Modeling veterans healthcare administration disclosure processes :
As with other large healthcare organizations, medical adverse events at the Department of Veterans Affairs (VA) facilities can expose patients to unforeseen negative risks. VHA leadership recognizes that properly handled disclosure of adverse events can minimize potential harm to patients and negative consequences for the effective functioning of the organization. The work documented here seeks to help improve the disclosure process by situating it within the broader theoretical framework of issues management, and to identify opportunities for process improvement through modeling disclosure and reactions to disclosure. The computational model will allow a variety of disclosure actions to be tested across a range of incident scenarios. Our conceptual model will be refined in collaboration with domain experts, especially by continuing to draw on insights from VA Study of the Communication of Adverse Large-Scale Events (SCALE) project researchers.
- Research Organization:
- Sandia National Laboratories (SNL-NM), Albuquerque, NM (United States)
- Sponsoring Organization:
- USDOE National Nuclear Security Administration (NNSA)
- DOE Contract Number:
- AC04-94AL85000
- OSTI ID:
- 1096264
- Report Number(s):
- SAND2013-8252; 476474
- Country of Publication:
- United States
- Language:
- English
Similar Records
Characteristics of Veterans With Depression Who Use the Veterans Choice Program of the Veterans Health Administration
Domain Shift Analysis in Chest Radiographs Classification in a Veterans Healthcare Administration Population
Initial Mobility Analysis for ORNL VA-EDH Synthetic Populations
Journal Article
·
Mon Nov 06 19:00:00 EST 2023
· Psychiatric Services
·
OSTI ID:2315660
Domain Shift Analysis in Chest Radiographs Classification in a Veterans Healthcare Administration Population
Journal Article
·
Thu Apr 10 20:00:00 EDT 2025
· Journal of Imaging Informatics in Medicine
·
OSTI ID:2573574
Initial Mobility Analysis for ORNL VA-EDH Synthetic Populations
Technical Report
·
Tue Apr 01 00:00:00 EDT 2025
·
OSTI ID:2573330