MEDICAL ERRORS...MORE

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editor's note- Recently, as indicated here and elsewhere, in Texas, a CRAZY thing happened, well, more like, a horrible abuse of the legal system happened. The Texas Medical Association, which has no mandate to oversee any profession (The Texas Medical Board, http://www.tmb.state.tx.us/ oversees practice of medicine in Texas) , filed a lawsuit against the Texas Board of Chiropractic Examiners, claiming , among other things, that Chiropractic Doctors should not be allowed legally to diagnose their own patients. The Texas Meddlesome Assn...er..uh "Texas Medical ASSn" says they did so, in order to "protect citizens of Texas".

This is ridiculous to the point of absurdity.

If the Texas Meddlesome ASSn, or "Texas Medical Association" as they prefer to be called,
cares so much about protecting the public, perhaps they should clean up their OWN profession.
Please read the following article.

http://metasearch.com/

The Quality of Health Care

Medical Error and Patient Injury: Costly and Often Preventable

Research Report

Andrew H. Smith, AARP Public Policy Institute

September 1998

Table of Contents:

• 
  
  Public Perception of Patient Safety and Medical Error


• 
  
  Incidence of Medical Error and Injury


• 
  
  Drugs and Medical Injury


• 
  
  Costs Resulting from Medical Injury


• 
  
  Why Do Medical Errors Happen, and How Should the Problem Be Addressed?
  


• 
  
  Addressing the Problem from a Systems Approach


• 
  
  Current Efforts to Address Medical Error From a Systems Perspective


• 
  
  Conclusion


• 
  
  Footnotes

Patient injuries that result from preventable medical errors are widespread
and costly.¹
One recent study found that more than one in six hospitalized patients suffered
medical injuries that prolonged their hospital stays.²
It has been estimated that total annual costs associated with injuries resulting
from medical error may be as high as $200 billion, the equivalent of nearly one
out of every five dollars spent on health care in America.³
Estimates of the frequency of medical errors and injuries and the costs
associated with them vary considerably, but even the most conservative estimates
indicate that the problem is widespread, very costly, and requires serious
attention.⁴

Preventable medical error and injury are of particular concern for older
people because there is evidence that they are injured at a substantially higher
rate than patients in other age groups. As

Figure 1 indicates, patients age 65 and older experience medical injury two
to four times as often as patients in age groups under the age of 45, according
to a landmark study published in 1991, the most recent age-specific data
available.⁵
Advancing age was the only demographic characteristic -- not gender, race,
ethnicity, or income -- associated with a significantly increased incidence of
medical injury and of injury due to "negligence."⁶
The evidence suggests that costs associated with preventable medical error and
injury, both in terms of human suffering and dollars spent by the Medicare
program to treat injured beneficiaries, are very significant.

Public Perception of Patient Safety and
Medical Error

There is a substantial amount of public concern about patient safety.⁷
In a 1997 national survey, respondents rated the current health care system as
only "moderately safe" -- safer than nuclear power and food handling, but less
safe than airplane travel and the workplace.⁸
(See

Table 1.) Forty-two percent of those surveyed said that they had been
involved, either personally or through a friend or relative, in a situation
where a medical mistake was made. Fifty-two percent of respondents stated that
they were satisfied with the measures currently in place to prevent medical
mistakes, but a large minority, 42 percent, said they were not satisfied.⁹
Not surprisingly, most of those who reported that they were not satisfied with
current measures were those who had been involved in some way with a medical
mistake.¹⁰

Table 1. Perceived Safety of Various Environments
Environment	Mean Scores
Airline travel	5.2
Workplace	5.2
Health care	4.9
Food handling	4.4
Nuclear power	4.2
Scores: 7=Safe, 1=Unsafe. Source: National Patient Safety Foundation at the AMA, "Public Opinion of Patient Safety Issues." Survey conducted by Louis Harris & Associates, September 1997.

Incidence of Medical Error and Injury

As noted above, recent estimates of the incidence of medical errors resulting

in injuries¹¹
reach as high as 17.7 percent of hospitalizations.¹²
One important study of medical injury is the 1990 Harvard Medical Practice Study
(Harvard Study), a population-based study of injuries resulting from medical
care during hospitalizations in New York. This study found that nearly 4 percent
of patients suffered an injury that caused their hospital stays to be prolonged,
or resulted in measurable disability.¹³
The Harvard Study, which used reviews of medical records to detect medical
injuries, found that almost 14 percent of those identified as having suffered
medical injury died as a result of their injuries. If the rate of deaths
resulting from medical error identified by the Harvard Study in New York were
consistent with rates in the other 49 states, that would mean that 180,000
Americans die annually as a result of medical injuries.¹⁴
That figure would be comparable to the number of deaths that would occur if
three jumbo-jets crashed every two days,¹⁵
and is approximately four times the number of traffic fatalities that occur
annually in America.¹⁶

Consistent with other studies that have found that most medical injuries are
due to errors, the Harvard Study determined that 69 percent of the medical
injuries identified were due to error, and were, therefore, preventable.¹⁷

Studies conducted more recently indicate that medical injury may be
substantially more common than suggested in the Harvard Study. Using a method
more likely to capture incidents of medical error than the earlier study,
Andrews and her colleagues found that 17.7 percent of patients whose care was
observed experienced at least one serious adverse event per hospitalization.¹⁸
The frequency of medical injuries was linked to severity of illness and length
of hospital stay, with the likelihood of experiencing a medical injury
increasing by 6 percent per day of hospitalization. One or more causes¹⁹
of medical injuries were determined in just over one half of cases in the study.
In 37.8 percent of cases, the adverse events were found to have been caused by
an individual; 15.6 percent had interactive causes; and 9.8 percent were due to
administrative decisions. Although 17.7 percent of patients experienced medical
injuries that prolonged their hospital stays, the study found that only 1.2
percent filed claims for compensation for their injuries.

Drugs and Medical Injury

Drugs have been found to be among the most common causes of medical injury.
In the Harvard study, 19.4 percent of the injuries detected were related to the
use of drugs, while the Andrews study determined that 9.3 percent of injuries
were medication-related.²⁰

A large percentage of adverse drug events (ADEs) have serious consequences,
and many of them are preventable. Bates and his colleagues found that of all
ADEs identified in their study, 1 percent were fatal, 12 percent
life-threatening, 30 percent serious, and 57 percent significant. Of ADEs that
were determined to have been preventable, 20 percent were life- threatening, and
43 percent were serious. (See

Figure 2.) Overall, 28 percent of the ADEs were judged preventable, but of
life-threatening and serious ADEs, 42 percent were determined to have been
preventable.²¹
Bates found rates of 6.5 ADEs and 5.5 potential ADEs per 100 non-obstetrical
admissions to tertiary-care hospitals. Classen and colleagues found that adverse
drug events complicated 2.43 percent of hospital admissions, adding
significantly to length of hospital stays and to costs.²²

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Costs Resulting from Medical Injury

The costs associated with injuries resulting from medical error are quite
substantial. As noted above, one recent estimate placed the total costs
associated with medical injury at as much as $200 billion annually.²³

Most studies that attempt to estimate costs associated with medical error
have focused on injuries resulting from the use or misuse of medications. In
their 1995 study, Johnson and Bootman estimated that costs associated with
drug-related illness and death that resulted primarily from patient
non-compliance, and inappropriate prescribing, and/or monitoring by health care
professionals equal $76.6 billion annually.²⁴
The costs calculated for drug-related illness and death were limited to those
that arose from medication use or misuse in an outpatient setting, with the
largest component of costs resulting from drug-related hospitalizations.²⁵

The ADEs identified in the Classen study, half of which were identified as
preventable, added 1.91 days to the mean length of hospital stays and resulted
in increased costs per stay of $2,262.²⁶

In a follow up to their earlier study, Bates and colleagues determined that
an additional 2.2 days of hospitalization were required for patients
experiencing an ADE, at an average added cost of $3,244. For ADEs identified as
preventable, patients stayed in the hospital an average of 4.6 extra days, at an
average additional cost of $5,857.²⁷

Why Do Medical Errors Happen, and How Should the
Problem Be Addressed?

