The Safety and Quality of Health Care
53CHAPTER 8
quality of care delivered in the United States to date. The results were
sobering. The authors found that, across a wide range of quality parameters, patients in the United States received only 55% of recommended
care overall; there was little variation by subtype, with scores of 54%
for preventive care, 54% for acute care, and 56% for care of chronic
conditions. The authors concluded that, in broad terms, the chances
of getting high-quality care in the United States were little better than
those of winning a coin flip.
Work from the Dartmouth Atlas of Health Care evaluating geographic variation in use and quality of care demonstrates that, despite
large variations in utilization, there is no positive correlation between
the two variables at the regional level. An array of data demonstrate,
however, that providers with larger volumes for specific conditions,
especially for surgical conditions, do have better outcomes.
Strategies for Improving Quality and Performance Many
specific strategies can be used to improve quality at the individual level,
including rationing, education, feedback, incentives, and penalties.
Rationing has been effective in some specific areas, such as persuading
physicians to prescribe within a formulary, but it generally has been
resisted. Education is effective in the short run and is necessary for
changing opinions, but its effect decays fairly rapidly with time. Feedback on performance can be given at either the group or the individual
level. Feedback is most effective if it is individualized and is given in
close temporal proximity to the original events. Incentives can be effective, and many believe that they will prove to be a key to improving
quality, especially if pay-for-performance with sufficient incentives is
broadly implemented (see below). Penalties produce provider resentment and are rarely used in health care.
Another set of strategies for improving quality involves changing
the systems of care. An example would be introducing reminders
about which specific actions need to be taken at a visit for a specific
patient—a strategy that has been demonstrated to improve performance in certain situations, such as the delivery of preventive services.
Another approach that has been effective is the development of “bundles” or groups of quality measures that can be implemented together
with a high degree of fidelity. Many hospitals have implemented a
bundle for ventilator-associated pneumonia in the intensive care unit
that includes five measures (e.g., ensuring that the head of the bed
is elevated). These hospitals have been able to improve performance
substantially. Another technique is SCAMPs, or Standardized Clinical
Assessment and Management Plans. These are care guidelines developed by clinicians who identify key steps in workflow and decisions to
help improve the process outcomes.
Perhaps the most pressing need is to improve the quality of care
for chronic diseases. The Chronic Care Model has been developed by
Wagner and colleagues (Fig. 8-3); it suggests that a combination of
strategies is necessary (including self-management support, changes
in delivery system design, decision support, and information systems)
and that these strategies must be delivered by a practice team composed of several providers, not just a physician.
Available evidence about the relative efficacy of strategies in reducing hemoglobin A1c (HbA1c) in outpatient diabetes care supports this
general premise. It is especially notable that the outcome was the
HbA1c level, as it has generally been much more difficult to improve
outcome measures than process measures (such as whether HbA1c was
measured). In this meta-analysis, a variety of strategies were effective,
but the most effective ones were the use of team changes and the use
of a case manager. When cost-effectiveness is considered in addition, it
appears likely that an amalgam of strategies will be needed. However,
the more expensive strategies, such as the use of case managers, probably will be implemented widely only if pay-for-performance takes hold.
The evidence linking better performance on quality metrics assessing process and outcomes varies greatly by condition. For example,
there is strong evidence that performing Pap smears results in better
outcomes in patients who develop cervical cancer, but the evidence for
many other conditions is far more tenuous.
National State of Quality Measurement In the inpatient setting, quality measurement is now being performed by a very large
proportion of hospitals for several conditions, including myocardial
infarction, congestive heart failure, pneumonia, and surgical infection
prevention; 20 measures are included in all. This is the result of the
Hospital Quality Initiative, which represents a collaboration among
many entities, including the Hospital Quality Alliance, The Joint Commission, the National Quality Forum, and the Agency for Healthcare
Research and Quality. The data are housed at the Centers for Medicare
and Medicaid Services, which publicly releases performance data on
the measures on a website called Hospital Compare (www.cms.gov/
Medicare/Quality-Initiatives-Patient-Assessment-Instruments/HospitalQualityInits/HospitalCompare.html). These data are reported voluntarily and are available for a very high proportion of the nation’s hospitals.
