Abstract
BackgroundTo overcome the limitations of administrative data in adequately adjusting for differences in patients’ risk of readmissions, recent studies have added supplemental data from patient surveys and other sources (e.g., electronic health records). However, judging the adequacy of enhanced risk adjustment for use in assessment of 30-day readmission as a hospital quality indicator is not straightforward. In this paper, we evaluate the adequacy of risk adjustment by comparing the one-year costs of those readmitted within 30 days to those not after excluding the costs of the readmission.MethodsIn this two-step study, we first used comprehensive administrative and survey data on a nationally representative Medicare cohort of hospitalized patients to compare patients with a medical admission who experienced a 30-day readmission to patients without a readmission in terms of their overall Medicare payments during 12 months following the index discharge. We then examined the extent to which a series of enhanced risk adjustment models incorporating code-based comorbidities, self-reported health status and prior healthcare utilization, reduced the payment differences between the admitted and not readmitted groups.ResultsOur analytic cohort consisted 4684 index medical hospitalization of which 842 met the 30-day readmission criteria. Those readmitted were more likely to be older, White, sicker and with higher healthcare utilization in the previous year. The unadjusted subsequent one-year Medicare spending among those readmitted ($56,856) was 60% higher than that among the non-readmitted ($35,465). Even with enhanced risk adjustment, and across a variety of sensitivity analyses, one-year Medicare spending remained substantially higher (46.6%, p < 0.01) among readmitted patients.ConclusionsEnhanced risk adjustment models combining health status indicators from administrative and survey data with previous healthcare utilization are unable to substantially reduce the cost differences between those medical admission patients readmitted within 30 days and those not. The unmeasured patient severity that these cost differences most likely reflect raises the question of the fairness of programs that place large penalties on hospitals with higher than expected readmission rates.
Highlights
To overcome the limitations of administrative data in adequately adjusting for differences in patients’ risk of readmissions, recent studies have added supplemental data from patient surveys and other sources
To evaluate the adequacy of risk adjustment when analyzing 30-day readmission rates, in this paper we take a novel approach that as far as we know has not been used before: we examine the longer term costs of those readmitted within 30 days and those not after excluding the costs of the readmission
We examined the difference in one-year costs of readmitted and non-readmitted patients who had spending that was below different dollar thresholds
Summary
To overcome the limitations of administrative data in adequately adjusting for differences in patients’ risk of readmissions, recent studies have added supplemental data from patient surveys and other sources (e.g., electronic health records). Studies over several decades have emphasized the inadequacy of administrative data-based risk adjustment models like that used in the US by the Centers for Medicare and Medicaid Services (CMS) in its 30-day hospital readmission profiling and penalty program, largely because administrative databases include only limited information on patient severity and disease burden [1,2,3,4,5,6,7]. Research papers have described models using additional variables from administrative databases (e.g., race/ethnicity and socio-economic status) as well as enhancing administrative data with self-reported and medical chart data, which capture previously unmeasured patient risk indicators such as health behavior, mental health status, functional health, socioeconomic vulnerabilities, and family and social support [2,3,4, 14,15,16,17,18,19,20,21,22,23,24]. The extent to which previously unmeasured disease burden and severity is captured in the enhanced models is unclear
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
