Implementation and Continuous Monitoring of an Electronic Health Record Embedded Readmissions Clinical Decision Support Tool

Gallagher, David; Zhao, Congwen; Brucker, Amanda; Massengill, Jennifer; Kramer, Patricia; Poon, Eric G.; Goldstein, Benjamin A.

doi:10.3390/jpm10030103

Cited by 24 publications

(32 citation statements)

References 24 publications

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“…The Epic unplanned readmission risk model has been investigated on our Duke patient population previously and reported out separately. 33 In this previous work, the area under curve (C-statistic) for Duke Hospital general medicine for this model in predicting unplanned readmissions was 0.694. The Epic readmission risk model V.1 calculates a score for readmission risk every 4 hours for inpatients.…”

Section: Methodsmentioning

confidence: 84%

“…This risk model has been previously evaluated by our institution and confirmed to have a reasonable discriminatory function to identify patients at higher risk of readmission. 33 Our understanding of the Epic readmission model is there is some possibility of customisation of the model’s variables and weightings, but our institution has not pursued that, so this project represents the implementation of the standard risk model. Using this risk model, we can more efficiently focus our interventions on those patients at the highest risk of unplanned readmission.…”

Section: Discussionmentioning

confidence: 99%

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Inpatient pharmacists using a readmission risk model in supporting discharge medication reconciliation to reduce unplanned hospital readmissions: a quality improvement intervention

et al. 2022

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IntroductionReducing unplanned hospital readmissions is an important priority for all hospitals and health systems. Hospital discharge can be complicated by discrepancies in the medication reconciliation and/or prescribing processes. Clinical pharmacist involvement in the medication reconciliation process at discharge can help prevent these discrepancies and possibly reduce unplanned hospital readmissions.MethodsWe report the results of our quality improvement intervention at Duke University Hospital, in which pharmacists were involved in the discharge medication reconciliation process on select high-risk general medicine patients over 2 years (2018–2020). Pharmacists performed traditional discharge medication reconciliation which included a review of medications for clinical appropriateness and affordability. A total of 1569 patients were identified as high risk for hospital readmission using the Epic readmission risk model and had a clinical pharmacist review the discharge medication reconciliation.ResultsThis intervention was associated with a significantly lower 7-day readmission rate in patients who scored high risk for readmission and received pharmacist support in discharge medication reconciliation versus those patients who did not receive pharmacist support (5.8% vs 7.6%). There was no effect on readmission rates of 14 or 30 days. The clinical pharmacists had at least one intervention on 67% of patients reviewed and averaged 1.75 interventions per patient.ConclusionThis quality improvement study showed that having clinical pharmacists intervene in the discharge medication reconciliation process in patients identified as high risk for readmission is associated with lower unplanned readmission rates at 7 days. The interventions by pharmacists were significant and well received by ordering providers. This study highlights the important role of a clinical pharmacist in the discharge medication reconciliation process.

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Section: Methodsmentioning

confidence: 84%

Section: Discussionmentioning

confidence: 99%

Inpatient pharmacists using a readmission risk model in supporting discharge medication reconciliation to reduce unplanned hospital readmissions: a quality improvement intervention

et al. 2022

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“…26,27 Combinations of clinical decision support and emerging digital health technologies may support transitions and provide the continuous care oncology patients need to reduce their readmission risk in the short-run. In Figure 8 , we provide a prototypical representation of readmission risk model applied to a patient encounter with potential recommended action in Epic - figure adapted from Gallagher et al 2020 28 with additional approval from the Epic Corporation. We believe that leveraging Shapley values in clinical decision support systems can play a critical role in supporting explainable AI that can be readily interpreted and trusted to make informed clinical care decisions personalized to the patient’s circumstances and clinical status.…”

Section: Discussionmentioning

confidence: 99%

Towards Predicting 30-Day Readmission among Oncology Patients: Identifying Timely and Actionable Risk Factors

Hwang

Urbanowicz

Lynch

et al. 2022

Preprint

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Purpose: Predicting 30-day readmission risk is paramount to improving the quality of patient care. Previous studies have examined clinical risk factors associated with hospital readmissions. In this study, we compare sets of patient, provider, and community-level variables that are available at two different points of a patient's inpatient encounter (first 48 hours and the full encounter) to train readmission prediction models in order to identify and target appropriate actionable interventions that can potentially reduce avoidable readmissions. Methods: Using EHR data from a retrospective cohort of 2460 oncology patients, two sets of binary classification models predicting 30-day readmission were developed; one trained on variables that are available within the first 48 hours of admission and another trained on data from the entire hospital encounter. A comprehensive machine learning analysis pipeline was leveraged including preprocessing and feature transformation, feature importance and selection, machine learning modeling, and post-analysis. Results: Leveraging all features, the LGB (light gradient boosted machines) model produced higher, but comparable performance: (AUC: 0.711 and APS: 0.225) compared to Epic (AUC: 0.697 and APS: 0.221). Given features in the first 48-hours, the random forest model produces higher AUC (0.684), but lower PRC (0.18) and APS (0.184) than the Epic model (AUC: 0.676). In terms of the characteristics of patients flagged by these models, both the full LGB and 48-hour (random forest) feature models were highly sensitive in flagging more patients than the Epic models. Both models flagged patients with a similar distribution of race and sex; however, our LGB and random forest models more inclusive flagging more patients among younger age groups. The Epic models were more sensitive to identifying patients with an average lower zip income. Our 48-hour models were powered by novel features at various levels: patient (weight changeover 365 days, depression symptoms, laboratory values, cancer type), provider (winter discharge, hospital admission type), community (zip income, marital status of partner). Conclusion: We demonstrated that we could develop and validate models comparable to existing Epic 30-day readmission models, but provide several actionable insights that could create service interventions deployed by the case management or discharge planning teams that may decrease readmission rates over time.

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“…When used in this way as clinical decision support tools, AI applications are embedded in clinicians' work in structured ways. For example, they might be designed to flag patients as high risk based on what is feasible in the organizational context and be expected in a certain percentage of cases to lead to additional clinical measures such as discussing an intervention with patients (Gallagher et al, 2020). In this paper, I argue that the discretionary authority accorded to AI applications based on reasonable expectations about their functioning is a significant kind of trust.…”

Section: Introductionmentioning

confidence: 98%

Trust in medical artificial intelligence: a discretionary account

Nickel

2022

Ethics Inf Technol

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This paper sets out an account of trust in AI as a relationship between clinicians, AI applications, and AI practitioners in which AI is given discretionary authority over medical questions by clinicians. Compared to other accounts in recent literature, this account more adequately explains the normative commitments created by practitioners when inviting clinicians’ trust in AI. To avoid committing to an account of trust in AI applications themselves, I sketch a reductive view on which discretionary authority is exercised by AI practitioners through the vehicle of an AI application. I conclude with four critical questions based on the discretionary account to determine if trust in particular AI applications is sound, and a brief discussion of the possibility that the main roles of the physician could be replaced by AI.

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Implementation and Continuous Monitoring of an Electronic Health Record Embedded Readmissions Clinical Decision Support Tool

Cited by 24 publications

References 24 publications

Inpatient pharmacists using a readmission risk model in supporting discharge medication reconciliation to reduce unplanned hospital readmissions: a quality improvement intervention

Inpatient pharmacists using a readmission risk model in supporting discharge medication reconciliation to reduce unplanned hospital readmissions: a quality improvement intervention

Towards Predicting 30-Day Readmission among Oncology Patients: Identifying Timely and Actionable Risk Factors

Trust in medical artificial intelligence: a discretionary account

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