Analyses demonstrate substantial regional variation in quality and
important differences among hospitals. Analyses by The Joint Commission for similar indicators reveal that performance on measures by
hospitals has improved over time and that, as might be hoped, lower
performers have improved more than higher performers.
Public Reporting Overall, public reporting of quality data is
becoming increasingly common. There are now commercial websites
that have quality-related data for most regions of the United States, and
Act
Plan
Do
Check
Adopt or abandon
strategies based
on results
Identify potential
improvement
strategies
Try out
strategies
Measure
effectiveness
of strategies
FIGURE 8-2 Plan-Do-Check-Act cycle. This approach can be used to improve
a specific process rapidly. First, planning is undertaken, and several potential
improvement strategies are identified. Next, these strategies are evaluated in
small “tests of change.” “Checking” entails measuring whether the strategies have
appeared to make a difference, and “acting” refers to acting on the results.
Productive
interactions
Informed,
activated
patient
Prepared,
proactive
practice team
Improved Outcomes
Selfmanagement
Support
Delivery
system
design
Decision
support
Clinical
information
systems
Community
Resources and policies
Health System
Organization of health care
FIGURE 8-3 The Chronic Care Model, which focuses on improving care for
chronic diseases, suggests that (1) delivery of high-quality care requires a range
of strategies that must closely involve and engage the patient and (2) team care is
essential. (From EH Wagner et al: Eff Clin Pract 1:2, 1998.)
54PART 1 The Profession of Medicine
Diagnosing patients’ illnesses is the essence of medicine. Patients
present to doctors seeking an answer to the question, “What is wrong
with me?” Ideally, no clinician would want to treat a patient without
knowing the diagnosis or, worse yet, erroneously treat a misdiagnosed
illness. From the earliest moments of medical school, the defining
quest toward becoming a knowledgeable and proficient physician
is learning how to put a diagnostic label on patients’ symptoms and
physical findings, and clinicians pride themselves on being “good
diagnosticians.” Yet the centuries-old paradigm of mastering a long
list of diseases, understanding their pathophysiology, and knowing the
cardinal ways they manifest themselves in signs and symptoms, while
still of fundamental importance, is being challenged by new insights
illuminated by the glaring spotlight of diagnostic errors. Basic internal
medicine diseases, such as asthma, pulmonary embolism, congestive heart failure, seizures, strokes, ruptured aneurysms, depression,
and cancer, are misdiagnosed at shockingly high rates, often with
20–50% of patients either being mislabeled as having these conditions
(false-positive diagnoses) or having their diagnosis missed or delayed
(false negatives). How and why do physicians so often get it wrong,
and what can we do to both diagnose and treat the problem of delayed
diagnosis or misdiagnosis?
Diagnosis is both an ancient art and a modern science. The current
science of diagnosis, however, goes far beyond what typically comes to
clinicians’ and patients’ minds when they conjure up images of stateof-the-art molecular, genetic, or imaging technologies. Improvements
in diagnosis are just as likely to come from other areas, many with
origins outside of medicine, as they are from advanced diagnostic
testing modalities. These diverse sciences that the field of diagnostic
safety has, and must, draw from include systems and human factors
9 Diagnosis: Reducing
Errors and Improving
Quality
Gordon Schiff
these data can be accessed for a fee. Similarly, national data for hospitals are available. The evidence to date indicates that patients have not
made much use of such data, but that the data have had an important
effect on provider and organization behavior. Instead, patients have
relied on provider reputation to make choices, partly because little
information was available until very recently and the information that
was available was not necessarily presented in ways that were easy for
patients to access. Problems still exist with quality metrics; many can be
“gamed,” and even though providers are now nearly universally using
electronic health records (EHRs), most metrics come from claims that
include many inaccuracies. More metrics that leverage EHRs are sorely
needed. However, many authorities think that, as more information
about quality becomes available, it will become increasingly central to
patients’ choices about where to access care.
