Overview of Quality Improvement Focus on Designing Reliable Interventions

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Overview of Quality ImprovementFocus on
Designing Reliable Interventions

• Greg Maynard MD, MS
• Professor of Clinical Medicine and Chief,
  Division of Hospital Medicine
• University of California, San Diego

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Quality Improvement Bridging the Implementation
Gap
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Working harder isnt always the answer
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The Evolving Culture of Medicine

• 20th Century Characteristics
• Autonomy
• Solo Practice
• Continuous learning
• Infallibility
• Individual Knowledge

• 21st Century Characteristics
• Teamwork systems
• Group practice
• Continuous improvement
• Multidisciplinary problem solving
• Change

Shine, KI. Acad.Med. 20027791-99
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How Do We Close the Gap? Essential Elements

• Institutional support and multidisciplinary teams
  
• Standardized order sets
• Infusion
• Subcutaneous which promote basal / bolus regimens
• Algorithms / protocols / policies
• Address dosing
• Nutritional intake
• Special situations TPN, enteral tube feedings,
  perioperative insulin, steroids
• Safety issues
• Transitions in care and discharge planning
• Metrics How will you know youve made a
  difference?
• Comprehensive educational program

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Traditional Quality Assurance
outliers
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• Quality Improvement

Before
After
better
worse
Quality
better
worse
Quality
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• Quality Improvement is
• Focus on processes of care
• Reduced variation by shifting entire practice
• A change in the design of care
• Quality Improvement is NOT
• Forcing people to work harder / faster / safer
• Traditional QA or peer review
• Creating order sets or protocols without
  monitoring use or effect

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Good Teamwork is Essential
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Features of a Good Team

• Safe
• no ad hominem attacks
• Inclusive
• open to all potential contributors
• values diverse views not a clique
• Open
• considers all ideas fairly
• Consensus seeking
• finds a solution all members can support

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Models for Improvement

• In use around the globe for decades
• Success in many fields of endeavor
• Healthcare late to the game!
• Alternative to the usual
• Predictable breakdowns in reliability leading to
  common problems
• Ignoring improvement concepts trying the first
  thing that comes to mind
• Not measuring effectiveness of implementation
  outcomes or process until bad events
  happen..again

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A Model for Improvement
Setting Aims Improvement requires setting
aims. The aim should be time-specific and
measurable, with a defined population.
Establishing MeasuresTeams use quantitative
measures to determine if a specific change
actually leads to an improvement.
Selecting ChangesAll improvement requires making
changes, but not all changes result in
improvement. Organizations therefore must
identify the changes that are most likely to
result in improvement.
Testing ChangesThe Plan-Do-Study-Act (PDSA)
cycle is shorthand for testing a change in the
real work setting by planning it, trying it,
observing the results, and acting on what is
learned. This is the scientific method used for
action-oriented learning.
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Features of Good Aim Statements

• Specific
• Measurable
• Aggressive yet Achievable
• Relevant
• Time-bound

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Sample Aim Statements

• Glycemic Control on the Wards
• Within 6 months the use of sliding scale only
  regimens will be reduced by half.
• Within 12 months the of patients with POC
  glucose testing achieving a mean glucose of lt 200
  mg/dL will improve from 65 to 85.
• Within 12 months the of our patients suffering
  from hypoglycemic events will be reduced from 11
  to 6.

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Measurement Principles

• Seek usefulness, not perfection
• Integrate measurement into daily routine
• Use qualitative and quantitative data
• Use sampling
• Plot data over time
• Use a balanced set of measures for all
  improvement efforts

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A Blend of Measures

• Structure
• Do you have a multidisciplinary steering
  committee?
• Do your SQIO sets include a prompt for A1c?
• Process
• of SQIO written using your order form
• with basal insulin
• Outcomes
• LOS, Mortality Glycemic control, Hypoglycemia

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Picabo Street and Communication
Olympic Gold Medal Winner.AND a Critical Care
Nurse!
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Picabo, ICU
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Hierarchy of Reliability
Predicted Success rate
Level

