Predicting Hospital Readmissions from Home Healthcare in Medicare Beneficiaries

Jones, Christine; Falvey, Jason R.; Hess, Edward; Levy, Cari; Nuccio, Eugene; Barón, Anna E.; Masoudi, Frederick A.; Stevens‐Lapsley, Jennifer E.

doi:10.1111/jgs.16153

Cited by 24 publications

(16 citation statements)

References 18 publications

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“…Most studies (25, 58%) have applied more than one ML technique, and the details of all these ML techniques are summarized in Table 1 . The most popular algorithm was tree-based methods (23, 53%), including decision trees (DT) [ 46 , 52 , 54 – 60 ], random forests (RF) [ 48 – 50 , 59 – 71 ] and boosted tree methods [ 47 , 49 – 51 , 53 , 54 , 59 , 64 – 67 , 71 – 77 ] (e.g. gradient descent boosting, XGboost, adaboost).…”

Section: Resultsmentioning

confidence: 99%

Application of machine learning in predicting hospital readmissions: a scoping review of the literature

Huang

Talwar

Chatterjee

et al. 2021

BMC Med Res Methodol

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Background Advances in machine learning (ML) provide great opportunities in the prediction of hospital readmission. This review synthesizes the literature on ML methods and their performance for predicting hospital readmission in the US. Methods This review was performed according to the Preferred Reporting Items for Systematic Reviews and Meta-Analysis Extension for Scoping Reviews (PRISMA-ScR) Statement. The extraction of items was also guided by the Critical Appraisal and Data Extraction for Systematic Reviews of Prediction Modelling Studies (CHARMS). Electronic databases PUBMED, MEDLINE, and EMBASE were systematically searched from January 1, 2015, through December 10, 2019. The articles were imported into COVIDENCE online software for title/abstract screening and full-text eligibility. Observational studies using ML techniques for hospital readmissions among US patients were eligible for inclusion. Articles without a full text available in the English language were excluded. A qualitative synthesis included study characteristics, ML algorithms utilized, and model validation, and quantitative analysis assessed model performance. Model performances in terms of Area Under the Curve (AUC) were analyzed using R software. Quality in Prognosis Studies (QUIPS) tool was used to assess the quality of the reviewed studies. Results Of 522 citations reviewed, 43 studies met the inclusion criteria. A majority of the studies used electronic health records (24, 56%), followed by population-based data sources (15, 35%) and administrative claims data (4, 9%). The most common algorithms were tree-based methods (23, 53%), neural network (NN) (14, 33%), regularized logistic regression (12, 28%), and support vector machine (SVM) (10, 23%). Most of these studies (37, 85%) were of high quality. A majority of these studies (28, 65%) reported ML algorithms with an AUC above 0.70. There was a range of variability within AUC reported by these studies with a median of 0.68 (IQR: 0.64–0.76; range: 0.50–0.90). Conclusions The ML algorithms involving tree-based methods, NN, regularized logistic regression, and SVM are commonly used to predict hospital readmission in the US. Further research is needed to compare the performance of ML algorithms for hospital readmission prediction.

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Section: Resultsmentioning

confidence: 99%

Application of machine learning in predicting hospital readmissions: a scoping review of the literature

Huang

Talwar

Chatterjee

et al. 2021

BMC Med Res Methodol

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“…A total of 31 reviewed studies (63.3%) evaluated the use of AI/ML applications in optimizing preoperative patient selection or projecting surgical costs, through prediction of hospital LOS, discharges, readmissions, and other cost-contributing factors ( Table 1 , Table 2 ). Sixteen studies (32.7%) evaluated AI/ML applications to accurately predict patient reoperations, operating time, hospital LOS, discharges, readmissions, or surgical and inpatient costs [ [14] , [15] , [16] , [17] , [18] , [19] , [20] , [21] , [22] , [23] , [24] , [25] , [26] , [27] , [28] , [29] ]. In addition, 16 studies (32.7%) used patients’ preoperative risk factors and other patient-specific variables to optimize the patient selection and surgical planning process through the use of AI/ML-based predictions of surgical outcomes and postoperative complications [ [30] , [31] , [32] , [33] , [34] , [35] , [36] , [37] , [38] , [39] , [40] , [41] , [42] , [43] , [44] ].…”

