As the healthcare industry moves forward to meet the demands of purchasers and payers for higher-quality, more effective, more cost-effective patient care with fewer errors and better care coordination, it is sobering to read the results of studies like the one recently conducted by Mihaela S. Stefan, M.D., and nine other researchers, and which was published in the Journal of General Internal Medicine in October. In the article that was created from the study, “Hospital Performance Measures and 30-day Readmission Rates,” Dr. Stefan and her colleagues aimed to “examine the association between hospital performance on Medicare’s Hospital compare process quality measures and 30-day readmission rates for patients with acute myocardial infarction (AMI), heart failure and pneumonia, and for those undergoing major surgery.”
Stefan and her colleagues pulled data from two different sources within the Medicare program: Hospital Compare process quality measures, and Medicare claims data on 30-day readmission rates for patients with those conditions. The team of researchers then created two composite measures of hospital performance for each condition, which they called an “overall measure” or “opportunity model” score, and an “appropriate care measure” score, based on performance along different dimensions. Then they compared those scores with 30-day readmissions rates. What they found could be considered discouraging. Here’s how the authors put it: “Higher performance scores were significantly associated with lower readmission rates for pneumonia, AMI, and orthopedic surgery, but the R correlation coefficients were low, ranging from 0.06 to 0.10. There was no statistically significant correlation between process measures and hospital readmission rates for HF, abdominal surgery, or cardiac/vascular surgery.”
Put more simply, the researchers continued, “In this large study of approximately 2,700 U.S. hospitals, we found little association between hospital performance on the process of care quality measures and hospital risk-standardized, 30-day all-cause readmission rates across a spectrum of medical and surgical conditions. Even when the associations were statistically significant,” the authors added, “the differences in the readmission rates of high- and low-performing hospitals were small.”
In short, following clinical guidelines and the evidence in the literature, even to the point of achieving admirable levels of clinical performance, will not in itself lead to statistically significant reductions in avoidable readmissions for patients with common chronic conditions.
What’s more, these findings by Dr. Stefan’s team follow upon a somewhat similar study performed by Robb D. Kociol, M.D., and his colleagues, and published in August of this year in the journal Circulation, and entitled, “National Survey of hospital Strategies to Reduce Heart Failure Readmissions: Findings From the Get With the Guidelines-Heart Failure Registry.” That study concluded that “A variety of strategies are employed by hospitals in an attempt to improve 30-day readmission rates for patients hospitalized with heart failure. Although more complete discharge and transitional care processes may be modestly associated with lower 30-day readmission rates, most current strategies are not associated with lower readmission rates.”
Importantly, Dr. Stefan and her co-authors note that hospitals cannot control many aspects around readmissions, particularly those related to socioeconomic patient population factors.
What’s clear here is that both types of work—the work to standardize patient care processes and to try to universalize best practices, and the work to reduce avoidable readmissions, are going to continue to be very difficult types of things to do going forward in healthcare. What’s more, we are absolutely in the first few collective steps in the journey of a thousand miles, when it comes to standardizing and optimizing patient care delivery processes in hospitals (not to mention in the outpatient sphere).
And our clinical information systems are only beginning to catch up to the pressing needs facing them in the emerging world of healthcare.
So there’s inevitably a kind of chicken-or-egg issue here: in order to optimize care practices, we will need exceptionally effective, newfangled clinical information systems and data analytics; yet in order to figure out exactly how to architect those solutions, we will need to be able to design those systems effectively to produce the results they need to produce.
All this speaks to what I’ve long noted: that the healthcare industry, uniquely among all the major industries in the U.S., is going through both its industrial revolution and its information age revolution at the same time. And that’s why efforts like these, to standardize care delivery and optimize patient care, are particularly difficult, because we lack both the standardization and optimization in our core industry processes that industries like manufacturing and transportation have had for decades; and we also lack the optimized information technology and available data we need to get to the next level.
But studies like these two will help clarify the situation in the trenches, as work moves forward to improve patient care delivery and to reduce avoidable readmissions. It will take years for some elements in all this work to be clarified. But there’s no question that informaticists, particularly clinical informaticists, will be deeply involved in all these initiatives and in the continuous analysis needed to interpret the data findings involved and turn them into continuous performance improvement.
