This article is contributed. See the original author and article here.






Story Background



Daily medication is a fact of life for a lot of patients. Too many, however, are not compliant with their medication regimens. Failure to follow doctors’ orders has a significant impact on health outcomes and healthcare costs. There are many reasons for patients not taking their medications, and many of those reasons stem directly from where and how those patients live and work. In fact, a sizeable percentage of patients are considered to be at moderate-to-high risk for financial insecurity, isolation, housing insecurity, transportation trouble, food insecurity, or health illiteracy.











 


Business Challenge 



Providers typically prescribe drugs to patients after a detailed diagnosis or treatment. They expect the patient to stick to the regimens.


According to the WHO, increased medication adherence had a greater impact on population health than any improvements in medical treatments, highlighting the pressing need to focus on the issue. Studies also estimate that non-adherence racks up $300b yearly in avoidable U.S. healthcare costs, 15% of which is linked to cardiovascular disease and statin medication non-adherence.


 











Business Outcomes 



Healthcare organizations face increased pressure to lower costs and improve care outcomes. The traditional approach of reactive patient care has been replaced by a predictive, data-driven approach.


 


Providers are armed with the right data and analytics capabilities that can target care management programs at the specific patients who need them, improving outcomes and helping to keep people healthy. Patients are provided with relevant information at the point of prescription to enhance medication adherence.











 


Solution Overview 



The figures above, along with encouragement from government programs such as CMS Stars, are driving the healthcare sector to study adherence patterns using predictive analytics to adjust care managers’ intervention strategies.


 


This model incorporated synthetic datasets on patient medication, encounters, and observation to analyze therapy progress and gaps on a year-to-year basis, covering a ten-year follow-up period. We used the frequency of drug dispensation per patient per year as a proxy for adherence and pegged the threshold cut-off at over the third quartile of the population. According to research, tracking medication fill rates may help measure non-adherence in some patients. In 1986, the Morisky Medication Adherence Scale, a simplified four-question survey demonstrated preliminary data on how the tool could assess non-adherence and overall treatment success in a smaller sample of hypertensive patients. The study found that patients who scored higher on the scale were significantly more likely to have their blood pressure under control after 42 months.


 


 


Thanks for reading, Shelly Avery |EmailLinkedIn 


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