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©2015 ECRI INSTITUTE
Data-Driven Quality
Improvement
Patricia Stahura, RN, MSN
Senior Patient Safety Analyst/Consultant
ECRI Institute
January 26, 2017
©2017 ECRI INSTITUTE2
• Power Point Slides viewed here• Today’s session is recorded• Today’s slides and recording will be
posted to the ECRI website.
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How to Ask Questions
Please submit your text questions and comments using the Questions Panel
Remember . . .
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How to Download Slides
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For Physicians:
CME Accreditation Statement:
This live activity has been planned and implemented in accordance
with the Essential Areas and Policies of the Accreditation Council for
Continuing Medical Education (ACCME). ECRI Institute is accredited by
the ACCME to provide continuing medical education for physicians.
AMA Credit Designation Statement:
ECRI Institute designates this live activity for a maximum of 0.75 AMA
PRA Category 1 credits tm. Physicians should claim only the credit
commensurate with the extent of their participation in the activity.
All faculty members involved in this January 26, 2017, live webinar
Data-Driven Quality Improvement have disclosed that there are no
conflicts or financial affiliations.
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For Nurses:
This activity has been approved for up to 1.0 California State Nursing contact
hours by the provider, Debora Simmons, who is approved by the California Board
of Registered Nursing, Provider Number CEP 13677. Credit will only be issued to
individuals that are individually registered and attend the entire program.
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To be eligible for credits:
Credit will only be issued to individuals that are individually registered and
attend the entire program. Each individual participant must log on prior to the
start of the program and remain on the line for the entirety of the program. This
is how individual timed attendance is verified. In addition you must complete an
attestation survey included in the post webinar evaluation at the conclusion of
the webinar. Once all that information is verified, qualified attendees will receive
a certificate via e-mail within 60 days of today’s program.
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About ECRI Institute
Independent, not-for-profit applied research institute
focused on patient safety, healthcare quality, risk
management
ECRI Institute resources about quality and safety
Obtain username and password by contacting us at
[email protected] with your name and contact information
■ Sign up to receive notifications of monthly webinars
50-year history, 450-person staff
■ Evidence-Based Practice Center under the Agency for
Healthcare Research and Quality (AHRQ)
■ Federally designated Patient Safety Organization
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Learning Objectives
1. Review definitions of quality data
2. Differentiate types, sources, and functions of quality data
3. Identify the elements of collection, analysis, and
reporting
4. Recognize the value of data-based decision making
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Quality Program
Team approach with assigned roles
Goal-directed: the Quadruple Aim
Work is structured around areas of interest to reach goals
Plan brings measures, outcomes, and focused studies to
gain buy-in and ownership of a complex process
Continuous evaluation and improvement
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Data Definitions
Word Description
Data Set of collected facts
Numerical Measured or variable data and counted or attribute data
Qualitative Text or words
Quantitative Numbers
Benchmark Measures its performance against that of best in class
Target Goal to be achieved
Threshold Point or level at which something begins or changes
Dashboard Combines all your data sources, like a road map
Indicator Established measures to determine level of performance
Trigger Efficient manner of screening to identify harm and identify cases
for more detailed review
Source: American Society for Quality. Quality glossary. [cited 2016 Dec 14]. http://asq.org/glossary/d.html
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Data Example What the Data Tell Us
Outcome ► Core measures
► Average HgB A1C for a
population of diabetes
patients
► What you made, the end product, the actual results
► Whether change occurred that leads to the intended outcome
► Whether the services and care delivered are meeting the
goals of the organization
Process ► Throughput
► Variability of the process
► Trends
► Whether actual practices follow the recommended sequence
► How smoothly the process works, efficiency
► Whether the parts/steps in the system are performing as
planned
► Whether an action was completed
Structural ► Findings from AHRQ Culture
of Patient Survey
► Staffing levels
► Volume of procedures
► Underlying processes
► Reflects conditions in which clinicians care for patients
Exception ► Incident reports
► Broken equipment reports
► Breakdowns in a process or a problem
► What are the specific exceptions that indicate times when our
processes are not working as planned
Activity ► How many activities on time
► Cost per activity
► How effectively are we completing improvement activities to
address areas identified as process weaknesses
Composite ► Patient safety indicators
► Adverse events/
1,000 patient-days
► Combine the results of multiple performance measures to
provide a more comprehensive picture of quality care
Types of Data
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“What” Data and “Why” Data
► “WHAT” data tell us what happened
■ In January we had 210 falls
■ You can’t fix “what”
► Other data tell us “WHY” something happened
■ 110 falls were related to medications and 100 were the
result of patients’ lack of awareness of their limitations
■ After you analyze and interpret, you can fix “why”
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Polling Question 1
Most raw data are _________________ data.
