A comprehensive Memorial Sloan Kettering Cancer Center real-world data model: Core clinical data elements

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Background: The 2016 21st Century Cures Act supports the use of Real-World Data (RWD) for regulatory decision/approval. Due to technological advances, a vast amount of health-related data are now available, but most are not standardized nor readily useable for research. Also, currently available standardized RWD models are not applicable across cancer types or oncology specialties (surgery, medical oncology, radiation oncology, pathology, radiology, etc.). To address these deficiencies Memorial Sloan Kettering Cancer Center (MSKCC) built a comprehensive, pan-cancer, pan-specialty RWD model. Methods: The Core Clinical Data Element (CCDE) data model incorporates aspects of existing academic and biopharma data models, including PRISSMM framework, ASCO’s mCODE, and NAACCR tumor registry model. The data model encompasses 11 domains that are critical to the understanding of the patient’s cancer journey, including: demographic, comorbidities, diagnosis, pathology, imaging, genomics, cancer surgeries, radiation oncology treatments, medical oncology treatments, cancer status/progression, and additional health information. To align with current standards, we are using ICD-10, ICDO3, CTACE V5.0, HL7, SNOMED and LOINC code sets. Further, this adaptable model allows for 5-10 disease specific elements to accommodate for disease heterogenicity and capture the differences among cancer types. Results: The CCDE database includes 1,126 of total data elements. MSKCC has 52,704 patients with MSK-IMPACT (Next-Generation sequencing platform with 505 genes panel) testing of which, we have identified 1,132 bladder cancer patients with at-least one year of cancer care follow-up for the initial curation cohort. Patients were identified as having an OncoTree bladder tumor type code that is assigned by a pathologist who attests the diagnosis by reviewing results from clinical tests on tumor specimens. To the date, 641 patients including 46,415 curated forms have been curated (Table). Conclusions: The comprehensive MSKCC’s CCDE data model standardizes the common and critical pan-cancer and pan-specialty elements for RWD. The dataset resulting from this curation efforts will provide robust structured and unified genomic and phenomic data across tumor types for future research enabling greater collaboration across various cancer types as well as oncology specialties.[Table: see text]

Implementing Electronic Health Record Default Settings to Reduce Opioid Overprescribing: A Pilot Study

Objective

To pilot test the effectiveness, feasibility, and acceptability of instituting a 15-pill quantity default in the electronic health record for new Schedule II opioid prescriptions.

Design

A mixed-methods pilot study in two health systems, including pre-post analysis of prescribed opioid quantity and focus groups or interviews with prescribers and health system administrators.

Methods

We implemented a 15-pill electronic health record default for new Schedule II opioids and assessed opioid quantity before and after implementation using electronic health record data on 6,390 opioid prescriptions from 448 prescribers. We then analyzed themes from focus groups and interviews with four staff members and six prescribers.

Results

The proportion of opioid prescriptions for 15 pills increased at both sites after adding an electronic health record default, with one reaching statistical significance (from 4.1% to 7.2% at CHC, P = 0.280, and 15.9% to 37.2% at WVU, P < 0.001). The proportion of 15-pill prescriptions increased among high-prescribing departments and among most high- and low-frequency prescribers, except for low-frequency prescribers at CHC. Sites reported limited challenges in instituting the default, although ease of implementation varied by electronic health record vendor. Most prescribers were not aware of the default change and stated that they made prescribing decisions based on patient clinical characteristics rather than defaults.

Conclusions

This pilot provides initial evidence that changing default settings can increase the number of prescriptions at the default level. This low-cost and relatively simple intervention could have an impact on opioid overprescribing. However, default settings should be selected carefully to avoid unintended consequences.