Clinician Attitudes and Perceptions of Point-of-Care Information Resources and Their Integration Into Electronic Health Records: Qualitative Interview Study
Bibliographic record
Abstract
BACKGROUND: Electronic health records (EHRs) are widely used in health care systems across the United States to help clinicians access patient medical histories in one central location. As medical knowledge expands, clinicians are increasingly using evidence-based point-of-care information (POCI) resources to facilitate clinical decision-making in medical practices. While these tools can improve clinical outcomes, few studies have assessed clinicians' opinions on integrating them with EHRs. OBJECTIVE: This study aims to assess clinicians' attitudes and the perceived value of POCI resources for finding medication- and disease-related information in clinical practice and their integration with EHRs. METHODS: Semistructured interviews were conducted with 10 clinicians from various roles and specialties between December 2021 and January 2022 at Brigham and Women's Hospital in Boston, Massachusetts. A content analysis approach was used to examine participants' responses and feedback on their current use of POCI resources, barriers and facilitators, mobile app use, and recommendations for improved integration. RESULTS: Of the 10 participants, 6 (60%) were female, 9 (90%) were aged <40 years, and 8 (80%) had ≤10 years of experience in clinical practice. While UpToDate was the most preferred disease-related information resource (n=9, 90%), preferences for medication-related resources varied, with 2 (20%) participants favoring Micromedex, 2 (20%) favoring Lexicomp, 2 (20%) favoring Brigham and Women's Hospital-specific drug administration guidelines, 2 (20%) favoring UpToDate, and 1 (10%) favoring Medscape. Most participants used their preferred tools weekly. Most clinicians preferred comprehensive POCI tools with clear, navigable layouts that eased and quickened the search for information. Features such as heavy text density, the lack of citations, and frequent log-ins to access the tool were viewed as barriers that limited content legibility, credibility, and accessibility. Access-related, tool-specific, and integration-related barriers were reported to negatively impact clinical workflow. Most (n=8, 80%) of the participants reported currently using mobile apps, reasoning that they facilitated quick and convenient searches for information; however, frequent updates, time-consuming log-ins, and high text density on smaller screens posed challenges. Most participants favored further integration of POCI resources with EHRs, with all reporting them being currently available as embedded links that launch externally. Some recommended that further integration would allow us to leverage existing POCI tool features, such as chatbots and knowledge links, as well as aspects of artificial intelligence and machine learning, such as predictive algorithms and personalized alert systems, to enhance EHR functionality. CONCLUSIONS: Participants favored integration to improve usability and optimize workplace efficiency by reducing the amount of time spent seeking answers to their medication- and disease-related questions. Recommendations on integration highlighted the need for stakeholder input in developing clinical decision support tools and interfaces that leverage advancements in artificial intelligence and machine learning while not compromising user experience or increasing time spent on tasks.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.016 | 0.026 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.005 | 0.004 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".