Pay-for-Performance Currently, providers in the United States
get paid the same amount for a specific service, regardless of the quality of care delivered. The pay-for-performance theory suggests that, if
providers are paid more for higher-quality care, they will invest in strategies that enable them to deliver that care. The current key issues in the
pay-for-performance debate relate to (1) how effective it is, (2) what
levels of incentives are needed, and (3) what perverse consequences
are produced. The evidence on effectiveness is limited, although a
number of studies are ongoing. With respect to incentive levels, most
quality-based performance incentives have accounted for merely 1–2%
of total payment in the United States to date. In the United Kingdom,
however, 40% of general practitioners’ salaries have been placed at
risk according to performance across a wide array of parameters;
this approach has been associated with substantial improvements in
reported quality performance, although it is still unclear to what extent
this change represents better performance versus better reporting. The
potential for perverse consequences exists with any incentive scheme.
One problem is that, if incentives are tied to outcomes, there may be a
tendency to transfer the sickest patients to other providers and systems.
Another concern is that providers will pay too much attention to quality measures with incentives and ignore the rest of the quality picture.
The validity of these concerns remains to be determined. Nonetheless,
it appears likely that, under health care reform, the use of various payfor-performance schemes is likely to increase.
■ CONCLUSIONS
The safety and quality of care in the United States could be improved
substantially. A number of available interventions have been shown
to improve the safety of care and should be used more widely; others
are undergoing evaluation or soon will be. Quality also could be dramatically better, and the science of quality improvement continues to
mature. Implementation of value-based approaches such as accountable
care that include pay-for-performance related to safety and quality
should make it much easier for organizations to justify investments in
improving safety and quality parameters, including health information
technology. However, many improvements will also require changing
the structure of care—e.g., moving to a more team-oriented approach
and ensuring that patients are more involved in their own care. Payment
reform focusing on value seems very likely to progress and will likely
include both positive incentives and penalties related to safety and
quality performance. Measures of safety are still relatively immature
and could be made much more robust; it would be particularly useful
if organizations had measures they could use in routine operations to
assess safety at a reasonable cost, and substantial research is addressing
this. Although the quality measures available are more robust than
those for safety, they still cover a relatively small proportion of the entire
domain of quality, and more measures need to be developed. The public
and payers are demanding better information about safety and quality
as well as better performance in these areas. The clear implication is that
these domains will have to be addressed directly by providers.
■ FURTHER READING
Bates DW et al: Effect of computerized physician order entry and a
team intervention on prevention of serious medication errors. JAMA
280:1311, 1998.
Bates DW et al: Two decades since to err is human: An assessment
of progress and emerging priorities in patient safety. Health Aff
(Millwood) 37:1736, 2018.
Berwick DM: Era 3 for medicine and health care. JAMA 315:1329,
2016.
Brennan TA et al: Incidence of adverse events and negligence in hospitalized patients. Results of the Harvard Medical Practice Study I. N
Engl J Med 324:370, 1991.
Chertow GM et al: Guided medication dosing for inpatients with
renal insufficiency. JAMA 286:2839, 2001.
Institute of Medicine. Report: To err is human: Building a safer
health system. 1999. https://www.nap.edu/resource/9728/To-Err-isHuman-1999--report-brief.pdf.
Institute of Medicine. Crossing the quality chasm: A new health
system for the 21st century. 2001. https://www.nap.edu/catalog/10027/
crossing-the-quality-chasm-a-new-health-system-for-the.
Landrigan C et al: Effect of reducing interns’ work hours on serious
medical errors in intensive care units. N Engl J Med 351:1838, 2004.
McGlynn EA et al: The quality of health care delivered to adults in the
United States. N Engl J Med 348:2635, 2003.
Pronovost P et al: An intervention to decrease catheter-related bloodstream infections in the ICU. N Engl J Med 355:2725, 2006. Erratum
in: N Engl J Med 356:2660, 2007.
Starmer AJ et al: Rates of medical errors and preventable adverse
events among hospitalized children following implementation of a
resident handoff bundle. JAMA 310:2262, 2013.
