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This project focuses on the value of standardization and shared learning in the development and validation process of data updates. It includes a data hosting and management plan, planning for a sustainable future, and initial results from the data cleaning process.
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Data Update Abby Crocker & Steve Kappell
Data Update • VMS Foundation QI Collaborative Measurement & Evaluation Strategy • Value of Standardization • Shared learning from our development & validation process • Data Hosting and Management Plan • Planning for a successful, sustainable future • Snapshot of initial results from data cleaning process • collaborative comparison lab rates HGB and CREA
Measurement and Evaluation Strategy • 3 armed approach • Individual Site PDSA cycles: primary data collection, internal data warehouse, process measures • Collaborative Metrics: team members, number of trainings, shared learning • Shared data system using “standardized” EMR data
Measurement and Evaluation Strategy • Shared data system using “standardized” EMR data • Evaluate the impact of the QI collaborative initiatives • How do lab rates compare across hospital? • What’s the “right” lab rate? • How do you change systems to get to the “right” lab rate? • How do you measure change to see if you got to the “right” lab rate? • How do you adjust for differences in patient populations?
Shared Data System • Identify common data elements • Inpatient admission information • Billing data • Laboratory results • Obtain “buy-in” from institutions • Time to develop extract • Commitment to upload the extract on a continuous basis • HIPAA compliance and BAA agreements
Shared Data System • Identify a place to store shared data • Secure data upload process • Secure and supportive analytic platform • Timely data access • Develop a data management strategy • Naming and storing files • Checking for structure changes • Checking for “invalid” data
Shared Data System • Standardize variable definitions across hospitals • Diagnostic Related Group (DRG) • Admission date & timestamp • Patient type
Standardize Variable Definitions: Admit timestamp • Registration date & time • Arrival date & time • ER admission date & time • Transfer to bed date & time • Observation status date & time • Inpatient admission date & time
Shared Data System • Identify measures of interest • Lab rates per patient day: what labs are of interest? What will we use as our numerator and denominator? • Plan a reporting process • Develop an analytic plan • Develop a reporting template • Disseminate information
Shared Data System • Learn from mistakes and keep improving the process • Validate the process, the data and the measures • Expect glitches (i.e. server upgrades)
Process & Data Validation • File upload process • File transfer process • File opening process • Analysis capabilities • Data structure of files received • Export functionality
Process & Data Validation • Subject inclusion and exclusion criteria • Adults, >18yo • Length of stay >24 hours • Exclude: maternity, inpatient psych, inpatient rehab and swing beds • Clarify the admit date & time • Clarify the lab codes • Check the expected counts of # discharges, # lab counts • Check complete lab profiles • Compare with VT 2014 hospital discharge data
Data Hosting & Management Plan • Continue with current system through June • At least? At most? • Work with current extracts • Use the NORC system • data management HEAVY
Data Hosting & Management Plan Looking ahead…. • Sustainable, secure, trusted system • Data management to be more systematized • Focus on analytics and putting the data in the hands of the users • Leverage known standardized datasets with all the experiential knowledge from this collaborative • Expand to incorporate additional data elements and define additional measures
Participating Sites: Data Validation & Update Status • CVMC ✔✔ thank you Kevin! • BMH ✔✔ thank you Frank! • SWVMC ✔ thank you Beth! • NVRH ✔ thank you Ryan! • DHMCthank you Diane, Susanne and Carol! • RRMC ✔ thank you Denise, Daniel and Joe! • PMC on hold • UVMMC ✔✔ thank you Melissa!