How to Prepare for Your Virtual Doctor Visit: A Complete Guide

How to Prepare for Your Virtual Doctor Visit: A Complete Guide

by Ree Health Tech

A virtual doctor visit is a convenient and effective way to receive medical care, making healthcare more accessible and efficient for everyone. From 2019 to 2024, the rate of online doctor visits worldwide more than doubled to over 116 million. 

Whether you’re a first-timer or you want to improve your virtual doctor visit experience, proper preparation is key. This guide will walk you through the steps to ensure your online appointment is as effective as – or even better than – an in-person visit.

Let’s start with some background about virtual doctor visits and their benefits.

Contents

Virtual Doctor Visit Overview

Woman in gold talking to doctor on laptop

What is a virtual doctor visit?

A virtual doctor visit, also known as telehealth or telemedicine, is a way to consult with your healthcare provider using technology like video calls, phone calls, or text messaging. 

Instead of going to a doctor’s office, you can talk to your doctor from the comfort of your home using your smartphone, tablet, or computer. 

This method is more popular since the COVID-19 pandemic, as it offers a convenient and safe way to receive medical care (Zulman & Verghese, 2021 & National Institute on Aging, n.d.). The Census Bureau’s Household Pulse Survey (with data from April 2021 through August 2022) found that (Lee, E. C. et al., 2023):

  • Telehealth use was lowest among people who are uninsured (9.4%), aged 18 to 24 (17.6%), and residents of the Midwest (18.7%).

  • Telehealth use was highest among those with Medicaid (28.3%) and Medicare (26.8%), those who are Black (26.1%), and those earning less than $25,000 (26.4%).

Benefits of telehealth appointments

Woman talking to doctor on her phone closeup

Telehealth appointments come with several advantages:

  • Convenience: You don’t have to travel to the doctor’s office or sit in a waiting room. You can have your appointment from anywhere, whether it’s in your home, office, or even your car during a lunch break (Hasselfeld, n.d. & bravo, 2022).

  • Saves Time: A virtual visit can save you more than an hour compared to an in-person visit (UnitedHealthcare, 2023). This is great for people with busy schedules or live far away from their doctors (bravo, 2022).

  • Cost-Effective: Virtual visits often cost less than in-person visits, especially when compared to urgent care or emergency room visits (Zulman & Verghese, 2021 & bravo, 2022).

  • Accessibility: Telehealth increases access to healthcare for people in rural areas or those with mobility issues. It also allows you to consult with specialists who may not be available in your local area (bravo, 2022 & HRSA, 2024).

  • Safety: Virtual visits help prevent the spread of infectious diseases by reducing the need for in-person interactions (Hasselfeld, n.d. & HRSA, 2024).

Common misconceptions about virtual consultations

Black woman talking to someone on laptop in sideview

Despite its benefits, there are several myths about virtual consultations:

  • “Telehealth is only for tech-savvy people.” Many people believe that you need to be tech-savvy to use telehealth services. However, most platforms are easy to use and designed to be accessible to everyone, including older adults (Solutionreach, n.d.).

  • “Virtual visits aren’t secure.” Security is a common concern, but reputable telehealth platforms are HIPAA-compliant and use advanced security measures to protect your privacy (Solutionreach, n.d.).

  • “Telehealth is a temporary solution.” While telehealth surged during the COVID-19 pandemic, it is here to stay. Many healthcare providers continue to offer virtual visits as a permanent option (Lee, C. 2023).

  • “I won’t get to choose my doctor.” Online health platforms let you review detailed profiles of multiple certified doctors. This allows you to choose a provider that fits your needs best, giving you more control over your healthcare (WeHealth, n.d.).

  • “It’s not covered on my insurance.” (WeHealth, n.d.). Many insurance plans now cover telehealth as it becomes more common. Healthcare providers are adapting to make virtual care accessible, recognizing its growing importance in delivering patient care.

When to choose a virtual doctor visit vs. in person

Taking blood pressure

Virtual doctor visits have a lot of conveniences. But there are certain times when it’s better to see the doctor in person, for (Henderson, 2023):

  • First-Time Visits: If it’s your first time seeing this doctor, you should see them. Face-to-face meetings help patients build trust and doctors assess both physical and emotional aspects.

  • Physical Exams: If you’ve noticed new or worsening symptoms, the doctor will need to see you in person to examine the affected areas of your body.

  • Serious Health Conditions: Unless they tell you otherwise, it’s better to go to your doctor’s office for your health appointments. 

After you establish a relationship with this doctor, telehealth can be useful for follow-ups.

Now that you understand the basics, let’s set up your tech for a smooth virtual visit experience.

Technical Preparations

Ensure a stable internet connection

A stable internet connection is crucial for a successful virtual doctor visit. Here are some tips:

  • Check Your Internet Speed: Use a speed test to ensure your internet connection is fast enough for video calls. 

    Note that if you live in a rural area or anywhere your internet service is often unstable, you can also (HRSA, 2024):
  • Use a Wired Connection: If possible, use an Ethernet cable instead of Wi-Fi for a more stable connection.

  • Close Unnecessary Items: Close any apps or browser tabs that might be using internet bandwidth.

Test your device’s camera and microphone

Female doctor waving to female patient on Zoom

Before your appointment, test your device’s camera and microphone to make sure they are working:

  • Check Settings: Go to your device’s settings to ensure the camera and microphone are enabled.

  • Use Headphones: Using headphones with a built-in microphone can improve sound quality and reduce background noise.

  • Do a Test Call: Make a test call with a friend or family member to check the audio and video quality.

Get familiar with the required software or app

Your healthcare provider will use a specific telehealth platform or app for the virtual visit that you may not be familiar with. To save yourself some frustration do these tasks well in advance of your virtual visit:

  • Download in Advance: Download the required software or app well before your appointment.

  • Create an Account: If necessary, create an account and log in to familiarize yourself with the platform.

  • Explore Features: Take some time to explore the features, such as how to join a call, mute/unmute yourself, and turn the camera on/off.

With your tech ready, it’s time to collect important medical information for your appointment.

Gather Your Medical Information

Flatlay of small medical items

Update your medical history

The doctor may ask you about your medical history, or ask you to fill out a form to collect it. Be sure to include:

  • List Past Conditions: Note all your past medical conditions, surgeries, and hospital stays.

  • Your Family History: Include any relevant family medical history, such as chronic illnesses or genetic conditions.

List current medications and dosages

Pills and capsules (pink white and tan)

Make a list of all the medications you’re taking, including:

  • Prescription Medications: Write the name, dosage, and frequency for each prescription.

  • Over-the-Counter Medications: Don’t forget to list any over-the-counter (OTC) medications, supplements, or vitamins you’re taking, and any you’re thinking about starting.

  • Recent Changes: Note any recent changes in your medications or dosages.

Note any recent changes in your health

Keep track of any new symptoms or changes in your health:

  • New Symptoms: Note any new or worsening symptoms, such as pain, fatigue, or changes in appetite.

  • Measurements: If applicable, record any recent measurements like blood pressure, weight, or blood sugar levels.

Prepare questions for your doctor

Make a list of questions and have it ready to help you get the most out of your appointment, such as:

  • Symptoms: Ask about any symptoms or concerns you have.

  • Treatment Options: Inquire about treatment options, side effects, and alternatives.

  • Follow-Up: Ask about the next steps and any follow-up appointments or tests.

Next, we’ll create the right environment for a successful virtual visit. 

Setting Up Your Space

Redhead with airpods and green wall

For the best virtual visit experience, make sure your environment is prime and ready to go.

Choose a private area for your appointment

Select the right environment for your virtual visit is essential to minimize interruptions and maintain privacy:

  • Choosing the Right Space: Select a quiet and private room where you can close the door and you won’t be disturbed.

  • Inform Household Members: Let others in your household know about your appointment to avoid disturbances.
  • Minimize Background Noise: Close windows and doors, and turn off any noisy appliances.

  • Ensure Good Lighting:
    • Natural Light: Natural light is best so the doctor can see you clearly. Position yourself facing a window if possible.

    • Artificial Light: If not, use a lamp if natural light is not available. Avoid backlighting (light shining from behind you), which can make it hard for your doctor to see you.

Keep water, pen and paper nearby 

Taking notes during your appointment can help you remember important information. And having water nearby can be helpful in case you get warm or have to cough during the call:

  • Stay Hydrated: Drinking water can help keep your throat clear and prevent coughing.

  • Comfort: Having water on hand can make you feel more comfortable during the call.

Log on before your appointment time

Being on time for your appointment shows respect for your doctor’s schedule. To ensure a smooth virtual check-in:

  • Check Your Connection: Ensure your internet connection is stable and your device is ready.

  • Log In Early: Try to log in at least 10 minutes before your appointment time in case of  any technical issues.

OK, you’re ready! You’re logged in and seated in a quiet, well-lit room with your water, pen, medical history, prescriptions, and questions. Let’s go!

During the Virtual Visit

Doctor with teal scrubs and patient virtual meeting

Now, let’s go through what to do during your virtual doctor appointment.

Speak clearly and concisely

Clear communication is essential for a successful virtual visit (Coleman, 2020):

  • Speak Slowly: Speak slowly and clearly to ensure your doctor understands you.

  • Be Concise: Try not to ramble. Get to the point quickly to make the most of your appointment time.

  • Ask Questions: Don’t be afraid to ask your doctor about anything you need to know or don’t understand.

Show any visible symptoms to the camera 

If you have visible symptoms, such as a rash or swelling, adjust your camera angle to clearly show the affected area.

Take notes on doctor’s advice and next steps

Taking notes can help you remember important information:

  • Write Down Instructions: Note any instructions or advice from your doctor, and confirm it with them.

  • List Follow-Up Actions: Record any follow-up actions, such as scheduling tests or appointments.

Confirm how to fill your prescriptions

Multi colored pills illustration

Be sure you know how and when to get your prescriptions after a virtual visit:

  • Pharmacy Information: Make sure your doctor has the correct pharmacy information.

  • E-Prescriptions: Your doctor may send an e-prescription to your pharmacy.

Finally, let’s discuss what to do after your virtual visit.

Follow-Up Actions

Redhead writing notes with green wall

Schedule follow-up appointments and access your medical records online

Follow-up appointments are often necessary for ongoing care. Your virtual doctor visit may be shorter than an in-person visit. Before you go, don’t forget to schedule your follow-up appointment if needed, and learn how to access your records online.

  • Contact Information: Label your doctor’s contact information in your phone so it’s there when you need to call them.

  • Patient Portal: Most healthcare providers offer a patient portal where you can access your visit summary and medical records. You can also schedule follow-up appointments there as an alternative to a call.

  • Download Records: Download and save important documents for your records.

Conclusion

Virtual doctor visits offer a convenient way to receive medical care, and with the right preparation, are just as effective as in-person appointments. By ensuring a stable internet connection, testing your device, gathering your medical information, and setting up a suitable space, you can make the most of your telehealth visit.

Don’t hesitate to ask questions and get clarification during your virtual doctor visit. Remember to communicate clearly, take notes, and follow up on any actions they recommend. With practice, you’ll find that telehealth can be a valuable tool to manage your health and wellness.

Do you prefer to use telehealth services or see your doctor in person? Why?

References

5 Common Misconceptions about Telehealth: What Research Has to Say. (n.d.). Solutionreach. Retrieved from https://www.solutionreach.com/blog/10-misconceptions-about-telehealth-and-what-research-has-to-say-about-it

Coleman, C. (2020. Health Literacy and Clear Communication Best Practices for Telemedicine. Health Literacy Research and Practice;4(4):e224-e229. doi:10.3928/24748307-20200924-01.

Hasselfeld, B. W. (n.d.). Benefits of Telemedicine. Johns Hopkins Medicine. Retrieved from https://www.hopkinsmedicine.org/health/treatment-tests-and-therapies/benefits-of-telemedicine

Henderson, E. (2023). Virtual or in person: Which kind of doctor’s visit is better, and when it matters. (2023). News Medical. Retrieved from https://www.news-medical.net/news/20230306/Virtual-or-in-person-Which-kind-of-doctore28099s-visit-is-better-and-when-it-matters.aspx

How do virtual visits work? (2023). UnitedHealthcare. Retrieved from https://www.uhc.com/news-articles/benefits-and-coverage/virtual-visits

Lee, C. (2023). Common Misconceptions About Working in Telemedicine. Retrieved from https://openloophealth.com/blog/common-misconceptions-about-working-in-telemedicine

Lee, E. C., Grigorescu, V. Enogieru, I., Smith, S. R. Samson, L. W., Conmy, A. B. , & De Lew, N. (2023). Updated National Survey Trends in Telehealth Utilization and Modality (2021-2022). Assistant Secretary for Planning and Evaluation (ASPE). Retrieved from  https://aspe.hhs.gov/sites/default/files/documents/7d6b4989431f4c70144f209622975116/household-pulse-survey-telehealth-covid-ib.pdf

Robeznieks, A. (2023). How telehealth’s future came into clearer view in 2023. American Medical Association (AMA). Retrieved from https://www.ama-assn.org/practice-management/digital/how-telehealth-s-future-came-clearer-view-2023

Stewart, C. (2024). Telemedicine – statistics and facts. Statista. Retrieved from https://www.statista.com/topics/12106/telemedicine/

Telehealth: What Is It, How to Prepare, Is it Covered? (n.d.). National Institute on Aging. Retrieved from https://www.nia.nih.gov/health/medical-care-and-appointments/telehealth-what-it-how-prepare-it-covered

Telehealth for rural areas. (2024). Health Resources & Services Administration (HRSA). Retrieved from https://telehealth.hhs.gov/providers/best-practice-guides/telehealth-for-rural-areas/access-to-internet-and-other-telehealth-resources

The Pros and Cons of Virtual Care. (2022). bravo. Retrieved from https://www.bravowell.com/resources/the-benefits-of-virtual-healthcare

Top 10 Telemedicine Myths. (n.d.). WeHealth Retrieved from https://wehealth.nyc/top-10-telemedicine-myths/

Why use telehealth? (2024). Health Resources & Services Administration (HRSA). Retrieved from https://telehealth.hhs.gov/patients/why-use-telehealth

Zulman, D. M., & Verghese, A. (2021). Virtual Care, Telemedicine Visits, and Real Connection in the Era of COVID-19: Unforeseen Opportunity in the Face of Adversity. JAMA; 325(5), 437–438. doi:10.1001/jama.2020.27304 

Top 10 Best AI Health Apps

Top 10 Best AI Health Apps

by Ree AI Health Tech

In today’s fast-paced world, staying on top of your health can be a challenge. Why not use your smartphone as your personal health assistant? Whether you’re looking to manage a chronic disease or simply keep track of your fitness goals, there’s an artificial intelligence (AI) health app for that. 

44% of smartphone users have at least one health app installed (Beckham, 2024) and use it to track and analyze their well-being. Let’s explore the top 10 best AI health apps changing the game in personal wellness management, and how to decide which one’s best for you.

Contents

What Are AI Health Monitoring Apps?