Diagnosis: Reducing Errors and Improving Quality
55CHAPTER 9
can also lead to biases and errors. Table 9-2 lists some of the common
cognitive biases that can lead diagnosis astray (this topic is discussed
further in Chap. 4).
However, clinicians will also benefit from having a better understanding of diagnosis as a “system” rather than just what takes place in
clinicians’ minds. Classic teaching exhorting trainees and practicing
physicians to have a broad differential and “high index of suspicion”
for various diseases is challenged not only by these unconscious biases
but also by limitations of human memory, information shortfalls,
constrained encounter time, system process failures, and the myriad
nonspecific symptoms that patients bring to clinicians. Many symptoms are self-limited, defy a precise diagnosis or etiology, and do not
portend harmful outcomes. Insights from safety and cognitive sciences
call for rethinking traditional approaches to diagnosis and suggest new
approaches to overcome current limitations (Table 9-3).
■ UNCERTAINTY IN DIAGNOSIS
Given variations and overlap in ways patients present, illnesses evolve,
and tests perform, it is often not possible or practical to “make” a
definitive diagnosis, particularly in the primary care setting early
in the course of a patient’s illness. Clinicians need to harness these
uncertainties to both engineer situational awareness of where things
Diagnostic
Process
Failures
Delayed,
Missed,
or Wrong
Diagnosis
Adverse
Outcomes
FIGURE 9-1 What is a diagnosis error? (Adapted from GD Schiff et al: Diagnosing
diagnosis errors: Lessons from a multi-institutional collaborative project, in
Advances in Patient Safety: from Research to Implementation. Vol. 2 Concepts and
Methodology, Rockville, MD, 2005, pp. 255-278, and GD Schiff, L Leape: Acad Med
87:135, 2012.)
TABLE 9-1 National Academy of Medicine Recommendations for
Improving Diagnosis in Health Care
1. Facilitate more effective teamwork in the diagnostic process among health
care professionals, patients, and their families.
2. Enhance professional education and training in the diagnostic process in
areas such as clinical reasoning; teamwork; communication with patients,
families, and other health care professionals; and appropriate use of
diagnostic tests.
3. Ensure that health information technologies support patients and health care
professionals in the diagnostic process.
4. Develop and deploy approaches to identify, learn from, and reduce diagnostic
errors and near misses in clinical practice including providing systematic
feedback on diagnostic performance.
5. Establish a work system and culture that supports the diagnostic process and
improvements in diagnostic performance.
6. Develop a reporting environment and medical liability system that facilitates
improved diagnosis by learning from diagnostic errors and near misses.
7. Design a payment and care delivery environment that supports the diagnostic
process.
8. Provide dedicated funding for research on the diagnostic process and
diagnostic errors.
engineering, reliability science, cognitive psychology, decision sciences,
forensic science, clinical epidemiology, health services research, decision analysis, network medicine, learning health systems theory, medical sociology, team dynamics and communication, risk assessment and
communication, information and knowledge management, and health
information technology, especially artificial intelligence and clinical
decision support. A clinician reading this chapter is likely to find this
list of overlapping and intersecting domains quite daunting. However,
rather than feeling overwhelmed, we urge readers to view them as the
basic science supports that will ultimately make their lives easier and
diagnosis more accurate and timely. Rather than feeling intimidated,
clinicians should feel a sense of relief and assurance in understanding
that good diagnosis does not rest entirely on their shoulders. Instead,
it is a systems property, where an infrastructure and a team, one that
especially includes the patient, can in a coordinated way work together
to achieve more reliable and optimal diagnosis.
■ EMERGENCE OF DIAGNOSIS ERROR AS AN
IMPORTANT PATIENT SAFETY ISSUE
Over the past decade, a series of studies culminating in a landmark
report from the U.S. National Academy of Medicine (NAM), Improving
Diagnosis in Health Care, have shone a spotlight on diagnostic errors.
