Search

Background: Screening over many years is required to optimize reductions in colorectal cancer (CRC) mortality. However, no prior trials have compared strategies for obtaining long-term adherence.Methods: Systems of Support to Increase Colorectal Cancer Screening and Follow-Up was implemented in an integrated health care organization in Washington State. Between 2008 and 2009, 4675 individuals aged 50 to 74 years were randomized to receive the usual care (UC), which included clinic-based strategies to increase CRC screening (arm 1), or, in years 1 and 2, mailings with a call-in number for colonoscopy and mailed fecal tests (arm 2), mailings plus brief telephone assistance (arm 3), or mailings and assistance plus nurse navigation (arm 4). Active-intervention subjects (those in arms 2, 3, and 4 combined) who were still eligible for CRC screening were randomized to mailings being stopped or continued in years 3 and 5. The time in compliance with CRC screening over 5 years was compared for persons assigned to any intervention and persons assigned to UC. Screening tests contributed time on the basis of national guidelines for screening intervals (fecal tests annually, sigmoidoscopy every 5 years, and colonoscopy every 10 years).Results: All participants contributed data, but they were censored at disenrollment, death, the age of 76 years, or a diagnosis of CRC. Compared with UC participants, intervention participants had 31% more adjusted covered time over 5 years (incidence rate ratio, 1.31; 95% confidence interval, 1.25-1.37; covered time, 47.5% vs 62.1%). Fecal testing accounted for almost all additional covered time.Conclusions: In a health care organization with clinic-based activities to increase CRC screening, a centralized program led to increased CRC screening adherence over 5 years. Longer term data on screening adherence and its impact on CRC outcomes are needed. Cancer 2017;123:4472-80. © 2017 American Cancer Society. [ABSTRACT FROM AUTHOR]

Introduction: The use of electronic health records (EHRs) for research is proceeding rapidly, driven by computational power, analytical techniques, and policy. However, EHR-based research is limited by the complexity of EHR data and a lack of understanding about data provenance, meaning the context under which the data were collected. This paper presents system flow mapping as a method to help researchers more fully understand the provenance of their EHR data as it relates to local workflow. We provide two specific examples of how this method can improve data identification, documentation, and processing. Background: EHRs store clinical and administrative data, often in unstructured fields. Each clinical system has a unique and dynamic workflow and EHR, which may be influenced by broader context such as documentation required for billing. Methods: We present a case study with two examples of using system flow mapping to characterize EHR data for a local colorectal cancer screening process. Findings: System flow mapping demonstrated that information entered into the EHR during clinical practice required interpretation and transformation before it could be accurately applied to research. We illustrate how system flow mapping shaped our knowledge of the quality and completeness of data in two examples: 1) determining colonoscopy indication as recorded in the EHR, and 2) discovering a specific EHR form that captured family history. Discussion: Researchers who do not consider data provenance risk compiling data that are systematically incomplete or incorrect. For example, researchers who are not familiar with the clinical workflow under which data were entered might miss or misunderstand patient information or procedure and diagnostic codes. Conclusions/Next steps: Data provenance is a fundamental characteristic of research data from EHRs. Given the diversity of EHR platforms and system workflows, researchers need tools for evaluating and reporting data availability, quality, and transformations. Our case study illustrates how system mapping can inform researchers about the provenance of their data as it pertains to local workflows. [ABSTRACT FROM AUTHOR]