Upload
informa-australia
View
302
Download
1
Tags:
Embed Size (px)
DESCRIPTION
Associate Professor Andrew Georgiou, Senior Research Fellow, Centre for Health Systems & Safety Research, Australian Institute of Health Innovation, University of New South Wales "The impact of the Electronic Medical Record (EMR) on Hospital Pathology Services - What is the evidence?" at the National Pathology Forum 2013. This annual conference provides a platform for the public and private sectors to come together and discuss all the latest issues affecting the pathology sector in Australia. For more information, please visit the conference website: http://www.informa.com.au/pathologyforum
Citation preview
Centre for Health Systems and Safety
Research
The impact of the electronic medical
record on hospital pathology
services – what is the evidence?
Associate Professor Andrew Georgiou
Senior Research Fellow
Outline • Background
o Existing evidence of the impact of
health IT on pathology services
o What do health professionals
think?
• Aim and Method
o What are the key performance
indicators?
• Results
o The impact on efficiency,
effectiveness and patient
outcomes and safety?
• Conclusion
Evidence of the impact of health IT
• 19 studies in total
• Most (7/11) showed
decreases in ordering
including a 27%
reduction in redundant
tests
• Four studies showed
improved adherence to
guidelines and improved
efficiency (up to 50%)
• Few studies across
multiple sites
Electronic ordering systems in the
Emergency Department
• Increase in time clinicians spend on
computers (up to 11% physicians
and 16% nurses)
• No change in time spent on patient
care
• Decreases in potential adverse drug
events (0.9/100 orders), laboratory
turnaround times
• 73% decrease in specimen
processing errors
What are health professionals
concerned about?
0%
5%
10%
15%
20%
25%
30%
35%
40%
45%
50%
secu
rity
inex
perie
nced
sta
ff
wor
k pr
actic
es
softw
are/
hard
war
e
educ
ation/
traini
ng
relatio
nshi
ps/com
mun
icat
ion
de-s
killin
gco
st
implem
enta
tion
roll o
ut
Issues of concern
Fre
qu
en
cy o
f p
art
icu
lar
issu
es a
s a
perc
en
tag
e o
f to
tal
co
ncern
s e
xp
ressed
by e
ach
pers
on
nel g
rou
p Senior staff - predominantly
management
Senior staff - management &
clinical
Staff - predominantly clinical
Research question
What is the impact of the
EMR on pathology
services, their work
processes and
relationships with other
departments, and on key
performance indicators?
Key performance metrics
Georgiou et al. Int J Med Info 2006
Test orderTest
processing
Test result
application
Costs Work practices
Test volumesRedundant test
rates
Guideline compliance
Turnaroundtimes
Doctor-lab communication
Patient management
Length of stay
Patient safety
Average turnaround time in minutes
Before implementation
(95% CI)
After implementation
(95% CI)
t test results*
All test assays 73.8 (72.2-95.4)
58.3 (57.1-59.4)
t=15.6 (df 184257)
p=0.000
Prioritised tests 44.6 (42.4-46.8)
40.1 (38.7-41.6)
t=3.3 (df 37830)
p=0.001
Non-prioritised
tests 81.5 (79.6-83.5)
65.9 (64.4-67.4)
t=12.6 (df 148493)
p=0.000
Tests in business
hours 81.8 (80.1-83.5)
69.0 (67.4-70.6)
t=10.7 (df 141219)
p=0.000
Tests outside
business hours 54.0 (50.6-57.4)
39.2 (37.8-40.5)
t=7.9 (df 37524)
p=0.000
Tests in control
ward 68.7 (63.9-73.5)
64.7 (60.4-69.0)
t=1.2 (df 12993)
p=0.218
Westbrook et al. (2006) J Clin Pathol
Test turnaround time significantly declined
Year 1 by 18.6% , Year 2 by 12.6%
Period No. of tests Mean in minutes
(95% CI)
All tests 2003 97851 35.35
(35.11,35.59)
2004 113752 28.77
(28.59,28.95)
2005 131022 25.14
(24.99,25.29)
TAT pre & post CPOE in four
hospitals
2005
Before 2006
After 2007
After Kruskal-Wallis
Hospital A - Median TAT 77 68 66 P<0.001
% tests using CPOE 75% 80%
Hospital B - Median TAT 145 129 108 P<0.001
% tests using CPOE 0-44% 57%
Hospital C- Median TAT 138 135 113 P<0.001
% tests using CPOE 29-38% 53%
Hospital D- Median TAT 141 139 128 P<0.001
% tests using CPOE 56-71% 74%
Median TAT in minutes
Volume of tests and
specimens* Average number of test assays per
patient did not change
92.5 assays/patient versus 103.2
(P=0.23)
Average number of specimens per patient
did not change
10.8/patient versus 11.7 (P=0.32)
*Westbrook et al. (2006) J Clin Pathol
Quality of pathology
ordering
Specification of
gentamycin specimens
Before 16% of gentamicin and 13% of vancomycin samples specified as peak or trough.
After significant increase - 73% for gentamicin and 77% for
vancomycin.
