Karin Hekkert1,2,3, Femke van der Brug3, Ine Borghans2, Sezgin Cihangir1, Cees Zimmerman4, Gert Westert3, Rudolf B Kool3. 1. Dutch Hospital Data, P.O. Box 9696, 3506 GR, Utrecht, The Netherlands. 2. Dutch Healthcare Inspectorate (IGZ), Stadsplateau 1, 3521 AZ, Utrecht, The Netherlands. 3. Radboud University Medical Center, Radboud Institute for Health Sciences, IQ Healthcare, P.O. Box 9101, IQ Healthcare 114 6500 HB, Nijmegen, The Netherlands. 4. Radboud University Medical Center, Intensive Care Unit 710, P.O. Box 9101, 6500 HB, Nijmegen, The Netherlands.
Abstract
IMPORTANCE: Hospital readmissions are being used increasingly as an indicator of quality of care. However, it remains difficult to identify potentially preventable readmissions. OBJECTIVES: To evaluate the identification of potentially preventable hospital readmissions by using a classification of readmissions based on administrative data. DESIGN AND SETTING: We classified a random sample of 455 readmissions to a Dutch university hospital in 2014 using administrative data. We compared these results to a classification based on reviewing the medical records of these readmissions to evaluate the accuracy of classification by administrative data. MAIN OUTCOME MEASURES: Frequencies of categories of readmissions based on reviewing records versus those based on administrative data. Cohen's kappa for the agreement between both methods. The sensitivity and specificity of the identification of potentially preventable readmissions with classification by administrative data. RESULTS: Reviewing the medical records of acute readmissions resulted in 28.5% of the records being classified as potentially preventable. With administrative data this was 44.1%. There was slight agreement between both methods: ƙ 0.08 (95% CI: 0.02-0.15, P < 0.05). The sensitivity of the classification of potentially preventable readmissions by administrative data was 63.1% and the specificity was 63.5%. CONCLUSIONS: This explorative study demonstrated differences between categorizing readmissions based on reviewing records compared to using administrative data. Therefore, this tool can only be used in practice with great caution. It is not suitable for penalizing hospitals based on their number of potentially preventable readmissions. However, hospitals might use this classification as a screening tool to identify potentially preventable readmissions more efficiently.
IMPORTANCE: Hospital readmissions are being used increasingly as an indicator of quality of care. However, it remains difficult to identify potentially preventable readmissions. OBJECTIVES: To evaluate the identification of potentially preventable hospital readmissions by using a classification of readmissions based on administrative data. DESIGN AND SETTING: We classified a random sample of 455 readmissions to a Dutch university hospital in 2014 using administrative data. We compared these results to a classification based on reviewing the medical records of these readmissions to evaluate the accuracy of classification by administrative data. MAIN OUTCOME MEASURES: Frequencies of categories of readmissions based on reviewing records versus those based on administrative data. Cohen's kappa for the agreement between both methods. The sensitivity and specificity of the identification of potentially preventable readmissions with classification by administrative data. RESULTS: Reviewing the medical records of acute readmissions resulted in 28.5% of the records being classified as potentially preventable. With administrative data this was 44.1%. There was slight agreement between both methods: ƙ 0.08 (95% CI: 0.02-0.15, P < 0.05). The sensitivity of the classification of potentially preventable readmissions by administrative data was 63.1% and the specificity was 63.5%. CONCLUSIONS: This explorative study demonstrated differences between categorizing readmissions based on reviewing records compared to using administrative data. Therefore, this tool can only be used in practice with great caution. It is not suitable for penalizing hospitals based on their number of potentially preventable readmissions. However, hospitals might use this classification as a screening tool to identify potentially preventable readmissions more efficiently.
Authors: Elsemieke A I M Meurs; Carl E H Siegert; Elien Uitvlugt; Najla El Morabet; Ruth J Stoffels; Dirk W Schölvinck; Laura F Taverne; Pim B J E Hulshof; Hilde J S Ten Horn; Philou C W Noordman; Josien van Es; Nicky van der Heijde; Meike H van der Ree; Maurice A A J van den Bosch; Fatma Karapinar-Çarkit Journal: Sci Rep Date: 2021-10-11 Impact factor: 4.379
Authors: N Salet; V A Stangenberger; F Eijkenaar; F T Schut; M C Schut; R H Bremmer; A Abu-Hanna Journal: Sci Rep Date: 2022-04-07 Impact factor: 4.379