Manual and automated methods for identifying potentially preventable readmissions: a comparison in a large healthcare system

作者：Jackson Ana H^*; Fireman Emily; Feigenbaum Paul; Neuwirth Estee; Kipnis Patricia; Bellows Jim

来源：BMC Medical Informatics and Decision Making, 2014, 14(1): 28.

DOI：10.1186/1472-6947-14-28

• Qualitative research
• Quality assurance
• Health care/methods
• Patient readmission/statistics & numerical data

Background: Identification of potentially preventable readmissions is typically accomplished through manual review or automated classification. Little is known about the concordance of these methods.
Methods: We manually reviewed 459 30-day, all-cause readmissions at 18 Kaiser Permanente Northern California hospitals, determining potential preventability through a four-step manual review process that included a chart review tool, interviews with patients, their families, and treating providers, and nurse reviewer and physician evaluation of findings and determination of preventability on a five-point scale. We reassessed the same readmissions with 3 M's Potentially Preventable Readmission (PPR) software. We examined between-method agreement and the specificity and sensitivity of the PPR software using manual review as the reference.
Results: Automated classification and manual review respectively identified 78% (358) and 47% (227) of readmissions as potentially preventable. Overall, the methods agreed about the preventability of 56% (258) of readmissions. Using manual review as the reference, the sensitivity of PPR was 85% and specificity was 28%.
Conclusions: Concordance between methods was not high enough to replace manual review with automated classification as the primary method of identifying preventable 30-day, all-cause readmission for quality improvement purposes.

• 出版日期2014-4-5