Westbrook et al. J Clin Pathol 2006
Heparin and warfarin
Proportion of patients reported to be on warfarin or heparin for aPTT & PT/INR
Pre-CPOE 3% (n=253) aPTT and 1.9% (n=161) PT/INR
Post-CPOE 3.9% (n=393) aPTT (p<0.005) and 2.6% (n=282) PT/INR (p<0.005)
Median laboratory turnaround time
Pre-CPOE 28 min. (aPTT) 34 min PT/INR Post-CPOE 21 min. (aPTT) (p<0.005) 23 minutes for
PT/INR (p<0.005)
The importance of quality
information exchange
• 43% of handwritten requests to
Microbiology contain patient related
clinical information e.g., “Recent
chemotherapy” “Previous MRSA”
“Recent travel”
• 97% of the clinical information were
deemed to impact on the
processing and/or interpretation of
lab tests
The impact of electronic ordering
on information exchange
Wound specimens with a request
specifying source and body site
Before electronic ordering (2005) 578 (69.6%)
One year later (2006) 774 (92.9%)
Two years later (2007) 814 (95.3%)
Three years later (2008) 877 (95.6%)
% of wound specimen requests where
admission reason was relevant
Before electronic ordering (2005) Not available
One year later (2006) 531 (63.7%)
Two years later (2007) 564 (66.0%)
Three years later (2008) 624 (68.0%)
Results from
regression analysis
Turnaround time was a significant factor
contributing to patients’ length of stay in
the emergency department. The model
accounted for 25.4% of variance (Adj.
R2=0.254)*
Laboratory errors
• Up to 68% of laboratory errors
occur in the pre-analytical test
ordering stage (Bonini, 2002)*
• Pre-analysis – all steps prior
to actual specimen analysis
• Errors can involve patient
and processing related
factors
*Errors in Laboratory Medicine Clin Chem 2002
Types of pre-analytical errors
• Mislabelled specimen
• Mismatched specimen
• Specimen suitability
• Leaking specimen
• Accident to specimen
• Insufficient specimen
9.7
12.7 13.5
0
4
8
12
16
2009 2010 2011
Error rate per 1000 test order episodes
N=1772
N=2282 N=2452
EMR errors = 464 EMR errors = 477
Errors per
1000 test order
episodes
Year
Incident Information Management System
(IIMS) reported errors
EMR Paper
Mislabelled specimen 0.1
(n=39)
0.31
(n=56) p<.001
Mismatched
specimen
0.49
(n=200)
1.42
(n=255) p<.001
Unlabelled specimen 1.37
(n=559)
1.65
(n=296) p<.01
“EMR test order problems”
Error details Total
Handwritten request on EMR 66.7% (n=451)
Order number problem (e.g.,
invalid number)
3.1% (n=21)
Multiple forms received
(Duplicates)
1.2% (n=8)
Incorrect order (e.g., swab
instead of fluid)
2.2% (n=15)
Change of tests 0.2% (n=1)
Add-on test 0.2% (n=1)
No information provided 26.5% (n=179)
Total 676
Comparison of Turnaround Time for test
orders with “EMR test order problem
EMR test order
problem
All test
orders
Median Data Entry time (mins) 8 5
Z=7.65, p<.001*
Median Total Lab TAT (mins) 263 82.14
Z=8.91, p<.001*
Total Episode Count (n=) 174 124119
*Wilcoxon signed-rank tests of significance
Missed test results
• Critical safety issue – increases
the risk of missed or delayed
diagnoses World Alliance for Patient Safety, WHO, 2008; Schiff, 2006
• Clinicians are concerned that their
test management practices are
not systematic Poon et al. Arch Int Med 2004
• Medico-legal concerns Berlin, AJR, 2009
• Impact on patient outcomes Roy et al. Ann Intern Med, 2005
How many results are missed for
hospital patients?
• Hospital inpatients 20% - 62% of tests are missed
• ED patients (discharged) 1% - 75% of tests are missed
Callen et al. BMJ Qual Saf 2011;20;194-199
• Ambulatory patients 7% - 62% laboratory tests missed
1% - 36% imaging tests missed
Callen et al. Jnl Gen Int Med, 2012
Study methods
Survey design (17 questions)
1 metropolitan ED; senior ED doctors
Significantly abnormal results
– not life threatening but need short-term
follow-up (e.g., chest x-ray with new shadow,
abnormal PSA)
Automatic patient notification methods
– Patient portal, Email, SMS, fax, mail or
phone
What types of tests were missed?
(%)
Are there standard policies and
procedures for patient notification of
results?
Who is responsible for notifying the
patient of a test result?
Perceptions of missed test results
19.2
26.9
53.9
In the past year I have missed an abnormal result that led to delayed
patient care
Yes (%)
No (%)
Don't know (%)
38.5
11.5
50
In the past year a colleague has missed an abnormal results that
led to delayed patient care
Yes (%)
No (%)
Don't know (%)
Safety considerations with health IT
implementation • Solutions need to be multipronged
• Policies, procedures and
responsibilities
• Role of patients, doctors, nurses,
clerical staff and laboratories in
the follow-up process
• Evaluation of information and
communication technology (ICT)
solutions
• Integrate solutions with work
practices of health professionals
Acknowledgements
Australian Research Council Linkage Grant (LP0347042) to evaluate
the impact of information and communication technologies on
organisational processes and outcomes: a multi-disciplinary, multi-
method approach (2003 – 2007)
Australian Research Council Linkage Grant (LP0989144) to
investigate the use of information and communication
technologies to support effective work practice innovation in the
health sector (2008 – 2012)
Quality Use of Pathology Program grant, Department of Health and
Ageing (2008-2009)
HREC Ethics approval Project No. 2005/058 and 2007/077