Factors Influencing Physician Adherence to Venous Thromboembolism Risk Assessment Model Recommendations: A Best–Worst Scaling

PURPOSE: Risk assessment models (RAM) use predictive algorithms to support physicians in personalizing care. For example, a RAM to determine the risk of venous thromboembolism (VTE-RAM) in hospitalized patients offered treatment recommendations that physicians adhered to at varying rates. We sought to investigate which factors influenced physician adherence to prophylaxis recommendations from a VTE-RAM implemented in an inpatient setting. METHODS: We assessed adherence to treatment recommendations for a VTE-RAM in the inpatient setting using both survey and real-world data sources. Inclusion criteria included being a hospitalist or resident in the inpatient setting, using a VTE-RAM at least 50 times, and having complete data from both sources available. Variation in treatment adherence from the two data sources was compared, and physicians were inductively segmented into high- and low-adherence groups. Seven possible factors influencing adherence were identified using prior research and prioritized using best-worst scaling (BWS). Results were stratified by adherence group, and differences in priorities were tested using Wald tests. RESULTS: We obtained data for 188 physicians, most of whom (56%) were unable to accurately state their rate of adherence. Two groups were identified: those with relatively high adherence (≥ 70%; n = 58) and those with relatively low adherence (< 70%; n = 130). High-adherence physicians prioritized that the VTE-RAM increases decision-making confidence (p < 0.001), reduces overtreatment of low-risk patients (p < 0.001), and facilitates documentation of low-risk patients (p = 0.029). Low-adherence physicians were more likely to prioritize that important risk factors are missing from the algorithm (p < 0.001), that the VTE-RAM disrupts workflow (p < 0.001), and that it is used owing to institutional policy requirements (p = 0.021). CONCLUSIONS: Observed physician adherence to VTE-RAM recommendations often differs from self-reported behavior. We found that several factors associated with high- and low-adherence physicians. High adherence was linked to confidence, reduced overtreatment, and documentation support, while low adherence reflected concerns about missing factors, workflow disruption, and institutional requirements. Aligning RAM design with physician priorities may improve adherence and clinical utility.

Authors

Institutions

Publication Details

Journal
American Journal of Cardiovascular Drugs
Published
2026-05-16
DOI
https://doi.org/10.1007/s40256-026-00799-4
Primary Topic
Venous Thromboembolism Diagnosis and Management
Type
article
Field-Weighted Citation Impact
0.00

Funders

Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Factors Influencing Physician Adherence to Venous Thromboembolism Risk Assessment Model Recommendations: A Best–Worst Scaling

Benjamin G Mittman, John F. P. Bridges, Nicola B. Campoamor, Michael B. Rothberg et al.
American Journal of Cardiovascular Drugs
Venous Thromboembolism Diagnosis and Management
article

Factors Influencing Physician Adherence to Venous Thromboembolism Risk Assessment Model Recommendations: A Best–Worst Scaling

Benjamin G Mittman, John F. P. Bridges, Nicola B. Campoamor, Michael B. Rothberg, Stephanie Moss
article en

Abstract

PURPOSE: Risk assessment models (RAM) use predictive algorithms to support physicians in personalizing care. For example, a RAM to determine the risk of venous thromboembolism (VTE-RAM) in hospitalized patients offered treatment recommendations that physicians adhered to at varying rates. We sought to investigate which factors influenced physician adherence to prophylaxis recommendations from a VTE-RAM implemented in an inpatient setting. METHODS: We assessed adherence to treatment recommendations for a VTE-RAM in the inpatient setting using both survey and real-world data sources. Inclusion criteria included being a hospitalist or resident in the inpatient setting, using a VTE-RAM at least 50 times, and having complete data from both sources available. Variation in treatment adherence from the two data sources was compared, and physicians were inductively segmented into high- and low-adherence groups. Seven possible factors influencing adherence were identified using prior research and prioritized using best-worst scaling (BWS). Results were stratified by adherence group, and differences in priorities were tested using Wald tests. RESULTS: We obtained data for 188 physicians, most of whom (56%) were unable to accurately state their rate of adherence. Two groups were identified: those with relatively high adherence (≥ 70%; n = 58) and those with relatively low adherence (< 70%; n = 130). High-adherence physicians prioritized that the VTE-RAM increases decision-making confidence (p < 0.001), reduces overtreatment of low-risk patients (p < 0.001), and facilitates documentation of low-risk patients (p = 0.029). Low-adherence physicians were more likely to prioritize that important risk factors are missing from the algorithm (p < 0.001), that the VTE-RAM disrupts workflow (p < 0.001), and that it is used owing to institutional policy requirements (p = 0.021). CONCLUSIONS: Observed physician adherence to VTE-RAM recommendations often differs from self-reported behavior. We found that several factors associated with high- and low-adherence physicians. High adherence was linked to confidence, reduced overtreatment, and documentation support, while low adherence reflected concerns about missing factors, workflow disruption, and institutional requirements. Aligning RAM design with physician priorities may improve adherence and clinical utility.

American Journal of Cardiovascular Drugs
Cleveland Clinic (US), The Ohio State University Wexner Medical Center (US), Case Western Reserve University (US)
Burroughs Wellcome Fund, National Institutes of Health
Peace, Justice and strong institutions
Openalex Percentile: Top 4%
Venous Thromboembolism Diagnosis and Management
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.