Understanding providers’ attitudes and key concerns toward incorporating CVD risk prediction into clinical practice: a qualitative study

Abstract

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.

Workload And Workflow Implications Associated With The Use Of Electronic Clinical Decision Support Tools Used By Health Professionals In General Practice: A Scoping Review

“…112 A study of an eCDS tool for assessing cardiovascular risk also highlighted clinicians' concerns about the cognitive burden of changing to a new way of calculating risk compared with the conventional method they had used until that point. 126 One study reported workload expressed as the number of follow-up consultations needed. This study examined eCDS tools for patients with upper respiratory tract infections, and found no signi cant difference in the proportion of follow-ups needed between the intervention and control arms.…”

“…16,[26][27][28][29][30][31][32][33][34][35][36][37][38][39][40][41] A prevalent view was that workload was 'already heavy' and that using eCDS tools would inevitably add burden. 36,38,39,[41][42][43][44][45][46][47][48] In the case of one tool to support delivery of preventive care through review of patients' lifestyle factors, the sense of lack of time for preventive care in general drove the view towards the tool increasing consultation duration. 42 Hirsch et al (2012), however, highlighted that even though the majority of physicians in their study subjectively appraised consultation duration as being extended (85%), there were more of these physicians who felt that the time extension was 'acceptable' than those who judged it to be 'unacceptable'.…”

“…49 The usual ow of tasks to complete during a consultation (often referred to as 'work ow' 31,[50][51][52][53][54][55][56][57][58][59][60][61][62][63][64] ) was commonly expected to be disrupted by eCDS tools, causing an increase in consultation duration. 28,31,33,36,50,[65][66][67] Speci c time-consuming functions of tools, such as reading text, additional data-entry and using tools which were stand-alone from the EMR 16,36,41,46,65,[68][69][70] , as well as perceptions of poor-or slow-functioning software 45,50,67,71 were also highlighted. A potential for negative impact of eCDS tools on the trajectory of the conversation with patients was expressed by some health professionals.…”

“…Among the 36 studies that indicated a perceived increase in consultation duration, the most commonly highlighted concerns related to existing time pressures and lack of time during a consultation for clinicians to interact with eCDS tools and/or to carry out resultant recommended actions. 16,[26][27][28][29][30][31][32][33][34][35][36][37][38][39][40][41] A prevalent view was that workload was 'already heavy' and that using eCDS tools would inevitably add burden. 36,38,39,[41][42][43][44][45][46][47][48] In the case of one tool to support delivery of preventive care through review of patients' lifestyle factors, the sense of lack of time for preventive care in general drove the view towards the tool increasing consultation duration.…”

The Untapped Potential of Nursing and Allied Health Data for Improved Representation of Social Determinants of Health and Intersectionality in Artificial Intelligence Applications: A Rapid Review

“…However, several papers described using some SDoH or intersectionality concepts as data points in the application or development of AI. Takamine et al [30] mention the lack of socioeconomic status consideration in risk prediction models as a reason for the non-adoption of AI. No other studies discussed or mentioned economic status.…”