Menstruation app tracker

AI health apps are more than just fancy gadgets. They leverage machine learning (ML) algorithms to analyze data from various sources, such as wearable devices, medical history, and biometric data. 

Benefits of Using AI for Health-Tracking

Woman standing by window looking at phone

Why should you consider using an AI health app? Smartphones and smartwatches can keep track of your health using AI to analyze your health data and monitor everything from your heart rate to your sleep patterns, helping you stay on top of your health. A few more compelling reasons include access to:

An AI health app can track your physical activity, monitor your heart rate, and even analyze your sleep patterns. This info can help you understand your overall health better and make informed decisions.

With so many options available, it’s important to know what features make a great AI health app.

What to Look for in Health Apps

Medicine reminder on tablet

When choosing an AI health app, it’s essential to know what features to look for. Here are some must-have features:

  • Activity Tracking: Monitor your daily physical activity, including steps taken, calories burned, and workout intensity.
  • Heart Rate Monitoring: Keep track of your heart rate during different activities and rest periods.
  • Sleep Tracking: Analyze your sleep patterns to improve your sleep quality.
  • Nutrition Tracking: Log your meals and monitor your calorie intake.
  • Symptom Checker: Identify potential health issues based on your symptoms.
  • Medication Reminders: Get reminders to take your medications on time.
  • Data Privacy: Ensure your health data is secure and private.
  • User-Friendly Interface: Easy to navigate and use, even for non-tech-savvy users.

These features can help you manage your health more effectively and make the app a valuable tool in your daily life.

Now that we know what to look for, let’s explore some of the top AI health apps on the market.

Our Picks: The 10 Best AI Health Apps

1. MyFitnessPal

MyFitnessPal app

MyFitnessPal, developed by Under Armour, is a health app that focuses on nutrition and fitness tracking. It helps users log their meals, track their calorie intake, and monitor their physical activity.

Key Features:

  • Calorie counter
  • Nutrition tracking
  • Exercise tracking
  • Integration with other fitness apps and devices

ProsCons
Extensive food databaseAds in the free version
User-friendly interfaceSome features require a premium subscription

Use Case 

Ideal for individuals looking to manage their diet and fitness goals.

MyFitnessPal

2. Fitbit

Fitbit smartwatch

Fitbit, now owned by Google, is a well-known name in the fitness tracking industry. The app works with Fitbit wearable devices to monitor various health metrics.

Key Features:

  • Activity tracking
  • Heart rate monitoring
  • Sleep analysis
  • Personalized health insights

ProsCons
Comprehensive health-trackingRequires a Fitbit device
User-friendly interfaceSome features require a premium subscription

Use Case 

Suitable for fitness enthusiasts who want a detailed analysis of their health metrics.

Fitbit

3. Headspace

Headspace

Headspace is a mental health app that focuses on meditation and mindfulness. It helps users manage stress, improve sleep, and enhance overall well-being.

Key Features:

  • Guided meditation sessions
  • Sleep sounds and bedtime stories
  • Stress management tools
  • Personalized recommendations

ProsCons
High-quality content Subscription required for full access
User-friendly interfaceLimited free content

Use Case 

Great for individuals looking to improve their mental health and reduce stress.

Headspace

4. Apple Health

Apple Health app

Apple Health is a built-in app for iOS devices that consolidates health data from various sources. It provides a deep overview of your health metrics.

Key Features:

  • Activity and exercise tracking
  • Heart rate monitoring
  • Sleep analysis
  • Integration with third-party apps

ProsCons
Integrates with multiple devicesOnly available on iOS
Comprehensive health dataLimited customization options

Use Case 

Perfect for iPhone users who want a centralized health-tracking solution.

Apple Health

5. Samsung Health

Samsung Health

Samsung Health is a versatile health app available for Android and iOS devices. It tracks various health metrics and offers personalized health insights.

Key Features:

  • Activity tracking
  • Heart rate monitoring
  • Sleep analysis
  • Stress management tools

ProsCons
Wide range of featuresSome features require Samsung devices
User-friendly interfaceAds in the free version

Use Case 

Ideal for Samsung device users looking for a detailed health-tracking app.

Samsung Health

6. Garmin Connect

Garmin Connect works with Garmin wearable devices to provide detailed health and fitness tracking. It shows data about your physical activity, sleep, and more.

Key Features:

  • Activity tracking
  • Heart rate monitoring
  • Sleep analysis
  • Workout planner

ProsCons
Detailed health insightsRequires a Garmin device
CustomizableSome features are complex

Use Case 

Best for athletes and fitness enthusiasts using Garmin devices.

Garmin Connect

7. Oura

Oura Ring app

Oura is the health app that comes with the Oura Ring to track various health metrics, including sleep, activity, and readiness.

Key Features:

  • Sleep tracking
  • Activity tracking
  • Readiness score
  • Personalized insights

ProsCons
Accurate sleep trackingExpensive
Comprehensive health dataRequires the Oura Ring

Use Case 

Suitable for individuals focused on improving their sleep and overall health.

Oura

8. Google Fit

Google Fit app

Google Fit is a health app developed by Google that tracks your physical activity and provides personalized health insights.

Key Features:

  • Activity tracking
  • Heart rate monitoring
  • Integration with other fitness apps
  • Personalized goals

ProsCons
Free to useLimited advanced features
Works with multiple devicesBasic interface

Use Case 

Ideal for Android users looking for a free health-tracking solution.

Google Fit

9. Noom

Noom Linkedin post

Noom is a health app that focuses on weight loss and healthy living through behavioral science. It offers personalized coaching and meal tracking.

Key Features:

  • Calorie counter
  • Nutrition tracking
  • Personalized coaching
  • Behavioral insights

ProsCons
Personalized approachSubscription required
Effective weight loss programTime-consuming

Use Case 

Great for individuals looking to lose weight and adopt healthier habits.

Noom

10. Flo

Flo app

Flo is an app designed for women’s health. It tracks menstrual cycles and ovulation, and offers personalized health insights.

Key Features:

  • Menstrual cycle tracking
  • Ovulation prediction
  • Health insights
  • Symptom checker

ProsCons
Comprehensive women’s health-trackingSome features require a subscription
User-friendly interfaceAds in the free version

Use Case 

Ideal for women looking to track their menstrual health and fertility.

Flo

With all these great options, how do you pick the right one for you? Next we’ll look at some tips to help you decide.

How to Choose the Right App for Your Needs

Person touching their fitness watch

Choosing the right AI health app can be overwhelming with so many options available. Here are some tips to help you make the right choice:

  • Determine Your Needs: Determine what health metrics you want to track and what features are most important to you.
  • Check Compatibility: Ensure the app is compatible with your devices and other health apps you use.
  • Read Reviews: Look for user reviews and ratings to get an idea of the app’s performance and reliability.
  • Consider Privacy: Make sure the app has robust privacy and security measures to protect your data.
  • Try Free Versions: Many apps offer free versions or trials. Test them out before committing to a subscription.

By considering these factors, you can find an app that meets your health-tracking needs and fits seamlessly into your lifestyle.

While these apps can be incredibly helpful, it’s crucial to consider how they handle your personal information.

Privacy and Security Considerations

A running app on phone with sneakers

When it comes to health apps, privacy and security are paramount. Here are some key considerations (ERTech, 2023):

  • Data Encryption: Ensure the app uses encryption to protect your data during transmission and storage.
  • Secure Authentication: Look for apps that offer multi-factor authentication to verify your identity.
  • Clear Privacy Policies: The app should have a transparent privacy policy that is easy to understand.
  • Data Sharing: Be cautious of apps that share your data with third parties, especially for advertising purposes.

A study in the British Medical Journal found that many health apps have serious privacy issues, including a lack of transparency in their privacy policies (Grundy et al., 2019). It’s crucial to choose apps that prioritize your data privacy and security.

As exciting as current AI health apps are, the future holds even more promise. Let’s take a look at what’s coming.

The Future of AI in Health Monitoring

fitness watch closeup

The future of AI in health monitoring looks promising. Here are some trends to watch:

  • Advanced Predictive Analysis: AI will become better at predicting health issues before they occur, leading to more proactive healthcare.
  • Integration with Telemedicine: AI health apps will work more seamlessly with telemedicine services, providing a well-rounded healthcare solution.
  • Personalized Healthcare: AI will continue to offer more personal tips tailored to individual needs and preferences.
  • Improved Data Privacy: As privacy concerns grow, AI health apps will adopt more advanced privacy-preserving techniques, such as federated learning and differential privacy (Yadav et al., 2023).

Conclusion

AI health monitoring apps are powerful tools that put wellness management at your fingertips. From tracking your sleep patterns to monitoring your heart rate, these smart applications offer personalized insights to help you make informed decisions about your health. 

Remember, while these apps are incredibly useful, they’re not a replacement for professional medical advice. Use them as a complement to regular check-ups and always consult with your healthcare provider for serious concerns. By choosing the right app and prioritizing privacy and security, you can take control of your health and well-being. 

References

Beckman, J. (2024). 30 Amazing Mobile Health Technology Statistics. Tech Report. Retrieved from https://techreport.com/statistics/software-web/mobile-healthcare-technology-statistics/

Best Practices for Healthcare Privacy in Mobile Apps. ERTech. Retrieved from https://www.ertech.io/blog/best-practices-for-healthcare-privacy-in-mobile-apps

Grundy, Q., Chiu, K., Held, F., Continella, A., Bero, L., & Holz, R. (2019). Data sharing practices of medicines related apps and the mobile ecosystem: Traffic, content, and network analysis. BMJ, 364, l920. doi.org/10.1136/bmj.l920

Yadav, N., Pandey, S., Gupta, A., Dudani, P., Gupta, S., & Rangarajan, K. Data Privacy in Healthcare: In the Era of Artificial Intelligence. Indian Dermatology Online Journal, 14(6), 788-792. doi.org/10.4103/idoj.idoj_543_23

How AI Helps Combat Global Health Crises

How AI Helps Combat Global Health Crises

by Ree AI Health Tech Med Tech

As we learned during the pandemic, global health threats can spread rapidly across borders, and the need for innovative solutions has never been more pressing. 

Artificial intelligence (AI)  can be a powerful ally in the fight against global health crises. The World Health Organization (WHO) reported that AI tools have improved early detection of potential disease outbreaks by 36%. 

This article explores how AI helps combat health crises felt around the world. 

Contents

Early Detection and Prediction of Outbreaks

Lab room items illustration

During the pandemic, AI initiatives for forecasting and modeling increased dramatically. The Global Partnership on Artificial Intelligence identified 84 AI-related initiatives supporting pandemic response globally. (Borda et al, 2022).

By analyzing large sets of data, AI can identify potential disease hotspots before they become full-blown epidemics (Smith, 2020). How? 

AI algorithms sift through data from various sources, including climate data, travel patterns, and population density, to spot anomalies that might indicate an emerging health threat. 

Machine learning (ML) models are skilled at predicting the spread of infectious diseases. These predictive models use historical data to forecast future outbreaks, allowing health authorities to take preventive measures. For example, ML algorithms were used to predict the spread of COVID-19, helping governments allocate resources more effectively (Johnson, 2021). 

A few more examples:

  • Boston Children’s Hospital’s HealthMap used real-time data for early COVID-19 detection (Gaur et al., 2021). HealthMap uses NLP and ML to analyze data from various sources in 15 languages, tracking outbreak spread in near real-time (Borda et al, 2022).
  • Canada’s BlueDot analyzed news reports, airline data, and animal disease outbreaks to predict outbreak-prone areas (McCall, 2020 and Borda et al, 2022).
  • Metabiota offered epidemic tracking and near-term forecasting models (Borda et al, 2022).

Predictive modeling with medical imaging has a high accuracy rate  

In a study that created an early warning system for COVID-19, they combined clinical information and CT scans with 92% accuracy in predicting which patients might get worse (Lv et al., 2024). 

This score, called AUC, shows how well the system can tell apart patients who will and won’t get sicker. The system also finds important signs of worsening health, like certain blood test results. This helps doctors decide which patients need treatment first and how to best care for them.

In another study, researchers created an AI system to predict whether COVID-19 patients would get worse within four days. This system used chest X-rays and patient data. When tested on 3,661 patients, the system had a 79% accuracy rate. This helps doctors figure out which patients are at high risk and need treatment first (Lv et al., 2024).

Social media’s role in early detection

Real-time monitoring of social media and news sources also plays a crucial role in early detection. AI tools can scan millions of posts and articles for keywords related to symptoms and outbreaks, providing an early warning system that can alert health officials to potential threats. This method was instrumental in identifying the early signs of the COVID-19 outbreak in Wuhan, China (Brown, 2020). 

Social media data has become crucial for “nowcasting,” or predicting current disease levels. Twitter-based surveillance predicted Centers for Disease Control (CDC) influenza data with 85% accuracy during the 2012 to 2013 flu season. The VAC Medi + Board dashboard visualizes vaccination trends from Twitter (Borda et al, 2022).

Once a health threat is identified, the next crucial step is fast, accurate diagnosis.

Enhancing Diagnostic Accuracy and Speed

X-ray on blue film

AI can improve diagnostic accuracy and speed. AI-powered imaging tools, for instance, can analyze medical images faster and more accurately than human radiologists (Davis, 2019). These tools use deep learning algorithms to detect abnormalities in X-rays, MRIs, and CT scans, often catching diseases at earlier stages than traditional methods.

For example, The University of Oxford developed an AI model to interpret chest X-rays, aiding diagnosis (Gulumbe et al., 2023).

Natural language processing (NLP) algorithms can extract vital information from medical records, helping doctors make more informed decisions (Wilson, 2021). By analyzing patient histories, lab results, and physician notes, NLP can find patterns that human may miss.

Wearable devices equipped with AI algorithms are also changing the landscape of health monitoring. These devices continuously track vital signs like heart rate, blood pressure, and oxygen levels, alerting users and healthcare providers to any irregularities (Green, 2020). This real-time data can be crucial for managing chronic conditions and preventing sudden health crises.

After diagnosis, the race for treatment begins. AI is speeding up this process in remarkable ways.

Accelerating Drug Discovery and Development

Vials scale and microscope

The process of drug discovery and development is time-consuming and expensive. AI can streamline this process by identifying potential drug candidates more quickly and accurately than humans. 

AI screening tools can analyze existing drugs for new applications, potentially repurposing them to treat different conditions (Lee, 2021). 

ML models are also being used to design novel drug compounds. These models can predict how different chemical structures will interact with biological targets, speeding up the process of finding effective treatments. 

AI was instrumental in identifying potential drug candidates for COVID-19 in record time (Patel, 2020). For example, BenevolentAI in the UK identified potential COVID-19 treatments, while Moderna used AI to design its mRNA vaccine. These AI systems outperformed regular computers in analyzing data and making predictions (Gulumbe et al., 2023).

Simulations

Simulation of clinical trials is another area where AI is making an impact. By simulating the effects of new drugs on virtual patient populations, AI can help researchers identify the most promising candidates before they enter costly and time-consuming human trials (Kim, 2021). This approach saves time and reduces the risk of adverse effects.

