This document discusses electronic health records (EHRs) and clinical decision support systems (CDS). It provides examples of different types of CDS, such as alerts and reminders, reference information, order sets, and diagnostic/treatment expert systems. The goal of health IT and CDS is to improve the quality of healthcare by making it safer, more timely, effective, efficient, patient-centered and equitable. However, human decisions are still necessary, so health IT should be designed to support, not replace, clinicians.

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Electronic Health Records (EHRs) &
Clinical Decision Support Systems (CDS)
Nawanan Theera-Ampornpunt, M.D., Ph.D.
March 29, 2021
www.SlideShare.net/Nawanan

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2003 M.D. (First-Class Honors)
2011 Ph.D. (Health Informatics), Univ. of Minnesota
Deputy Dean for Operations
Lecturer, Department of Clinical Epidemiology & Biostatistics
Faculty of Medicine Ramathibodi Hospital
Mahidol University
Interests: Health IT for Quality of Care, Social Media
IT Management, Security & Privacy
nawanan.the@mahidol.ac.th
SlideShare.net/Nawanan
นพ.นวนรรน ธีระอัมพรพันธุ์ Dr.Nawanan Theera-Ampornpunt
Introduction

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▪ An Informatics Doctor
▪ Digital Health Transformation
▪ Health IT, Smart Hospital & CDS
Outline

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An Informatics Doctor:
A Real-Life Personal Story

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https://www.youtube.com/watch?v=MuoDaJAqQ6c

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Class Discussion
What ideas come to mind?
Digital Health
Transformation
When you think of

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https://medium.com/@marwantarek/it-is-the-perfect-storm-ai-cloud-bots-iot-etc-4b7cbb0481bc

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http://www.ibtimes.com/google-deepminds-alphago-program-defeats-human-go-champion-first-time-ever-2283700
http://deepmind.com/ http://socialmediab2b.com
An Era of Smart Machines

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englishmoviez.com
Rise of the Machines?

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Digitizing Healthcare?
http://www.bloomberg.com/bw/stories/2005-03-27/cover-image-the-digital-hospital

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“Big data is like teenage sex:
everyone talks about it,
nobody really knows how to do it,
everyone thinks everyone else is doing it,
so everyone claims they are doing it...”
-- Dan Ariely @danariely (2013)
Substitute “Big data” with “AI”, “Blockchain”, “IoT”
of your choice.
-- Nawanan Theera-Ampornpunt (2018)

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Health &
Health Information

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Let’s take a look at
these pictures...

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Image Source: https://en.wikipedia.org/wiki/Industrial_robot (KUKA Roboter GmbH)
“Smart” Manufacturing

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Image Sources: http://isarapost.net/home/?p=17760
http://www.telecomjournalthailand.com/ตอบโจทย์โมเดลทางธุรกิจ/
“Smart” Banking

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ER - Image Source: nj.com
Healthcare (On TV)

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(At an undisclosed hospital)
Healthcare (Reality)

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• Life-or-Death
• Difficult to automate human decisions
– Nature of business
– Many & varied stakeholders
– Evolving standards of care
• Fragmented, poorly-coordinated systems
• Large, ever-growing & changing body of
knowledge
• High volume, low resources, little time
Why Healthcare Isn’t (Yet) “Smart”?

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“Smart Hospital”

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https://www.youtube.com/watch?v=gxz9ZVvduGc
Connecting People to a Healthy Future With
Personalized Care – Kaiser Permanente

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Back to
something simple...

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To treat & to care
for their patients
to their best
abilities, given
limited time &
resources
Image Source: http://en.wikipedia.org/wiki/File:Newborn_Examination_1967.jpg (Nevit Dilmen)
What Clinicians Want?

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Why Aren’t We Talk About These Words?
http://hcca-act.blogspot.com/2011/07/reflections-on-patient-centred-care.html

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The Goal of Health Care
The answer is already obvious...
“Health”
“Care”

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• Safe
• Timely
• Effective
• Patient-Centered
• Efficient
• Equitable
Institute of Medicine, Committee on Quality of Health Care in America. Crossing the quality
chasm: a new health system for the 21st century. Washington, DC: National Academy
Press; 2001. 337 p.
High Quality Care

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Information Is Everywhere in Healthcare

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WHO (2009)
Components of Health Systems

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Health IT
Health
Information
Technology
Goal
Value-Add
Means

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Hospital Information System (HIS) Computerized Physician Order Entry (CPOE)
Electronic
Health
Records
(EHRs)
Picture Archiving and
Communication System
(PACS)
Various Forms of Health IT

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m-Health
Health Information
Exchange (HIE)
Biosurveillance
Telemedicine &
Telehealth
Images from Apple Inc., Geekzone.co.nz, Google, PubMed.gov, and American Telecare, Inc.
Personal Health Records
(PHRs)
Health IT Beyond Hospitals

