Do d Module 1 Classroom

7/28/2019 Do d Module 1 Classroom

1/71

copyright 2008 by the Trustees of Columbia University in the City of New York Rights Reserved 1

Patient Safety Analysis Training:

A DoD/AHRQ Partnership

Module 1:Introduction to Patient Safety Analysis

and Event Management

Harold S. Kaplan

Barbara Rabin Fastman


2/71

2copyright 2008 by the Trustees of Columbia University in the City of New York Rights Reserved

Module Outline

Medical ErrorGrowing concerns

Types of events and errors; terminology

Medical Event ManagementSources of event data

Event reporting systems


3/71


Objectives

Participants will be able to: Explain how studying medical events

can provide information to improve patientsafety

Define the various types of events and errors

Explain the goals and critical elements of aneffective event reporting system

Describe the event management process


4/71


Growing Public Concern

About Medical Errors Headlines in newspapers

about human error in

hospitals Numerous articles in the

medical literature

Governmental attention


5/71


Annual Accidental Deaths


6/71


To Err is Human

Institute of

MedicineReport 1999


7/71


Report

Recommendations Establish a national focus of

research, tools, and protocols to enhanceknowledge base about patient safety

Create safety systems inside healthcare organizations through implementation ofsafe practices at the delivery level

Identify and learn from errors throughreporting systems both mandatory andvoluntary


8/71


Errors Provide Useful Information

We can learn more from our failures thanfrom success

Our processes can be improved

when studied Give me a fruitful error

anytime, full of seeds,

bursting with its own

corrections. You can keepyour sterile truth to yourself.

Vilfred Pareto


9/71


Types of Events


10/71


Misadventures

The event actually

happened, and some

level of harmevenpossibly

deathoccurred.


11/71


No Harm Events

The event actually

occurred, but no

harm was done.


12/71


Near Miss Events

The potential forharm may have beenpresent, butunwantedconsequences wereprevented because a

recovery actionwas taken.


13/71


Recovery:

Planned or Unplanned Planned recovery

built into our

processes

Unplanned recovery

lucky catches

Study of recoveryactions is valuable.


14/71


Dangerous SituationsAn accident waiting to happen


15/71


Exercise:

A patient is taken to the OR. The

wristband is checked, and it is realizedthat the wrong patient was brought in.

Misadventure?

No-harm event?

Near miss?

Planned recovery?

Unplanned recovery?


16/71


Exercise:

A patient is found sitting on the floor ofhis room. He claims that he fell. He did

not hit his head. He is examined, andthere are no signs of injury.

Misadventure?

No-harm event?

Near miss?

Planned recovery?

Unplanned recovery?


17/71


Types of Errors

Activeerrors committed by those

in direct contact with the human-

system interface (human error)

Latentdelayed consequences oftechnical and organizational actions

and decisions


18/71


Events Happen When:

latentunderlying conditions

+active human failure

= Event

ActiveError

LatentConditions

Event


19/71


Sharp End Active Failures

Individuals at the sharp end are in direct

contact with the human-system interface

They administer care to patientsTheir actions and decisions may result in

active failures


20/71


Types of Errors

Active (Human) Errors skill-based

rule-based

knowledge-based

Latent Errors (conditions orfailures) technical

organizational

Other (patient related and other)


21/71


Skill-Based Error

Failure in the performance of a routine task

that normally requires little conscious effort

Example: locking your keys in the car

because youre distracted by

someone calling your name


22/71


Rule-Based Error

Failure to carry out a procedure or protocol

correctly, or choosing the wrong rule

Example: not waiting your turn at a 4-way

stop sign


23/71


Knowledge-Based Error

Failure to know what to do in a new situation

(problem solving at conscious level)

Example: not knowing what to do

when the traffic light is out


24/71


Rule-Based vs.