Section: Resultsmentioning

confidence: 99%

“…Several of the reviewed studies used AI/ML models to accurately predict the risk of a range of postoperative complications and adverse events [ 19 , 29 , [47] , [50] , [51] , [60] ]. TKA and total hip arthroplasty revisions and reoperations are also modeled with AI/ML algorithms in some studies, [ 15 , 16 , 21 , 64 ] as well as hospital readmissions [ 20 , 21 , 26 , 27 ]. In the postoperative period, AI/ML tools offer surgeons the ability to predict patients’ outcomes after surgery, including functional outcomes and PRO scores [ 14 , 32 , 33 , 43 , 45 , [48] , [53] , [54] , [57] , [58] , [59] , [61] ].…”

Section: Discussionmentioning

confidence: 99%

Artificial Learning and Machine Learning Decision Guidance Applications in Total Hip and Knee Arthroplasty: A Systematic Review

et al. 2021

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Background: Artificial intelligence (AI) and machine learning (ML) modeling in hip and knee arthroplasty (total joint arthroplasty [TJA]) is becoming more commonplace. This systematic review aims to quantify the accuracy of current AI-and ML-based application for cognitive support and decision-making in TJA. Methods: A comprehensive search of publications was conducted through the EMBASE, Medline, and PubMed databases using relevant keywords to maximize the sensitivity of the search. No limits were placed on level of evidence or timing of the study. Findings were reported according to the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines. Analysis of variance testing with post-hoc Tukey test was applied to compare the area under the curve (AUC) of the models. Results: After application of inclusion and exclusion criteria, 49 studies were included in this review. The application of AI/ML-based models and average AUC is as follows: cost prediction-0.77, LOS and discharges-0.78, readmissions and reoperations-0.66, preoperative patient selection/planning-0.79, adverse events and other postoperative complications-0.84, postoperative pain-0.83, postoperative patient-reported outcomes measures and functional outcome-0.81. Significant variability in model AUC across the different decision support applications was found (P < .001) with the AUC for readmission and reoperation models being significantly lower than that of the other decision support categories. Conclusions: AI/ML-based applications in TJA continue to expand and have the potential to optimize patient selection and accurately predict postoperative outcomes, complications, and associated costs. On average, the AI/ML models performed best in predicting postoperative complications, pain, and patientreported outcomes and were less accurate in predicting hospital readmissions and reoperations.

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“…There is an emerging trend of developing predictive models. By introducing abundant variables in EMR database [43], collecting social determinants of health [44], and using sophisticated machine learning methods can build a surprisingly accurate but complex readmission prediction model [2,16,45,46]. There is always a huge information gap between the transition from hospital to home, and missing data are not uncommon for home care patients.…”

Section: Discussionmentioning

confidence: 99%

LACE Score-Based Risk Management Tool for Long-Term Home Care Patients: A Proof-of-Concept Study in Taiwan

Chen

Huang

et al. 2021

IJERPH

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Background: Effectively predicting and reducing readmission in long-term home care (LTHC) is challenging. We proposed, validated, and evaluated a risk management tool that stratifies LTHC patients by LACE predictive score for readmission risk, which can further help home care providers intervene with individualized preventive plans. Method: A before-and-after study was conducted by a LTHC unit in Taiwan. Patients with acute hospitalization within 30 days after discharge in the unit were enrolled as two cohorts (Pre-Implement cohort in 2017 and Post-Implement cohort in 2019). LACE score performance was evaluated by calibration and discrimination (AUC, area under receiver operator characteristic (ROC) curve). The clinical utility was evaluated by negative predictive value (NPV). Results: There were 48 patients with 87 acute hospitalizations in Pre-Implement cohort, and 132 patients with 179 hospitalizations in Post-Implement cohort. These LTHC patients were of older age, mostly intubated, and had more comorbidities. There was a significant reduction in readmission rate by 44.7% (readmission rate 25.3% vs. 14.0% in both cohorts). Although LACE score predictive model still has room for improvement (AUC = 0.598), it showed the potential as a useful screening tool (NPV, 87.9%; 95% C.I., 74.2–94.8). The reduction effect is more pronounced in infection-related readmission. Conclusion: As real-world evidence, LACE score-based risk management tool significantly reduced readmission by 44.7% in this LTHC unit. Larger scale studies involving multiple homecare units are needed to assess the generalizability of this study.

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Predicting Hospital Readmissions from Home Healthcare in Medicare Beneficiaries

Cited by 24 publications

References 18 publications

Application of machine learning in predicting hospital readmissions: a scoping review of the literature

Application of machine learning in predicting hospital readmissions: a scoping review of the literature

Artificial Learning and Machine Learning Decision Guidance Applications in Total Hip and Knee Arthroplasty: A Systematic Review

LACE Score-Based Risk Management Tool for Long-Term Home Care Patients: A Proof-of-Concept Study in Taiwan

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