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)