Care Coordination and Interoperable Health IT Systems

1. Care Coordination and Interoperable Health IT Systems Unit 12: Data Driven Care Coordination Strategy Lecture a – Care Coordination Data Evaluation This material (Comp 22 Unit 12) was developed by The University of Texas Health Science Center at Houston, funded by the Department of Health and Human Services, Office of the National Coordinator for Health Information Technology under Award Number 90WT0006. This work is licensed under the Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License. To view a copy of this license, visit http://creativecommons.org/licenses/by-nc-sa/4.0/.

2. Data Driven Care Coordination StrategyLecture a – Learning Objectives • Objective 1: Identify care coordination data sources (Lecture a) • Objective 2: Demonstrate care coordination effectiveness using analytics (Lecture a) • Objective 3: Evaluate interoperability opportunities and challenges (Lecture b)

3. Care Coordination Data Sources • Electronic health records (EHRs) systems data: • Primary care physician practice EHRs • Specialty physician EHRs • Facilities, programs, and services EHRs • Integrated health systems EHRs

4. Care CoordinationData Sources (Cont’d – 1) • Public health departments data: • Disease surveillance data • Provide public health alerts and recommendations • Supply population health data

5. Care CoordinationData Sources (Cont’d – 2) • Potential patient population data sources: • Patient / family portals data • Care management data • Telehealth and tele-monitoring data • Care coordination programs data • Research data

6. Care Coordination Data Sources (Cont’d – 3) • Potential patient population data sources: • EHR systems data • Physician Quality Reporting System (PQRS) data • Meaningful Use data • Survey data • Health risk assessments data

7. Care Coordination Data Sources (Cont’d – 4) • Integrated systems electronic health technology systems data • Health plans HEDIS data • Health information exchange (HIE) data sources • Third-party payers data • Specialty Care data

8. Care Coordination Data Sources (Cont’d – 5) • Hospitals and facilities data • Post-acute programs and service organizations data • Radiology, laboratory, and pharmacy data • Federal, state, and municipal health and human services departments data • Centers for disease control data

9. Coordinated Data Benefits • Coordinating information, patients, and other providers allow physicians to: • Know and use patient histories • Follow up with patients and other providers • Manage patient populations and use evidence-based care • Employ electronic tools to prevent medical errors

10. Analytic Tools to Evaluate Efficiency • Analysis resources include: • Institute for Healthcare Improvement (IHI) • Commercially available software and applications for qualitative analysis • Customized and / or commercial software products for manipulation and analysis • Agency for Healthcare Research and Quality (AHRQ) Patient-Centered Medical Home Evaluation guidelines

11. Analytic Tools to Evaluate Efficiency (Cont’d – 1) • Care Coordination evaluation analysis tools: • Fuzzy Set Analysis • Statistical Process Control • Logic Models • Formative Evaluation • Private and public tools

12. Analytic Tools to Evaluate Efficiency (Cont’d – 2) • Care coordination processes facilitated by information technology systems • Used as measureable and comparative data such as • Demographics • Clinical notes • Medication management information • Registries information and data • Measurements and performance reporting

13. PCMH Model • Care Coordination related patient data collected in the Patient-Centered Medical Home (PCMH) • Analyzed using the simple Plan-Do-Study-Act (PDSA) Model for Improvement

14. Plan-Do-Study-Act (PDSA) Analysis • PDSA: simple, yet powerful tool for testing and analyzing changes on a small scale • Asks the questions: • What are we trying to accomplish? • How we know that a change is an improvement? • What changes can we make that will result in improvement?

15. Going In-Depth with Analysts • Analytics programs use models that correlate the data with the care coordination outcomes and make recommendations • Analysts work closely with clinicians to be able to recognize whether the results of the models are meaningful and relevant • The capacity and expertise of analysts and analytical tools are critical factors

16. Evaluating Efficiency of Coordination Strategies • Consider all factors, even if you may not be able to collect data on all of them • Factors to consider: • Practice or organization-specific factors • Larger health care environment factors • Intervention components

17. Expert Analysis Tools: STATA • Commercially available general-purpose statistical software package • Capabilities include: • Data management • Statistical analysis • Graphics • Simulations • Regression • Custom programming

18. Expert Analysis Tools: Apache Pig • Commercially available platform for processing and analyzing large data sets • Consists of: • High-level language for expressing data analysis programs • Framework for processing these programs

19. Logic Models • Logic models, or a model for theory of change, are useful for showing why and how an intervention might improve outcome • Analysts and clinicians work together to describe the theory of change • The logic model should identify factors that might affect outcomes, either directly or indirectly, by affecting implementation of the intervention

20. Logic Models Resources • A Guide to Real-World Evaluations of Primary Care Interventions • Logic Model Workbook • Logic Models: The Foundation to Implement, Study, and Refine Patient-Centered Medical Home Models • Logic Model Development Guide

21. Unit 12: Data Driven Care Coordination StrategySummary – Lecture a – Care Coordination Data Evaluation • Data from a variety of sources can be used to coordinate patient care • Analytic tools can be employed to evaluate the efficiency of coordination strategies • There are various ways to evaluate interventions in care coordination

22. Unit 12: Data Driven Care Coordination StrategyReferences – Lecture a References A., Peikes, D., Ph.D., M.P.A., Taylor, E. F., Ph.D, M.P.P, Genevro, J., Ph.D., & Meyers, D., M.D. (2014, October). A Guide to Real-World Evaluations of Primary Care Interventions: Some Practical Advice. Retrieved March 07, 2016, from https://pcmh.ahrq.gov/page/pcmh-evaluation-guide Innovation Network, Inc. Logic Model Workbook. Washington, DC: Innovation Network, Inc.; n.d. www.innonet.org/client_docs/File/logic_model_workbook.pdf Institute for Healthcare Improvement. (n.d.). Retrieved March 07, 2016, from http://www.ihi.org/ Petersen D, Taylor EF, Peikes D. Logic Models: The Foundation to Implement, Study, and Refine Patient-Centered Medical Home Models. AHRQ Publication No.13-0029-EF. Rockville, MD: Agency for Healthcare Research and Quality; March 2013. https://pcmh.ahrq.gov/page/pcmh-evaluation-guide W.K. Kellogg Foundation. Logic Model Development Guide. Battle Creek, MI: W.K. Kellogg Foundation; December 2001:35-48. http://www.wkkf.org/resource-directory/resource/2006/02/wk-kellogg-foundation-logic-model-development-guide

23. Unit 12: Data Driven Care Coordination StrategyLecture a – Care Coordination Data Evaluation This material was developed by The University of Texas Health Science Center at Houston, funded by the Department of Health and Human Services, Office of the National Coordinator for Health Information Technology under Award Number 90WT0006.