Transcript David Bates

Slaying the HIT Dragon
Nice, 2010
David Bates, MD, MSc
Chief, Division of General Internal Medicine,
Brigham and Women’s Hospital
Medical Director of Clinical and Quality
Analysis, Partners Healthcare
Past Board Chair, American Medical
Informatics Association
Overview
Background
Specific technologies
– Computerized physician order entry
The right medication-related decision support
– Bar-coding
– Smart pumps
– Computerization of handovers
– Results management (outside hospital)
Transforming care
Conclusions
The Dragon
HIT offers enormous promise for improving
safety and quality
– But many organizations have struggled
– Some reports that safety has even gotten
worse
– Technology is expensive and failure is hard to
contemplate
When to move? And who will win?
Barriers for Hospitals
Capital
Uncertainty about vendor systems
Typically stuck with one vendor
Computerized physician order entry
represents a major behavioral change
Lack of standards
Little interoperability of clinical data with
outside world
No financial incentives to deliver safer care
Typical Scenario
CEO has many competing priorities
Hard to pick among specific HIT solutions
– Big ones take time
– Risk of failure higher with this than with a new MRI for
example
– Many purchases are infrastructure—ROI tricky
Have been uncertainties about whether
upgrades will cause problems
– Standardization vs. local tailoring
Hard to decide when to pull the trigger
Message of Today
Stars are now in alignment
Federal financial incentives now in place
Additional incentives to organizations for
delivering safer care
Vendor systems are improving rapidly
– Still not perfect but good enough
Data exchange also coming fast
Time to get off the sidelines
Meaningful Use Matrix and Decision
Support: Hospitals 2011
10% all orders through CPOE
Drug-drug, drug-allergy, drug-formulary checks
Up-to-date problem list
Generate lists of patients by condition
Implement one clinical decision rule related to a
high-priority condition
Inpatient Prevention
55% reduction in serious
medication error rate with CPOE
Bates, JAMA, 1998
83% reduction in overall
medication error rate
Bates, JAMIA, 2000
NEPHROS study
Effect of real-time decision support for
patients with renal insufficiency
Of 17,828 patients, 42% had some
degree of renal insufficiency
Interv
Control
Dose
Frequency
67%
59%
54%
35%
LOS 0.5 days shorter
Chertow et al, JAMA 2001
Medication Safety: Refining the Rules
In most systems most alerts get overridden
We identified a highly selected set of drug alerts
for the outpatient setting
Over 6 months, 18,115 alerts
– 12,933 (71%) non-interruptive
– 5,182 (29%) interruptive
Of interruptive, 67% were accepted
Shah, JAMIA 2006
Dispensing Errors and Potential
ADEs: Before and After Barcode Technology
Implementation
1.00%
Before Period (115164
doses observed)
After Period (253984
doses observed)
0.88%
0.80%
0.61%
0.60%
31%
reduction*
63%
reduction*
0.40%
0.19%
0.20%
0.07%
0.00%
Dispensing Error Rate
* p<0.0001 (Chi-squared test)
Potential ADE Rate
Projections for errors
prevented per year
at study hospital:
>13,500 medication
dispensing errors
>6,000 potential
ADEs
Poon, Ann Intern Med, 2006
Safe IV Systems: Smart Pumps
Smart pumps can warn nurse when
administering IV drugs
Few administration errors get caught
– Yet intravenous errors can be especially dangerous
Case
Heparin bolus dose of 4000 units, followed by
an infusion of 890 units/hr
– 4000 unit bolus dose was given appropriately
– But nurse misinterpreted the order and programmed the
infusion device to deliver 4000 U/hour, not 890 U/hour
ISMP Newsletter Feb 6, 2002
Smart pump alerted nurse
Take-Away Messages of
Smart Pump Controlled Trial
Serious IV med errors were frequent and
could be detected using smart pumps
However, no impact on the serious med
error or preventable ADE rate was found
– Likely because of poor compliance
Behavioral and technologic factors must
be addressed if smart pumps are to
achieve their potential
Rothschild et al, Crit Care Med 2005
Coverage-Related Events
Before data showed patients being crosscovered at 5-fold excess risk of adverse
event
After computerized sign-out introduction,
no excess risk
– Included medications
Simple from informatics perspective but
major benefit
Petersen, Jt Comm Jl
Dilbert
Results Manager Home Page
The Assessment Tool
AHRQ/NQF/Leapfrog “Flight Simulator”
Assessment Tool for CPOE
Hospital
logs on
(Password
access)
Obtain
patient
criteria
Complete
sample
test
Program
patient
criteria
(Adult or
pediatric)
(HM if AMB)
Review
patient
descriptions
Hospital selfreports
results
on website
Review
scoring
Download
and print
30 – 40
test orders
Score
generated
against
weighted
scheme
Enter
orders into
CPOE
application
and record
results
Review
orders and
categories
Aggregate
score to
Leapfrog
Report
generated
Order category
scores viewed
by hospital
Safety Results of CPOE Decision
Support Among Hospitals
62 hospitals voluntarily participated
Simulation detection only 53% of orders
which would have been fatal
Detected only 10-82% of orders which
would have caused serious ADEs
Almost no relationship with vendor
Metzger et al, Health Affairs 2010
Jane Metzger, Emily Welebob, David W. Bates, Stuart Lipsitz, and David C. Classen,
Mixed Results In The Safety Performance Of Computerized Physician Order Entry,
Health Affairs, Vol 29, Issue 4, 655-663
Copyright ©2010 by Project HOPE, all rights reserved.
Have to Implement Well
Changes like CPOE and bar-coding are
transformational
Can cause major problems if not handled
well
– Are now guides about what to do, what to
avoid
Keys to success with CPOE
– Strong clinical and administrative leadership
High-Performing Healthcare
System Initiatives
6 network-wide initiatives
One focuses on IT
– Inpatient CPOE
– Outpatient EHR
Another on safety
– Standardizing medication-related decision support
– Implementing proactive tools to look for ADEs,
implementing standard web-based reported
– Making more uniform decisions about administration
– Standardizing information exchanged at transfers
What Will It Take to Transform
Care? Safety
Key issue is making essential processes
more reliable
– New approaches like CPOE, bar-coding, etc
– Checklists
And central line infection rates (Pronovost)
And rates of ventilator-associated pneumonia
Surgical checklists in the operating room
(Gawande)
Will likely need dozens of checklists
Also essential to measure performance in
on-going way
Conclusions
Information technology is becoming ubiquitous
in healthcare—near a tipping point
– All organizations should get on the bandwagon—time
is now
– CAN slay the dragon—but need to play cards right
– Tools like simulator can help
EHRs and HIT more broadly can provide major
benefits with respect to safety
–
–
–
–
–
Checklists
Reliable processes
Right decision support
HIT is simply a tool—part of a program
But nearly every other effort to improve
safety/quality/efficiency will rely on HIT
Conclusions--Leadership
Leadership must be involved, supportive
– Clinical
– Administrative
– HIT is NOT like plumbing
Will be more things than any organization can
afford
– Prioritization process key
What vendor you pick is not the only decision
– Need effective processes for incremental
improvement
– All organizations will need some in-house expertise
– Processes around decision support especially
important
“Insanity is doing the
same things the same
way and expecting
different results”
Albert Einstein