For Publishers
DiscoveryMetadataPeer reviewHostingPublishing
For Researchers
JoinPublishReviewCollect
Register
Blog
About
Search
Advanced search
My ScienceOpen
Search
For Publishers
For Researchers
Blog
About
8
views
25
references
 Top references
cited by
2
    
0 reviews
 Review
0
comments
 Comment
0
recommends
+1 Recommend
2 collections
    0
    shares
      170
      similar
       All similar
      • Record: found
      • Abstract: found
      • Article: not found

      Implementation Experience with a 30-Day Hospital Readmission Risk Score in a Large, Integrated Health System: A Retrospective Study

      research-article

      Read this article at

      ScienceOpenPublisherPMC
      Bookmark
          There is no author summary for this article yet. Authors can add summaries to their articles on ScienceOpen to make them more accessible to a non-specialist audience.

          Abstract

          Background

          Driven by quality outcomes and economic incentives, predicting 30-day hospital readmissions remains important for healthcare systems. The Cleveland Clinic Health System (CCHS) implemented an internally validated readmission risk score in the electronic medical record (EMR).

          Objective

          We evaluated the predictive accuracy of the readmission risk score across CCHS hospitals, across primary discharge diagnosis categories, between surgical/medical specialties, and by race and ethnicity.

          Design

          Retrospective cohort study.

          Participants

          Adult patients discharged from a CCHS hospital April 2017–September 2020.

          Main Measures

          Data was obtained from the CCHS EMR and billing databases. All patients discharged from a CCHS hospital were included except those from Oncology and Labor/Delivery, patients with hospice orders, or patients who died during admission. Discharges were categorized as surgical if from a surgical department or surgery was performed. Primary discharge diagnoses were classified per Agency for Healthcare Research and Quality Clinical Classifications Software Level 1 categories. Discrimination performance predicting 30-day readmission is reported using the c-statistic.

          Results

          The final cohort included 600,872 discharges from 11 Northeast Ohio and Florida CCHS hospitals. The readmission risk score for the cohort had a c-statistic of 0.6875 with consistent yearly performance. The c-statistic for hospital sites ranged from 0.6762, CI [0.6634, 0.6876], to 0.7023, CI [0.6903, 0.7132]. Medical and surgical discharges showed consistent performance with c-statistics of 0.6923, CI [0.6807, 0.7045], and 0.6802, CI [0.6681, 0.6925], respectively. Primary discharge diagnosis showed variation, with lower performance for congenital anomalies and neoplasms. COVID-19 had a c-statistic of 0.6387. Subgroup analyses showed c-statistics of > 0.65 across race and ethnicity categories.

          Conclusions

          The CCHS readmission risk score showed good performance across diverse hospitals, across diagnosis categories, between surgical/medical specialties, and by patient race and ethnicity categories for 3 years after implementation, including during COVID-19. Evaluating clinical decision-making tools post-implementation is crucial to determine their continued relevance, identify opportunities to improve performance, and guide their appropriate use.

          Supplementary Information

          The online version contains supplementary material available at 10.1007/s11606-021-07277-4.

          Related collections

          Most cited references25

          • Record: found
          • Abstract: found
          • Article: not found

          Dissecting racial bias in an algorithm used to manage the health of populations

          Ziad Obermeyer, Brian Powers, Christine Vogeli … (2019)
          Health systems rely on commercial prediction algorithms to identify and help patients with complex health needs. We show that a widely used algorithm, typical of this industry-wide approach and affecting millions of patients, exhibits significant racial bias: At a given risk score, Black patients are considerably sicker than White patients, as evidenced by signs of uncontrolled illnesses. Remedying this disparity would increase the percentage of Black patients receiving additional help from 17.7 to 46.5%. The bias arises because the algorithm predicts health care costs rather than illness, but unequal access to care means that we spend less money caring for Black patients than for White patients. Thus, despite health care cost appearing to be an effective proxy for health by some measures of predictive accuracy, large racial biases arise. We suggest that the choice of convenient, seemingly effective proxies for ground truth can be an important source of algorithmic bias in many contexts.
          Article 0 comments Cited 898 times – based on 0 reviews     Review now
            Bookmark
            • Record: found
            • Abstract: found
            • Article: not found

            Risk prediction models for hospital readmission: a systematic review.

