Integrating Quality Measures into EHRs: Available Tools and Lessons Learned

1. Integrating Quality Measures into EHRs: Available Tools and Lessons Learned November 17, 2010

2. Meaningful Use Summary • Quality reporting requirements in the Final Rule • Hospitals have 15 mandatory quality measures • 2 ED throughput measures • 7 stroke measures • 6 VTE measures • Eligible professionals have 3 required core measures and 3 additional measures selected from a menu of 38 • Core measures include: hypertension measurement, tobacco use and intervention, and adult weight management • Alternative core measures include weight management in children, flu shots for patients over 50 and childhood immunizations • The final rule reinforced the requirement that all data must be in structured format, all requirements must be met using certified EHR technology, and that the rules apply to all patients, not just Medicare and Medicaid patient.

3. Data Driven Healthcare Organization Why This Journey to be a Data Driven Organization? • It is not the computer itself that has value, • but the access to information, • the ability to track and avoid errors, • the speed and reliability in finding what we need, in making decisions, and in implementing care without error or delay. • Computers let us achieve a level of quality care that we could never achieve without them. Q U A L I T Y

4. Data Driven Healthcare Organization Data is Used to Assess Needs, Drive Interventions and Report Performance in a Closed-loop Continuous Improvement Model Clinical, Operational or Research Goals GOALS Data Acquisition Data Integration Data Analysis ANALYSIS Establish Standards of Care Screen and Identify Patients Provide Data-Driven Support to Clinicians INTERVENTION Measure Quality, Outcomes and Costs Report Results REPORTING

5. Getting Started What is the Current State of Your Data? • What do we currently measure? • How do we measure it? • Why do we measure it? • Does a data dictionary exist for all data elements? • What is the current state of our data and it’s integrity? • Does existing workflow support data collection requirements and outcome objectives?

6. Getting Started Laying the Foundation • Data Use Models — Information needs must be focused on solving identified problems across the organization • Data Governance — Establish data owners/stewards and processes for making data related decisions • Data Quality — Determine how data quality will be measured and how quality issues will be remediated. Data quality criteria needs to be set for each data field of interest. Is the data valid? eg. Indicate preliminary and final on lab tests. Does the data make sense? • Data Standards — Organizational decisions on data definitions, medical vocabularies and naming conventions must be made early and consistently enforced

7. Components of a Data Driven Healthcare Organization Data Driven Use Models Clinical Intelligence Financial Intelligence • Quality/Outcomes Management • High-Risk/Cost Case Management • Adverse Event Detection • Infection Control • Compliance Reporting • Drug Utilization Reviews • Patient Cost Analysis • Service Line Analysis • Procedure Cost Analysis • Referral Analysis • Revenue Cycle Analysis and Coding Optimization • Market Analysis Operational Intelligence Research and Surveillance Predictive • Capacity Planning and Management • Patient Flow Management (ED and Inpatient) • Resource Utilization • Patient Scheduling/Access • Health Services Research • Comparative Effectiveness • Drug Outcomes Research • Drug Safety Surveillance • Disease Surveillance • Patient Recruitment Prospective Retrospective

8. Components of a Data Driven Healthcare Organization Are You Set Up to Manage Data? • Do medical staff bylaws and policies support outcome objectives? • What tools are available to collect aggregate data? • Does the reappointment process support review of data at the individual physician level (how do I compare to my peers?) • Do we have IT tools to support the amount of data to be collected?(SQL db, Business Objects) • Do we have the IT tools to distribute data in a meaningful format?(physician report cards) • Does a committee structure exist to guide us through this journey?

9. Components of a Data Driven Healthcare Organization Defining Data Management Data Architecture “Blueprint” for the data warehouse or data mall Technical Architecture Data Governance Server configuration and administration, security management, database administration Data ownership and stewardship roles, responsibilities and workflow Data Acquisition Metadata Definitions, algorithms, impact analysis and data lineage reporting Integration of data from disparate source systems Analysis and Reporting Dashboards, drill-down analytics and operational reporting

10. Components of a Data Driven Healthcare Organization Journey to Data Maturity DISTINCTIVE Full Realization Data stewardship integrated with operational committee structure ADVANCED Partial Integration Stewardship implemented at dept level Enterprise strategy defined Foundational projects planned FOUNDATIONAL Building Blocks to Success Stewardship capability defined Consistent reusable data between applications BASIC Starting Gate

11. Components of a Data Driven Healthcare Organization Governance Challenge Strategy Project Sponsors Leadership Prioritization Vision Integrated Governance Model Policy Groups Accountability Oversight Information Services Project Committees