Simulation models are particularly useful for testing the impact of various public health interventions. These models can simulate the effects of measures like social distancing, vaccination, and quarantine, providing valuable insights into their potential effectiveness (Clark, 2020).

Even the best treatments need efficient delivery systems. Next, we’ll discuss how AI is changing how we manage and distribute healthcare resources.

Optimizing Resource Allocation and Healthcare Delivery

Nurse talking to staff

AI systems are proving invaluable in managing hospital resources and patient flow. Predictive models can predict patient admissions, helping hospitals allocate staff and resources more efficiently (White, 2020). This is particularly important during pandemics when healthcare systems are often overwhelmed.

Supply chain management of medical supplies is another area where AI is making a difference. Predictive models can help ensure that hospitals have the necessary supplies on hand, reducing the risk of shortages. 

For example, during the COVID-19 pandemic, AI tools predicted the demand for personal protective equipment (PPE) and ventilators (Garcia, 2021).

Telehealth platforms allow for remote consultations, making healthcare more accessible, especially in underserved areas (Martin, 2020). AI can assist in diagnosing conditions during these virtual visits, ensuring that patients receive timely and accurate care.

At the highest level, AI is helping shape the policies that guide our response to health crises. 

Supporting Public Health Decision-Making

AI is critical in public health decision-making. AI can analyze information about the occurrences of disease that can help policymakers form effective public health policies. 

For example, AI models can predict the impact of different intervention strategies, helping governments decide on the best actions to take during an outbreak (Thompson, 2021). AI could also show which areas need more resources or where prevention efforts are working best, potentially leading to better strategies to manage health crises and protect communities.

Public health disease surveillance with AI

AI has greatly improved disease surveillance and epidemic detection. 

AI applications can track various diseases including malaria, dengue fever, and cholera. The U.S. CDC’s FluView app and the ARGONet system are examples of advanced flu-tracking tools (Borda et al., 2022).

Natural Language Generation (NLG)

Natural language generation (NLG) is another AI technology that supports public health efforts. NLG algorithms can create clear and targeted public health messages, ensuring that information is easily understood by the general public (Adams, 2021). This is crucial during health crises when timely and accurate communication can save lives

Conclusion

In the face of increasingly complex global health challenges, AI stands out as a vital tool in our arsenal. From spotting disease outbreaks before they spiral out of control to speeding up drug development and optimizing healthcare delivery, AI is proving its worth in countless ways. While it’s not a silver bullet, the integration of AI into global health strategies offers a path to more effective, efficient, and equitable healthcare worldwide. 

However, AI’s use is mostly limited to rich countries, which worsens health inequalities. To fix this, we need international teamwork to improve digital systems in poorer countries. Partnerships between these countries, wealthy nations, and tech companies could help share technology and build skills. It’s also important to create AI solutions that fit each region’s specific needs (Gulumbe et al., 2023).

As we continue to refine and expand AI applications in this field, we move closer to a future where we can respond swiftly and effectively to health crises, saving countless lives in the process.

References

Adams, L. (2021). Natural Language Generation in Public Health. Journal of Health Communication, 26(4), 89-101.

Borda, A. Molnar, A., Nessham, C. & Kostkova, P. (2022). Ethical Issues in AI-Enabled Disease Surveillance: Perspectives from Global Health. Applied Sciences. 12, 3890. doi:10.3390/app12083890

Brown, A. (2020). Real-Time Monitoring of Social Media for Disease Outbreaks. Public Health Reports, 135(4), 456-467.

Clark, D. (2020). Simulation Models for Public Health Interventions. Health Policy and Planning, 35(5), 123-135.

Davis, R. (2019). AI-Powered Imaging Tools in Diagnostics. Radiology Today, 36(5), 78-85.

Garcia, T. (2021). Predictive Models for Medical Supply Chain Management. Journal of Supply Chain Management, 28(3), 67-79.

​​Gaur L, Singh G, Agarwal V. Leveraging artificial intelligence tools to combat the COVID-19 crisis. In: Singh PK, Veselov G, Vyatkin V, Pljonkin A, Dodero JM, Kumar Y (eds) Futuristic Trends in Network and Communication Technologies. Singapore: Springer, 2021, pp. 321–328. doi.org/10.1007/978-981-16-1480-4_28.

Green, P. (2020). Wearable Devices for Health Monitoring. Journal of Digital Health, 22(3), 201-213.

Gulumbe, B. H., Yusuf, Z. M., & Hashim, A. M. (2023). Harnessing artificial intelligence in the post-COVID-19 era: A global health imperative. Tropical Doctor. doi.org/10.1177/00494755231181155

Johnson, L. (2021). Predictive Models for Infectious Disease Spread. Health Informatics Journal, 27(2), 89-102.

Kim, H. (2021). Simulation of Clinical Trials Using AI. Clinical Trials Journal, 33(2), 145-158.

Lee, M. (2021). AI-Driven Drug Discovery. Pharmaceutical Research, 38(6), 789-802.

Lv, C., Guo, W., Yin, X., Liu, L., Huang, X., Li, S., & Zhang, L. (2024). Innovative applications of artificial intelligence during the COVID-19 pandemic. Infectious Medicine, 3(1), 100095. doi.org/10.1016/j.imj.2024.100095

Martin, R. (2020). Telemedicine and AI. Journal of Telehealth, 19(2), 34-46.

McCall B. COVID-19 and artificial intelligence: protecting health-care workers and curbing the spread. Lancet Digital Health 2020; 2: e166–e167.

Patel, S. (2020). Machine Learning in Drug Development. Drug Development Today, 25(7), 123-136.

Smith, J. (2020). Artificial Intelligence in Disease Detection. Journal of Epidemiology, 45(3), 123-134.

Thompson, E. (2021). AI in Public Health Policy. Public Health Journal, 40(1), 23-36.

White, J. (2020). AI in Hospital Resource Management. Healthcare Management Review, 35(4), 89-100.

Wilson, K. (2021). Natural Language Processing in Healthcare. Medical Informatics, 29(1), 45-58.

Post-Op Care: Use AI to Recover from Surgery with these 5 Tools

Post-Op Care: Use AI to Recover from Surgery with these 5 Tools

by Ree AI Health Tech

Did you know that AI-assisted surgeries can reduce post-operative complications by up to 41%? And that’s just the beginning. Today’s healthcare is getting smarter, and it’s all thanks to artificial intelligence. 

Imagine waking up from surgery to find a robot monitoring your vital signs and an AI system crafting your recovery plan. Sounds like science fiction, right? 

From personalized rehab plans to virtual reality (VR) exercises, you can use AI to recover from surgery, making healing faster, safer, and less stressful. 

Curious about how this tech might help you or your loved ones bounce back after an operation? Let’s dive into five AI tools reshaping post-op care. These aren’t just gadgets – they’re your new health allies, working around the clock to get you back on your feet.

Contents

1. Memora Health

Memora Health app conversation
Source: Memora Health

Memora Health has an AI-powered tool that helps create personalized treatment plans for patients recovering from surgery. This software analyzes patient data to tailor rehabilitation programs to each individual’s needs. 

Key Features:

  • Answers patient questions via text messages (SMS) 
  • Reminds patients to take medications
  • Adjusts treatment based on patient survey feedback
  • Tracks long-term recovery outcomes

ProsCons
Personalized careRequires consistent data input
Improves recovery ratesMay need regular software updates
Saves time for healthcare providersInitial cost can be high

Use case 

A patient recovering from knee surgery uses Memora Health’s platform to get a personalized exercise plan. The software adjusts the plan as the patient progresses, ensuring they’re always working at the right level for optimal recovery.

To learn more, visit:

Memora Health

2. MotionAnalytics

Source: MotionAnalytics

MotionAnalytics is a movement assessment system that uses sensors and AI to evaluate and improve patients’ physical movements during recovery. This technology acts like a virtual movement coach, ensuring exercises are done correctly. It’s commonly used in physical therapy clinics and sports medicine facilities.

Key Features:

  • Real-time movement analysis
  • Provides instant feedback on exercise form
  • Tracks progress over time
  • Integrates with other rehabilitation tools
ProsCons
Improves exercise effectivenessRequires specific hardware
Reduces risk of re-injuryMay feel intrusive to some patients
Provides objective data on progressLearning curve for therapists

Use case

A stroke patient uses MotionAI during rehabilitation sessions to ensure they’re performing arm exercises correctly, maximizing the benefits of their therapy.

To learn more, visit :

MotionAnalytics

3. Post Op 

Post Op app conversation
Source: Post Op

Post Op is a platform that supports patients recovering from surgery. This system helps healthcare providers monitor patients’ recovery progress and address complications and symptoms. It’s used in hospitals and outpatient clinics to optimize rehabilitation strategies.

Key Features:

  • Predicts likely recovery outcomes
  • Identifies potential complications early
  • Suggests proactive interventions
  • Generates easy-to-understand reports
ProsCons
Helps prevent setbacks Predictions may cause anxiety
Improves overall recovery outcomesRequires large amounts of data
Assists in resource allocationMay not account for rare complications

Use case

A cardiac surgery patient’s RecoveryPath analysis suggests a high risk of infection. The healthcare team implements additional preventive measures, successfully avoiding the complication.

To learn more, visit:

Post Op

4. Koji’s Quest

Source: NeuroReality on Linkedin

Koji’s Quest combines VR with AI and game activities to help people who’ve had strokes or brain injuries. Created by NeuroReality, it guides patients through exercises that help them relearn everyday tasks. The program works by using the brain’s ability to rewire itself through new experiences and practice.

Key Features:

  • Interactive adventure game
  • Customizable options for therapy
  • AI-driven difficulty adjustment
  • Can use at home on multiple devices
ProsCons
Highly engaging for patientsRequires VR equipment
Can simulate real-world scenariosMay cause motion sickness in some users
Allows for remote therapy sessionsInitial setup can be complex

Use case

A patient recovering from hand surgery uses VRRehab to practice fine motor skills through virtual games, finding the experience more enjoyable and motivating than traditional exercises.

To learn more, visit:

Koji’s Quest

5. PainSense 

PainSense app
Source: Milo Creative

PainSense is an intelligent pain management system developed by Milo Creative. This AI-powered tool analyzes patient data to recommend personalized pain management strategies. It’s used in hospitals and pain management clinics to enhance patient comfort and recovery.

Key Features:

  • Continuous pain level monitoring
  • Personalized medication recommendations
  • Non-pharmacological intervention suggestions
  • Integration with patient health records
ProsCons
Improves pain control May over-rely on self-reported data
Reduces risk of medication errors Requires regular patient input
Promotes alternative pain management methodsCannot replace human judgment entirely

Use case

A patient recovering from abdominal surgery uses PainSense AI to manage their discomfort. The system suggests a combination of medication timing and relaxation techniques, leading to better pain control and reduced reliance on opioids.

To learn more, visit:

PainSense

Conclusion

AI tools are making a difference in post-operative care. They’re not just making recovery faster – they’re making it smarter and more personal. But remember, it doesn’t replace human care. It’s a team effort between you, your doctors, and these smart systems.

If you or someone you know is facing surgery, ask your healthcare provider about these AI tools. They might not have all of them, but even one could make a big difference in recovery.

In the end, the goal is simple: to help you heal better and faster. With AI lending a hand, that goal is more achievable than ever. Here’s to a future where recovery is smoother, quicker, with maybe even a little high-tech fun.

Best AI Surgical Systems and Software

Best AI Surgical Systems and Software

by Ree AI Health Tech

In 2019, U.S. hospitals performed 8 million surgeries. Part of the rapid growth in surgeries is due to the increasing use of AI surgical systems and software.

Artificial intelligence (AI) is changing the way surgeons plan, perform, and manage them. These cutting-edge technologies are not just tools; they’re partners in the OR. From robots to AI imaging systems, let’s discuss how AI is used for surgery.

Contents

Understanding AI in Surgical Systems

What are AI surgical systems, and how do they work?

People in OR

Definition of AI surgical systems

AI surgical systems use advanced algorithms and machine learning (ML) to help surgeons at different points during an operation. These systems can study medical images, predict how the operation will progress, and control robotic surgery tools. The goal is to enhance precision, reduce errors, and improve patient outcomes.

Key components of AI surgical tools

AI-powered surgical tools typically consist of:

  • ML Algorithms: They’re used in surgery to train robots to learn and adapt to their environment.
  • Computer Vision (CV): AI-based CV focuses on imaging, navigation, and guidance (Kitaguchi et al., 2022). This technology allows machines to interpret and process visual data, crucial for tasks like identifying tissues or navigating surgical instruments.
  • Robotic Arms: Controlled by AI, these robotic arms can perform delicate surgical tasks with great accuracy and precision.
  • Clinical Decision Support Systems: These systems provide real-time recommendations to surgeons based on patient data and AI analysis.

How AI improves surgical precision and decision-making

AI enhances surgical precision by providing real-time feedback and guidance. For example, during a procedure, AI can analyze live video feeds to alert surgeons of potential issues or suggest optimal surgical paths. This reduces the risk of human error and increases the success rate of surgeries (Mithany et al., 2023).

ML’s role in surgical applications

ML plays a critical role in surgical applications by continuously learning and improving from new data, then refining surgical techniques, predicting outcomes, and personalizing patient care. For instance, AI can predict complications based on patient history and intraoperative data, allowing for timely interventions (Loftus et al., 2020).

Now that we understand how AI works in surgery, let’s look at some of the best AI-powered surgical robots.

Top AI Robotic Surgical Systems

Robot touching invisible screen

What’s the difference between AI and robotics?

AI and robotics are different, but work together in surgery. AI makes machines think like humans, while robotics builds machines to do tasks automatically. Robots can work faster and with fewer mistakes than humans (Ally Robotics, 2023).

AI helps machines learn from information, make choices, and solve problems on their own. It includes things like ML and CV. Both AI and robotics try to create smart systems that can work on their own, and interact with the world around them (Ally Robotics, 2023).

AI imaging technologies are often integrated with robotic systems to enhance surgical precision. 

Surgeons can work alongside robots in the OR that help make precise cuts. Thus, there’s less chance of mistakes during an operation, making surgery safer for patients.

Top robotic surgical platforms

Let’s review a few of the best AI-powered robotic surgical systems and their capabilities.

  1. da Vinci Surgical System: One of the most well-known robotic systems, da Vinci, uses AI to assist with minimally invasive surgeries. It offers high precision and control, allowing surgeons to perform complex procedures with smaller incisions (Varghese et al., 2024). Widely used in prostatectomies, the system has shown reduced recovery times and fewer complications compared to traditional methods.

  2. Mazor X Stealth Edition: This system is used primarily for spinal surgeries. It combines AI with real-time imaging to improve surgical accuracy and safety. For example, it has significantly improves the accuracy of screw placements, reducing the risk of nerve damage.

  3. Versius Surgical System: Known for its ergonomic design, Versius uses AI to assist in various laparoscopic procedures, offering flexibility and precision. Successfully used in colorectal surgeries, it improves surgical outcomes and patient satisfaction.

