This document discusses quality measurement and improvement in healthcare. It covers the role of information technology (IT) and informatics in measuring and improving healthcare quality. While IT has potential to enhance quality data, the impact on outcomes is mixed. Barriers include issues with some quality measures, challenges for specific provider types, and ethical concerns. Overall, effectively utilizing IT requires substantial effort and the healthcare system must continuously learn and improve to achieve high performance.

2. The Culture of Health Care
Quality Measurement and Improvement
Lecture c
This material (Comp 2 Unit 7) was developed by Oregon Health & Science University, funded by the Department
of Health and Human Services, Office of the National Coordinator for Health Information Technology under
Award Number IU24OC000015. This material was updated in 2016 by Bellevue College under Award
Number 90WT0002.
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/.

3. Quality Measurement and
Improvement
Learning Objectives
• Define health care quality and the major types of quality
measures: structural, process, and outcome measures
(Lecture a).
• Describe the current state of health care quality in the
United States (Lecture a).
• Discuss quality measures used in various health care
settings in the US, including those required for the
HITECH meaningful use program (Lecture b).
• Describe the role of information technology in measuring
and improving health care quality (Lecture c).
• Describe the results of current health care quality efforts
in the United States (Lecture c).
3

4. Role of IT and Informatics
• Need electronic data (IT) and means to understand it (informatics)
• Series of case studies demonstrate real-world use for quality
measurement and improvement (Fowles et al., 2008)
• Standards emerging for measures and their reporting
– Quality Reporting Document Architecture (QRDA) for quality reports
(Alschuler et al., 2007)
– Hospital Quality Measures Format (HQMF) for individual measures
(http://www.hqmf.org)
– eMeasures: Effort to “retool” 113 quality measures easy to extract from
EHR data and express in HQMF
o http://www.qualityforum.org/Projects/eg/eMeasures/Electronic_Quality_Meas
ures.aspx
4

5. Role of IT and Informatics
Continued
• In inpatient settings
– University HealthSystem Consortium (UHC) sites at HIMSS Analytics
Stage 4 or higher adoption have higher scores on quality measures
(HIMSS Analytics, 2006)
– “Most wired” hospitals more likely to have higher quality measures
(Weinstock 2015)
• Mixed results in outpatient settings
– Presence of EHR not correlated with better quality in treatment of
diabetes measures (Crosson et al., 2007) and general ambulatory
quality measures (Linder et al., 2007; Romano & Stafford, 2011)
– Targeted quality improvement does lead to better outcomes (Cebul et
al., 2011)
• Better quality “not automatic” and requires substantial effort (Baron,
2007)
5

6. Case Studies, Best Practices, and
Education Resources
• Institute for Healthcare Improvement
(ihi.org)
• American Society for Quality (ASQ.org)
• National Heart, Lung, and Blood Institute
(NHLBI.org)
• National Institutes of Health (NIH.org)
• AHRQ: Centers for Excellence and case
studies (CAHPS.AHRQ.org)
6

7. Resources for Quality-Related
Activities
• Systems and Tools
– Source systems: Clinical systems, EHRs, and so on
– Decision support systems
– Data warehousing, analytics, and reporting tools
• Organizational and executive support
– Data governance
– Quality activities
• Staffing
– All staff
– Information technology
o Analysts, application support, etc.
– Informatics: CNIO, CMIO
– Training and education of quality program and use of systems
7

8. Supporting the Advancement of
Health IT
• Health IT certification requirements
• Standards development organizations
– Example: http://www.HL7.org
• Integrating the Healthcare Enterprise (IHE)
• Commonwell Health Alliance
• Other industry resources
8

9. Demonstrating Effective Use of
Health IT
• HIMSS Davies Award of Excellence
winners
• HIMSS Analytics EMRAM model
• HIMSS Continuity of Care Maturity model
• HIMSS IT Value Suite
• KLAS Research
9

