The Big Pharma Needscape for EHR Analytics

The community vectors receiving value from electronic health records (EHR) is broad.  EHR data has uses from epidemiology and populations at one end of the spectrum, and genotyping in computational biology for specific proteins at the other.  EHR is prominent in healthcare legislation, issues of interest to governments vis-à-vis personalized medicine and genetic testing, and the number of lives represented is growing.

At some point soon – if it has not happened already – the patient will realize the value of EHR is not as much “Wow, my doctors are talking to each other and sharing my medical record,” but rather “My daughter was not prescribed x because EHR data mining showed the chance of an adverse reaction was lower for kids like her when they take y.”  This ‘behind the curtain’ intelligence that invariably pulls medicine-and society-toward deeper personalization as an index of the increase in mine-able data will save more lives than relieve headaches of coordinating care.

But what about ‘Big Pharma’?  As the innovators and distribution centers of the drug interventions, are there compelling value propositions for them to analyze EHR data?  Most certainly, and these areas give just a start:

  • Pharmacovigilance.  This concept has been around for some time, seems the most obvious, and EHR seems to have a critical value proposition in drug safety and interactions.   And the model is so simple and direct, the data sizes so large and rich, that – for example – the European Union (through its executive body, the European Commission) proctored a study identifying the most important adverse events to search for in the EHR pool.   The question is not how to use it, but how to use it best.
  • In Clinical Trials.  Dr. Michael Kahn of Children’s Hospital, Denver, presented a paper to the National Institute of Health () and the first benefit listed was “Query EHR database to establish number of potential study Candidates”.  The ability to use EHR databases to save time during participant selection and recruitment seems compelling.
  • Comparative Effectiveness.  Earlier this year, Steven Labkoff, MD, FACP formerly of Pfizer presented at the National Academy of Science conference “Electronic Health Records: Where Do We Go From Here?”  Labkoff discussed the benefits Big Pharma can receive by aggressively using EHR data.  A key mention by Labkoff is that new players-payors, PBMs, US government- are now in the market asking for “pharmacoeconomic justification” that a drug is better and cheaper than something on the formulary.  Geisinger Health Network weighed in simply on how EHR can help on comparative effectiveness.
  • Marketing & Branding.  Bringing all the insights together from these areas tied to a single drug make a much stronger case that prescribing it has increased therapeutic value.  As consumers and prescribers have more channels of data-centric information on their treatments, sound, large sample EHR-type data backing up claims of a drug’s benefits become critical.

Taken together, the EHR as our collective health repository and ‘effect’ database can make the pharma expenditures drop noticeably in many silos from picking the compounds to bring to market, testing and validating them, and ensuring they are used well once in the market.