A critical evaluation of healthcare quality improvement and how organizational context drives performance

This thesis explored healthcare quality improvement, considering the general question of why the last decade's worth of quality improvement (QI) had not significantly improved quality and safety. The broad objective of the thesis was to explore how hospitals perform when completing QI projects and whether any organizational characteristics were associated with that performance.

First the project evaluated a specific QI collaborative undertaken in the Veterans Affairs (VA) healthcare system. The goal of the collaborative was to improve patient flow throughout the entire care process leading to shorter hospital length of stay (LOS) and an increased percentage of patients discharged before noon. These two goals became the primary outcomes of the analysis, which were balanced by three secondary quality check outcomes: 30-day readmission, in-hospital mortality, and 30-day mortality.

The analytic model consisted of a five-year interrupted time-series examining baseline performance (two-years prior to the intervention), the year during the QI collaborative, and then two-years after the intervention to determine how well improvements were maintained post intervention. The results of these models were then used to create a novel 4-level classification model. Overall, the analysis indicated a significant amount of variation in performance; however, sub-group analyses could not identify any patterns among hospitals falling into specific performance categories.

Given this potentially meaningful variation, the second half of the thesis worked to understand whether specific organizational characteristics provided support or acted as key barriers to QI efforts. The first step in this process involved developing an analytic model to describe how various categories of organizational characteristics interacted to create an environment that modified a QI collaborative to produce measureable outcomes. This framework was then tested using a collection of variables extracted from two surveys, the categorized hospital performance from part one, and data mining decision trees. Although the results did not identify any strong associations between QI performance and organizational characteristics it generated a number of interesting hypotheses and some mild support for the developed conceptual model.

Overall, this thesis generated more questions than it answered. Despite this feature, it made three key contributions to the field of healthcare QI. First, this thesis represents the most thorough comparative analysis of hospital performance on QI and was able to identify four unique hospital performance categories. Second, the developed conceptual model represents a comprehensive approach for considering how organizational characteristics modify a standardized QI initiative. Third, data mining was introduced to the field as a useful tool for analyzing large datasets and developing important hypotheses for future studies.