1. Negligent and/or incompetent providers

As a recent survey reveals, many people believe that medical errors and
injuries occur because there are just too many "bad doctors" and other health
care professionals performing in a negligent manner.²⁸
Medical injury is viewed as primarily the result of allowing incompetent and/or
careless providers to continue in the practice of medicine, and of hospital
under-staffing and other cost-cutting practices.²⁹
It has frequently been observed that relatively few providers are sanctioned by
the medical profession and/or state entities charged with enforcing standards of
medical practice despite evidence of widespread negligence.³⁰

Those who believe that medical negligence and an ineffective oversight system
are largely responsible for medical error and injury have responded in a number
of ways. For example, they promoted the development and use of a practitioner
databank. As a result, the National Practitioner Data Bank (NPDB) was created.
The NPDB collects and releases information (to authorized entities) relating to
medical malpractice payments, adverse licensure actions, certain types of
professional review actions, and reports of Medicare and Medicaid sanctions
taken against physicians, dentists, and some other health care practitioners.³¹
They have also defended the laws that govern medical malpractice actions against
a strong effort from the medical community to enact legal reforms that would
curtail malpractice litigation.³²

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2. Inevitable human error and systems failures

A contrasting view holds that the problem of medical error and injury results
primarily from systems failures. Proponents of this view acknowledge that there
are incompetent and impaired providers who commit errors that result in patient
injury, and that few physicians face disciplinary actions. However, they
observe, there is little evidence that negligence is the major cause of medical
error, or that rooting out negligent and incompetent providers would solve the
problem.

Those who subscribe to a "systems approach" to medical error, drawing on
psychological and human factors research, argue that human beings, no matter how
careful and conscientious they are, will make mistakes.³³
They also note that because the practice of medicine is complex, there are a
great many opportunities for mistakes to occur, and that the high level of
complexity makes it unrealistic to depend on promoting individual perfection as
the method to avoid mistakes that result in patient injury. For example, in one
study of an intensive care unit, it was determined that patients received an
average of 178 "activities" each day.³⁴
The average number of errors per patient per day was 1.7, or slightly less than
1 percent. Thus, the unit was functioning correctly 99 percent of the time.³⁵
Leape notes, however, that even an accuracy rate of 99.9 percent may not prove
adequate, noting that a 99.9 percent accuracy rate would translate to:

• Two unsafe landings at O'Hare airport each day;


• 16,000 pieces of lost mail per hour; and


• 32,000 bank checks deducted from the wrong account every hour.³⁶
  

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Addressing the Problem from a Systems
Approach

One medical specialty, anesthesiology, has already made significant
improvements in its safety record. Mortality resulting from errors in anesthesia
has been reduced by 95 percent over the past 15 years.³⁷

Recognizing system factors, rather than carelessness or incompetence as the
most important causes of medical error, anesthesiologists designed fail-safe
systems and developed and implemented training programs to avoid errors.³⁸

The success story in anesthesiology illustrates the possibilities and
problems for other areas of medical practice. Errors and the resulting injuries
in anesthesiology, unlike those in many areas of medical practice, tend to be
dramatic and severe.³⁹
Information about incidents and the circumstances surrounding them were,
therefore, available to those attempting to understand the problems, and the
reasons the errors occurred were often transparent. These factors were conducive
to understanding the problems and developing approaches to correct them.

A number of scholars believe that the most important reason that medicine has
failed to develop more effective ways to prevent error is that, except in the
case of the practice of anesthesiology, there has been little opportunity to
study the reasons that errors occur. Information about medical error is
inadequate for researchers because most errors go unreported. Unlike errors in
anesthesiology, which, as noted above, cannot easily be hidden, errors occurring
in other areas of medical practice tend to be less frequently obvious and
dramatic in effect. In what some call medicine's culture of blame, there
is good reason not to volunteer information that an error has occurred when it
might otherwise remain undiscovered. In the medical culture, error cannot be
accepted; physicians are taught in medical school and during residency to learn
and practice error-free medicine, i.e., to be perfect. Error is treated
as a moral failing,⁴⁰
and it is not surprising that mistakes are driven "underground."

Advocates of the systems approach argue that, for medicine to enjoy the
success observed in anesthesiology, it is essential to overcome the barriers to
full reporting of medical errors. For researchers to devise ways to prevent
and/or to absorb⁴¹
errors and prevent injuries, they must learn precisely how and why errors and
their resulting injuries take place. They must have access to detailed and
comprehensive information on errors, and full information can be obtained only
if there is full disclosure of errors.

Current Efforts to Address Medical Error
From a Systems Perspective

A number of initiatives have been developed to study and address the problem
of medical error using a systems approach. Examples include:

• The National Coordinating Council for Medication Error Reporting and
  Prevention (NCC MERP), an organization of pharmacy and health care
  professional groups, the U.S. Food and Drug Administration, the U.S.
  Pharmacopoeia, and consumer organizations, among others, has developed
  numerous recommendations to prevent medication errors. These recommendations,
  addressed to pharmaceutical manufacturers, packagers and repackagers,
  hospitals and hospital pharmacies, outpatient pharmacies, physicians and other
  health care personnel, should lead to the safer use of drugs in all settings.
  

  Among NCC MERP's recommendations: (1) print warnings only on caps and
  ferrules of injectables; (2) make intravenous drug names visible on both sides
  of the container; and (3) print drug names in type that is at least as large
  as company names and logos.⁴²
  

  
  

  The organization is also encouraging the use of its "Medication Error Index
  for Categorizing Errors," a new indexing system that will help researchers to
  track medication errors in a consistent, systematic manner.⁴³
  Widespread use of the index should result in the efficient collection and
  compilation of data on medication error, and thereby allow the development of
  recommendations that could lessen the chance for patient injury.
  

• The National Patient Safety Foundation at the AMA (NPSF) and the National
  Patient Safety Partnership (NPSP) constitute two major initiatives to (1)
  study medical error and (2) develop systems-based responses to reduce the
  incidence of medical error and absorb errors when they do occur so that the
  errors do not reach the patient.


