ERIC V. JACKSON, JR., MD, MBA

ASSOCIATE DIRECTOR, VALUE INSTITUTE DIRECTOR, HEALTH CARE DELIVERY SCIENCE

CHRISTIANA CARE HEALTH SYSTEM ADJUNCT – FACULTY, JOHNS HOPKINS SCHOOL OF MEDICINE

The Science of Safety: Creating Systems of Profound Knowledge

No Disclosures

Basic ResearchMolecular and Cellular BiologyPhysiology and GeneticsNeurosciences

Translational ResearchDisease MechanismsDisease ModelsDrug DiscoveryClinical Studies

Clinical Research/TrialsDrug TrialsVaccine TrialsDevice Trials

TraditionalModel

Discovery Increasing Amounts of Information

Institute of Medicine, Sept 2012

International Comparison of Spending on Health, 1980–2009

0

1000

2000

3000

4000

5000

6000

7000

8000

1980 1985 1990 1995 2000 2005

United StatesNorwaySwitzerlandCanadaNetherlandsGermanyFranceDenmarkAustraliaSwedenUnited KingdomNew Zealand

0

2

4

6

8

10

12

14

16

1980 1985 1990 1995 2000 2005

United StatesFranceSwitzerlandGermanyCanadaNetherlandsNew ZealandDenmarkSwedenUnited KingdomNorwayAustralia

Average spending on health per capita ($US PPP)

Total expenditures on health as percent of GDP

SOURCE: Organization for Economic Cooperation and Development, OECD Health Data 2011 (June 2011).

Premiums Rising Faster Than Inflation and Wages

0

25

50

75

100

125

2000 2001 2002 2003 2004 2005 2006 2007 2008* 2009*

Insurance premiumsWorkers' earningsConsumer Price Index

1112

1314

1617

18 18 18 1819 19 19

20 2021 21

22 2223

24

18

0

5

10

15

20

25

1999

2000

2001

2002

2003

2004

2005

2006

2007

2008

2009

2010

2011

2012

2013

2014

2015

2016

2017

2018

2019

2020

* 2008 and 2009 NHE projections. Source: K. Davis, Why Health Reform Must Counter the Rising Costs of Health Insurance Premiums, (New York: The Commonwealth Fund, Aug. 2009).

Projected Average Family Premium as a Percentage of Median Family Income,

2008–2020

Cumulative Changes in Insurance Premiums and Workers’ Earnings,

2000–2009

Percent Percent

108%

32%

24%

Projected

To Err is Human…

98,000 preventable deaths/yr from medical errors in the United StatesCosts of Errors – Estimated at $17-29 billion/year

Institute of Medicine Report - 1999

Mortality from Amenable Causes in Four Countries for People under 65 (1999 – 2007)

Health Affairs, Sept 2012

“Health care may be the most entrenched, change-averse industry in the United States”

CLAYTON M. CHRISTENSENCIRCA 2000 -

HARVARD BUSINESS SCHOOL

An institutional approach is needed

Defining the Triple Aim

Improving the individual (patient) experienceImproving the health of populationsReducing the per capita costs of care for populations

Health Affairs May/June 2008

Donald Berwick, M.D.Senior Fellow Center for American Progress

Centers of Excellence

System Level Perspective

Swiss Cheese Model of Error

Accident Theories

Normal Accident Theory (NAT)-Perrow

2 main elements2 main elementsComplexityTight coupling

High Reliability Organization (HRO) theoryComplex, high-hazard organizations that maintain low failure rates relative to risks

Characteristics of High Reliability Organizations

Safety as highest priorityPreoccupation with failureOpen environment for discussing errorCommunication that permits/encourages all in command hierarchy to speak upRewards for safety actions

Heavy reliance on training for Heavy reliance on training for hazardous situationshazardous situations

What is preventable harm?

