Optimizing e-Consultations to Adolescent Medicine Specialists: Qualitative Synthesis of Feedback From User-Centered Design
Bibliographic record
Abstract
BACKGROUND: e-Consultations between primary care physicians and specialists are a valuable means of improving access to specialty care. Adolescents and young adults (AYAs) face unique challenges in accessing limited adolescent medicine specialty care resources, which contributes to delayed or forgone care. e-Consultations between general pediatricians and adolescent medicine specialists may alleviate these barriers to care. However, the optimal application of this model in adolescent medicine requires careful attention to the nuances of AYA care. OBJECTIVE: This study aims to qualitatively analyze feedback obtained during the iterative development of an e-consultation system for communication between general pediatricians and adolescent medicine specialists tailored to the specific health care needs of AYAs. METHODS: We conducted an iterative user-centered design and evaluation process in two phases. In the first phase, we created a static e-consultation prototype and storyboards and evaluated them with target users (general pediatricians and adolescent medicine specialists). In the second phase, we incorporated feedback to develop a functional prototype within the electronic health record and again evaluated this with general pediatricians and adolescent medicine specialists. In each phase, general pediatricians and adolescent medicine specialists provided think-aloud feedback during the use of the prototypes and semistructured exit interviews, which was qualitatively analyzed to identify perspectives related to the usefulness and usability of the e-consultation system. RESULTS: Both general pediatricians (n=12) and adolescent medicine specialists (n=12) perceived the usefulness of e-consultations for AYA patients, with more varied perceptions of potential usefulness for generalist and adolescent medicine clinicians. General pediatricians and adolescent medicine specialists discussed ways to maximize the usability of e-consultations for AYAs, primarily by improving efficiency (eg, reducing documentation, emphasizing critical information, using autopopulated data fields, and balancing specificity and efficiency through text prompts) and reducing the potential for errors (eg, prompting a review of autopopulated data fields, requiring physician contact information, and prompting explicit discussion of patient communication and confidentiality expectations). Through iterative design, patient history documentation was streamlined, whereas documentation of communication and confidentiality expectations were enhanced. CONCLUSIONS: Through an iterative user-centered design process, we identified user perspectives to guide the refinement of an e-consultation system based on general pediatrician and adolescent medicine specialist feedback on usefulness and usability related to the care of AYAs. Qualitative analysis of this feedback revealed both opportunities and risks related to confidentiality, communication, and the use of tailored documentation prompts that should be considered in the development and use of e-consultations with AYAs.
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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 | 0.056 | 0.103 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.003 | 0.005 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.002 | 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".