Learnin g Public Health Informatics Nuts and Bolts: Competency-based Informatics Training at CDC

1. Learning Public Health Informatics Nuts and Bolts: Competency-based Informatics Training at CDC Herman Tolentino, MD Lead, Public Health Informatics Fellowship Program 2014 AeHIN Hour Office of Surveillance, Epidemiology, and Laboratory Services Public Health Informatics Fellowship Program

2. Outline • Informatics overview • Nuts and Bolts: Cycles and Layers • Public Health Informatics Applied Definition • Definition Dissected • Informatics Competencies • Training Programs • Q&A 2

3. Informatics involves the study of the information dimensions of systems at different levels of organization, from molecules to populations Adapted by Tolentino from Ted Shortliffe’s presentation for Biomedical Informatics http://sci.asu.edu/news/bmi_symposium/downloads/EdwardShortliffe_presentation.pdf 3

4. System Nuts and BoltsMental Models: Cycles and Layers TRANSFORMATIONS CREATION OF VALUE UNFOLDS OVER TIME LAYERS OF COMPLEX SYSTEMS INTERACTING COMPONENTS 4

5. What is Public Health Informatics?Definition 4 3 to improve population health capture, manage, analyze, use information 2 1 knowledge about systems (that) Systematic application (of) PHIFP definition 5

6. What is Public Health Informatics?Definition 4 END 3 to improve population health capture, manage, analyze, use information 2 1 knowledge about systems (that) Systematic application (of) MEANS PHIFP definition 6

7. 4 Improving Population Health Population health is defined as the health outcomesof a group of individuals, including the distributionof such outcomes within the group Kindig, DA, Stoddart G. (2003). What is population health? American Journal of Public Health, 93, 366-369. Kindig DA. (2007). Understanding Population Health Terminology. Milbank Quarterly, 85(1), 139-161. 7

8. Improving Population HealthStakeholders Within a Complex Health System Community Clinical care delivery system Governmental public health infrastructure Employers and businesses Government agencies other than public health Education sector The media 8 Institute of Medicine, For the Public's Health: The Role of Measurement in Action and Accountability (2011)

9. Measuring Population HealthWe cannot improve what we do not measure. – Lord Kelvin DISTAL FACTORS Cultural context Political context Education Poverty Social connections Workplace Environment PHYSIOLOGIC FACTORS Cholesterol Blood glucose Blood pressure Immunity DISEASES/INJURIES Diabetes Cardiovascular disease Infection Violence HEALTH OUTCOMES Function/disability Sense of well-being Death PROXIMAL FACTORS Diet Activity level Alcohol Smoking Self-identity Adapted from Parrish G. Measuring Population Health Outcomes. Prev Chronic Dis 2010;7(4):A71. URL: http://www.cdc.gov/pcd/issues/2010/jul/10_0005.htm. 9

10. Measuring Population HealthThe Health System Feedback Loop – A Complicated Business Causal Web of Health Status and Determinants Information Systems capture, manage, analyze, use Data, information, knowledge Health system performance Policies Programs Decisions Interventions Collective action Health System Public health is an information-driven enterprise. Information systems enable feedback loops that drive public health programs and policies. Adapted by H Tolentino from Sterman, John D. "Learning from evidence in a complex world." American Journal of Public Health96.3 (2006); and Parrish G. Measuring Population Health Outcomes. Prev Chronic Dis 2010;7(4):A71. URL: http://www.cdc.gov/pcd/issues/2010/jul/10_0005.htm. 10

11. Classification of Population Health Measurements Measures of current health status Health services research Evidence-based policy Process and outcome measures Projections and risk estimation Evidence-based medicine Epidemiology Etiology and determinants McDowell, Ian, Robert A. Spasoff, and Betsy Kristjansson. "On the classification of population health measurements." Am J of Public Health 94.3 (2004). 11

12. Measuring Population HealthThe Health System Feedback Loop Public health is an information-driven enterprise. Information systems enable feedback loops that drive public health programs and policies. Adapted by H Tolentino from Sterman, John D. "Learning from evidence in a complex world." American Journal of Public Health96.3 (2006); and Parrish G. Measuring Population Health Outcomes. Prev Chronic Dis 2010;7(4):A71. URL: http://www.cdc.gov/pcd/issues/2010/jul/10_0005.htm. 12

