Quality Measurement and Improvement Lecture 3_slides

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/.

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

Improvement
References – Lecture c
References
Alschuler, L., Bennett, C., Kallem, C., Kuhl, J., & Yu, F. (2007). Quality Reporting Document
Architecture (QRDA) Initiative—Phase I Final Report. Ann Arbor, MI: Health Level Seven.
Retrieved from http://www.hl7.org/Library/Committees/pedsdata/QRDA%20Phase%
20I%20Public%20Report.pdf
Ashworth, M., Medina, J., & Morgan, M. (2008). Effect of social deprivation on blood pressure
monitoring and control in England: A survey of data from the quality and outcomes framework.
British Medical Journal, 337, a2030.
Auerbach, A., Landefeld, C., & Shojania, K. (2007). The tension between needing to improve care and
knowing how to do it. New England Journal of Medicine, 357, 608–613.
Baker, D., Persell, S., Thompson, J., Soman, N., Burgner, K., Liss, D., & Kmetik, K. (2007). Automated
review of electronic health records to assess quality of care for outpatients with heart failure.
Annals of Internal Medicine, 146, 270–277.
Baron, R. (2007). Quality improvement with an electronic health record: Achievable, but not automatic.
Annals of Internal Medicine, 147, 549–552.
Berwick, D. (2009). Measuring physicians’ quality and performance: Adrift on Lake Wobegon. JAMA,
302, 2485–2486.
Boyd, C., Darer, J., Boult, C., Fried, L., Boult, L., & Wu, A. (2005). Clinical practice guidelines and
quality of care for older patients with multiple comorbid diseases: Implications for pay for
performance. JAMA, 294, 716–724.
25

Improvement
References – Lecture c Continued
Brook, R. (2010). The end of the quality improvement movement: long live improving value. Journal of
the American Medical Association, 304, 1831–1832.
Campbell, S., Reeves, D., Kontopantelis, E., Sibbald, B., & Roland, M. (2009). Effects of pay for
performance on the quality of primary care in England. New England Journal of Medicine, 361,
368–378.
Casalino, L., Alexander, G., Jin, L., & Konetzka, R. (2007). General internists’ views on pay-for-
performance and public reporting of quality scores: a national survey. Health Affairs, 26, 492–
499.
Cebul, R., Love, T., Jain, A., & Hebert, C. (2011). Electronic health records and quality of diabetes
care. New England Journal of Medicine, 365, 825–833.
Centers for Medicare & Medicaid Services (CMS) (2015, November 25). CMS updates its quality
strategy to build a better, smarter, and healthier health care delivery system [Web log post]
Retrieved from https://blog.cms.gov/2015/11/25/cms-updates-its-quality-strategy-to-build-a-
better-smarter-and-healthier-health-care-delivery-system
Chan, K., Fowles, J., & Weiner, J. (2010). Electronic health records and reliability and validity of quality
measures: a review of the literature. Medical Care Research and Review, 67(5), 503–527.
Commonwell Health Alliance. (2016). Why Commonwell Health Alliance. Retrieved from
http://www.commonwellalliance.org
Crosson, J. C., Ohman-Strickland, P. A., Hahn K. A., et al. (2007). Electronic medical records and
diabetes quality of care: Results from a sample of family medicine practices. Annals of Family
Medicine, 5 (3), 209–215.
26

Improvement
References – Lecture c Continued 2
Doran, T., Fullwood, C., Gravelle, H., Reeves, D., Kontopantelis, E., Hiroeh, U., & Roland, M. (2006).
Pay-for-performance programs in family practices in the United Kingdom. New England Journal
of Medicine, 355, 375–384.
Doran, T., Fullwood, C., Reeves, D., Gravelle, H., & Roland, M. (2008). Exclusion of patients from pay-
for-performance targets by English physicians. New England Journal of Medicine, 359, 274–284.
Fonarow, G., Abraham, W., Albert, N., Stough, W., Gheorghiade, M., Greenberg, B., & Young, J.
(2007). Association between performance measures and clinical outcomes for patients
hospitalized with heart failure. JAMA, 297, 61–70.
Fowles, J., Kind, E., Awwad, S., Weiner, J., & Chan, K. (2008). Performance measures using
electronic health records: Five case studies. Washington, DC: Commonwealth Fund. Retrieved
from http://www.commonwealthfund.org/publications/publications_show.htm?doc_id=685103
Friedman, C. P., Wong, A. K., & Blumenthal, D. (2010). Achieving a nationwide learning health system.
Science Translational Medicine, 2(57), 57cm29–57cm29.
Fung, C., Lim, Y., Mattke, S., Damberg, C., & Shekelle, P. (2008). Systematic review: The evidence
that publishing patient care performance data improves quality of care. Annals of Internal
Medicine, 148, 111–123.
Glickman, S., Ou, F. S., DeLong, E. R., et al. (2007). Pay for performance, quality of care, and
outcomes in acute myocardial infarction. JAMA, 297, 2373–2380.
Hazlehurst, B., Sittig, D. F., Stevens V. J., et al. (2005). Using natural language processing to assess
tobacco care quality in the EMR. American Journal of Preventive Medicine, 29, 434–439.
27

