Utilizing Patient Reported Outcome Measures (PROMs) in ambulatory oncology in Alberta: Digital reporting at the micro, meso and macro level
Bibliographic record
Abstract
Cancer patients experience numerous distressing symptoms and concerns across the course of their illness, which negatively influence their quality of life. Regardless of cancer type, unmanaged symptoms can lead to adverse downstream consequences. Patient Reported Outcome Measures (PROMs) can be used to inform patient care and lead to targeted symptom management but simply gathering this information does not improve outcomes for the patient. Patient generated information must be easy for the clinicians to access and interpret if it is to be used to inform care delivery in ambulatory oncology facilities. This pragmatic work responded to this need. One Canadian provincial ambulatory oncology jurisdiction implemented digital tracking of PROMs over time in the provincial Electronic Medical Record (EMR) to support full integration of PROMs into standard care workflows and processes. Due to an inability within the EMR for direct patient entry, a hybrid data-entry was designed where the patient completes a paper-based PROM in the waiting room, and after clinical review, a clinician documents this along with their clinical assessment in the EMR. Several digital dashboards were developed which report PROMs data at the micro (individual), meso (clinic) and macro (program) levels. Using PROMs routinely in these provincial practice settings has numerous benefits including enhanced patient-clinician communication, assisting with problem detection, management of symptoms, and improving outcomes for patients. There are over 60,000 unique patients represented in our PROMs database, and over 300,000 unique screening events captured. The PROMs data is now used at all levels of the provincial cancer jurisdiction to provide targeted person centred care (micro), to staff appropriately at a clinic or program level (meso), and for capacity planning for provincial programs (macro). A new provincial EMR is currently being implemented which has an associated patient portal. Based on the success of this work, integration of direct entry of PROMs by the patient prior to the appointment and an associated workflow for symptom management is underway in this jurisdiction.
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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 | 0.010 | 0.027 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.008 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.000 | 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".