Although big data analytics can help providers target high-risk patients for population health interventions, current models may miss other factors that play a role in selecting patients for the programs, according to a blog post published by Health Affairs.
Instead, healthcare organizations would be better off using a hybrid approach to identify high-risk patients, such as a model that incorporates data analytics and human insight, write James Colbert, senior medical director for population health at Verisk Health, and Ishani Gangul, M.D., an internist and primary care physician at the Massachusetts General Hospital Ambulatory Practice of the Future.
They suggest organizations first use claims and electronic health record data to identify patients most likely to benefit from complex care management programs. From there, primary care physicians and clinicians can review the list, factoring in what they know about their patients' personal situations.
"Leveraging provider insights can allow an organization to select patients for complex care management who will truly benefit from additional services or home-based interventions, and may in turn save providers time by helping to manage some of their most complex patients," the authors write.
Finally, they suggest organizations also consider patient-reported data, such as health literary scores, to not only predict patients at risk, but those who understand their health challenges and want to make changes to improve their outcomes.
To learn more:
- read the post