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M. Alarakhia



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    OS18 - Minding the Gap in Our Healthcare Policies (ID 34)

    • Event: e-Health 2018 Virtual Meeting
    • Type: Oral Session
    • Track: Clinical Delivery
    • Presentations: 1
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      OS18.01 - Developing a Data Quality Assessment Framework for Primary Care (ID 385)

      M. Alarakhia, The eHealth Centre of Excellence; Waterloo/CA

      • Abstract
      • Slides

      Purpose/Objectives: The connecting South West Ontario Program’s Primary Care Data Sharing Proof of Concept (PCDS POC) project, funded by eHealth Ontario, is enabling a sub-set of primary care electronic medical record (EMR) data to be shared as part of Ontario’s integrated electronic health record (EHR). The POC is being conducted across four Family Health Teams in southwest Ontario. A key objective is to improve data quality to enhance data sharing; ensuring the data being shared is accurate, complete and timely and can be used to inform clinical care. The purpose of this presentation is to explain the framework, including tools and templates that was developed by the project team to assess and monitor data quality improvements.

      Methodology/Approach: To create the framework, existing data quality frameworks were assessed and customized to meet project needs. The tools focused on three dimensions: a) completeness: how well the data in the EMR reflects the actual medical state of the patient; b) correctness: how up-to-date the data is in the EMR; and 3) comparability: how much of the data in the EMR is comparable (e.g. coded and easily analyzed). The framework enables the evaluation of these dimensions across several elements of EMR data including: problem list, past medical and surgical history, medications, immunizations, allergies, and risk factors. The framework consists of two data quality assessment tools (an objective and a subjective tool). An online survey to assess the clinician’s perception of their level of confidence of their EMR data and a checklist used to validate data quality by completing a mini chart review. For both tools, a percentage of completeness, correctness and comparability is calculated, and averaged to calculate an overall data quality score.

      Finding/Results: Our analysis resulted in a number of key findings. First, clinicians recognized the necessity of data standardization, but also recognized the need for resources (human and time) to achieve desired outcomes. In addition, continued reinforcement is needed to remind clinicians to motivate change including showing them what their data looks like. Some sites addressed their interest for reinforcement by creating a data quality scorecard that showed data quality status by physician and site. An additional consideration for future work included data capture inconsistencies across clinicians and vendor variation in terms of completion of data queries to support data quality plans. In terms of benefits, standardizing EMR data was shown to enable more accurate identification of patients with complex care needs; allowing clinicians to better focus on providing preventative care. This finding, of course, is vital to consider when the POC is put into the larger system goals of improving patient outcomes and ensuring appropriate use of health system resources.

      Conclusion/Implications/Recommendations: The PCDS POC has designed a framework to assess and monitor data quality improvements over time. The framework includes a number of tools that can be utilized by primary care sites. The tools and templates reveal important potential being developed during a POC centred on Primary Care Data Sharing and offer considerable potential for decision-makers considering scaling and spreading of such an initiative.

      140 Character Summary: Development and validation of a data quality assessment framework to support primary care sites in assessing and monitoring data quality improvement activities.

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