P3-4 Institutional implementation of electronic patient reported outcome measures and palliative care telemedicine during the COVID pandemic at the national cancer institute of milan
Bibliographic record
Abstract
Routine assessment of Patient Reported Outcome Measures (PROMs) is an indicator of integration between oncology and palliative care (PC), yet seldom applied in clinical practice. Electronic PROMs assessment (ePROMs), are a promising option. The institutional implementation of an ePROM assessment system, integrated into the electronic clinical records, was the aim of an implementation project since 2019 at our institution. The project (‘Patients’ Voices’) underwent 4 phases, predevelopment, software development and piloting, feasibility, post-development. Convergent mixed method design was applied with a websurvey on healthcare providers (HCPs), qualitative study on patients and HCPs and quantitative studies. The program has now developed the system and its integration with the hospital electronic chart and assessed its feasibility on cross-sectional and longitudinal assessment. Cross-section and longitudinal assessment has been performed in out and inpatient oncology and palliative care settings using the Psychological distress thermometer, Edmonton Symptom Assessment Scale and Therapy Impact Questionnaire. On 441 patients screened, 309 successfully completed the ePROM at baseline (70%; 95%CI 66% to 74%). Feasibility, usability together with the association of non compliance with patients and setting characteristics will be presented. Results demonstrate good patient compliance, acceptability and usability of the system with variability among wards and tools applied, e.g. higher compliance in the Palliative care clinic. Another important technical evolution of digital medicine has been stimulated by the worldwide COVID-19 pandemic outbreak. Telemedicine emerged as an important mean to reduce risks of transmission. A review of the literature shows that, in general telemedicine access increased during the pandemic and that it was felt useful and feasible, yet its efficacy in combining or substituting in person visits needs to be further confirmed by specific research. Our experience on outpatient palliative care patients during COVID-19 is based on a longitudinal observational study and was conducted from April to December 2020 with the aim of assessing feasibility, patients’ experience and satisfaction. Consecutive patients were screened for video consultations feasibility. Either the patients or their caregivers were contacted via video or phone consultation recording reason of the call and intervention performed. Patients or caregivers contacted at least twice were eligible for a phone interview to evaluate their experience with the service. Among 572 screened patients, 282 (49%, 95%CI 45% to 52%) were eligible for video consultation (accepted, had technology, did not lack help). 112 patients had at least two contacts, and 11% had one or more video-calls. 56% of the calls were done with patients, 30% with caregivers and 14% with both. In most cases (63%) the patient/caregiver requested the consultation. Reasons for tele-consultation included uncontrolled symptoms (66%), new symptoms onset (20%), therapy clarifications (37%) and update on diagnostic tests (28%). Most interventions were therapy modifications (70%) and appointments’ rescheduling (51%). Most users reported high satisfaction scores (range 1–5, mean 3.9 and 4.2 patients and caregivers respectively), no communication issues, and the great majority declared they would use telemedicine also after the pandemics (83% and 84%). Beyond its feasibility, clinical impact and cost effectiveness of telemedicine in palliative care need to be further studied.
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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 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".