David W Bates
medication errors in hospitals
Using information technology to reduce rates of
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The author is also professor of anaesthesia at Stanford
University School of Medicine, Stanford, California.
Competing interests: The author is the secretary of the
Anesthesia Patient Safety Foundation. He also licensed simulation
technology to CAE-Link in 1992, for which he received a licence
and royalties on the sale of patient simulators. He is also, periodi-
cally, a paid consultant to MedSim, the company that now owns
the rights to the licensed simulation technology.
Leape L. Error in medicine. JAMA 1994;272:1851-7.
Cooper J, Newbower R, Long C, McPeek B. Preventable anesthesia
mishaps: a study of human factors. Anesthesiology 1978;49:399-406.
Gaba D, Maxwell M, DeAnda A. Anesthetic mishaps: breaking the chain
of accident evolution. Anesthesiology 1987;66:670-6.
Lunn J. Epidemiology in anaesthesia. London: Edward Arnold, 1986.
Derrington M, Smith G. A review of studies of anaesthetic risk, morbidity,
and mortality. Br J Anaesth 1987;59:815-33.
Warden JC, Borton CL, Horan BF. Mortality associated with anaesthesia
in New South Wales, 1984-1990. Med J Aust 1994;161:585-93.
Cooper JB, Newbower R, Kitz R. An analysis of major errors and equip-
ment failures in anesthesia management: considerations for prevention
and detection. Anesthesiology 1984;60:34-42.
Cheney FW. The American Society of Anesthesiologists closed claims
project: what have we learned, how has it affected practice, and how will it
affect practice in the future? Anesthesiology 1999;91:552-6.
Runciman WB, Sellen A, Webb RK, Williamson JA, Currie M, Morgan C,
et al. The Australian incident monitoring study. Errors, incidents and
accidents in anaesthetic practice. Anaesth Intensive Care 1993;21:506-19.
10 Webb RK, Currie M, Morgan CA, Williamson JA, Mackay P, Russell WJ, et
al. The Australian incident monitoring study: an analysis of 2000 incident
reports. Anaesth Intensive Care 1993;21:520-8.
11 Bhasale AL, Miller GC, Reid SE, Britt HC. Analysing potential harm in
Australian general practice: an incident-monitoring study. Med J Aust
12 Beckmann U, West LF, Groombridge GJ, Baldwin I, Hart GK, Clayton
DG, et al. The Australian incident monitoring study in intensive care:
AIMS-ICU. The development and evaluation of an incident reporting
system in intensive care. Anaesth Intensive Care 1996;24:314-9.
13 Orkin FK, Cohen MM, Duncan PG. The quest for meaningful outcomes
[editorial]. Anesthesiology 1993;78:417-22.
14 Petty C. The anesthesia machine. New York: Churchill Livingstone, 1987.
15 Arens J. Practice parameters: some new, others under development or
revision. ASA Newsletter 1999;63:26-8.
16 Eichhorn JH, Cooper JB, Cullen DJ, Maier WR, Philip JH, Seeman RG.
Standards for patient monitoring during anesthesia at Harvard Medical
School. JAMA 1986;256:1017-20.
17 Drui A, Behm R, Martin W. Predesign investigation of the anesthesia
operational environment. Anesth Analg 1973;52:584-91.
18 Weinger M, Herndon B, Gaba D. The effect of electronic record keeping
and transesophageal echocardiography on task distribution, workload,
and vigilance during cardiac anesthesia. Anesthesiology 1997;87:144-55.
19 Xiao Y, Milgram P, Doyle D. Incident evolution and task demands: an
analysis and a field study of “going sour” incidents [abstract]. Proceedings
of the Human Factors Society’s 36th Annual Meeting 1992:1279-83.
20 Mackenzie CF, Craig GR, Parr MJ, Horst R. Video analysis of two emer-
gency tracheal intubations identifies flawed decision-making. Anesthesiol-
ogy 1994;81:763-71.
21 DeAnda A, Gaba D. The role of experience in the response to simulated
critical incidents. Anesth Analg 1991;72:308-15.
