Challenges and opportunities for patient

safety with clinical information systems

Professor Johanna Westbrook PhD, FACMI, FACHI, FTSE,

Centre for Health Systems and Safety Research

Australian Institute of Health Innovation

Macquarie University, Australia

Patient Safety and Cost Pressure, Zurich, 15th November 2019

Australian Institute of Health Innovation

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AIHI

Australian Institute of Health Innovation

Centre for Healthcare Resilience and Implementation Science – Prof J Braithwaite

Centre for Health Informatics – Prof E Coiera

Centre for Health Systems and Safety Research – Prof J Westbrook

Centre for Health Systems & Safety Research, AIHI

Applied research targeting safety

Centre for Health Systems & Safety Research

Focus on applied research to improve patient safety and investigate

the role of information technology in supporting improvements

➢ Medication Safety

➢ Electronic Decision Support and Human Factors

➢ Diagnostic Informatics

➢ Work Innovation and Communication

➢ Aged and Community Care Services

➢ Data Analytics for patient safety

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Patient safety economics

• Patient safety is a critical policy issue and

active engagement with stakeholders is

essential.

• The cost to patients, healthcare

systems and societies is considerable.

• Greater investment in prevention is

justified and solid foundations for patient

safety need to be in place.

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Disability Adjusted Life Years (DALYs) associated with adverse

events causing patient harm in OECD countries 2015

~65 DALYS/100,000 population

No. of years lost due to ill health/disability resulting from adverse events

OECD “best buys”

• VTE prevention strategies

• Infection control interventions (protocols to improve safety of central line catheters, urinary catheters & managing ventilated patients)

• Procedural checklists

• Medication management and reconciliation (across settings)

• Pressure injury prevention protocols

• Patient hydration and nutrition standards.

[Slawomirski, Auraaen and Klazinga 2017]

Agency for Healthcare Research and Quality (AHRQ)

• Between 2010 and 2015 - 21% decline in adverse events for Medicare patients treated in hospital

• Estimated to have avoided 125,000 deaths; saved US$28 Billion in heath costs as result of safety improvements.

• Greatest benefits accrued from reductions in adverse drug events – 42%

Benefits of Safety Improvement Activities

▪ 10% of care delivered to patients causes harm

▪ 30% of health care is low value or wasteful

▪ 60% of care delivered is consistent with consensus or evidence-based guidelines

Technology is providing enormous new possibilities to address these challenges

Challenges for Health Systems Internationally

60:30:10 Problem (Braithwaite et al)

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To design & implement safe and effective

systems and technology

it is fundamental to understand clinical work

+

how safe care is delivered in practice

Clinical work conceptualised as linear, governed by

rules, regulations, policies and procedures

Work-as-imagined (WAI) – Hollnagel et al 2017

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But healthcare really looks like this … Work-as-done (WAD)

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Health Care is a complex adaptive system

❖Agents are autonomous often pursuing different agendas

❖Behaviour is emergent

❖Agents work in networks. They share some common rules for behaving and work together without a central source of direction.

❖Dynamic and use experimentation. Trial things and then adapt behaviours.

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A complex adaptive system in action

Aims for ‘as few

things as possible to

go wrong’.

Error reduction is

through a ‘find & fix’

approach to

problems

or

‘whack-a-mole’!

Traditional approach to patient safety

Approaches to Patient Safety

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The amazing thing about health care isn’t that it produces adverse events

in 10% of all cases, but that it produces safe care in 90% of cases.

Complementary Approaches to Patient Safety

WHO Goal:Reduce severe, avoidable harm related to medications by >50% over 5 years

globallyCHSSR | AIHI | MACQUARIE UNIVERSITY

• Global costs associated with medication errors – US$41 billion annually

• Medication errors – single most preventable cause of patient harm

• Information technology shows promise in making a significant impact on medication safety

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Medication Safety - Change from paper to electronic – impact on safety

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)

Do electronic medication management systems (eMM) reduce errors?

➢ eMM – resulted in a reduction of ~ US$50 per admission

➢ Entire hospital with 39,000 annual admissions = releasing

US$ 1.9M each year

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New Errors facilitated by the eMM!

❖ Reviewed 1164 post eMMS prescribing errors

❖ 493 (42.4%) facilitated by the eMM, but low risk of patient harm 11/493

❖ Most frequent type Incorrect selection from drop-down menus = 43%

82% no harm to patients

13% low harm

4% moderate

1% severe

4 contributed to patient death

Benefits and potential risks

Embedding a model of linear workflow

“..Systems often appear to be imbued with a formal stepwise notion of health care work.” (Ash et al 2004)

Inflexibility in systems can led to errors

E.G. A drug ordered 3 times per day was discontinued, but one dose had been given.

The system would not allow the nurse to chart the one dose because the system considered it an incomplete execution of a task.

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Island Health CEO apologizes in wake

of report on IHealth system

A report into the $174-million electronic health record system in Nanaimo [Canada] validates safety risks raised by doctors, and suggests Island Health should have spent more time tailoring the software to the needs of front-line workers before introducing it.

Cindy E. Harnett / Times Colonist November 17, 2016

Continued discord between IT systems & clinical work processes need more adaptive systems

31OFFICE I FACULTY I DEPARTMENT

Increased mortality following CPOE

Significant impacts of the system on workflow, including that:

❖ interventions such as medications could not be initiated as quickly as with the paper-based system

❖staff were taken away from clinical areas in order to attend to processes associated with the system

❖Communication was heavily reliant on the system rather than oral communication, reducing opportunities for ensuring messages had been received, and quick changes following consultation.

