quality improvement science and patient safety research dan france, ph.d., mph center for clinical...
TRANSCRIPT
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Quality Improvement Science and Patient Safety Research
Dan France, Ph.D., MPH
Center for Clinical Improvement
Vanderbilt University Medical Center
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Outline
• Quality Improvement – Need for the engineering mentality/systems
thinking in healthcare
• Patient Safety
• Student Project
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Engineering
• Design/Analysis
• Systems Engineering
• Engineering Management
• Quality Management
• Quality Engineer
• Industrial Engineer
• Health System Engineer
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Part I. Quality Improvement
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Background
• Institute of Medicine (IOM) reports– Nov 1999: To Err is Human – March 2001: Crossing the Quality Chasm
• Brief History of QI– Scientific Management (Taylor, 1911)
• Assembly lines
– Statistical Process Control (Shewhart, 1931)– Quality Improvement (Deming, 1955)– Lean Production (Womack, 1990)
• Mass customization
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Expert knowledgeContent knowledge
System ThinkingStatistical VariationScientific MethodPsychology of Change
Traditional Improvement Continuous Quality Improvement
Improvement in Healthcare
Paul Batalden MD
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What is Quality
Quality is the degree to which we meet or exceed customer expectations
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Quality Assurance versus Quality Improvement
• Quality Assurance– meet a specification or standard– Take sample measurements to measure
performance
• Quality Improvement– continual process to improve current
performance– Continual measurement and data feedback
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The Relation Between Quality Inspection, Regulation, Management, and Improvement
Design
RedesignManagement & Improvement
Numberof
Providers
0
Inspection & Regulationfor Public Safety
Level of Quality
SanctionsResearch &Development
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IOM Definition of Quality
• Six Dimensions of Quality in Healthcare– Safe– Effective– Timely– Patient centered– Efficient– Equitable
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QI is a ScienceDefined Methodology
• Focus on systems (Systems theory)• Develop ideas for change and test them (Scientific
method)• Understand the variation of data measured
continuously over time (SPC)• Understand reasons and motivation of people to
act on data (Common cause, special cause variation, diffusion of innovation)
• Use a balanced set of measures (Value compass)
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QI is a Discipline • QI research is funded by AHRQ and NIH• QI research is published in peer review
journals such as NEJM and JAMA• QI science is taught in schools of public
health, business schools, graduate programs in engineering, management and education, medical schools in health services research, biostatistics, public health
• There is a national Quality Scholars program in healthcare
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Variation in PracticeInstitute of Medicine
• Overuse (eg. Antibiotics, C-Section)
• Underuse (eg. Mammography, Beta-Blockers)
• Misuse (eg. Medical errors)
The issue is unnecessary variation
i.e., appropriateness of care
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Six Sigma
• Domestic Airline Fatality– 6– 99.99966% “Right”
• Mammography Screening– 1.7 – 56% “Right”
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QI is a Science: Statistical ApproachVariation and Improvement
Lessons about Variation
• Once we begin to measure important quality characteristics and outcomes, we notice variation.
• We question measurements that display no variation.
• Often, single data points alone are uninformative, but data displayed over time can provide information for improvement.
• The primary purpose of understanding variation is to enable prediction.
• Interaction among process variables produces sources of variation: materials, methods, procedures, people, equipment, information, measurement, and environment.
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A process
... a series of linked steps, often but not necessarily sequential, designed to ...
cause some set of outcomes to occur transform inputs into outputs generate useful information add value
Walter Shewhart: a system of causes
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Constant (convergent) systems
follow the laws of mathematical probability:
How the process behaved in the pastpredicts how it should behave in the future
non-constant (divergent) systems follow the laws of chaos theory:
How the process behaved in the pastdoes not predict how it should behave in the future
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Random variation
represents the sum of many small variations, arising from real but small causes that are inherent in—and part of—any real process
follows the laws of probability— behaves statistically as a random probability function
because random variation represents the sum of many small causes, it cannot be traced back to a root cause
represents " appropriate " variation
different processes have different levels of random variation random variation is a matter of measurement, not goal setting
is a physical attribute of the process
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Assignable variation
represents variation arising from a single cause that is not part of the process (system of causes)
represents " inappropriate " variation
therefore can be traced, identified, and eliminated (or implemented)
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Registration Times
– These are actual times it took triage level 2 patients to register in the Emergency Department of a hospital:
15 67 4 14 10
12 54 3 7 11
14 83 54 17 20
10 53
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Parametric frequency distribution
Value observed
Nu
mb
er
of
tim
es
ob
serv
ed
(Num
ber,
ra
te,
perc
enta
ge,
prop
ortio
n)
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Parameters: mean and variance
Value observed
Nu
mb
er
of
tim
es
ob
serv
ed
(Num
ber,
ra
te,
perc
enta
ge,
prop
ortio
n)
center (mean, median)
spread (variance, standarddeviation, range)
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Probability-based boundaries
Frequency Distribution
Value observed
Nu
mb
er
of
tim
es
ob
serv
ed
(Num
ber,
ra
te,
perc
enta
ge,
prop
ortio
n)
2.575 std. devs. 2.575 std. devs.
