Development of an Electronic Database for Acute Pain Service Outcomes
Bibliographic record
Abstract
From mitigating complications during hospital stay to reducing the incidence of chronic pain, improving pain management positively impacts short‐ and long‐term outcomes of treatment. Quality assurance has become the focus of many health care programs in an effort to confront the challenges presented by pain and its management. The analysis of treatment strategies and patient responses form the basis of a reflective, evidence‐based practice, which can raise awareness of about the importance of adequate pain management. The capabilities of an electronic database can be exploited to organize large amounts of data, which can then be used to characterize symptoms and analyze treatment responses. However, the types of data to collect, the burden of data collection on workers, and costs must be considered before implementing an electronic database for research and/or everyday practice. This study tested the relevance and usability of an electronic database in an acute pain service by assessing several variables important to database development and evaluation. BACKGROUND: Quality assurance is increasingly important in the current health care climate. An electronic database can be used for tracking patient information and as a research tool to provide quality assurance for patient care. OBJECTIVE: An electronic database was developed for the Acute Pain Service, University of Alberta Hospital (Edmonton, Alberta) to record patient characteristics, identify at‐risk populations, compare treatment efficacies and guide practice decisions. METHOD: Steps in the database development involved identifying the goals for use, relevant variables to include, and a plan for data collection, entry and analysis. Protocols were also created for data cleaning quality control. The database was evaluated with a pilot test using existing data to assess data collection burden, accuracy and functionality of the database. RESULTS: A literature review resulted in an evidence‐based list of demographic, clinical and pain management outcome variables to include. Time to assess patients and collect the data was 20 min to 30 min per patient. Limitations were primarily software related, although initial data collection completion was only 65% and accuracy of data entry was 96%. CONCLUSIONS: The electronic database was found to be relevant and functional for the identified goals of data storage and research.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.022 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".