Clinician Needs and Requirements for a Decision Aid Navigator: Qualitative Study
Bibliographic record
Abstract
Background: Decision aids (DAs) are important tools that support shared decision-making (SDM) between clinicians and patients, enabling patients to be more informed and engaged in decisions regarding their care. The use of DAs can increase patient knowledge, reduce decisional regret, and engage the clinician and patient in meaningful dialog. Despite proven effectiveness in enhancing patient-centered care, a gap remains in clinician use of DAs. Known clinician barriers to using DAs include (1) time constraints, (2) concerns about the match between patient need and available DAs, (3) forcing users to leave the electronic health record (EHR) to access DAs, and (4) the burden of manually entering data into the DA. Objective: This qualitative study identified the needs and requirements of clinicians to inform the design of a clinician-facing, EHR-integrated, Substitutable Medical Applications, Reusable Technologies (SMART; SMART Health IT) on Fast Healthcare Interoperability Resources (FHIR) (HL7) app, the Decision Aid Navigator (DEAN; University of Colorado Anschutz Medical Campus). The Navigator identifies and surfaces DAs that are relevant to a patient's health care conditions (eg, atrial fibrillation), current care (eg, not on anticoagulation), and demographics (eg, check the youngest age for the stroke prevention), and facilitates documentation of SDM discussions and decisions. Methods: We conducted 13 semistructured interviews with clinicians who were recruited from 4 academic medical centers. Interviews included a demonstration of an initial, mid-fidelity, DEAN app prototype that was designed to address DA use and barriers described in the literature. The interviews focused on clinician context and use of the prototype, affordances and barriers to using the system, and clinician needs and requirements of the system. We used qualitative content analysis to code and reduce the data, using a consensus-making approach, and identify emerging themes. Results: We identified 3 overarching themes: (1) streamlined functionality may simplify workflow and decrease the burden of DA use and SDM; (2) clinicians need appropriate competencies to effectively use the Navigator and relevant DAs; and (3) trust that the Navigator suggests prevetted DAs. Unanimously, clinicians shared that the DEAN Navigator should be integrated into the EHR. To accomplish this clear priority, clinicians stated that they needed the requisite competencies to successfully use the tool within their workflow and build trust with the tool itself. Conclusions: Better tools to support and harness the benefits of SDM are needed. Overcoming the barriers of using DAs is paramount. Tools designed and developed to support DA use must be integrated into the EHR efficiently to create an opportunity for uptake of the technology by busy clinicians. If tools like DEAN can streamline the cumbersome process of documenting the use of DAs, more clinicians may potentially use DAs with their patients, given the right context and appropriate DA.
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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 | 0.038 | 0.068 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.010 | 0.008 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".