Transcript NURS586I Nursing and Clinical Informatics

Electronic Health Records:
Promises and Pitfalls
Leanne M. Currie, RN, PhD
Associate Professor
UBC School of Nursing
[email protected]
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Overview
 What is informatics?
 How can the electronic health record support clinical
work?
 What are problems associated with electronic health
records?
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Health Informatics
• The application of information technology to
facilitate the creation and use of health related
data, information and knowledge. (Canada
Health Infoway)
InformationCommunication
Science
Decision
Science
Computer
Science
<Healthcare Domain>
Medicine, Pharmacy,
Occupational therapy,
Physical therapy, Public
Health
<Nursing>
Science
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Organizational
Behaviour
Scope of the field of informatics
 Clinical Informatics
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Electronic health record (EHR)
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Simulation

Computer adaptive testing
 Consumer health informatics

Clinical decision support
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Computer readable guidelines

Personal health record
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Clinical documentation

Patient portal

Digital literacy

Social media

Technology use in homes
Systems design

Health information exchange

Standardized terminologies

Information retrieval

Data mining

Telehealth/Virtual Health
 Telephone advice lines
 Video conference to provide
access to healthcare
professional
Public Health Informatics
 Surveillance systems
 Antibiotic Rx  outbreak


Global Health Informatics
 Low-resource settings

Radiology informatics
 Picture archiving systems
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Bioinformatics
 Personalized medicine