13. Modern Measurement Challenges(Big) Data Issues – 5Vs Variety Velocity Volume Veracity Voids Sources Formats Types Structures Transmission Storage Retrieval Computation Consumption Creation/generation Transmission Computation Consumption Completeness Comprehensiveness Fragments Representativeness Source (trust) Content integrity 13

14. Measuring Population HealthThe Health System Feedback Loop – Simplified The Health System Adapted by H Tolentino from Sterman, John D. "Learning from evidence in a complex world." American Journal of Public Health96.3 (2006); and Parrish G. Measuring Population Health Outcomes. Prev Chronic Dis 2010;7(4):A71. URL: http://www.cdc.gov/pcd/issues/2010/jul/10_0005.htm. 14

15. 3 Information Value Cycle • Organizational context • Information needs • Change management issues • Resources (financing, workforce) • Information systems architecture • Inputs • Process • Outputs • Outcomes • Impact • Capture Methods • Data Types & Formats • Data Standards • Data Quality Improve population health • Decision support • Situation awareness • Disseminate health information • Storage/Retrieval • Transformation • Exchange • Protection (security) • Integration Adapted from (1) Taylor, R. S. (1982). Value-added processes in the information life cycle. Journal of the American Society for Information Science, 33, 341-346; (2) SB Thacker,et al. (2012) . Public health surveillance in the US: Evolution and challenges. MMWR Supplement, July 27, 2012, 61:3-9. • Visualization • Classification • Aggregation, linkage • Knowledge representation 15

16. Information Value Cycle Information systems must generate value in each step. WISDOM DATA ACTION KNOWLEDGE INFORMATION Adapted from (1) Taylor, R. S. (1982). Value-added processes in the information life cycle. Journal of the American Society for Information Science, 33, 341-346; (2) SB Thacker,et al. (2012) . Public health surveillance in the US: Evolution and challenges. MMWR Supplement, July 27, 2012, 61:3-9; (3) M Laventure, B Brandt., Minnesota Department of Health, Karen Zeleznak Bloomington Division of Public Health, 2005. 16

17. 2 What is a System? • It is a whole with a purpose and has interacting, interdependent parts. • Parts cannot provide system function, examples: Wheels alone cannot carry passengers from points A to B, but the whole car does. • Implementing solutions with a limited understanding of a system and its interactions with larger systems can lead to unintended consequences with time delays • Examples: Traffic congestion, air pollution, siloed information systems, siloed professional practice Air pollution = large scale disaster Traffic jam = nuisance of urban life Car = technological feat 17

18. An Information SystemPeople, Process and Technology • The system that captures, manages, analyzes and uses information is made up of people, process and technology. • Alignment of all three enables smooth functioning of the IS. • Alignment with organizational context enables IS to deliver impact to organization bottom line. 18

19. DISC: Data, the Information Systemand its Context Knowledge about systems comes from various disciplines. • Context components: • Environment: Technology trends and advancements, socio-cultural factors, health system factors, legislation, other information systems, other systems • Organization: Mission, structure, resources information culture, values, informatics capacity, programs and policies • Information system components (structure): • People: End users, system administrators, decision makers • Process: Business activities supported by information system • Technology: Paper, computing and communication devices, communication networks, software applications that support information management • System interactions: • Both environmental and organizational contexts affect how information systems are designed, developed, implemented and used. • Organizational performance can be a cause or a consequence of information generation and use, and vice versa (reinforcing loops). Information System Data Context • Data: • Data • Information • Knowledge Adapted from (1) Heeks R, Bathnagar S. Understanding success and failure in information age reform. Reinventing Government in the Information Age, Heeks, Richard (Ed), Routledge, London, 1999, pp 49-74. (2) Avgerou C. (2008). The significance of context in information systems and organizational change. Information Systems Journal, 11 (1): 43-63. (3) Ramaprasad, A., and A. Rai. "Envisioning management of information." Omega 24.2 (1996): 179-193. 19

20. 1 Looking for Informatics ProblemsGaps in value creation Context problems Data problems IS problems 20

21. IVC – ApplicationThe solutions of today may be the problems of tomorrow. Opportunities for prevention of downstream problems Opportunities for improvement – rethinkingor reframing existing problems Design/Development Phase Implementation/Maintenance Phase 21