• The NPSF was founded by the American Medical Association in 1997, but is
  now an independent foundation supported by a broad range of organizations,
  including health care professional organizations, consumer organizations,
  insurance companies, managed care organizations, and academicians. The NPSP
  was founded by the U.S. Veterans Administration, and like the NPSF, has a
  broad range of participating organizations. The NPSF and NPSP have recently
  linked their efforts to promote research into the causes and cures for medical
  error and injury. Among the projects they are working on together are:


• (1) an effort to design a voluntary, confidential, non-punitive system
  that would promote the reporting of essential data that would allow
  researchers to learn the nature of systems failures that lead to injury; and
  


• (2) a survey of health care providers and the medical culture as it
  relates to patient safety.

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Conclusion

The systems approach has been successfully employed in non-health care
settings that are, like health care, high risk enterprises. Both the airline
industry's Aviation Safety Reporting System (ASRS) and the National Aeronautics
and Space Administration's (NASA) "Close-Call" reporting system were developed
through use of the systems approach.⁴⁴

As noted above, the success achieved in anesthesiology through the use of a
systems approach to improve patient safety strongly suggests that applying that
approach would be appropriate in other areas of medical practice. Before systems
changes to prevent medical error and patient injury can be devised and
implemented, the weaknesses in the complex systems of medical care that allow,
or even promote, medical errors must be identified and understood. A great deal
of research must be performed before the goal of substantially reducing rates of
preventable injury can be realized.

The systems approach promises significant reductions of preventable medical
error and injury in the future. It cannot, however, eliminate current and future
needs for patient compensation when a preventable injury does occur, despite
systems improvements. Neither can it adequately address errors/injuries that
arise from provider incompetence and/or impairment. Those are matters that must
continue to be addressed through legal and administrative mechanisms.

The work of the NPSF, NPSP, and NCC MERP, among other organizations, to
coordinate and support research and disseminate its results, should lead to
safer medical practice, fewer patient injuries, and reduced health care costs.
Success in preventing or absorbing medical error should prove beneficial to
Medicare beneficiaries, who most frequently suffer medical injuries, and could
save the Medicare program billions of dollars currently devoted to treating
preventable medical injuries.

---

Footnotes

¹ "Medical error" may be defined as "an unintended act (either of
omission or commission) or one that does not achieve its intended outcomes."
Leape, Lucien. "Error in Medicine." Journal of the American Medical
Association 272(23):1851-57 (Dec. 21, 1994).

² Andrews, Lori B., Carol
Stocking, Thomas Krizek, et al. "An Alternative Strategy for Studying Adverse
Events in Medical Care." Lancet 349:309-13 (Feb. 1, 1997).

³ Perrone, J. "Designing a Safer, Smarter Health Care System: AMA
Foundation Looks at Ways to Prevent Mistakes," American Medical News
40(40):1 (Oct. 27, 1997).

⁴ Reduction of medical error
is listed as one of "Six National Aims" in the Report of the President's
Advisory Commission on Consumer Protection and Quality in the Health Care
Industry (March 1998).

⁵ Patients, Doctors, and Lawyers: Medical Injury, Malpractice
Litigation, and Patient Compensation in New York. The Report of the Harvard
Medical Practice Study to the State of New York. Harvard Medical Practice
Study, 1990, 6-23.

⁶ Ibid.

⁷ "Public Opinion of Patient Safety Issues: Research Findings,"
National Patient Safety Foundation at the AMA, September 1997.

⁸ Ibid.

⁹ Ibid.

¹⁰ Ibid.

¹¹ "Medical injuries" here refer to "iatrogenic injuries," i.e.,
injuries or conditions resulting from treatment by physicians or surgeons.

¹² Andrews, et al. (1997).

¹³ Harvard Medical Practice Study (1990).

¹⁴ Leape (1994).

¹⁵ Ibid.

¹⁶ There were 43,910 deaths in 1997 resulting from motor vehicle
accidents. National Center for Health Statistics. "Births, Marriages, Divorces,
and Deaths for February 1997. Monthly Vital Statistics Report." 46: 2. (1997).

¹⁷ Leape (1994).

¹⁸ Andrews and her colleagues used a prospective, observational
approach that followed the care of all patients admitted over a period of time
to three units of a teaching hospital, as opposed to the Harvard Medical
Practice Study that used retrospective reviews of medical records. Andrews, et
al. (1997).

¹⁹ "Interactive causes" refers to "interactions between individuals,
or between individuals and hospital entities, or between hospital entities, such
as the failure of a consultant team to communicate adequately with the
requesting team." Andrews, et al. (1997) at p. 311.

²⁰ Harvard Medical Practice
Study (1990).

²¹ Bates, David W., David J. Cullen, Nan Laird, et al. "Incidence of
Adverse Drug Events and Potential Adverse Drug Events: Implications for
Prevention." Journal of the American Medical Association 274(1): 29-34
(July 5, 1995).

²² Classen,, David C., Stanley L. Pestotnik, R. Scott Evans, et. al.
Adverse Drug Events in Hospitalized Patients," Journal of the American
Medical Association 277(4):301-06 (Jan. 22/29, 1997).

²³ Perrone (1997).

²⁴ Johnson, Jeffrey A. and J. Lyle Bootman. "Drug-Related Morbidity
and Mortality: A Cost-of-Illness Model," Archives of Internal Medicine
155:1949-56 (Oct. 6, 1995). This estimate includes all types of medication
error, both preventable and non-preventable. It does not include costs
associated with injuries that are the result of unforseeable
allergic/idiosyncratic responses or those that occur when the provider knows
that there are risks associated with a drug but prescribes it anyway because, in
his/her judgment, the potential benefits outweigh the risks.

²⁵ When indirect costs due to non-compliance are added to the direct
cost figures, total economic costs rise to approximately $100 billion. Berg, J.S.,
J. Dischler, J.J. Raia, and N. Palmer-Shevlin, "Medication Compliance: A
Healthcare Problem," Annals of Pharmacotherapy 27(9):S3-S22 (1993).

²⁶ Ibid.

²⁷ Bates, David W., Nathan Spell, David J. Cullen, et al. "The Costs
of Adverse Drug Events in Hospitalized Patients," Journal of the American
Medical Association 277(4):307-11 (Jan. 22/29, 1997).

²⁸ See Richards, Edward P. and Katharine C. Rathbun, Law and the
Physician: A Practical Guide. Little, Brown, and Co.:New York (1996).

²⁹ Ibid.

³⁰ See, for example, Public Citizen, "16,638 Questionable Doctors."
(March 1998). It is noted that, although there have been more disciplinary
actions taken against physicians recently, few have been required to stop
practicing medicine, even for a short time. In 1996, 16,638 physicians were
disciplined by state boards or federal agencies. The rate of "serious
disciplinary actions" was 3.96 per 1,000 doctors (2,731 actions).

³¹ Title IV of the Health Care Quality Improvement Act of 1986 (P.L.
99-660) established the National Practitioner Data Bank (NPDB). Regulations
governing the NPDB may be found at 45 CFR Part 60. The information in the NPDB
is available only to state licensing boards, hospitals and other health care
entities, professional societies, certain Federal agencies, and others as
specified in the law. Only hospitals are mandated by law to query the Data Bank.

³² Nonetheless, many states passed "tort reform" measures in the wake
of the alleged medical malpractice insurance crisis of the late 1980s. They
included such measures as placing caps on possible damage awards (particularly
on awards for "pain and suffering"), restrictions on statutes of limitations,
limitations of plaintiff attorneys' fees, and other measures to discourage
potential complainants from filing malpractice actions.

