For Ramathibodi Hospital Administration School's Healthcare CIO Certificate Program, 6th Class (Class of 2015) on September 17, 2015

1. Evaluation of
Health IT Implementation
Sep 17, 2015
Nawanan Theera-Ampornpunt, M.D., Ph.D.

2. Informatics Evaluation Methods Book
Friedman & Wyatt (2006)

3. Why Evaluate Projects?
• Promotional: To encourage more use
• Scholarly: To confirm or create scientific
knowledge
• Pragmatic: To know what works and what fails
• Ethical: To ensure appropriateness & justify its
use or its budget
• Medicolegal: To reduce liability risks
Friedman & Wyatt (2006)

4. Complexity of Evaluation in Informatics
Friedman & Wyatt (2006)
Medicine &
Health care
Evaluation
Methodology
Information
Systems

5. Marchewka JT (2006)
Project Life Cycle & SDLC

6. Marchewka JT (2006)
IT Project Management Deliverables

7. Class Exercise 1
• How would you evaluate the success
of your project to implement
Computerized Physician Order Entry
(CPOE) in your hospital?
– What defines success
– Measurement methods

8. • DeLone & McLean’s IS Success Model (1992;2003)
• Revised model in 2003 adds “Service Quality”
Various Ways to Measure Success
DeLone & McLean (1992; 2003)

9. Health IT as Healthcare Interventions
• Donabedian’s Model
Donabedian (1966), Friedman & Wyatt (2006)
Structure Processes Outcomes

10. Class Exercise 2
• Can you provide some examples of
measures in each aspect in the
Donabedian’s model that help evaluate
health IT project success?

11. A Mindset for Evaluation
• Tailor the study to the problem
• Collect data useful for making decisions
• Look for intended and unintended effects
• Study the resource while it is under development and
after it is deployed
• Study the resource in the lab and in the field
• Go beyond the developer’s point of view
• Take the environment into account
• Let the key issues emerge over time
• Be methodologically Catholic and eclectic
Friedman & Wyatt (2006)

12. Evaluation vs. Traditional Research
• Different goals
• Who (clients or evaluators) determines the agenda
• Evaluation actively seeks unanticipated effects as well
as anticipated ones
• Both lab and in-situ evaluations important for evaluation
• Evaluations often employ many data-collection paradigm
Friedman & Wyatt (2006)

13. Evaluation Approaches
• Objectivist vs. Subjectivist approaches
• Objectivist characteristics
– Information resources, users, and processes can be measured
– Rational persons should agree on important measures and
desirable outcomes
– It is possible to disprove a hypothesis, but never to fully prove
one
– Quantitative measurement is superior and more precise to
qualitative methods
– We can assess which resource is superior through comparisons
Friedman & Wyatt (2006)

14. Evaluation Approaches
• Objectivist vs. Subjectivist approaches
• Subjectivist characteristics
– What is observed depends fundamentally on the observer
– Context is crucial
– Different perspectives can be legitimately valid on desirable
outcomes
– Verbal description can be highly illuminating
– Evaluation is viewed as an exercise in argument, rather than
demonstration
Friedman & Wyatt (2006)

15. Objectivist Approaches
Objectivist
• Comparison-Based Approach
• Objectives-Based Approach (against stated goals)
• Decision-Facilitation Approach (evaluation to resolve
issues important for decision-making for further
development)
• Goal-Free Approach (purposefully blinded to intended
effects)
Friedman & Wyatt (2006)

16. Subjectivist Approaches
Subjectivist
• Quasi-Legal Approach (e.g. a mock trial)
• Art Criticism Approach
• Professional Review Approach (e.g. site visit by
experienced peers)
• Responsive/Illuminative Approach (derived from
ethnography)
Friedman & Wyatt (2006)

17. Objectivist Studies
• Measurement studies
– “Studies undertaken to develop and refine methods for making
measurements”
– E.g. development and validation of measurement methods,
tools, questionnaires
• Demonstration studies
– Studies that use measurement “methods to address questions of
direct importance in informatics”
– Descriptive studies (no independent variables)
– Comparative studies (investigator creates a contrasting set of
conditions, as in experiments & quasi-experiments)
– Correlational studies (explore hypothesized relationships among
variables that were not manipulated)
Friedman & Wyatt (2006)

18. Study Designs
• Experiments
– Randomized controlled trials
• Quasi-Experiments
– Non-randomized interventions
– Investigator still controls assignment of subjects to
interventions but not through randomization
• Observational Studies
– Investigator has no control over assignment of
subjects into groups
Friedman & Wyatt (2006)

19. Quasi-Experiments
Harris et al. (2006)

20. Quasi-Experiments
Harris et al. (2006)

21. Quasi-Experiments
Harris et al. (2006)

22. Quasi-Experiments
Harris et al. (2006)

23. Observational Studies
• Cohort studies
– Observe subjects with different exposures over time and
compare outcomes
• Case-control studies
– Compare subjects with outcome of interests (cases) and without
(controls) retrospectively to determine differences in exposure
• Cross-sectional studies
Mann (2003)