Map treatments to your genome
 Identify disease genome
Foundational fields
 Computer science:
 Algorithmic methods for representing and transforming
information
 Information science
 Origins, collection, storage, retrieval, transmission & utilization
of information
 Library science
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Why is informatics important to
patient safety and quality experts?
 Quality experts need to be aware of:
 Data integrity
 Technology induced errors
 Nursing/Pharmacy/Medical informatics is an expected
entry-to-practice competency in Canada (and in US)
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Why Informatics?
• Data and information are required to manage
patient care
– If you can’t name it, you can’t manage it
– Need to build systems that:
•
•
•
•
Support clinicians’ work
Maximize safe care
Can adapt to changes in knowledge
Can capture information that ‘can’t be quantified’ (i.e., free text)
 Need to make collection of data for re-use a part of the health care
delivery process.
•
Not to be confused with pushing the job of “data
collection” to the frontline (already busy) clinician.
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The Vision
• Data should be:
• Captured as a byproduct of care
• Entered only once (and verified if needed)
• Use and re-used for:
1. Share information and data (view reports and others‘notes)
2. Real time decision support (guideline integration, knowledge
translation)
3. Administrative reporting (must know what you will get OUT of
system)
4. Research
5. Practice-based evidence (knowledge discovery)
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Data collection as a By-Product of
care
 Data that are collected as part of clinical work should be
able to be automatically integrated into the
documentation system
 E.g., dynamap data should be automatically uploaded into
the documentation system
 E.g., audio-record ‘report’ (e.g., shift report) and
automatically transcribe into format that can re-use the
data (e.g., Dragon-talk software and Natural language
programming).
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Bar Coding for ‘data collection
as a by-product of care’
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Data Re-Use
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In the past……
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1. Sharing Information
 Pros:
 Remote & Asynchronous viewing
 Multiple concurrent viewers
 Decrease repeat tests (cost savings)
 Cons:
 Potential for privacy breach
 Requires national standards for health information exchange
Info-structure costly to implement
 Presumes digital literacy
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Infostructure in Canada
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Interoperability definitions
 Technical Interoperability
 ensures that systems can send and receive
data successfully. It defines the degree to
which the information can be successfully
“transported” between systems.
 Semantic Interoperability
 ensures that the information sent and received
between systems is unaltered in its meaning. It is
understood in exactly the same way by both
the sender and receiver.
 Process Interoperability
 is the degree to which the integrity of workflow
processes can be maintained between systems.
This includes maintaining/conveying information
such as user roles between systems.
From: Dolin, R (2011) Approaching Semantic Interoperability with HL7. JAMIA.
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3005878/
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Uses of Terminologies, Taxonomies
and Ontologies
 Modeling knowledge
 Sematic (meaning) relationships between concepts
 Automating guideline integration
 Storing in database
 As a record
 Monitoring data
 For real time decision support
 Querying database
 For aggregate data analysis (local, regional, national, international)
 Transferring data
 From one setting to another
 Billing
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 E.g., ICD10 codes
iEHR
 Interoperable EHR supports clinical information-sharing
generated primarily on the basis of clinical assessments.
Includes:
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Clinical Observations
Professional Services
Health Conditions
Care Compositions
Allergy/Intolerance
Patient Note
Clinical Documents
 Discharge/Care Summary
 Referral
 Clinical Observation Document
 EHR Clinical Summary/Profile Retrieval
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Estimated Cost Savings in Canada
http://www.documentcloud.org/documents/690256-final-infoway-emr-benefits-english-summary.html
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2. Real-time Decision Support (for
clinicians)
 Pros:
 Supports guideline adherence
 Prevents adverse events (e.g., drug-drug interaction
 Can be used for quality data tracking
 Cons:
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Complex & time consuming to develop
Alert fatigue
Technology induced errors if user interface confusing
Presumes data collection complete from previous users
Requires comprehensive infostructure and standards
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CPOE
http://jamia.bmj.com/content/20/3/470.full.pdf+html
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ATHENA - GLINDA
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ATHENA - GLINDA
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ATHENA - GLINDA
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Evidence about CDSS
(Bright et al. 2012)
Outcome
Examples
Evidence Level (outcomes)
Clinical outcomes
LOS, mortality, QOL, Adv.Event
Low
Morbidity
Mod (OR=0.88)
Preventative care or
recommended Tx
High (OR=1.42 – 1.57)
Tests
Mod (OR=1.72)
Workflow/
efficiency
# patients seen
Insufficient data
Relationshipcentered outcomes
Patient satisfaction
Insufficient data
Economic outcomes
Cost
Mod (trend toward lower Tx costs)
Cost effectiveness
Insufficient data
Provider acceptance
Low (often not reported)
Provider satisfaction
Moderate (higher satisfaction if developed
locally)
Provider use
Low (overall low use)
Implementation
Insufficient data
Process measures
Healthcare Use
•
Clinician workload
Bright TJ, Wong A, Dhurjati R, Bristow E, Bastian L, Coeytaux RR, Samsa G, Hasselblad V, Williams JW, Musty MD, Wing L, Kendrick AS, Sanders GD, Lobach D.
of please
clinical decision-support
systems:
a systematic
review. Ann Intern Med. 2012 Jul 3;157(1):29-43. doi: 10.7326/0003-4819-157-1-201207030-00450.
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2015
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3. Administrative reporting
 Pros:
 Automated reports:
 reduce time to get administrative data
 Reduce cost of medical records department
 Near to ‘real-time’ administrative reporting
 (currently time lag due to manual data abstraction)
 Cons:
 Data collection pushed to front-line person
 Uncertain how data is used for decision making
 Information overload  not using data
 Can’t get data out unless planned during system design
 May require additional time/expertise when building system
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http://ihtsdo.org/fileadmin/user_upload/doc/download/doc_StarterGuide_Current-en-US_INT_20140222.pdf
http://www.himss.org/library/interoperability-standards/pharmacy-health-it-standards
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4. Use Data for Research/Practicebased evidence
 Pros:
 Big Data (Massive data)
 Larger data sets
 Have full population data
 Data mining techniques can be applied
 Time to get data faster because no need for manual data extraction
 Cons:
 Potential for Privacy breach
 Can’t get data out if system not designed for it
 Can take longer if data are ‘dirty’
 Qualitative data might not be used
 Might miss context of quantitative data
http://www.himss.org/library/interoperability-standards/pharmacy-health-it© Leanne Currie, 2015 please do not share without permission
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Syndromic Surveillance
http://www.phac-aspc.gc.ca/fluwatch/13-14/w15_14/index-eng.php
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What Happens when Systems are
poorly designed?
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Avoid this Scenario
Permission obtained from Randy Glasbergen for use in presentations only
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Software Development Team
Bill
Microsoft Corp Circa 1978
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Work-around
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Clever and Useful Work-around
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Work-around
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http://www.cbc.ca/news/canada/british-columbia/pharmacists-failure-to-check-drug-risks-leads-to-horrible-death© Leanne Currie, 2015 please do not share without permission
1.2787185
Technology induced errors
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What Are Technology-induced Errors?
 Technology-induced errors are those sources of error
that “arise from the:
(a) design and development of a technology
(b) implementation and customization of a technology
(c) interactions between the operation of a new technology
and the new work processes that arise from a technology’s
use”
 (Borycki & Kushniruk, 2008, p. 154)
 More specific than unintended consequences which look
at all negatives outcomes of use
 (Borycki & Keay, 2010; Kushniruk et al., 2005)
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USP MEDMARX Computer TechnologyRelated Harmful Errors (2006)
Cause
Number
Barcode, medication mislabeled
20
Information management system
1,176
Computer screen display unclear/ confusing
137
Dispensing device involved
3,181
Barcode, failure to scan
114
Computer entry (general, other than CPOE)
10,752
CPOE
24,715
Barcode, override warning
41
Total from176,409 medication error records 43,372
http://www.jointcommission.org/SentinelEvents/SentinelEventAlert/sea_42.htm
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Model of Technology Induced Error
(Borycki et. al, 2009)
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Magrabi F1, Ong MS, Runciman W, Coiera E. An analysis of computer-related patient safety incidents to inform the development
of ©
a Leanne
classification.
J Amplease
Med Inform
Assoc.
2010 Nov-Dec;17(6):663-70.
doi: 10.1136/jamia.2009.002444.
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
22 Ways a System Caused Medical
Misleading
Default
Values
Errors
Koppel et al. (2005)
 Default values determined by smallest dose in pharmacy
 prescribe 10 mg, even though 20 or 30 is most common

