Data Mining and Pharmaceutical Bar Tending

In a recent article in OR/MS Today (a sequel to his excellent article in Analytics), Douglas Samuelson writes about various OR opportunities as the health care industry in the U.S. reacts to the Mother of All Health Care Bills.  OR in health care also happens to be the theme of this month's INFORMS blog challenge (for which today's post is my entry). 

Let me focus on one particular statement in Samuelson's latest article:

Another aspect of uncoordinated care is polypharmacy, the use of multiple prescription medications in combination, with too little attention to possible interactions. According to the medical examiner’s official report, polypharmacy killed high-profile celebrities Anna Nicole Smith and Michael Jackson, both of whom used multiple doctors and multiple pharmacies. Safety testing is usually done one medication at a time, so interactions can take quite some time to become identified and publicized. This is a growing problem, and information technology offers a promising answer.

I've seen statistics asserting that accidental drug interactions cause a staggering number of "adverse results" (dying being counted as an adverse result).  Those events include not only prescriptions of multiple drugs whose interactions are unknown, but prescriptions of combinations with known interactions where the prescriptions may be issued by multiple doctors and/or filled at multiple pharmacies.  My physician's office asks patients to bring all their meds with them on visits (but of course I don't).  My guess is that many patients never think to mention some medications they are taking (or incorrectly remember names, which is fairly easy given the choice between an unpronounceable generic name and a brand name devoid of mnemonic value).  There are also patients who partially self-medicate (borrowing unprescribed medications from friends or relatives, taking left over pills from long-expired prescriptions, or ordering cheap drugs over the Internet, from suppliers who couldn't spell "prescription", let alone recognize one).

Since drug manufacturers cannot possibly test all possible combinations of medications, to a large extent risky interactions have to be identified the hard way.  As Samuelson states in the quote above, information technology (combined with data analysis) offers some hope there.  If changes to the health care system lead to more thorough (and more accurate) record keeping, there is an opportunity for data analysis to ferret out potential problems, which can then be tested in laboratories.  Better use of electronic record keeping will also help pharmacists detect when a customer is purchasing drugs that may interact in unfortunate ways, even if the purchases are being made at multiple pharmacies.

I think there is one more opportunity for "predictive analytics", though, and that is in identifying patients likely to be at greatest risk of an adverse interaction.  Just as considerable effort is going into profiling likely terrorists, so that time and energy screening travelers or inspecting cargo can be focused where it will do the most good, we can think about profiling patients.  If analytics allows us to identify patients at greatest risk, whether it be due to their errors or to errors by doctors or pharmacists, then perhaps either government or insurers can intervene (assign a case manager, warn pharmacists to ask extra questions, use a cell phone application to nag the patient, ...) and reduce the danger.  Making intelligent, data-driven decisions to achieve the best possible outcome:  sounds like OR to me.