Clinician user involvement in the real world: Designing an electronic tool to improve interprofessional communication and collaboration in a hospital setting
Bibliographic record
Abstract
OBJECTIVES: User involvement is vital to the success of health information technology implementation. However, involving clinician users effectively and meaningfully in complex healthcare organizations remains challenging. The objective of this paper is to share our real-world experience of applying a variety of user involvement methods in the design and implementation of a clinical communication and collaboration platform aimed at facilitating care of complex hospitalized patients by an interprofessional team of clinicians. METHODS: We designed and implemented an electronic clinical communication and collaboration platform in a large community teaching hospital. The design team consisted of both technical and healthcare professionals. Agile software development methodology was used to facilitate rapid iterative design and user input. We involved clinician users at all stages of the development lifecycle using a variety of user-centered, user co-design, and participatory design methods. RESULTS: Thirty-six software releases were delivered over 24 months. User involvement has resulted in improvement in user interface design, identification of software defects, creation of new modules that facilitated workflow, and identification of necessary changes to the scope of the project early on. CONCLUSION: A variety of user involvement methods were complementary and benefited the design and implementation of a complex health IT solution. Combining these methods with agile software development methodology can turn designs into functioning clinical system to support iterative improvement.
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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 | 0.035 | 0.057 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.002 | 0.002 |
| 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".