³³ For a brief overview of relevant developments in cognitive
psychology and human factors research, see Leape, p. 1853 (1994).

³⁴ An "activity" is defined as any interaction between health care
personnel and patients that presents an opportunity for an adverse patient
outcome.

³⁵ Leape (1994).

³⁶ W.E. Deming, written communication quoted in Leape (1994).

³⁷ Orkin, P.K. "Patient Monitoring During Anesthesia as an Exercise
in Technology Assessment." In Saidman, L. J. and N.T. Smith, eds. Monitoring in
Anesthesia 3rd Ed. London, England: Butterworth Publishers, Inc. (1993).

³⁸ See Gaba, D.M., "Human Errors in Anesthetic Mishaps,"
International Anesthesiology Clinics 27(3):137-47 (Fall 1989). Also see
Cooper, J.B., R.S. Newbower, and P.J. Kitz, "An Analysis of Major Errors and
Equipment Failures in Anesthesia Management: Considerations for Prevention and
Detection," Anesthesiology 60(1):34-42 (Jan. 1984).

³⁹ Leape, p. 1856 (1994).

⁴⁰ Ibid.

⁴¹ It is recognized that errors are inevitable in any human endeavor,
including the provision of health care. Error "absorption" refers to the notion
that well-designed error prevention systems will "absorb" errors, keeping them
from reaching the patient and causing injury.

⁴² See U.S.P., "Medications Errors Council Recommends Changes to
Medical Product Packaging and Labeling," The Standard (Sep. 16, 1997).

⁴³ U.S.P., "Medication Errors Council Promotes Categorization Index,"
The Standard (October 1996).

⁴⁴ See Helmreich, R.L. "Managing Human Error in Aviation,"
Scientific American 276(5):62-67 (May 1997).

---

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Written by Drew Smith, AARP Public Policy Institute

September 1998

©1998 AARP

May be copied only for noncommercial purposes and with attribution; permission
required for all other purposes.

Public Policy Institute, AARP, 601 E Street, NW, Washington, DC 20049

ABSTRACT OF THE HARVARD MEDICAL PRACTICE STUDY

Brennan T, Leape L, Laird N, Hebert L, Localio A, Lawthers A, et al. Incidence of adverse events and negligence in hospitalized patients: results of the Harvard Medical Practice Study I. N Engl J Med 1991;324:370-6.[Abstract]

Abstract

BACKGROUND. As part of an interdisciplinary study of medical injury and malpractice litigation, we estimated the incidence of adverse events, defined as injuries caused by medical management, and of the subgroup of such injuries that resulted from negligent or substandard care. METHODS. We reviewed 30,121 randomly selected records from 51 randomly selected acute care, nonpsychiatric hospitals in New York State in 1984. We then developed population estimates of injuries and computed rates according to the age and sex of the patients as well as the specialties of the physicians. RESULTS. Adverse events occurred in 3.7 percent of the hospitalizations (95 percent confidence interval, 3.2 to 4.2), and 27.6 percent of the adverse events were due to negligence (95 percent confidence interval, 22.5 to 32.6). Although 70.5 percent of the adverse events gave rise to disability lasting less than six months, 2.6 percent caused permanently disabling injuries and 13.6 percent led to death. The percentage of adverse events attributable to negligence increased in the categories of more severe injuries (Wald test chi 2 = 21.04, P less than 0.0001). Using weighted totals, we estimated that among the 2,671,863 patients discharged from New York hospitals in 1984 there were 98,609 adverse events and 27,179 adverse events involving negligence. Rates of adverse events rose with age (P less than 0.0001). The percentage of adverse events due to negligence was markedly higher among the elderly (P less than 0.01). There were significant differences in rates of adverse events among categories of clinical specialties (P less than 0.0001), but no differences in the percentage due to negligence. CONCLUSIONS. There is a substantial amount of injury to patients from medical management, and many injuries are the result of substandard care.

SO MANY MEDICAL ERRORS...SO LITTLE TIME

Since the Texas Medical Association set themselves
up to decide what is safe and what is not, and to pursue a course of suing the
Board of Chiropractic Examiners in Texas because they don't think Chiropractic
Doctors should be able to diagnose their patients, I thought I should look more
into the Medical Doctor's side of safety, since the pretext of the lawsuit by
the TMA was "protecting the safety of Texas citizens" (my interpretation of
their assertion).

There are so MANY errors committed by Medical
doctors, that a government page is setup to classify them.

A	Back to Top

Active Error (or Active Failure) – The terms
"active" and "latent" as applied to
errors were coined by
James Reason.(1,2)
Active errors occur at the point of contact between a human and some aspect of a
larger system (eg, a human-machine interface). They are generally readily
apparent (eg, pushing an incorrect button, ignoring a warning light) and almost
always involve someone at the frontline.
Latent errors (or
latent conditions), in contrast, refer to less apparent failures of
organization or design that contributed to the occurrence of errors or allowed
them to cause harm to patients.



Active failures are sometimes referred to as errors at the "sharp
end," figuratively referring to a scalpel. In other words, errors at the
sharp end are noticed first because they are committed by the person closest to
the patient. This person may literally be holding a scalpel (eg, an orthopedist
who operates on the wrong leg) or figuratively be administering any kind of
therapy (eg, a nurse programming an intravenous pump) or performing any aspect
of care. To complete the metaphor, latent errors are those at the other end of
the scalpel—the "blunt
end"—referring to the many layers of the health care system that affect the
person "holding" the scalpel.



 

1. Reason JT. Human Error. New York, NY: Cambridge University Press; 1990. [
go
to PSNet listing ]

2. Reason J. Human error: models and management. BMJ. 2000;320:768-770. [

go to PubMed ]

Adverse Drug Event (ADE) – An adverse event involving medication use.



Examples:

 

 

• anaphylaxis to penicillin


• major hemorrhage from heparin


• aminoglycoside-induced renal failure


• agranulocytosis from chloramphenicol

As with the more general term
adverse event,
there is no necessary relation to error or poor quality of care. In other words,
ADEs include expected adverse drug reactions (or "side effects") defined below,
as well as events due to error.



Thus, a serious allergic reaction to penicillin in a patient with no prior such
history is an ADE, but so is the same reaction in a patient who does have a
known allergy history but receives penicillin due to a prescribing oversight.



Ignoring the distinction between expected medication side effects and ADEs due
to errors may seem misleading, but a similar distinction can be achieved with
the concept of preventability. All ADEs due to error are preventable, but other
ADEs not warranting the label
error may also be
preventable.




 

Adverse Drug Reaction – Adverse effect produced by the use of a
medication in the recommended manner. These effects range from "nuisance
effects" (eg, dry mouth with anticholinergic medications) to severe reactions,
such as anaphylaxis to penicillin.

Adverse Event – Any injury caused by medical care.



Examples:

 

• pneumothorax from central venous catheter placement


• anaphylaxis to penicillin


• postoperative wound infection


• hospital-acquired delirium (or "sun downing") in elderly patients

Identifying something as an adverse event does not imply "error,"
"negligence," or poor quality care. It simply indicates that an undesirable
clinical outcome resulted from some aspect of diagnosis or therapy, not an
underlying disease process.