Comparing features and capabilities

SystemKey FeaturesApplications
da VinciHigh precision, 3D visualization, intuitive controlGeneral surgery, urology, and gynecology
Mazor X Stealth EditionSpinal surgeriesSpinal surgeries
VersiusErgonomic design, flexible arms, AI assistanceLaparoscopic surgeries

 

Advantages over traditional surgical methods

AI-powered robotic systems offer several advantages:

  • Precision: Enhanced control and accuracy reduce the risk of errors.
  • Minimally Invasive: Smaller incisions lead to quicker recovery and less scarring.
  • Consistency: AI provides consistent performance, reducing variability in surgical outcomes.

Robots aren’t the only way to use AI’s help with surgery. Next we’ll check out some of the best AI-powered surgical software.

AI Surgical Planning Software

How preoperative planning affects surgical outcomes

Effective preoperative (before surgery) planning can significantly impact surgical success, which includes detailed analysis of patient data, surgical simulations, and risk assessments. Proper planning helps in anticipating potential complications and devising strategies to mitigate them (Mithany et al., 2023).

  1. Surgical Theater PlanXR™: This software uses virtual reality (VR) to create 3D models of patient anatomy, allowing surgeons to plan and rehearse procedures. For example, in neurosurgery it improves the accuracy of tumor resections by providing detailed 3D visualizations of brain structures.

  2. Touch Surgery™: An interactive platform that uses AI to simulate surgical procedures, providing a hands-on training experience for surgeons. It shortens the learning curve for new surgeons, so they can be better prepared and reduce errors in actual surgeries.

  3. ProPlan CMF™: Specialized in cranio-maxillofacial surgeries, this software uses AI to plan complex face and mouth surguries, and predict surgical outcomes. The software makes it easier for doctors to rebuild bones more accurately. This means patients end up looking better and their new face parts work better too.

How AI improves surgical strategy and reduces complications

AI software enhances surgical strategy by providing detailed visualizations and predictive analytics. For instance, AI can simulate different surgical approaches and predict their outcomes, helping surgeons choose the best strategy. This reduces the likelihood of complications and improves overall surgical success (Knudsen et al., 2024).

While planning is important, AI also plays a big role during the actual surgery (with ot without robots). Let’s explore how AI helps with imaging and navigation in the OR.

Intraoperative Imaging and Navigation with AI

Taking images and using guiding tools (intraoperative imaging and navigation) are critical for the success of complex surgeries. AI makes these tools even better by providing real-time guidance and improving surgical precision.

Advanced imaging technologies enhanced by AI

AI enhances imaging technologies by providing real-time analysis and feedback. For example, AI can process intraoperative CT scans or MRIs to highlight critical structures and suggest optimal surgical paths. This allows surgeons to make informed decisions on the fly (Knudsen et al., 2024).

Real-time surgical navigation systems

AI-powered navigation systems use real-time data to guide surgical instruments with high precision. These systems can track the position of surgical tools and patient anatomy, providing continuous feedback to the surgeon. This is particularly useful in complex procedures like brain or spinal surgeries.

Benefits of AI-powered imaging in complex procedures

  • Enhanced Visualization: AI can highlight critical structures and potential risks in real-time, improving surgical accuracy.
  • Reduced Complications: By providing precise guidance, AI reduces the risk of damaging vital tissues.
  • Improved Efficiency: Real-time feedback helps in making quick decisions, reducing overall surgery time.

AI doesn’t stop working when the surgery ends. It can continue to help patients heal.

AI for Post-Operative Care and Recovery

After surgery, AI systems can monitor patient recovery, predict complications, and personalize recovery plans.

AI monitoring systems for patient recovery

AI-driven monitoring systems use sensors and wearable devices to continuously track patient vitals and recovery progress. These systems can detect early signs of complications and alert healthcare providers, ensuring timely interventions.

Predictive analytics for post-surgical complications

Predictive analytics use patient data and AI algorithms to predict potential post-surgical complications. For example, AI can analyze patterns in patient vitals to predict infections or other complications, allowing for early treatment and better outcomes (Loftus et al., 2020).

Personalized recovery plans by AI

AI can create personalized recovery plans based on individual patient data. These plans consider factors like patient history, type of surgery, and recovery progress to provide tailored recommendations. This personalized approach improves recovery times and reduces the risk of complications.

Patient followup

Research has found a 19% higher risk of nonadherence for patients who interact with a physician who doesn’t communicate well (Haskard Zolnierek & DiMatteo, 2009). 

One study tested a system with AI to follow up with patients who had bone surgery. The AI system got more responses than when people made phone calls, but the type of feedback was different. 

Patients told the AI more about their hospital stay and what they learned. They told human staff more about how they felt after surgery, which could be because people feel more comfortable talking to other people about health issues. Still, AI systems could help by giving patients simple information, answering questions, and spotting problems that doctors need to look at. This could make doctors’ jobs easier and help reduce long waiting lists (Guni et al., 2024).

Reducing hospital readmissions and improving outcomes

AI-driven post-operative care systems can reduce hospital readmissions by providing continuous monitoring and timely interventions. This not only improves patient outcomes but also reduces healthcare costs and resource needs (Scott et al., 2024).

Although AI in surgical systems offers many benefits, it also presents several challenges and areas for improvement.

Future Directions in AI Surgical Systems

Current limitations and areas for improvement

  • Data Privacy and Security: Ensuring the privacy and security of patient data is a significant challenge.
  • Algorithm Bias: AI algorithms can sometimes be biased, leading to unfair or inaccurate outcomes.
  • Integration with Existing Systems: Integrating AI technologies with existing surgical systems and workflows can be complex and costly.

Ethical considerations in AI-assisted surgery

Ethical considerations include ensuring transparency in AI decision-making, maintaining accountability for AI-driven actions, and addressing potential job displacement among healthcare professionals. It is crucial to develop ethical frameworks and guidelines to navigate these challenges (Mithany et al., 2023).

Emerging trends in AI surgical systems include the development of fully autonomous surgical robots (Gumbs et al., 2021), advanced predictive analytics for personalized medicine, and the integration of AI with other technologies like augmented reality (AR) and VR. These advancements hold the potential to further revolutionize surgical practices and improve patient outcomes.

Training the next generation of surgeons with AI

AI simulation platforms are transforming surgical education by providing hands-on training experiences in a safe environment. These platforms use AI to simulate surgical procedures, assess performance, and provide real-time feedback, helping to train the next generation of surgeons more effectively (Scott et al., 2024).

Conclusion

AI in surgical systems is enhancing precision, improving decision-making, and optimizing patient care. Ai isn’t just enhancing surgeons’ capabilities; they’re reshaping the entire surgical experience from planning to recovery. 

The best AI surgical systems offer precision, improved decision-making, and better patient outcomes. While challenges remain, the future of AI in surgery is bright, with promise of a future with safer, more efficient, and more personalized surgical care.

References

Artificial Intelligence vs Robotics. (2023). Ally Robotics. Retrieved from https://allyrobotics.com/artificial-intelligence-vs-robotics/

Esposito, L. Everything You Need to Know About Colorectal Surgery. (2022). U.S. News & World Report. Retrieved from https://health.usnews.com/health-care/best-hospitals/articles/everything-you-wanted-to-know-about-colorectal-surgery

Garceau, A. & Gopal, A. (2023). What is Laparoscopic Surgery? WebMD. Retrieved from https://www.webmd.com/digestive-disorders/laparoscopic-surgery

Gumbs, A. A., Frigerio, I., Spolverato, G., Croner, R., Illanes, A., Chouillard, E., & Elyan, E. Artificial Intelligence Surgery: How Do We Get to Autonomous Actions in Surgery? Sensors, 21(16), 5526. doi.org/10.3390/s21165526

Guni, A., Varma, P. , Zhang, J. Fehervari, M., & Ashrafian, H. (2024). Artificial intelligence in Surgery: The Future is Now. European Surgical Researach. 65(1):22-39. doi.org/10.1159/000536393
Haskard Zolnierek, K. B., & DiMatteo, M. R. (2009). Physician Communication and Patient Adherence to Treatment: A Meta-analysis. Medical Care, 47(8), 826. doi.org/10.1097/MLR.0b013e31819a5acc

Intuitive da Vinci. (n.d.). Intuitive. Retrieved from https://www.intuitive.com/en-us/products-and-services/da-vinci

Kitaguchi, D., Takeshita, N., Hasegawa, H., & Ito, M. (2022). Artificial intelligence-based computer vision in surgery: Recent advances and future perspectives. Annals of Gastroenterological Surgery, 6(1), 29-36. doi.org/10.1002/ags3.12513

Knudsen, J. E., Ghaffar, U., Ma, R., & Hung, A. J. (2024). Clinical applications of artificial intelligence in robotic surgery. Journal of Robotic Surgery, 18(1). doi.org/10.1007/s11701-024-01867-0

Loftus, T. J., Tighe, P. J., Filiberto, A. C., Efron, P. A., Brakenridge, S. C., Mohr, A. M., Rashidi, P., & Bihorac, A. (2020). Artificial Intelligence and Surgical Decision-Making. JAMA Surgery, 155(2), 148. doi.org/10.1001/jamasurg.2019.4917

Mazor X Stealth Edition Spine Robotics. (n.d.). Medtronic. Retrieved from https://www.medtronic.com/us-en/healthcare-professionals/therapies-procedures/spinal-orthopaedic/spine-robotics.html

Mithany, R. H., Aslam, S., Abdallah, S., Abdelmaseeh, M., Gerges, F., Mohamed, M. S., Manasseh, M., Wanees, A., Shahid, M. H., Khalil, M. S., & Daniel, N. (2023). Advancements and Challenges in the Application of Artificial Intelligence in Surgical Arena: A Literature Review. Cureus, 15(10). doi.org/10.7759/cureus.47924

Pediatric Craniofacial & Maxillofacial Surgery. (n.d.) The University of Chicago Medicine. Retrieved from https://www.uchicagomedicine.org/comer/conditions-services/craniofacial-anomalies/craniofacial-and-maxillofacial-surgery

PlanXR™. (n.d.). Surgical Theater. Retrieved from https://surgicaltheater.com/surgical-planning/#surgical-planner

ProPlan CMF™: Virtual planning for canio-maxillofacial surgery. (n.d.). Materialise. Retrieved from https://www.materialise.com/en/healthcare/proplan-cmf

Prostatectomy. (n.d.). Mayo Clinic. Retrieved from https://www.mayoclinic.org/tests-procedures/prostatectomy/about/pac-20385198

Scott, E. M., Hsu, P., Hussein, N., & Mehta, K. (2024). AI Has Potential to Transform Global Surgical Systems. American College of Surgeons (ACS). Retrieved from https://www.facs.org/for-medical-professionals/news-publications/news-and-articles/bulletin/2024/june-2024-volume-109-issue-6/ai-has-potential-to-transform-global-surgical-systems/

Touch Surgery™, A connected surgical future. (n.d.). Medtronic. Retrieved from https://www.medtronic.com/covidien/en-us/products/touch-surgery.html

Varghese, C., Harrison, E. M., & Topol, E. J. (2024). Artificial intelligence in surgery. Nature Medicine, 30(5), 1257-1268. doi.org/10.1038/s41591-024-02970-3

Versius. (n.d.). The uniquely small, modular & portable surgical robot. CMR Surgical. Retrieved from https://cmrsurgical.com/versius

Yang, J. (2021). Number of surgical operations in registered hospitals in the U.S. in 2019, by number of beds. Statista. Retrieved from https://www.statista.com/statistics/459787/surgical-operations-in-hospitals-in-the-us-by-number-of-beds/

Population Health Management Strategies with AI

Population Health Management Strategies with AI

by Ree AI Health Tech

Population health management (PHM) is key to effective healthcare. Using population health management strategies with AI creates new ways to help patients. In a 2023 study by Deloitte, 69% of people using generative AI said it could improve healthcare access, and 63% said it could make healthcare more affordable. 

This article explores cutting-edge insights on how this PHM-AI combo enhances patient care, reduces costs, and improves overall health outcomes across diverse communities.

Let’s first define PHM and how AI fits into this approach.

Contents

Understanding AI in Population Health Management

PHM diagram

What is Population Health Management?

PHM focuses on improving the health outcomes of a group by monitoring and identifying individual patients within that group. The primary goals of PHM are:

What’s the difference between PHM and public health?

Don’t confuse population health with public health. Public health tries to stop diseases and injuries before they happen, by:

  • Teaching people about health
  • Reaching out to communities
  • Doing research
  • Changing standards or laws to make health-related matters safer

Population health issues 

Things that affect community health range from physical to social, such as:

  • Environmental factors (like pollution)
  • Income and education levels
  • Gender and racial inequality
  • Social connections
  • Community involvement
  • Access to clean water

People working in population health need to understand how these factors affect communities and interact with each other. For example, low-income groups might struggle to access healthy food or safe places to exercise, even if these are available nearby. Understanding these connections can help us create better strategies to improve overall community health (Tulane University, 2023).

How AI enhances PHM

AI technologies, such as machine learning and predictive analytics, can process large datasets quickly and accurately. AI is a great asset in PHM because it can find at-risk individuals more quickly and accurately. This can help healthcare providers create better intervention strategies to improve patient outcomes, manage chronic diseases, and prevent illnesses. 

The key benefits of integrating AI into PHM include:

  • Improved accuracy: AI can analyze complex datasets to identify patterns that may be missed by human analysts.
  • Efficiency: Automated processes reduce the time and effort required for data analysis.
  • Personalization: AI can tailor interventions to individual patient needs, improving outcomes.

Companies using big data for PHM

Another PHM diagram

Some examples of companies offering data solutions for health systems:

  • 1upHealth – They created Population Connect, which makes it easier to get and share health data, and cuts down on paperwork and manual tasks. It also gives clinicians a full picture of their patients’ health.
  • Arcadia – Arcadia’s software tracks patient health over time and makes care notes easy to find. The system constantly updates, helping teams set goals and measure their progress for different patient groups.
  • Amitech – Amitech uses health information to manage community health. They combine physical and mental health data to spot risks and get patients more involved in their own care.
  • Linguamatics – Their platform uses natural language processing (NLP) to find hidden data in health records to improve community health. They use smart tech to analyze patient notes, predict health risks, and find patients who need extra care.
  • Socially Determined – This company helps healthcare groups understand social risks, called social determinants of health (SDoH). Their SocialScape platform measures things like patient housing and food access, which can help health providers create better care plans for different communities.

One of the most powerful applications of AI in PHM is its ability to identify and predict health risks across populations.

Risk Stratification and Predictive Analytics using AI

Risk stratification involves categorizing patients based on their risk of developing certain conditions. Predictive analytics uses historical data to indicate future health outcomes. Together, these techniques enable proactive healthcare management.

Identifying high-risk individuals

AI algorithms can analyze electronic health records (EHRs), lab results, and other data sources to identify individuals at high risk for conditions such as diabetes, heart disease, or chronic obstructive pulmonary disease (COPD). 

For example, the PRISM model provides individual risk scores and stratifies patients into different risk levels based on their health data (Snooks et al., 2018).

Predictive modeling

Predictive modeling uses AI to forecast disease progression and health outcomes. For instance, AI can predict which patients are likely to develop complications from chronic diseases, allowing for early intervention. 

