1. When Location Doesn’t Matter: When the Quality of Care is at Stake Johanna Warren MD, Jessica Flynn MD, and Scott Fields MD MHA Oregon Health & Sciences University

2. Objectives 1.Demonstrate how quality indicators may be used to assist in the care of patients regardless of where the care occurs. 2.Describe the process of building a set of quality indicators from the ground up. 3.Define the skill set of a functional Quality Data Team, including the interface between the clinician and Data Team to yield meaningful data.

3. Logistics 90 minutes, 3 objectives Facilitated Group Discussion Individual Activity - Workbooks

4. QUALITY OF CARE IS IMPORTANT; LOCATION OF CARE DOESN’T MATTER

5. Transformation We need to meet the medical needs of our society within a sustainable financial model. We need to embrace the use of data to evaluate our own personal performance. We need to teach faculty, clinic staff, residents, and students a new set of skills. We need academic departments to assist in developing infrastructures to provide data and analytics.

6. Your Practice Data Critical to our success is the data from our own practices: – Clinical performance – Operational effectiveness – Productivity for the individual & the practice – Patient satisfaction

7. Can be Clinic-Based

8. Can be Team-Based

9. Can be Individual

10. Can be Patient Satisfaction

11. Your Quality Program Should Reflect Your Values Perspectives to consider: – Patient – Clinician – Payor – Society Care may be provided anywhere within a continuum of environments

12. Workbook Activity Define your practice type/setting. What are your values?

13. BUILD YOUR QUALITY INDICATORS & DATA TEAM

14. Where do you start? Start with the basics. Build a line of inquiry that is understandable and meaningful. Who are our patients? What are their medical problems? Why are they seen? How well do we care for our patients?

15. Location of Care Doesn’t Matter All of these questions are important regardless of where the care is provided. Patient safety and health outcomes should be optimized in every location. Consider monitoring: – frequency of utilization of various care environments – Impact of transitions of care between environments

16. Workbook Activity What question(s) would you start with for your patients/organization?

17. OHSU Family Medicine First Focus: The “PCP” Field Need to identify who our patients “belong to” Challenged by working with two separate EMRs that don’t “cross-talk” very well Responsibility for identification and EMR field population ultimately lies with PCP, discharge resident & attending Motivation by individual residents & faculty to complete this task waxes and wanes

18. The PCP Field

19. Current OHSU Family Medicine Focus: Care Transitions Inherently dangerous for the patient Motivated to improve: – Patient care – Patient satisfaction Motivated to reduce: – Admissions – Readmissions – Length of Stay (LOS)

20. Care Transitions OHSU Family Medicine Inpatient Service 4 Teams: – 3 Clinic based – 1 Maternity Care Admissions from: – 4 OHSU Family Medicine clinics – County and Community Safety Net Clinic Systems – “Crystal Patients”

21. “Crystal Patients” Identifying ALL OHSU Family Medicine patients admitted to ANY service – Special attention to patients on surgical services Included on our FMIS Teams

22. Crystal Report

23. Improving Care Transitions We wanted to know: 1.Number of admissions per clinic team 2.Readmission rates and timeframes 3.Follow up contact after discharge? 4.…and before the readmission?

24. What is Important to You? After you have the data to answer your first basic question(s), what are your next inquiries?

25. Building a Quality Data Team Access to data and balanced scorecards do not equal a quality improvement program. Use your tools to evolve your systems. “Ground level change” in each smaller system must occur to obtain transformational change. We need Data Teams.

26. Quality Data Team A Quality Data Team must: – understands the mission of the organization – Have the skill to present data in a meaningful and understandable manner to clinicians and operational leaders Skills required: – Technical – Interpersonal

27. Quality Data Team You need to lead your team with strong clinical guidance: – Relevance of clinical issues – Relationship of clinical content – Balance between technical requirements and clinical content

28. Example: Delivery Report

29. Example: Antenatal QI Tool

30. Who Should Lead Your Data Team? Does s/he have the skills required? If not, how can s/he acquire these skills?

31. IMPLEMENT YOUR QUALITY TOOLS

32. Meaningful Data (to you!) What is your practice environment? What does your balanced scorecard look like? What do you care about tracking? With whom do you share the reports? – Your clinicians – Your residents – Your teams – Your hospital administrators – Your patients

33. Example from Our Practice Problem: Large clinical practice, hospital- based care of patient often “divorced” from medical home clinic-based outpatient care. Specific Struggles: Discharge Follow-Up – Residents spending time on phones (on hold) – Various availability for follow up for providers – No system in place for follow up calls – No way to track follow up attendance

34. Example from Our Practice Change: Introduction of “Clinic-Based” Teams for med/peds admissions Clinics now tasked with “Reaching In” and facilitating care Date of Change: 7/1/2011 Need “before” and “after” data

35. Example from Our Practice Reports available to outpatient clinic team RN “Dot phrases” generated and sent to: – Clinic Team RN – Clinic Team scheduler – PCP Nurse and/or scheduler should call the patient while s/he is ADMITTED to the hospital RN follow up calls after discharge

36. Data Reports Currently we can Track – Admissions to all services – Readmissions – Follow up visits before readmissions Future directions for FMIS Data Reports – # 48h hour follow up phone calls made – Patient satisfaction

37. Admissions Data - monthly

38. Admissions Report – 6 mo trend

39. OHSU FMIS Admissions: Adults & Peds

40. Report on Readmissions

41. Drilling Down the Readmission Data

42. Readmission Trends

43. Does our “clinic-based team” model work?

44. Interesting peaks July… Fewer residents due to FMLA… Significant? Perhaps. Point for further study? Definitely.

45. 2011 Admissions & Readmissions

46. Readmission Trend 11% per month 15% per month

47. OHSU FMIS Patients Seen before Readmission? 22% 19% 59%

48. SWF Clinic: % seen* before readmission 40% *clinic visits only. Not currently able to capture home or SNF visits

49. What will we do with this data? Identify differences in follow up rates between clinics – Ability to examine opportunities and differences btw clinic methods Small “n” for readmissions: – Allows us to identify trends, frequently readmitted patients and/or conditions

50. What barriers to you anticipate? …in getting your partner(s)/organization to develop a Quality Data Team? …in implementing your Quality Initiative(s)?

51. Questions? Comments? Thank you.