Literature DB >> 28034774

The use of the Risk Assessment and Prediction Tool in surgical patients in a bundled payment program.

James Slover1, Kathleen Mullaly2, Raj Karia3, John Bendo4, Patricia Ursomanno5, Aubrey Galloway6, Richard Iorio7, Joseph Bosco8.   

Abstract

OBJECTIVES: The purpose of this study was to evaluate the relationship between the Risk Assessment and Predictor Tool (RAPT) and patient discharge disposition in an institution participating in bundled payment program for total joint replacement, spine fusion and cardiac valve surgery patients.
METHOD: Between April 2014 and April 2015, RAPT scores of 767 patients (535 primary unilateral total joint arthroplasty; 150 cardiac valve replacement; 82 spinal fusions) were prospectively captured. Total RAPT scores were grouped into three levels for risk of complications: <6 = 'high risk', between 6 and 9 = 'medium risk', and >9 = 'low risk' for discharge to a post-acute facility. Associations between RAPT categories and patient discharge to home versus any facility were conducted. Multivariate analysis was performed to determine if there was any correlation between RAPT score and discharge to any facility.
RESULTS: 70.5% of total joint patients, 80.7% of cardiac valve surgery patients and 70.7% of spine surgery patients were discharged home rather than to a post-acute facility. RAPT risk categories were related to discharge disposition as 72% of those in the high risk group were discharged to a facility and 91% in the low risk group were discharged to home in the total joint replacement cohort. In the cardiac cohort, only 33% of the high risk group was discharged to a facility, and 94% of the low risk group was discharged to home. In the spinal fusion cohort, 60% of those in the high risk group were discharged to a facility and 86% in the low risk group were discharged to home. Multivariate analysis showed that being in the high risk category versus low risk category was significantly associated with substantially increased odds of discharge to a facility.
CONCLUSION: The RAPT tool has shown the ability to predict discharge disposition for total joint and spine surgery patients, but not cardiac valve surgery patients, where the majority of patients in all categories were discharged home, at an institution participating in a bundled payment program. The ability to identify discharge disposition pre-operatively is valuable for improving care coordination, directing care resources and establishing and maintaining patient and family expectations.
Copyright © 2017 IJS Publishing Group Ltd. Published by Elsevier Ltd. All rights reserved.

Entities:  

Keywords:  Arthroplasty; Bundled payment; Cardiac valve replacement; Risk Assessment and Prediction Tool; Spinal fusions; Total joint replacement; Value base care

Mesh:

Year:  2016        PMID: 28034774     DOI: 10.1016/j.ijsu.2016.12.038

Source DB:  PubMed          Journal:  Int J Surg        ISSN: 1743-9159            Impact factor:   6.071


  11 in total

1.  Development of a machine learning algorithm predicting discharge placement after surgery for spondylolisthesis.

Authors:  Paul T Ogink; Aditya V Karhade; Quirina C B S Thio; Stuart H Hershman; Thomas D Cha; Christopher M Bono; Joseph H Schwab
Journal:  Eur Spine J       Date:  2019-03-27       Impact factor: 3.134

2.  Predicting discharge placement after elective surgery for lumbar spinal stenosis using machine learning methods.

Authors:  Paul T Ogink; Aditya V Karhade; Quirina C B S Thio; William B Gormley; Fetullah C Oner; Jorrit J Verlaan; Joseph H Schwab
Journal:  Eur Spine J       Date:  2019-04-02       Impact factor: 3.134

3.  Non-routine discharge disposition is associated with post-discharge complications and 30-day readmissions following craniotomy for brain tumor resection.

Authors:  Nikita Lakomkin; Constantinos G Hadjipanayis
Journal:  J Neurooncol       Date:  2017-12-05       Impact factor: 4.130

4.  CORR Synthesis: How Might the Preoperative Management of Risk Factors Influence Healthcare Disparities in Total Joint Arthroplasty?

Authors:  Chloe C Dlott; Daniel H Wiznia
Journal:  Clin Orthop Relat Res       Date:  2022-03-18       Impact factor: 4.755

5.  An Electronic Medical Record-Based Discharge Disposition Tool Gets Bundle Busted: Decaying Relevance of Clinical Data Accuracy in Machine Learning.

Authors:  Alexander S Greenstein; Jack Teitel; David J Mitten; Benjamin F Ricciardi; Thomas G Myers
Journal:  Arthroplast Today       Date:  2020-10-14

6.  Readmission, Complication, and Disposition Calculators in Total Joint Arthroplasty: A Systemic Review.

Authors:  Cole M Howie; Simon C Mears; C Lowry Barnes; Jeffrey B Stambough
Journal:  J Arthroplasty       Date:  2020-11-03       Impact factor: 4.757

7.  Association of Social Behaviors With Community Discharge in Patients with Total Hip and Knee Replacement.

Authors:  Kevin T Pritchard; Ickpyo Hong; James S Goodwin; Jordan R Westra; Yong-Fang Kuo; Kenneth J Ottenbacher
Journal:  J Am Med Dir Assoc       Date:  2020-10-09       Impact factor: 7.802

Review 8.  The Risk Assessment and Prediction Tool (RAPT) after Hip and Knee Replacement: A Systematic Review.

Authors:  Cristiano Sconza; Stefano Respizzi; Guido Grappiolo; Marco Monticone
Journal:  Joints       Date:  2019-07-25

9.  Modifying the RAPT Score to Reflect Discharge Destination in Current Practice.

Authors:  Eric Cohen; Daniel B C Reid; Matthew Quinn; Devin Walsh; Jeremy Raducha; Leigh Hubbard; John Froehlich
Journal:  Arthroplast Today       Date:  2020-12-21

10.  HSS@Home, Physical Therapist-Led Telehealth Care Navigation for Arthroplasty Patients: A Retrospective Case Series.

Authors:  Charles Fisher; Elizabeth Biehl; Matthew P Titmuss; Rachelle Schwartz; Chandra Sekhar Gantha
Journal:  HSS J       Date:  2019-08-22
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