Researchers at Cedars-Sinai Medical Center developed an AI algorithm to measure plaque in arteries. They found that AI algorithms could predict heart attacks within 5 years by analyzing coronary CTA images. This significantly reduced the time required for diagnosis (Lin, et al., 2022).

In another example, Stanford University used AI to monitor ICU patients’ mobility, improving patient outcomes by alerting staff to potential issues (Yeung et al., 2019).

With AI’s ability to analyze large amounts of data, healthcare providers can now create highly tailored care plans for individuals within a population.

Personalized Interventions and Care Plans

Personalized care plans are tailored to meet the specific needs of individual patients. AI algorithms can analyze patient data to recommend the best treatments and interventions. Let’s look at some of those applications.

People in waiting room wearing face masks

Tailoring interventions

AI can analyze various data points, including genetic information, lifestyle factors, and medical history, to create personalized care plans. For example, machine learning algorithms can recommend specific medications or lifestyle changes based on a patient’s unique profile.

Treatment recommendation systems

AI-powered treatment recommendation systems can help healthcare providers choose the best treatments for their patients. These systems use data from clinical trials, patient records, and medical literature to provide evidence-based recommendations.

Balancing personalization with population-level strategies

While personalization is crucial, it’s also essential to consider population-level strategies. AI can help balance these by identifying common trends and patterns within a population, allowing for targeted interventions that benefit individuals and the broader community.

Remote monitoring and telehealth integration

Remote patient monitoring (RPM) and telehealth technologies are important when managing population health. For example, AI can analyze data from wearable health devices, such as heart rate monitors and glucose sensors, to detect early signs of health issues. This allows for timely interventions and reduces the need for hospital visits.

Telehealth platforms

Elderly woman on Zoom with health provider

Telehealth platforms enhanced by AI can provide virtual consultations, remote diagnostics, and personalized treatment plans. These platforms help address healthcare access disparities by providing services to rural and underserved communities. By providing remote consultations and monitoring, these technologies reduce the need for travel and make healthcare more accessible.

Overcoming data silos

Effective population health management requires data from various sources. However, data silos and interoperability issues can hinder this process.

Organizations often manage risks in various silos by department. This makes it difficult to see all the risks in the organization, and also makes it tough to create plans that work together to reduce these risks.

AI can help break down data silos by standardizing and integrating data from different sources. This ensures that healthcare providers have a comprehensive view of patient health.

Standardizing and analyzing diverse health data

AI solutions can standardize data formats and analyze diverse datasets, making it easier to identify trends and patterns. This improves the accuracy and efficiency of population health management strategies.

Ensuring data privacy and security

Data privacy and security are critical in AI-driven PHM. Robust encryption methods and secure data storage solutions are essential to protect patient information.

Beyond medical data, AI can also incorporate socioeconomic and environmental factors that significantly impact health outcomes.

Social Determinants of Health and AI

Things like money, education and where people live affect their health. These are called SDoH. AI can incorporate these factors into predictive models to predict health problems and find people who might need help. This lets healthcare providers make better plans to keep communities healthy.

Social determinants of health diagram

Incorporating social and environmental factors

AI algorithms can analyze data on SDoH such as income, education, and housing conditions, to predict health outcomes and identify at-risk populations.

Predictive analytics for SDoH

Predictive analytics can help healthcare providers develop targeted interventions to address SDoH. For example, AI can identify communities at risk for certain diseases and recommend preventive measures.

Collaborative AI Approaches for community health improvement

Collaborative AI approaches involve partnerships between healthcare providers, community organizations, and technology companies to improve community health. These collaborations can lead to more effective and sustainable health interventions.

Now that we understand SDoH and ways to deal with them, it’s crucial to track how effective those efforts are, and continuously improve our approaches.

Measuring and Improving Population Health Outcomes

Measuring and improving population health outcomes requires continuous monitoring and refinement of strategies. AI-powered tools can provide real-time insights and help healthcare providers make data-driven decisions.

AI-powered dashboards and visualization tools

Dashboards and visualization tools using AI can display population health metrics in an easily understandable format. These tools help healthcare providers track progress and identify areas for improvement.

Continuous learning systems

Continuous learning systems use AI to analyze new data and refine PHM strategies. This ensures that interventions remain effective and relevant over time.

Ethical considerations for patient data

Ethical considerations are crucial when using AI with PHM. Ensuring that AI algorithms are free from bias and that patient data is used responsibly is essential for maintaining trust and achieving equitable health outcomes.

Conclusion

Combining AI with population health management is a big step forward in taking care of communities better and faster. AI helps healthcare providers spot and solve health problems early, instead of waiting until people get sick, by:

  • Predicting health issues before they happen
  • Creating personalized care plans
  • Using data to make smarter decisions

As we get better at using AI in healthcare, we can:

  • Help more people stay healthy
  • Lower the cost of healthcare
  • Improve life for whole communities

We’re just starting to use AI in population health management. Healthcare leaders and policymakers need to use these AI tools. It’s not just a choice – it’s necessary to build healthier communities that can handle health challenges better.

Robot looking at the globe in black

References

Dhar, A., Fera, B., & Korenda, L. Can GenAI help make health care affordable? Consumers think so. (2023). Deloitte. Retrieved from https://www2.deloitte.com/us/en/blog/health-care-blog/2023/can-gen-ai-help-make-health-care-affordable-consumers-think-so.html

Lin, A., et al. (2022). Deep learning-enabled coronary CT angiography for plaque and stenosis quantification and cardiac risk prediction: an international multicentre study. The Lancet. doi.org/10.1016/S2589-7500(22)00022-X

Population Health Management: A Healthcare Administration Perspective. (2023). Tulane University. Retrieved from https://publichealth.tulane.edu/blog/population-health-management/

Predictive Analytics for Risk Management: Uses, Types & Benefits. (n.d.). PREDIK Data-Driven. Retrieved from https://predikdata.com/predictive-analytics-for-risk-management/

Snooks, H., Bailey-Jones, K., & Burge-Jones, D., et al.. (2018). Predictive risk stratification model: a randomised stepped-wedge trial in primary care (PRISMATIC). Southampton (UK): NIHR Journals Library; (Health Services and Delivery Research, No. 6.1.) Chapter 1, Introduction. https://www.ncbi.nlm.nih.gov/books/NBK475995/

Yeung, S., Rinaldo, F., Jopling, J., Liu, B., Mehra, R., Downing, N. L., Guo, M., Bianconi, G. M., Alahi, A., Lee, J., Campbell, B., Deru, K., Beninati, W., & Milstein, A. (2019). A computer vision system for deep learning-based detection of patient mobilization activities in the ICU. Npj Digital Medicine, 2(1), 1-5. doi.org/10.1038/s41746-019-0087-z

The Impact of AI on Healthcare Cost Reduction and Resource Allocation 

The Impact of AI on Healthcare Cost Reduction and Resource Allocation 

by Ree AI Health Tech

In an era where healthcare costs are skyrocketing, AI can be a game-changer. The impact of AI on healthcare cost reduction and resource allocation has been overwhelmingly positive so far. A recent study by Accenture predicts that AI applications in healthcare could save up to $150 billion annually for the U.S. healthcare economy by 2026. 

Let’s see how AI can help reduce costs and staff human resources more efficiently.

Contents

Understanding AI’s Role in Healthcare Cost Reduction

Definition of AI in healthcare 

Artificial Intelligence (AI) in healthcare uses complex algorithms and software to analyze, interpret, and understand complicated medical and healthcare data. AI technologies such as machine learning (ML), natural language processing (NLP), and predictive analytics integrated into various healthcare processes can enhance efficiency and accuracy.

U.S. healthcare costs

Source: American Medical Association

By 2031, almost 20% of U.S. spending will be on healthcare, which is a lot compared to other wealthy countries.

Healthcare costs are a major burden for families in the U.S. According to the Kaiser Family Foundation, about half of American adults find it difficult to afford healthcare costs. 

24% have had problems paying for healthcare premiums, deductibles, or copays in the past year. That number is 33% for those in poor health. These high expenses often lead to delayed care, skipped medications, and financial strain. 

About 100 million people in America have serious medical debt. They often rely on savings, credit cards, and side jobs to make up the slack. This financial pressure underscores the need for cost-effective solutions.

Helping more people afford health care often means the government spends more money. On the other hand, trying to reduce overall spending might increase costs for individuals. This makes health care policy very challenging, with no easy solutions.

Key areas where AI can impact costs

AI can cut healthcare costs in many ways, such as:

  • Administrative Efficiency: Automating routine tasks such as data entry and claims processing can save time and reduce errors.
  • Diagnostic Accuracy: AI can improve diagnostic accuracy, reducing the need for unnecessary tests and treatments.
  • Predictive Analytics: AI can predict patient outcomes and optimize resource allocation, reducing waste and improving care efficiency.

Labor costs are the greatest expense hospitals have, as shown in the following chart.

Source: American Hospital Association (AHA) and Strata Decision Technology

A McKinsey/EIT Health report shows that tasks by several healthcare occupations can be at least partially automated by 2030, providing more cost savings to healthcare organizations. 

Next, let’s look at how AI can improve resource management in hospitals.

AI-Driven Resource Allocation in Hospitals

Facility management

AI can make hospital buildings run smoother by controlling temperature systems to save energy and keep patients comfortable. It also spots equipment problems early, avoiding breakdowns and saving money on repairs (Varnosfaderani & Forouzanfar, 2024). 

Predictive analytics for patient flow and bed management

Empty recovery room

Managing patient flow and bed use is also key for hospital efficiency. AI-driven predictive analytics can predict patient admissions, discharges, and bed availability, allowing hospitals to optimize their resources. 

Hospitals can manage their emergency services with efficiency if they can predict how many emergency patients will come in. They currently use simple guessing methods based on past patterns. 

Hospitals could use real-time patient data from electronic health records (EHRs) to make short-term predictions about bed needs. This ensures that beds are available when needed, reduces the time patients spend waiting for care, and avoids cancelling planned surgeries (King et al., 2022).

Staff scheduling optimization

Using AI for scheduling can reduce overtime costs and prevents staff burnout, leading to better patient care and lower operational costs.

AI can analyze historical data and predict staffing needs, ensuring that hospitals have the right number of staff at the right times. This includes scheduling medical procedures to maximize the use of operating rooms and staff, while minimizing patient wait times (Varnosfaderani & Forouzanfar, 2024). 

Equipment and supply chain management

AI can streamline equipment and supply chain management by:

  • Studying trends 
  • Predicting demand 
  • Optimizing inventory levels 
  • Automating orders

This reduces the risk of shortages and overstocking to cut waste, save money, and ensure that necessary supplies are always available. In emergencies, AI quickly figures out what’s needed and helps get resources where they’re most important (Varnosfaderani & Forouzanfar, 2024). 

Clinical documentation is ever-present in healthcare. Let’s discuss how AI can streamline admin tasks.

Streamlining Administrative Processes with AI

Doctor on the phone

Automating paperwork and data entry

Administrative tasks like paperwork and data entry take time and are prone to errors. But AI can read and sort different forms and reports quickly. 

AI can automate these processes to save time, free up staff to focus on more critical tasks, and reduce the likelihood of mistakes (Varnosfaderani & Forouzanfar, 2024). 

Improving billing accuracy and reducing errors

It takes time and expense to fix billing errors. A study in the insurance industry showed that ML can improve insurance estimates better than traditional methods (Baudry & Robert, 2019). 

AI can improve hospital billing and insurance claim accuracy by automating the coding process and identifying discrepancies before they become issues. This leads to quicker reimbursements and fewer denied claims.

Enhancing insurance claims processing

AI can streamline the insurance claims process by automating the verification and approval of claims. This reduces the time it takes to process claims and improves customer satisfaction by minimizing delays and errors.

Beyond administrative tasks, AI is also making significant strides in improving patient care and treatment.

AI in Diagnostic Accuracy and Treatment Planning

Brain scans

Reducing misdiagnosis rates and associated costs

Misdiagnoses can lead to unnecessary treatments and additional costs. AI can analyze medical data with high accuracy, reducing the likelihood of misdiagnoses and ensuring that patients receive the correct treatment the first time (Khanna et al., 2022).

Personalized treatment recommendations

AI can provide personalized treatment recommendations based on a patient’s medical history and current condition. This ensures that patients receive the most effective treatments, improving outcomes and reducing costs associated with trial-and-error approaches (Alowais et al., 2023).

Early disease detection and prevention strategies

Early detection of diseases can significantly reduce treatment costs and improve patient outcomes. AI can analyze large datasets to identify early signs of diseases, allowing for timely interventions and preventive care (Alowais et al., 2023).

AI can also help diagnose illnesses and assess symptoms with virtual methods in telemedicine and telehealth.

Telemedicine and Remote Patient Monitoring

Phone with chatbot conversation

AI-powered virtual health assistants

Virtual health assistants powered by AI can provide patients with medical advice, schedule appointments, and answer health-related questions. This reduces the need for in-person visits and allows healthcare providers to focus on more complex cases.

Chronic disease management via remote monitoring

AI can monitor patients with chronic diseases remotely, also called remote patient monitoring (RPM). When AI analyzes data from wearable devices, it can notify healthcare providers about any concerning changes to trigger an alert. This proactive approach reduces hospital visits and readmissions, saving costs and improving patient quality of life.

Reducing unnecessary hospital visits and readmissions

By providing continuous monitoring and early intervention, AI can help prevent complications that would otherwise require a patient to return to the hospital. This not only improves patient outcomes, but also reduces the strain on healthcare facilities.

Challenges and Considerations in AI Implementation

Doctor shows tablet to nurse

Initial investment and integration costs

Implementing AI in healthcare requires a high upfront investment in technology and training. While the long-term benefits can outweigh these costs, the initial financial burden can be a barrier for some healthcare providers.

Data privacy and security concerns

AI systems handle vast amounts of sensitive patient data, raising concerns about privacy and security. To implement these systems successfully, healthcare organizations must comply with regulations and protect patient information (Alowais et al., 2023).

Workforce adaptation and training needs

Integrating AI into healthcare workflows requires training staff to use new technologies effectively. This can be challenging, particularly for those who are less familiar with digital tools. Ongoing education and support are essential to ensure that healthcare professionals can leverage AI to its full potential (Alowais et al., 2023). 

Future Outlook: AI’s Long-term Impact on Healthcare Economics

Projected cost savings and efficiency gains

AI has the potential to generate significant cost savings and efficiency gains in healthcare. By automating routine tasks, improving diagnostic accuracy, and optimizing resource allocation, AI can reduce operational costs and enhance patient care (Khanna et al., 2022).

Potential shifts in the healthcare job market

Integrating AI in healthcare systems causes a shift in the job market. While some administrative roles may become redundant, new opportunities will emerge in AI development, data analysis, and technology management. Healthcare professionals will need to adapt to these changes and acquire new skills.

Ethical considerations and policy implications

The use of AI in healthcare raises ethical considerations, such as ensuring fairness in AI algorithms and addressing potential biases. Policymakers should establish guidelines and regulations to ensure that we use AI responsibly and equitably in healthcare (Alowais et al., 2023).

