overview of quality improvement focus on designing reliable interventions greg maynard md, ms...

Overview of Quality Improvement

Focus on Designing Reliable Interventions

Greg Maynard MD, MSProfessor of Clinical Medicine and Chief,

Division of Hospital MedicineUniversity of California, San Diego

Quality Improvement: Bridging the Implementation

Gap

Implementation Gap

Scientific understanding

Patient care

Pro

gres

s

Time

Working harder isn’t always the answer……

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. 2002;77:91-99

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 you’ve made a difference?

• Comprehensive educational program

Traditional Quality Assurance

worsebetterworsebetter Quality Quality

threshold

Before After

outliers

Quality Improvement

worsebetter worsebetter Quality Quality

AfterBefore

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

Good Teamwork is Essential

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

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

Establishing MeasuresTeams use quantitative measures to determine if a specific change actually leads to an improvement.

Setting Aims Improvement requires setting aims. The aim should be time-specific and measurable, with a defined population.

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.

A Model for Improvement

Features of Good Aim Statements

• Specific

• Measurable

• Aggressive yet Achievable

• Relevant

• Time-bound

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 < 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%.

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

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

Picabo Street and Communication

Olympic Gold Medal Winner….AND a Critical Care Nurse!

“Picabo, ICU”

Hierarchy of Reliability

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 Protocol enhanced (by other QI and high reliability strategies)reliability strategies)

Oversights identified and addressed in Oversights identified and addressed in real timereal time

Level

4

1

2

3

5

Predicted

Success rate

40%

50%

65-85%

90%

95+%

Order sets w/ embedded insulin orders:

Standardization?

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

CAUTION!!!!

Be Sure to Insert a Brain Between Protocol and Patient!

• Education for broad range of providers

• Consider special team of focused providers

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

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

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!

Act Plan

Study Do

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

Examples: integration of best practice

• A1c level within last 30 days.

• Specify hyperglycemic diagnosis

• Each patient should have a glycemic target.

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

Proper diagnosis

Diagnosis:Uncontrolled –or– Controlled

Diabetes type: 1 2 Gestational –or–

Secondary to another cause;Specify

–or– Stress/situational hyperglycemia

Improves reimbursement: define “uncontrolled DM” and monitor coding accuracy

Order set docmentation translates into ICD-9

Identify non-critical care glycemic target

• Preprandial target 90–130 mg/dL; maximum random glucose < 180 mg/dL (ADA/AACE consensus target)

• 80–150 mg/dL

• Preprandial target 90–130 mg/dL for most patients, 90–150 mg/dL if hypoglycemia risk factors

“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 definitionHow to generate reportWho acts on report

Putting this in place moves you up hierarchy of reliability.

Opportunity to Learn from variation!

Hierarchy of Reliability

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 Protocol enhanced (by other QI and high reliability strategies)reliability strategies)

Oversights identified and addressed in Oversights identified and addressed in real timereal time

Level

4

1

2

3

5

Predicted

Success rate

40%

50%

65-85%

90%

95+%

•eliminate variation arising from professionals; retain variation arising from patients

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

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

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

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

The Use of Basal Insulin Increases(sliding scale only regimens decline)

Percent Sliding Scale Insulin Only

0

10

20

30

40

50

60

70

80

Per

cent

10/20/03

New Order Set

01/20/04

CPOE - TH

30-90 patients sampled per month, no formal analysis done, results sustained

Glycemic Control• Days 1 – 14 of admission• Exclude patients with < 8 POC tests• 5,800 patients• 37,516 patient days• 111,473 POC tests• By patient stay

– % of patients with mean glucose < 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

Percent of patients with mean glucose < 180 mg/dL

56%

58%

60%

62%

64%

66%

68%

70%

72%

74%

Baseline Order Set Algorithm

62 %

69%

73%

5800 patients w/ > 8 POC glucose values, day 1-14 values: p value < .02 (Pearson chi-square statistic)

Percent of patients with a glucose average <=180

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

1st order setBaseline Order set Algorithm

Percent Patient Days w/ all Controlled Glucose(no values < 60 or > 180 mg/dL)

0%

10%

20%

30%

40%

50%

60%

Baseline Order Set Algorithm

44%48%

53%

37,516 Patient Days monitored in 5800 patients with > 8 POC glu tests, day 1-14: (Pearson chi-square statistic p < .001)

Clinical Inertia Improves with Order Set and Algorithm

Glycemic Control "Inertia" Improves with Interventions

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1 2 3 4 5 6 7 8

Hospital Day

Pro

po

rtio

n o

f p

atie

nts

wit

h m

ean

d

aily

glu

< 1

80 m

g/d

L

Baseline

Order set

Algorithm

p < 0.001 for time period

Oh no! What Oh no! What about…… about……

HYPOGLYCEMIA!

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

Percent of Patient Days with Hypoglycemia / Extreme Hypoglycemia decreased by 30% and 31%, respectively. (Pearson chi square p < .02)

> 53,000 patient days > 148,000 POC glu tests

Percent of patients with any glucose <=60 mg/dl

0%

2%

4%

6%

8%

10%

12%

14%

16%

18%

Complete CPOE implementation of

Approximately 100 fewer patients with Hypoglycemia per year

Month

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

The first time subcutaneous insulin is ordered, the prescriber is asked for an actionable glycemic target. A prompt to order HbA1C is also presented.

The weight and markers of insulin sensitivity are elicited, as well as the form of the patient’s nutritional intake. (in this case, the patient is an obese 80 kg woman eating regular meals)

The Total Daily Dose (TDD) is calculated for the clinician, based on the information provided on the patient’s obesity and weight. The TDD can be adjusted by the physician. Alternate methods of calculating the TDD are also presented.