Thus, pneumothorax from central venous catheter placement counts as an adverse
event regardless of insertion technique. Similarly, postoperative wound
infections count as adverse events even if the operation proceeded with optimal
adherence to sterile procedures, the patient received appropriate antibiotic
prophylaxis in the peri-operative setting, and so on. (See also
iatrogenic)

Anchoring Error (or Bias) — Refers to the common
cognitive trap of allowing first impressions to exert undue influence on the
diagnostic process. Clinicians often latch on to features of a patient's
presentation that suggest a specific diagnosis. Often, this initial diagnostic
impression will prove correct, hence the use of the phrase "anchoring heuristic"
in some contexts, as it can be a useful rule of thumb to "always trust your
first impressions." However, in some cases, subsequent developments in the
patient's course will prove inconsistent with the first impression. Anchoring
bias refers to the tendency to hold on to the initial diagnosis, even in the
face of disconfirming evidence.



1. Redelmeier DA. Improving patient care. The cognitive psychology of missed
diagnoses. Ann Intern Med. 2005;142:115-120.

[go to PubMed]



2. Croskerry P. Cognitive forcing strategies in clinical decisionmaking. Ann
Emerg Med. 2003;41:110-120.

[go to PubMed]



3. Croskerry P. The importance of cognitive errors in diagnosis and strategies
to minimize them. Acad Med. 2003;78:775-780.

[go to PubMed]

APACHE –The Acute Physiologic and Chronic Health Evaluation (APACHE)
scoring system has been widely used in the United States. APACHE II is the most
widely studied version of this instrument (a more recent version, APACHE III, is
proprietary, whereas APACHE II is publicly available); it derives a severity
score from such factors as underlying disease and chronic health status.(1,2)
Other points are added for 12 physiologic variables (ie, hematocrit, creatinine,
Glasgow Coma Score, mean arterial pressure) measured within 24 hours of
admission to the ICU. The APACHE II score has been validated in several studies
involving tens of thousands of ICU patients.



 

1. Knaus WA, Draper EA, Wagner DP, Zimmerman JE. APACHE II: a severity of
disease classification system. Crit Care Med. 1985;13:818-29.[

go to PubMed ]



 

2. Knaus WA, Wagner DP, Zimmerman JE, Draper EA. Variations in mortality and
length of stay in intensive care units. Ann Intern Med. 1993;118:753-61.[

go to PubMed ]

Authority Gradient – Refers to the balance of decision-making power or
the steepness of command hierarchy in a given situation. Members of a crew or
organization with a domineering, overbearing, or dictatorial team leader
experience a steep authority gradient. Expressing concerns, questioning, or even
simply clarifying instructions would require considerable determination on the
part of team members who perceive their input as devalued or frankly unwelcome.



Most teams require some degree of authority gradient; otherwise roles are
blurred and decisions cannot be made in a timely fashion. However, effective
team leaders consciously establish a command hierarchy appropriate to the
training and experience of team members.



Authority gradients may occur even when the notion of a team is less well
defined. For instance, a pharmacist calling a physician to clarify an order may
encounter a steep authority gradient, based on the tone of the physician's voice
or a lack of openness to input from the pharmacist. A confident, experienced
pharmacist may nonetheless continue to raise legitimate concerns about an order,
but other pharmacists might not.

Availability Bias (or Heuristic) — Refers to the tendency to assume, when
judging probabilities or predicting outcomes, that the first possibility that
comes to mind (ie, the most cognitively "available" possibility) is also the
most likely possibility. For instance, suppose a patient presents with
intermittent episodes of very high blood pressure. Because episodic hypertension
resembles textbook descriptions of pheochromocytoma, a memorable but uncommon
endocrinologic tumor, this diagnosis may immediately come to mind. A clinician
who infers from this immediate association that pheochromocytoma is the most
likely diagnosis would be exhibiting availability bias. In addition to
resemblance to classic descriptions of disease, personal experience can also
trigger availability bias, as when the diagnosis underlying a recent patient's
presentation immediately comes to mind when any subsequent patient presents with
similar symptoms. Particularly memorable cases may similarly exert undue
influence in shaping diagnostic impressions.



1. Redelmeier DA. Improving patient care. The cognitive psychology of missed
diagnoses. Ann Intern Med. 2005;142:115-120.

[go to PubMed]



2. Croskerry P. Cognitive forcing strategies in clinical decisionmaking. Ann
Emerg Med. 2003;41:110-120.

[go to PubMed]



3. Croskerry P. The importance of cognitive errors in diagnosis and strategies
to minimize them. Acad Med. 2003;78:775-780.

[go to PubMed]









 

B	Back to Top

Bayesian Approach – Probabilistic reasoning in which test results (not
just laboratory investigations, but history, physical exam, or any aspect for
the diagnostic process) are combined with prior beliefs about the probability of
a particular disease. One way of recognizing the need for a Bayesian approach is
to recognize the difference between the performance of a test in a population
vs. in an individual. At the population level, we can say that a test has a
sensitivity and specificity of, say, 90%—ie, 90% of patients with the condition
of interest have a positive result and 90% of patients without the condition
have a negative result. In practice, however, a clinician needs to attempt to
predict whether an individual patient with a positive or negative result does or
does not have the condition of interest. This prediction requires combining the
observed test result not just with the known sensitivity and specificity, but
also with the chance the patient could have had the disease in the first place
(based on demographic factors, findings on exam, or general clinical gestalt).

Benchmark – A "benchmark" in health care refers to an attribute or
achievement that serves as a standard for other providers or institutions to
emulate.



Benchmarks differ from other "standard of care" goals, in that they derive from
empiric data—specifically, performance or outcomes data. For example, a
statewide survey might produce risk-adjusted 30-day rates for death or other
major adverse outcomes. After adjusting for relevant clinical factors, the top
10% of hospitals can be identified in terms of particular outcome measures.
These institutions would then provide benchmark data on these outcomes. For
instance, one might benchmark "door-to-balloon" time at 90 minutes, based on the
observation that the top-performing hospitals all had door-to-balloon times in
this range.



In the present example regarding infection control, benchmarks would typically
be derived from national or regional data on the rates of relevant nosocomial
infections. The lowest 10% of these rates might be regarded as benchmarks for
other institutions to emulate.



The article below provides an excellent discussion of the principles of
benchmarking and the specific steps in using outcomes data to generate
benchmarks.



Kiefe CI, Weissman NW, Allison JJ, et al. Identifying achievable benchmarks of
care: concepts and methodology. Int J Qual Health Care. 1998;10:443-47. [

go to pubmed ]

Blunt End – The "blunt end" refers to the many layers of the health care
system not in direct contact with patients, but which influence the personnel
and equipment at the “sharp
end” who do contact patients. The blunt end thus consists of those who set
policy, manage health care institutions, design medical devices, and other
people and forces, which, though removed in time and space from direct patient
care, nonetheless affect how care is delivered.



Thus, an error programming an intravenous pump would represent a problem at the
sharp end, while the institution’s decision to use multiple different types of
infusion pumps, making programming errors more likely, would represent a problem
at the blunt end. The terminology of “sharp” and “blunt” ends corresponds
roughly to “active
failures” and “latent
conditions.”






