The Ontario oncology electronic medical record (EMR): The integration of end user and provincial needs.
Bibliographic record
Abstract
248 Background: The use of ambulatory electronic medical record (EMR) systems within oncology provides an opportunity for aligning provincial, local and end-user patient-centred quality indicators in the design, delivery and evaluation of clinical care and resource utilization. The aim of this provincial initiative is to define the “meaningful use” for the Oncology EMR by identifying the essential data elements and functional requirements required to facilitate integrated care, information standards (both local and provincial), and system integration needs. This paper presents the results of a provincial field study designed to determine end-user needs for information and quality metrics. Methods: Data collection included two separate onsite focus groups at each of the 13 regional cancer programs, with a focus on Clinical and Operational requirements. A total of 141 participants, representing physicians, interprofessional clinical team members, administrators and health information specialists were involved. An additional online survey was used for optimal engagement, with a total of 194 respondents, primarily nurses and physicians. Inclusion and exclusion criteria were developed to assist in coding and distillation of concepts generated. Results: A total of 1,598 ideas were generated (Clinical = 997, Operational = 601). Multiple rounds of content analysis were used to eliminate duplicates, identify common themes and distill the wealth of information down to the “vital few” discrete information requirements that should be included in the oncology EMR. At this time, 63 clinical and 55 operational concepts have been identified to support clinical care as well as operational planning and system evaluation. The online survey has helped define the data required for a Provincial Oncology Patient Profile within the EMR. Conclusions: The study employed significant consultation to merge end user and existing provincial quality measurement needs in order to define the Ontario Oncology EMR. A full spectrum of quality indicators identified through these processes will inform the future provincial priorities for information standards and quality monitoring that will be facilitated by a standardized EMR.
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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 | 0.012 | 0.048 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.006 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".