22 Galletly D, Mushet N. Anaesthesia system errors. Anaesth Intensive Care
23 Gaba D. Human performance issues in anesthesia patient safety. Problems
in Anesthesia 1991;5:329-50.
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Using information technology to reduce rates of
medication errors in hospitals
David W Bates
Data continue to show that medication errors are
frequent and that adverse drug events, or injuries due
to drugs, occur more often than necessary.1–4 In fact, the
frequency and consequences of iatrogenic injuries
seems to dwarf the frequency of other types of injuries
that have received more public attention, such as aero-
plane and automobile crashes.2 A recent meta-analysis
reported an overall incidence of 6.7% for serious
adverse drug reactions (a term that excludes events
associated with errors) in hospitals.4 Between 28% and
56% of adverse drug events are preventable.3 5–7
Though the reasons this issue has received so little
attention are complex, the reasons that medical injuries
occur with some frequency are perhaps less so; medicine
is more or less a cottage industry, with little standardisa-
tion and relatively few safeguards in comparison to, say,
manufacturing. In fact, most of the systems in place in
medicine were never formally designed, and this holds
for the entire process of giving drugs.
Take, for example, the allergy detection process used
in our hospital several years ago, which was similar to
that used in most hospitals at the time. Physicians, medi-
cal students, and nurses all asked patients what their
Summary points
Although information technologies are widely
used in hospitals, relatively few data are available
regarding their impact on the safety of the
process of giving drugs
Exceptions are computerised physician order
entry and computerised physician decision
support, which have been found to improve drug
Other innovations, including using robots to fill
prescriptions, bar coding, automated dispensing
devices, and computerisation of the medication
administration record, though less studied, should
all eventually reduce error rates
The medication system of the future will include
these and other technologies, all electronically
Education and debate
Division of General
Medicine and
Primary Care,
Brigham and
Women’s Hospital,
75 Francis Street,
Boston, MA 02115,
David W Bates
BMJ 2000;320:788–91
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allergies were. This information was recorded at several
sites in the medical record, though there was no one
central location. The information was also required to be
written at the top of every order sheet, although in prac-
tice this was rarely done. The pharmacy recorded the
information in its computerised database, but it found
out about allergies only if the information was entered
into the orders, and often it was not. Checking by physi-
cians and pharmacy and nursing staff was all manual.
This information was not retained between the inpatient
and outpatient settings, or from admission to admission.
Not surprisingly, about one in three orders for drugs to
which a patient had a known allergy slipped through.3
This system has been replaced by a system in which all
allergies are noted in one place in the information
system, drugs are mapped to “drug families” (for exam-
ple, penicillin) so that checking of drugs within classes
can be done, information is retained over time, and
checking is performed by the information system, which
does not fatigue.
Using information technologies to
prevent medication errors
Several interventions involving information systems
have been shown to reduce medication errors consid-
erably, and many others have promise but have not
been sufficiently studied. Among these are computer-
ised physician order entry, computerised physician
decision support (which is often, though not necessar-
ily, linked with order entry), robots for filling prescrip-
tions, bar coding, automated dispensing devices, and
computerisation of the medication administration
record (fig 1).
It is essential to state at the outset, however, that
information technologies are not a panacea, and that
they may make some things better and others worse8;
the net effect is thus not entirely predictable, and it is
vital to study the impact of these technologies. They
have their greatest impact in organising and making
available information, in identifying links between
pieces of information, and in doing boring repetitive
tasks, including checks for problems. The best
medication processes will thus not replace people but
will harness the strengths of information technology
and allow people to do the things best done by people,
such as making complex decisions and communicating
with each other.
Computerised physician order entry
Computerised physician order entry (CPOE) is an
application in which physicians write orders online.
