How do data and creativity relate?

2007 “gets the right care to people when they need it and then captures the results for improvement”

Requesting Data 12 networks Opt in and out of query requests Tracking Definitions

Inventory Automated queries Custom queries Statistical Tools

WWAMI region Practice & Research Network ~50 Primary care WWAMI clinics ~15 data connected clinics CHCs and RHCs Underserved populations Many serving rural populations Collaboration with national network of practice based research networks Native American Clinical Research Network ~20 Tribal communities 1 data connected clinic Serving American Indian populations in urban and reservation community settings

Examples Behavioral health Benchmarking – 1.5 million patient lives Chronic kidney disease Colorectal cancer screening Northwest Pharmacogenomic Research Network Acute Pain Lung cancer screening HealthShare Montana HIE Research Portal Chronic opioid therapy best practices for non- cancer chronic pain Joined Alcohol and Drug Abuse Institute’s Clinical Trial Network Individual trials Behavioral Health Teratogens Diabetes Networks Alcohol and Drug Abuse Institute – Clinical Trials Network PCORNet CMMI Practice Transformation Network Seeking New Grants

Discover the breadth and depth of data across the network Federated Information Dictionary Tool Creating views of metadata and simple interactive summaries to convey content and usability Improve comprehension of the dataset to allow for creative thinking about research questions and goodness of fit for research projects Share a standard data dictionary Hurdle free inventory

Pitfalls Denominator, do you have the population you think you have Extraction errors Inconsistent data over time Variability in code uses across sites (ICD-9/10, CPT) Lack of consistent harmonized codes across (medications, labs) Medication data are complex, no dose quantities unless it’s fill data Lack coded key info (med hx, fam hx, smoking status, race/ethnicity) Difficulty defining events or relationships (i.e., a “visit” or “stay,” who the primary care provider is) Recommendations Get good consultation Create data validation procedures Use test datasets early in your studies to work out the kinks Expect messiness Create the right exclusion and inclusion criteria Understand the data provenance