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Health Information Management and Systems Society (HIMSS). (2016). Davies Award of Excellence
winners case studies. Retrieved from http://www.himss.org/search/searchresults.aspx?
Keywords=davies+winners+case+studies&Topic=2798
Health Level Seven International. (2016). Health Level Seven. Retrieved from http://www.HL7.org
HealthIT.gov (2016). ONC Health IT Certification Program. Retrieved from https://www.healthit.gov/
policy-researchers-implementers/about-onc-health-it-certification-program
Hibbard, J., Greene, J., & Daniel, D. (2010). What is quality anyway? Performance reports that clearly
communicate to consumers the meaning of quality of care. Medical Care Research and Review,
67(3), 275–93.
HIMSS (Health Information and Management Systems Society). (2014). Lakeland HealthCare
selected as 2014 HIMSS Enterprise Davies Award recipient. Retrieved from
http://www.himss.org/News/NewsDetail.aspx?ItemNumber=34979
HIMSS. (2016). Health IT value suite. Retrieved from http://www.himss.org/ValueSuite
HIMSS Analytics. (2016a). Continuity of care maturity model. Retrieved from
https://app.himssanalytics.org/emram/continuity.aspx
HIMSS Analytics. (2016b). Electronic medical record adoption model (EMRAM). Retrieved from
http://www.himssanalytics.org/provider-solutions
HIMSS Analytics. (2016c). EMRAM stage 7 case studies. Retrieved from
http://www.himssanalytics.org/case-study-list
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HIMSS Analytics. (2016d). Validated stage 6 & 7 providers list. Retrieved from
http://www.himssanalytics.org/stage7
IHE (Integrating the Healthcare Enterprise). (2016). Integrating the healthcare enterprise. Retrieved
from http://www.ihe.net
IHE. (2016). Quality, research and public health. Retrieved from
http://www.ihe.net/Quality_Research_and_Public_Health
Jha, A., Orav, E., Ridgway, A., Zheng, J., & Epstein, A. (2008). Does the Leapfrog program help
identify high-quality hospitals? Joint Commission Journal on Quality and Patient Safety, 34, 318.
Kelly, A., Thompson, J., Tuttle, D., Benesch, C., & Holloway, R. (2008). Public reporting of quality data
for stroke: Is it measuring quality? Stroke, 39, 3367–3371.
KLAS Research. (2016). KLAS (home page). Retrieved from http://www.klasresearch.com
Krumholz, H., & Lee, T. (2008). Redefining quality—Implications of recent clinical trials. New England
Journal of Medicine, 358, 2537–2539.
Landon, B., & Normand, S. (2008). Performance measurement in the small office practice: Challenges
and potential solutions. Annals of Internal Medicine, 148, 353–357.
Lee, S., & Walter, L. (2011). Quality indicators for older adults: preventing unintended harms. JAMA,
306, 1481–1482.
Lindenauer, P., Remus, D., Roman, S., Rothberg, M., Benjamin, E., Ma, A., & Bratzler, D. (2007).
Public reporting and pay for performance in hospital quality improvement. New England Journal
of Medicine, 356, 486–496.
29

