Clinical Quality and Safety
The minimization of clinical variation in the treatment of patients can result in significant and sustainable savings. Clinical variation has been widely recognized as a key determinant in the ceaseless rise in healthcare costs. However, it is only with the use of modern data discovery tools, that these variations can be easily documented through the analysis of multiple, interconnected data sets, such as clinician cost, pharmacy data, length of stay, OR time points and many more.
The Problem with Variation
The study of variation in healthcare can be directly traced to the work of Walter Shewhart and W. Edwards Deming. The concepts of statistical control of processes and the related technical tool of the control chart, provided the basic framework for process and quality improvement. Control charts provide a basic way to visualize the data, but using modern data discovery technology, variance and outliers are spotted more easily and directly.
These concepts led to generally accepted ideas around warranted variation and unwarranted variation:
Warranted variation in healthcare attributable to objective patient care factors
Unwarranted variation in health care service delivery refers to differences that cannot be explained by illness, medical need, or the results of evidence-based medicine.
Sources of clinical variation
- Preference-sensitive care: the choice of care is driven by the patient’s own preferences.
- Supply-sensitive care: strongly correlated with healthcare system resource capacity
- Underuse: This involves discontinuity of care and a lack of systems that would facilitate the appropriate use of services. This results in insufficient use of care.
- Misuse: This involves the failure to accurately understand or communicate the risks and benefits of alternative treatments, and inappropriate prescriptions for medications or tests. This results in improperly utilized care.
- Overuse: This involves an overdependence on the acute-care sector, and a lack of infrastructure necessary to support the management of chronically ill patients in other settings. This results in the inappropriate use of care.(1)(2)
Importance of outlier identification
Clinical variation data is available from public sources such as the Dartmouth Atlas in the United States and the NHS Atlas of Variation in Healthcare in the United Kingdom. At the macro level, these data are instructive to help identify individual hospitals manage outliers. Most healthcare communities draw a reference from local practice patterns and as a result of these comparisons can be effective as well.
- Variation by physician - Find unseen relationships and trends. The analysis of physician practice patterns. Physician practice patterns play a key role in the overall cost to deliver appropriate healthcare. The goal with physician practice pattern analysis is not to penalize outliers, but rather to move the average level of care provided.
- Variation by diagnosis - At the patient level, variations in diagnostic findings as well as diagnostic costs profiles can be analyzed for efficiency and outcome effectiveness.
Qlik Sense® is designed to support self-service visualization in a scalable, secure, and governable way. Qlik Sense® can be deployed on a single server and scale vertically and horizontally to address the availability and processing requirements of your deployment, whether on premise or in the cloud.
The Qlik Sense® Clinical Variation App is able to analyze available data at the physician level, the individual patient level, the local community level, or the population as a whole. This flexibility is enabled by the Qlik associative engine. The Qlik Indexing Engine (QIX) is the associative, in-memory data analytics engine. This memory-based application tier delivers highly interactive self-service visualizations, search, and calculations at runtime.