A Methodology for Development of Clinical Performance Monitoring Applications
Bibliographic record
Abstract
Clinical performance monitoring applications enable performance management of care processes in clinical settings. Although information technology has been advocated as a solution to support the provision of better care, the development of clinical performance monitoring applications is often a non-trivial task. A high rate of failure in IT healthcare project implementations has been reported in the literature due to the disconnect between clinicians and the development team. Furthermore, challenges inherent to the configuration of the healthcare system add to the complexity of developments. Often data sources are not adequately structured or cannot be accessed in a timely fashion; processes are uncoordinated or ill-defined; a plethora of information technologies across different healthcare organizations make interoperability problematic; and there are concerns related to privacy and security. Getting the right information to measure the achievement of the right goals at the right time for the right people is the main task to address when developing clinical performance monitoring applications. In this thesis we propose a development methodology that combines technical and managerial aspects of application development following a user-centered approach. It involves the engagement of stakeholders and users throughout in a three phase iterative process of modeling, implementation and evaluation to ensure user acceptance and adoption of applications when deployed. In particular, our focus is on the development of mobile clinical performance monitoring applications, where raw data about clinical problems are logged by healthcare providers and then transformed into meaningful reports that will support decision-making. The development methodology is evaluated using a case study of a Resident Practice Profile (RPP) application that was developed by a team lead by Dr. Gary Viner from the University of Ottawa medical school.
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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 | 0.025 | 0.051 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.004 | 0.006 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.006 | 0.003 |
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".