Abstract P2-13-16: A multidisciplinary approach to implement personalized multifaceted care plans in standard breast cancer care: A quality improvement project
Bibliographic record
Abstract
Abstract Background: IOM, ASCO and Commission on Cancer have emphasized the provision of survivorship care plans (SCPs) to cancer patients. Cancer Care Ontario (CCO) aims to ensure that SCPs are developed and shared with healthcare providers (HCPs) to facilitate integrated cancer care. In 2016, a randomized mixed-methods study at St. Michael’s Hospital (SMH) provided a paper based Personalized Multifaceted Care Plan (PMCPs) to breast cancer (BC) patients, which included an upfront treatment plan. The PMCP was generated by entering non-repetitive patient data into a care plan database software developed by the team. The study results showed that patients and primary care providers (PCPs) found PMCP to be useful. Despite the benefits, implementation of SCPs as standard of care remained a challenge due to lack of designated personnel for developing SCP and limited resources causing poor provider buy-in. In 2017, we initiated a quality improvement (QI) project to implement the PMCP by integrating it into existing workflow by involving the care team. Methods: This REB approved QI project was done in two phases. Phase 1 was to identify key members to create the PMCP and to secure the software to the SMH IT server. In phase 2, the PMCPs were delivered to newly diagnosed invasive BC patients, receiving chemotherapy. The PMCPs were also shared with respective PCPs. At SMH, new patients diagnosed with BC are seen initially at the Multidisciplinary Clinic (MDC) by a medical and radiation oncologist, and a patient navigator (PN) to plan treatment. In phase 1, the PN who coordinated the MDC was identified as the most appropriate care team member to initiate the PMCP. To incorporate the PMCP into the PN’s existing workflow, a Transfer of Accountability (TOA) document was designed using the care plan software to replace a patient summary that was created in silos. The TOA was electronically shared with the team and had most of the information provided in the PMCP. The primary outcome measures were proportion of information completed to generate the PMCP and care plans delivered by patients’ care team. Secondary Outcome measures were patient and provider satisfaction with PMCP assessed using surveys at three time points and single time point respectively. Data were summarized using median and IQR as well as frequency and percentages. Data was also shared at QI team meetings, practice patterns were observed for changes and incorporated into subsequent PDSA cycles. Results: From 2017 to 2019, 90% (120/134) of eligible BC patients were provided with the PMCP. At the first 2 PDSA cycles 100% PMCP preparation and delivery was done by research staff. We have achieved 80% compliance in Phase 1 by involving the PN in data entry for PMCP while preparing the TOA document. TOA had been incorporated into standard of care at MDC and a recent nursing project found that HCPs surveyed perceived TOA to be a useful tool in information exchange and care coordination. Patient satisfaction surveys for PMCP found their responses were consistent over all time points with high levels of agreement. Surveys also showed that 73% (86/117) patients agreed that the PMCP helped with communication with their PCPs and 81% (95/117) agreed it helped with communication with the team. Of the 25 PCPs and 5 oncology HCPs surveyed, 91% (22/25) PCPs and 100% (5/5) oncology HCPs agreed that the PMCP should be incorporated into BC care. Conclusions: Implementation of SCPs with a multidisciplinary approach is feasible in BC care. Integration of SCPs within the existing work flow and involvement of members of the care team can lead to increased SCP uptake, better care coordination, sustainability and provider buy-in. With the high patient and provider satisfaction with PMCP, and recommendation by CCO, we are in the process of integrating PMCPs for BC patients as standard of care in the oncology clinic. Citation Format: Rashida Haq, Amy Kong, Ronita Lee, Geetha Mukherji, Christine Brezden-Maesley, Pauline Gulasingam. A multidisciplinary approach to implement personalized multifaceted care plans in standard breast cancer care: A quality improvement project [abstract]. In: Proceedings of the 2019 San Antonio Breast Cancer Symposium; 2019 Dec 10-14; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2020;80(4 Suppl):Abstract nr P2-13-16.
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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 | 0.125 | 0.062 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.005 | 0.003 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.006 | 0.013 |
| Research integrity | 0.002 | 0.006 |
| Insufficient payload (model declined to judge) | 0.008 | 0.002 |
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".