Leveraging Data for Performance Improvement: Transforming into a Quality & Cost-Saving Machine

1. Utilizing Data to Support Performance Improvement : Transforming into a Quality & Cost-Saving Machine Indiana Primary Health Care Association May 3, 2016

2. Affiliation Disclosure I, Heather Budd, work for Azara Healthcare, a company which sells a healthcare reporting and analytics software to community health centers.

3. The Urgency of Improvement UDS to offer incentive bonus for high performers. UDS Performance is now public: US News, web, etc. Bonus not available to those doing 70 chart audit method. Health plans will not be likely to accept data flaw excuses in shared savings/risk evaluations. Fix your data now. In early years incremental improvement will be key to $ success. Independent Practice Association Shared Savings, and other new reimbursement methods.

4. Money is an Important Motivator Those of us working in FQHCs are mission-driven, but mission alone does not pay the bills. “No Money - No Mission.”

5. System Level Various programs across the country are looking to create financial alignment to improve the US health care system. • Affordable Care Act and ACOs (accountable care organizations) create alignment between various stakeholders in the healthcare delivery system to create cost savings • Trend toward Value vs. Volume in patient care & PCMH

6. Organization Level • IPAs (independent practice associations) for Value-based Purchasing, Shared Services and group negotiation of Payer contracts • Shared Savings/Risk Programs with Payers • Pay for Reporting/Performance Programs with Payers and States

7. Provider and Staff Level • Financial bonus for providers and staff for meeting or exceeding quality goals • Enabling funding for additional resources to do the work to support all of these activities • Reward or Return on Investment is not just in dollars – it can also be in funding the right person for a role that then facilitates other improvement

8. Strategic Vision -> Action Distilling Initiatives into Manageable Measures of Focus

9. Strategic Initiatives and Measure Alignment HEDIS & PCMH Meaningful Use Sweet Spot UDS Clinical Measures ACO Measures

10. Data Foundation for “Sharing the Care” REACTIVE PROACTIVE

11. DRVS Scorecards to reflect each CHC’s Quality Focus Make the QI Focus Measures part of daily life…

12. Dashboards as High Level Performance Signals

13. Connecting the Data Dots Understanding Documentation Behavior and Data Results

14. Bridging the Quality Chasm Performance Data Reflects Quality of Care Delivered, Financial Reward Achieved CARE Document to reflect quality of care SYSTEMS RESULTS Maximize credit and $ for work done Set up and maintain systems Follow key standards and offer feedback Standardize key workflows Better Quality & Experience, Lower Cost Engage staff in continuous quality improvement Data at Point of Care for coordination and QI Accurate Data for Reporting

15. Assembling the Right Team:Roles & Responsibilities A cross-functional team is critical to the success of the project to ensure quality of data capture, accuracy, extraction and measure results. Data is not just an IT project. 15

16. Getting to the Bottom of Data • Map existing workflows for key measures • Standardize best practices that result in usable data • Train staff. Use data and analytics to see if staff is documenting in the fields that link to QI reports. • Rinse and Repeat.

17. Structured vs. Unstructured Data There is tremendous value in recording data using a common vocabulary and methodology. Creates data which can be recognized, ordered, analyzed, reported & shared. Data not captured in structured fields is not reportable. Radio buttons, Locked down Pick-lists, Checkboxes, NDC-ID (Meds), ICD-9/10/SNOMED(Dx), LOINC (Labs), CPT (Procedures) Dictation, Transcription, Voice recognition typing, Free text, Memo fields