            Devan Kansagara, Honora Englander, Amanda Salanitro … (2011)
            Predicting hospital readmission risk is of great interest to identify which patients would benefit most from care transition interventions, as well as to risk-adjust readmission rates for the purposes of hospital comparison. To summarize validated readmission risk prediction models, describe their performance, and assess suitability for clinical or administrative use. The databases of MEDLINE, CINAHL, and the Cochrane Library were searched from inception through March 2011, the EMBASE database was searched through August 2011, and hand searches were performed of the retrieved reference lists. Dual review was conducted to identify studies published in the English language of prediction models tested with medical patients in both derivation and validation cohorts. Data were extracted on the population, setting, sample size, follow-up interval, readmission rate, model discrimination and calibration, type of data used, and timing of data collection. Of 7843 citations reviewed, 30 studies of 26 unique models met the inclusion criteria. The most common outcome used was 30-day readmission; only 1 model specifically addressed preventable readmissions. Fourteen models that relied on retrospective administrative data could be potentially used to risk-adjust readmission rates for hospital comparison; of these, 9 were tested in large US populations and had poor discriminative ability (c statistic range: 0.55-0.65). Seven models could potentially be used to identify high-risk patients for intervention early during a hospitalization (c statistic range: 0.56-0.72), and 5 could be used at hospital discharge (c statistic range: 0.68-0.83). Six studies compared different models in the same population and 2 of these found that functional and social variables improved model discrimination. Although most models incorporated variables for medical comorbidity and use of prior medical services, few examined variables associated with overall health and function, illness severity, or social determinants of health. Most current readmission risk prediction models that were designed for either comparative or clinical purposes perform poorly. Although in certain settings such models may prove useful, efforts to improve their performance are needed as use becomes more widespread.
            Article 0 comments Cited 183 times – based on 0 reviews     Review now
              Bookmark
              • Record: found
              • Abstract: found
              • Article: not found

              Potentially avoidable 30-day hospital readmissions in medical patients: derivation and validation of a prediction model.

              Drahomir Aujesky, Jeffrey Schnipper, Deborah Williams … (2013)
              Because effective interventions to reduce hospital readmissions are often expensive to implement, a score to predict potentially avoidable readmissions may help target the patients most likely to benefit. To derive and internally validate a prediction model for potentially avoidable 30-day hospital readmissions in medical patients using administrative and clinical data readily available prior to discharge. Retrospective cohort study. Academic medical center in Boston, Massachusetts. All patient discharges from any medical services between July 1, 2009, and June 30, 2010. Potentially avoidable 30-day readmissions to 3 hospitals of the Partners HealthCare network were identified using a validated computerized algorithm based on administrative data (SQLape). A simple score was developed using multivariable logistic regression, with two-thirds of the sample randomly selected as the derivation cohort and one-third as the validation cohort. Among 10 731 eligible discharges, 2398 discharges (22.3%) were followed by a 30-day readmission, of which 879 (8.5% of all discharges) were identified as potentially avoidable. The prediction score identified 7 independent factors, referred to as the HOSPITAL score: h emoglobin at discharge, discharge from an o ncology service, s odium level at discharge, p rocedure during the index admission, i ndex t ype of admission, number of a dmissions during the last 12 months, and l ength of stay. In the validation set, 26.7% of the patients were classified as high risk, with an estimated potentially avoidable readmission risk of 18.0% (observed, 18.2%). The HOSPITAL score had fair discriminatory power (C statistic, 0.71) and had good calibration. This simple prediction model identifies before discharge the risk of potentially avoidable 30-day readmission in medical patients. This score has potential to easily identify patients who may need more intensive transitional care interventions.
              Article 0 comments Cited 161 times – based on 0 reviews     Review now
                Bookmark
                All references