0.5% 0.5%
99%
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Statistical Process Control Chart
Time
Ob
serve
d
valu
e
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Random variation
Process Control Chart
Time
(How the process behaves over time)
T1 T2 T3 T4 T5 T6 T7 T8 T9
Ob
serve
d
valu
e
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Assignable variation
Process Control Chart(How the process behaves over time)
Time
T1 T2 T3 T4 T5 T6 T7 T8 T9
Ob
serve
d
valu
e
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Managing assignable variation
track it to root causes
Find a data point that probably represents assignable variation (usually a statistical outlier)
(React to individual fluctuations in the data)
eliminate (or implement) the assignable cause
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UNDESIRABLEOUTCOMES
ENVIRONMENTPEOPLE
POLICIESPROCEDURES
EQUIPMENT
Communication (8) MD to MD MD to family RN to family
Judgement accuracy of diagnosis (10) timing of treatment (5)
Central Lines (2)
Use of non-VUMC equipment (2)
Staffing (6)
Access to MD & treatmentafter regular hours (7)
Technical issues (7) mgmt of cultures from lab placing/monitoring central lines giving or not giving meds fluid management DKA
Following IV policies (4)
Accepting test results from referring hospitals (3)
Frequency of tests/x-rays (3)
On-Call system forsub-specialties (4)
SUMMARY
Number patient encounters: 281,000Total number of claims: 25Total Cost Incurred: $7,426,815Median Case: $ 100,000
RISK MANAGEMENT PROJECT
d:\RiskMgtProjFishbone
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Tampering:
Shewhart proved that tamperingdoes not just waste time and effort --
it seriously harms process performance
Using assignable methodsin an attempt to manage
random variation
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Statistical process control charts
Show the probability that an observation arose from the underlying process — that is,
the probability that a particular point's deviation from the center represents only "random" variation arising from the system of causes that make up the process, as opposed to "assignable" variation representing an identifiable, intruding cause.
separate random from assignable variationbased on statistical probabilityusing control limits, runs, trends, and other patterns
in longitudinal data.
They
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A trend
55.000000
UCL 76.56000
LCL 33.44000
8.500000
UCL 27.77571
LCL 0.00000
Psych Inpatient Admits / Month# patients
# patients
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QI is a Science: Statistical ApproachOverall Improvement Strategy
Outcome
Remove special causes Process change Process change
Unstable processSpecial causes presentAverage is too high
Stable processCommon causevariation is highAverage is too high
Stable processCommon causevariation reducedAverage too high
Stable processCommon causevariation lowAverage reduced
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CAP protocol compliance
Implementation Group -- Loose Abx Compliance
0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
-23 -21 -19 -17 -15 -13 -11 -9 -7 -5 -3 -1 1 3 5 7 9 11 13 15 17
Month relative to CPM implementation
Pro
po
rtio
n c
om
pli
ant
P chart - 0.01 control limits
Baseline Implementation
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Using data to improve
The minimum standard: an annotated time series
Start with a run chart (80% of total value)1.
Add center and goal lines (anchors the eye - now 95% of total value)2.
Add control limits (in appropriate zones)3.
