Bill Lockwood, Chairman/Publisher
Bill Lockwood, Chairman/Publisher

I read an interesting article in a Wall Street Journal supplement (Dec. 9, 2021) on “The Future of Everything/Data.” What caught my eye was an article by Ron Winslow on “Mining the Gold in Patient Records.” He mentioned a company in California, Atropos Health, that introduced a product to commercialize technology being developed at Stanford to convert data in electronic health record (EHR) databases to information for patient care. The company developed a tool called Prognostogram that it claims can answer most physician inquiries about patient treatments within 24 hours.

It offers real-time retrieval of a descriptive summary of how hundreds or thousands of similar patients were treated for a diagnosis. At Stanford the thinking is this could become a routine part of a patient visit within a decade. However, when you start combining data from various EHR systems you run into a problem due to lack of standards on the way patient information is entered. This is what caused IBM to abandon Watson’s venture into treatment advice for cancer patients. Combining patient data from electronic health records turned out to be a lot more difficult than imagined. This happens to be a major barrier to interoperability.

My point in bringing up the article is how valuable the data is that resides in a given system, whether it be in an electronic health record system used by physicians, or in a pharmacy management system. Epic Systems, a leading provider of electronic health record systems, searched its database to find that routine breast, colon, and cervical cancer screening dropped by more than 85% during the first weeks of the COVID-19 pandemic. The report the company issued helped motivate people to make up for missed screenings. Researchers are using the patient records in Epic’s Cosmos database to generate reports on population-level healthcare trends. Essentially what we have here is mining data to allow physicians to provide better outcomes.

I can make an analogy to the same trend in pharmacy. One example is identifying patients for a med sync program. This is data mining. Another is a company that provides a way to prevent adverse drug events for people on multiple medications. There are examples on the business side as well, with the use of pharmacy data to better manage prescription drug inventory and prescription pricing. Data mining with point-of-sale data enables better decisions over the front-end product mix.

I could offer up more examples, but I think you get the point. The data resident in pharmacy systems can lead to not only better patient care and but also to a more profitable pharmacy. CT

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