Professor Johanna Westbrook

Centre for Health Systems and Safety Research

Australian Institute of Health Innovation

Macquarie University, Australia

5th Annual eMedication Management Conference

15th March 2016

Assessing the Effects of eMM in

Paediatrics

Australian Institute of Health Innovation

Centre for Health Systems and Safety

Research

Programs of Research

Medication Safety and e-Health

Communication and Work Innovation

Human Factors Evaluation and Design

Pathology and Imaging Informatics

Safety & Integration of Aged and Community

Care Services

Primary Care Safety and eHealth

Res

earc

h M

eth

od

s D

evel

op

men

t

Key Research Findings About eMM in

Adult Hospitals

Sample: 3200 patient admissions; 17,000 prescribing errors

Prescribing errors declined by >50% (p<0.0001)

44% (p=0.0002) reduction in serious prescribing error rate25/100 admissions 14/100 admissions(95%CI 21-29) (95%CI 10-18)

No significant change on the control wards (p=0.4)

New Errors !

Occurred frequently, but low risk of patient harm

Most frequent type

Incorrect selection from drop-down menus = 43%

Changes in eMM features, training, work processes

Effects of eMM on Medication

Administration Error Rates

Controlled pre post

study

226 nurses

administering 7451

medications on 6 wards

Observe & record drug

details- compare with

charts

Post eMMS

Significant reduction on the

intervention wards of

4.24 errors/100 administrations (95%CI: 0.15-8.32, p=0.04) compared to

control wards.

Wrong timing errors had the greatest

decline by

3.35 /100 administrations (95%CI: 0.01-6.69, p<0.05) compared with

control wards.

Change in serious medication

administration errors

Significant reduction in serious (ie potential ADEs)

MAEs on intervention compared to control wards

4.20% 1.83%(95%CI 3.25, 5.15%) (95%CI 1.20, 2.46%)

Pre Post

10

HEADER INFO

$$

$

Cost-effectiveness Results

11

eMM – resulted in a reduction of $63-66 per

admission

Cardiology ward = ~$100,000 reduction p.a.

due to a reduction ~ 80 ADEs p.a.

Entire hospital with 39,000 annual admissions =

releasing $2.5M each year

Success reliant upon systems integrating and supporting

work and communication processes

How does eMM impact decision-making

and work practices?

12

Direct Observations Nurses & Doctors

70 nurses observed for 276.9 hours59 doctors observed for 356.3 hours

AIM: To measure changes in how nurses and doctors distributed their time across work tasks pre and post eMMS

Where?

With what?

With whom?

What task?

Interruptions

Work Observation

Method By Activity

Timing -

• Nurses and Doctors with eMM experienced no significant

changes in % of time spent on:

Medication Tasks;

Direct Care;

Professional Communication

Compared to those without eMM

• Doctors with eMM spent more time with other doctors

(p=0.003) and with patients (p=.009).

• Nurses with eMM spent less time with doctors (p=0.0001).

Results

15

Influence on team

and individual

decision-making

processes

What impact does eMMS decision

support have during ward rounds? 48% of medication orders triggered alerts

17% read

No prescriptions changed

Junior doctors at night

16:30-22:30

Observational study - 65 hours

78% of those alerts were read

5% resulted in a change in prescribing

Alert Fatigue – Dr Melissa Baysari

19

Alert Volume in

eMMs

“How many alerts can you fire at users before they

become ineffective? “

Using experiments to

investigate

20

Paediatric

patients

Complex medication decision process – age, weight, dosing ranges etc

Estimated 19.2% of paediatric inpatients experience an ADE (Gazarian et al, 2012)

Limited data on med. administration errors

Few studies measure actual harm resulting from errors

Is eMMthe

Solution?

Westbrook JI1, Li L1, Raban MZ1,

Baysari MT1, Mumford V1, Prgomet

M1, Georgiou A1, Kim T1, Lake R1,

McCullagh C2, Dalla-Pozza L2,

Karnon J4, O’Brien TA2,

Ambler G3, Day R5, Cowell CT2,

Gazarian M5 ,Worthington R2,

Lehmann CU6, White L7,

Barbaric D2, Gardo A2,

Kelly M7, Board N8, Kennedy P9

Delivering safe and effective care for children in hospital with eHealth systems – 5 year NHMRC Partnership GrantResearch Team

