© leanne currie, 2015 please do not share without permission electronic health records: promises...

© Leanne Currie, 2015 please do not share without permission

Electronic Health Records: Promises and Pitfalls

Leanne M. Currie, RN, PhD

Associate Professor

UBC School of Nursing

[email protected]

mailto:[email protected]


Overview

What is informatics?

How can the electronic health record support clinical work?

What are problems associated with electronic health records?


Health Informatics

<Nursing> Science

Computer Science

• The application of information technology to facilitate the creation and use of health related data, information and knowledge. (Canada Health Infoway)

Organizational Behaviour

Information-Communication

Science

Human Factors

Decision Science

<Healthcare Domain>Medicine, Pharmacy, Occupational therapy, Physical therapy, Public Health


Scope of the field of informatics Clinical Informatics

Electronic health record (EHR) Clinical decision support

Computer readable guidelines

Clinical documentation

Systems design Health information exchange

Standardized terminologies

Information retrieval

Data mining

Public Health Informatics

Surveillance systems

Antibiotic Rx outbreak

Global Health Informatics

Low-resource settings

Radiology informatics

Picture archiving systems

Health education technology

Simulation

Computer adaptive testing

Consumer health informatics Personal health record

Patient portal

Digital literacy

Social media Technology use in homes

Telehealth/Virtual Health

Telephone advice lines

Video conference to provide access to healthcare professional

Bioinformatics

Personalized medicine Map treatments to your genome

Identify disease genome

1924!


Foundational fields

Computer science: Algorithmic methods for representing and transforming

information

Information science Origins, collection, storage, retrieval, transmission &

utilization of information Library science


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)


Why Informatics?

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.

• 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)

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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).


Bar Coding for ‘data collection as a by-product of care’

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Data Re-Use


In the past……


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


Infostructure in Canada

15© Leanne Currie, 2015 please do not share without permission

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/


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 E.g., ICD10 codes


iEHR Interoperable EHR supports clinical information-sharing

generated primarily on the basis of clinical assessments. Includes: 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


Estimated Cost Savings in Canada

http://www.documentcloud.org/documents/690256-final-infoway-emr-benefits-english-summary.html




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: 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


CPOE

http://jamia.bmj.com/content/20/3/470.full.pdf+html


ATHENA - GLINDA


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)

Process measures Preventative care or recommended Tx

High (OR=1.42 – 1.57)

Tests Mod (OR=1.72)

Workflow/efficiency

# patients seen Insufficient data

Clinician workload

Relationship-centered outcomes

Patient satisfaction Insufficient data

Economic outcomes Cost Mod (trend toward lower Tx costs)

Cost effectiveness Insufficient data

Healthcare Use Provider acceptance Low (often not reported)

Provider satisfaction Moderate (higher satisfaction if developed locally)

Provider use Low (overall low use)

Implementation Insufficient data

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• 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. Effect of 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.


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


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


4. Use Data for Research/Practice-based 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-standards


Syndromic Surveillance

http://www.phac-aspc.gc.ca/fluwatch/13-14/w15_14/index-eng.php


What Happens when Systems are poorly designed?


Avoid this Scenario

Permission obtained from Randy Glasbergen for use in presentations only


Software Development Team

Bill

39

Microsoft Corp Circa 1978


Work-around


Clever and Useful Work-around

© Leanne Currie, 2015 please do not share without permission42

Work-around


Technology induced errors


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)


USP MEDMARX Computer Technology-Related 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

http://www.jointcommission.org/SentinelEvents/SentinelEventAlert/sea_42.htm


Model of Technology Induced Error

(Borycki et. al, 2009)


Magrabi F1, Ong MS, Runciman W, Coiera E. An analysis of computer-related patient safety incidents to inform the development of a classification. J Am Med Inform Assoc. 2010 Nov-Dec;17(6):663-70. doi: 10.1136/jamia.2009.002444.


Misleading Default Values 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

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)

22 Ways a System Caused Medical Errors - Koppel et al. (2005)


Computerized Clinical Decision Support Systems


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



2. Anticipate Needs and Deliver in Real Time- "Latent needs" are present but have not been

consciously realized

Creswick N, Westbrook JI, Braithwaite J. Understanding communication networks in the emergency department.BMC Health Serv Res. 2009 Dec 31;9:247.



3. Fit into the User's Workflow



4. Little Things Can Make a Big Difference- Usability testing



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


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)

Long Term Memory• Schema

Construction• Schema

Automation

Working Memory7 +/- 2 information units

• Interruptions• Information overload• Confusing user interface design


7. Simple interventions work best (con’t)e.g., 4 Visual Options


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


How can informaticians work with patient safety experts to support the triple aim?


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.


http://3mhealthinformation.wordpress.com/2014/12/01/the-ihi-triple-aim-and-informatics/

http://www.healthcare-informatics.com/blogs/mark-hagland/ihi-s-goldmann-making-explicit-link-between-ehrs-and-triple-aim

patient

centre

d

design

Inte

ropera

bili

ty and

usabilit

y

Manage costs of

implementation – long

term view of ROI

Well-designed decision support, with

coordinated effors


What’s going on in BC?


http://ubccpd.ca/course/ehits-2015


Informatics in Canada - InspireNet


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


THANK YOU! Questions??

[email protected]