This system has probably had the largest impact of any
automated intervention in reducing medication errors;
the rate of serious errors fell 55% in one study9 and the
rate of all errors fell 83% in another.10 Computerisation
of ordering improves safety in several ways: firstly, all
orders are structured, so that they must include a dose,
route, and frequency; secondly, they are legible and the
orderer can be identified in all instances; thirdly, infor-
mation can be provided to the orderer during the
process; and fourthly, all orders can be checked for a
number of problems including allergies, drug interac-
tions, overly high doses, drug-laboratory problems
(giving a patient a drug when they have a known
biochemical factor that predisposes them to risk), and
whether the dose is appropriate for the patient’s liver
and kidney function (fig 2). A large decrease in the
number of errors can be achieved by computerising
the process even without providing much decision
support; in one study even a simple system reduced
medication errors by 64%.10
Computerised decision support is also valuable for
reducing the frequency of adverse drug events, even
when not linked to computerisation of the ordering
process. In an elegant series of studies, the group from
LDS Hospital in Salt Lake City, Utah, showed large
reductions in adverse drug events due to antibiotics.11
Also, a community hospital in Phoenix, Arizona, used a
computerised alert system to target 37 drug-specific
adverse reactions—for example, arrhythmia caused by
digoxin—for which they looked for patients receiving
digoxin who had hypokalaemia.12
They detected
opportunities to prevent injury at a rate of 64 per 1000
admissions; 44% of the true positive alerts had not
been recognised by the physician.12
This approach
works partly by helping clinicians to associate key
Physician order entry
Computerised design support
Electronic order transcription
Bar coding
Automated dispensing devices
Bar coding
Automated dispensing devices
administration record
Computerised medication
administration record
Computerised monitoring
of adverse drug events
Fig 1 Role of automation by stage in the medication process.
Automation of some functions may affect more than one stage
Fig 2 Computerised checking of a chemotherapy dose. The
computer calculates the body surface area, displays the calculation,
and asks if it is correct. The dose is then checked against a table of
doses, with daily and weekly limits. If a dose limit is exceeded the
order is suspended until it can be reviewed and approved
Education and debate
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pieces of data, which can be problematic given the
overwhelming stream of data confronting them.
Though computerisation of ordering dramatically
decreases the overall rate of medication errors, compu-
terised decision support may be especially important
for preventing errors that actually result in injury. In
one study, computerised order entry with relatively
limited decision support resulted in a larger decrease
in near misses (84%) than in errors that actually
resulted in injury (17%)9—but in a later evaluation, after
more decision support had been added, the rate of
errors resulting in injury fell from 2.9 to 1.1 per 1000
patient days.10
Robots for filling prescriptions
Automation may also reduce error rates in filling pre-
scriptions. Robots have been used for this in some
large hospitals for some time, and more recently in
smaller hospitals, and they are increasingly being used
in the outpatient setting. No published data are
available, but in one unpublished study a robot
decreased the dispensing error rate from 2.9% to 0.6%
(PE Weaver and VJ Perini, American Society of Health
System Pharmacists, 1998).
Bar coding
Although few data from health care are available, bar
coding of drugs also seems useful for reducing error
rates.13 The major barrier to implementation has been
that drug manufacturers have not been able to agree
on a common approach; this should be legislated. Bar
coding is widely used in many industries outside medi-
cine; it results in error rates about a sixth of those due
to keyboard entry and is less stressful to workers. Some
hospitals in the United States have already successfully
implemented bar coding—for example, at Concord
Hospital in New Hampshire bar coding was associated
with an 80% fall in medication administration errors
(D DePiero, personal communication). Bar coding can
rapidly ensure that the drug at hand is actually the
intended one and can also be used to record who is
giving and receiving it, as well as various time intervals.
Automated dispensing devices
Automated dispensing devices can be used to hold
drugs at a location and dispense them only to a specific
Such devices, especially if linked with bar
coding and interfaced with hospital information
systems, can decrease medication error rates substan-
tially. Without these links the effect of these devices is
unclear14–16; in one study such a system was actually
associated with an increase in medication errors.17
Automated medication administration record
Another key part of the medication use process is the
medication administration record, on which the
clinicians who actually administer drugs record what
has been given. Computerisation of this part of the
process, especially if linked to computerised order
entry, could reduce errors and allow detection of other
types of errors relating to the quantities of drugs that
are to be taken “as needed.”