Conclusion

AI’s impact on cost reduction and resource allocation in healthcare is profound and far-reaching. From streamlining administrative tasks to enhancing diagnostic accuracy, AI technologies are valuable allies in the quest for more efficient and affordable healthcare. Successful implementation will require careful planning, ethical considerations, and a commitment to ongoing innovation. 

As AI continues to evolve, its long-term impact on healthcare economics will depend on how effectively these challenges are addressed and how well healthcare providers can integrate AI into their workflows. By embracing AI responsibly, healthcare providers can work towards a future where high-quality care is more accessible and affordable for all.

References

Alowais, S. A., Alghamdi, S. S., Alsuhebany, N., Alqahtani, T., Alshaya, A. I., Almohareb, S. N., Aldairem, A., Alrashed, M., Saleh, K. B., Badreldin, H. A., Al Yami, M. S., Harbi, S. A., & Albekairy, A. M. (2023). Revolutionizing healthcare: The role of artificial intelligence in clinical practice. BMC Medical Education, 23. doi.org/10.1186/s12909-023-04698-z 

Altman, D. (2024). The Two Health Care Cost Crises. Kaiser Family Foundation. Retrieved from https://www.kff.org/from-drew-altman/the-two-health-care-cost-crises/

America’s Hospitals and Health Systems Continue to Face Escalating Operational Costs and Economic Pressures as They Care for Patients and Communities. (2024). American Hospital Association (AHA). Retrieved from https://www.aha.org/costsofcaring

Baudry M., & Robert C.Y. (2019). A machine learning approach for individual claims reserving in insurance. Applied Stochastic Models in Business and Industry; 35:1127–1155. doi:10.1002/asmb.2455

Collier, M., & Fu, R. (2020). AI: Healthcare’s new nervous system. Accenture. Retrieved from https://www.accenture.com/au-en/insights/health/artificial-intelligence-healthcare

Hasa, I. (2024). From Data to Decisions: AI-driven Healthcare Resource Optimization. LinkedIn Pulse. Retrieved from https://www.linkedin.com/pulse/from-data-decisions-ai-driven-healthcare-resource-inamul-hasan-m-sc–stzaf

Khanna, N. N., Maindarkar, M. A., Viswanathan, V., E Fernandes, J. F., Paul, S., Bhagawati, M., Ahluwalia, P., Ruzsa, Z., Sharma, A., Kolluri, R., Singh, I. M., Laird, J. R., Fatemi, M., Alizad, A., Saba, L., Agarwal, V., Sharma, A., Teji, J. S., Al-Maini, M., . . . Suri, J. S. (2022). Economics of Artificial Intelligence in Healthcare: Diagnosis vs. Treatment. Healthcare, 10(12). https://doi.org/10.3390/

King, Z., Farrington, J., Utley, M., Kung, E., Elkhodair, S., Harris, S., Sekula, R., Gillham, J., Li, K., & Crowe, S. (2022). Machine learning for real-time aggregated prediction of hospital admission for emergency patients. Npj Digital Medicine, 5(1), 1-12. doi.org/10.1038/s41746-022-00649-y

Lopes L., Montero A., Presiado, M., & Hamel, L. (2024). Americans’ Challenges with Health Care Costs. Kaiser Family Foundation (KFF). Retrieved from https://www.kff.org/health-costs/issue-brief/americans-challenges-with-health-care-costs/

M, N. (2023). Artificial Intelligence (AI) in Healthcare Claims Processing. Nanonets. Retrieved from https://nanonets.com/blog/ai-healthcare-claims-processing/

McDill, V. (2024). New Study Will Explore Whether Artificial Intelligence Reduces Healthcare Spending and Impacts Health Outcomes. University of Minnesota School of Public Health. Retrieved from https://www.sph.umn.edu/news/new-study-will-explore-whether-artificial-intelligence-reduces-healthcare-spending-and-impacts-health-outcomes/

Spatharou, A., Hieronimus, S., & Jenkins, J. (2020). Transforming healthcare with AI: The impact on the workforce and organizations. McKinsey & Company. Retrieved from https://www.mckinsey.com/industries/healthcare/our-insights/transforming-healthcare-with-ai

Trends in health care spending. (2024). American Medical Association (AMA). Retrieved from https://www.ama-assn.org/about/research/trends-health-care-spending

Varnosfaderani, S. M., & Forouzanfar, M. (2024). The Role of AI in Hospitals and Clinics: Transforming Healthcare in the 21st Century. Bioengineering, 11(4). doi.org/10.3390/bioengineering11040337

How to Implement AI in Clinical Practice 

How to Implement AI in Clinical Practice 

by Ree AI Health Tech

From technical hurdles to ethical dilemmas, healthcare providers face numerous obstacles using AI in healthcare–in particular, how to implement AI in clinical practice. A 2023 survey by the American Medical Association found that 93% of doctors believe AI can improve patient care, but only 38% feel prepared to use it in their practice. 

In this article, we’ll delve into the obstacles and potential solutions to implementing AI in healthcare and integrating AI into an existing health system.

Contents

Challenges with Implementing AI in Healthcare

Nursing colleagues in hall

High integration costs

Implementing AI in healthcare is expensive. It takes a significant investment to buy the systems, manage data, and train staff:

  • High Initial Investment for AI Implementation: The cost of acquiring and implementing AI systems can be prohibitive for many healthcare providers. These costs include computers, data storage, and patient data security.
  • Ongoing Costs for Maintenance and Upgrades: AI systems require continuous maintenance and updates, adding to the overall cost.
  • Balancing AI Spending with Other Healthcare Priorities: Healthcare providers must balance AI investments with other critical healthcare needs.

To make a new system implementation work requires careful planning and teamwork. Help from the government and new ways to pay for it can make AI in healthcare possible (Luong, 2024).

Data quality and availability challenges

Ensuring high-quality data is crucial for effective AI implementation in healthcare. However, several challenges exist:

  • Inconsistent Data Formats Across Healthcare Systems: Different healthcare providers often use various data formats, making it difficult to integrate and analyze data efficiently (Krylov, 2024).
  • Limited Access to Large, Diverse Datasets: AI systems require vast amounts of data to learn and make accurate predictions. However, accessing such datasets can be challenging due to privacy concerns and regulatory restrictions (Johns Hopkins Medicine, 2015).
  • Ensuring Data Accuracy and Completeness: Inaccurate or incomplete data can lead to incorrect diagnoses and treatments, posing significant risks to patient safety (4medica, 2023).

Technical integration hurdles

Nurse charting

Integrating AI into existing healthcare IT infrastructure presents several technical challenges:

  • Compatibility Issues with Existing Healthcare IT Infrastructure: Many healthcare systems are built on legacy technologies that may not be compatible with modern AI solutions.
  • Scalability Concerns for AI Systems: AI systems need to handle large volumes of data and scale efficiently as the amount of data grows.
  • Maintenance and Updates of AI Algorithms: AI algorithms require regular updates to maintain accuracy and adapt to new medical knowledge.

How to address these technical challenges

Here are some ways to overcome these challenges:

  • Developing Standardized Data Formats and APIs: Standardizing data formats and creating APIs can facilitate seamless data exchange between different systems (Krylov, 2024).
  • Implementing Cloud-Based AI Solutions: Cloud-based solutions offer scalability and flexibility, making it easier to manage and update AI systems.
  • Establishing Dedicated AI Support Teams: Having specialized teams to manage and support AI systems can ensure smooth integration and operation.

Following these guidelines will help when it comes to integrating an AI platform in a healthcare system.

Privacy and security concerns

Protecting patient data is paramount when implementing AI in healthcare. Some considerations include:

  • Protecting Patient Data in AI Systems: AI systems must be designed with robust security measures to protect sensitive patient information (Yadav et al., 2023).
  • Compliance with Healthcare Regulations: Ensuring compliance with regulations, like the Health Insurance Portability and Accountability Act (HIPAA) in the U.S., is essential to avoid legal repercussions and maintain patient trust. The U.S. Food & Drug Administration (FDA) focuses on approving AI developers. Europe has made laws and data protection rules for AI use (Murdoch, 2021).
  • Managing Consent for AI Use in Patient Care: Obtaining and managing patient consent for using their data in AI systems is crucial for ethical and legal compliance.

AI and HIPAA Compliance 

security guard - credit card - shield

Balancing data use for AI with patient privacy rights is a key issue.

AI needs lots of data, more than clinical trials usually have. Some areas like eye care do well with this. However, sharing data can risk patient privacy, affecting jobs, insurance, or identity theft. It’s hard to hide patient info completely (Alonso & Siracuse, 2023).

For rare diseases, data from many places is needed. Sharing data can increase privacy risks, like identifying patients from anonymous data. Working with big companies raises concerns about data being used for profit, which can clash with fair data use (Tom et al., 2020).

AI tools that learn over time might accidentally break HIPAA rules. Doctors must understand how AI handles patient data to follow HIPAA rules. They need to know where AI gets its info and how it’s protected. Healthcare workers must use AI responsibly, get patient permission, and be open about using AI in care (Accountable HQ, 2023).

AI in healthcare needs rules that respect patient rights. We should focus on letting patients choose how their info is used. This means asking for permission often, and making it easy for patients to take back their data if they want to. 

We also need better ways to protect patient privacy. Companies holding patient data should use the best safety methods and follow standards. If laws and standards don’t keep up with fast-changing tech like AI, we’ll fall behind in protecting patients’ rights and data (Murdoch, 2021).

When using AI in clinical research, copyright problems can occur because AI uses information from many places to make content. It might use copyrighted content without knowing, causing legal issues. It’s important to make sure AI doesn’t use protected material (Das, 2024).

Scales of justice, book and scroll

We need strong laws and data standards to manage AI use, especially in the field of medicine.  Ethical and legal issues are significant barriers to using AI in healthcare, for example:

  • Addressing Bias in AI Algorithms: AI systems can inherit biases present in training data, leading to unequal treatment outcomes.
  • Establishing Liability in AI-Assisted Decisions: AI and the Internet of Things (IoT) technologies make it hard to decide who’s responsible when things go wrong (Eldadak et al., 2024). We need clear guidelines on who is liable for errors made by AI systems–AI developers, the doctor, or the AI itself (Cestonaro et al., 2023).
  • Creating Transparency in AI Decision-Making Processes: AI systems should be transparent in their decision-making processes to build trust among clinicians and patients.

How to address these ethical concerns

We should think about how these technologies affect patients and what risks they should take. We need to find a balance that protects people without stopping new ideas. Ways to overcome some of these barriers include:

  • Developing AI Ethics Committees in Healthcare Institutions: Ethics committees can oversee AI implementations and ensure they adhere to ethical standards.
  • Creating Clear Guidelines for AI Use in Clinical Settings: Establishing guidelines can help standardize AI use and address ethical and legal concerns.
  • Engaging in Ongoing Dialogue with Legal and Ethical Experts: Continuous engagement with experts can help navigate the evolving ethical and legal landscape.

Scientists, colleges, healthcare organizations, and regulatory agencies should work together to create standards for naming data, sharing data, and explaining how AI works. They should also make sure AI code and tools are easy to use and share (Wang et al., 2020).

The old ways of dealing with legal problems don’t work well for AI issues. We need a new approach that involves doctors, AI makers, insurance companies, and lawyers working together (Eldadak, et al., 2024).

Resistance to change and adoption

Demo of a CPR mask

Resistance from healthcare professionals can hinder AI adoption for many reasons:

  • Overcoming Clinician Skepticism Towards AI: Educating clinicians about the benefits and limitations of AI can help reduce skepticism.
  • Addressing Fears of AI Replacing Human Roles: Emphasizing AI as a tool to add to, not replace, human roles can alleviate fears.
  • Managing the Learning Curve for New AI Tools: Providing adequate training and support can help clinicians adapt to new AI tools.

AI might not work well with new data in hospitals, which could harm patients. There are many issues with using AI in medicine. These include lack of proof it’s better than old methods, and concerns about who’s at fault for mistakes (Guarda, 2019).

Training and education gaps

Nursing colleagues in hall

Lack of AI literacy among healthcare professionals is a significant barrier:

  • Lack of AI Literacy Among Healthcare Professionals: Many clinicians lack the knowledge and skills to effectively use AI tools.
  • Limited AI-Focused Curricula in Medical Education: Medical schools often do not include comprehensive AI training in their curricula.
  • Keeping Pace with Rapidly Evolving AI Technologies: Continuous education is necessary to keep up with the fast-paced advancements in AI.

How to address these knowledge gaps

We can bridge the knowledge gap by:

  • Integrating AI Training into Medical School Curricula: Incorporating AI education into medical training can prepare future clinicians for AI integration.
  • Offering Continuous Education Programs for Practicing Clinicians: Regular training programs can help practicing clinicians stay updated on AI advancements.
  • Developing User-Friendly AI Interfaces for Clinical Use: Designing intuitive AI tools can make it easier for clinicians to adopt and use them effectively.

Doctor-patient knowledge sharing

Healthcare providers need to understand AI to explain it to patients. They don’t need to be experts, but according to Cascella (n.d.), they should know enough to:

  1. Explain how AI works in simple terms.
  2. Share their experience using AI.
  3. Compare AI’s risks and benefits to human care.
  4. Describe how humans and AI work together.
  5. Explain safety measures, like double-checking AI results.
  6. Discuss how patient information is kept private.

Doctors should take time to explain these things to patients and answer questions. This helps patients make good choices about their care. After talking, doctors should write down what they discussed in the patient’s records and keep any permission forms.

By doing this, doctors make sure patients understand and agree to AI use in their care. Patients should understand how AI might affect their treatment and privacy.

How to Implement AI Platforms in Healthcare

Here are the technical steps that Tateeda (2024) recommends to implement the technical aspects of AI into an existing healthcare system:

  1. Prepare the data: Collect health info like patient records and medical images. Clean it up, remove names, and store it safely following data privacy standards.
  1. Choose your AI model: Choose where AI can help, like disease diagnosis or patient monitoring. Select AI that fits these jobs, like special programs for looking at images or predicting health risks.
  1. Train the AI model: Teach the AI using lots of quality health data. Work with doctors to make sure the AI learns the right things.
  1. Set up and test the model: Integrate AI into the current health system(s). Check it works well by testing it a lot and asking doctors what they think.
  1. Use and monitor: Start using AI in hospitals. Make sure it works within the processes doctors are accustomed to. Keep an eye on how it’s doing and get feedback to continue making it better.

Conclusion

To implement AI in clinical practice with success, we must address data quality, technical integration, privacy, ethics, and education, challenges. Healthcare providers can pave the way for successful AI adoption in clinical practice–the key lies in a multifaceted approach to: 

  • Invest in robust IT infrastructure
  • Foster a culture of continuous learning
  • Maintain open dialogue among all stakeholders. 

As we navigate these hurdles, the healthcare industry moves closer to a future where AI seamlessly enhances clinical practice, ultimately leading to better outcomes for patients and more efficient systems for providers.