Technology constrained rather than facilitated work

Han et al 2006, Pediatrics

Medication safety in paediatrics

5 year NHMRC Partnership Grant ($1.1m)

Sydney Children’s Hospital Network

NSW Kids & Families

eHealth NSW Ministry for Health

Is eMM the Solution?

Research - Policy and

management driven &

funded

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Measuring changes in prescribing error rates pre and post eMM

• Stepped Wedge Cluster Randomised Controlled Trial (n=3549 patients; 26,369 medication orders)

• Wards randomised for IT implementation over 10 weeks; collected data at baseline and each week

• Examined clinical prescribing errors (e.g wrong dose, wrong drug) for all patients & procedural errors (e.g incomplete order) for a random 68.3% (n= 2424 patients) sample.

• Longest followup period for trial was 10 weeks post eMM implementation

• Additional 1 year followup 12 months post eMM (1046 patients)

• Total of 35,003 orders reviewed for 4595 patients

Stepped-wedge cluster RCT study design

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Randomised wards; measured medication error rates at each step

Clinical Prescribing Errors – Changes in error rates pre, post-eMM and 12 months post eMM

CHSSR | AIHI | MACQUARIE UNIVERSITY – Not for distribution 35

Paper (n=1865 patients)

Maximum time 10 weeks post eMM

(n=1684 patients)

One year post eMM

(n= 1046 patients)

No. of Errors / Orders

Error rate per

100 orders

95% CINo. of Errors

/ Orders

Error rate per 100

orders

95% CINo. of Errors

/ Orders

Error rate per

100 orders

95% CI

1690 / 11322 14.9 14.2, 15.7 2652 / 15047 17.6 17.0, 18.3 995 / 8634 11.5 10.8, 12.3

A significant 22.8% reduction in clinical prescribing error rate - pre eMM compared to 12 months

post eMM (14.9/100 orders to 11.5/100)

Following eMM implementation there was an initial significant increase in clinical errors (14.9 to 17.6/100 orders)

Clinical Prescribing Error Rates by Ward pre, post-eMM and 12 months post eMM

CHSSR | AIHI | MACQUARIE UNIVERSITY – Not for distribution 36

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Comm Trav Surgical Orthopaedic

Edgar Stephens Wade Turner

Clubbe Middleton Hunter Baillie

Results of 122 interviews with

staff (drs & nurses) at different times

post eMMimplementation

Issues Identified

CHSSR | AIHI | MACQUARIE UNIVERSITY 38

Workarounds - Nurse with drug trolley

Guarding Against Selective Attention

“The Invisible Gorilla Strikes Again” Drew et al 2013

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“The Invisible Gorilla Strikes Again”

24 radiologists were asked to review ~ 200 CT

scans of five patients for typical lung cancer

screening.

Drew et al 2013 Psychological Science

11/16/2019

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Results

➢ 20 out of 24 radiologists missed the gorilla

➢ 25 non-trained reviewers all missed the gorilla

“It’s important to be willing to look for more than one

thing, to set yourself up for success.”

Evidence that targeted decision support can be highly effective

Impact of decision support on repeat laboratory test ordering rates

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Repeat testing for 5073 children under 1 year in ICUs significantly (p<0.0001) declinedfollowing the introduction of electronic test ordering

Li et al. 2014 What is the effect of e-pathology ordering on test re-ordering for paediatric patients? Studies in Health Technology and Informatics, 204, IOS press, 74-79.

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But…….

A large body of work demonstrating that doctors override alerts (i.e. click past alerts

without following recommendations), up to 95% of alerts

Alert fatigue - mental state resulting from excessive numbers of

alerts being triggered

Leads to:

• User frustration and annoyance

• Prescribers overwhelmed by alerts

• Learn to ignore all alerts

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When and why decision support may be effective?

What impact does eMMS decision support have during ward rounds?

▪58.5 hrs,14 teams,

96 orders

▪48% of medication orders

triggered alerts

▪17% read

▪No orders changed

Junior doctors’ response to computerised alerts at night 16:30-22:30

• Observational study - 65 hours

• 78% of alerts were read

• 5% resulted in a change in prescribing

Context Matters

“How many alerts can you fire at users before they become ineffective? “

An alert regarding - Alert Fatigue!

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How do hospitals decide what alerts to

implement?

❖Survey of 26 Australian hospitals (Page N, et al, JPPR, 2018)

❖All had Allergy and Drug-Drug interaction checking

❖69% also had dose-range checking

Configuration decisions fueled by a perception that alerts change

prescriber behaviour and improve patient outcomes – But very few local

evaluations

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❖ Drug-condition – 5/6 studies showed positive effects

❖ Drug-drug interaction – 2/6 studies

❖ Corollary order alerts – 1/6 studies

❖ No one studied the combined effects of multiple alert types

An evidence-based approach to decision support selection

33% of patients, 67% of ICU patients experience potential DDI

Few studies report harm

Bucsa et al, 2013 - Only ~2% were harmed by DDIs (e.g. Bradycardia; abdominal pain)

15,000 alerts???

Does the size of the problem warrant the solution?

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Drug-drug Interaction (DDI) Alerts

What is the size and nature of the problem to be addressed?

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❖Understand the nature of the problems to be addressed

❖Recognise IT systems need to be integrated into complex adaptive systems

❖Measure effects and implementation processes using rigorous methods – monitor for both expected and unexpected changes

❖Importance of collaborations between researchers, implementers, clinicians, consumers, policy makers working together to reap the benefits of clinical IT systems

Opportunities & challenges for patient safety with

Clinical information systems

Johanna.westbrook@mq.edu.au

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Thank You