Computerised adverse drug event detection
To monitor how any process is performing, it is essen-
tial to be able to measure its outcomes. Traditional
monitoring relies on self reporting, which radically
underestimates adverse drug events, detecting only
about 1 in 20.18 However, computerised data can be
used to detect signals (such as use of an antidote or a
high concentration of a drug) that are associated with
an adverse reaction.19 20
A pharmacist can then
evaluate the incident and determine whether it
represents an adverse drug event, and these data can
then be used for root cause analyses. In a head to head
comparison with chart review and spontaneous
reporting, a computerised monitor was found to detect
45% of events detected by any method, compared with
64% for chart review and only 4% for voluntary
reporting.20 The cost of the computerised monitoring
was only 20% of that for chart review. This is the first
practical way to monitor the medication process on an
ongoing basis.
Diffusion of these technologies
The tools that are now available should eventually be
used in all hospitals; the overall approach should be
analogous to that used in infection control, in which
data about complications are used to continuously
improve the system. Given the potential impact of
these technologies, their diffusion has been surpris-
ingly slow. One reason may be the lack of research
showing how much of a difference the technologies
make. Funding for such research has been relatively
limited, and relatively little support has come from the
developers of the technologies. Another, more impor-
tant reason is lack of demand from the healthcare
industry. Safety has not been a high priority in
medicine, in part because the problem of safety is gen-
erally undervalued. One reason for this lack of appre-
ciation is that medical accidents occur in ones and twos
rather than in large groups; moreover, many of those
involved are ill and elderly. Fortunately, public concern
about the issue is substantial, and increasing, and the
healthcare industry is beginning to take a more active
The medication system of the future
In future, physicians will write orders online and get
feedback about problems like allergies and decision
support to help them choose the best treatment. The
orders will be sent electronically to the pharmacy,
where most will be filled by robots; complex orders will
be filled by pharmacists. Pharmacists will be much
more clinically oriented and will focus on promoting
optimal prescribing and identifying and solving
problems. Automated dispensing devices will be used
by nurses to provide drugs to patients. All drugs,
patients, and staff will be bar coded, making it possible
to determine what drigs have been given to whom, by
whom, and when.
Several information technologies have been shown to
improve the safety of drugs. Computerised physician
order entry seems to be the most potent of these, and
it can be expected to become even more useful as more
data become computerised. The technology can be
expected to diffuse rapidly as all major vendors are
Education and debate
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developing such systems and many are pursuing inter-
net based applications which would allow ordering and
provide a common platform. Information technology
should also improve safety in other parts of the
process, including dispensing and administering, but
the full benefits will not be achieved until all the com-
ponents are electronically linked.
The net result of the above will be a much safer sys-
tem, which will still require substantial human guidance.
Moreover, the people using the system will have fewer
menial tasks and a more rewarding role: physicians will
discuss drug choices with patients and other providers
rather than worrying about missing an allergy; pharma-
cists will deal with complex drug orders, counsel
physicians about choices, and investigate problems that
occur, rather than simply filling prescriptions; and
nurses will talk with patients and monitor for adverse
reactions, rather than just passing pills.
I thank Joshua Borus for help with preparation of the
Competing interests: DB has received honoraria for
speaking from the Eclipsys Corporation, which has licensed the
rights to the Brigham and Women’s Hospital Clinical
Information System for possible commercial development, and
from Automated Healthcare, which makes robots that dispense
drugs. He is also a consultant and serves on the advisory board
for McKesson MedManagement, a company that helps hospitals
to prevent adverse drug events, and is on the clinical advisory
board for Becton Dickinson.
Brennan TA, Leape LL, Laird N, Hebert L, Localio AR, Lawthers AG, et
al. Incidence of adverse events and negligence in hospitalized patients:
results from the Harvard Medical Practice Study. I. N Engl J Med
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et al. The nature of adverse events in hospitalized patients: results from
the Harvard Medical Practice Study. II. N Engl J Med 1991;324:377-84.
