User Requirements and Conceptual Design for an Electronic Data Platform for Interhospital Transfer Between Acute Care Hospitals: User-Centered Design Study
Bibliographic record
Abstract
Background: The transfer of patients between hospitals, that is, interhospital transfer (IHT), introduces discontinuity of care, including gaps in health information transfer, which may worsen patient outcomes. Objective: This is the first phase of a 5-year research study. Our goals are (1) to understand the gaps in health information exchange (HIE) and the clinician experience in accessing and using the electronic health record (EHR) during IHT and (2) to identify clinician user requirements for the development of an internal EHR solution for IHT. Methods: We used prior work on HIE during IHT, coupled with a user-centered design (UCD) process to engage in discussions with clinical users and gather input on EHR workflow during IHT patient admission and planning. A total of 8 UCD sessions were held between February and July 2023, involving 18 clinicians who interact with the EHR during IHT, including 3 medicine residents, 10 advanced practice providers (APPs), and 5 direct care attendings-all responsible for caring for IHT patients at Brigham and Women's Hospital Cardiology, Medicine, Oncology, and intensive care unit services. Discussions highlighted facilitators and barriers and suggested improvements for data access and availability at the time of transfer. UCD sessions were recorded, analyzed, and coded by 2 independent reviewers to identify common themes driving suboptimal HIE. User requirements were derived from the sessions with users and iteratively refined throughout the process. Results: Qualitative analysis revealed that a significant number of frontline clinicians experience suboptimal availability of clinical information in the EHR at the time of IHT, including gaps in communication, incomplete data, and inefficient access to clinical data. User requirements emerged from these themes and primarily focused on information prioritization, data accessibility, and workflow and efficiency. Conclusions: Notable levels of missing information and inefficient access to clinical data were reported by end users caring for IHT patients at the time of transfer. Conducting user research to understand the current process of IHT, involving users in conceptual design and information architecture, and generating prototypes for feedback from users can aid in designing a solution that meets user needs. The results of these early UCD activities will be used to develop and implement a data platform to support clinicians during IHT.
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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 | 0.107 | 0.113 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".