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What Is a Complication, Anyway?
April 18, 2016

What Is a Complication, Anyway?

When asked “what is your surgical site infection rate?” one will get an answer that is almost certainly removed from reality.
What is needed is a single (ie, binary) designation of whether the patient had a major complication or not from his or her inpatient care: that is, a comprehensive and composite measurement of inpatient outcome. To do this, our research group has turned to the writings of Walter Shewhart, the grandfather of manufacturing quality control.
 
Shewhart was the architect of the concept of Statistical Process Control. He recognized that even with precise industrial manufacturing methods there was very slight variation in the measurements of final widgets that were produced. Common cause variation around the desired specification was acceptable because the final product effectively served its purpose. However, special cause variation was the consequence of failed processes that yielded an end-product that was unacceptable, and if manufacturing processes were producing a defective product, it needed to be promptly identified and corrected. Defective products were consistently ±3-sigma from the manufacturing specification. Can we use statistical process control for the development of a composite measure of inpatient outcomes in hospital care?
 
Inpatient length-of-stay has become the focus of our quest to apply statistical process control for the composite measurement of severe complications of care. The duration of hospital stay is critically important to hospitals and to insurers. Patients are expected to be discharged in a standard period of time unless an untoward event has occurred. Surgeons recognize this as well, and prompt discharge of post-operative patients with an uneventful recovery is expected. Patients stay for longer periods of time when things have not gone as expected. Thus, we developed linear prediction models from national databases (eg, Medicare data) to define the expected length-of-stay for elective surgical cases when no coded complications were identified, and then apply that prediction model to all cases to identify prolonged length of stay (prLOS) outliers.
 
Our statistical process control method begins by taking all operative cases of a given type (eg, coronary artery bypass grafting) and we arrange them in temporal sequence of when they were performed during the time period of study. The temporal sequencing in important since statistical process control has time as a dimension in addition to normative statistical distribution. For each hospital being evaluated, the total observed days of hospitalization and the total predicted days are set equal to each other by multiplying the predicted value by the observed:predicted ratio. This adjustment accounts for the local culture of hospitalization practice but maintains the relative weight of the prediction variables. A control chart is then designed and 3-sigma outliers are identified.
 
Studying Length-of-Stay Outliers
We have found that length-of-stay outliers have been a consistent method for the identification of severe complications of inpatient care. Length-of-stay outliers have significantly higher costs than those patients with coded complications of care but are not outliers. Length-of-stay outliers have highly significant odds ratios in the prediction of 90-day post-discharge deaths and readmissions to the hospital. Even those patients that are length-of-stay outliers but are without coded complications at discharge have higher costs, predict 90-day post-discharge deaths and readmissions, and upon audit consistently have complications that have escaped being coded and have resulted in protracted hospitalization.
 
We have used this metric in the risk-adjusted evaluation of hospital performance in cardiac surgery that was recently published in The American Journal of Accountable Care.
 
The use of prolonged length-of-stay outliers has laid a strong foundation for the definition of inpatient complications of surgical care. Occasional cases have been rapidly discharged to skilled nursing facilities when major adverse events have occurred and have escaped identification by prolonged hospitalization. There are uncommon cases where poor discharge planning prior to elective surgical procedures may lead to prolonged hospitalization because of disposition issues and constitute economic morbidity, but not a complication of care. For the vast majority of cases, we have found 3-sigma length-of-stay outliers to be an effective composite measure of inpatient adverse outcomes.
 
This metric can serve as an effective method to monitor and improve results of surgical care in much the same way that Shewhart envisioned statistical process control over 80 years ago.

 
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