How Digital Health Platforms Affect Healthcare Costs

by Ree AI Health Tech Med Tech

As healthcare costs continue to go up, digital health platforms are emerging as powerful cost-cutting tools. The global digital health market size was estimated at $240.9 billion in 2023 and is projected to grow at a compound annual growth (CAGR) of 21.9% from 2024 to 2030. 

These platforms are not just fancy apps or websites. From telehealth to AI-powered diagnostics, digital health applications are changing healthcare for the better. 

How do these platforms trim the fat from our bloated healthcare system? Let’s explore the ways digital health can make healthcare more affordable for everyone.

Contents

Telemedicine: Healthcare at Your Fingertips

Telemedicine brings healthcare right to your home, office, or wherever you are. It’s like having a doctor in your pocket! But how does this convenience translate to cost savings?

Woman in green sweater talking to doctor on Zoom

Virtual doctor visits reduce travel and waiting room costs

A study published in the Journal of Medical Internet Research found that telehealth visits saved patients an average of 100 minutes of travel time and $50 in travel costs per visit (Snoswell et al., 2020).

Think about the last time you went to the doctor. How much time did you spend traveling and sitting in the waiting room? With telehealth, those time and money costs disappear. 

Fewer ER visits

How often have you wondered if that late-night stomach ache was worth a trip to the ER? Telehealth tools like AI chatbots can help you make that decision without leaving home. 

Cost savings for both patients and healthcare providers

It’s not just patients who save money. Healthcare providers benefit too. Telehealth services have been found to reduce healthcare costs for providers and patients. Even better, many insurers now have an allowance to cover the cost of certain telehealth visits.

Preventive Care: Stopping Problems Before They Start

Have you ever heard the saying “an ounce of prevention is worth a pound of cure”? Digital health platforms are making this old adage more relevant than ever.

How digital platforms promote healthy habits

Fitness app in the gym

From step counters to diet trackers, digital health apps are helping us stay healthier. But do they really make a difference? A study by Ernsting et al. (2017) found that users of health and fitness apps were 34% more likely to meet physical activity guidelines compared to non-users.

Wearable devices and their impact on early detection

glucose monitor on arm with phone app showing glucose level

Smartwatches surpass the practical use of telling time–they’re becoming powerful health monitors. For example, Apple Watch’s ECG feature can detect atrial fibrillation with 98% accuracy, potentially preventing strokes and saving lives (Perez et al., 2019).

How AI and big data can predict health risks and reduce costs

Big Data Analytics in healthcare uses AI, machine learning and deep learning tools to help doctors find the best treatments for each patient, which can reduce waste. This lets doctors predict health problems  and start treatments early, which can save lives. This could change how common certain diseases are and save money on healthcare (Batko & Ślęzak, 202​​2).

Cost savings through prevention vs. treatment

Prevention isn’t just better for our health—it’s better for our wallets too. The Centers for Disease Control and Prevention estimates that chronic diseases that are avoidable through preventive care account for 75% of the nation’s healthcare spending.

Streamlined Administrative Processes

Paperwork is no one’s favorite part of healthcare. Digital platforms are making administrative tasks faster, easier, and more cost-effective.

Automated appointment scheduling and reminders

Have you ever forgotten a doctor’s appointment? Digital reminders can help. 

Smartwatch with phone and dumbbells

Ulloa-Pérez et al. (2022) found that sending an extra text reminder for high-risk appointments reduced no-shows in primary care and mental health offices, and same-day cancellations in primary care offices. 

Targeting reminders using risk prediction models (predictive analytics) can efficiently use healthcare resources, potentially preventing hundreds of missed visits monthly. This approach saves costs compared to messaging all patients, though implementing the risk model has some costs.

Digital health records reduce paperwork and administrative errors 

Nurse charting

Remember when doctors used to write prescriptions by hand? Digital health records make all kinds of admin work more efficient. A study in the Journal of the American Medical Informatics Association found that electronic health records with AI can reduce medication and billing errors.

Cost savings through improved workflow and resource allocation

Efficient workflows mean better care at lower costs. A study in the Journal of Medical Internet Research found that digital health platforms improved hospital workflow efficiency by 25%, leading to annual cost savings of $1.2 million for a mid-sized hospital (Luo et al., 2019).

Person looking at white overlay

Data-Driven Insights for Better Decision Making

In the age of big data, information is power. Healthcare is no exception. With all this digital information, doctors can make smarter choices about your health. 

How big data analytics improve treatment plans

A study in the Journal of Big Data found that big data analytics improved treatment efficacy by 30% and reduced treatment costs by 20% (Dash et al., 2019).

Cost savings from shorter and fewer hospital stays

Nurse standing in a recovery room

Have you ever wondered how hospitals decide how many beds they need? Predictive analytics is the answer. It can reduce hospital bed shortages and decrease operational costs.

Hospital stays are expensive, but RPM can help shorten them. RPM allows patients to be discharged an average of 2 days earlier, resulting in cost savings of $7,000 per patient.

Personalized medicine and its impact on cost reduction

One size doesn’t fit all in healthcare. Targeted treatments are more effective and cost-effective. 

  • Personalized treatment plans based on genetic data improve treatment efficacy and reduce adverse drug reactions (ADRs).
ECG monitor closeup on stomach

Remote Patient Monitoring: Reducing Hospital Stays

Sometimes, the best hospital care happens outside the hospital. 

Remote patient monitoring (RPM) allows health providers to keep an eye on patients without keeping them in the hospital. From smart pills to wearable sensors, remote monitoring technologies are diverse and growing. 

