Though a comparison between manual and automated methods for identifying potentially preventable readmissions in a large healthcare system found that the software identified many more readmissions as potentially preventable, the study concluded that "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."
Researchers 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. They also reassessed the same readmissions with 3Ms Potentially Preventable Readmission (PPR) software, and examined between-method agreement and the specificity and sensitivity of the PPR software using manual review as the reference.
The results of the study, published April 5 in BMC Medical Informatics and Decision Making, found that automated classification and manual review respectively identified 78 percent and 47 percent of readmissions as potentially preventable. Overall, the classification methods agreed about the preventability of 56 percent of readmissions. Using manual review as the reference, the sensitivity of PPR was 85 percent and specificity was 28 percent.
"Substantial differences existed between manual review and automated classification methods, with PPR identifying many more readmissions as potentially preventable," states the article's authors. "This may have occurred because PPR uses a sole criterion to identify potential preventability: clinical relatedness to the index admission. In contrast, manual review classified as non-preventable many readmissions that were clinically related to the index stay."
"To a lesser extent, manual review also identified potentially preventable readmissions that PPR did not identify," added the authors, who also indicated that the "subjective" process of manual review might have resulted in misclassifications.
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