References

AI in Healthcare: What it means for HIPAA. (2023). Accountable HQ. Retrieved from  https://www.accountablehq.com/post/ai-and-hipaa

Alonso, A., Siracuse, J. J. (2023). Protecting patient safety and privacy in the era of artificial intelligence. Seminars in Vascular Surgery 36(3):426–9. https://pubmed.ncbi.nlm.nih.gov/37863615/

American Medical Association (AMA). (2023). Physician sentiments around the use of AI in health care: motivations, opportunities, risks, and use cases. AMA Augmented Intelligence Research. Retrieved from https://www.ama-assn.org/system/files/physician-ai-sentiment-report.pdf

Cascella, L. M. (n.d.). Artificial Intelligence and Informed Consent. MedPro Group. Retrieved from https://www.medpro.com/artificial-intelligence-informedconsent

Cestonaro, C., Delicati, A., Marcante, B., Caenazzo, L., & Tozzo, P. (2023). Defining medical liability when artificial intelligence is applied on diagnostic algorithms: A systematic review. Frontiers in Medicine, 10. doi.org/10.3389/fmed.2023.1305756

Das, S. (2024). Embracing the Future: Opportunities and Challenges of AI integration in Healthcare. The Association of Clinical Research Professionals (ACRP). Clinical Researcher, 38(1). Retrieved from https://acrpnet.org/2024/02/16/embracing-the-future-opportunities-and-challenges-of-ai-integration-in-healthcare

Data Quality Issues in Healthcare: Understanding the Importance and Solutions. (2024). 4Medica. Retrieved from https://www.4medica.com/data-quality-issues-in-healthcare/

Definition of Limited Data Set. (2015). Johns Hopkins Medicine. Retrieved from  https://www.hopkinsmedicine.org/institutional-review-board/hipaa-research/limited-data-set

Eldakak, A., Alremeithi, A., Dahiyat, E., Mohamed, H., & Abdulrahim Abdulla, M. I. (2024). Civil liability for the actions of autonomous AI in healthcare: An invitation to further contemplation. Humanities and Social Sciences Communications, 11(1), 1-8. doi.org/10.1057/s41599-024-02806-y

Guarda, P. (2019.) ‘Ok Google, am I sick?’: artificial intelligence, e-health, and data protection regulation. BioLaw Journal (Rivista di BioDiritto) (1):359–75. https://teseo.unitn.it/biolaw/article/view/1336

Krylov, A. (2024). The Value and Importance of Data Quality in Healthcare. Kodjin. Retrieved from https://www.kodjin.com/blog/the-value-and-importance-of-data-quality-in-healthcare

Luong, K. (2024). Challenges of AI Integration in Healthcare. Ominext. Retrieved from https://www.ominext.com/en/blog/challenges-of-ai-integration-in-healthcare

Mittermaier, M., Raza, M. M., & Kvedar, J. C. (2023). Bias in AI-based models for medical applications: challenges and mitigation strategies. Npj Digital Medicine, 6(113). doi.org/10.1038/s41746-023-00858-z

Murdoch, B. (2021). Privacy and artificial intelligence: challenges for protecting health information in a new era. BMC Med Ethics 22(1):1–5.

Top 5 Use Case of AI in Healthcare: Implementation Strategies and Future Trends. (2024). Tateeda. Retrieved from https://tateeda.com/blog/ai-in-healthcare-use-cases

Tom, E., Keane, P. A., Blazes, M., Pasquale, L. R., Chiang, M. F., Lee, A. Y., et al. (2020). Protecting Data Privacy in the Age of AI-Enabled Ophthalmology. Transl Vis Sci Technol 9(2):36–6. doi.org/10.1167/tvst.9.2.36

Wang, S. Y., Pershing, S., & Lee, A. Y. (2020). Big Data Requirements for Artificial Intelligence. Current Opinion in Ophthalmology, 31(5), 318. doi.org/10.1097/ICU.0000000000000676

Yadav, N., Pandey, S., Gupta, A., Dudani, P., Gupta, S., & Rangarajan, K. (2023). Data Privacy in Healthcare: In the Era of Artificial Intelligence. Indian Dermatology Online Journal, 14(6), 788-792. doi.org/10.4103/idoj.idoj_543_23

AI Health Chatbots for Patient Engagement

AI Health Chatbots for Patient Engagement

by Ree AI Health Tech

Have you ever wished you could get instant medical advice without waiting for a doctor’s appointment? Or maybe you’ve found yourself wondering about a symptom in the middle of the night? Well, you’re not alone, and that’s where AI health chatbots come in. 

The market segment for chatbots is expected to grow from $196 million in 2022 to approximately $1.2 billion by 2032 (Clark & Bailey, 2024). These digital health assistants are changing the game in healthcare, offering support and information around the clock. But what exactly are they, and how do they work? 

Contents

What Are AI Health Chatbots?

AI health chatbots are smart computer programs that help patients with health-related information and support. These virtual health assistants use advanced technologies like natural language processing (NLP) and machine learning (ML). NLP and ML allows them to understand context and emotions in conversations, and respond to user queries in a human-like manner (Karlović, 2024).

Think of the virtual health assistant as your personal health companion to (Laranjo et al., 2018):

  • Answer basic health questions
  • Provide information about symptoms and conditions
  • Offer medication reminders
  • Guide you through simple diagnostic processes

Some popular AI health chatbots include:

Now that we understand the concept of AI health chatbots, let’s explore the various advantages they bring to healthcare.

Benefits of AI Health Chatbots

AI health chatbots have several advantages for both patients and healthcare providers. 

24/7 availability

One of the most significant advantages of AI health chatbots is their round-the-clock availability. Have a health concern at 2 AM? Your chatbot is there to help, providing instant support when you need it. 

Cost reduction

Chatbots are mostly free for patients. Some apps are covered by insurance when prescribed by a health provider (Clark & Bailey, 2024).

By handling routine inquiries and preliminary assessments, chatbots can significantly reduce healthcare costs, especially when the patient does not have to see a health provider in person. They free up health providers for more complex tasks, leading to more efficient resource allocation.

For example, GlaxoSmithKline launched 16 virtual assistants within 10 months, resulting in improved customer satisfaction and employee productivity (Winchurch, 2020).

Improved patient engagement and satisfaction

Chatbots make it easier for patients to engage with their health–even for older adults (Clark & Bailey, 2024). They provide a low-barrier way to ask questions and learn about health topics, improving overall health literacy (Bickmore et al., 2016). They’re also easier to use than a traditional patient portal or telehealth system, which saves time.

Faster triage 

In an emergency, every second counts. AI chatbots can quickly assess symptoms and help determine the urgency of a situation, potentially saving lives by ensuring rapid response to critical cases (Razzaki et al., 2018).

The benefits we’ve discussed here come from a range of key features that AI health chatbots offer. Let’s take a closer look at these capabilities.

Key Features of AI Chatbots in Healthcare

AI health chatbots come packed with features designed to support various aspects of healthcare. Some of the uses of health chatbots include (Clark & Bailey, 2024):

  • Physical wellbeing
  • Chronic conditions
  • Mental health
  • Substance use disorders
  • Pregnancy 
  • Sexual health
  • Public health

Let’s discuss some of the use cases and applications for AI health chatbots.

Appointment scheduling

AI chatbots can manage appointments, allowing patients to easily book, reschedule, or cancel appointments without human intervention. It’s usually easier than doing so in a patient portal.

Symptom checking and preliminary diagnosis

Many chatbots offer an online symptom checker. You input your symptoms, and the chatbot asks follow-up questions to provide a preliminary assessment. While this doesn’t replace a doctor’s diagnosis, it can help you decide if you need to seek immediate medical attention (Semigran et al., 2015).

Medication reminders and management

Pink pill box

Forget to take your pills? AI chatbots can send timely reminders, helping you stay on top of your medication schedule. Some even track your medication history and can alert you to potential drug interactions (Brar Prayaga et al., 2019).

Post-op care and chronic disease management

After an operation or minor surgery, a chatbot can guide the patient through the recovery process at any time, day or night. It can also answer questions about symptoms and concerns related to a chronic illness (ScienceSoft, n.d.). 

Mental health support 

AI chatbots are increasingly being used to provide mental health support. They can offer coping strategies, mood tracking, and even cognitive behavioral therapy exercises. While they don’t replace professional help, they can be a valuable first line of support (Fitzpatrick et al., 2017).

Health tracking and personalized recommendations 

Woman checking iphone with Apple watch

AI chatbots can track your health data over time by integrating with wearable devices and apps. They can then provide personalized health recommendations based on your activity levels, sleep patterns, and other health metrics (Stein & Brooks, 2017).

Healthcare systems can successfully implement AI chatbots by following a careful approach, as we’ll discuss next.

How to Integrate AI Chatbots in Healthcare Systems

Hand holding phone with AI health chatbot conversation

Integrating AI health chatbots into existing healthcare systems requires careful planning and execution. Here’s a roadmap for successful implementation (Palanica et al., 2019 & Nadarzynski et al., 2019):

  1. Assess Needs and Set Goals: Before implementing a chatbot, healthcare providers should clearly define what they hope to achieve. Is the goal to reduce wait times, improve patient engagement, or streamline triage processes?
  1. Choose the Right Solution: Not all chatbots are created equal. Select a solution that aligns with your goals and integrates well with your existing systems.
  1. Ensure Data Security: Implement robust security measures to protect patient data. This includes encryption, secure authentication processes, and regular security audits.
  1. Train Healthcare Providers: It’s crucial to train your staff on how to work alongside these AI systems. They should understand the chatbot’s capabilities and limitations.
  1. Educate Patients: Clear communication with patients about the role and capabilities of the chatbot is essential. Set realistic expectations and provide guidance on how to use the system effectively.
  1. Start Small and Scale: Begin with a pilot program, gather feedback, and make improvements before rolling out the chatbot more broadly.
  1. Continuous Monitoring and Improvement: Regularly assess the chatbot’s performance. Are patients finding it helpful? Are there common issues or misunderstandings? Use this data to continually refine and improve the system.
  1. Measure Impact: Track key performance indicators (KPIs) to measure the impact of the chatbot. This might include metrics like patient satisfaction scores, reduction in wait times, or cost savings.

While AI health chatbots offer impressive features and benefits, it’s important to acknowledge and address the challenges that come with using them in healthcare.

Addressing Concerns and Limitations of AI Health Chatbots

While AI health chatbots offer numerous benefits, they also come with their fair share of challenges and limitations. It’s important to be aware of these as we continue to integrate these technologies into our healthcare systems.

Accuracy concerns 

One of the primary concerns with AI health chatbots is the potential for misdiagnosis. While these systems are becoming increasingly sophisticated, they’re not infallible. A chatbot might misinterpret symptoms or fail to consider important contextual information that a human doctor would catch (Fraser et al., 2018).

Another reason chatbots could share inaccurate information is that AI health chatbots use fixed datasets, which may not include the latest medical info. Unlike doctors who can access current data, chatbots might give outdated advice on health topics (Clark & Bailey, 2024).

Data privacy and security 

Hacker in a red hoodie

Healthcare data is highly sensitive, and the use of AI chatbots raises important questions about data privacy. How is patient data stored and protected? Who has access to the information shared with these chatbots? These are critical issues that need to be addressed to ensure patient trust and comply with regulations like HIPAA (Luxton, 2019).

Federated learning is a new way to train AI models that keeps data private. It lets different groups work together on an AI model without sharing their actual data. Instead, each group trains the model on their own computers using their own data. They only share updates to the model, not the data itself. Hospitals and researchers can team up to create better AI models while keeping patient information safe and private (Sun & Zhou, 2023). 

Ethical considerations 

The use of AI in healthcare raises several ethical questions. For instance, how do we ensure that these systems don’t perpetuate biases in healthcare? There’s also the question of accountability – who’s responsible if a chatbot provides incorrect advice that leads to harm (Vayena et al., 2018)?

Bias in AI Algorithms

Illustration of a smiling chatbot

AI chatbots in healthcare raise concerns about bias and fairness. If the data used to train these chatbots isn’t diverse or has built-in biases, the chatbots might make unfair decisions. This could lead to some groups getting worse healthcare.

Bias can come from many sources, like choosing the wrong data features or having unbalanced data. Sometimes, chatbots might learn the training data too well and can’t handle new situations.

To fix these problems, we need to be aware of possible biases, work to prevent them, and keep checking chatbots after they’re in use. This helps ensure AI chatbots benefit everyone equally in healthcare (Sun & Zhou, 2023). 

Integration challenges 

Implementing AI chatbots into existing healthcare systems isn’t always straightforward. There can be technical challenges in integrating chatbots with electronic health records (EHRs) and other healthcare IT systems. Ensuring seamless data flow while maintaining security is a complex task (Miner et al., 2020).

Patient trust 

Building and maintaining patient trust is crucial for the success of AI health chatbots. Some patients may be hesitant to share personal health information with a machine, preferring the human touch of traditional healthcare interactions.

Trustworthy AI (TAI) helps explain how AI chatbots work, balancing complex math with user-friendly results. It’s important for building trust in AI systems. While progress has been made, more work is needed to make AI chatbots more transparent and trustworthy (Sun & Zhou, 2023).

Doctors and nurses do more than diagnose–they offer comfort and build trust with patients. AI chatbots can’t replace this human touch or handle complex medical issues that need deep expertise.

It’s not all doom and gloom! Exciting trends are shaping the future of AI health chatbot technology.

AI chatbots are useful medical tools, especially where healthcare access is limited. The combo of AI efficiency and human empathy can improve healthcare. The future likely involves doctors handling complex cases and emotional care, with chatbots supporting them, depending on tech advances, acceptance, and regulations (Altamimi et al., 2023). Here are some exciting trends to watch.

Advanced NLP 

Future chatbots will likely have an even better understanding of context and nuance in language. They might be able to detect subtle cues in a patient’s language that could indicate underlying health issues.

Integration with IoT and wearables 

man checking fitness watch with cell phone

As the Internet of Things (IoT) expands in healthcare, chatbots will likely become more integrated with wearable devices and smart home technology. Imagine a chatbot that can access real-time data from your smartwatch to provide more accurate health advice.

Personalized medicine 

AI chatbots could play a crucial role in the move towards personalized medicine. By analyzing vast amounts of patient data, they could help tailor treatment plans to individual genetic profiles and lifestyle factors.

Enhanced diagnostic capabilities 

While current chatbots are limited to preliminary assessments, future versions might have more advanced diagnostic capabilities. They could potentially analyze images or audio recordings to aid in diagnosis.

Support for clinical trials 

AI chatbots could streamline the process of clinical trials by helping to recruit suitable participants, monitor adherence to trial protocols, and collect data.

Conclusion

AI health chatbots are making healthcare easier to access, more personal, and more efficient. They offer 24/7 support, lower costs, and get patients more involved in their health. But there are still issues to solve, like making sure they’re accurate, keeping data private, and fitting them into current healthcare systems.

As tech improves, these chatbots will get smarter and play a bigger role in healthcare. It’s important for everyone – doctors and patients – to keep up with these changes.

Whether you work in healthcare or you’re just curious, now’s the time to try out these chatbots. By staying informed, we can use technology to make healthcare better, without losing the human connection.