22. Continuous Quality ImprovementBy the time you have improved it, it’s already obsolete! Iteration 1 Iteration 2 Iteration X Iteration 3 Improving a surveillance system through an iterative approach. 22

23. Information Systems in the EnterpriseSystem of Systems: Potential Interfaces Law Enforcement Foodborne Illness Environmental Health Department of Labor Occupational Safety Injury Prevention Health Care 23

24. Systems Interoperability in the EnterpriseImportance of standards Eisenhower Interstate System Information Super Highway Plug n’ Play 24

25. Layers of InteroperabilityAlignment challenges • Human-to-human interoperability as important if not more than machine-to-machine interoperability. • Corresponding layers may be asymmetrically developed 25

26. Informatics Problem Solving A Systematic Approach to Apply Knowledge to Real World Issues • A problem is defined as a recognized gap between a current state and a future state. Problem solving is a systematic approach to get to the future state. • From a living systems perspective, an informatics problem is like a “disease” or disorder within an information system that prevents creation of value. • There may be organizational or environmental determinants that lead to development of informatics problems. 26

27. Information “Pathologies” • Preventable gaps in distributed information processing • When Information that can be applied to a decision-making process is: • Producible and not produced • Procurable and not procured • Transmissible and not (accurately) transmitted • Applicable and not (accurately) applied Scholl W. Restrictive control and information pathologies in organizations. Journal of Social Issues, 1999. 27

28. Diagnosing and Treating Informatics ProblemsThe Systematic Application Determinants Collect data Analyze Diagnose Prognose Recommend or provide informatics solutions Follow up Track outcomes Problem 28

29. Diagnosing and Treating Informatics ProblemsWith living systems… Determinants Collect data Analyze Diagnose Prognose Recommend or provide treatment or intervention Follow up Track outcomes Problem 29

30. How do we become experts in informatics problem solving? Systematic Application – PHIFP Problem Solving Framework B. PROBLEM MANAGEMENT C. PERFORMANCE IMPROVEMENT A. PROBLEM SOLVING INPUTS Tolentino H, Papagari S, Kuruchittham V, Reese P, Franzke L, Koo D (2011). Development of a problem-solving framework for public health informatics. Poster session presented at: 2011 Public Health Informatics Network (PHIN) Conference; 22 August 2011; Atlanta, GA. 30

31. PHIFP Problem Solving FrameworkSimplified Version – Three Components A B C Tolentino H, Papagari S, Kuruchittham V, Reese P, Franzke L, Koo D (2011). Development of a problem-solving framework for public health informatics. Poster session presented at: 2011 Public Health Informatics Network (PHIN) Conference; 22 August 2011; Atlanta, GA. 31

32. To be competent, you have to feel bad. – Hubert Dreyfus Informatics Competencies

33. Expert Performance Ericsson, K. Anders, Ralf T. Krampe, and Clemens Tesch-Römer. "The role of deliberate practice in the acquisition of expert performance." Psychological review 100.3 (1993): 363.

34. Informatics Competencies (Domains)Supporting Public Health Practice Public Health Practice Informatics Practice Community Dimensions of Practice Cultural Competence Communication Analysis, Assessment and Evaluation LEARNING UNLEARNING Public Health Sciences Leadership and Systems Thinking PHIFP Competencies, 2009

35. CDC Informatics fellowships

36. CDC Informatics FellowshipsDifferent folks, different strokes • Public Health Informatics Fellowship Program (PHIFP): 2-year assignment to a CDC center, institute or office in Atlanta, GA; masters/doctoral; 4-6/year, begins summer; accepts international fellows • Applied Public Health Informatics Fellowship Program (APHIF): 1-year assignment to a state or local health department; masters/doctoral; 8-10/year, begins summer • Informatics Training in Place Program (I-TIPP): 1-year fellowship for existing employees of state or local health departments; bachelors; 8-10/year, begins summer

37. Informatics Workforce Pipeline PHIFP, APHIF TIPP • Short term training: • Internships • Faculty development • Capacity building InfoAids • PHI introductory course Pipeline adapted from Ramesh Krishnamurthy, WHO

38. You in 2015

39. Contact: htolentino@cdc.gov http://www.cdc.gov/PHIFP Questions? Public health informatics is the systematic application of knowledge about systems that capture, manage, analyze and use information to improve population health.