 

C	Back to Top

Checklist – Algorithmic listing of actions to be performed in a given
clinical setting (eg, Acute Cardiac Life Support [ACLS] protocols for treating
cardiac arrest) to ensure that, no mater how often performed by a given
practitioner, no step will be forgotten. An analogy is often made to flight
preparation in aviation, as pilots and air-traffic controllers follow
pre-take-off checklists regardless of how many times they have carried out the
tasks involved.

Clinical Decision Support System (CDSS) – Any system designed to improve
clinical decision making related to diagnostic or therapeutic processes of care.
CDSSs thus address activities ranging from the selection of drugs (eg, the
optimal antibiotic choice given specific microbiologic data [1])
or diagnostic tests (2)
to detailed support for optimal drug dosing (3,4)
and support for resolving diagnostic dilemmas.(5)




Structured antibiotic order forms (6)
represent a common example of paper-based CDSSs. Although such systems are still
commonly encountered, many people equate CDSSs with computerized systems in
which software algorithms generate patient-specific recommendations by matching
characteristics, such as age, renal function, or allergy history, with rules in
a computerized knowledge base.



The distinction between decision support and simple reminders can be unclear,
but usually reminder systems are included as decision support if they involve
patient-specific information. For instance, a generic reminder (eg, “Did you
obtain an allergy history?”) would not be considered decision support, but a
warning (eg, “This patient is allergic to codeine.”) that appears at the time of
entering an order for codeine would be.



 

 

1. Evans RS, Pestotnik SL, Classen DC, et al. A computer-assisted management
program for antibiotics and other antiinfective agents. N Engl J Med.
1998;338:232-238.

[

go to PubMed ]

 

2. Harpole LH, Khorasani R, Fiskio J, Kuperman GJ, Bates DW. Automated
evidence-based critiquing of orders for abdominal radiographs: impact on
utilization and appropriateness. J Am Med Inform Assoc. 1997;4:511-521.

[

go to PubMed ]

 

3. Walton RT, Harvey E, Dovey S, Freemantle N. Computerised advice on drug
dosage to improve prescribing practice. Cochrane Database Syst Rev.
2001:CD002894.

[

go to PubMed ]

 

4. Chertow GM, Lee J, Kuperman GJ, et al. Guided medication dosing for
inpatients with renal insufficiency. JAMA. 2001;286:2839-2844.

[

go to PubMed ]

 

5. Friedman CP, Elstein AS, Wolf FM, et al. Enhancement of clinicians'
diagnostic reasoning by computer-based consultation: a multisite study of 2
systems. JAMA. 1999;282:1851-1856.

[

go to PubMed ]

 

6. Avorn J, Soumerai SB, Taylor W, Wessels MR, Janousek J, Weiner M.
Reduction of incorrect antibiotic dosing through a structured educational order
form. Arch Intern Med. 1988;148:1720-1724.

[

go to PubMed ]

Close Call – An event or situation that did not produce patient injury,
but only because of chance. This good fortune might reflect robustness of the
patient (eg, a patient with penicillin allergy receives penicillin, but has no
reaction) or a fortuitous, timely intervention (eg, a nurse happens to realize
that a physician wrote an order in the wrong chart). Such events have also been
termed "near miss"
incidents.

Competency – Having the necessary knowledge or technical skill to perform
a given procedure within the bounds of success and failure rates deemed
compatible with acceptable care.

Complexity Science (or Complexity Theory) - Provides an approach to
understanding the behavior of systems that exhibit non-linear dynamics, or the
ways in which some adaptive systems produce novel behavior not expected from the
properties of their individual components. Such behaviors emerge as a result of
interactions between agents at a local level in the complex system and between
the system and its environment.(1,2)




At first, this may sound indistinguishable from the “systems thinking” commonly
encountered in the patient safety literature. Some people probably use these
terms loosely and occasionally interchangeably, but complexity theory differs
importantly from systems thinking in its emphasis of the interaction between
local systems and their environment (such as the larger system in which a given
hospital or clinic operates). It is often tempting to ignore the larger
environment as unchangeable and therefore outside the scope of quality
improvement or patient safety activities. According to complexity theory,
however, behavior within a hospital or clinic (eg, non-compliance with a
national practice guideline) can often be understood only by identifying
interactions between local attributes and environmental factors.



Another key feature of complexity theory is the emphasis on achieving deep
understanding of a given problem prior to engaging in efforts to change
practice. For instance, instead of simply identifying that providers’ behavior
fails to comply with some target guideline and then implementing an “off the
shelf” means of achieving behavior change (eg, a financial incentive),
complexity theorists might identify what currently works well in a given
practice and the attitudes or structures that provide the basis for what works
well. This process may then reveal an important negative interaction between
local values and perceptions about the national guideline. A more effective
change strategy may then emerge in which the national guideline is adapted for
the local setting. The alternative approach of attempting to force behavioral
change may lead to no improvement or, worse, perverse collateral effects. This
phenomenon is certainly familiar when the complex adaptive system in question is
an ecosystem; complexity theorists advocate that we view health care systems
through a similar lens and not rush into change strategies, however plausible
they may seem. The two references below provide concrete examples to flesh out
the ideas of complexity theory and distinguish it from other major theories of
organizational behavior.(1,2)


 

 

1. Rhydderch M, Elwyn G, Marshall M, Grol R. Organisational change theory
and the use of indicators in general practice. Qual Saf Health Care.
2004;13:213-217.

[

go to PubMed ]

 

2. Plsek PE, Wilson T. Complexity, leadership, and management in healthcare
organisations. BMJ. 2001;323:746-749.

[

go to PubMed ]

Computerized Physician Order Entry or Computerized Provider Order Entry
(CPOE) – Refers to a computer-based system of ordering medications and often
other tests. Physicians (or other providers) directly enter orders into a
computer system that can have varying levels of sophistication. Basic CPOE
ensures standardized, legible, complete orders, and thus primarily reduces
errors due to poor handwriting and ambiguous abbreviations. Almost all CPOE
systems offer some additional capabilities, which fall under the general rubric
of Clinical Decision Support System (CDSS). Typical CDSS features involve
suggested default values for drug doses, routes of administration, or frequency.
More sophisticated CDSSs can perform drug allergy checks (eg, the user orders
ceftriaxone and a warning flashes that the patient has a documented penicillin
allergy), drug-laboratory value checks (eg initiating an order for gentamicin
prompts the system to alert you to the patient’s last creatinine), drug-drug
interaction checks, and so on. At the highest level of sophistication, CDSS
prevents not only errors of commission (eg, ordering a drug in excessive doses
or in the setting of a serious allergy), but also of omission. (For example, an
alert may appear such as, "You have ordered heparin; would you like to order a
PTT in 6 hours?" Or, even more sophisticated: "The admitting diagnosis is hip
fracture; would you like to order heparin DVT prophylaxis?")

Confirmation Bias - Refers to the tendency to focus on evidence that
supports a working hypothesis, such as a diagnosis in clinical medicine, rather
than to look for evidence that refutes it or provides greater support to an
alternative diagnosis.(1,2)
Suppose that a 65-year-old man with a past history of angina presents to the
emergency department with acute onset of shortness of breath. The physician
immediately considers the possibility of cardiac ischemia, so asks the patient
if he has experienced any chest pain. The patient replies affirmatively. Because
the physician perceives this answer as confirming his working diagnosis, he does
not ask if the chest pain was pleuritic in nature, which would decrease the
likelihood of an acute coronary syndrome and increase the likelihood of
pulmonary embolism (a reasonable alternative diagnosis for acute shortness of
breath accompanied by chest pain). The physician then orders an EKG and cardiac
troponin. The EKG shows nonspecific ST changes and the troponin returns slightly
elevated.



