Prem N Ramkumar1, Jaret M Karnuta1, Sergio M Navarro2, Heather S Haeberle3, Richard Iorio4, Michael A Mont5, Brendan M Patterson1, Viktor E Krebs1. 1. Department of Orthopedic Surgery, Cleveland Clinic, Cleveland, OH. 2. Said Business School, University of Oxford, Oxford, UK. 3. Department of Orthopaedic Surgery, Baylor College of Medicine, Houston, TX. 4. Department of Orthopaedic Surgery, Brigham & Women's Hospital, Boston, MA. 5. Department of Orthopaedic Surgery, Lenox Hill, New York, NY.
Abstract
BACKGROUND: The primary objective was to develop and test an artificial neural network (ANN) that learns and predicts length of stay (LOS), inpatient charges, and discharge disposition for total hip arthroplasty. The secondary objective was to create a patient-specific payment model (PSPM) accounting for patient complexity. METHODS: Using 15 preoperative variables from 78,335 primary total hip arthroplasty cases for osteoarthritis from the National Inpatient Sample and our institutional database, an ANN was developed to predict LOS, charges, and disposition. Validity metrics included accuracy and area under the curve of the receiver operating characteristic curve. Predictive uncertainty was stratified by All Patient Refined comorbidity cohort to establish the PSPM. RESULTS: The dynamic model demonstrated "learning" in the first 30 training rounds with areas under the curve of 82.0%, 83.4%, and 79.4% for LOS, charges, and disposition, respectively. The proposed PSPM established a risk increase of 2.5%, 8.9%, and 17.3% for moderate, major, and severe comorbidities, respectively. CONCLUSION: The deep learning ANN demonstrated "learning" with good reliability, responsiveness, and validity in its prediction of value-centered outcomes. This model can be applied to implement a PSPM for tiered payments based on the complexity of the case.
BACKGROUND: The primary objective was to develop and test an artificial neural network (ANN) that learns and predicts length of stay (LOS), inpatient charges, and discharge disposition for total hip arthroplasty. The secondary objective was to create a patient-specific payment model (PSPM) accounting for patient complexity. METHODS: Using 15 preoperative variables from 78,335 primary total hip arthroplasty cases for osteoarthritis from the National Inpatient Sample and our institutional database, an ANN was developed to predict LOS, charges, and disposition. Validity metrics included accuracy and area under the curve of the receiver operating characteristic curve. Predictive uncertainty was stratified by All Patient Refined comorbidity cohort to establish the PSPM. RESULTS: The dynamic model demonstrated "learning" in the first 30 training rounds with areas under the curve of 82.0%, 83.4%, and 79.4% for LOS, charges, and disposition, respectively. The proposed PSPM established a risk increase of 2.5%, 8.9%, and 17.3% for moderate, major, and severe comorbidities, respectively. CONCLUSION: The deep learning ANN demonstrated "learning" with good reliability, responsiveness, and validity in its prediction of value-centered outcomes. This model can be applied to implement a PSPM for tiered payments based on the complexity of the case.
Authors: Thomas G Myers; Prem N Ramkumar; Benjamin F Ricciardi; Kenneth L Urish; Jens Kipper; Constantinos Ketonis Journal: J Bone Joint Surg Am Date: 2020-05-06 Impact factor: 5.284
Authors: Safa C Fassihi; Abhay Mathur; Matthew J Best; Aaron Z Chen; Alex Gu; Theodore Quan; Kevin Y Wang; Chapman Wei; Joshua C Campbell; Savyasachi C Thakkar Journal: J Orthop Date: 2021-11-25
Authors: Cesar D Lopez; Anastasia Gazgalis; Venkat Boddapati; Roshan P Shah; H John Cooper; Jeffrey A Geller Journal: Arthroplast Today Date: 2021-09-03