Penetration of Artificial Intelligence in Population Health Management

The population health management technology helps to improve the health of segmented communities and reduces treatment costs. Advanced initiatives in the field of population health include an in-depth review of data to identify patterns within a target group.

Hospitals that exploit these patterns successfully will better grasp the care needs of particular populations, and advise successful outreach. This highly focused approach to public health will significantly improve the quality of treatment. Most healthcare agencies, through expanding existing services and targeting new patient groups, seek to continue the upward trend of public health initiatives.

What is population health management?

Population health management is the compilation of patient data from various health information technology systems, the processing of that data into one comprehensive, actionable patient record, and the actions by which care providers can enhance both clinical and financial outcomes.

Population Health Management (PHM) seeks to enhance a population’s health outcomes by tracking individual patients within the population and recognizing them. PHM services commonly use a Business Intelligence (BI) platform to collect data to provide a detailed clinical image of each patient. With that data, providers can monitor clinical results, and ideally enhance them, while raising costs.

Population health management trends

More payers are offering prevention and disease management benefits

Employers and health insurers recognize the financial advantages of being as safe as possible for staff and enrolments. Thirty percent of employers surveyed by the National Health Business Group listed illness or medical condition management as the most successful method for reducing the cost of the benefit. The number of employers offering employees an opportunity to complete a biometric test increased from 14 percent to 22 percent over the same span.

Data collection with wearable health technology

In the last few years, wearable technology’s success has skyrocketed. Healthcare companies have already begun to harness the data wearable technologies they can generate and this trend will continue to gain momentum.

Wearable technology monitors health indicators, such as heart rate, level of exercise and patterns of sleep. More advanced devices in health technology have been introduced in recent years, including blood pressure monitoring, seizure tracking, respiration rate, and even ECG data. Such type of information allows patients to monitor their health actively over time and encourages them to share reliable data about their daily health.

Digital security of IoT and AI

Health systems will use the Internet of Things (IoT) to access and evaluate new patient information from medical devices, software, wearables, home monitors and more. Artificial Intelligence (AI) and applied analytics support healthcare organizations with administrative and marketing activities, by accessing patient data, automating outreach. Data-driven organizations must recognize needs within the community in a changing healthcare environment in order to implement more successful strategies to promote public health.

Here’s how population health management technology is embracing the advent of Artificial Intelligence

One of the latest health IT developments is using Artificial Intelligence (AI) in Healthcare environments. For many healthcare professionals and front-end providers, it has become a black box when they seek for conceptualizing it at the point of treatment. Most hospitals, outpatient clinics, and post-acute settings use decision support systems in some form or fashion and some of these methods do not necessarily fit in the concept or methodologies of Artificial Intelligence and other people interpret AI in slightly different ways.


It is generally accepted that many of the factors that form individuals’ and communities’ health and welfare have their origins beyond the traditional health care system. Recent AI methodological advances allow multi-level modeling to combine individual-level data with sociomarkers, observable measures of social conditions, at group level to enhance disease monitoring, disease prediction, and public health interventions implementation and evaluation.


Most applications of health care analytics rely on clinician queries seeking to test hypotheses about their patients and treatment options. Some numerous patterns and trends will not be revealed through the different forms of data that make up public health because physicians neglected to ask the right question. AI approaches that provide “unsupervised learning,” however, open up a whole new path for the exploration of hidden treatment gaps and clusters of best practices.

Unsupervised learning considers all the data and all the possibilities of identifying patterns, classes or phenomena that elude conventional approaches within the dataset. Using their record systems, including EMRs, financial data, patient-generated data, and socio-economic data, AI-deploying healthcare organizations will automatically discover patient groups that share specific combinations of characteristics.

AI to benefit public health programs

According to the CDC, nearly 2.6 million people die annually in the United States and about one-third of these deaths can be avoided by community health initiatives. Death related to cancer, diabetes, heart, and respiratory disease and stroke have decreased significantly due to active public health awareness campaigns. The campaign’s wide scope makes this an expensive affair.

Artificial Intelligence and Machine Learning may help define particular populations or areas where health issues occur and specifically target them through prevention and treatment programs. AI lets health professionals analyze data in a comprehensive, real-time manner and determines the population at risk of disease. Opioid abuse is one of the health-care areas that can benefit from AI. AI will help recognize counties, states, and regions suffering from primary opioid abuse and mistreatment. Public education programs with higher overdose rates could be best geared at that specific region.

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The future of population health management with artificial intelligence

Healthcare organizations are concentrating increasingly on the social determinants of health, such as transportation, housing security, and food safety. Artificial intelligence is well suited for identifying the needs of patients and helping providers identify the data. Population Health Management (PHM) implementation and transformation is a global priority for most health systems, and AI and machine learning are rapidly being used in several ways, from keeping people safe to support clinical decision-making, and have gained considerable media coverage. They can deliver a powerful mechanism for a fundamental shift in personalized healthcare when these two initiatives come together.