
==== Front
Neurooncol Adv
Neurooncol Adv
noa
Neuro-Oncology Advances
2632-2498
Oxford University Press US

10.1093/noajnl/vdae090.041
vdae090.041
Final Category: Data Sciences/AI Advances
AcademicSubjects/MED00300
AcademicSubjects/MED00310
DSAI-09 APPLYING CONSENSUS QUALITY OF CARE MEASURES FOR BRAIN METASTASES (BMETS-QC) TO PATIENT EHR DATA, A PILOT STUDY
Anderson Roger UVA Cancer Center, Charlottesville, VA, USA

Bonilla Gloribel UVA Cancer Center, Charlottesville, VA, USA

Fadul Camilo UVA Health, Charlottesville, VA, USA

8 2024
02 8 2024
02 8 2024
6 Suppl 1 2024 SNO/ASCO CNS Metastases Conference i13i13
© The Author(s) 2024. Published by Oxford University Press, the Society for Neuro-Oncology and the European Association of Neuro-Oncology.
2024
https://creativecommons.org/licenses/by-nc/4.0/ This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (https://creativecommons.org/licenses/by-nc/4.0/), which permits non-commercial re-use, distribution, and reproduction in any medium, provided the original work is properly cited. For commercial re-use, please contact journals.permissions@oup.com

Abstract

PURPOSE

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.

METHODS

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).

RESULTS

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.

CONCLUSION

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.
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pmc