• No protocol (State of Nature)
• Decision support exists but not linked to order
  writing, or prompts within orders but no decision
  support
• Protocol well-integrated (into orders at
  point-of-care)
• Protocol enhanced (by other QI and high
  reliability strategies)
• Oversights identified and addressed in real time

1
40
50
2
3
65-85
4
90
95
5
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Order sets w/ embedded insulin orders
Standardization?
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High Reliability Design Solutions (as applied to
Insulin Protocol)

• Standardize insulin choices for common situations
• MD must opt out of default choices (not opt in)
• Prompts for basal insulin if over glycemic
  target, prompts for HgA1c, etc.
• Scheduled assessments of glycemic control /
  insulin regimen
• Redundant responsibility to maintain glycemic
  target

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CAUTION!!!! Be Sure to Insert a Brain Between
Protocol and Patient!

• Education for broad range of providers
• Consider special team of focused providers

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Engineering Change Hints for Success

• Empower nursing
• Expedite passage through medical staff committees
• Better to implement an imperfect, compromise
  change than no change at all
• Provide hot line or support for difficult
  situations
• Follow metrics continuously as you implement

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Engineering Change Hints for Success

• Measure, learn, and over time eliminate variation
  arising from professionals retain variation
  arising from patients
• Keep big picture in mind
• Negotiate speed bumps
• Time delays in getting data
• Incomplete buy-in
• Go around obstacles instead of through them (can
  always go back to them later)
• Some who disagree with you may be correct
• Make changes painless as possible make it easy
  to do the right thing

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PDSA Plan-Do-Study-Act

• The use of PDSA has been referred to as the
  democratization of the scientific method.
  (Paul Miles, MD)
• Do small scale tests of change.
• Everyone can do it!

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Benefits of rapid cycle change

• Increases belief that change will result in
  improvement
• Allows opportunities for failures without
  impacting performance
• Provides documentation of improvement
• Adapts to meet changing environment
• Evaluates costs and side-effects of the change
• Minimizes resistance upon implementation

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Examples integration of best practice

• A1c level within last 30 days.
• Specify hyperglycemic diagnosis
• Each patient should have a glycemic target.

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A1c Level

• Incorporate prompt for A1c level in insulin order
  sets and protocols.
• Ordering can be accomplished with checkbox
• Monitor performance, feedback to providers
• Glycemic control team obtains it

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Proper diagnosis

• Diagnosis
• Uncontrolled or ? Controlled
• Diabetes type ? 1 ? 2 ? Gestational or
• Secondary to another causeSpecify
• or ? Stress/situational hyperglycemia
• Improves reimbursement define uncontrolled DM
  and monitor coding accuracy
• Order set docmentation translates into ICD-9

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Identify non-critical care glycemic target

• Preprandial target 90130 mg/dL maximum random
  glucose lt 180 mg/dL (ADA/AACE consensus target)
• 80150 mg/dL
• Preprandial target 90130 mg/dL for most
  patients, 90150 mg/dL if hypoglycemia risk
  factors

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Actionable Glycemic Target

• The what is common to all institutions push
  for changes in regimens when glycemic target not
  being met.
• Variable by institution
• Glycemic target definition
• How to generate report
• Who acts on report
• Putting this in place moves you up hierarchy of
  reliability.
• Opportunity to Learn from variation!

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Hierarchy of Reliability
Predicted Success rate
Level

• No protocol (State of Nature)
• Decision support exists but not linked to order
  writing, or prompts within orders but no decision
  support
• Protocol well-integrated (into orders at
  point-of-care)
• Protocol enhanced (by other QI and high
  reliability strategies)
• Oversights identified and addressed in real time

1
40
50
2
3
65-85
4
90
95
5

• eliminate variation arising from professionals
  retain variation arising from patients

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Setting

• Academic teaching medical centers with over 400
  beds
• Adult inpatients on non-critical care wards with
  POC glucose testing.
• Nov 2002 Dec 2005
• Excluded
• Critical care, OB, Psych, Senior Behavioral Health

