medication errors in hospitals Using information technology to reduce rates of

doi:10.1136/bmj.320.7237.788

2000;320;788-791

BMJ

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

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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.

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and transesophageal echocardiography on task distribution, workload,

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analysis and a field study of “going sour” incidents [abstract]. Proceedings

of the Human Factors Society’s 36th Annual Meeting 1992:1279-83.

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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

safety

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

linked

Education and debate

Division of General

Medicine and

Primary Care,

Brigham and

Women’s Hospital,

75 Francis Street,

Boston, MA 02115,

USA

David W Bates

chief

dbates@

partners.org

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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

Prescribing

Physician order entry

Computerised design support

Transcription

Electronic order transcription

Dispensing

Robots

Bar coding

Automated dispensing devices

Administration

Bar coding

Automated dispensing devices

Medication

administration record

Computerised medication

administration record

Monitoring

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

patient.14

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

interest.21

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.

Conclusions

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

manuscript.

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

1991;324:370-6.

Leape LL, Brennan TA, Laird NM, Lawthers AG, Localio AR, Barnes BA,

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-

tions for prevention. JAMA 1995;274:29-34.

Lazarou J, Pomeranz BH, Corey PN. Incidence of adverse drug reactions

in hospitalized patients: a meta-analysis of prospective studies. JAMA

1998;279:1200-5.

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

costs of adverse drug events in hospitalized patients. JAMA 1997;227:

307-11.

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,

1994:141-59.

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

1998;280:1317-20.

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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.

Gaps

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

accidents

Safety is increased by understanding and

reinforcing practitioners’ normal ability to bridge

gaps

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,

USA

Richard I Cook

associate director

VA Patient Safety

Center of Inquiry

(GAPS), Cincinnati,

VAMC, Cincinnati,

OH 45220, USA

Marta Render

director

continued over

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