Literature DB >> 26699343

National projections of time, cost and failure in implantable device identification: Consideration of unique device identification use.

Natalia Wilson1, Jennifer Broatch2, Megan Jehn3, Charles Davis4.   

Abstract

BACKGROUND: U.S. health care is responding to significant regulation and meaningful incentives for higher quality care, patient safety, electronic documentation and data exchange. FDA's Unique Device Identification (UDI) Rule, a relatively new regulation aligned with these goals, requires standard labeling of medical devices by manufacturers. This lays the foundation for UDI scanning and documentation in the electronic health record, expected to change the landscape of medical device identification and postmarket surveillance.
METHODS: We developed national projections for time, cost and failure in implant identification prior to revision total hip and knee arthroplasty (THA/TKA) using American Association of Hip and Knee Surgeons 2012 membership survey data, Nationwide Inpatient Sample 2011 data and THA/TKA demand projection data.
RESULTS: Our projections suggest that cumulative surgeon time spent identifying failed implants could reach 133,000 h in 2030, representing opportunity to perform over 500,000 15 min established patient office visits. Staff time could reach 220,000 h with a cost of $3.3m. Failed implants that cannot be identified may be greater than 50,000 preoperatively and 25,000 intraoperatively in 2030.
CONCLUSION: Study projections indicate significant time, cost and inability to identify failed implants, supporting need for improvement of implant documentation. FDA's UDI Rule sets the foundation for UDI scanning and documentation in the electronic health record, a process poised to serve as the standard system for device documentation.
Copyright © 2015 Elsevier Inc. All rights reserved.

Entities:  

Keywords:  Implantable device identification; Unique device identification

Mesh:

Year:  2015        PMID: 26699343     DOI: 10.1016/j.hjdsi.2015.04.003

Source DB:  PubMed          Journal:  Healthc (Amst)        ISSN: 2213-0764


  5 in total

1.  Application of deep learning algorithm in automated identification of knee arthroplasty implants from plain radiographs using transfer learning models: Are algorithms better than humans?

Authors:  Anjali Tiwari; Amit Kumar Yadav; Vaibhav Bagaria
Journal:  J Orthop       Date:  2022-05-26

2.  Knee Implant Identification by Fine-Tuning Deep Learning Models.

Authors:  Sukkrit Sharma; Vineet Batta; Malathy Chidambaranathan; Prabhakaran Mathialagan; Gayathri Mani; M Kiruthika; Barun Datta; Srinath Kamineni; Guruva Reddy; Suhas Masilamani; Sandeep Vijayan; Derek F Amanatullah
Journal:  Indian J Orthop       Date:  2021-09-28       Impact factor: 1.033

3.  Automated Identification of Orthopedic Implants on Radiographs Using Deep Learning.

Authors:  Ravi Patel; Elizabeth H E Thong; Vineet Batta; Anil Anthony Bharath; Darrel Francis; James Howard
Journal:  Radiol Artif Intell       Date:  2021-03-17

4.  Advancing Patient Safety Surrounding Medical Devices: A Health System Roadmap to Implement Unique Device Identification at the Point of Care.

Authors:  Natalia A Wilson; James E Tcheng; Jove Graham; Joseph P Drozda
Journal:  Med Devices (Auckl)       Date:  2021-11-30

5.  Advancing Patient Safety Surrounding Medical Devices: Barriers, Strategies, and Next Steps in Health System Implementation of Unique Device Identifiers.

Authors:  Natalia A Wilson; James E Tcheng; Jove Graham; Joseph P Drozda
Journal:  Med Devices (Auckl)       Date:  2022-06-21
  5 in total

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