12. CPOE Benefits Example of Immediate Indicators for CPOE What percent of physicians place orders using CPOE? What is the Target? Measuring 30-60-90 days from implementation with a goal of 70% for Community Hospitals 80% for more academic centers and high deployment of hospitalists. What percent of orders are done via Communication Type? Number of physician pharmacy call-backs to clarify medication orders? What percent of delinquent charts per month? Monthly ALOS per DRG Number of cancelled/voided diagnostic orders (laboratory & radiology per week/month) Turn Around Time (TAT) from diagnostic order to result documentation (Both Stat/Now and Routine Categories) "Overall" scores for AMI,CAP, CHF, and Pregnancy JCAHO core measures Number per 100 admissions of Adverse Drug Events (ADEs) Turn around time from order to administration of medication Number of non-formulary medication orders per week/month (TNF) ICU LOS TAT for Blood Admin from Order to First Administration

13. CPOE Benefits Example of Long Term Indicators for CPOE Total cost per discharge (for DRGs of interest) Use of peer-reviewed Order Sets (pathways and protocols) Physician and Nursing Number of unexpected admission/ readmission to higher level of care per week/month Number of Ventilator Days /ICU stay Number of Dietary Order Clarifications Orders within a Care Plan % of patients receiving Electronic Prescriptions ED % of ED Physicians using Electronic Prescriptions Reason for Radiology Exam Completed (Compare only the pre to post conversion statistics) Appropriate Reason for Exam (When field is changed to reflect another choice) Reason for Consult Given (Free Text Field Use — Y or N) Total cost per discharge (for DRGs of interest) Use of peer-reviewed Order Sets (pathways and protocols) Physician and Nursing Number of unexpected admission/readmission to higher level of care per week/month

14. CPOE Benefits Indentify Baseline Data • Determine what current data elements can be collected and utilized as baseline data • Baseline data must be used to determine benefits and return on investment

15. CPOE Benefits Score Card Example

16. CPOE Benefits Benefits Example

17. Lessons Learned Impact on the Organization Data Continuum Identify Readmissions Detection of Existing Condition and Assessment of Risk of Condition Developing Monitoring of Patient at Risk of Condition Developing Intervention for Managing Condition or for Managing Risk Evaluate Potential for Readmission • Develop a solution that sits above enterprise revenue cycle and clinical applications to make data accessible and actionable • Identifies and proactively manages patients with high risk conditions • Manages core measure patients concurrently (while outcomes can be improved) not just retrospectively

18. Lessons Learned Ask Your Organization • Key Questions • What is the current state of your data? • Are you set up to manage data (governance)? • What is the most efficient data management solution (architecture)? • What is your capability to derive intelligence from data to effect process improvement (informatics)? • What is your budget and timeframe (scope)?

19. Lessons Learned Everything Must be Done With the Final Destination in Mind • Start slow and grow, avoid taking on too many improvements at once • Avoid unstructured text as much as possible • Establish a culture whereby quality is top of mind • More than a single committee • Each committee meeting quality is woven into the discussion • Communication strategy for entire organization so they gain understanding • Organizational change methodology • Leverage for each time change the documentation or ordering pathways and associated processes

20. Case Study Debbie Newman, MBA, CPHIMSDirector of Process ImprovementLicking Memorial Health Systems

21. Licking Memorial Health Systems – Licking County, Ohio

22. CMS Core measure requirements Hospital based activity CMS and State - Anticipated core measure requirements Expansion into cross-organization activity Expansion into claims based measures 2014 CMS and State – What’s coming? ED throughput (admit decision to admit, door to admit) Global immunization measures CMS Requirements & Effects

23. What’s the big deal? Hospital requirements = physician requirements CMS Requirements & Effects

24. LMHS Lessons Learned • Early adoption can have it’s complexities • Example – Maternity laceration prevalence • Example – Community Report Cards • Software not created to mine data • Is a purchased EMR truly set up to evaluate quality? • Are reports available to account for CMS complexities? • Infrastructure not in place to affect data • How do we evaluate outliers? • Who is involved in evaluations? • How do we communicate to physicians / clinicians?

25. Electronic Medical Record Observations • Access – Total access for all providers? • Real time – Scanning delays – true access when needed? • Printing – Where and when appropriate?

26. Electronic Medical Record Observations • Downtime – What is the process for downtime? • Duplication – Where are areas for simplification?

27. Hospital or Physician OfficeElectronic Medical Record Success Stories • Recall notification • Who received a particular implant? • Best practice • How are we managing our Core Measure patients?

28. Software Limitations & Lessons Learned • Basic software may be too versatile • Not set up for monitoring quality • Garbage in – garbage out • Need standard responses to measure quality • Need central Quality Committee • Early adoption has its downfalls

29. Quality Report Cards

31. Question and Answers Have a question? Beverly Bell BBell20@csc.com Debbie Newman Dnewman@LMHealth.org

32. THANK YOU