Identifying preventable readmissions: an achievable goal or waiting for Godot?
Hospital readmission rates have captured the attention of policymakers, administrators, researchers and healthcare providers over the last decade. This has been spurred in no small part by the Hospital Readmissions Reduction Program, which began in the USA in 2012 and requires the Centres for Medicare and Medicaid Services to reduce payments to acute care facilities with high rates of readmission within 30 days of discharge for selected conditions. After years of intense research to find an objective measure of preventable readmissions, it seems as imminent as the arrival of Godot. Whether preventable readmissions can be objectively defined or represent a valid patient-centred measure of quality are unclear.
While the search continues for validated and objective measures of readmission, emerging commercial software programs using administrative data to flag potentially preventable readmissions (PPRs) are marketed as a solution to labour-intensive chart review. One example is the 3M Potentially Preventable Readmissions Grouping Software, a widely used proprietary program.1 Using hospital administrative data, the program identifies readmissions with diagnoses that are ‘clinically related’ to the index admission and flags them as potentially preventable. Readmissions are risk-adjusted for case mix and severity of illness. Although the program has yet to be validated, its ease of use and promise of producing an objective measure of PPR have resulted in quick uptake by many organisations.
In their BMJ Quality and Safety publication, Borzecki et al2 aim to determine whether the 3M PPR software can accurately identify preventable readmissions for pneumonia, one of the conditions with financial penalties for US hospitals with high readmission rates. Using administrative data from the US Veterans Affairs (VA) healthcare system, the researchers randomly selected 100 pneumonia readmissions for manual chart review. They developed a tool including four explicit quality measure domains related to admission work-up, in-hospital evaluation/treatment, discharge readiness and postdischarge period. Each domain received equal weighting and postdischarge information was available through the VA’s comprehensive linked database. Using this tool, the investigators compared quality scores for cases flagged by the software as PPR (PPR-yes) and unavoidable readmissions (PPR-no). They found that PPR-yes readmissions actually had slightly higher-quality scores compared with those deemed as unavoidable, although the difference did not reach significance. Whether the cases flagged as PPR-yes truly represent real avoidable patient readmissions is unclear. As the authors note, their findings may indicate that PPR-yes cases are no more preventable than PPR-no cases. Alternatively, preventability assessment may require information outside the scope of health records. For example, assessing the quality of discharge instructions may require direct observation. These results give us pause when considering preventable readmission rates as a quality metric.
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