Presented at the 11th Healthcare CIO Certificate Program, School of Hospital Management, Faculty of Medicine Ramathibodi Hospital, Mahidol University, Bangkok, Thailand on February 17, 2021

1. Evaluation of
Health IT Implementation
February 17, 2021
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.