Key result
Combining an automated readmission model with clinical insight identified 22% and 9% of model-classified low-medium-risk patients as high risk based on nurses and physicians, respectively.
Why the study?
Automated electronic health record readmission prediction models are increasingly used for prevention efforts, but their accuracy remains moderate.
Does combining clinicians' risk assessments with an automated prediction model improve the identification of elderly patients at high risk for 30-day readmission?
Observational (n=371)
Yes
Does combining clinicians' risk assessments with an automated prediction model improve the identification of elderly patients at high risk for 30-day readmission?
Combining an automated readmission prediction model with clinical insight from nurses and physicians improves the identification of high-risk elderly patients for 30-day readmission.
May refine readmission risk stratification when clinical insight supplements automated models; leaves open effects on outcomes or resource use.
AIM: This study explores the potential benefit of combining clinicians' risk assessments and the automated 30-day readmission prediction model. BACKGROUND: Automated readmission prediction models based on electronic health records are increasingly applied as part of prevention efforts, but their accuracy is moderate. METHODS: This prospective multisource study was based on self-reported surveys of clinicians and data from electronic health records. The survey was performed at 15 internal medicine wards of three general Clalit hospitals between May 2016 and June 2017. We examined the degree of concordance between the Preadmission Readmission Detection Model, clinicians' readmission risk classification and the likelihood of actual readmission. Decision trees were developed to classify patients by readmission risk. RESULTS: A total of 694 surveys were collected for 371 patients. The disagreement between clinicians' risk assessment and the model was 34.5% for nurses and 33.5% for physicians. The decision tree algorithms identified 22% and 9% (based on nurses and physicians, respectively) of the model's low-medium-risk patients as high risk (accuracy 0.8 and 0.76, respectively). CONCLUSIONS: Combining the Readmission Model with clinical insight improves the ability to identify high-risk elderly patients. IMPLICATIONS FOR NURSING MANAGEMENT: This study provides algorithms for the decision-making process for selecting high-risk readmission patients based on nurses' evaluations.
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Flaks‐Manov et al. (2021) conducted an observational in Risk for 30-day hospital readmission (n=371). Clinicians' risk assessments combined with automated prediction model vs. Automated prediction model alone was evaluated on Concordance between prediction model, clinicians' classification, and actual readmission. Combining an automated readmission model with clinical insight identified 22% and 9% of model-classified low-medium-risk patients as high risk based on nurses and physicians, respectively.
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