                Author and article information

                Contributors
                Anita D. Misra-Hebert:
                ORCID: http://orcid.org/0000-0003-3079-7025
                misraa@ccf.org
                Journal
                Journal ID (nlm-ta): J Gen Intern Med
                Journal ID (iso-abbrev): J Gen Intern Med
                Title: Journal of General Internal Medicine
                Publisher: Springer International Publishing (Cham )
                ISSN (Print): 0884-8734
                ISSN (Electronic): 1525-1497
                Publication date (Electronic): 7 February 2022
                Pages: 1-8
                Affiliations
                [1 ]GRID grid.239578.2, ISNI 0000 0001 0675 4725, Healthcare Delivery and Implementation Science Center, , Cleveland Clinic, ; Cleveland, OH USA
                [2 ]GRID grid.239578.2, ISNI 0000 0001 0675 4725, Department of Internal Medicine, , Cleveland Clinic, ; 9500 Euclid Avenue Suite G10, Cleveland, OH 44195 USA
                [3 ]GRID grid.239578.2, ISNI 0000 0001 0675 4725, Department of Quantitative Health Sciences, , Cleveland Clinic, ; Cleveland, OH USA
                [4 ]GRID grid.239578.2, ISNI 0000 0001 0675 4725, Neurological Institute, , Cleveland Clinic, ; Cleveland, OH USA
                [5 ]GRID grid.239578.2, ISNI 0000 0001 0675 4725, Department of Pharmacy, , Cleveland Clinic, ; Cleveland, OH USA
                [6 ]GRID grid.430779.e, ISNI 0000 0000 8614 884X, The Institute for H.O.P.E.TM, , MetroHealth System, ; Cleveland, OH USA
                [7 ]GRID grid.239578.2, ISNI 0000 0001 0675 4725, Clinical Transformation, , Cleveland Clinic, ; Cleveland, OH USA
                [8 ]GRID grid.239578.2, ISNI 0000 0001 0675 4725, Department of Hospital Medicine, , Cleveland Clinic, ; Cleveland, OH USA
                [9 ]GRID grid.239578.2, ISNI 0000 0001 0675 4725, Department of Biomedical Engineering, Lerner Research Institute, , Cleveland Clinic, ; Cleveland, OH USA
                [10 ]GRID grid.239578.2, ISNI 0000 0001 0675 4725, Center for Neurological Restoration, Neurological Institute, , Cleveland Clinic, ; Cleveland, OH USA
                Author information
                http://orcid.org/0000-0003-3079-7025
                Article
                Publisher ID: 7277
                DOI: 10.1007/s11606-021-07277-4
                PMC ID: 8821785
                PubMed ID: 35132549
                SO-VID: de04719e-31eb-41e6-85a6-b466973faf2e
                Copyright © © The Author(s) under exclusive licence to Society of General Internal Medicine 2022
                License:

                This article is made available via the PMC Open Access Subset for unrestricted research re-use and secondary analysis in any form or by any means with acknowledgement of the original source. These permissions are granted for the duration of the World Health Organization (WHO) declaration of COVID-19 as a global pandemic.

                History
                Date received : 8 June 2021
                Date accepted : 10 November 2021
                Categories
                Subject: Original Research

                ScienceOpen disciplines: Internal medicine
                Keywords: hospital readmission,electronic medical record,decision support model
                Data availability:
                ScienceOpen disciplines: Internal medicine
                Keywords: hospital readmission, electronic medical record, decision support model

                Comments

                Comment on this article

                scite_

                Similar content170

                See all similar

                Cited by2

                See all cited by

                Most referenced authors891

                See all reference authors