Virtual Care Use for Patients with Hematologic Malignancies - a Natural Policy Experiment
Bibliographic record
Abstract
Introduction: The use of virtual care for patients with hematologic malignancies significantly increased during the COVID-19 pandemic, and its use has continued as an effective means for care delivery. In Ontario, Canada's largest province, all physicians are remunerated through publicly funded payment programs. On December 1, 2022, a policy change from the Ministry of Health (MoH) resulted in a significant fee decrease for virtual visits by phone for specialists (new phone consultations payable at $15.00 CAD, follow up visits payable at 85% of the equivalent in-person rate). We aimed to assess how this payment policy change affected physician decision-making to use virtual care by phone for patients with hematologic malignancies. Methods: We identified patients with hematologic malignancies at Princess Margaret Cancer Centre who had a visit between June 1 and November 30, 2022 (Period 1- prior to the MoH policy change), and visits between December 1, 2022 and May 31, 2023 (Period 2 - after the MoH policy change). We performed an interrupted time series analysis to assess the impact of the policy change, which was implemented on December 1, 2022. The primary outcome was the number of phone visits per week for an initial consultation or follow-up visit during the study period. A multivariable linear model was fitted to weekly number of encounters and time trends were modeled through a linear trend. The baseline trend was defined as the trend in weekly virtual care by phone use in Period 1, the step change was defined as the change in weekly virtual care by phone use immediately following the MoH policy change, and the trend change estimated the change in slope between Period 1 and Period 2. Autocorrelation and partial autocorrelation plots as well as the Durbin-Watson (DW) statistic were used to examine temporal autocorrelation in the residuals from the linear regression analysis. Results: There were 29,578 visits during the study period to either an allogeneic stem cell transplantation clinic, leukemia clinic, lymphoma or myeloma clinic (including autologous stem cell transplantation), or radiation therapy review clinic: 15,550 visits in Period 1 and 14,028 visits in Period 2. Of these, 291 were new patient consultations (152 period 1, 139 period 2) and 29,287 follow-up visits (15,398 Period 1, 13,889 Period 2). A majority of visits in both cohorts (90%) were in person or virtual care by video, with 10% of visits in both cohorts being virtual care by phone visits. Autocorrelation was not significant, and therefore the model was not adjusted. Prior to the MoH policy change, the use of virtual phone visits was significantly increasing with a baseline trend of 1.33 (95% CI 0.68-1.98, p<0.001). Visits by phone significantly decreased following the funding policy change with a step change of -22.50 (95% CI -36.82 to -8.19, p=0.003) and a trend change of -1.10 (95% CI -2.07 to -0.14, p=0.03). When stratified by new patient consultations or follow-up visits, the step change was not significant for new patients (1.37, 95% CI -1.20-3.94, p=0.29), but was significant for follow-up visits (-71.48, 95% CI -138.34 to -4.62, p=0.04). In contrast, the use of in-person or video visits did not significantly change with a baseline trend of 2.33 (95% CI -0.37 to 5.03, p=0.09), step change of -47.60 (95% CI -107.18 to 11.98, p=0.11) and trend change of -2.52 (95% CI -6.52 to 1.48, p=0.21). Discussion: Change in physician payment for phone visits resulted in a significant change in their use at the time of policy implementation, which was sustained over time. This effect was observed primarily in follow-up visits, likely reflecting the low overall utilization of phone for new consultations. These findings suggest that virtual care utilization was motivated in part by physician financial incentives, rather than clinical appropriateness. Since access to virtual care may enhance the patient care experience and remove barriers to accessing care for patients with hematologic malignancies, this study highlights the negative unintended consequences on care provision of this policy change and the need to better balance incentives across modalities of virtual care to encourage appropriate use.
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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 | 0.024 | 0.038 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.007 | 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".