Disruptive Transformation for Hospital Systems and a Couple of Places where Statisticians can Help

For many years hospitals and physicians have been paid by insurance companies for each procedure they perform.  This may seem reasonable since every procedure performed, from discussing a patient's disease to performing the most complicated surgery, requires resources.  However, it creates a perverse set of financial incentives for physicians and hospital systems.  Sicker patients lead to more procedures which lead to more revenue.  The financial incentive is to make patients sicker!

Physicians and hospital administrators recognize the wrongness of this incentive structure.  Only criminally anti-social individuals would actively pursue "upselling" as a legitimate means of increasing hospital revenue.  Therefore, in order to obscure and minimize the effect of this financial incentive, physicians are shielded from the costs of the procedures they perform and hospital administrations typically do not monitor the health of their patient population.

There is a movement in healthcare to impose financial incentives for healthcare providers to make patients healthier.  Recent changes in (1) the rules by which the Centers for Medicare and Medicaid Services (CMS) must operate and (2) federal law regarding the implementation of electronic health records, are beginning to make this change a reality. 

As of 2012 CMS can work with hospitals or groups of physicians to create "Accountable Care Organizations" (ACOs).  Under this payment structure, care providers are given a fixed fee for each patient for whom they are responsible; if they can save money in the care of that patient they get to pocket the savings.  This is similar to the fee structure of Health Maintenance Organizations that were reviled by patients in the 1980's because they were financially incentivized to minimize patient interactions. 

There are some key differences which, if taken advantage of, can lead to a different outcome for ACOs.  First, with ACOs and other "risk bearing" healthcare organizations there are penalties when patients do not do well.  This leads to a question into which statistics can offer insight; since everybody is different, what does it mean for a patient to be doing well? Second, the opportunities for communication between hospital systems and patients have vastly improved since the 1980’s.  Try searching for “frustrated with hospital” on twitter and you will readily see that communication from the patient to the hospital is already very robust.  Third, due to the Affordable Care Act, there is a growing percentage of the population who are directly responsible for choosing their own health insurance.  It is natural to demand the most expensive insurance from one’s employer if a choice of health insurance provider is not part of the hiring process.  However, when an individual is deciding between plans with vastly different prices, choosing a plan that encourages maximizing the number of procedures no longer seems like the obvious choice – it shouldn’t have been anyway.

In addition to changing fee structures, as of 2012 healthcare organizations are required to maintain electronic medical records.  The original intention of this law was to encourage the free exchange of health information between providers in order to minimize duplication of effort; if a patient has an x-ray at one hospital, it should not be repeated the next day if they show up at a different hospital.  In practice, this objective has not yet been realized because every hospital has its own EMR and those systems are not interoperable – even if they were purchased from the same vendor.  However, what has been created is a vast trove of data about the health of individual patients.  The potential of this huge amount of data to affect all aspects of healthcare cannot be overstated.  In particular, risk bearing hospital systems now have both the financial incentives and the necessary data to track the health of their patient population and be proactive in the treatment of disease.  Successful physician-statistician collaborations are needed to turn this data into information that hospital systems can act upon.

Driving uptake of IT and Statistics in Healthcare

The strongest and earliest driver encouraging hospitals to implement electronic medical records originates from CMS in the form of a couple of different penalties.  The “meaningful use” requirement is being implemented in three increasingly strict phases (phase 1, phase 2 and phase 3).  Those hospitals that are deemed not to be utilizing their electronic medical records in a meaningful way will be penalized, beginning in 2015, with a 1% decrease in CMS payouts.  The penalty increases by 1% yearly up to a total of 5% for consistent failure to achieve meaningful use – tens of millions of dollars for hospitals with large Medicare and Medicaid populations.  Most of the meaningful use definitions require solutions that are straightforward even if they are technically complicated to implement.  As of early 2014, I am not aware of any “meaningful use” applications that involve statistical solutions, though I can imagine improvements to the current versions that might.  Here I discuss a statistical approach to identifying homogeneous groups of patients (one of the elements of meaningful use in phase 2).  Whether improvements like these are financially viable will depend heavily on the way that financial incentives are structured for hospitals.

The second penalty (again a 1% incrementally increasing penalty), and the one that has led the industry to seek out statistical solutions, is a reduction in payment for hospitals with too many patients who are readmitted within 30 days of discharge.  There has been an explosion of statistical models attempting to predict early readmission – there is an open access survey in JAMA for those who want greater detail.  To my knowledge, all of the models attempting to accomplish this are logistic regressions which, in their final form, rely on a clearly defined set of data (independent variables) with which to make predictions.  Implementing these models in practice is challenging because electronic records do not follow fixed standards, patient populations vary significantly between hospitals, and every hospital record system is plagued by missing and incorrectly coded data. Finally, it is not always clear how far back into a record one must go.  A typical statistical approach to modeling the time varying state of a patient is to assume that all the relevant information for predicting the future is available in the present (see hidden Markov model and memorylessness).  However, if two patients come to the hospital with skin infections, and one was diagnosed years earlier with diabetes, the severity of their infection and their chances of returning within 30 days are very different.

I have described some of the disruptive changes that hospitals are undergoing as a result of changing incentives and the availability of electronic health records.  The availability of this data will disrupt healthcare delivery at all levels; insurance companies, contract research organizations, pharma, regulators and consumers are all seeing (and will continue to see) disruption. 

Perhaps the most exciting thing for statisticians is the availability of a vast array of statistical challenges in the healthcare industry that are financially viable, able to tolerate uncertainty and just downright fun to work on.  We will likely never get to a point where computers can be trusted to make medical decisions for patients, but a tricorder reminiscent of Star Trek might be just around the corner, and even a 1% increase in efficiency for the half-trillion dollar drug discovery industry would be tremendously valuable.