Improving quality care through it/analytics for team-based care interventions.

Abstract

e18263 Background: Team based care is essential to achieve coordinated, high-quality care. We recognized a responsibility to identify new treatment patients to provided targeted, team based interventions. Previous attempts at identifying new patients were difficult, manual, and delayed. Our aim was to provide personnel with the appropriate means to identify new patients for services such as patient education, financial navigation, social work assessments and outreach calls for care coordination. As a result of multiple barriers and challenges, only 51% of our new patients were being identified. This project fit well in the larger context of the transformation of health care delivery through our participation in the Oncology Care Model, which places an emphasis on team based care. Methods: We used the Plan-Do-Study-Act continuous improvement model to develop an automated data set to identify new treatment patients across the oncology service line and leveraged our Information Technology (IT) Analytics team. A report was established based utilizing logic to identify the first appointment scheduled within the department and a secondary tab to look back at new patients within a three week time frame. Information is housed within a business intelligence software system that is easy for end users to see and understand. The initial report was validated for accuracy at an identification rate of 93% and the 3 week “look back” report gives the ability to identify the remaining patients. Results: We have seen marked improvements metrics as a result of the utilization of this report. Our Financial Navigators have been able to sustain out of pocket estimate calls at > 94% YTD 2018. Social Work initial patient assessments have sustained at 79% for the last 6 months, and we have seen nearly 400 documented oncology patient education encounters each month. Identification of these patients allows us to target patient education, outreach, and psychosocial assessments at initiation of therapy. Care Coordinators are able to establish patient lists to track eligibility for survivorship at the appropriate time points. Conclusions: This project demonstrates the value of using IT/Data Analytics for continuous quality improvement projects related to patient centered team based care. The cross collaboration of stakeholders in data analysis should be explored as a mechanism to transform care through key services and interventions.