The Top Seven Analytics-Driven Approaches for Reducing Diagnostic Error and Improving Patient Safety

The Top Seven Analytics-Driven
Approaches for Reducing Diagnostic
Error and Improving Patient Safety
̶̶ Kirston Scott & Tracy Vayo

© 2016 Health Catalyst
Proprietary. Feel free to share but we would appreciate a Health Catalyst citation.
Diagnostic Errors and Improving Patient Safety
Diagnostic errors, from missed diagnoses
to misdiagnoses, are all too common in
healthcare—and gathering attention as a
prominent healthcare concern.
In 2015, The National Academy of
Medicine (NAM)—formerly known as the
Institute of Medicine—published a report
in the New England Journal of Medicine
that described the industry’s growing
concern as diagnostic errors become
more clinically and financially damaging.

Diagnostic errors are clinically and
financially costlier today than ever before.
Efforts to identify, monitor, and reduce the
rates of such errors therefore require greater
attention and more dedicated resources
than they’ve received in the past.
DR. BRYAN OSHIRO
Chief Medical Officer, Senior VP
To get [the diagnosis] right, and to get it right the first
time is one of the greatest challenges in healthcare.
It is more important now than ever before due to the
complexity and cost and available cures and
treatments today.”

Accurate, timely diagnosis helps ensure
patients receive the most appropriate care.
Health systems that prioritize diagnostic
process improvements make the most
efficient use of resources and tests, and
ensure the correct diagnostic procedures
and protocols are used.
This presentation takes a closer look at
diagnostic error, from how it’s defined to
what the research reveals about its clinical
and financial impacts and emphasizes the
importance of healthcare analytics.

Back to Basics: Defining Diagnostic Error
NAM defines 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.”
A diagnosis is considered wrong when it’s
inaccurate, incomplete (it doesn’t represent
the patient’s true condition or doesn’t
reveal enough detail for optimal treatment),
or is determined too late to guide effective
treatment decisions.

To explain the different types of diagnostic error, the Society to Improve
Diagnosis in Medicine (SIDM) uses three main categories:
When the diagnostic
tests don’t provide
an explanation for a
patient’s complaints
(common in patients
with chronic fatigue
or chronic pain).
Missed Diagnosis
When the original
diagnosis is found to
be incorrect because
the true cause is
discovered later (i.e.,
heart attack patient
diagnosed with acid
indigestion).
Wrong Diagnosis
The most common
type of diagnostic
error, in which the
diagnosis should
have been made
earlier.
Delayed Diagnosis

Most diagnostic errors are made by conscientious
clinicians practicing in first-rate medical organizations.”
The SIDM also addresses the
misconception that diagnostic errors
primarily result from sub-optimal care.
According to SIDM, most malpractice claims
for diagnostic error involve heart attack,
cancer, or stroke. The delayed diagnosis of
breast cancer is also very common.

The Consequences of Diagnostic Error
The case for reducing diagnostic error
intensifies when healthcare organizations
realize how common it is.
Despite the prevalence of diagnostic error in
the industry, many health systems don’t realize
the severe consequences of these errors.
Examples from recent research demonstrate
how frequently diagnostic errors occur
and how they impact patient safety
and healthcare costs.

Patient Safety Consequences
The Patient Safety Network reported on two
studies that assessed death tolls due to
diagnostic error:
1. A Harvard Medical Practice study found that
diagnostic error accounted for 17 percent of
preventable deaths in hospitalized patients.
2. The second study of autopsy results over
four decades, found that approximately nine
percent of patients experienced a major
undetected diagnostic error in their lifetime.

These studies indicate that diagnostic error
is linked to thousands of deaths each year.
One multicenter review, which examined
669 cases from 22 institutions, revealed
that 28 percent of the study’s reported
diagnostic errors were considered major
(life-threatening or resulted in patient death
or permanent disability).
Forty-one percent of the reported errors
were considered moderate.

The same analysis of malpractice claims
determined that the most frequent outcomes
of diagnostic errors were death, significant
permanent injury, major permanent injury,
and minor permanent injury.
Diagnostic errors more often resulted in
death than other allegation groups and
were the leading cause of claims-
associated death and disability.

Another diagnosis-related factor that
impacts patient outcomes is communication.
NAM emphasizes the importance of
communication, delineating that for a
diagnosis to be useful to patients,
providers must explain it in a way
that helps patients make the appropriate
treatment decisions or lifestyle changes.
Providers must explain why a particular
test is so critical and why it must be done
in a timely fashion.

Financial Consequences
Diagnostic error is responsible for costs associated
with permanent disabilities, unnecessary testing,
lost productivity, and increased insurance payouts.
A 1986–2010 analysis of malpractice claims
assessed the financial consequences of diagnostic
error to find an inflation-adjusted, 25-year sum of
diagnosis-related payments of $38.8 billion (or a
mean per-claim payout of $386,849).
Diagnostic errors were the leading type of claim
(28.6 percent) and accounted for the highest
proportion of total payments (35.2 percent).