“limited empirical evidence of the validity and reliability of the available definitions of preventable harm”

Nabhan et al. BMH Health Services Research 2012 12:128

The Seven Themes of Preventable Harm

1. 44% - Presence of an identifiable modifiable cause2. 23% - Reasonable adaptation to a process will

prevent future recurrence3. 16% - Lack of adherence to guidelines implies

preventability 4. 7% - Morbidity adjusted risk estimates using

observed over expected models to account for preventable vs. inevitable harm

5. 6% - All harm is preventable6. 2% - Comparison with another cohort shows different

incidence7. 2% - Historical comparison (events with declining

incidence over time)Nabhan et al. BMH Health Services Research 2012 12:128

n =127, (Jan 2001 –Jun 2011)

A Classification for Unsafe Acts

Unsafe acts

Unintende d

ActionLapse

Violation

Mistake

Memory failures

Attentional failuresSlip

Intended action

Rule-based mistakesKnowledge-based mistakes

Routine violationsExceptional violationsActs of Sabotage

BasicErrorTypes

Why do hospitals fail to learn from failure?

Case Study

A Harvard Business School doctoral candidate performed an in-depth study on work system failures on the front lines of care delivery in hospitalsOnly hospitals with national reputations for nursing excellence were chosen (magnet, etc.)Qualitative data from 239 hours of observation of 26 nurses at 9 different hospitals were obtained.

California Management Review, Winter 2003

Case Study (cont.)

Key Definitions

Two types of process failures defined & observedError – the execution of a task that is either unnecessary or incorrectly carried out and that could have been avoided with appropriate distribution of pre-existing information.Problem – a disruption in the worker’s ability to execute a prescribed task because either something the worker needs is unavailable in the time, location, condition, or quantity desired and, hence , the task cannot be executed as planned; or something is present that should not be, interfering with the designated task.

California Management Review, Winter 2003

Case Study Results

Results (194 failures observed)

Five broad types of problems (n=166)1. Missing or incorrect information2. Missing or broken equipment3. Waiting for a (human or equipment) resource4. Missing or incorrect supplies5. Simultaneous demands on their time

Three broad types of errors (n=28)1. Errors made by nurse2. Errors made by other people3. Unnecessary execution of tasks resulting from faulty process

flows

California Management Review, Winter 2003

Study Conclusions

First-order problem solving (avoid)Second-order problem solving (promote)

33 minutes per 8hr/nursing shift were lost coping with preventable system failures.

California

Management Review, Winter 2003

Three Positive Human Attributes that Prevent Organizational Learning

Individual VigilanceNursing units designed to maximize individual unit efficiency.Empowerment of workers

California Management Review, Winter 2003

How should we measure preventable harm ?

Is patient harm preventable or inevitable?

Spectrum of medical errors as defined by egregiousness and contribution to outcome

Blameworthy

Not Blameworthy

Coincidence Direct Cause

amputation ofamputation ofthe wrong legthe wrong leg

Death after inpatient CPR with Death after inpatient CPR with right bronchus intubationright bronchus intubation

Death with coagulopathyDeath with coagulopathyand no thoracentesisand no thoracentesisto check for empyemato check for empyema

Second heart attack Second heart attack with no with no ββ

blocker blocker after 1after 1stst

heart attackheart attack

Eff Clin Prac. 2000;6:261-269

Contribution to Outcome

Potential Measurement Strategies

A High-Sensitivity – Low-Specificity StrategyAssume all harm is preventableLink care received to outcome

A Low-Sensitivity – Low-Specificity StrategyAdjust for preventability

The Way Forward

Develop scalable measuresMake estimates of measurement error transparentSeparate hospital efforts to learn from policy efforts to judge.

Nice try…that’s great theory but…. I need something actionable!!

