623. Exploring 'Slicer Dicer', an Extraction Tool in EPIC, for Clinical and Epidemiological Analysis. (4th December 2021) - Saini, Vikram; Jaber, Tariq; Como, James D; Lejeune, Keith; Bhanot, Nitin
Abstract: Background: Electronic Health Record (EHR) implementation has created an unprecedented library of patient data. Data extraction tools provide an opportunity to retrieve clinico-epidemiological information on a wide scale. Slicer Dicer is a data exploration tool in the EPIC EHR that allows one to customize searches on large patient populations. This software contains a variety of models that present de-identified information from EPIC's Caboodle database. We explored the applicability and potential utility of this tool utilizing the diagnosis of Lyme disease as an example. Methods: The following steps outline an overview of data extraction utilizing ICD-10 codes around Lyme disease at our health system. Step 1-3: Denominator chosen as 'All Patients' over a 3-year period, 'Slicing' of the data by 'Lyme disease, unspecified' was applied to these results, and the 'sliced' data was categorized by year of diagnosis (Slide 1). Step 4: This data was further arranged by month of diagnosis for trend analysis (Slide 2). Step 5: Sub-diagnosis was applied for Lyme arthritis (Slide 3). Step 6: Further 'slicing' was/can be done by other variables, such as 'Hospitalization, ' 'Encounter Diagnosis, ' and 'ED Diagnosis' (Slide 4). Step 7-8: Output was 'sliced' by 'Age' (Slide 5) and 'Postal Code' (Slide 6). Slide 1. EPIC EHR screen capture showing 3-year period data Data shown here represents 'All patients' chosen as the denominator further sliced by 'Lyme disease, unspecified' and categorized by the year of diagnosis. Slide 2. EPIC EHR screen capture showing data further arranged by month of diagnosis Results: Macro-level data of period prevalence on Lyme disease over 3 years (Slide 1), seasonal trends (Slide 2), specific sub-diagnosis (Slide 3), output by setting of diagnosis (Slide 4), and demographic information of our patient population (Slides 5, 6) was revealed by application of these parameters. Slide 3. EPIC EHR screen capture showing application of sub-diagnosis for Lyme arthritis Slide 4. EPIC EHR screen capture showing further slicing by multiple variables like hospitalization and diagnosis Slide 5. EPIC EHR screen capture showing slicing of data by demographic information (Age) Conclusion: Slicer Dicer can provide a snapshot for preliminary data analysis prior to investing time and commitment to a project. The appeal of this tool is that it mines de-identified data and thus does not require initial IRB approval. This opens an avenue for potential full research projects based on the results obtained and helps generate preliminary hypotheses through analysis of healthcare. Slide 6. EPIC EHR screen capture showing slicing of data by demographic information (Postal Code) Disclosures: All Authors : No reported disclosures