Electronic Health Record Data in Cancer Learning Health Systems: Challenges and Opportunities

Post, Andrew R.; Burningham, Zachary; Halwani, Ahmad

doi:10.1200/cci.21.00158

Cited by 5 publications

(3 citation statements)

References 119 publications

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“…Fragmentation of health information is another challenge, with individual patient data often contained in different systems, that may or may not be designed for oncology workflows. 13 This fragmentation poses challenges in interoperability 14 and maintenance of data pipelines. Often, EHR source records are not fully accessible, limiting the implementation of standard and repeatable data quality standards.…”

Section: Background and Objectivementioning

confidence: 99%

Raising the Bar for Real-World Data in Oncology: Approaches to Quality Across Multiple Dimensions

Castellanos,

Wittmershaus,

Chandwani

2024

JCO Clin Cancer Inform

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PURPOSE Electronic health record (EHR)–based real-world data (RWD) are integral to oncology research, and understanding fitness for use is critical for data users. Complexity of data sources and curation methods necessitate transparency into how quality is approached. We describe the application of data quality dimensions in curating EHR-derived oncology RWD. METHODS A targeted review was conducted to summarize data quality dimensions in frameworks published by the European Medicines Agency, The National Institute for Healthcare and Excellence, US Food and Drug Administration, Duke-Margolis Center for Health Policy, and Patient-Centered Outcomes Research Institute. We then characterized quality processes applied to curation of Flatiron Health RWD, which originate from EHRs of a nationwide network of academic and community cancer clinics, across the summarized quality dimensions. RESULTS The primary quality dimensions across frameworks were relevance (including subdimensions of availability, sufficiency, and representativeness) and reliability (including subdimensions of accuracy, completeness, provenance, and timeliness). Flatiron Health RWD quality processes were aligned to each dimension. Relevancy to broad or specific use cases is optimized through data set size and variable breadth and depth. Accuracy is addressed using validation approaches, such as comparison with external or internal reference standards or indirect benchmarking, and verification checks for conformance, consistency, and plausibility, selected on the basis of feasibility and criticality of the variable to the intended use case. Completeness is assessed against expected source documentation; provenance by recording data transformation, management procedures, and auditable metadata; and timeliness by setting refresh frequency to minimize data lags. CONCLUSION Development of high-quality, scaled, EHR-based RWD requires integration of systematic processes across the data lifecycle. Approaches to quality are optimized through knowledge of data sources, curation processes, and use case needs. By addressing quality dimensions from published frameworks, Flatiron Health RWD enable transparency in determining fitness for real-world evidence generation.

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Section: Background and Objectivementioning

confidence: 99%

Raising the Bar for Real-World Data in Oncology: Approaches to Quality Across Multiple Dimensions

Castellanos,

Wittmershaus,

Chandwani

2024

JCO Clin Cancer Inform

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“…MASCC's systematic review provided the call to action that the field needed to advance our understanding of mucositis risk and develop the next-generation of risk stratification tools. However, to move beyond these highly siloed, hypothesis-driven studies, significant investment – scientifically, financially and logistically – is required to collect the data needed to enable more robust risk prediction efforts with greater power and clinical influence [20 ▪▪ ]. To achieve this in a reasonable timeframe, coordinated approaches that leverage on data (including biospecimens) that are routinely collected in clinical workflow are needed, underscoring the potential of big data to accelerate advances in mucositis risk prediction.…”

Section: Big Data To Predict Cancer Treatment-induced Toxicities: Muc...mentioning

confidence: 99%

Using real world data to advance the provision of supportive cancer care: mucositis as a case study

Wardill

Sonis

Blijlevens³

2022

Current Opinion in Supportive &Amp; Palliative Care

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Purpose of reviewFor decades, clinical decision making and practice has been largely informed by data generated through randomized clinical trials (RCTs). By design, RCTs are highly restricted in both scope and scale, resulting in narrow indications and iterative advances in clinical practice. With the transition to electronic health records, there are now endless opportunities to utilize these 'real world' data (RWD) to make more substantive advances in our understanding that are, by nature, more applicable to reality. This review discusses the current paradigm of using big data to advance and inform the provision of supportive cancer care, using mucositis as a case study. Recent findingsGlobal efforts to synthesize RWD in cancer have almost exclusively focused on tumor classification and treatment efficacy, leveraging on routine tumor pathology and binary response outcomes. In contrast, clinical notes and billing codes are not as applicable to treatment side effects which require integration of both clinical and biological data, as well as patient-reported outcomes.

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“…Sharing patient data from cancer care centers will better our understanding of individual patients' needs and advance clinical practices that will improve patient outcomes [13]. However, significant hurdles impede the effective sharing of uniform clinical oncology information across care providers.…”

Section: Introductionmentioning

confidence: 99%

Navigating Precision Oncology: Insights from an Integrated Clinical Data and Biobank Repository Initiative across a Network Cancer Program

Aryal,

Bizhanova,

Joseph

et al. 2024

Cancers

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Advancing cancer treatment relies on the rapid translation of new scientific discoveries to patient care. To facilitate this, an oncology biobank and data repository program, also referred to as the “Moonshot” program, was launched in 2021 within the Integrated Network Cancer Program of the Allegheny Health Network. A clinical data program (CDP) and biospecimen repository were established, and patient data and blood and tissue samples have been collected prospectively. To date, the study has accrued 2,920 patients, predominantly female (61%) and Caucasian (90%), with a mean age of 64 ± 13 years. The most common cancer sites were the endometrium/uterus (12%), lung/bronchus (12%), breast (11%), and colon/rectum (11%). Of patients diagnosed with cancer, 34% were diagnosed at stage I, 25% at stage II, 26% at stage III, and 15% at stage IV. The CDP is designed to support our initiative in advancing personalized cancer research by providing a comprehensive array of patient data, encompassing demographic characteristics, diagnostic details, and treatment responses. The “Moonshot” initiative aims to predict therapy responses and clinical outcomes through cancer-related biomarkers. The CDP facilitates this initiative by fostering data sharing, enabling comparative analyses, and informing the development of novel diagnostic and therapeutic methods.

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Electronic Health Record Data in Cancer Learning Health Systems: Challenges and Opportunities

Cited by 5 publications

References 119 publications

Raising the Bar for Real-World Data in Oncology: Approaches to Quality Across Multiple Dimensions

Raising the Bar for Real-World Data in Oncology: Approaches to Quality Across Multiple Dimensions

Using real world data to advance the provision of supportive cancer care: mucositis as a case study

Navigating Precision Oncology: Insights from an Integrated Clinical Data and Biobank Repository Initiative across a Network Cancer Program

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