A. What
B. Why
C. Outcome
D. Composite
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Characteristics of Quality Data
Characteristic Description
Important To the hospital/clinic/patients and families
Valid Means what it should
Feasible Can be done or can be demonstrated
Reliable Can be replicated if pulled again
Predictable Providers document the same way consistently
Evidence
based
Blends the best available scientific knowledge
with clinical expertise
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Data Sources: Where Do We Find Data?
► Chart review
► Data from electronic health record (EHR):
electronic clinical quality measures (eCQM)
► Observations
► Claims
► Billing data
► Administrative data
► Surveys and interviews
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Data Functions: What Is the Purpose of Data?
Data prove quality Data drive quality
■ Tangible measure
■ Supports and authenticates
mission, vision, strategy
■ Connect EHR data with
quality goals
■ Quantifies Quadruple Aim
(i.e., readiness, better care,
better health, lower costs)
■ Monitors, protects, and
controls
■ Points to areas of future or
further quality improvement
efforts
■ Measures drive to
improvement
■ Alerts and triggers
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Data Objectives: Why Do We Measure?
► Regulations and accreditation
► Payment and reimbursement
► Standardization for comparisons
► Quality assurance to keep it the same
► Performance improvement to make it better
► Information for stakeholders
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Deciding What to Measure
► Quadruple Aim
► Regulation and accreditation
► “Report once”
► Key processes of care and services
► High risk—high volume—problem prone
►Patient experience
■ Quality/performance improvement—“Make me better”
■ Patient satisfaction—“Keep me comfortable”
■ Patient safety—“Don’t hurt me”
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Are the Data Meaningful?
► Generate more answers than questions
► Answer the question
► Provide insight
► Current, not too old or stale
► Valuable
► Actionable
► Make sense clinically
► Transparent
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Data Collection
►Data collection procedure
■ Chart abstraction
■ Direct observation
■ Who will collect data
■ Make sure it captures the workflow
■ Recording the data
■ Will the data need coding
■ Data entry or formatting
■ Frequency
■ Missing or incorrect data
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Using the EHR for Data Collection
Advantages Disadvantages
■ Staff trained in EHR use and data
entry
■ Information complete and consistent
■ Documentation is accurate and
timely
■ Data can be extracted from
reportable fields
■ Data analysis becomes less labor
intensive
■ Electronic interfacing of lab and
radiology data allows for rapid access
■ Easily accessed, shared, and
exchanged
■ Third-party data collection—
abstraction and submission
■ Data may be missing
■ Lack of experience
■ Data are incorrect
■ Data cannot be extracted from free text
■ Provider documentation deficiencies,
especially “negation” (provider should have
done the task but didn’t)
■ Extraction gaps or errors
■ Data abstraction
■ Bugs or glitches in the system
■ Incomplete file submission may result from
formatting files or data elements
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Is “Data” the Same as “Information”?
Data
■ Facts, raw, unorganized, input, disparate, random,
low value
Information
■ Evolved, structured, refined, organized, processed,
analyzed, data set, context, meaning
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Reviewing the Data
► Raw data
► Information
► Seven tools of quality
■ Cause and effect diagram
■ Check sheet
■ Control chart
■ Flowchart
■ Histogram
■ Pareto chart
■ Scatter diagram
Source: American Society for Quality. Quality glossary. [cited 2016 Dec 14]. http://asq.org/glossary/d.html
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Analysis and Decision Making
► Data complexity
► Siloed and scattered
► Meaningful information
► Deep dive
► Identify areas and causes of variability
► Process of translating data
► Interpretation
► Database, pivot tables, time series
► Dashboard
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Elements of a Quality Dashboard
► Financial data
(outcome + satisfaction + safety / cost = value)
► Clinical quality
► Operational data
► Satisfaction data
► Condensed to 15–30 metrics
► Graphic display
► Triggers targets or threshold
► Benchmarks
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Polling Question 2
On quality dashboards, the colors red, yellow, and green
have standardized meanings:
Does not meet, Meets, Exceeds
A. True
B. False
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sample dashboardRM Risk Management
Measure, Key Performance Indicator Target 1stQ 2ndQ 3rdQ 4thQ Annual
Incident Reports
