Analytics, patient portals among key population health tools for the underserved 'silent middle'

Computer showing analytics
The "silent middle"—patients that are neither sick nor well—could benefit from tech-based population health efforts. (Richard Khor)

Using a three-pronged approach of advanced analytics, patient portals and supplemental care services, healthcare organizations can strengthen population health efforts for the “silent middle”: an underserved group that is neither sick nor well, but could benefit from preventative services.

Although providers typically focus their population health efforts on the sickest and most expensive patients, the much larger cohort of people “quietly developing yet-to-be-diagnosed chronic conditions” can be addressed through technology and convenient care, Peter Goldbach, M.D., chief medical officer of RediClinic, the walk-clinic chain owned by Rite Aid, wrote in an op-ed for Hospitals & Health Networks.

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Advanced analytics can comb through a variety of clinical factors to help providers identify patients in the silent middle, while patient portals can facilitate engagement and customize preventative care based on risk factors. Care clinics, nurse call lines and shared decision-making between clinicians and patients can help facilitate low-cost treatment options.

“The best online portals exploit a combination of digital, telephonic and in-person channels to interact with individuals in a manner that works for them,” Goldbach wrote. “Text, chat and video—as well as the tried and true telephone—present myriad ways for individuals to access health guidance, and choose how they want to receive it and apply it to their daily life. Some portals allow individuals to integrate hundreds of wellness devices and apps that go beyond popular fitness wearables.”

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Last month, Geisinger Health System unveiled a “radical” new population health initiative that focuses on preventative care as well as socioeconomic issues like housing. Meanwhile, researchers with Kaiser Permanente Colorado have used predictive modeling and cluster analytics to identify patients who need specialty care.