IHI Global Trigger Tool

Automation of measuresNurse Review – Physician Oversight

Trigger Tool6 Modules

Care(all records)

Medication(all records)

SurgicalIntensive 

Care

Perinatal

Emergency Department

Concurrent Use of Global Trigger Tools

Leading Indicator:

Measures things that 

are in some way a 

precursor to harm

Lagging Indicator:

Direct measure of 

harm

Trajectory of Care…..Leading and Lagging Indicators

Patient Harm

Leading Indicator

Lagging Indicator

Examples of Leading Indicators

Clostridium difficile positive stoolPTT greater than 100 secondsINR greater than 6Glucose less than 50 mg/dlRising BUN/Serum Creatinine two times over baselineDVT Prophylaxis not ordered for a high risk patient

Level Description Event

A Circumstances or events occurred that had the capacity to cause error.

Harm does not reach patient

B Error occurred but did not reach the patient.

C Error occurred that reached the patient but did not cause patient harm.

D Error occurred that reached the patient and required monitoring to preclude harm or confirm that it caused no harm

E Error occurred that may have contributed to or resulted in temporary harm and required intervention

Harm reaches patient

F Error occurred that may have contributed to or resulted in harm and required an initial or prolonged hospital stay.

G Error occurred that contributed to or resulted in permanent patient harm.

H Error occurred that required intervention to sustain the patient’s life.

I Error occurred that may have contributed to or resulted in patient death

The NCC MERP Index for Categorizing Errors

IHI Global AHRQ Patient Hospital VoluntaryTrigger Tool Safety Indicators Reporting System

IHI Severity LevelE (temporary harm) 204 23 0F (temporary harm) 124 7 2G (permanent harm) 8 1 2H (sustain life) 14 0 0I (death) 4 4 0Total 354 35 4

HospitalHospital A 161 13 0 Hospital B 92 13 3Hospital C 101 9 1Total 354 35 4

Health Affairs 30,4(2011) 581-589

90 seconds

Pronovost, National Press Club, Sept 2012

Clinical Complexity

ICU physicians have 180 activities per patient per dayChronic Disease: a 79 year old patient with osteoporosis, osteoarthritis, type 2 diabetes, hypertension, and chronic obstructive pulmonary disease: 19 medications per day

Institute of Medicine, Best Care at Lower Cost Report, 2012

INR

GLU

BUN

Re- admit

ADT admit

lab results

lab results

lab results

encounters

patient of interest

“POI alert”order to

nursing PAL

Q4h reportemailed

pager alert

dashboarddisplay

CommunicatePOI

Reportable data

Triggers

Work Processes

Analytics

POI

Nursing PAL alert

29-Mar-2012 12:33PowerChart Patient AlertFIN/ECD: 987654321DOB: 3/7/1965 FemaleUnit: C3D 3C01 AINR: 8Resulted: 12:30 03/29/2012

Thu. Mar 29, 2012

Paging of POI Alerts

Use of the Modified Early Warning Score Decreases Code Blue Events

Jt Comm J Qual Patient Saf 2010;35(12):598-603

Prediction of cardiac arrest in critically ill patients presenting to the ED using a machine learning HR variability score vs. MEWS

Critical Care 2012, 16R108

Integration of Early Physiological Responses Predicts Later Illness Severity in Preterm Infants (Physiscore)

APGAR(standard of care)

CRIB SNAP-II SNAPPE-II Physiscore

Time from birth

5 mins 12 hours 12 hours 12 hours 3 hours after birth

Accuracy 0.69 0.85 0.82 0.87 0.91

Effort Manual Manual Manual Manual Manual

Invasive Testing X X

Sci Transl Med 2, 48ra65 2010

• Identifies premature infants at risk for major complications• Useful for resource allocation, managing infant transport, staffing-ratio

Sci Transl Med 2, 48ra65 2010

29-Mar-2012 12:33PowerChart Patient AlertFIN/ECD: 987654321DOB: 10/7/2012 FemaleUnit: C3D 3C01 AEarly Sepsis Warning (hisk risk) start ABX & Fluid now!!, may arrest in 10 hours, Activate Sepsis mobile appResulted: 12:30 03/29/2012

Thu. Mar 29, 2012

Paging of POI Alerts

Thank You