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Questions

• What is current state? Baseline Nov 02-Oct 03.
• Insulin Use Patterns
• Glycemic Control
• Hypoglycemia
• Other
• What is effect of implementing a standardized
  SQIO set?
• Main Intervention 1 Nov
  03-May 05
• What is the incremental effect of an insulin
  management protocol?
• Main Intervention 2 May
  05-Dec 05

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Intervention 1 (Nov 2003)A Basic Subcutaneous
Insulin Order Set

• Basal / Nutritional / Correction dose
  terminology introduced
• Multiple correction dose scales available, based
  on total insulin dose required.
• Sliding scale only regimens discouraged
• Check box simplicity
• Some guidance for dosing and adjustment
• Hypoglycemia protocol incorporated
• Paper, then CPOE versions

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Intervention 2 (May 2005)Insulin Management
Protocol

• One page algorithm
• Glycemic Target
• Prompt for A1C
• DC Oral Hypoglycemic Agents
• Guidance on dosing
• Suggested regimens for eating patient, NPO
  patient, patient on enteral nutrition
• Guidance on dosing adjustment
• Introduced with case based teaching

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The Use of Basal Insulin Increases(sliding scale
only regimens decline)
30-90 patients sampled per month, no formal
analysis done, results sustained
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Glycemic Control

• Days 1 14 of admission
• Exclude patients with lt 8 POC tests
• 5,800 patients
• 37,516 patient days
• 111,473 POC tests
• By patient stay
• of patients with mean glucose lt 180 mg /dL
• By patient day
• of patient days when all glucose values were
  between 60 180 mg / dL
• Pearson chi-square statistic to compare
• TP 1 (Baseline) Nov 02 Oct 03
• TP2 (Order Set) Nov 03 Apr 05
• TP3 (Algorithm) May 05 Dec 05

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73
69
62
5800 patients w/ gt 8 POC glucose values, day 1-14
values p value lt .02 (Pearson chi-square
statistic)
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Algorithm
Baseline
Order set
1st order set
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53
48
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37,516 Patient Days monitored in 5800 patients
with gt 8 POC glu tests, day 1-14
(Pearson chi-square statistic p lt .001)
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Clinical Inertia Improves with Order Set and
Algorithm
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Oh no! What about HYPOGLYCEMIA!
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Hypoglycemia

• All non critical care patients with POC values
• 11,057 patients / 53,466 days / 148,466 POC tests
• Hypoglycemia 60 mg/dL
• Extreme Hypoglycemia 40 mg/dL
• By patient day
• of patient days with one or more hypoglycemic
  events
• Pearson chi-square statistic to compare
• TP 1 (Baseline) Nov 02 Oct 03
• TP2 (Order Set) Nov 03 Apr 05
• TP3 (Algorithm) May 05 Dec 05

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Percent of Patient Days with Hypoglycemia /
Extreme Hypoglycemia decreased by 30 and 31,
respectively. (Pearson chi square p lt .02) gt
53,000 patient days gt 148,000 POC glu tests
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Approximately 100 fewer patients with
Hypoglycemia per year
Month
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Summary

• Large opportunities for improvement
• A safety and quality issue
• Systems approach is needed
• SHM and others now provide resources to assist
  implementation teams with all essential
  elements
• Use Talking Points, local anecdote, and small
  sample data to gain institutional support
• Reduced hypoglycemia can be compatible with
  improved glycemic control on the wards
• Controversy exists, but time for action is now

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The first time subcutaneous insulin is ordered,
the prescriber is asked for an actionable
glycemic target. A prompt to order HbA1C is
also presented.
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The weight and markers of insulin sensitivity are
elicited, as well as the form of the patients
nutritional intake. (in this case, the patient is
an obese 80 kg woman eating regular meals)
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The Total Daily Dose (TDD) is calculated for the
clinician, based on the information provided on
the patients obesity and weight. The TDD can be
adjusted by the physician. Alternate methods of
calculating the TDD are also presented.