| Date post: | 27-Jun-2015 |
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Centre for Health Systems and Safety
Research
Professor Johanna Westbrook
Centre for Health Systems & Safety Research
How doctors’ and nurses’ patterns of
work and communication change
following eMMS
Centre for Health
Systems & Safety
Research
To produce world-class evidence which
informs policy and practice, focusing on
patient safety and the evaluation of
information and communication
technologies (ICT) in the health sector
Effectiveness
Impact of eMMS on medication error rates?
Efficiency
How do eMMS impact work of health
professionals?
Why should we care about study design?
What are some of the measurement challenges
and how can we design useful evaluation studies?
Outline
Results – Post eMMS
Prescribing errors declined by > 50% from:
406 (95% CI 374-437) 185 (95% CI 160-210)
per 100 admissions; p<0.0001
No significant change in errors on control
wards
Greatest reduction from orders which were
illegible, incomplete or illegal
National inpatient medication chart 5-10%
Serious prescribing errors
Intervention wards – significant 44% (p=0.0002)
reduction in serious prescribing error rate
25/100 admissions 14/100 admissions
(95%CI 21-29) (95%CI 10-18)
No significant change on the control wards (p=0.4)
MAE Post eMMS
Across all clinical error categories - a
significant reduction on the intervention wards
of 4.24 clinical 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 the intervention wards compared to the control wards
4.20% 1.83% (95%CI 3.25, 5.15%) (95%CI 1.20, 2.46%)
Pre Post
This eMMS
is going to
take too
long
Systems are
promoted for their
ability to improve
work efficiency
and safety
- Less time on administrative tasks
- More time for patient care
Research Evidence
Qualitative accounts - both significantly hinders and assists work efficiency
Quantitative evidence is sparse
Most studies on doctors’ work in ambulatory care and critical care
Know little about the impact on nurses on general wards
No Australian studies
Use of an eMMS
Aim: To measure changes in how nurses
and doctors distributed their time across
work tasks pre and post eMMS
Changes in time spent on:
Medication tasks
Direct Care
Professional Communication
Controlled Pre and Post Study
Control
Control
Intervention
Intervention
Control
Control
eMMS
eMMS
4 Wards
Pre Year 1
4 Wards
Post Year 3
Direct Observations Nurses & Doctors
70 nurses observed for 276.9 hours
59 doctors observed for 356.3 hours
Work Observation
Method By Activity
Timing -
Where?
With what?
With whom?
What task?
Interruptions
Results
Did nurses/doctors on the eMMS wards
spend more or less time on direct care,
medication tasks and professional
communication compared to colleagues on
the control wards?
No Significant
Differences
Nurses Comparison of time distribution
control and eMMS Wards
% Time
P Value
Direct Care Control 22.1 0.23
eMMS 26.1
Medication Control 23.7 0.28
eMMS 22.6
Prof Comm. Control 20.1 0.57
eMMS 22.8
Doctors Comparison of time distribution
control and eMMS Wards
% time
P Value
Direct Care Control 19.7 0.08
eMMS 25.7
Medication Control 7.4 0.4
eMMS 8.5
Prof Comm. Control 36.6 0.8
eMMS 37.6
No Significant
Differences
Time Nurses Spent with Others
Baseline
33% of nurses time is spent with patients
50% spent with other nurses
5% with doctors
4% with Relatives
Changes Following eMMS
Nurses on the eMMS wards spent less time with
doctors (p=0.0001).
4.2% less time than nurses on the control
wards.
Due to both fewer interactions (tasks per hour)
and shorter interactions (mean task time).
Time Doctors Spent with Others
Baseline
18% of doctors’ time is spent with patients
63% spent with other doctors
10% with nurses
4% with relatives
Changes Following eMMS
for Doctors
Doctors on the eMMS wards spent more time
with other doctors (p=0.003).
6% more time than doctors on the control
wards.
Doctors spent more time with patients
(p=.009)
6% more time than doctors on the control
wards.
Available at JAMIA.BMJ.Com
Why should we care about
study design?
Before and after
Versus
Controlled before and after
Controlled Pre and Post Study
Control
Control
Intervention
Intervention
Control
Control
eMMS
eMMS
4 Wards
Pre Year 1
4 Wards
Post Year 3
Before After
Differences in controlled versus
uncontrolled studies
Control
Control
Intervention
Intervention
Control
Control
eMMS
eMMS
4 Wards
Pre Year 1
4 Wards
Post Year 3
Comparison over time
Comparison with & without eMMS taking account of how both have changed from baseline
How did nurses’s work change from
year 1 (before) to year 3 (after)?
Nurses now spending significantly more time on
medication tasks (p=0.001)
20.2% year 1 23.1% year 3
Nurses now spending more time on direct care
(p=0.003)
20.2% year 1 24.2% year 3
These changes were experienced by all nurses
regardless of the eMMS
Looking for the expected
and unexpected
Selective attention
The Gorilla Strikes Again! –
Drew and colleagues presented 24 radiologists with typical lung cancer screening CT scans
20/24 radiologists (83%) 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.”
Drew et al Psychol Science 2013
Context Matters
Understanding how systems impact
work will be influenced by context
Example:
Decision support
When, who and how it will impact
What impact does decision support
have during ward rounds?
Study – Teaching Hospital
58.5 hours direct observation of 14 teams on ward rounds
48% of medication orders triggered alerts
17% of alerts were read
No prescriber read the entire content of an alert.
No prescriptions were changed
Senior clinicians during ward-rounds are
the decision-makers but do not receive the
alerts
No junior doctor was observed questioning a
senior doctor’s decision following the triggering
of an alert
JAMIA, 2011
Junior doctors at night
16:30-22:30
Observational study - 65 hours
78% of those alerts were read ≈ 50% read completely
5% resulted in a change in prescribing
2 hospital wards
Ward A 47 staff
Ward B 54 staff
How often do you seek advice from this
person about medication decisions/tasks?
• Each shape is a staff member • Each line is a medication advice seeking connection • Arrow indicate the direction of the advice sought • Networks of medication advice seeking at least weekly ie
they sought medication advice weekly or more frequently
Junior Drs are hubs of medication information
Senior Drs were isolates
Prescribing error rates
19.4 / 100 patient days Sample of 240 admissions
9.0/100 patient days Sample of 428 admissions
Conclusions regarding impact of eMMS
on work
eMMS not associated with significant redistribution of time
Some interactions change - implications of these for safety and quality
should be investigated
On wards with eMMS there were significant reductions in both
prescribing and medication administration errors
Only one aspect of work patterns – ie time distribution
eMMS will influence work practice and workflow in a multitude of
expected and unexpected ways requiring investigation
Study design may have a significant impact on the results.
What’s next ? – Cost-effectiveness study
Team of researchers and hospital staff who made
this work possible Andrew Georgiou
Ling Li
Margaret Reckmann
Melissa Baysari
Nerida Creswick
Joanne Callen
Ric Day
Jeffrey Braithwaite
William Runciman
Richard Paoloni
Katherine Gibson
John Cullen
Louise Robertson
Rosemary Burke
Connie Lo
Kate Richardson
Maureen Heywood
Fiona McWhinnie
Amanda Woods
Naomi Malouf
Margaret Williamson
Jackie Laurens
Silvia Fazekas
Rosemary Richman
Joanne Villaret
Natasha Smith
Amanda Ampt
Melissa Pignone
David Roffe
Clinical ward staff
Pharmacy staff
ISD Staff
Thank You
Centre for Health Systems & Safety Research
Australian Institute of Health Innovation
UNSW Medicine
This program of research has been supported by funding from the NHMRC &
ARC
