Exploratory Co-Design on Electronic Health Record Nursing Summaries: Case Study
Bibliographic record
Abstract
Background: Although electronic health record nursing summaries aim to provide a concise overview of patient data, they often fall short of meeting nurses' information needs, leading to underutilization. This gap arises from a lack of involvement of nurses in the design of health information technologies. Objective: The purpose of this exploratory co-design case study was to solicit insights from nurses regarding nursing summary design considerations, including key information types and the preferred design prototype. Methods: We recruited clinical nurses (N=33) from 7 inpatient units at a university hospital in the Midwestern United States using a purposive sampling method. We used images from a simulated nursing summary to generate visual card versions of the 46 information types currently included in an electronic health record vendor-generated nursing summary. Participants selected which cards to include and arranged them in their designs based on their perceived relevance of the information types to the summary and their preferred reading layout. The nurses' perceived relevance of information types to the summary was analyzed by quantifying the frequency of included cards, while the nurses' preferred reading layout was analyzed by quantifying the occurrence of closely paired cards to identify common groupings. After participants evaluated the information type cards, debriefing interviews were conducted and analyzed thematically to explore their rationales for the desired content and its arrangement. Results: The participants demonstrated a high level of engagement in the activities. On average, all 33 participants included 61% (n=28) of the total information types (n=46). The most frequently included cards were "unit specimen" (results of the analysis of body fluid, tissue, or urine), "activity," "diet," and "hospital problems," each included by 33 participants. Participants most frequently preferred adjacency of the following pairs: "activity" and "diet" (paired by 26 participants; 79%) and "notes to physicians" and "notes to treatment team" (paired by 25 participants; 76%). Participants preferred arranging the cards to improve information accessibility, focusing on key information types. Conclusions: Involving nurses in the co-design process may result in more useful and usable designs, thereby reducing the time required to navigate nursing summaries. Future work should include refining and evaluating prototypes based on the designs created by the nurses.
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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 | 0.028 | 0.057 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.005 | 0.003 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".