1Centre for Health Systems and Safety Research, Australian Institute of Health

Innovation, Macquarie University, Sydney, Australia, 2The Sydney Children’s Hospitals Network, 3The Sydney Children’s Hospitals Network and The University of Sydney, 4University of Adelaide, 5School of Medical Sciences, Faculty of Medicine, University of New South Wales, 6Vanderbilt University, USA, 7Office of Kids and Families NSW Health, 8Australian Commission for Safety & Quality in Health Care9eHealth NSW Health Ministry

Aim – Assess the impact of eMM in

paediatrics

Design: Stepped wedge cluster randomised

controlled trial

Cost effectiveness

Impact on medication

errors, harm, LOS

To assess the effects of the eMM/EMR on

workflow, efficiency, waste

Use evidence to make changes to eMM prior to

site 2 implementation

Randomised Controlled Trial

23

Patients randomised

into 2 groups

No intervention

Intervention

Patients allocated to a control or intervention group

Cluster Randomised controlled Trial

Control Cluster

Intervention Pts

Baseline Post Intervention

Control PtsControl Cluster

Control Cluster

Control Cluster

Control Cluster

Intervention Pts

Intervention Pts

Intervention Pts

Intervention Wards

Control Cluster

Control PtsControl Cluster

Control Cluster

Control Wards

Control PtsControl PtsControl

Wards

Intervention Pts

Intervention Pts

Intervention Pts

Intervention Pts

Intervention Wards

Cluster (RCT) – not possible to randomise patients to groups and therefore you randomise according to a cluster e.g. a ward, a GP practice etc.

Randomise

Time Measure - 2 points in time

Outcomes

Some wards never get the intervention

Stepped Wedge Cluster

Randomised Controlled Trial

25Randomise the order in which wards receive the EMM

WA

RD

S

Ran

do

mis

ed

Measure multiple times

• Prescribing errors/harm

• Med Admin errors/harm

• Average LOS

eMM

Baseline Implementation of eMM starts

• 8 Wards at Westmead Children’s Hospital

• Wards have been randomised

• eMM rolled out one week apart

• Collect data at baseline and the next 10 weeks on all wards

Prescribing errors – Review patients charts to identify errors >1200 records

Medication administrations – Direct observation of the administration process, >5200 medication administrations

Clinical review to assess both potential and actual harm

Stepped-wedge cluster randomised

controlled trial (SWCRCT)

Direct Observation of the Medication Administration Process

Capture of specific administration procedures designed to support

safety

Identification of harm associated with medication errors

Some Specific Challenges:

27

28

Precise Observation System for the Safe

Use of Medicines (POSSUM)

6 observers timetabled across

8 wards at key times from

7:00 – 21:00

Weekdays & weekends

Identify compliance with

various procedures

Designed to reduce errors and harm to patients

Research evidence shows– compliance is

variable, policies poorly define the D-C process

Few studies have investigated whether D-C makes a

difference & error rates

Some evidence it may be associated with error rates

due to diffusion of responsibility

Resource and time intensive

eMM need to build in processes to allow for multiple

nurses sign-off

Double Checking (D-C) of Medications

Assessment of Double-Checking

30

Defining what constitutes double checking; the steps

and the dimensions

Independence

Priming

Completeness

Assessing Harm when Medication Errors

are Identified

Errors resulting in Identify harm indicators

Higher than

recommended dose

or concentration of

the drug

Symptoms: drowsiness, nausea

Signs: pinpoint pupils, lower level of consciousness,

respiratory depression

Medications: naloxone, abrupt cessation of opioid

Tests: blood gasses – high CO2, O2 saturation <95%

Actions: any code or arrest

Care record: increased monitoring, increased level of

care (e.g. transfer to ICU), family notified, incident report

filed, discharge delayed

Lower than

recommended dose

or concentration of

the drug

Symptoms: complaints of pain, agitation/restlessness

Signs: high pulse rate, high BP, pain scores noted and

increasing

Medications: additional doses of analgesic/s

Example of Harm Assessment Guide for

paediatric opioid errors for use in record review

Important new safety information about: Medication error rates, detection rates, severity and harm associated with

errors

Compliance with medication administration procedures (e.g. double checking)

& extent to which compliance is in fact associated with improved safety

Contextual factors- (interruptions and multi-tasking) and errors

New data on the effectiveness of eMM to reduce: Medication errors (prescribing and medication administration)

Harms associated with those errors

Impact on work

Cost-effectiveness study of eMM in paediatrics

Outcomes

Evidence to drive change in practice & policy

Evidence to drive change in practice, policy & eMM design

Special thanks to our SCHN partners

johanna.westbrook@mq.edu.au