Of course, ordering an EKG and testing cardiac enzymes is appropriate in the
work-up of acute shortness of breath, especially when it is accompanied by chest
pain and in a patient with known angina. The problem is that these tests may be
misleading, since positive results are consistent not only with acute coronary
syndrome but also with pulmonary embolism. To avoid confirmation in this case,
the physician might have obtained an arterial blood glass or a D-dimer level.
Abnormal results for either of these tests would be relatively unlikely to occur
in a patient with an acute coronary syndrome (unless complicated by pulmonary
edema), but likely to occur with pulmonary embolism. These results could be
followed up by more direct testing for pulmonary embolism (eg, with a helical CT
scan of the chest), whereas normal results would allow the clinician to proceed
with greater confidence down the road of investigating and managing cardiac
ischemia.



This vignette was presented as if information were sought in sequence. In many
cases, especially in acute care medicine, clinicians have the results of
numerous tests in hand when they first meet a patient. The results of these
tests often do not all suggest the same diagnosis. The appeal of accentuating
confirmatory test results and ignoring nonconfirmatory ones is that it minimizes
cognitive dissonance.(3)




A related cognitive trap that may accompany confirmation bias and compound the
possibility of error is “anchoring
bias”—the tendency to stick with one’s first impressions, even in the face
of significant disconfirming evidence.



 

 

1. Croskerry P. The importance of cognitive errors in diagnosis and
strategies to minimize them. Acad Med. 2003;78:775-780.

[

go to PubMed ]

 

2. Redelmeier DA. Improving patient care. The cognitive psychology of missed
diagnoses. Ann Intern Med. 2005;142:115-120.

[

go to PubMed ]

 

3. Pines JM. Profiles in patient safety: confirmation bias in emergency
medicine. Acad Emerg Med. 2006;13:90-94.

[

go to PubMed ]

Crew Resource Management – Crew resource management (CRM), also called
crisis resource management in some contexts (eg, anesthesia), encompasses a
range of approaches to training groups to function as teams, rather than as
collections of individuals. Originally developed in aviation, CRM emphasizes the
role of "human factors"-the effects of fatigue, expected or predictable
perceptual errors (such as misreading monitors or mishearing instructions), as
well as the impact of different management styles and organizational cultures in
high-stress, high-risk environments.



CRM training develops communication skills, fosters a more cohesive environment
among team members, and creates an atmosphere in which junior personnel will
feel free to speak up when they think the something is amiss. Some CRM programs
emphasize education on the settings in which errors occur and the aspects of
team decision making conducive to "trapping" errors before they cause harm.
Other programs may provide more hands-on training involving simulated crisis
scenarios followed by debriefing sessions in which participants assess their own
and others' behavior.

Critical Incidents – A term made famous by a classic human factors study
by Cooper (1)
of “anesthetic mishaps,” though the term had first been coined in the 1950s.
Cooper and colleagues brought the technique of critical incident analysis to a
wide audience in health care but followed the definition of the originator of
the technique.(2)
They defined critical incidents as occurrences that are “significant or pivotal,
in either a desirable or an undesirable way,” though Cooper and colleagues (and
most others since) chose to focus on incidents that had potentially undesirable
consequences. This definition by itself conveys little—what does “significant or
pivotal” mean? It is best understood in the context of the type of investigation
that follows, which is very much in the style of
root cause
analysis. Thus, “significant or pivotal” means that there was significant
potential for harm (or actual harm), but also that the event has the potential
to reveal important hazards in the organization. In many ways, it is the spirit
of the expression in quality improvement circles, “every defect is a treasure.”(3)
In other words, these incidents, whether
close calls or
disasters in which significant harm occurred, provide valuable opportunities to
learn about individual and organizational factors that can be remedied to
prevent similar incidents in the future.



 

 

1. Cooper JB, Newbower RS, Long CD, McPeek B. Preventable anesthesia
mishaps: a study of human factors. Anesthesiology. 1978;49:399-406.

[

go to PubMed ]

 

2. Flanagan JC. The critical incident technique. Psychol Bull.
1954;51:327-358.

[

go to PubMed ]

 

3. James BC. Every defect a treasure: learning from adverse events in
hospitals. Med J Aust. 1997;166:484-487.

[

go to PubMed ]








 

D	Back to Top

Decision Support – Refers to any system for advising or providing
guidance about a particular clinical decision at the point of care. For example,
a copy of an algorithm for antibiotic selection in patients with community
acquired pneumonia would count as clinical decision support if made available at
the point of care. Increasingly, decision support occurs via a computerized
clinical information or order entry system. Computerized decision support
includes any software employing a knowledge base designed to assist clinicians
in decision making at the point of care.



Typically a decision support system responds to "triggers" or "flags"—specific
diagnoses, laboratory results, medication choices, or complex combinations of
such parameters—and provides information or recommendations directly relevant to
a specific patient encounter. For instance, ordering an aminoglycoside for a
patient with creatinine above a certain value might trigger a message suggesting
a dose adjustment based on the patient's decreased renal function.








 

E	Back to Top

Error – An act of commission (doing something wrong) or omission (failing
to do the right thing) that leads to an undesirable outcome or significant
potential for such an outcome. For instance, ordering a medication for a patient
with a documented allergy to that medication would be an act of commission.
Failing to prescribe a proven medication with major benefits for an eligible
patient (eg, low-dose unfractionated heparin as venous thromboembolism
prophylaxis for a patient after hip replacement surgery) would represent an
error of omission.



Errors of omission are more difficult to recognize than errors of commission but
likely represent a larger problem. In other words, there are likely many more
instances in which the provision of additional diagnostic, therapeutic, or
preventive modalities would have improved care than there are instances in which
the care provided quite literally should not have been provided. In many ways,
this point echoes the generally agreed-upon view in the health care quality
literature that underuse far exceeds overuse, even though the latter
historically received greater attention. (See definition for for
Underuse,
Overuse, Misuse.)



In addition to commission vs. omission, three other dichotomies commonly appear
in the literature on errors:
active failures
vs. latent conditions,
errors at the "sharp end"
vs. errors at the "blunt
end," and slips vs.
mistakes.

Error Chain – Error chain generally refers to the series of events that
led to a disastrous outcome, typically uncovered by a
root cause
analysis. Sometimes the chain metaphor carries the added sense of
inexorability, as many of the causes are tightly coupled, such that one problem
begets the next. A more specific meaning of error chain, especially when used in
the phrase break the error chain, relates to the common themes or categories of
causes that emerge from root cause analyses. These categories go by different
names in different settings, but they generally include (1) failure to follow
standard operating procedures (2) poor leadership (3) breakdowns in
communication or teamwork (4) overlooking or ignoring individual fallibility and
(5) losing track of objectives. Used in this way, break the error chain is
shorthand for an approach in which team members continually address these links
as a crisis or routine situation unfolds. The checklists that are included in
teamwork training programs have categories corresponding to these common links
in the error chain (e.g., establish team leader, assign roles and
responsibilities, monitor your teammates).








