Using De-Identified Data: 10 practical steps. Jennifer Creasman CTSI Consultation Services
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1 Using De-Identified Data: 10 practical steps Jennifer Creasman CTSI Consultation Services
2 RDB De-identified Flat Files, reduced EHR data but Still A Tidal Wave of Data Number of Tables: 16 Total File Size: 209 GB and growing You can t read these into MExcel or MSAccess you need other tools.. 54 Clinical Data Colloquium
3 Assumptions: Ten Practical Steps to Staying on Top of a Tidal Wave of EHR Data You have a clearly defined research question You have clinical expertise in the area of research If you decide to work with a Data Scientist /Programmer -- All steps still apply 55 Clinical Data Colloquium
4 Step 1: Find the Right Tool for You! Language-oriented Software Programs UCSF Licenses: licensed-software MyResearch Platform Single-user license option 56 Clinical Data Colloquium
5 Step 2: Develop Your Programming Skills How Do I Become a Better Programmer? 10,000 hours rule: Just do it! Data Science Courses Join a group Become friends with other programmers. Eat lunch with them. 57 Clinical Data Colloquium
6 Step 3: Understand Your data Multiple diagnoses per encounter Working with RDB De-identified Flat Files Review the RDB documentation Study the relationships between the tables Open the files Look at the data 58 Clinical Data Colloquium
7 Observations: One record per patient De-identified: Patient File Introductory sentence Arial 18pt font Picture of patient file First bullet point Secondary bullet point Tertiary bullet point 59 Clinical Data Colloquium
8 Observations: Multiple encounters per patient De-identified: Encounter File Introductory sentence Arial 18pt font picture of encounter file First bullet point Secondary bullet point Tertiary bullet point 60 Clinical Data Colloquium
9 Observations: Multiple values per encounter De-identified: Flowsheet Data Introductory sentence Arial 18pt font Picture of flowsheet data First bullet point Secondary bullet point Tertiary bullet point 61 Clinical Data Colloquium
10 Step 4: Don t Drown in Your Data! Reduce the files to only include your study cohort SAS program examples are available in RDB folder Easy to use if identifying patients using ICD9/10 codes and/or patient s demographic data Otherwise, complex programming might be required Start with a smaller set of variables 62 Clinical Data Colloquium
11 Step 5: Get Dirty! Own and drive the research Review the literature: What variables are expected to be there? Develop an explicit instructions on how outcomes and covariates should be derived from the available data Provide an explicit and exact recipe for new calculated variables Understand limitations of the data 63 Clinical Data Colloquium
12 Step 6: Develop Project Specific Documentation Plan Ahead Create a mock Table 1 Start with word descriptions Specify variable names, don t leave anything open to interpretation Table 1. Characteristics of inpatient PAD populations with and without depression Variable D No D Age [Patient_Age] Gender [Patient_Sex] BMI [Vitals_BMI] Race [Patient_Ethnicity] Smoking [Patient_smoking] P Calculated Variable Description reinsertion whether a foley catheter was reinserted during the hospitlization for any reason with any time period new foley placement time is after during hospital admission time for hospitalizati patients that we admitted on without a foley catheter qmonth change new placement time minus previous placement time >25 days 64 Clinical Data Colloquium
13 Step 6: Develop Project Specific Documentation Each variable might require a separate calculation Calculated Variable Description reinsertion whether a foley catheter was reinserted during the hospitlization for any reason with any time period new foley placement time is after during hospital admission time for hospitalizat patients that we admitted ion without a foley catheter qmonth change new placement time minus previous placement time >25 days 65 Clinical Data Colloquium
14 Step 7: Create Reproducible Code Tips for Easy Reproducibility Read raw de-identified files directly into programming software Use only the program to manipulate the data Add text to describe the purpose of the program at a high level and imbed it in the code to describe the specific task Have an organized file structure /Data /Documents /Programs 66 Clinical Data Colloquium
15 Step 8: Ask for Help UCSF Resources Cores at UCSF UCSF Library Workshops & Special Events Data Science Training Opportunities CTSI s Consultation Services Study design experts to help define covariates and outcomes from EHR Programming experts to help translate your recipe into code And more Image credit: springcreekanimalhospital.com 67 Clinical Data Colloquium
16 Step 9: Prepare an Analytical Dataset and Codebook (see Step 6) Merge study variables into one nice data frame Each row represents a different a observation, with potentially repeated observations for individual patients Each column represents a different variable in your dataset 68 Clinical Data Colloquium
17 Step 10: Analyze and Publish Your Results Easier said than done! Project started 1/2015, submitted 4/2016 and published 4/2017 Project started 6/2016 and was published 3/ Clinical Data Colloquium
18 Real-time Feedback On your phone, tablet, laptop Go to: slido.com Enter event code: clinicaldata 70 Clinical Data Colloquium
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