18. DM A1c Order and Result Workflow Map MA/LPN evaluates last A1c date to determine need. Standing order allows MA/LPN to place an order for A1c every 91 days for DM patients. Pt. due for A1c? Patient Arrives Front Desk Checks in Patient MA/LPN Rooms Patient MA/LPN performs vital signs Paper A1c Result Arrives Yes No Pre-visit planning and huddle to avoid missed opportunities Continue with visit. MA/LPN enters into the non-billable encounter created, enter result into PathLabs tab or PathLabs type on the Order screen. If A1c needed MA/LPN orders and draws blood. If sent to reference lab Prioritize most urgent patients and designate outreach staff to call and schedule a visit with PCP, diabetic educator, pharmacy educator, etc. Outreach and proactive care coordination activities Perform analysis in house. If being sent to LabCorp, Package sample. Enter result on the superbill, or Path Labs tab, or PathLabs type on the Order Screen on the order. Run registry reports weekly for DM Patients who have not been in for a visit in >90 days. Run registry reports weekly for DM Patients who were in last week and sort by A1c result. A1c Complete Result returned via LabCorp interface.

19. Low Hanging Fruit for QI- Diabetes Registry Sort by A1c ascending to find patients who have not been tested. Demo Data

20. Low Hanging Fruit for QI- Diabetes Registry • Sort by A1c descending to find : • Data quality issues such as Glucose results mis-entered in A1c field • Out of control patients at risk for stroke, hospitalization, etc. Demo Data

21. Maintain the Integrity of the Bridge Structure • Make data hygiene part of your daily, weekly, monthly, and annual routines. • Daily: Visit Planning Tools • Weekly: Registry and Care Management Reports • Monthly: QI and Performance Improvement, Data Validation of a subset of measures • Annual: UDS Reports (check stubborn data points like birth weight and trimester of entry quarterly) • Assign responsibility and accountability for these activities.

22. Team Based Care and Visit Planning Embedding Quality Improvement in Daily Care Delivery for PCMH and Improved Outcomes to Transform into a Quality Machine

23. Care Team Members Patient Resource Managers Discharge FU RNs Provider Care Managers Health Educators Patient CDE Nutrition BH MA/LPN Referral Clerks Team Office Manager Front Desk

24. Comprehensive Standing Orders for MAs • Usually standing orders are fragmented; leads to a lack of clarity. Need one comprehensive set which is part of the Policy and Procedures. • Empower MAs to do support their provider by giving them the freedom and trust to follow the protocol. • Standing orders create the basis for use of the visit planning report as a foundation for trust to delegate in team-based care.

25. MA/LPN Comprehensive Standing Orders Standing Orders for MAs

26. What is a Visit Planning Report? Facilitates more efficient pre-visit planning sessions by allowing care teams to review alerts for patients with upcoming appointments • Does the work MAs/ LPNs already do manually, using EHR data and electronic calculation of alerts • Displays only relevant and actionable items to help teams prepare for visits • Displays active diagnoses and relevant risk factors

27. Visit Planning ReportCombines Registry & Preventative Care Alerts, by Provider, ordered by appointment, in one report. 1:25 PM l Friday, September 11, 2015 Visit Reason: Well Child Visit Gomez, Jose DOB: 11/23/2006 Gender: M Phone: 522-113-5837 PCP: Cranston, Bill MRN: 780239 Age: 9 Risk Level: Moderate Language: Spanish Diagnoses_______________________Alert ______________ _ Message_________ Most Recent Date _ __Most Recent Result Asthma Nutritional Counseling Missing Physical Activity Counseling Missing Risk Factors______________________ BMI Percentile Overdue 8/15/2014 90 OBS Asthma Severity Overdue 8/15/2014 3:45 PM l Friday, September 11, 2015 Visit Reason: Headaches Perkins, Sonja DOB: 3/18/1962 Gender: F Phone: 522-788-5001 PCP: Gunther, Eric MRN: 5112866 Age: 53 Risk Level: High Language: English Diagnoses_______________________Alert ______________ _ Message_________ Most Recent Date _ __Most Recent Result DM, HTN, DEP, COPD Mammogram Missing Pap Smear Missing Risk Factors______________________ A1c Overdue 8/15/2014 10.2 SAD, SMIP BP Result out of Range 8/15/2014 150/95 Eye Exam Missing Flu Missing Tobacco Status Missing LDL Overdue 5/15/2013 90.1 Demo Data