Reports from patient surveys, malpractice claims, and safety organizations, such as the ECRI and the National Patient Safety Foundation
(now part of Institute for Healthcare Improvement), have found that
diagnostic errors are the leading type of medical error. Although errors
in diagnosis are defined in various ways, the NAM Committee defined
diagnostic error as “the failure to (a) establish an accurate and timely
explanation of the patient’s health problem(s) or (b) communicate that
explanation to the patient.” One way to visualize diagnostic errors is
through a Venn diagram (Fig. 9-1), which illustrates the fact that many
things can go wrong in the diagnostic process (e.g., failure to ask an
important history question, physical examination sign overlooked, laboratory specimen erroneously switched between two patients, x-ray not
followed up), but this usually does not result in a wrong diagnosis or
patient harm. Similarly, a patient can be misdiagnosed but unharmed,
without any identifiable error in the care received. Our greatest concern is where these three circles intersect, with conservative estimates
suggesting that 40,000–80,000 patients die each year in U.S. hospitals
alone from diagnostic errors. The NAM report outlined eight recommendations that are the foundation for this chapter (Table 9-1).
■ NEW WAYS TO THINK ABOUT DIAGNOSIS AND
DIAGNOSTIC ERRORS
Medical textbooks have historically given attention to “clinician
reasoning” and associated cognitive heuristics and biases. Errors
in clinical reasoning can be summarized in three broad groups: (1)
hasty judgments, (2) biased judgments, and (3) inaccurate probability
estimates. Research from cognitive psychology has identified scores
of common mental shortcuts or “heuristics” humans are prone to use
in everyday life, many of which are useful for efficient diagnosis but
TABLE 9-2 Selected Cognitive Biases Contributing to Diagnostic
Errors
1. Premature closure: accepting a diagnosis before it has been fully verified
2. Anchoring: tendency to fixate on a specific symptom or piece of information
early in the diagnostic process with subsequent failure to appropriately
adjust
3. Confirmation bias: tendency to look for confirming evidence to support one’s
diagnostic hypothesis, rather than disconfirming evidence to refute it
4. Search satisficing: tendency to call off a search, satisfied once a piece of
data or presumed explanation is found, and not considering/searching for
additional findings or diagnoses
5. Availability bias: tendency to give too much weight to diagnoses that come
more readily to mind (e.g., recent dramatic case)
6. Base-rate neglect: failing to adequately take into account prevalence of a
particular disease (e.g., erroneously interpreting a positive test as indicating
disease in a low-prevalence population using a test with 5% false-positive
rate)
7. Knowledge deficit (on part of provider, with accompanying lack of
awareness)
8. Framing bias: Judgement overly influenced by the way the problem was
presented (how it was framed in words, settings, situations)
9. Social/demographic/stereotype bias: biases from personal or cultural beliefs
about women, minorities, or other patient groups for whom prejudices may
distort diagnostic assessment
56PART 1 The Profession of Medicine
TABLE 9-3 New Models for Conceptualizing Diagnosis and Diagnosis Improvement
TRADITIONAL WAYS OF THINKING ABOUT DIAGNOSIS
AND DIAGNOSTIC ERROR NEW PARADIGMS/BETTER WAYS TO THINK ABOUT DIAGNOSIS AND IMPROVING DIAGNOSIS
General
A good diagnostician gets it right the first time, almost all
of the time
Diagnosis is an inexact science with inherent uncertainties
Goal is to minimize errors and delays via more reliable systems and follow-up
Lore of masterful/skillful academic expert diagnostician
who knows/recalls everything; need to look to them if
seeking diagnostic excellence
Less reliance on (fallible) human memory
Quality diagnosis is based on well-coordinated distributed network/team of people and reliable processes
All patients entitled to receive quality diagnosis, regardless of where and from whom they receive care
Diagnosis is the doctor’s job Co-production of diagnosis among clinicians (including lab, radiology, specialists, nurses, social workers)
and, especially, the patient and family
Patients often viewed as overly anxious, exaggerating,
time-consuming, questioning, with sometimes
unreasonable demands and expectations
Patients are key allies in diagnosis; hold key information
Need to address understandable/legitimate fears, desires for explanations
