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

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:

• Improving health outcomes
• Reducing healthcare costs
• Enhancing patient care quality

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

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.

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

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.

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.

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