Clinic # of incident reports Freq
Clinic Incident reports filed within 24 hrs of event 90%
SREs/SIs
Clinic # of Serious reportable events/serious incidents Freq
RM Dept Determination of preventability completed by RM
within 30 days of SRE
90%
RM Dept # of SREs not billed because of preventability
analysis
Freq
Root-Cause Analyses (RCAs)
RM Dept # of RCAs completed per qtr Freq
QA Quality Management Health Care Aquired Infections
IC Dept Total HAI 1
IC Dept CAUTI 1.06
IC Dept SSI 0.86
IC Dept CLABSI 0.54
IC Dept C.Diff 0.9
IC Dept Employee Influenza Vaccination Rate 90%
Antibiotic Stewardship
Ph Dept #convert IV to oral 60%
Ph Dept Culture/test prior to treatment 70%
Key IndicatorsQM Dept Mortality/risk adjusted 0.9 1.5 1.3 1.2 1.2QM Dept Readmission 15.5 17 14 23 17QM Dept C-sect before 39 weeks 22% 32 34 30 30QM Dept Patient Satisfaction 80% 78% 76% 79% 78%QM Dept All cause readiness 88% 90 90 91 90
HEDIS
QM Dept Diabetes screen complete 90%
QM Dept Tobacco counseling 95%
QM Dept Cancer screen 65%
PS Patient Safety
PSO PSI composite 1%
PSO Adverse events/1000 pt days 8%
PSO Adeverse events/100 admissions 4%
PSO % admissions with AE 23%
Dashboard Key - Performance
Improved/exceeded expectations T
Acceptable/needs improvement T-10
Not meeting target, action needed >T-10
Proprietary and Confidential
Copyright ECRI Institute, 2016
Dashboard Key - Performance
Improved/exceeded
expectations T
Acceptable/needs
improvement T-10
Not meeting target, action
needed
>T-
10
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Military Health System (MHS) Dashboard• Internally transparent for staff with CAC
• Website to gain access https://carepoint.health.mil
©2017 ECRI INSTITUTE32
How Do You Know There Is Improvement?
Look at the data, your measurement
Check against the goal for expected compliance level
Match the data against the target
Check against the threshold
Minimal acceptable level of performance
All improvement will require change, but not all change
will result in improvement
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Analysis . . . Ask WHY ?
Look for patterns and trends
Determine the cause
Identify opportunities for improvement
Convert the data into actionable data
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Sound Decision Making:
Continue or Change Direction?
Magnitude
Direction
Variability
Rate
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Continue or Change Direction?
Question What It Tells You Quality Tool
Magnitude How much? Too much or too little
How does it compare to others
Limits
Goals
Benchmarks
Direction Better or worse?
Increasing or
decreasing?
Improving or declining
Fewer or more
Longer or shorter
Crossing
averages
Trends
Variability Is it under control or
out of control?
Nice and steady improvement
Predictable versus random
Bouncing all over the place
Smooth or spikes
Control
charts
Rate How fast is it
changing?
Slow or fast change
Slow and steady
Plenty of time versus emergency
Trends
Slopes
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©2017 ECRI INSTITUTE36
Jan
ua
ry
Feb
rua
ry
Ma
rch
Ap
ril
Ma
y
Jun
e
July
Au
gu
st
Se
pte
mb
er
Oct
ob
er
No
ve
mb
er
De
cem
be
r
Ye
ar
in R
ev
iew
CLABSI Rate 0.020 0.063 0.000 0.000 0.000 0.125 0.031 0.000 0.170 0.113 0.167 0.068 0.063
# of CLABSI 1 4 0 0 0 7 2 0 8 6 5 3 36
# of central l ine days 50 63 44 32 48 56 64 38 47 53 30 44 569
Central Line Utilization Rate 0.12 0.15 0.12 0.09 0.12 0.17 0.16 0.10 0.13 0.12 0.08 0.11 0.12
# of central l ine days 50 63 44 32 48 56 64 38 47 53 30 44 569
# of patient days 402 422 378 352 396 324 388 392 376 428 400 388 4646
Charts reviewed 0 0 0 0 0 0 0 0 0 0 0 0 0
Checklist Incompletion Percent N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A# of incomplete process checks
before procedure 0 0 0 0 0 0 0 0 0 0 0 0 Before Procedure0
# of incomplete steps
prior to l ine insertion 0 0 0 0 0 0 0 0 0 0 0 0
Prior to Line Insertion0
# of incomplete steps
during the procedure 0 0 0 0 0 0 0 0 0 0 0 0
During the Procedure0
# of incomplete steps
after the procedure 0 0 0 0 0 0 0 0 0 0 0 0
After the Procedure0
View Reports (Dashboard) >>
Central Line-Associated Bloodstream Infection (CLABSI) Worksheet for Year
Data Worksheet
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Central Line-Associated Bloodstream Infection (CLABSI) Dashboard for Year
Comments
0.12
0.15
0.12
0.09
0.12
0.17 0.16
0.10
0.13 0.12
0.08
0.11
0.00
0.05
0.10
0.15
0.20
Central Line Utilization Rate (central line days /patient days)
0.020
0.063
0.000 0.000 0.000
0.125
0.031
0.000
0.170
0.113
0.167
0.068
0.0000.0200.0400.0600.0800.1000.1200.1400.1600.180
CLABSI Rate (central line count /central line days)
Magnitude
Direction
Variability
Rate
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Polling Question 3
Refer to the previous slide:
Your supervisor asks you to explain the CLABSI* dashboard
performance report. You tell her that ___________.
