DSAI-09 APPLYING CONSENSUS QUALITY OF CARE MEASURES FOR BRAIN METASTASES (BMETS-QC) TO PATIENT EHR DATA, A PILOT STUDY

Optimal management of patients with brain metastases requires coordinated, interprofessional care, that is well-documented in their electronic health record (EHR). There are unquantified inconsistencies and gaps in the care for this complex medical diagnosis. We explored the feasibility of applying a peer-reviewed set of 29 quality of care (QOC) consensus measures to commercially available aggregated, limited EHR data. These measures address care planning, treatment timing, assessment of QoL, referrals to palliative care and hospice, and survival. Patients with BMETS were selected (N=43) from a an EHR data warehouse sourced from IQVIA’s collaboration with the Guardian Research Network (GRN). Data extraction followed curation rules developed by the study team and performed by GRN. Patients’ EHR data was manually reviewed by trained staff. Both structured data and unstructured text notes were reviewed and coded. Dates of diagnosis, medical services, and death were masked by indexing to birth date. For each indicator, a patient’s EHR data was coded as done (+), not done(-), or inconclusive (missing). Of 29 indicators, 23 could be ascertained in > 85% of cases. Instances of lower ascertainment occurred for patients lost to follow-up, missing dates of service, no information on planned or actual dose, or missing prognostic scores. BMETS-QC processes ascertained as not performed (-) mostly involved a lack of documented patient-centered care (i.e., shared decision-making, patient education, and psychosocial support). Finally, 12 indicators required the use of unstructured data and required data curation with manual review or NLP. Most BMETS-QC indicators appear feasible to collect in EHR data with curation processes. An important finding is that providers may fail to document key elements of patient-centered care or timely prognostic information. To handle missing data, patients must have their BMETS care recorded within a single EHR system.