Unlocking the Potential of Machine Learning to Improve Population Health

Machine learning has driven developments in a wide number of fields. However, its applications to healthcare and medicine have yet to be utilized to their full advantage. One of the potential applications of the technology in the healthcare realm, along with artificial technology, deals with improving population health.

By looking at the distribution of outcomes within and between different groups of people, population health researchers hope to improve outcomes across the board. This analysis often involves working with large databases of global data.

Machine learning has a great deal of potential in helping to sort through the data and take novel approaches to analysis. This could drive different ways of thinking about healthcare delivery across populations.

Applying Machine Learning to Population Health Analysis

Taking a population health approach to treatment requires understanding how socioeconomic factors play into risk for disease and response to treatment interventions. No one would dispute that sociomarkers — measurable indicators of social conditions in which a person is embedded — have a massive impact on the wellbeing of individuals and their communities. However, accounting for these factors in the health care setting remains challenging.

Recently, researchers created multi-level modeling methods to combine individual health data with sociomarkers at a community level. This sort of technology has helped improve disease surveillance and even predict disease in certain cases.

For example, one study used multi-level modeling technology to predict hospitalization of pediatric asthma patients. Utilizing the technology can help medical professionals provide intervention earlier, preventing morbidity altogether.

The potential for machine learning in recognizing healthcare patterns in studied populations is greater than even before. The introduction of wearable devices and the Internet make it especially relevant. These developments help collect unprecedented amounts of data, allowing clinicians greater insight into physiological variability within individuals and populations.

This understanding, in turn, drives precision medical developments to improve prevention and diagnosis of disease as well as treatment. Public health analysts can use the data to create community-level interventions based on the specific behaviors of a population and its members. Artificial intelligence and machine learning are necessary for processing and interpreting the incredible amount of data which could be generated by these devices.

Bridging the Divide Between Population Health and Care

Precision medicine involves tailoring approaches to prevention and treatment based on the individual situation of the person. Many individuals think of precision medicine in terms of genomics. However, there is also an element of population health in providing tailored care.

The public health data generated by population health data must ultimately be applied to the individual patient. At the same time, it is difficult for clinicians to consider all of the relevant socioeconomic data points when providing care.

Machine learning also has a role to play in translating public health data for use in the clinic. This will ultimately enhance the community-linked and patient-centered approach to care advocated for by population health.

Artificial intelligence could be integrated in electronic health records (EHRs) to help keep track of patient preferences, analyze population-based risk factors and improve overall quality of care delivered. By taking into account socioeconomic factors, such a system could also get individuals more involved in their care by pointing to specific sources of information and care outside of the clinic.

Thus, while helping create more personalized care plans, EHRs would also remove the social barriers to care experienced by many patients, leading to the creation of healthier communities. Some studies have already shown how machine learning can improve accuracy of prognoses by taking into account a wider range of patient data. It may not be long before such systems become a standard feature of EHRs.

The Path Forward for Machine Learning in Population Health

Some benefits of machine learning for population health have already been demonstrated, however, the full potential has not yet been reached. Part of the issue derives from a basic mistrust of the technology, especially in the context of clinical diagnosis.

These systems can analyze more health data than clinicians in terms of making recommendations. It is important to note this does not necessarily mean the conclusion they offer is the correct one, so some skepticism is warranted.

To be sure, adoption of these technologies should enhance clinical decision-making rather than replace it. However, other concerns have also been raised in terms of scalability and data integration. This is particularly the case when it comes to using it with EHRs, which bring up additional problems like security, privacy and ethics.

When it comes to using technology to improve population health, it is important not to undermine the privacy and autonomy of the individual. This issue occurred in early implementation of artificial intelligence in social media analytics. It led to stigmatization, which could prove extremely detrimental in care delivery settings.

There is still a limit to machine learning, a technology still in its infancy. As American patient populations become older, the complexity of their health and social situations is increasing. Artificial intelligence can help deal with the complexity, but clinicians still need to exercise sharp judgment and perhaps become more involved with engineering efforts to tailor the technology to the healthcare market.

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