1371. Automated continuous monitoring of clinical outcomes using cumulative sum methods and variable life-adjusted display charts
Authors: Clemens M. Schirmer, MD, PhD, FAANS ; Michel Lacroix, MD; Stanley Pugsley, MD; Neil Martin, MD (Wilkes Barre, PA)
Robust systems for monitoring quality in neurosurgical practice are lacking. Statistical process control methods, utilizing the increasingly available routinely collected electronic patient records, could be used in creating surveillance systems that could lead to rapid detection of periods of deteriorating standards. We aimed to develop and test a CUmulative SUM (CUSUM) based surveillance system that could be used in continuous monitoring of clinical outcomes, using routinely collected data and display data using variable life-adjusted display (VLAD) charts that account for the expected specialty practice parameters.
We utilize prospective and independently abstracted Geisinger quality data across the dimensions or morbidity and mortality, length of stay, readmissions and complications, charges and cost. A surveillance system based on the Observed minus Expected (O-E) as well as the 2- sided Log-Likelihood CUSUM charts, displayed in VLAD charts was developed based on data since 2013. Data is identified based on ICD-9 and -10 principal and discharge diagnosis, procedure code or MS-DRG attribution.
The VLAD-CUSUM system allows to develop a comprehensive assessment of the neurosurgical practice and offers the ability to drill down in multiple dimensions across sub-specialties, individual practitioners and disease entities. The VLAD component offers the ability discern temporal trends and set detection windows for favorable or suboptimal practice patterns. Unexpected singular events can be visually identified and differentiated from systematic opportunities for improvement (OFI).
The VLAD-CUSUM method can be used in continuous monitoring of clinical outcomes using routinely collected data. Used prospectively, they could lead to the prompt detection of periods of suboptimal standards and rapid detection of OFI and facilitate near “real-time” performance monitoring allowing early detection and intervention in altered performance. Careful interpretation of charts for group and individual operators may prove helpful in detecting and differentiating systemic from individual practice variation.