10. EHRs Can Augment Data Used in
Quality Measures
• Coded information in EHR
– Improves ability to assess diabetes quality measures (Tang, 2007)
– Administrative (or “claims”) data insufficient to calculate HEDIS
measures—EHR data can improve accuracy of calculating HEDIS
measures (Pawlson, Scholle, & Powers, 2007)
• But some measures are in narrative text that is harder to access
– In heart failure, important data inaccessible in clinical notes, especially
exclusion data for medications (Baker et al., 2007)
– Some data can be extracted by natural language processing (NLP) as
effectively as manual abstractors in areas such as smoking cessation
advice (Hazlehurst et al., 2005), diabetic foot exam (Pakhomov et al.,
2008), and congestive heart failure (CHF) (Pakhomov et al., 2008)
• Overall, EHR data quality is mixed for quality measurement;
important attributes to improve include are granularity, timeliness,
and comparability (Chan, Fowles, & Weiner, 2010)
10

11. Results of Current Approaches
• Does better performance on measures
lead to improved patient outcomes?
• What problems arise from current
approaches?
• Results in other countries, such as in
England
11

12. English Quality and Outcomes
Framework (QOF)
• P4P program that ties 25% of pay to 129 quality indicators
• Initial assumption In England was 75% achievement, but it ended up
being 97%, which increased costs (Doran et al., 2006)
– Most quality improvement occurred during pre-evaluation period and
has since leveled out (Campbell et al., 2009)
• Other findings of note
– Ability to exclude patients in measures has not led to “gaming” of
system (Doran et al., 2008)
– Major “unintended consequence” has been excess focus on EHR and
prompts for quality measures (McDonald & Roland, 2009)
– Gap for care in more “deprived” areas has been reduced (Ashworth,
Medina, & Morgan, 2009)
12

13. Better Performance on Measures
= Better Outcomes? Yes
• Patients choosing top-performing hospital or
surgeon had one-half mortality of those who
chose one in lowest quartile (Jha & Epstein,
2006)
• Participation in HQA associated with lower
mortality for MI, pneumonia, and CHF (Jha et al.,
2007)
• Adopting Leapfrog Group practices associated
with better quality and lower mortality for acute
MI (Jha et al., 2008)
13

14. Better Performance on Measures
= Better Outcomes? No
• Process measures in hospitals predict small differences in mortality
in MI, CHF, and pneumonia (Werner & Bradlow, 2006)
• CHF measures of ACC/AHA have little relationship to mortality or
rehospitalization rates (Fonarow et al., 2007)
• Participation of hospitals in MI P4P quality effort did not improve
quality of care or better outcomes (Glickman et al., 2007)
• Smoking cessation quality metric did not correlate with actual
smoking cessation (Reeves et al., 2008)
• Door-to-balloon measure for acute MI not correlated with other
quality measures or mortality (Wang et al., 2009)
• CMS heart failure measures not associated with better outcomes
(Patterson et al., 2010)
14

15. Effect of Public Reporting of
Quality
• Systematic review finds scant evidence for documented benefit in
quality of care (Fung et al., 2008)
• Combining public reporting with P4P improves performance on
measures, whereas reporting alone does not (Lindenauer et al.,
2007)
• US general internists support financial incentives for quality,
although they have concerns about public reporting, especially its
impact on incentive to care for sicker or more complex patients
(Casalino et al., 2007)
• Public report cards in Canada did not improve indicators for MI or
heart failure (Tu et al., 2009)
• Patients have difficulty understanding; better approach consists of a
framework and plain language (Hibbard, Greene, & Daniel, 2010)
15

16. Problems from Current
Approaches
• Measurement issues
• Challenges for certain practice
environments
• Ethical issues
16

17. Measurement Issues
• Elderly patients often have complex comorbidities that render
recommendations in guidelines (and performance measures) inappropriate
(Boyd et al., 2005)
– Experience to date in England (Doran et al., 2008) and US (Persell et al., 2010)
show most physician-recorded exceptions appropriate
• Medicare patients’ care dispersed among many physicians, so it’s hard to
attribute quality (Pham et al., 2007)
• New results in clinical trials can render some measures obsolete (e.g.,
lowering cholesterol, diabetes) (Krumholz & Lee, 2008)
• Some measures have unintended consequences, (e.g., time to first
antibiotic dose in pneumonia) (Wachter et al., 2008)
• Multiplicity of measures leads to conflict reports (e.g., in stroke care) (Kelly
et al., 2008)
• Most physicians don’t have large enough practice caseloads to reliably
measure differences (Scholle et al., 2008; Nyweide et al., 2009)
– Need to focus on multiple measures and all payers (Berwick et al., 2009)
17