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"Teen use turns upward"
1992 17.3%1993 19.0%
% high school seniorswho smoke daily
USA Today, June 21, 1994
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"Teen use turns upward"
USA Today, June 21, 1994
1984 18.8%1985 19.6%1986 18.7%1987 18.6%1988 18.1%1989 18.9%1990 19.2%1991 18.2%1992 17.3%1993 19.0%
% high school seniorswho smoke daily
(average moving range = 0.778)
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% high school seniors smoking
0
5
10
15
20
25
84 85 86 87 88 89 90 91 92 93
Year
% h
igh
sch
oo
l se
nio
rs w
ho
sm
oke
dai
ly
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% high school seniors smoking
0
5
10
15
20
25
84 85 86 87 88 89 90 91 92 93
Year
% h
igh
sch
oo
l se
nio
rs w
ho
sm
oke
dai
ly
Mean = 18.64%
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% high school seniors smoking
0
5
10
15
20
25
84 85 86 87 88 89 90 91 92 93
Year
% h
igh
sch
oo
l se
nio
rs w
ho
sm
oke
dai
ly
Mean = 18.64%Avrg Moving Range = 0.778%Upper Process Limit = 20.71%Lower Process Limit = 16.57%
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Part II. Patient Safety
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Heinrich Triangle
Major Error
Minor Errors
Intercepted Errors(Near Misses)
1
29
300
Information on Major Errors
Information onMinor Errors
Information onNear Misses
Error
Knowledge
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Parallel Universe
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Essential System Characteristics
• Uses available technologies
• Real-time data
• Feedback providing (closing the loop)
• Designed to succeed (safe)
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ALCOA
“At ALCOA I have a real fine data system so that I knew every minute of every day the health and safety condition of 140,000 people. We shared the information across the whole place so that we had real-time learning among the people. The information was not there for me. It was for 140,000 people to learn from shared experiences. Without information having to travel up through some appointment process and maybe some day gets distributed so you can learn something. It was there every day. If we had an incident in Sumatra, the people in Jamaica knew it tomorrow morning and they did something about it to avoid the same kind of circumstances. When I asked for the data at Treasury, it took them a long time to get it for me and when they did, it turned out that their lost workday rate in the Treasury, that has about the same number of employees, was 20 times higher than ALCOA’s.”
Paul O’Neill, Treasury Secretary
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J.T. Reason
“major residual safety problems do not belong exclusively to either the technical or the human domains. Rather, they emerge from as yet little understood interactions between the technical and social aspects of the system”
J.T. Reasons,
Safety at Sea and in the Air-Taking Stock Together
Symp., Nautical Institute, 1991
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Disney
“But, ultimately, even the most conscientious Cast Members cannot do it alone. Guests, too, have an essential role to play in making every visit to our parks safe.”
Paul S. Pressler,
Chairman, Walt Disney Parks and Resorts
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Aviation Safety Network
“Without a doubt 2001 was the year with the highest aviation caused fatalities ever. However, when we take a closer look at the figures we can see that 34 fatal multi-engined airliner accidents were recorded, which was an all-time low since 1946.”
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Learning Objectives
• Implement a blame-free reporting culture• Improve or expand chemotherapy taxonomy/definitions
– Preventable adverse drug events and near misses– Operational barriers (i.e., delays) as errors?
• Evaluate wireless technologies as an electronic resources and reporting tool– Integrate into daily workflow– Extend to bedside
• Apply Computerized Order Entry/Decision support• Quality Improvement via multidisciplinary teamwork
based on data feedback
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Intelligent Chemo Delivery System
Clinical Improvement(generate hypotheses, tests of change)
Chemotherapy Registry(tracks metrics over time)
Blame-Free ADE Reporting(Process focused)
Decision Support System(Inbedded safety logic)
Perfect Chemotherapy Delivery
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Chemo Events – Data Capture
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Standardized Reporting
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Lesson Learned• Leadership and organizational culture are as critical for
patient safety as structure• Vertical and horizontal organization communication
are essential components of surveillance, prioritization• Team communication (chatter) is key to developing
safe culture and trust; Foundation for safety “pattern recognition”
• Timely data feedback drives safety improvement• Healthcare can learn much about systems thinking
from other industries and cultures• Tightly coupled systems are more prone to failure than
highly adaptive systems
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Student Project
1. Develop and test taxonomy for systems errors in Emergency Medicine
– What system factors in the ER increase the likelihood that care providers will commit errors?
– How to measure these factors?
2. Clinic Redesign - Orthopedics– Room Utilization tracking program