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Linder, J., Ma, J., Bates, D., Middleton, B., & Stafford, R. (2007). Electronic health record use and the
quality of ambulatory care in the United States. Archives of Internal Medicine, 167, 1400–1405.
Lynn, J., Baily, M., Bottrell, M., et al. (2007). The ethics of using quality improvement methods in health
care. Annals of Internal Medicine, 146, 666–673.
McDonald, R., Harrison, S., Checkland, K., Campbell, S., & Roland, M. (2007). Impact of financial
incentives on clinical autonomy and internal motivation in primary care: ethnographic study.
British Medical Journal, 334, 1357–1359.
McDonald, R., & Roland, M. (2009). Pay for performance in primary care in England and California:
comparison of unintended consequences. Annals of Family Medicine, 7, 121–127.
Miller, F., & Emanuel, E. (2008). Quality-improvement research and informed consent. New England
Journal of Medicine, 358, 765–767.
Milstein, A., & Lee, T. (2007). Comparing physicians on efficiency. New England Journal of Medicine,
357, 2649–2652.
Mohan, V., & Hersh, W. (2011). EHRs and health care quality: Correlation with out-of-date, differently
purposed data does not equate with causality. Archives of Internal Medicine, 171, 952–953.
National Quality Forum. (2016) Electronic quality measures (eMeasures). Retrieved from http://www.
qualityforum.org/Projects/e-g/eMeasures/Electronic_Quality_Measures_(eMeasures).aspx
Nyweide, D., Weeks, W., Gottlieb, D., Casalino, L., & Fisher, E. (2009). Relationship of primary care
physicians’ patient caseload with measurement of quality and cost performance. JAMA, 302,
2444–2450.
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O’Brien, S., DeLong, E., & Peterson, E. (2008). Impact of case volume on hospital performance
assessment. Archives of Internal Medicine, 168, 1277–1284.
Olsen, L., Aisner, D., & McGinnis, J. (Eds.). (2007). The learning healthcare system—Workshop
summary. Washington, DC: National Academies Press.
Olsen, L., & Young, P. L. (2010). The Healthcare Imperative: Lowering costs and improving outcomes:
Workshop series summary. Washington, DC: National Academies Press.
Pakhomov, S., Bjornsen, S., Hanson, P., & Smith, S. (2008). Quality performance measurement using
the text of electronic medical records. Medical Decision Making, 28, 462–470.
Pakhomov, S., Hanson, P., Bjornsen, S., & Smith, S. (2008). Automatic classification of foot
examination findings using statistical natural language processing and machine learning. Journal
of the American Medical Informatics Association, 15(2), 198–202.
Patterson, M., Hernandez, A., Hammill, B., et al. (2010). Process of care performance measures and
long-term outcomes in patients hospitalized with heart failure. Medical Care, 48, 210–216.
Pawlson, L., Scholle, S., & Powers, A. (2007). Comparison of administrative-only versus administrative
plus chart review data for reporting HEDIS hybrid measures. American Journal of Managed Care,
13, 553–558.
Persell, S., Dolan, N., Friesema, E., Thompson, J., Kaiser, D., & Baker, D. (2010). Frequency of
inappropriate medical exceptions to quality measures. Annals of Internal Medicine, 152, 225.
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PBMC Health. (2013). Hospitals & health networks 2013 most wired innovator award finalist. Retrieved
from http://www.pbmchealth.org/news-and-events/in-the-news/peconic-bay-medical-center-
hospitals-health-networks-2013-mo
Pham, H., Schrag, D., O’Malley, A., Wu, B., & Bach, P. (2007). Care patterns in Medicare and their
implications for pay for performance. New England Journal of Medicine, 356, 1130–1139.
Porter, M. (2010). What is value in health care? New England Journal of Medicine, 363, 2481–2483.
Pronovost, P., Goeschel, C., & Wachter, R. (2008). The wisdom and justice of not paying for
“preventable complications.” JAMA, 299, 2197–2199.
Reeves, G., TY Wang, T. Y., Reid, K. J., et al. (2008). Dissociation between hospital performance of
the smoking cessation counseling quality metric and cessation outcomes after myocardial
infarction. Archives of Internal Medicine, 168, 2111–2117.
Romano, M. J., & Stafford, R. S. (2011). Electronic health records and clinical decision support
systems impact on national ambulatory care quality. Archives of Internal Medicine, 171 (10),
897–903.
Russom, P. (2008). Definitions of data governance. Retrieved from https://tdwi.org/articles/2008/05/27/
data-governance-strategies-helping-your-organization-comply-transform-and-integrate.aspx
Scholle, S., Roski, J., Adams, J., et al. (2008). Benchmarking physician performance: Reliability of
individual and composite measures. American Journal of Managed Care, 14, 829–838.
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References
Shih, A., Davis, K., Schoenbaum, S., Gauthier, A., Nuzum, R., & McCarthy, D. (2008). Organizing the
U.S. health care delivery system for high performance. Washington, DC: Commonwealth Fund.
Retrieved from http://www.commonwealthfund.org/publications/publications_show.htm?doc_id
=698139
Snyder, L., & Neubauer, R. (2007). Pay-for-performance principles that promote patient-centered care:
An ethics manifesto. Annals of Internal Medicine, 147, 792–794.
Tang, P. C., Ralston, M., Arrigotti, M. F., Qureshi, L., & Graham, J. (n.d.). Comparison of
Methodologies for Calculating Quality Measures Based on Administrative Data versus Clinical
Data from an Electronic Health Record System: Implications for Performance Measures.
American Medical Informatics Association.
Tu, J., Donovan, L., Lee, D., et al. (2009). Effectiveness of public report cards for improving the quality
of cardiac care: the EFFECT study: A randomized trial. JAMA, 302, 2330–2337.
Vonnegut, M. (2007). Is quality improvement improving quality? A view from the doctor's office. New
England Journal of Medicine, 357, 2652–2653.
Wachter, R., Flanders, S., Fee, C., & Pronovost, P. (2008). Public reporting of antibiotic timing in
patients with pneumonia: Lessons from a flawed performance measure. Annals of Internal
Medicine, 149, 29–32.
Wang, T., Fonarow, G., Hernandez, A., et al. (2009). The dissociation between door-to-balloon time
improvement and improvements in other acute myocardial infarction care processes and patient
outcomes. Archives of Internal Medicine, 169, 1411–1419.
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Weinstock, M., Hoppszallern, S. (2015). 2015 most wired. H&HN. July. 26-39. Retrieved from
http://www.hhnmag.com/ext/resources/inc-hhn/pdfs/2015/MostWired_2015_complete1.pdf
Relationship between Medicare’s hospital compare performance measures and mortality rates. JAMA,
296, 2694–2702.
Werner, R., Goldman, L., & Dudley, R. (2008). Comparison of change in quality of care between
safety-net and non-safety-net hospitals. JAMA, 299, 2180–2187.
34

The Culture of Health Care
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

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