Skill-Based Error Curve

Knowledge-based

Errors

Time

Skill-based


25/71


Blunt End Latent Failures

Individuals at the blunt end take actions

and/or make decisions that affect technical

and organizational policies and procedures These actions and decisions may result in

latent failures


26/71

26copyright 2008 by the Trustees of Columbia University in the City of New York Rights Reserved 26copyright 2008 by the Trustees of Columbia University in the City of New York Rights Reserved

Types of Errors

Active (Human) Errors skill-based

rule-based

knowledge-based

Latent Errors (conditions orfailures) technical

organizational Other (patient related and

other)


27/71


Latent Categories

(conditions or failures) Technicalproblems with physical items

example: design flaw in software

Organizationalproblems resulting from decisional elements

example - unclear procedure


28/71


Latent Examples

Technical incorrect installation of equipment

faulty seals on a blood bag

forms that are difficult to use

Organizational decisions made by a regulatory body

way in which new staff is oriented rational management decisions thatmay still contribute to an event


29/71


Event

Example

Management decision to delay

computerization

Patient readmitted; penicillin allergy

Chart unavailable

Patient given penicillin; allergic reaction


30/71


Exercise:

An experienced physician is ordering a

medication. She is interrupted by a telephone

call. When she gets back to reading thetubes, she has a mental slip and orders the

med for the wrong patient. What kind of

mistake is this?

Technical?

Organizational?

Human?


31/71


Exercise:

A new infusion pump is introduced in thehospital. The nurse assigned to operate itduring the first week relies on an internal

procedure to operate the instrument; however,the procedure is incomplete and leaves outcrucial information necessary for operation. .What kind of mistake is this?

Technical?Organizational?

Human?


32/71


The Titanic

A disasterthat was

set up


33/71


Latent Conditions on Titanic

Inadequate number of lifeboats


34/71



No transverse overheads on water

tight bulkheads


35/71



No shake down or practice cruise totrain crew


36/71



No training for officers on handling of

large single rudder ships


37/71



Only one radio channel


38/71


Error Data Sources

Event reporting systems

Audits

Medical records Observation

Patient safety indicators

Simulation


39/71


Event Reporting

To what purpose?

What are its critical elements?

What are the barriers? How is the data used?


40/71


Goals of Event Management

Prevent failure but if you cant,

Make failure visible and

Prevent adverse effects of failure or

Mitigate the adverse effects


41/71


Three Major Functions of Event

Reporting Systems Modeling of new or unique events

Monitoring Events / Risks

type, Cause, Change

Mindfulness

awareness of hazards

active engagement, ownership

feedback

effect on safety culture


42/71


A Quality Event

Reporting System

Standardize reporting

Provide tools that capture the full complexity

of events in a way that is easy to understand Emphasize process improvement based on

multiple rather than single events

Collect events with and without harm, near-miss events, and dangerous situations


43/71


Compliance vs. Adoption

Mandatory reporting: Staff merely comply when ordered to do so

Voluntary reporting: Staff are engaged in patient safety efforts


44/71


Benefits ofNear Miss

Event Reporting Tell us why misadventures donthappen

Misadventures are often atypical. Near misses

and no harm events give relative proportions ofclasses of system failures and help define risk

Raise awareness of system hazards

Data (and lessons) can be shared

Chris Johnson, 2001

University of Glasgow


45/71


The Event Management Process

Detection

Selection

Investigation

Classification & Description

Computation

Interpretation


46/71


Detection

Basic event information is captured:

where and when in process

type of person involved

narrative description

how discovered

event type/category

contributing factors

recovery/mitigation

step(s)

etc.

Noticing and Recording the Event

**Detection rates should be high**


47/71


Heinreichs Ratio1

1 Major injury

29 Minor injuries

300 No-injury

accidents

300

29

1

It has been proposed that reporting systems could be evaluated on

the proportion of minor to more serious incidents.