28. The 3 buckets of Visit Planning Alerts: 1. MA performs: • BP • BMI (weight and height) • Monofilament Foot Exam • FIT /FOBT (give pt. kit and instructions) • A1c Point of Care (order and perform) • Flu Immunization (order and give) • PCV Immunization (order and give) • Depression Screening • Tobacco Assessment & Advice to Quit 2. MA creates an order/referral: • Pap • Mammogram • Eye Exam 3. MA reminds the provider: • Child/Adolescent Physical Activity and Nutritional Counseling • Weight Counseling for Adults with BMI >24.9 and <18.5 • Asthma Severity Assessment (open template for provider) • A1c Lab • LDL • Nephropathy Screen • Colonoscopy

29. Huddle (limit to 5-10 minutes) • Have a consistent time and stand for meeting. Any team member can initiate. • Must Discuss: • Patients with special intervention needs • Patients with risk factors • Any scheduling bottlenecks anticipated, and plans to workaround • Organize for extra services if needed: • Behavioral Health • Care Coordination • Diabetes, Asthma, Nutrition Education

30. Rx for Data Usage in Your Practice Integrating data into the practice on a daily, monthly, quarterly, and yearly basis

31. Appetite for Data: CHCs who are the greatest consumers of their data have the best chance of success on all levels of performance.

32. Rx for Integrating Data Into Every day Practice • Report type- Visit Planning (VP), Registry, Measure Analyzer, Compliance, Scorecard/Dashboard? • What-specifically what report- Diabetes Labs and Services for X Providers, for what period- last month of patients. • Who-will be responsible for running it? • When-should they run it? Daily at 7:45am. • Why-what’s the goal? To identify needed care for today. • Where-will we use the data? Share results in huddle. • Responsible Administrator- who will ensure accountability for report being run consistently?

33. Who should have access to Reporting and Analytics Data? • IT/Applications Staff • MA/LPN • RN/RN Care Manager • Provider • Quality Director • Health Home Director • BH Consultant • Pharmacist • Operations • Clinical Leadership • Executive Director and Leadership • Establish an practice Administrator for Reporting and Analytics. • Make sure everyone is comfortable logging in to DRVS. It’s not enough to train once. • If there is concern about multiple users having access to the data, we can restrict access. • You cannot break DRVS- feel free to experiment.

34. General Tips for Success • Create Your Own Accountability- Put an OutlookReminder in your Calendar for running reports. • Share results • with your team by posting where everyone can see it (break room, etc.), at regular times (monthly, etc.). • with the practice on the Quality Board or other common space • Celebrate success at staff meetings consistently- make it a recurring agenda item. • Goal is for the whole practice to be aware of important measures and at any given point, how the practice is doing.

35. Pre-visit Planning/Huddle Preparation • Potential Barriers • Solutions to Barriers 35

36. Understanding the Value of a Common Reporting Platform How does EHR data end up in a data warehouse? How can we compare data from different EHRs? How do standard mapping practices support trust in data across centers and EHR platforms?

37. Key Components of Centralized Data Reporting and Analytics Solutions for PCAs, Networks, and Community Health Centers Dashboards

38. Managing Multi-Center Programs with Data • Manage and improve health outcomes: • training on evidence-based clinical practice guidelines, technical assistance support on information technology and quality improvement projects, patient education materials • Quality improvement strategies: • problem assessment, identification, study, corrective action, monitoring, evaluation, and assessment • Health Information Technology: • to gather and analyze data, and to generate reports that can be used to track progress towards quality improvement goals • Needed a credible source of data across control and selected practices • 12 Measures: • A1c tested • A1c <7 • A1c 9-7 • A1c >9 or untested • BP recorded • BP <130/80 • Eye Exam • BMI recorded • BMI 18.5-24.9 • LDL tested • LDL <100 • Tobacco Users