Bates DW, Cullen D, Laird N, Petersen LA, Small SD, Servi D, et al. Inci-
dence of adverse drug events and potential adverse drug events: implica-
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Lazarou J, Pomeranz BH, Corey PN. Incidence of adverse drug reactions
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Classen DC, Pestotnik SL, Evans RS, Lloyd JF, Burke JP. Adverse drug
events in hospitalized patients. Excess length of stay, extra costs, and
attributable mortality. JAMA 1997;277:301-6.
Bates DW, Spell N, Cullen DJ, Burdick E, Laird N, Peterson LA, et al: The
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Bates DW, Leape LL, Petrycki S. Incidence and preventability of adverse
drug events in hospitalized adults. J Gen Intern Med 1993;8:289-94.
Sheridan TB, Thompson JM. People versus computers in medicine. In:
Bogner MS, ed. Human error in medicine. Hillsdale, NJ: Lawrence Erlbaum,
Bates DW, Leape LL, Cullen DJ, Laird N, Petersen LA, Teich JM, et al.
Effect of computerized physician order entry and a team intervention on
prevention of serious medication errors. JAMA 1998;280:1311-6.
10 Bates DW, Teich J, Lee J, Seger D, Kuperman GJ, Boyle D, et al. The impact
of computerized physician order entry on medication error prevention.
J Am Med Informatics Assoc 1999;6:313-21.
11 Evans RS, Pestotnik SL, Classen DC, Clemmer TP, Weaver LK, Orme
JF Jr, et al. A computer-assisted management program for antibiotics and
other antiinfective agents. N Engl J Med 1998;338:232-8.
12 Raschke RA, Golihare B, Wunderlich TA, Guidry JR, Liebowitz AI, Peirce
JC, et al. A computer alert system to prevent injury from adverse drug
events. Development and evaluation in a community hospital. JAMA
13 Chester MI, Zilz DA. Effects of bar coding on a pharmacy stock replenish-
ment system. Am J Hosp Pharm 1989;46:1380-5.
14 Barker KN. Ensuring safety in the use of automated medication dispens-
ing systems. Am J Health Syst Pharm 1995;52:2445-7.
15 Allan EL, Barker KN, Malloy MJ, Heller WM, Dispensing errors and
counseling in community practice. Am Pharm 1995;35:25-33.
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21 Massachusetts Hospital Association Bulletin 1999 March 3.
Gaps in the continuity of care and progress on
patient safety
Richard I Cook, Marta Render, David D Woods
The patient safety movement includes a wide variety of
approaches and views about how to characterise patient
safety, study failure and success, and improve safety. Ulti-
mately all these approaches make reference to the
nature of technical work of practitioners at the “sharp
end” in the complex, rapidly changing, intrinsically haz-
ardous world of health care.1 2 It is clear that a major
activity of technical workers (physicians, nurses, techni-
cians, pharmacists, and others) is coping with complexity
and, in particular, coping with the gaps that complexity
spawns.3 Exploration of gaps and the way practitioners
anticipate, detect, and bridge them is a fruitful means of
pursuing robust improvements in patient safety.
The notion of gaps is simple. Gaps are discontinuities
in care. They may appear as losses of information or
momentum or interruptions in delivery of care. In
practice gaps rarely lead to overt failure. Rather, most
gaps are anticipated, identified, and bridged and their
Summary points
Complex systems involve many gaps between
people, stages, and processes
Analysis of accidents usually reveals the presence
of many gaps, yet only rarely do gaps produce
Safety is increased by understanding and
reinforcing practitioners’ normal ability to bridge
This view contradicts the normal view that
systems need to be isolated from the unreliable
human element
We know little about how practitioners identify and
bridge new gaps that occur when systems change
Education and debate
VA Patient Safety
Center of Inquiry
(GAPS), University
of Chicago,
Chicago, IL 60637,
Richard I Cook
associate director
VA Patient Safety
Center of Inquiry
(GAPS), Cincinnati,
VAMC, Cincinnati,
OH 45220, USA
Marta Render
continued over
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