Quality metrics at scale: Deriving time to first cancer treatment from electronic health records.

Authors

null

Arjun Sondhi

Flatiron Health, New York, NY

Arjun Sondhi, Heather Silver, Alexandra Jacob, Lindsay Gaido Bramwell, Jonathan Levine, Erica Dominic, Giselle Geno, Anisa Xhaja, Gabrielle Betty Rocque

Organizations

Flatiron Health, New York, NY, University of Alabama at Birmingham, Birmingham, AL, O'Neal Comprehensive Cancer Center at The University of Alabama at Birmingham, Birmingham, AL

Research Funding

Pharmaceutical/Biotech Company
Flatiron Health

Background: Measurements of cancer care quality are critical for internal administration and participation in value-based care programs, towards the goal of improving patient outcomes. Data from electronic health records (EHRs) have the potential to allow for automatic computation of quality measures at a larger scale than manual chart review. This requires the creation of algorithms that process data and apply custom logic to define a clinically meaningful measurement. In this work, we describe the development and performance of an algorithm for deriving time from first primary oncologist visit to first cancer treatment using EHR data. Methods: This EHR-derived measure of time to first treatment (TTT) was implemented at an academic medical center for all patients who had a visit since 01/01/2011 and a cancer-related diagnosis code. We defined a patient’s primary oncologist as the first medical oncologist who they visited at least 5 times. These criteria were chosen to find the first provider at the site who the patient likely established care with and received treatment from. Then the patient’s TTT is computed as the number of days between their first visit date with this oncologist and the date of their first antineoplastic drug administration. Data on other treatment modalities, such as surgery and radiation therapy, were not available at the time of algorithm development. We allow for patients to receive their first treatment up to 60 days before their first visit to account for inpatient treatments before a primary outpatient oncologist is established. This algorithm’s performance was assessed through manual chart review among a sample of 38 patients. Results: Our algorithm correctly assigned 37 out of 38 patients (97.4%) to their primary oncologist, and computed the correct TTT for 28 out of 38 patients (73.4%). Incorrect TTT computations were mostly due to missingness of systemic therapy administrations in the structured EHR data; these were primarily for oral therapies. Conclusions: This work demonstrates the feasibility of measuring a patient’s TTT from structured EHR data alone. In particular, a patient’s initial visit with their primary oncologist can be derived with high accuracy. From this index date, the accuracy of the metric is dependent on the availability of structured treatment data. To further improve the EHR-derived metric, future work should incorporate other treatment modalities through structured data sources such as billing codes, and improve completeness of systemic therapy data in a scalable way, such as from natural language processing techniques.

Reasons for incorrect TTT computation.

Total Patients with Incorrect TTT10 (26.3%)
Missing oral therapy4 (10.5%)
Missing clinical trial therapy2 (5.3%)
Missing infusion therapy2 (5.3%)
Missing surgery1 (2.6%)
Incorrect primary oncologist attributed1 (2.6%)

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Abstract Details

Meeting

2023 ASCO Quality Care Symposium

Session Type

Poster Session

Session Title

Poster Session B

Track

Health Care Access, Equity, and Disparities,Technology and Innovation in Quality of Care,Palliative and Supportive Care

Sub Track

Use of IT/Analytics to Improve Quality

Citation

JCO Oncol Pract 19, 2023 (suppl 11; abstr 580)

DOI

10.1200/OP.2023.19.11_suppl.580

Abstract #

580

Poster Bd #

N3

Abstract Disclosures

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