New Commands Not Checked Against Previous Ones
 System permitted entering new dose without canceling old dose
 Patients received the sum of the old and new doses
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Poor Readability
 Patient names in small font, names listed alphabetically
 Name not on all screens

Memory Overload
 View up to twenty screens to see all of a patient's medications
 Complicated Workflow
 System design conflicted with hospital workflow
 e.g., nurses kept a separate set of paper records that they entered into the
system at the end of the shift
 e.g. CPOE “work-around” - Reynolds, Peres, Tatham et al. (2005)
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Computerized Clinical Decision
Support Systems
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Ten Commandments for Effective
Clinical Decision Support (Bates, et al.
2008)
1. Speed Is Everything
- If the decision support takes too long to appear, it
will be useless
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Ten Commandments for Effective
Clinical Decision Support (Bates, et al.
2008)
2. Anticipate Needs and Deliver in Real Time
- "Latent needs" are present but have not been
consciously realized
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2015 please
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share without
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Creswick
N, Westbrook
JI, Braithwaite
J. Understanding
communication networks in the emergency department.BMC Health Serv Res. 2009 Dec 31;9:247.
Ten Commandments for Effective
Clinical Decision Support (Bates, et al.
2008)
3. Fit into the User's Workflow
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Ten Commandments for Effective
Clinical Decision Support (Bates, et al.
2008)
4. Little Things Can Make a Big Difference
- Usability testing
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Ten Commandments for Effective
Clinical Decision Support (Bates, et al.
2008)
5. Clinicians Will Strongly Resist Stopping
-
Allow clinicians to exercise their own judgment and
override nearly all reminders and to "get past" most
guidelines
6. Changing Direction Is Easier than Stopping
- provide clinicians with best practice alternatives
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Ten Commandments for Effective
Clinical Decision Support
7. Simple Interventions Work Best
-fit guideline on a single screen
Cognitive Load: Short-term memory (working memory) is limited in
capacity to about seven informational units (7 plus or minus 2
units of information)
Working Memory
7 +/- 2 information
units
•
•
•
Long Term Memory
• Schema
Construction
• Schema
Automation
Interruptions
Information overload
Confusing user interface design
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7. Simple interventions work best (con’t)
e.g., 4 Visual Options
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Ten Commandments for Effective
Clinical Decision Support
8. Ask for Additional Info Only When You Really Need It

get data from the system
9. Monitor Impact, Get Feedback, and Respond

be prepared for variation

track the frequency of alerts and reminders and user responses on
a regular basis
10. Manage and Maintain Knowledge-based Systems

Create and maintain a ‘rules engine’ to ensure that all decision
logic is reviewed at regular intervals

Consider housing knowledge management in Quality & Patient
Safety committee
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How can informaticians work with
patient safety experts to support
the triple aim?
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Among the things most EHRs don’t do
(per Ron Goldman, CEO, IHI)
 they don’t support the basic functions of patient-centered medical homes
and “medical neighborhoods”
 don’t support robust panel management and the creation of patient
registries
 they don’t enhance care team communication or handovers in real time
 don’t help develop coordination with care managers and community
health workers
 don’t effectively track abnormal lab test results and their resolution
 don’t provide adequate referral to specialists or feedback from specialists
back to primary care physicians and patients
 don’t support patient-reported outcomes measurement; don’t integrate
with patient self-management apps; and don’t provide for rich clinical
data mining.
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http://3mhealthinformation.wordpress.com/2014/12/01/the-ihi-triple-aim-and-informatics/
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aim
What’s going on in BC?
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http://ubccpd.ca/course/ehits-2015
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Informatics in Canada - InspireNet
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http://www.e-healthconference.com/
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Summary
 eHealth is here to stay
 Need to be thoughtful about use and applications
 Patient safety and quality experts need to be involved in
system design
 If you don’t design what you want to track, someone will
design it for you
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THANK YOU! Questions??
[email protected]
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