39. Data Warehouse Infrastructure and Extraction Security & De-Identification Data Warehouse PRESENTATION LAYER Reporting & Analytics Application CLEANSING & ORGANIZATION Data Normalization SOURCE DATA Athena Vitera Health Port Meditab Epic McKesson Compu Group AllScripts NextGen Success EHS GE eCW

40. The Value of a Common Data Platform Extraction and Normalization: Data is extracted nightly from practices and normalized so different A1c result names from EHRs can all be interpreted as A1c. eClinicalWorks NextGen Epic Health Center A Health Center C Health Center D Health Center B Glycosylated HbA1c Glycohemogolobin Glycohemogolobin Glycosylated HbA1c A1c Glucose HbA1c A1c Glucose HbA1c A1c Glucose HbA1c A1c HA1c HbA1c HbA1c GHb HA1c HbA1c HbA1c HA1c Glycated hemoglobin LOINC 17855-8 LOINC 17855-8 LOINC 17855-8 LOINC 17855-8 Glycosylated Hemoglobin A1c Glycosylated Hemoglobin A1c Calc. Glycosylated Hemoglobin A1c Glycohemogolobin 1 2 3 4 DATA EXTRACTIONS Centralized Reporting A1C results Centralized Reporting Normalization

41. The Value of a Common Data Platform Data Warehouse: After normalization, all result values are interpreted as A1c, and practice segregated data is used to calculate performance in the reporting layer. Central Reporting Data Warehouse Health Center B A1c Health Center A A1c Health Center A Central Reporting Data Glycosylated HbA1c Glycohemogolobin A1C Glycosylated Hemoglobin A1c Glycosylated HbA1c LOINC 17855-8 LOINC 17855-8 HbA1c HbA1c A1c A1c Glucose HbA1c HA1c GHb Glycated hemoglobin

42. Peer Comparison, Data Validation by Drill Down, and Troubleshooting Drill Down with Providers, and Common Data Pitfalls

43. Troubleshooting and Validating Data When there are Questions about Data Validity or Performance Improvement Plateaus…Investigate data and workflows. • Validate that what you think is true, is true. • Rogue workflows can develop quickly… • Get to the bottom of it: • Examine location and provider variation • Look for outliers- focus on providers whose results are outside of the pack • Use data to theorize, but you may have to ask and observe as well

44. Measure Analyzer- Cervical Cancer Screening • Evaluate the layers of organizational performance with customizable scorecards. • Monitor EHR adoption and training efficacy. • Compare performance by center, and investigate down to the provider and patient level. • Identify candidates for best practice sharing. Or Click on Full Report to see details . Double-click on the measure to drill down.

45. Center Trend and Performance Comparison • Cervical Cancer Screening - TY July 2014 • Evaluate which centers are the best performers, and how your center compares to others.

46. All Center Comparative Analytics • Cervical Cancer Screening - TY July 2014 • Evaluate which centers are the best performers to identify best practices. • For lagging centers, drill down for more information. Double-click anywhere in the bar to drill down.

47. Your Center Comparative Analytics • Cervical Cancer Screening – Month, Dec 2013- March 2014 • Choose the appropriate time period- use shorter periods to evaluate PDSAs. 1 2 3

48. Your Center Comparative Analytics • Cervical Cancer Screening – Month, Dec 2013- March 2014 • Evaluate performance across different locations • Was one of these centers a PDSA pilot? • Are there physical layout, equipment, staffing or other differences that make these • locations significantly different? Double-click anywhere in the bar to drill down.

49. One Location in Your Center - Comparative Analytics • Cervical Cancer Screening – Month, Dec 2013- March 2014 • Evaluate performance trend for one location. Double-click anywhere in the bar to drill down.

50. Provider Performance Variation- Chart view • Cervical Cancer Screening – Month, Dec 2013- March 2014 • Provider performance variation can be a factor of practice preference, staff differences, • equipment, or other. Focus on the high performers with significant denominators to harvest best practices. Focus on the tail to identify rogue workflows and training issues. 50