![]() ![]() EPIC EHR screen capture showing slicing of data by demographic information (Age) EPIC EHR screen capture showing further slicing by multiple variables like hospitalization and diagnosis ![]() EPIC EHR screen capture showing application of sub-diagnosis for Lyme arthritis 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. ![]() EPIC EHR screen capture showing data further arranged by month of diagnosis EPIC EHR screen capture showing 3-year period dataĭata shown here represents 'All patients' chosen as the denominator further sliced by 'Lyme disease, unspecified' and categorized by the year of diagnosis. Step 7-8: Output was ‘sliced’ by ‘Age’ (Slide 5) and ‘Postal Code’ (Slide 6). Step 6: Further ‘slicing’ was/can be done by other variables, such as ‘Hospitalization,’ ‘Encounter Diagnosis,’ and ‘ED Diagnosis’ (Slide 4). Step 5: Sub-diagnosis was applied for Lyme arthritis (Slide 3). Step 4: This data was further arranged by month of diagnosis for trend analysis (Slide 2). 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). The following steps outline an overview of data extraction utilizing ICD-10 codes around Lyme disease at our health system. We explored the applicability and potential utility of this tool utilizing the diagnosis of Lyme disease as an example. This software contains a variety of models that present de-identified information from EPIC’s Caboodle database. Slicer Dicer is a data exploration tool in the EPIC EHR that allows one to customize searches on large patient populations. Data extraction tools provide an opportunity to retrieve clinico-epidemiological information on a wide scale. Electronic Health Record (EHR) implementation has created an unprecedented library of patient data. ![]()
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