Leveraging patient questions and questioning of diagnosis to stimulate rethinking the diagnosis where
needed
Diagnosis and treatment as separate stages in patient care
(i.e., make a diagnosis, then treat)
Prioritizing diagnostic efforts to target treatable conditions
More integrated strategies and timing for testing and treatment depending on urgency for treatment
Clinical practices
Order lots of tests to avoid missing diagnoses Judicious ordering: targeted, well-organized data and testing
Appreciation of test limitations (false positive or negative, incidental findings, overdiagnosis, test risks)
and resulting harms
More referrals to avoid missing rarer/specialized
diagnoses; concomitant utilization barriers (copays, prior
authorization) to minimize overuse
“Pull systems” to lower barriers and make it easier to pose questions, obtain real-time virtual consults
Co-management approaches to enable collaborative watch-and-wait conservative strategies where
appropriate
Frequent empirical drug trials when uncertain of diagnosis Conservative use of drugs to avoid confusing clinical picture or labeling patients with diseases they may
not have
Physician attention/efforts to ensure disease screening Automating, delegating clerical functions; teamwork to free up physician cognitive time
Diagnosis errors and challenges
Diagnostic error viewed as a personal failing
Errors classified as either “system” or “cognitive”
Many errors/delays rooted in processes and system design/failures
Errors multifactorial with interwoven, interacting, and inseparable cognitive and system factors
Errors are infrequent; hit-and-miss ways to learn about
errors
Errors are common; systematic proactive follow-up is needed to recognize potential for errors
Surveilling of high-risk situations and one’s own diagnostic performance and outcomes
Clinicians’ reactions: denial, defensive, others to blame,
pointing to others also making similar errors
Culture of actively and nondefensively seeking to uncover, dig deep to learn from, and share errors and
lessons
Dreading complex, frustrating diagnostic dilemmas Welcoming/enjoying intellectual/professional challenges
Adequate support (time, help, consultations) for more complex patients
Diagnoses as distinct labels, events Diagnoses can be indistinct, interacting comorbidities, socially constructed, multifactorial, evolving over
time, or have overlapping genotype-phenotype expressions
Documentation/communication
Viewed as time-consuming, mindless, primarily to
document for billing code and/or bulwark against
malpractice claims
Documentation as useful tool for reflecting, crafting, sharing assessments, differential diagnosis,
reflecting about unanswered questions
Opportunities for decision support interacting with computer
Notes open for patients to read to help understand and critique diagnosis
Say and write as little as possible about uncertainties, lest
it be used against you in malpractice allegation
Share uncertainties to maximize communication and engagement with other caregivers, patients
Don’t let patient know about errors so they don’t become
angry, mistrustful, or sue
Patients have right to honest disclosure; often find out about errors anyway (e.g., cancer evolves);
anticipate, engage their concerns
Patients advised to call if not better; no news is good news
(test results: “We’ll call if anything is abnormal.”)
Systematic proactive follow-up to close loop on tests and symptoms, to check how patient is doing,
monitor outcomes
Global remedies
Knowing/memorizing more medical knowledge Knowing more about the patient (including psychosocial, past history, environmental contexts)
Attention to the “objective” data (physical exam, tests) to
reliably make diagnoses
Renewed emphasis on history, history-taking, listening
Acknowledgement of ubiquitous subjective cognitive biases; efforts to anticipate, recognize, counteract
Exhortations to have “high index of suspicion” of various
diagnoses
Less reliance on memory recall of lectures/reading; more just-in-time info look-up
Affordances, alerts to red flags engineered into workflow
Delineation of “don’t miss” diagnoses with design of context-relevant decision support reminders
Ensuring physician is copied on everything, thorough/
voluminous notes, widespread reminders/alerts
Biggest problem is no longer lack of access to information, but rather information overload; strategies to
organize, minimize
Continuing medical education (CME) courses to expand
medical knowledge
Real-time, context-aware reminders of pitfalls, critical differential diagnoses, and key differentiating
features.
Ready access to medical references, second opinions
(Continued)
