Physician Data Analytics – Population Health in the “Oldest Old”

Last month, I commented on physician analytics related to tracking medications, finding discontinued medications and monitoring of chronic conditions. This month I would like to talk about data analytics related to the “oldest old” – or people over the age of 90.



Background and Findings
Senior citizens over the age of 90 are called our “oldest old.” They are the fastest growing segment of the U.S. population. Now a landmark study initiated in 2003 of over 1,600 members of a retirement community in Southern California is revealing factors that contribute to living longer (The 90+ Study, C. Kawas, et.al. The Institute for Memory Impairments and Neurological Disorders (UCI MIND) at University of California, Irvine, 2014).

Participants of The 90+ Study are visited every six months to obtain information about diet, activities, medical history, medications and numerous other factors, including various cognitive and physical tests.

Some of the major findings from The 90+ Study are:
• People who exercised lived longer than those who did not exercise;
• People who participated in social activities, such as bridge or book clubs, also lived longer;
• Vitamins did not seem to affect longevity;
• People who drank moderate amounts of alcohol or coffee lived longer than those who abstained;
• People who were overweight in their 70s lived longer than normal or underweight people did;
• Over 40 percent of people aged 90 and older suffer from dementia, while almost 80 percent are disabled. Both are more common in women than men;
• 40 percent of the time, what seemed to be Alzheimer’s disease in people over 90 was actually not. By studying the brains of these subjects after death, many showed evidence of microscopic strokes.

Collection of Basic Clinical and Other Patient Information
So, just based on The 90+ Study as an example, what clinical and patient information could you collect to help improve the long-term viability and health of your patients? This information could include:
• Vitamins and Over-the-Counter Medications;
• Exercise / Physical Activity Level;
• Socializing Level (i.e. participating in various social activities);
• Alcohol Consumption;
• Tobacco Use;
• H&P (Age, Height, Weight, Waist Size, BMI);
• Basic Blood Tests (HgA1c, C-LDL, etc.).

Use of Physician Analytics in Population Health
Next month I would like to discuss how basic clinical information (as noted above) can be applied to population health.

The North Texas Regional Extension Center (NTREC) has grant-subsidized services to help you implement an EHR. For a preview of our services, check out this video.