There’s no denying the significant impact
diagnostic error has on patient safety and cost.
The consequences of diagnostic error for patients
are countless: the worry, angst, and stress
patients feel when they miss work to come in for
additional visits is just one of many examples.
More severely, a delayed diagnosis could lead to
an increase in morbidity and mortality.
Given the significant patient outcome and
financial consequences of diagnostic error, it
is an industry wide problem health systems
are compelled to solve.

Where to Start: The Top Seven Analytics-Driven
Approaches for Reducing Diagnostic Error
NAM’s 2015 report described several strategies
for improving diagnosis, from establishing formal
medical school curricula about misdiagnosis to
developing standard processes to determine
how frequently diagnostic errors occur.
But improvements won’t happen without
healthcare analytics.
Healthcare analytics is at the heart of reducing
diagnostic errors, as it provides a source of truth
about which diagnostic procedures are effective
in which circumstances, as well as which ones
are less likely to improve outcomes.

Healthcare analytics is at the heart of
reducing diagnostic errors, as it provides
a source of truth about which diagnostic
procedures are effective in which
circumstances, as well as which ones
are less likely to improve outcomes.
With analytics, we don’t have to wait for the publication
of huge studies before we can consider improvement
work; we now have the power to review healthcare data
on our local population and our current practices to
glean insights quickly and to turn our facilities into
continuous learning labs.”
- Dr. Bryan Oshiro

Health systems can reduce diagnostic error by
implementing seven analytics-driven approaches:
1. Key Process Analysis (KPA) to Target Improvement Areas
2. Always Consider Delayed Diagnosis
3. Diagnose Earlier Using Data
4. Use the Choosing Wisely Initiative as a Guide
5. Understand Patient Populations Using Data
6. Collaborate with Improvement Teams
7. Include Patients and Their Families

1: Use Key Process Analysis (KPA) to
Target Improvement Areas
Target improvement areas using an 80/20
Pareto analysis to identify cost-driving
clinical areas and care process variation.
The analysis should combine clinical
and financial data to highlight the best
opportunities for improvement and cost
reduction and guide the development of
applications for improvement initiatives.

1: Use Key Process Analysis (KPA) to
Target Improvement Areas
The analysis should reveal areas with the
greatest cost and volume, and show the
degree of variation within subpopulations
within select clinical programs.
Health Catalyst has developed a tool called
the Key Process Analysis (KPA) Application,
which substantially automates this process
for easy analysis.

2: Always Consider Delayed Diagnosis
Keeping in mind that the cause of delayed
treatment is often delayed diagnosis, health
systems should consider time-sensitive
conditions in which they often fail to meet
time targets for intervention.
In incidents of delayed treatment, health
systems should always ask, “Is delayed
diagnosis the cause?”

3: Diagnose Earlier Using Data
Health systems should use analytics to
determine within their population those
conditions that are being identified later.
For example, do you only identify diabetes
patients when they have complications—
not when they have prediabetes?
In these cases, health systems
should use data to determine what
might be done to diagnose earlier.

4: Use the Choosing Wisely Initiative as a Guide
The Choosing Wisely Initiative promotes patient-
physician conversations about unnecessary medical
tests and procedures, and delayed diagnosis.
Diagnostic accuracy is critically dependent on the
partnership between clinicians and patients.
The initiative also promotes the use of evidence-
based, safe, and necessary testing.

4: Use the Choosing Wisely Initiative as a Guide
Health systems should use Choosing Wisely recommendations
as their guide, using analytics to answer several key questions:
Are we doing too many unnecessary tests (i.e., routine X-
rays/CTs/MRIs for low back pain)?
Do patients who receive certain imaging or lab tests always
meet indications?
Are we taking too much time to get to the right tests?
Are we mistaking symptoms for the wrong condition with a
dramatically different treatment and risk (e.g., heartburn and
heart attack can cause very similar symptoms)?
How do the above factors influence a delayed diagnosis or
wrong diagnosis?
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5: Understand Patient Populations Using Data
Use analytics to understand characteristics of
your patient populations and inform screening
decisions.
In the absence of specific guidelines or
regulations, providers need to implement
local standards that balance costs,
potential benefits, and the potential
for and impact of false-positives.

6: Collaborate with Improvement Teams
Connect with improvement teams already
underway in a particular domain, such as
cardiovascular or respiratory.
Inquire about their greatest diagnostic challenges.
Use analytics to understand the size and impact
of delayed, mistaken, or missed diagnoses.
Then investigate possible causes
and quantify the impact of improvement.

6: Collaborate with Improvement Teams
Consider ways to improve communication and
reduce wasteful tests that may delay diagnosis.
This could be something as simple as adopting
the practice of always asking patients:
“Have you been seen for this problem before?
When and where?”

