Predicting Hospital Readmission in Medicaid Patients with Heart Failure Using Administrative and Claims Data.
Objective To develop a model that predicts the risk of 30-day, all-cause readmission in Medicaid patients hospitalized for heart failure. Design Retrospective study of a population cohort to create a predictive model. Setting and Participants We analyzed 2016-2019 Medicaid claims data from seven US...
| Publicado en: | Perspectives in Health Information Management Vol. 20; no. 3; pp. 5 - 6 |
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| Autores principales: | , , |
| Formato: | research tables/charts Journal Article |
| Publicado: |
American Health Information Management Association
Summer/Fall2023
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=173909541&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 173909541 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 15594122 2QEL jtl: Perspectives in Health Information Management issn: 15594122 maglogo: N pubinfo: dt: Summer/Fall2023 vid: 20 iid: 3 pid: 6825 pub: American Health Information Management Association place: Chicago, Illinois artinfo: ui: 173909541 173909541 173909541 173909541 ppf: 5 ppct: 1 formats: fmt: @attributes: type: T tig: atl: Predicting Hospital Readmission in Medicaid Patients with Heart Failure Using Administrative and Claims Data. aug: au: Yun, Jaehyeon Ahuja, Vishal Heitjan, Daniel F. sug: subj: Readmission Medicaid Heart Failure Therapy Time Risk Assessment Prediction Models Human Adult Middle Age Male Female Retrospective Design Inpatients Prospective Studies United States Logistic Regression Comorbidity Health Resource Utilization Hospitalization Predictive Validity Sensitivity and Specificity ROC Curve Descriptive Statistics Heart Failure Diagnosis Office Visits Patient Discharge Billing and Claims Adult: 19-44 years Middle Aged: 45-64 years Male Female ab: Objective To develop a model that predicts the risk of 30-day, all-cause readmission in Medicaid patients hospitalized for heart failure. Design Retrospective study of a population cohort to create a predictive model. Setting and Participants We analyzed 2016-2019 Medicaid claims data from seven US states. We defined a heart failure admission as one in which either the admission diagnosis or the first or second clinical (discharge) diagnosis bore an ICD-10 code for heart failure. A readmission was an admission for any condition (not necessarily heart failure) that occurred within 30 days of a heart failure discharge. Methods We estimated a mixed-effects logistic model to predict 30-day readmission from patient demographic data, comorbidities, past healthcare utilization, and characteristics of the index hospitalization. We evaluated model fit graphically and measured predictive accuracy by the area under the receiver operating characteristics curve (AUC). Results 6,859 patients contributed 9,336 heart failure hospitalizations; 2,667 (28.6 percent) were 30-day readmissions. The final model included age, number of admissions and emergency room visits in the preceding year, length of stay, discharge status, index admission type, US state of admission, and past diagnoses. The observed vs. predicted plot showed good fit, and the estimated AUC of 0.745 was robust in sensitivity analyses. Conclusions and Implications Our model robustly and with moderate precision identifies Medicaid patients hospitalized for heart failure who are at a high risk of readmission. One can use the model to guide the development of post-discharge management interventions for reducing readmissions and for rigorously adjusting comparisons of 30-day readmission rates between sites/providers or over time. pubtype: Academic Journal doctype: research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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