24. Measurements
Friedman & Wyatt (2006) Source: http://ibis.health.state.nm.us/resources/ReliabilityValidity.html

25. Measurement Validity & Reliability
Source: http://ibis.health.state.nm.us/resources/ReliabilityValidity.html

26. Measurement Validity
• Content Validity & Face Validity
• Criterion-Related Validity
– Predictive validation
– Concurrent validation
• Construct Validity
– Convergent validity
– Divergent/discriminant validity
• Not the same as internal validity &
external validity of scientific studies
Friedman & Wyatt (2006)

27. Measurement Reliability
• Test-retest reliability
• Interrater reliability
– E.g. Kappa, intraclass correlations
• Internal consistency reliability
– E.g. Cronbach’s alpha

28. Threats to Internal Validity: Biases
• Assessment bias
• Allocation and recruitment bias
• The Hawthorne Effect (the tendency for humans to
improve their performance if they know it is being
studied)
• Data collection biases
– Checklist effect
– Data completeness effect (more complete data in intervention cases
than controls)
– Feedback effect
– Carryover effect (spillover effect)
– Placebo effect
– Second-look bias
Friedman & Wyatt (2006)

29. Threats to Internal Validity
Harris et al. (2006)

30. Threats to Internal Validity: Confounding
Harris et al. (2006)

31. Threats to External Validity
• Study generalizability
– Sample representativeness
– Intervention (including implementation strategies)
– Context
• Developers as evaluators
Friedman & Wyatt (2006)

32. Making Conclusions
• Internal and external validity
• Correlation vs. causation
• Acknowledgement of study limitations
• Anticipated vs. unanticipated effects
• Lessons learned

33. Special Study Methods Used in Informatics
• Surveys
– Study design: Cross-sectional vs. longitudinal
– Subjects
– Sampling methods
• Census
• Random sampling (simple, stratified, cluster)
• Nonproblability sampling (purposive sampling,
quota sampling, etc.)
– Sampling frame

34. Surveys
• Survey Methodology
– Survey delivery methods: paper, electronic
(e-mail, web site)
– Survey administration: self-administered vs.
investigator-administered
– Survey instrument (items)
– Survey design
– Item wording

35. Errors in Survey Studies
• Sampling errors
• Coverage errors
• Nonresponse errors
• Measurement errors
• Processing errors
OMB (2001)

36. Survey Book
Dillman et al. (2014)

37. Special Study Methods Used in Informatics
• Time and Motion Studies
(Time-Motion Studies)
• Economic Analysis
– Cost-effectiveness analysis
– Cost-benefit analysis
– Cost-utility analysis
– Economic impact analysis
– Return on investment analysis

38. Special Study Methods Used in Informatics
• Qualitative Studies
– Interviews
– Focused groups
– Usability evaluations
– Content analysis

39. Special Study Methods Used in Informatics
• Software Testing & Evaluation
Methodology
• Testing Levels
– Unit testing
– Integration testing
– System testing
– System integration testing
http://en.wikipedia.org/wiki/Software_testing

40. Software Testing & Evaluation
• Software Testing Objectives
– Installation testing
– Compatibility testing
– Smoke and sanity testing
– Regression testing
– User acceptance testing
– Functional testing
– Usability testing
– Alpha & beta testing
– Software performance testing
– Security testing
http://en.wikipedia.org/wiki/Software_testing

41. Software Testing & Evaluation
• Approaches
– White-box testing
– Black-box testing
– Gray-box testing
http://en.wikipedia.org/wiki/Software_testing

42. Image source: Senoo et al. (2007)
Nonaka SECI Model
During
Implementation,
Near Go-Live &
Post Go-Live
After Action
Review (AAR) /
Postmortem
Meeting,
Project Evaluation
Before & After
Project Kick-off,
During Project
Planning
During
Implementation,
Near Go-Live
Training
Project Evaluation as Part of Project’s KM

43. “Half the money I spend on
advertising is wasted; the trouble is
I don't know which half.”
-- John Wanamaker
http://www.quotationspage.com/quote/1992.html, http://en.wikipedia.org/wiki/John_Wanamaker

44. References
• DeLone WH, McLean ER. Information systems success: the quest for the
dependent variable. Inform Syst Res. 1992 Mar;3(1):60-95.
• DeLone WH, McLean ER. The DeLone and McLean model of information
systems success: a ten-year update. J Manage Inform Syst. 2003
Spring;19(4):9-30.
• Donabedian A. Evaluating the quality of medical care. Millbank Mem Q.
1966;44:166-206.
• Friedman CP, Wyatt JC. Evaluation methods in biomedical informatics. 2nd
ed. New York (NY): Springer; 2006. 386 p.
• Harris AD, McGregor JC, Perencevich EN, Furuno JP, Zhu J, Peterson DE,
Finkelstein J. The use and interpretation of quasi-experimental studies in
medical informatics. J Am Med Inform Assoc. 2006 Jan-Feb;13(1):16-23.

45. References
• Mann CJ. Observational research methods. Research design II: cohort,
corss sectional, and case-control studies. Emerg Med J. 2003;20:54-60.
• Office of Management and Budget, Office of Information and Regulatory
Affairs, Statistical Policy Office. Statistical policy working paper 31:
Measuring and reporting sources of error in surveys. 2001 Jul.