A Model to Optimize Follow-up Care and Reduce Hospital Readmissions After Radical Cystectomy

Radical cystectomy has one of the highest readmission rates across all surgical procedures at approximately 25%. Our objective is to develop a mathematical model to optimize outpatient follow-up regimens for radical cystectomy.

We used delay-time analysis, a systems engineering approach, to maximize the probability of detecting patients susceptible to readmission through office visits and telephone calls. Our data source includes patients readmitted after radical cystectomy from the Healthcare Cost and Utilization Project's State Inpatient Database in 2009 and 2010 as well as from our institutional bladder cancer database from 2007 to 2011. We measured the time interval from hospital discharge to the point when a patient first exhibits concerning symptoms. Our primary endpoint is 30-day hospital readmission. Our model optimized the timing and sequence of follow-up care after radical cystectomy.

The timing of office visits and telephone calls is more important in detecting a patient at risk of readmission than the sequence of these encounters. Patients are most likely to exhibit concerning symptoms between 4 and 5 days after discharge. An optimally placed office visit can detect up to 16% of potential readmissions, which can be increased to 36% with 1 office visit followed by 4 telephone calls.

Our model improves the detection of concerning symptoms after radical cystectomy by optimizing the timing and number of outpatient encounters. By understanding how to design better outpatient follow-up care for radical cystectomy patients, we can help reduce the readmission burden for this population.

The Journal of urology. 2015 Dec 09 [Epub ahead of print]

Naveen Krishnan, Xiang Liu, Mariel S Lavieri, Michael Hu, Alexander Helfand, Benjamin Li, Jonathan E Helm, Chang He, Brent K Hollenbeck, Ted A Skolarus, Bruce L Jacobs

University of Michigan Medical School. University of Michigan College of Engineering; Departments of Industrial & Operations Engineering. , University of Michigan College of Engineering; Departments of Industrial & Operations Engineering. , University of Michigan College of Engineering; Departments of Industrial & Operations Engineering. , University of Michigan; Department of Urology. , University of Michigan; Department of Urology. , Indiana University Kelley School of Business. , University of Michigan; Department of Urology; Dow Division of Health Services Research. , University of Michigan; Department of Urology; Dow Division of Health Services Research; Division of Oncology. , University of Michigan; Department of Urology; Dow Division of Health Services Research; Division of Oncology; VA HSR&D Center for Clinical Management Research; VA Ann Arbor Healthcare System. , University of Pittsburgh Department of Urology.

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