18. Challenges for Certain Practice
Environments
• Small numbers in small hospitals can inflate
performance relative to large hospitals (O’Brien, DeLong,
& Peterson, 2008)
• “Safety-net” hospitals—ongoing funding and staffing
issues impact ability to provide quality care. Mission
could be adversely affected by P4P (Werner, Goldman,
& Dudley, 2008) and worsen already existing disparities
(Casalino et al., 2007)
• Small practices have limited time, multiple payers, and
low capital investment (Landon & Normand, 2008)
– Is it overly burdensome? (Vonnegut, 2007)
18

19. Ethical Issues
• Patient consent
– Treat as part of care or like research with human subjects protection
(Lynn et al., 2007; Snyder & Neubauer, 2007; Miller & Emanuel, 2008)?
• Not paying for preventable complications
– Some obvious (e.g., objects left in patients), others less so (e.g.,
ventilator-associated pneumonia) (Pronovost, Goeschel, & Wachter,
2008)
• Tensions between
– “Customers” and “purchasers” in US system (Milstein & Lee, 2007)
– “Needing to improve care and knowing how to do it” (Auerbach,
Landefeld, & Shojania, 2007)
– Physician internal motivations—not found to be adversely impacted in
one UK study (McDonald et al., 2007)
19

20. How Can We Achieve a “High-
Performance” Health Care System?
• Need a “learning” health care system (Olsen, Aisner, &
McGinnis, 2007)
– Must build infrastructure, including informatics, to learn what
works (Olsen, 2010; Friedman, 2010)
• Should be guided by principles (Shih et al., 2008)
– Patients have access to care and information but are also
accountable
– System must provide coordination of care and aim for
continuous learning and improvement
• Should we be thinking more of “value” than “quality?”
(Porter, 2010; Brook, 2010)
20

21. 2016 CMS Quality Strategy Goals
• Six priority goals
– Safer care
– Patient/family engagement
– Communication across care coordination
– Effective prevention and treatment of chronic
disease
– Healthy living best practices
– Affordable care
21

22. How to Achieve Quality
Strategy Goals?
CMS will:
• Measure and publicly report providers’ performance and
cost
• Provide technical assistance and foster learning
networks
• Adopt evidence-based National Coverage
Determinations
• Create incentives for quality and value
• Set quality standards for providers
• Create processes to evaluate quality improvement
22

23. Quality Measurement and
Improvement
Summary – Lecture c
• There is an important role for informatics
and IT in helping to measure health care
quality
• The research to date shows conflicting
results for quality improvement efforts
• There are also challenges in measuring
quality, particularly in certain practice
environments, as well as ethical issues to
resolve
23

24. Quality Measurement and
Improvement
Summary
• There are three major types of health care quality
measures: structural, process, and outcome measures
• Many different health care quality measures are used in
a variety of settings, from health plans to inpatient to
outpatient, including those that are part of the HITECH
meaningful use program
• Information technology has an important role in
measuring and improving health care quality
• The results of current health care quality efforts in the
United States show mixed successes and a number of
challenges
24

25. Quality Measurement and
Improvement
References – Lecture c
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25

26. Quality Measurement and
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References – Lecture c Continued
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26

27. Quality Measurement and
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(2007). Association between performance measures and clinical outcomes for patients
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27

28. Quality Measurement and
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References – Lecture c Continued 3
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28

29. Quality Measurement and
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29

30. Quality Measurement and
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30

31. Quality Measurement and
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31

32. Quality Measurement and
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32

33. Quality Measurement and
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References
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33

34. Quality Measurement and
Improvement
References – Lecture c Continued 9
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34

35. The Culture of Health Care
Quality Measurement and
Improvement
Lecture c
This material was developed by Oregon Health &
Science University, funded by the Department of
Health and Human Services, Office of the National
Coordinator for Health Information Technology
under Award Number IU24OC000015. This
material was updated in 2016 by Bellevue College
under Award Number 90WT0002.
35