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Ordering Transcription Dispensing Administration
Computerized
Physician
Order Entry
(CPOE)
Automatic
Medication
Dispensing
Electronic
Medication
Administration
Records
(e-MAR)
Barcoded
Medication
Administration
Barcoded
Medication
Dispensing
Health IT for Medication Safety

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Hospital A Hospital B
Clinic D
Policymakers
Patient at
Home
Hospital C
HIE Platform
Health Information Exchange (HIE)

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• Safe
–Drug allergies
–Medication Reconciliation
• Timely
–Complete information at point of
care
• Effective
–Better clinical decision-making
Being Smart in Healthcare

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• Efficient
–Faster care
–Time & cost savings
–Reducing unnecessary tests
• Equitable
–Access to providers & knowledge
• Patient-Centered
–Empowerment & better self-care
Being Smart in Healthcare

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(IOM, 2001)
(IOM, 2000) (IOM, 2011)
Landmark Institute of Medicine Reports

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• Humans are not perfect and are bound to
make errors
• Highlight problems in U.S. health care
system that systematically contributes to
medical errors and poor quality
• Recommends reform
• Health IT plays a role in improving patient
safety
Summary of These Reports

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Image Source: (Left) http://docwhisperer.wordpress.com/2007/05/31/sleepy-heads/
(Right) http://graphics8.nytimes.com/images/2008/12/05/health/chen_600.jpg
To Err is Human 1: Attention

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Image Source: Suthan Srisangkaew, Department of Pathology, Facutly of Medicine Ramathibodi Hospital
To Err is Human 2: Memory

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External Memory
Knowledge Data
Long Term Memory
Knowledge Data
Inference
DECISION
PATIENT
Perception
Attention
Working
Memory
CLINICIAN
Elson, Faughnan & Connelly (1997)
Clinical Decision Making

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• The real place where most of the
values of health IT can be achieved
– Expert systems
• Based on artificial intelligence,
machine learning, rules, or
statistics
• Examples: differential
diagnoses, treatment options
(Shortliffe, 1976)
Clinical Decision Support Systems (CDS)

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– Alerts & reminders
• Based on specified logical conditions
• Examples:
–Drug-allergy checks
–Drug-drug interaction checks
–Reminders for preventive services
–Clinical practice guideline integration
Clinical Decision Support Systems (CDS)

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Example of “Alerts & Reminders”
Reducing Errors through Alerts & Reminders

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• Reference information or evidence-
based knowledge sources
–Drug reference databases
–Textbooks & journals
–Online literature (e.g. PubMed)
–Tools that help users easily access
references (e.g. Infobuttons)
More CDS Examples

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Image Source: https://webcis.nyp.org/webcisdocs/what-are-infobuttons.html
Infobuttons

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• Pre-defined documents
–Order sets, personalized “favorites”
–Templates for clinical notes
–Checklists
–Forms
Other CDS Examples

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Image Source: http://www.hospitalmedicine.org/ResourceRoomRedesign/CSSSIS/html/06Reliable/SSI/Order.cfm
Order Sets

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• Simple UI designed to help clinical
decision making
–Abnormal lab highlights
–Graphs/visualizations for lab results
–Filters & sorting functions
Other CDS Examples

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Image Source: http://geekdoctor.blogspot.com/2008/04/designing-ideal-electronic-health.html
Abnormal Lab Highlights

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External Memory
Knowledge Data
Long Term Memory
Knowledge Data
Inference
DECISION
PATIENT
Perception
Attention
Working
Memory
CLINICIAN
Elson, Faughnan & Connelly (1997)
Clinical Decision Making
Abnormal lab
highlights

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External Memory
Knowledge Data
Long Term Memory
Knowledge Data
Inference
DECISION
PATIENT
Perception
Attention
Working
Memory
CLINICIAN
Elson, Faughnan & Connelly (1997)
Clinical Decision Making
Drug-Drug
Interaction
Checks

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External Memory
Knowledge Data
Long Term Memory
Knowledge Data
Inference
DECISION
PATIENT
Perception
Attention
Working
Memory
CLINICIAN
Elson, Faughnan & Connelly (1997)
Clinical Decision Making
Diagnostic/Treatment
Expert Systems (e.g. AI)

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• CDS as a replacement or supplement of
clinicians?
– The demise of the “Greek Oracle” model (Miller & Masarie, 1990)
The “Greek Oracle” Model
The “Fundamental Theorem” Model
Friedman (2009)
Wrong Assumption
Correct Assumption
Clinical Decision Support Systems (CDS)

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▪ An informatics doctor thinks like a doctor but
designs IT applications to help other doctors
▪ Digital Health Transformation is, first and
foremost, about Health Care, not digital
technologies
▪ EHRs & CDS are examples of useful Health IT
▪ Human decisions are still necessary in
healthcare, so many health IT should work to
support human decisions as CDS
Summary