Impact on reducing hospital readmissions

Nobody likes going back to the hospital. Remote monitoring can help prevent that. A study in the New England Journal of Medicine found that remote monitoring reduced hospital readmissions for heart failure patients by 50% (Perez et al., 2019).

Management of chronic conditions from home

Gentleman taking his blood pressure in tan shirt

Chronic conditions are a major driver of healthcare costs. Remote monitoring can help manage these conditions more effectively. 

A 2024 study showed that telehealth reduces healthcare costs by cutting down on hospital visits, travel time, and missed work, especially for managing chronic conditions. This benefits both patients and healthcare systems financially (Prasad Vudathaneni et al., 2024).

Increasing Access to Specialized Care

Specialized care can be hard to access, especially in rural areas. Digital health isn’t just about general care – it’s also bringing expert help to more people.

Telehealth solutions for rural and underserved areas

Rural healthcare access is a major challenge. Telehealth can help bridge that gap. A study in Health Affairs found that telehealth increased access to specialty care in rural areas by 54%.

Telehealth also faces challenges like high setup costs and outdated payment models, especially in rural areas. Its success depends on cost distribution, clinical outcomes, and indirect savings. Hospitals need funding and strategies to reach underserved groups and ensure fair access to telehealth (Anawade et al., 2024).

Virtual second opinions and their impact on treatment decisions

Getting a second opinion can be life-changing. Virtual platforms make it easier than ever. Virtual second opinions can change the diagnosis or treatment plan in over one-third of cases, potentially avoiding unnecessary procedures and costs.

Conclusion

Digital health platforms are powerful allies to counteract rising healthcare costs. By leveraging technology for prevention, efficiency, and data-driven insights, these platforms are making healthcare more accessible and affordable. From applications like telehealth reducing unnecessary ER visits to catching illnesses early with AI-powered diagnostics, the potential for cost savings is huge. 

As patients, we can embrace these digital tools to take control of our health and potentially lower our healthcare expenses. For healthcare providers, adopting these platforms could lead to more efficient operations and better patient outcomes. 

What do you think about these digital health innovations? Have you used any of these technologies in your own healthcare journey? 

References

Anawade, P. A., Sharma, D., & Gahane, S. (2024). A Comprehensive Review on Exploring the Impact of Telemedicine on Healthcare Accessibility. Cureus, 16(3). doi.org/10.7759/cureus.55996

Batko, K., & Ślęzak, A. (2022). The use of Big Data Analytics in healthcare. Journal of Big Data, 9(1). doi.org/10.1186/s40537-021-00553-4

Centers for Disease Control and Prevention. (2021). Chronic diseases in America. Retrieved from https://www.cdc.gov/chronicdisease/resources/infographic/chronic-diseases.htm

Dash, S., Shakyawar, S. K., Sharma, M., & Kaushik, S. (2019). Big data in healthcare: Management, analysis and future prospects. Journal of Big Data, 6(1), 1-25. doi.org/10.1186/s40537-019-0217-0

Ernsting, C., Dombrowski, S. U., Oedekoven, M., & Kanzler, M. (2017). Using smartphones and health apps to change and manage health behaviors: A population-based survey. Journal of Medical Internet Research, 19(4), e101.

Grand View Research. (2024). Digital Health Market Size, Share & Trends Analysis Report By Technology (Healthcare Analytics, mHealth), By Component (Hardware, Software, Services), By Application, By End-use, By Region, And Segment Forecasts, 2024 – 2030. Retrieved from https://www.grandviewresearch.com/industry-analysis/digital-health-market

Luo, L., Li, J., Liang, X., Zhang, J., & Guo, Y. (2019). A cost-effectiveness analysis of a mobile-based care model for community-dwelling elderly individuals. Journal of Medical Internet Research, 21(5), e13563.

Perez, M. V., Mahaffey, K. W., Hedlin, H., Rumsfeld, J. S., Garcia, A., Ferris, T., Balasubramanian, V., Russo, A. M., Rajmane, A., Cheung, L., Hung, G., Lee, J., Kowey, P., Talati, N., Nag, D., Gummidipundi, S. E., Beatty, A., Hills, M. T., Desai, S., … Turakhia, M. P. (2019). Large-scale assessment of a smartwatch to identify atrial fibrillation. New England Journal of Medicine, 381(20), 1909-1917.

Personalized Medicine Coalition. (2020). The personalized medicine report: Opportunity, challenges, and the future. Retrieved from http://www.personalizedmedicinecoalition.org/Userfiles/PMC-Corporate/file/The-Personalized-Medicine-Report1.pdf

Prasad Vudathaneni, V. K., Lanke, R. B., Mudaliyar, M. C., Movva, K. V., Kalluri, L. M., & Boyapati, R. (2024). The Impact of Telemedicine and Remote Patient Monitoring on Healthcare Delivery: A Comprehensive Evaluation. Cureus, 16(3). doi.org/10.7759/cureus.55534

Snoswell, C. L., Taylor, M. L., Comans, T. A., Smith, A. C., Gray, L. C., & Caffery, L. J. (2020). Determining if telehealth can reduce health system costs: Scoping review. Journal of Medical Internet Research, 22(10), e17298.

Ulloa-Pérez, E., Blasi, P. R., Westbrook, E. O., Lozano, P. , Coleman, K. F., & Coley, R. Y.  (2022). Pragmatic Randomized Study of Targeted Text Message reminders to Reduce Missed Clinic Visits. The Permanente Journal, 26(1), doi/10.7812/TPP/21.078

Winstead, E. (2023). Telehealth Can Save People with Cancer Time, Travel, and Money. National Cancer Institute. Retrieved from https://www.cancer.gov/news-events/cancer-currents-blog/2023/telehealth-cancer-care-saves-time-money

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