1. Heinreich HW Industrial Accident Prevention, NY And London 1941

Detection


48/71


Selection

If the detection level is high, there will be

many events Events must be prioritized as to the type/depth

of investigation it will receive


49/71


Investigation Options

Routine Investigation

collect standardized event information

track and Trend

Root Cause Analysis

in-depth investigation

build causal tree

Selection


50/71


Rules for Filtering Events

New, unique or worrisome

Probability for patient harm (severity)

Probability of recurrence Potential for organizational risk

Likelihood of recovery

Expert judgment

Detectability

Combination of above

Selection


51/71


Tool: Risk Matrix

Selection


52/71


Tool: Fuzzy Matching

Similarity functionthat identifies reportsthat are most similar

to a selected or newevent

If many similar events

are identified, furtherinvestigation may berecommended

Selection


53/71


54/71


Example of a Causal TreeInvestigation

O Patient Given

A Blood (& Dies)

A+Unit notremoved

from prior

ER-case

Head nurse

distracted

NoneA+ Unit left

on infusion-

pumpwith O units

Blood unit not

checked for

type wheninfused

Group specific

blood ordered

in chaoticsituation

Greatconfusio

n

in ER

Inexperienced nurses in

ER due to

strike

SOP inextreme

emergency

inadequate

A+Unitnot

removed

when O

units hung

Root CausesRoot Causes

RecoveryRecoveryFailureFailure


55/71



Event classification affects availability of

information for learning:

Classifications trigger information processing

routines that direct the decision makers

attention

Cl ifi i & D i i


56/71


Believing is Seeing

You see what you expect to see

You see what you have labels to see

classification and expectation are key

You see what you have the skills to manage

Everything else is a blur

There lies the developing unexpected event


Weick K, Sutcliffe K, 2001

Cl ifi i & D i i


57/71


Event Coding

Assign descriptive event codes based on

these criteria:

where and when in the process anevent occurred

Example: Pharmacy filled prescription incorrectly

where and when in the process an event wasdiscovered

Example: Almost administered the wrong medication


Cl ifi ti & D i ti


58/71


Casual Coding

Example: The Eindhoven Classification Model,Medical Version

20 codes divided into:

latent (Technical, Organizational)

active (Human)

other

aim for 3-7 root cause codes for each event, a mixture

of active and latent


C t ti


59/71


Computation

See patterns or trends in the

data Focus on areas of risk

Monitor any changes that

have been implemented

within the organization

Looking at data in aggregate to see

patterns and trends


60/71

C t ti


61/71


Types of Data

Comparison and Analysis: Production of frequency distribution and

trend charts

Identification of events meeting a broad

range of parameters (conjunctive queries)

Similarity matching

Computation

Comp tation


62/71


Conjunctive Query

Identification of events meeting broad

range of user-specified parameters

Indicates which items on the form tomatch against

Example: Search for all medication

omissionsdiscovered by an RNon a

weekend

Computation

Computation


63/71


Usefulness of

Similarity Matching For a routine event, if there are many similar

events, do an RCA

For a high-risk event, if there are similarevents that have already undergone an

expanded investigation, link the cases rather

than repeat the RCA

Computation


64/71


ALERT!


65/71

copyright 2008 by the Trustees of Columbia University in the City of New York Rights Reserved

Interpretation

Using data to make measured system changes

Computation reports provide information that

identifies the high risk areas and trends

In Interpretation, we explore these areas and

trends in search of process improvement

opportunities

65


66/71


Dont Tamper

Interpretation


67/71


Indications of Success

Overall risk of events will decreaseover time as process improvementsare implemented

Patterns of data will change thefrequency distributions ofconsequent, antecedent, and causal

codes will change over time

Interpretation

Weick K, Sutcliffe K, 2001


68/71


Examples of Analysis Tools

Root Cause Analysis (RCA)

causal or risk trees

Data Mining and Case-Based Reasoning (CBR)trend and cluster analysis

Failure Mode and Effects Analysis (FMEA)

Probabilistic Risk Assessment (PRA)

Sense-Making


69/71


The Event Management Process

Detection

Selection

Investigation


Computation Interpretation


70/71


Summary

Medical Errorgrowing concerns

types of events and errors; terminology

Medical Event Managementsources of event data

event reporting systems


71/71

71copyright 2008 by the Trustees of Columbia University in the City of New York Rights Reserved 71copyright 2008 by the Trustees of Columbia University in the City of New York Rights Reserved

Thank you

An Introduction to Medical Event

Reporting-Module 1

Do d Module 1 Classroom

Documents

Transcript of Do d Module 1 Classroom