A. Improvement is declining
B. The CLABSI rate is going in the wrong direction
C. Performance is out of control
D. It’s bad
E. A, B, and C
* Central line–associated bloodstream infection
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Decision Making
Skill set
Blend experience and evidence
Synthesize disparate data
Magnitude, direction, variability, rate
Identify risks
Identify areas of variation and opportunities
Bias
Gut instinct
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Gut Instincts
► Experience and/or emotional filter
► No hard analytical data or information
► Only source available
► Refined analytics but instincts rule
► Refine intuitive decision making
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Value of Data-Based Decision Making
► Quantifies
► Supports a common language
► Follows a framework
► Identifies risks
► Defines fact versus opinion
► Increases credibility and reliability
► Bias free
► Explains performance
► Classifies priorities
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Make the Decision
► Analysis paralysis
► Learn to make the best decision possible
► Even if the data set is incomplete
► Law of diminishing returns
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Reporting the Right Level of Data
Level of organization Level of information
Senior leaders Summary, strategic
Emerging concerns
Long-term strategic and financial goals
Committees Dig deep, granular or specific cases
Recommendations
Directors Shorter-term tactical goals for the month,
quarter, year
Managers/supervisors Meeting daily and weekly
Staff My work and contribution
Organization’s work
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Quality Stories
Rudyard Kipling wrote, “If history were taught in the form of
stories, it would never be forgotten.”
Data will be remembered if presented in the right way
Dashboards, slides, spreadsheets, or graphs tell a story
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Tips on Writing a Quality Report
► Determine who is your audience
► Be concise and well organized
► Make it easy to scan
► Engage the audience
► Make report culturally appropriate
► Be thoughtful about statistics and data
Source: Agency for Healthcare Research and Quality. Tips on writing a quality report. 2011 Jul [cited 2016 Dec
14]. http://www.ahrq.gov/professionals/quality-patient-safety/talkingquality/resources/writing/index.html
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Acting on the Data
Provide feedback to providers
Report both successes and deficiencies
Plan, Do, Study, Act (PDSA)
5 S’s: Sort, Straighten, Shine, Standardize, Sustain
Set monitoring schedules to evaluate and maintain
improvement
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Summary: Remember the Purpose of Data
Data prove quality Data drive quality
■ Tangible measure
■ Supports and authenticates
mission, vision, strategy
■ Connects EHR data with
quality goals
■ Quantifies Quadruple Aim
(i.e., readiness, better care,
better health, lower costs)
■ Monitors, protects, and
controls
■ Points to areas of future or
further quality improvement
efforts
■ Measures drive to
improvement
■ Alerts and triggers
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References Agency for Healthcare Research and Quality. Tips on writing a quality report. 2011 Jul [cited
2016 Dec 14]. http://www.ahrq.gov/professionals/quality-patient-
safety/talkingquality/resources/writing/index.html
American Health Information Management Association (AHIMA). Data quality management
model (2015 update). 2015 Oct [cited 2016 Dec 15].
http://library.ahima.org/PB/DataQualityModel#.WDOF5OQVCUk
American Society for Quality:
o Quality glossary. [cited 2016 Dec 14]. http://asq.org/glossary/d.html
o Quality tools and templates. 2016 [cited]. http://asq.org/learn-about-quality/tools-
templates.html
Centers for Medicare and Medicaid Services:
o Domestic Lean Goddess. Quality improvement video series. CFMC, the Learning and
Action Network National Coordinating Center.
https://www.cms.gov/Medicare/Provider-Enrollment-and-
Certification/QAPI/Downloads/QAPI-Domestic-Lean-Goddess.pdf
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References (cont.)
o MMS Blueprint. CMS measures management system blueprint (the Blueprint) v 12.0.