7: Include Patients and Their Families
Health systems must engage not only patients,
but also patients’ family members who may
have a big picture view of patient’s lives.
This strategy will lead to better patient
engagement, improved diagnostic accuracy,
stronger compliance to treatment, and,
ultimately, better outcomes.
Enabling patients and families to contribute
valuable input will facilitate an accurate and
timely diagnosis and improve care.

Reducing Diagnostic Error: A Success Story
To tackle the significant clinical and
financial consequences of sepsis, one
large medical center reached out to Health
Catalyst to leverage advanced healthcare
analytics solutions to improve sepsis care.
Sepsis, a life-threatening disease caused
by an infection, is responsible for
approximately 200,000 deaths, more than
750,000 hospitalized patients, and 570,000
emergency department visits each year.
Sepsis cost $20.3 billion in 2011. (5.2% of total 2011 inpatient costs.)

The system’s previous partnership with Health
Catalyst gave it a foundation from which to
deploy the Health Catalyst’s Population
Analytics Advanced Application-Sepsis Module.
This scalable platform integrated clinical,
financial, operational, and other data sources to
enable the health system’s teams to track
interventions and their impact on sepsis rates
and financial measures.

The health system also formed a cross-functional
team, who used the sepsis analytics platform to
define cohorts and recommend best practices.
Targeting provider compliance with the 3-hour
sepsis bundle the health system used advanced
healthcare analytics to track the rate at which
nurses complied with the guidelines.
The goal is full compliance with the assessment
and 3-hour sepsis bundle program in order to
reduce time diagnosis of sepsis and prevent
progression to severe sepsis or septic shock.

Working closely with Health Catalyst advisors
to facilitate implementation and adoption, the
health system developed a prototype electronic
algorithm for automating sepsis assessment.
The results are impressive: they’ve tracked an
80 percent relative increase in compliance and
a 41 percent absolute increase in nurses’
compliance with completing assessments.

In less than 12 months, the health
system significantly improved outcomes
and reduced costs, with an average
decrease in mortality of 22 percent and
more than $1.3 million in cost savings.

Reducing Diagnostic Error Is a Healthcare Imperative
Simply put, reducing diagnostic error is a
healthcare imperative because it will
improve patient safety and outcomes,
reduce costs, and protect resources.
With accurate and timely diagnosis,
healthcare systems can help patients get
the care they need (when they need it),
while avoiding spending money and
wasting resources on treatments that
are unlikely to help the patient.

Reducing Diagnostic Error Is a Healthcare Imperative
As Dr. Oshiro explains, appropriate,
data-driven diagnosis is the first clinical
step in outcomes improvement:
With seven approaches grounded in
analytics as their guide, health systems
can start tackling diagnostic error.
We talk about ‘having a single source of truth’ in
our work; diagnostic accuracy is the [source of
truth] for all treatment.”

For more information:
“This book is a fantastic piece of work”
– Robert Lindeman MD, FAAP, Chief Physician Quality Officer

More about this topic
Link to original article for a more in-depth discussion.
The Top Seven Analytics-Driven Approaches for Reducing Diagnostic Error and Improving Patient
Safety
How to Improve Sepsis Diagnosis and Outcomes through Innovative Healthcare Analytics
Success story
How to Reduce Sepsis Mortality Rates by 22%
Success story
The Top Six Early Detection and Action Must-Haves for Improving Outcomes
Kirstin Scott, Senior Medical Writer; Tracy Vayo, VP of Knowledge Services
The Top Five Essentials for Outcomes Improvement
Leslie Hough Falk, Customer Engagement, VP; Ann Tinker, Engagement Executive, VP
Analytics in Medical Practice: How to Get Physicians On Board and Engaged
Dr. Bryan Oshiro, Chief Medical Officer, Senior VP

Kirstin Scott has a background in instructional design. She began work at Health Catalyst
in 2014 after 12 years as a medical writer at Intermountain Healthcare. There she worked
primarily with Women and Newborns and Pediatric Specialty Clinical Programs.
Other Clinical Quality Improvement Resources
Click to read additional information at www.healthcatalyst.com
Tracy Vayo joined Health Catalyst in 2014 as Director of Knowledge Development,
bringing over 25 years of experience in various facets of healthcare and clinical writing and
education. Immediately prior to her work at Health Catalyst, Tracy spent nearly 13 years
directing the efforts of a clinical publication team at Intermountain Healthcare – creating
innovative care process models, patient education, and implementation tools for clinical
best practice improvement. Prior to that she directed multidisciplinary education programs
at Primary Children’s Hospital and created and managed cardiac rehabilitation and wellness programs
for several hospital systems. Tracy holds a Master’s degree in clinical exercise science and a
Bachelor’s degree in health education with a communications emphasis.

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