Live SessionModule #10
Patient Safety at a Glance:
Chapter 17 Technology in
Healthcare and E-Iatrogenesis
HCM520
Quality and Patient Safety
Overview
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International struggle to cope with demand
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Increasing population
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Demographic changes
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Greater numbers of elderly
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Patients with multiple long-term conditions
Seeking new/innovative ways to cope
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Technological innovation
► Reduce risks with care provision
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Improving communication
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Promoting accessibility
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Assisting with decision making
► Inadvertent error risk occurs
Course Code and Title
Epidemiology of IT-Related
Errors
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Healthcare Technology
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Used by all healthcare professionals in developed countries
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Increasingly used in developing countries
Technical Complications
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Account for 13% of adverse healthcare events
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~400 patients/year die in UK due to medical devices
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Expected to increase
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Lack of understanding
Course Code and Title
Nature of Technology-Related
Errors in Healthcare Settings
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Key Term
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E-iatrogenesis: errors and adverse events resulting from
the use of technology
Can involve software/hardware
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Needed but unavailable
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Malfunctions during use
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Used in ways other than intended
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Interacts with other technology in unintended ways
Results due to design and implementation in an
organization
Course Code and Title
Technological Design: Latent
Example
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Technology
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Designed with safety in mind
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Help address surrounding error producing conditions
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Help address human shortcomings
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Not fool-proof, may bring inherent risks
Latent Condition Example
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Bar-code verification technology
► Help reduce medication errors
► Identifies correct patient, correct medication
► Scans patient wristband against medication to be
administered
► Minimize risk of confusing patients/doses
Course Code and Title
Technological Design: Human
Cognition Example
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Prescribing systems
► Holding prescribing-related information
► Alert prescribers to incorrect dose, contraindications,
allergies
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Display of info can impact behavior
► Large pop-up alerts become tiring (alert fatigue)
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Usability analysis: Heuristic Evaluation
► Minimize design-related errors thru assessment:
Course Code and Title
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Simple dialogue, Minimize memory load
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Consistent feedback
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Shortcuts
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Effective error messages
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Prevent errors
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Facilitate documentation
Need for Human-Centered Design
Course Code and Title
Implementation
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E-iatrogenesis can be result of implementation
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If users are not trained
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If system does not fit with work practices
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Unintended patterns of usage may result
Workarounds get created
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Eg: system requires large number of clicks
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May delay data entry until later in workday
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May result in out-of-date records
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Inability to access important information
Mitigating strategies
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Include process mapping
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Assess existing workflows
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Re-design process
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Course Code and Title
Consult users
Reduce Technology-Related Errors:
Safe implementation
► Toolkits for change
► Guide implementers through whole cycle
► Conception, planning, implementation,
onging maintenance
► Example: Stratis Health’s Health
Information Technology Toolkit for Critical
Access and Small Hospitals
► http://www.stratishealth.org/expertise/h
ealthit/hospitals/htoolkit.html
► Based on US context, transferable to other
countries
Course Code and Title
Reduce Technology-Related
Errors: National Guidelines
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Aim to reduce errors
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2010 UK’s NPSA guidelines for safe on-screen display
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Advice for designers
► Displaying text, symbol, drug names, numbers, units of measure,
etc
► Eg: busy users misread numbers due to trailing zeros
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5.0mg may be read as 50mg resulting in overdose
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NPSA recommends leaving trailing zeros out
NPSA’s guide to design of infusion devices
► Hardware, software specification/recommendation
► Some
devices do not alert if syringe/plunger not secured
► Potential for disengaging
Course Code and Title
Reduce Technology-Related Errors:
Safe Implementation/Design
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Global initiatives for implementation/design sources of
error
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WHO Dept of Essential Health Technologies
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Oversee international efforts
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Develop policies/guidelines for use/implementation of
technology on empirical evidence
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4 sub-streams
► Blood Transfusion Safety
► Clinical Procedures
► Diagnostic Imaging/Medical Devices
► Diagnostics/Laboratory Technology
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Strengthen existing technology within economic ability
Course Code and Title
Function of IT Systems in
Healthcare
Course Code and Title
Assumptions Underlying Intro
of IT in Healthcare
Course Code and Title
Conclusion
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Tech systems made headway to ensure care is
safe/efficient
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Can introduce new sources of error
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Can introduce potentially avoidable harm
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Design- and implementation- related initiatives
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Reduce risk
Technology is tool
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Not fool-proof to eliminate human error/latent error
Health Administration Press
Chapter 3: Variation in Medical Practice and
Implications for Quality
Chapter Outline
• Background Variation in Medical Practice
• Variation in Healthcare
• Analyzing Variation
• Using Variation Data to Drive Healthcare Quality Initiatives
• Baylor Scott & White Health Case Study
• Conclusion
• Study Questions
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Health Administration Press
Background:
Random Versus Assignable Variation
• Variation is the difference between an observed event
and a standard or norm.
• Random variation is an attribute of the event or
process, adheres to the laws of probability, and cannot
be traced to a root cause.
• Assignable variation arises from a single or small set of
causes that are not part of the event or process and can
be traced and identified and then implemented or
eliminated.
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Health Administration Press
Background:
Process, Outcome, and Performance Variation
• Process variation refers to different usage of a therapeutic
or diagnostic procedure in an organization, geographic
area, or other grouping of healthcare providers.
• E.g., fecal occult blood testing, sigmoidoscopy, colonoscopy, or a combination of
these options for screening for colorectal cancer
• Outcome variation occurs when different results follow
from a single process.
• Performance variation is the difference between any given
result and the optimal result.
• It is arguably the most important category of variation.
