Application of Predictive Modelling to Improve the Discharge Process in Hospitals

Hisham, Sayed; Rasheed, Shahina Abdul; D’Souza, Brayal Carry

doi:10.4258/hir.2020.26.3.166

Cited by 2 publications

(1 citation statement)

References 15 publications

(17 reference statements)

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“…and even patient-reported data (status of completion of outcome questionnaires and discharge checklists, etc.) [ 14 , 25 ] may improve predictive utility, this would need to be further validated, ideally using machine learning techniques [ 38 , 40 ]. Nonetheless, the rapid adoption of APIs provides an opportunity to use external data sources to improve predictions beyond EHR data alone [ 38 – 41 ].…”

Section: Discussionmentioning

confidence: 99%

Early Expected Discharge Date Accuracy During Hospitalization: A Multivariable Analysis

et al. 2023

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Introduction Accurate estimation of an expected discharge date (EDD) early during hospitalization impacts clinical operations and discharge planning. Methods We conducted a retrospective study of patients discharged from six general medicine units at an academic medical center in Boston, MA from January 2017 to June 2018. We retrieved all EDD entries and patient, encounter, unit, and provider data from the electronic health record (EHR), and public weather data. We excluded patients who expired, discharged against medical advice, or lacked an EDD within the first 24 h of hospitalization. We used generalized estimating equations in a multivariable logistic regression analysis to model early EDD accuracy (an accurate EDD entered within 24 h of admission), adjusting for all covariates and clustering by patient. We similarly constructed a secondary multivariable model using covariates present upon admission alone. Results Of 3917 eligible hospitalizations, 890 (22.7%) had at least one accurate early EDD entry. Factors significantly positively associated (OR > 1) with an accurate early EDD included clinician-entered EDD, admit day and discharge day during the work week, and teaching clinical units. Factors significantly negatively associated (OR < 1) with an accurate early EDD included Elixhauser Comorbidity Index ≥ 11 and length of stay of two or more days. C-statistics for the primary and secondary multivariable models were 0.75 and 0.60, respectively. Conclusions EDDs entered within the first 24 h of admission were often inaccurate. While several variables from the EHR were associated with accurate early EDD entries, few would be useful for prospective prediction.

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Section: Discussionmentioning

confidence: 99%

Early Expected Discharge Date Accuracy During Hospitalization: A Multivariable Analysis

et al. 2023

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Predicting next-day discharge via electronic health record access logs

Zhang

Yan

Malin

et al. 2021

Journal of the American Medical Informatics Association

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Objective Hospital capacity management depends on accurate real-time estimates of hospital-wide discharges. Estimation by a clinician requires an excessively large amount of effort and, even when attempted, accuracy in forecasting next-day patient-level discharge is poor. This study aims to support next-day discharge predictions with machine learning by incorporating electronic health record (EHR) audit log data, a resource that captures EHR users’ granular interactions with patients’ records by communicating various semantics and has been neglected in outcome predictions. Materials and Methods This study focused on the EHR data for all adults admitted to Vanderbilt University Medical Center in 2019. We learned multiple advanced models to assess the value that EHR audit log data adds to the daily prediction of discharge likelihood within 24 h and to compare different representation strategies. We applied Shapley additive explanations to identify the most influential types of user-EHR interactions for discharge prediction. Results The data include 26 283 inpatient stays, 133 398 patient-day observations, and 819 types of user-EHR interactions. The model using the count of each type of interaction in the recent 24 h and other commonly used features, including demographics and admission diagnoses, achieved the highest area under the receiver operating characteristics (AUROC) curve of 0.921 (95% CI: 0.919–0.923). By contrast, the model lacking user-EHR interactions achieved a worse AUROC of 0.862 (0.860–0.865). In addition, 10 of the 20 (50%) most influential factors were user-EHR interaction features. Conclusion EHR audit log data contain rich information such that it can improve hospital-wide discharge predictions.

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Application of Predictive Modelling to Improve the Discharge Process in Hospitals

Cited by 2 publications

References 15 publications

Early Expected Discharge Date Accuracy During Hospitalization: A Multivariable Analysis

Early Expected Discharge Date Accuracy During Hospitalization: A Multivariable Analysis

Predicting next-day discharge via electronic health record access logs

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