1. DATA, TRACKING, AND QUALITY OUTCOMES Chris Espersen, MSPH

2. Overview  Data overload  What data to collect  How to collect data  Data fidelity  Data dissemination  Your questions

3. Data Overload  Other agencies “helpful” data  Regurgitated data  Expanding funding requirements  Differing data definitions  TMI!!!

4. What data to collect  Evolution of data  Funding requirements  Quality improvement data  Finding the data that works for us

5. How to collect data: ?s to ask  What am I measuring?  What population do I want to measure this for?  Why does it matter?  What are my numerator and denominator?  Data dictionary

6. How to collect data: Systems  Infrastructure  Programs?  People?  Data for more than one purpose  Are the data in your system?  Are there data you are already collecting that are applicable?  Are others collecting similar data?  What difference are these data going to make to your organization?  One time funding?  Aligned with needs of clientele?  Existing or potential reimbursement systems?

7. Data fidelity  Don’t work on QI until you are reasonably sure your data are accurate  Won’t ever be perfect  Small changes  Encourage questions!  Audit your own data  Don’t take anyone’s data at face value  Run reports 2 different ways  Ask if It makes sense

8. Data Dissemination  Share those data!  QI teams  Community partners  Fellow grantees  Legislature  People with $$

9. Data, data, data!  Love your data  Use your data  Question your data  Share your data

10. Thank you!  Questions?  cespersen@phcinc.net cespersen@phcinc.net