 

F	Back to Top

Face Validity – The extent to which a technical concept, instrument, or
study result is plausible, usually because its findings are consistent with
prior assumptions and expectations.



 

Failure Mode and Effect Analysis (FMEA) – Error analysis may involve
retrospective investigations (as in
Root Cause
Analysis) or prospective attempts to predict "error modes." Different
frameworks exist for predicting possible errors. One commonly used approach is
failure mode and effect analysis (FMEA), in which the likelihood of a particular
process failure is combined with an estimate of the relative impact of that
error to produce a "criticality index." By combining the probability of failure
with the consequences of failure, this index allows for the prioritization of
specific processes as quality improvement targets. For instance, an FMEA
analysis of the medication dispensing process on a general hospital ward might
break down all steps from receipt of orders in the central pharmacy to filling
automated dispensing machines by pharmacy technicians. Each step in this process
would be assigned a probability of failure and an impact score, so that all
steps could be ranked according to the product of these two numbers. Steps
ranked at the top (ie, those with the highest "criticality indices") would be
prioritized for error proofing.

Failure to Rescue – "Failure to rescue" is shorthand for failure to
rescue (ie, prevent a clinically important deterioration, such as death or
permanent disability) from a complication of an underlying illness (eg, cardiac
arrest in a patient with acute myocardial infarction) or a complication of
medical care (eg, major hemorrhage after thrombolysis for acute myocardial
infarction). Failure to rescue thus provides a measure of the degree to which
providers responded to adverse occurrences (eg, hospital-acquired infections,
cardiac arrest or shock) that developed on their watch. It may reflect the
quality of monitoring, the effectiveness of actions taken once early
complications are recognized, or both.



The technical motivation for using failure to rescue to evaluate the quality of
care stems from the concern that some institutions might document adverse
occurrences more assiduously than other institutions.(1,2)
Therefore, using lower rates of in-hospital complications by themselves may
simply reward hospitals with poor documentation. However, if the medical record
indicates that a complication has occurred, the response to that complication
should provide an indicator of the quality of care that is less susceptible to
charting bias.



Initial studies of mortality and complication rates after surgical procedures
indicated that lower rates of failure to rescue correlated with other plausible
quality measures.(1,2)
Rates of failure to rescue have since served as outcome measures in prominent
studies of the impacts of nurse-staffing ratios (3,4)
and nurse educational levels (5)
on the quality of care. Examples of the specific "rescue-able" adverse
occurrences in such studies include pneumonia, shock, cardiac arrest, upper
gastrointestinal bleeding, sepsis, and deep venous thrombosis.(4)
Death after any of these in-hospital occurrences would count as failure to
rescue, on the view that early identification by providers can influence the
risk of death.



The AHRQ technical report that developed the AHRQ Patient Safety Indicators (6)
reviews the evidence supporting failure to rescue as a measure of the quality
and safety of hospital care. Although failure to rescue made the final set of
approved indicators, the expert panels that reviewed each candidate indicator
identified some unresolved concerns about its use. For instance, patients with
advanced illnesses may be particularly difficult to rescue from complications
such as sepsis and cardiac arrest. Moreover, patients with advanced illness may
not wish "rescue" from such complications. The initial studies that examined
failure to rescue focused on surgical care, where these issues may not be as
problematic. Nonetheless, the concept of failure to rescue is an important one
and finds increasing application in studies of health care quality and safety.

 

1. Silber JH, Williams SV, Krakauer H, Schwartz JS. Hospital and patient
characteristics associated with death after surgery. A study of adverse
occurrence and failure to rescue. Med Care. 1992;30:615-629.

[

go to PubMed ]

 

2. Silber JH, Rosenbaum PR, Schwartz JS, Ross RN, Williams SV. Evaluation of
the complication rate as a measure of quality of care in coronary artery bypass
graft surgery. JAMA. 1995;274:317-323.

[

go to PubMed ]

 

3. Aiken LH, Clarke SP, Sloane DM, Sochalski J, Silber JH. Hospital nurse
staffing and patient mortality, nurse burnout, and job dissatisfaction. JAMA.
2002;288:1987-1993.

[

go to PubMed ]

 

4. Needleman J, Buerhaus P, Mattke S, Stewart M, Zelevinsky K.
Nurse-staffing levels and the quality of care in hospitals. N Engl J Med.
2002;346:1715-1722.

[

go to PubMed ]

 

5. Aiken LH, Clarke SP, Cheung RB, Sloane DM, Silber JH. Educational levels
of hospital nurses and surgical patient mortality. JAMA. 2003;290:1617-1623.

[

go to PubMed ]

 

6. McDonald KM, Romano PS, Geppert J, et al. Measures of Patient Safety
Based on Hospital Administrative Data—The Patient Safety Indicators. Rockville,
MD: Agency for Healthcare Research and Quality; 2002. AHRQ Publication No.
02-0038.

Available at:

http://www.ahrq.gov/clinic/evrptfiles.htm#psi.

Forcing Function – An aspect of a design that prevents a target action
from being performed or allows its performance only if another specific action
is performed first. For example, automobiles are now designed so that the driver
cannot shift into reverse without first putting her foot on the brake pedal.
Forcing functions need not involve device design. For instance, one of the first
forcing functions identified in health care is the removal of concentrated
potassium from general hospital wards. This action is intended to prevent the
inadvertent preparation of intravenous solutions with concentrated potassium, an
error that has produced small but consistent numbers of deaths for many years.








 

H	Back to Top

Health Literacy – Individuals' ability to find, process, and comprehend
the basic health information necessary to act on medical instructions and make
decisions about their health.(1)




 

 

1. Ad Hoc Committee on Health Literacy for the Council on Scientific Affairs
AMA. Health literacy: report of the Council on Scientific Affairs. JAMA.
1999;281:552-7. [

go to PubMed ]




 

Heuristic – Loosely defined or informal rule often arrived at through
experience or trial and error (eg, gastrointestinal complaints that wake
patients up at night are unlikely to be functional). Heuristics provide
cognitive shortcuts in the face of complex situations, and thus serve an
important purpose. Unfortunately, they can also turn out to be wrong.

The Health Insurance Portability and Accountability Act (HIPAA) – The
Health Insurance Portability and Accountability Act of 1996 (HIPAA) contains new
federal regulations intended to increase privacy and security of patient
information during electronic transmission or communication of "protected health
information" (PHI) among providers or between providers and payers or other
entities.



"Protected health information" (PHI) includes all medical records and other
individually identifiable health information. "Individually identifiable
information" includes data that explicitly linked to a patient as well as health
information with data items with a reasonable potential for allowing individual
identification.



HIPAA also requires providers to offer patients certain rights with respect to
their information, including the right to access and copy their records and the
right to request amendments to the information contained in their records.



Administrative protections specified by HIPAA to promote the above regulations
and rights include requirements for a Privacy Officer and staff training
regarding the protection of patients' information.

High Reliability Organizations (HROs) – High reliability organizations
refer to organizations or systems that operate in hazardous conditions but have
fewer than their fair share of adverse events. (1,2)
Commonly discussed examples include air traffic control systems,