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Health Administration Press
Variation in Medical Practice:
Warranted vs. Unwarranted Variation
• Warranted variation is based on differences in patient
preference, disease prevalence, or other patient-related
factors.
• Unwarranted variation is variation that cannot be
explained by patient preference or condition or
evidence-based medicine.
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Health Administration Press
Variation in Medical Practice:
Wennberg’s Three Categories
• Wennberg has identified three categories of care in
which unwarranted variation indicates different
possible problems:
• Effective care
• Preference-sensitive care
• Supply-sensitive care
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Health Administration Press
Variation in Medical Practice:
Sources of Unwarranted Variation in Medical Practice
• Potential sources of unwarranted variation:
• Inadequate patient involvement in decision making
• Inequitable access to resources
• Poor communication
• Role confusion
• Misinterpretation or misapplication of clinical evidence
• Clinician uncertainty
• Economic incentives
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Health Administration Press
Variation in Medical Care:
Tools for Quality Improvement
• Where best practices have been identified:
• Clinical guidelines
• Benchmarking and report cards
• Academic detailing
• Pay-for-performance
• When care is “preference sensitive”:
• Comparative effectiveness research
• Initiatives to increase patient engagement
• When care is “supply sensitive”:
• Outcomes research and effectiveness research
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Health Administration Press
Analyzing Variation:
The Challenge of Attribution
• Particularly in the context of population health, multiple
providers may have had the opportunity to influence the
outcome of interest.
• Where patients are retrospectively attributed to providers for
performance measurement, the rules used for that attribution
can greatly influence how providers appear to perform.
• E.g., compared to a “default” attribution rule, 11 alternative rules tested in
the context of using commercial health plan claims data to assign
Massachusetts physicians to the categories of “low cost,” “average cost,”
“high cost,” and “low sample size” assigned 17% to 61% of physicians to a
different performance category.*
*Mehrotra, A., J. L. Adams, J. W. Thomas, and E. A. McGlynn. “The effect of different attribution rules on individual physician
cost profiles.” Ann Intern Med 152, no. 10 (May 18 2010): 649-54.
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Health Administration Press
Analyzing Variation:
The Challenge of Attribution
• Careful thought needs to go into the design/choice of
any retrospective attribution rule to ensure the
resulting performance measurement holds the relevant
provider(s) accountable for quality or cost.
• Different attribution rules may, therefore, need to be
applied to different measures contributing to the overall
evaluation of the quality or cost of care provided to a
population.
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Health Administration Press
Analyzing Variation: Tools for Analysis
• League tables and caterpillar charts, which order
providers from lowest to highest performers on a
chosen measure, are frequently used to examine
variation.
• They are vulnerable to misinterpretation, as the instinct is to focus on
numeric ordering, missing the uncertainty around each provider’s point
estimate and thus the fact that much of the ordering reflects random
variation.
• Better tools are forest and funnel plots.
• Both can avoid the ranking issue.
• Funnel plot makes clear allowance for additional variability among providers
with small volume.
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Health Administration Press
Analyzing Variation: Tools for Analysis
Forest plot showing
variation in heart
failure 30-day riskstandardized
mortality in
Medicare patients
for hospitals in
Dallas County (July
2013 – June 2016)
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Health Administration Press
Analyzing Variation: Tools for Analysis
Funnel plot showing
variation in heart
failure 30-day riskstandardized mortality
in Medicare patients
for hospitals in Dallas
County (July 2013 –
June 2016)
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Health Administration Press
Analyzing Variation: Tools for Analysis
• Statistical process control (SPC) is an approach adopted from
industrial manufacturing that appeals widely across healthcare
improvement activities.
• It combines statistical significance tests with graphical analysis of
summary data as the data are produced, most often in control charts,
which plot measured points together with upper and lower reference
thresholds (calculated using historical data) that define the range of
the random variation.
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Health Administration Press
Analyzing Variation: Tools for Analysis
• SPC can provide the time sensitivity so important to pragmatic
improvement.
• BUT, having originated in a setting characterized by repetitive
manufacturing of identical products, care must be taken in its
application to healthcare, where individual patient characteristics
vary widely and influence both the appropriateness of delivery of
certain processes of care and the outcomes that can be expected
from that care.
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Health Administration Press
Using Variation Data to Drive Healthcare Quality
Initiatives
• National quality improvement efforts applying variation
include:
• Medicare’s Hospital Value-Based Purchasing Program
• Medicare’s Hospital Readmissions Reduction Program
• Individual hospitals and healthcare systems with the
capacity and infrastructure for quality monitoring and
improvement use variation data to identify opportunities
for quality improvement and/or evaluate the effectiveness
of quality initiatives.
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Health Administration Press
Case Study: Baylor Scott & White Health
• Increasing use of evidence-based heart failure therapies
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Health Administration Press
Conclusion
• Keys to successful management (rather than
elimination) of variation in pursuit of quality healthcare
include the ability to
• identify variation;
• distinguish between random and assignable
variation;
• determine the meaning, importance, or value of the
observed variation relative to some standard; and
• implement methods that will take advantage of or
rectify what the variation reveals.
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Health Administration Press
Study Questions
1. While exploring opportunities to improve processes of care for a
group practice, you find no variability in compliance with the US
Preventive Services Task Force’s recommendations for colorectal
cancer screening across the practice’s physician over time. Is this
absence of variation optimal? Why or why not?
2. Distinguish between random and assignable variation. Discuss the
relevance of each of these to measuring quality of care and to the
design and evaluation of quality improvement initiatives.
3. Describe the three categories of care identified by Wennberg
(2011), the possible opportunities for improvement unwarranted
variation might indicate in each of these categories, and the goals of
health services research and quality improvement initiatives in each
category.
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