Quality Control in the Healthcare Industry: An Analysis of Surgery Turnover Times

For virtually all hospitals, utilization rates are a critical managerial indicator of efficiency and are determined in part by turnover time. Turnover time is defined as the time elapsed between surgeries, during which the operating room is cleaned and preparedfor the next surgery. Lengthier turnover times result in lower utilization rates, thereby hindering hospitals’ ability to maximize the numbers of patients that can be attended to. In this thesis, we analyze operating room data from a two year period provided byEvangelical Community Hospital in Lewisburg, Pennsylvania, to understand the variability of the turnover process. From the recorded data provided, we derive our best estimation of turnover time. Recognizing the importance of being able to properly modelturnover times in order to improve the accuracy of scheduling, we seek to fit distributions to the set of turnover times. We find that log-normal and log-logistic distributions are well-suited to turnover times, although further research must validate this finding. Wepropose that the choice of distribution depends on the hospital and, as a result, a hospital must choose whether to use the log-normal or the log-logistic distribution. Next, we use statistical tests to identify variables that may potentially influence turnover time. We find that there does not appear to be a correlation between surgerytime and turnover time across doctors. However, there are statistically significant differences between the mean turnover times across doctors. The final component of our research entails analyzing and explaining the benefits of introducing control charts as a quality control mechanism for monitoring turnover times in hospitals. Although widely instituted in other industries, control charts are notwidely adopted in healthcare environments, despite their potential benefits. A major component of our work is the development of control charts to monitor the stability of turnover times. These charts can be easily instituted in hospitals to reduce the variabilityof turnover times. Overall, our analysis uses operations research techniques to analyze turnover times and identify manners for improvement in lowering the mean turnover time and thevariability in turnover times. We provide valuable insight into a component of the surgery process that has received little attention, but can significantly affect utilization rates in hospitals. Most critically, an ability to more accurately predict turnover timesand a better understanding of the sources of variability can result in improved scheduling and heightened hospital staff and patient satisfaction. We hope that our findings can apply to many other hospital settings.

Accuracy of Recognition and Diagnosis of Comorbid Depression in the Nursing Home

A variety of research has documented high levels of depression among older adults in the health care setting. Additional research has shown that care providers in health care settings are not very effective at diagnosing comorbid depression.This is a troublesome finding since comorbid depression has been linked to a number of negative outcomes in older adults. Early results have indicated that comorbid depression may be associated with a number of unfavorable consequences ranging from impairments in physical functioning to increased mortality.The health care setting with arguably the highest rate of physical impairment is the nursing home and it is the nursing home where the effects of comorbid depression may be most costly. Therefore, the current analysis uses data from the Institutional Population Component of the NationalMedical Expenditure Survey (US Department of Health and Human Services, 1990) to explore rates of both recognized and unrecognized comorbid depression in the nursing home setting. Using a constructed proxy variable representative of the DSM-III-R diagnosis of depression, results indicate that approximately 8.1% of nursing home residents have an unrecognized potential comorbid depression.