2016 Jun 7 [cited]. https://www.cms.gov/Medicare/Quality-Initiatives-Patient-
Assessment-Instruments/MMS/MMS-Blueprint.html
o A process tool framework. https://www.cms.gov/Medicare/Provider-Enrollment-and-
Certification/QAPI/Downloads/ProcessToolFramework.pdf
ECRI Institute:
o Quality improvement/quality assurance toolkit. 2012 Aug 1 [cited].
https://www.ecri.org/components/PPRM/Pages/QAToolkit.aspx
o The use of EHRs for quality improvement [webinar]. 2013 May 29 [cited].
https://www.ecri.org/components/HRSA/Pages/AC_EHRsforQI.aspx
Groves P, Kayyali B, Knott D, Van Kuiken. Center for US Health System Reform Business
Technology Office. The big-data revolution in US health care: accelerating value and
innovation. McKinsey Co. 2013 Jan [cited].
http://www.mckinsey.com/industries/healthcare-systems-and-services/our-insights/the-
big-data-revolution-in-us-health-care
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References (cont.)HealthIT.gov:
o EHR incentives & certification. Meaningful use definitions & objectives. 2015 Feb 6
[cited]. https://www.healthit.gov/providers-professionals/meaningful-use-definition-
objectives
o Learn EHR basics. 2014 May 21 [cited]. https://www.healthit.gov/providers-
professionals/learn-ehr-basics
Institute for Healthcare Improvement:
o Griffin FA, Resar RK. IHI global trigger tool for measuring adverse events, 2nd ed. IHI
Innovation Series white paper. [cited].
http://www.ihi.org/resources/pages/IHIWhitePapers/IHIGlobalTriggerToolWhitePaper.
aspx
o Plan-Do-Study-Act (PDSA) worksheet. [cited].
http://www.ihi.org/resources/Pages/Tools/PlanDoStudyActWorksheet.aspx
o Kuhn TM, Barr MS, Gardner LA, Baker DW. EHR-based quality measurement &
reporting: critical for meaningful use and health care improvement. A policy paper of
the American College of Physicians. 2010 Feb [cited].
https://www.acponline.org/acp_policy/policies/ehr_quality_measurement_critical_me
aning_hc_2010.pdf
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References (cont.)Model Systems Knowledge Translation Center. Charts and graphs to communicate research
findings. [cited]. http://www.msktc.org/Knowledge-Translation/Charts-Graphs-2
Moen RD, Norman CL. Circling back: clearing up myths about the Deming cycle and seeing
how it keeps evolving. Qualityprogress.com. 2010 Nov [cited].
http://www.apiweb.org/circling-back.pdf
Myatt M. 6 Tips for making better decisions. Forbes. 2012 Mar 28 [cited].
http://www.forbes.com/sites/mikemyatt/2012/03/28/6-tips-for-making-better-
decisions/#71d74ffe9f54
National Committee for Quality Assurance (NCQA). HEDIS measures. [cited].
http://www.ncqa.org/HEDISQualityMeasurement/HEDISMeasures.aspx
National Quality Forum. Phrase book: a plain language guide to NQF jargon.
Paranjpe P. How to use data analytics to engage physicians. Health Technology
Management 2016 Feb 23 [cited]. https://www.healthmgttech.com/how-to-use-data-
analytics-to-engage-physicians
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References (cont.)
Rosen A. Are we getting better at measuring patient safety? AHRQ PSNET. Patient Safety
Network. 2010 Nov [cited]. https://psnet.ahrq.gov/perspectives/perspective/94/are-
we-getting-better-at-measuring-patient-safety#Table
Sittig DF, Singh H. Electronic health records and national patient-safety goals. N Engl J
Med 2012 Nov 8;367(19): 1854-60.
http://www.nejm.org/doi/pdf/10.1056/NEJMsb1205420 PubMed:
https://www.ncbi.nlm.nih.gov/pubmed/23134389
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Upcoming Webinar Dates and Topics
Date* Topic
February 23, 2017 Introducing the Global Trigger Tool to Improve Quality and Patient Safety
March 23, 2017 Healthcare Resolution and
Disclosure
April 27, 2017 Caring for the Second Victim
* All webinars are held the fourth Thursday of the month
from 1–2 p.m. eastern.
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[email protected]
(610) 825-6000, x5800
Thank you!
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