Predicting Inpatient Payments Prior to Lower Extremity Arthroplasty Using Deep Learning: Which Model Architecture Is Best?

Karnuta, Jaret M.; Navarro, Sergio M.; Haeberle, Heather S.; Helm, J. Matthew; Kamath, Atul F.; Schaffer, Jonathan; Krebs, Viktor E.; Ramkumar, Prem N.

doi:10.1016/j.arth.2019.05.048

Cited by 39 publications

(40 citation statements)

References 23 publications

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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%

“…The application of AI/ML to clinical decision-making in hip and knee arthroplasties may result in optimized outcomes by aiding in accurate patient selection and surgical planning during the perioperative period. Several studies in our review demonstrated the use of AI/ML models to predict hospital LOS and readmissions and associated inpatient costs after total joint arthroplasty [ 14 , [22] , [23] , [24] , [25] , [27] , [28] , [29] , 46 ]. Other studies have demonstrated AI/ML potential to reduce unnecessary expenditures and create risk-adjusted reimbursement models [ 24 , 27 , 28 , 46 ].…”

Section: Discussionmentioning

confidence: 99%

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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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Section: Resultsmentioning

confidence: 99%

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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“…At the policy level, DL models have been applied to proposing a risk‐based, patient‐specific payment model (including tiered reimbursements) aligned with the complexity of cases 56 . Early studies are evaluating the performance of different models to advance value based health care through better prediction of outcomes and costs including inpatient procedural costs prior to joint replacement surgery 59 . Ultimately, systems using AI may better contend with an array of datasets and account for variations in care across populations.…”

Section: Discussionmentioning

confidence: 99%

Advanced decision‐making using patient‐reported outcome measures in total joint replacement

Jayakumar

Bozic

2020

Journal Orthopaedic Research

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Up to one‐third of total joint replacement (TJR) procedures may be performed inappropriately in a subset of patients who remain dissatisfied with their outcomes, stressing the importance of shared decision‐making. Patient‐reported outcome measures capture physical, emotional, and social aspects of health and wellbeing from the patient's perspective. Powerful computer systems capable of performing highly sophisticated analysis using different types of data, including patient‐derived data, such as patient‐reported outcomes, may eliminate guess work, generating impactful metrics to better inform the decision‐making process. We have created a shared decision‐making tool which generates personalized predictions of risks and benefits from TJR based on patient‐reported outcomes as well as clinical and demographic data. We present the protocol for a randomized controlled trial designed to assess the impact of this tool on decision quality, level of shared decision‐making, and patient and process outcomes. We also discuss current concepts in this field and highlight opportunities leveraging patient‐reported data and artificial intelligence for decision support across the care continuum.

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“…Machine learning is a powerful and effective tool for medical study. Machine learning has seen many applications in the fields of health care management [31][32][33], health care cost prediction [34,35], and health care insurance [36][37][38]. Various machine learning models, which are of better performance compared with traditional statistics models, have been used in the field of identification of surgeries with high cancellation risk as well [4].…”

Section: Introductionmentioning

confidence: 99%

Key Experimental Factors of Machine Learning-Based Identification of Surgery Cancellations

Zhang

Cui

Gong

et al. 2021

Journal of Healthcare Engineering

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This study aimed to provide effective methods for the identification of surgeries with high cancellation risk based on machine learning models and analyze the key factors that affect the identification performance. The data covered the period from January 1, 2013, to December 31, 2014, at West China Hospital in China, which focus on elective urologic surgeries. All surgeries were scheduled one day in advance, and all cancellations were of institutional resource- and capacity-related types. Feature selection strategies, machine learning models, and sampling methods are the most discussed topic in general machine learning researches and have a direct impact on the performance of machine learning models. Hence, they were considered to systematically generate complete schemes in machine learning-based identification of surgery cancellations. The results proved the feasibility and robustness of identifying surgeries with high cancellation risk, with the considerable maximum of area under the curve (AUC) (0.7199) for random forest model with original sampling using backward selection strategy. In addition, one-side Delong test and sum of square error analysis were conducted to measure the effects of feature selection strategy, machine learning model, and sampling method on the identification of surgeries with high cancellation risk, and the selection of machine learning model was identified as the key factors that affect the identification of surgeries with high cancellation risk. This study offers methodology and insights for identifying the key experimental factors for identifying surgery cancellations, and it is helpful to further research on machine learning-based identification of surgeries with high cancellation risk.

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Predicting Inpatient Payments Prior to Lower Extremity Arthroplasty Using Deep Learning: Which Model Architecture Is Best?

Cited by 39 publications

References 23 publications

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

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

Advanced decision‐making using patient‐reported outcome measures in total joint replacement

Key Experimental Factors of Machine Learning-Based Identification of Surgery Cancellations

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