CT Pulmonary Angiogram Clinical Pretest Probability Tool: Impact on Emergency Department Utilization.

OBJECTIVE: Currently, computed tomographic pulmonary angiogram (CTPA) for evaluating acute pulmonary embolism (PE) in Emergency Departments (EDs) is overused and with low yields. The goal of this study is to assess the impact of an evidence-based clinical decision support (CDS) tool, aimed at optimizing appropriate use of CTPA for evaluating PE. METHODS: The study was performed at EDs in a large healthcare system and included 9 academic and community hospitals. The primary outcome was the percent difference in utilization (number of CTPA performed/number of ED visits) and secondary outcome was yield (percentage of CTPA positive for acute PE), comparing 12 months before (6/1/2021-5/31/2022) vs. 12 months after (6/1/2022-5/31/2023) a system-wide implementation of the CDS. Univariate and multivariable analyses using logistic regression were performed to assess factors associated with diagnosis of acute PE. Statistical process control (SPC) charts were used to assess monthly trends in utilization and yield. RESULTS: Among 931,677 visits to Emergency Departments, 28,101 CTPAs were performed on 24,675 patients. 14,825 CTPAs were performed among 455,038 visits (3.26%) pre-intervention; 13,276 among 476,639 visits (2.79%) post-intervention, a 14.51% relative decrease in CTPA utilization (chi-square, p<0.001). CTPA yield remained unchanged (1371/14825=9.25% pre- vs. 1184/13276=8.92% post-intervention; chi-square, p=0.34). Patients with COVID diagnosis prior to CTPA had higher probability of acute PE. SPC charts demonstrated seasonal variation in utilization (Friedman test, p=0.047). DISCUSSION: Implementing a CDS based on validated decision rules was associated with a significant reduction in CTPA utilization. The change was immediate and sustained for 12 months post-intervention.

Representing narrative evidence as clinical evidence logic statements.

Objective: Clinical evidence logic statements (CELS) are shareable knowledge artifacts in a semistructured "If-Then" format that can be used for clinical decision support systems. This project aimed to assess factors facilitating CELS representation. Materials and Methods: We described CELS representation of clinical evidence. We assessed factors that facilitate representation, including authoring instruction, evidence structure, and educational level of CELS authors. Five researchers were tasked with representing CELS from published evidence. Represented CELS were compared with the formal representation. After an authoring instruction intervention, the same researchers were asked to represent the same CELS and accuracy was compared with that preintervention using McNemar's test. Moreover, CELS representation accuracy was compared between evidence that is structured versus semistructured, and between CELS authored by specialty-trained versus nonspecialty-trained researchers, using χ2 analysis. Results: 261 CELS were represented from 10 different pieces of published evidence by the researchers pre- and postintervention. CELS representation accuracy significantly increased post-intervention, from 20/261 (8%) to 63/261 (24%, P value < .00001). More CELS were assigned for representation with 379 total CELS subsequently included in the analysis (278 structured and 101 semistructured) postintervention. Representing CELS from structured evidence was associated with significantly higher CELS representation accuracy (P = .002), as well as CELS representation by specialty-trained authors (P = .0004). Discussion: CELS represented from structured evidence had a higher representation accuracy compared with semistructured evidence. Similarly, specialty-trained authors had higher accuracy when representing structured evidence. Conclusion: Authoring instructions significantly improved CELS representation with a 3-fold increase in accuracy. However, CELS representation remains a challenging task.

Characteristics of knowledge content in a curated online evidence library.

Objective: To describe types of recommendations represented in a curated online evidence library, report on the quality of evidence-based recommendations pertaining to diagnostic imaging exams, and assess underlying knowledge representation. Materials and Methods: The evidence library is populated with clinical decision rules, professional society guidelines, and locally developed best practice guidelines. Individual recommendations were graded based on a standard methodology and compared using chi-square test. Strength of evidence ranged from grade 1 (systematic review) through grade 5 (recommendations based on expert opinion). Finally, variations in the underlying representation of these recommendations were identified. Results: The library contains 546 individual imaging-related recommendations. Only 15% (16/106) of recommendations from clinical decision rules were grade 5 vs 83% (526/636) from professional society practice guidelines and local best practice guidelines that cited grade 5 studies (P < .0001). Minor head trauma, pulmonary embolism, and appendicitis were topic areas supported by the highest quality of evidence. Three main variations in underlying representations of recommendations were "single-decision," "branching," and "score-based." Discussion: Most recommendations were grade 5, largely because studies to test and validate many recommendations were absent. Recommendation types vary in amount and complexity and, accordingly, the structure and syntax of statements they generate. However, they can be represented in single-decision, branching, and score-based representations. Conclusion: In a curated evidence library with graded imaging-based recommendations, evidence quality varied widely, with decision rules providing the highest-quality recommendations. The library may be helpful in highlighting evidence gaps, comparing recommendations from varied sources on similar clinical topics, and prioritizing imaging recommendations to inform clinical decision support implementation.