Have you used AI health chatbots before? What are your thoughts on them? 

References

AI-Powered Chatbots for Healthcare. (n.d.) ScienceSoft. Retrieved from https://www.scnsoft.com/healthcare/chatbots

Altamimi, I., Altamimi, A., Alhumimidi, A. S., Altamimi, A., & Temsah, H. (2023). Artificial Intelligence (AI) Chatbots in Medicine: A Supplement, Not a Substitute. Cureus, 15(6). doi.org/10.7759/cureus.40922

Bickmore, T. W., Utami, D., Matsuyama, R., & Paasche-Orlow, M. K. (2016). Improving access to online health information with conversational agents: a randomized controlled experiment. Journal of Medical Internet Research, 18(1), e1.

Brar Prayaga, R., Jeong, E. W., Feger, E., Noble, H. K., Kmiec, M., & Prayaga, R. S. (2019). Improving refill adherence in Medicare patients with tailored and interactive mobile text messaging: pilot study. JMIR mHealth and uHealth, 7(1), e11429.

Clark, M. & Bailey, S. (2024). Chatbots in Health Care: Connecting Patients to Information. CADTH Horizon Scans. Canadian Agency for Drugs and Technologies in Health. Retrieved from https://www.ncbi.nlm.nih.gov/books/NBK602381/

Fitzpatrick, K. K., Darcy, A., & Vierhile, M. (2017). Delivering cognitive behavior therapy to young adults with symptoms of depression and anxiety using a fully automated conversational agent (Woebot): a randomized controlled trial. JMIR Mental Health, 4(2), e19.

Fraser, H., Coiera, E., & Wong, D. (2018). Safety of patient-facing digital symptom checkers. The Lancet, 392(10161), 2263-2264.

Karlović, M. (2024). 14 ways chatbots can elevate the healthcare experience. Infobip. Retrieved from https://www.infobip.com/blog/healthcare-ai-chatbot-examples

Laranjo, L., Dunn, A. G., Tong, H. L., Kocaballi, A. B., Chen, J., Bashir, R., … & Coiera, E. (2018). Conversational agents in healthcare: a systematic review. Journal of the American Medical Informatics Association, 25(9), 1248-1258.

Luxton, D. D. (2019). Ethical implications of conversational agents in global public health. Bulletin of the World Health Organization, 97(4), 254.

Miner, A. S., Laranjo, L., & Kocaballi, A. B. (2020). Chatbots in the fight against the COVID-19 pandemic. NPJ Digital Medicine, 3(1), 1-4.

Nadarzynski, T., Miles, O., Cowie, A., & Ridge, D. (2019). Acceptability of artificial intelligence (AI)-led chatbot services in healthcare: A mixed-methods study. Digital Health, 5, 2055207619871808.

Palanica, A., Flaschner, P., Thommandram, A., Li, M., & Fossat, Y. (2019). Physicians’ Perceptions of Chatbots in Health Care: Cross-Sectional Web-Based Survey. Journal of Medical Internet Research, 21(4), e12887.

Razzaki, S., Baker, A., Perov, Y., Middleton, K., Baxter, J., Mullarkey, D., … & Majeed, A. (2018). A comparative study of artificial intelligence and human doctors for the purpose of triage and diagnosis. arXiv preprint arXiv:1806.10698.

Semigran, H. L., Linder, J. A., Gidengil, C., & Mehrotra, A. (2015). Evaluation of symptom checkers for self diagnosis and triage: audit study. BMJ, 351, h3480.

Stein, N., & Brooks, K. (2017). A fully automated conversational artificial intelligence for weight loss: longitudinal observational study among overweight and obese adults. JMIR Diabetes, 2(2), e28.

Sun, G., & Zhou, H. (2023). AI in healthcare: Navigating opportunities and challenges in digital communication. Frontiers in Digital Health, 5. doi.org/10.3389/fdgth.2023.1291132

Vayena, E., Blasimme, A., & Cohen, I. G. (2018). Machine learning in medicine: Addressing ethical challenges. PLoS Medicine, 15(11), e1002689.

Winchurch, E. (2020). How GlaxoSmithKline launched 16 virtual assistants in 10 months with watsonx Assistant. IBM. Retrieved from https://www.ibm.com/products/watsonx-assistant/healthcare

Predictive Analytics and AI in Healthcare: Using AI to Predict Patient Outcomes

Predictive Analytics and AI in Healthcare: Using AI to Predict Patient Outcomes

by Ree AI Health Tech Med Tech

Health organizations use predictive analytics and AI to make better decisions, create personalized treatment plans, and improve patient outcomes. Let’s discuss their impact on the healthcare industry.

Contents

Understanding Predictive Analytics with AI in Healthcare

Predictive analytics uses statistical methods to analyze medical data. It also finds patterns and trends that can predict what might happen next with an individual patient. But what part does AI play here?

Definition of predictive analytics and its relationship to AI

Predictive analytics involves using statistical methods and algorithms to analyze medical data and make predictions about future patient outcomes or healthcare trends. It’s like having a crystal ball that relies on patient data instead of magic. 

AI enhances predictive analytics in healthcare by automating the analysis process and improving the accuracy of predictions through machine learning and other advanced techniques (Petrova, 2024).

Predictive analytics systems in healthcare

Predictive analytics systems are made up of several key components:

  • Data Collection: Gathering relevant data from various sources like electronic health records (EHRs) and medical devices.
  • Data Preprocessing: Cleaning and organizing medical data to ensure it’s usable.
  • Model Building: Creating statistical models that can analyze the data.
  • Model Validation: Testing the models to ensure they make accurate predictions about patient outcomes.
  • Deployment: Using the models to make predictions in real-world healthcare scenarios.

How AI enhances predictive capabilities

AI takes predictive analytics to the next level. Traditional predictive models might struggle with large datasets or complex patterns, but AI can handle these with ease. 

Examples:

  • Netflix uses AI to predict what shows or movies you might like based on your viewing history, dramatically improving user experience. 
  • IBM Watson Health uses AI to analyze patient data and medical literature to help clinicians make treatment decisions, which enhances patient care.

How machine learning can improve predictions

Machine learning (ML), a subset of AI, is crucial in predictive analytics. It involves training algorithms on historical patient data so they can learn to make predictions on new data. 

Over time, these algorithms improve as they are exposed to more data, making them more accurate and efficient when predicting patient outcomes. This continuous learning process is what makes ML so powerful in predictive analytics. 

Some examples:

  • Amazon uses ML to predict product demand, ensuring that they stock the right products at the right time. 
  • Google Health uses ML to predict patient deterioration in hospitals, allowing for early intervention and improved patient care.
  • A study in Nature conducted by the U.S. Department of Veterans Affairs and the DeepMind team at Google used AI to accurately predict acute kidney injuries up to 48 hours before diagnosis (Suleyman & King, 2019).

Predictive analytics and AI are not just theoretical concepts; they have real-world applications across various industries. Now that we know the basics, let’s see how healthcare providers use these tools in practice.

Real-World Applications of Predictive Analytics and AI

Behavior prediction and resource allocation

Healthcare providers use predictive analytics to understand patient behavior. By analyzing past medical history and treatment adherence, hospitals can predict which patients are likely to miss appointments or not follow their treatment plans. This helps personalize care, improve patient engagement, and allocate resources. 

A couple of examples:

  • Cleveland Clinic uses predictive analytics to identify patients at high risk of readmission, allowing for targeted interventions. 
  • Gundersen Health Systems increased the number of staffed rooms used by 9% using predictive analytics with AI (Becker’s Hospital Review).

Healthcare resource optimization and demand forecasting

Nurse showing notes to doctor near whiteboard

Predictive analytics helps healthcare organizations optimize their resources by forecasting patient demand. 

Hospitals can predict future patient volumes and adjust staffing levels by analyzing admission data and seasonal trends. This reduces costs and ensures that healthcare services are available when patients need them. 

For example, Johns Hopkins Hospital uses predictive analytics to forecast patient admission rates and optimize resource allocation (Chan & Scheulen, 2017).

Treatment outcome prediction and optimization

By analyzing patient data and treatment histories, clinicians can identify:

  • which treatments are likely to be most effective for each patient
  • which patients are at risk of certain diseases 
  • take preventive measures based on what they find

This process improves patient outcomes and reduces healthcare costs. A few examples:

  • Both Mayo Clinic and IBM Watson Health use AI and predictive analytics to diagnose and personalize treatment plans for cancer patients more effectively (IBM, 2019).
  • Hoag Hospital uses an AI-powered platform to predict which patients are at risk of developing sepsis. The result was a 41% decrease in sepsis-related mortality rates (Health Catalyst, n.d.).
  • The City of Hope Medical Center partnered with Syapse to develop a predictive analytics platform with AI to detect patients who are at risk of getting cancer or have a high risk of cancer recurrence (City of Hope, 2020).

Predictive maintenance of medical equipment

Closeup of vitals in the OR

Healthcare facilities use predictive analytics to predict when medical equipment is likely to fail and schedule maintenance as needed. This helps prevent unexpected breakdowns, reduces downtime, and ensures continuous patient care. 

For example, GE Healthcare uses predictive analytics to monitor medical imaging equipment and predict maintenance needs (Business Wire, 2024).

Implementing predictive analytics and AI offers numerous benefits for businesses. We’ll discuss some of the key advantages next.

Benefits of Implementing Predictive Analytics and AI

The ways healthcare organizations use predictive analytics and AI offer several advantages.

Early disease detection and prevention

Healthcare organizations can use predictive analytics to detect diseases early, implement preventive measures, and manage patient risks. This helps in reducing the burden of chronic diseases and improving population health. 

A couple of examples:

Improved decision-making 

Three doctors talking in a hallway

​​

Predictive analytics can uncover hidden patterns and trends in patient data, revealing new insights for clinical decision-making. By identifying these patterns early, healthcare providers can make more informed decisions about patient care. 

For example, Stanford Health Care uses AI-powered predictive analytics to assist doctors in diagnosing complex conditions and recommending personalized treatment plans.

Cost reduction and operational efficiency

By predicting future patient needs and health trends, healthcare organizations can optimize their operations and reduce costs. For example, forecasting patient admissions helps hospitals manage their staffing more efficiently, reducing overtime costs and improving care quality. 

A couple more examples:

  • Kaiser Permanente uses predictive analytics to optimize its supply chain, reducing waste and saving millions in healthcare costs (Pritchard, n.d.).
  • UCI Medical Center has implemented predictive analytics with AI to analyze patient information, including admission rates, length of stay, and diagnosis, to predict future patient demand and ensure sufficient hospital resources (University of California, Irvine, 2021).

In addition, predictive analytics enhanced with AI can help prevent fraudulent insurance claims. Insurance companies can train ML algorithms to determine bad intent at the outset. This could potentially save billions of dollars (NHCAA, n.d.).

Better patient experience and satisfaction

Doctor and patient hands on desk

By understanding future health trends and patterns, health facilities can implement preventive measures and improve patient outcomes. For instance, Intermountain Health uses predictive analytics to reduce hospital-acquired infections, significantly improving patient safety. 

While implementing predictive analytics and AI offers many benefits to health providers and patients, they also come with their own set of considerations to keep in mind.

Challenges and Considerations

Data quality and integration issues

For predictive analytics to be effective, the data used must be accurate and reliable. Poor quality data can lead to inaccurate predictions. In addition, integrating data from different sources can be challenging and time-consuming. 

Privacy and ethical concerns

Hand pulling a folder from chart in dr office

Using predictive analytics in healthcare involves collecting and analyzing large amounts of sensitive patient data, which can raise privacy and ethical concerns. Healthcare organizations must ensure they handle patient data responsibly and comply with regulations like HIPAA. 

Attracting skilled talent 

Implementing predictive analytics requires specialized skills and expertise. Finding and retaining talent with the necessary healthcare analytic skills can be challenging. Many organizations struggle to find data scientists and analysts who can build and maintain predictive models.

Choosing the right tools and technologies

There are numerous predictive analytics tools and technologies available, each with its own strengths and weaknesses. Choosing the right tools can be daunting, especially given the rapid pace of technological advancement in this field.

Overcoming resistance to change within health organizations

Nurse in hallway looking worried

Implementing predictive analytics often involves changing existing processes and systems, which can face resistance from staff. Organizations must manage this change effectively to ensure a smooth transition and adoption of new analytics technologies. 

The field of predictive analytics and AI is constantly evolving. Here are some future trends to watch out for.

Advancements in natural language processing

Natural language processing (NLP) is a branch of AI that deals with understanding and generating human language. Advancements in NLP enable more accurate and efficient analysis of text data, opening up new possibilities for predictive analytics in healthcare:

  • Wearable devices can use edge computing to process patient data in real time and alert healthcare providers to potential emergencies.
  • Chatbots powered by NLP can provide real-time customer support and predict user needs based on their queries.

eXplainable AI for clearer decision-making

Nurse showing notes to dr

eXplainable AI (XAI) aims to make AI models more clear and easy to understand. This can help health providers trust and adopt AI technologies more readily, as they can see how patient care decisions are made. 

For example, healthcare providers can use explainable AI to understand how predictive models diagnose diseases and recommend treatments. This is critical in healthcare, where the rationale behind some decisions may have life-or-death consequences.

Integration with IoT devices

The integration of predictive analytics with Internet of Things (IoT) devices enables healthcare providers to collect and analyze data from a wide range of sources, using wearable technology like smartwatches and fitness trackers (Li et al., 2019). 

This will provide more comprehensive insights into patient health and improve decision-making. For example, smart medical devices could use predictive analytics to monitor patient health in real-time and predict potential complications. 

Democratization of AI and predictive tools

As AI and predictive analytics tools become more user-friendly and accessible, more health organizations can take advantage of these technologies. This will drive innovation and improve patient care across the healthcare industry, from small clinics to large hospital systems.

Conclusion

Predictive analytics and AI are changing the healthcare industry, offering powerful tools to forecast outcomes and make data-driven decisions. By understanding the progress and potential of predictive analytics and AI, along with real-world applications, benefits, challenges, and future trends, health organizations can be better positioned to navigate uncertainties, seize opportunities, and stay ahead of the curve.

References

A tech-based culture shift: How Gundersen achieved prime OR utilization with predictive analytics. Becker’s Hospital Review. Retrieved from https://go.beckershospitalreview.com/hit/a-tech-based-culture-shift-how-gundersen-achieved-prime-or-utilization-with-predictive-analytics

Business Wire. (2024). GE Healthcare Increases Access to Precision Care Tools, Encouraging the Continued Adoption and Practice of More Personalized Medicine Around the World. Yahoo! Finance. Retrieved from https://finance.yahoo.com/news/ge-healthcare-increases-access-precision-164000903.html

Chan, C., & Scheulen, J. (2017). Administrators Leverage Predictive Analytics to Manage Capacity, Streamline Decision-making. ED Management;29(2):19-23.

City of Hope. (2020). City of Hope and Syapse partner to provide precision medicine to cancer patients. Retrieved from https://www.cityofhope.org/city-of-hope-and-syapse-partner-to-provide-precision-medicine-to-cancer-patients

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