Background Facilitating appropriate care delivery using electronic health record (digital health) tools is increasing. However, frequently used determinants frameworks seldom address key barriers for technology-associated implementation. Methods Semi-structured interviews were conducted in two contexts: the national Veterans Health Affairs (VA) following implementation of an electronic dashboard, a population health tool, and the Michigan Anticoagulation Quality Improvement Initiative (MAQI2) prior to implementation of a similar electronic dashboard. The dashboard is designed for pharmacist or nurse use to monitor safe outpatient anticoagulant prescribing by physicians and other clinicians We performed rapid qualitative inquiry analysis and selected implementation strategies. Through a stakeholder focus group session, we selected implementation strategies to address determinants and facilitate implementation in the MAQI2 sites. Results Among 45 interviewees (32 in VA, 13 in MAQI2), we identified five key determinants of implementation success: (1) clinician authority and autonomy, (2) clinician self-identity and job satisfaction, (3) documentation and administrative needs, (4) staffing and work schedule, and (5) integration with existing information systems. Key differences between the two contexts included concerns about information technology support and prioritization within MAQI2 (prior to implementation) but not VA (after implementation) and concerns about authority and autonomy that differed between the VA (higher baseline levels, more concerns) and MAQI2 (lower baseline levels, less concern). Conclusions The successful implementation of electronic health record tools requires unique considerations that differ from other types of implementation, must account for the status of implementation, and should address the effects of the tool deployment on clinical staff authority and autonomy. Interviewing both post-implementation and pre-implementation users can provide a robust understanding of implementation determinants.
Background Although risk prediction has become an integral part of clinical practice guidelines for cardiovascular disease (CVD) prevention, multiple studies have shown that patients’ risk still plays almost no role in clinical decision-making. Because little is known about why this is so, we sought to understand providers’ views on the opportunities, barriers, and facilitators of incorporating risk prediction to guide their use of cardiovascular preventive medicines. Methods We conducted semi-structured interviews with primary care providers (n = 33) at VA facilities in the Midwest. Facilities were chosen using a maximum variation approach according to their geography, size, proportion of MD to non-MD providers, and percentage of full-time providers. Providers included MD/DO physicians, physician assistants, nurse practitioners, and clinical pharmacists. Providers were asked about their reaction to a hypothetical situation in which the VA would introduce a risk prediction-based approach to CVD treatment. We conducted matrix and content analysis to identify providers’ reactions to risk prediction, reasons for their reaction, and exemplar quotes. Results Most providers were classified as Enthusiastic (n = 14) or Cautious Adopters (n = 15), with only a few Non-Adopters (n = 4). Providers described four key concerns toward adopting risk prediction. Their primary concern was that risk prediction is not always compatible with a “whole patient” approach to patient care. Other concerns included questions about the validity of the proposed risk prediction model, potential workflow burdens, and whether risk prediction adds value to existing clinical practice. Enthusiastic, Cautious, and Non-Adopters all expressed both doubts about and support for risk prediction categorizable in the above four key areas of concern. Conclusions Providers were generally supportive of adopting risk prediction into CVD prevention, but many had misgivings, which included concerns about impact on workflow, validity of predictive models, the value of making this change, and possible negative effects on providers’ ability to address the whole patient. These concerns have likely contributed to the slow introduction of risk prediction into clinical practice. These concerns will need to be addressed for risk prediction, and other approaches relying on “big data” including machine learning and artificial intelligence, to have a meaningful role in clinical practice.
PURPOSE Unlike in many community-based settings, benzodiazepine (BZD) prescribing to older veterans has decreased. We sought to identify health care system strategies associated with greater facility-level reductions in BZD prescribing to older adults. METHODSWe completed an explanatory sequential mixed methods study of health care facilities in the Veterans Health Administration (N = 140). Among veterans aged ≥75 years receiving long-term BZD treatment, we stratified facilities into relatively high and low performance on the basis of the reduction in average daily dose of prescribed BZD from October 1, 2015 to June 30, 2017. We then interviewed key facility informants (n = 21) who led local BZD reduction efforts (champions), representing 11 high-performing and 6 lowperforming facilities.RESULTS Across all facilities, the age-adjusted facility-level average daily dose in October 2015 began at 1.34 lorazepam-equivalent mg/d (SD 0.17); the average rate of decrease was −0.27 mg/d (SD 0.09) per year. All facilities interviewed, regardless of performance, used passive strategies primarily consisting of education regarding appropriate prescribing, alternatives, and identifying potential patients for discontinuation. In contrast, champions at high-performing facilities described leveraging ≥1 active strategies that included individualized recommendations, administrative barriers to prescribing, and performance measures to incentivize clinicians.CONCLUSIONS Initiatives to reduce BZD prescribing to older adults that are primarily limited to passive strategies, such as education and patient identification, might have limited success. Clinicians might benefit from additional recommendations, support, and incentives to modify prescribing practices.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.