Health Informatics for Clinical Research (November 25, 2021)

RACE 624/RAME 624
Health Informatics for
Clinical Research
Nawanan Theera-Ampornpunt, M.D., Ph.D.
November 25, 2021
Parts of the slides copied/adapted from
Dr. Oraluck Pattanaprateep

Objectives
•Understand the field of biomedical and health
informatics and its relationships with other fields
•Recognize roles of health IT in health care
•Understanding nature of health information and health
information standards and its potential implications for
research

What Is “Informatics”
•French: informatique = the science and technology
of information processing using computers (Greenes &
Shortliffe, 1990)
•“[T]he discipline focused on the acquisition, storage,
and use of information in a specific setting or
domain” (Hersh, 2009)
•“[T]he science of information”
(Bernstam et al, 2010)

Medical Informatics
• “Ancient” term
• Being retired
• Future use discouraged by experts
• Only retained in titles of professional organizations
Main Problems
• Medical = Doctor? (e.g. not nursing?)
• Medical informatics vs. Clinical informatics

Better Terms
• Biomedical informatics
• Health informatics
• Biomedical and Health informatics
A Few Subtleties
• Health informatics suggests the goal is “health”
• Health informatics vs Public health informatics
• Health informatics includes Bioinformatics?
• No clear winner between
Biomedical informatics vs. Health informatics

But What Is M/B/H Informatics Anyway?
• Medical computing/computers in medicine?
• ‘[R]eferring to biomedical informatics as “computers in medicine”
is like defining cardiology as “stethoscopes in medicine”.’ (Bernstam et
al, 2010)
• “[T]he field concerned with the cognitive, information processing,
and communication tasks of medical practice, education, and
research, including the information science and technology to
support these tasks”
(Greenes & Shortliffe, 1990)

More Definitions of M/B/H Informatics
• “[T]he field that is concerned with the optimal use of information,
often aided by the use of technology, to improve individual health,
health care, public health, and biomedical research” (Hersh, 2009)
• “[T]he application of the science of information as data plus
meaning to problems of biomedical interest” (Bernstam et al, 2010)

Summary About M/B/H Informatics
• Focuses more on information, not technology
• Task-oriented view:
Collection Processing
Storage
Utilization
Communication/
Dissemination/
Presentation

Summary About M/B/H Informatics
• Areas under the domain of M/B/H informatics
• Health service delivery (health care)
• Medical, dental, nursing, pharmacy, etc.
• IT management in health care organizations
• Public health
• Policy & administration, epidemiology, environmental health, health services
research, etc.
• Individual patient/consumer’s health
• Education of health professionals
• Biomedical research (clinical trials, public health research, research in
biomedical sciences)

So....What Is Information?
Data-Information-Knowledge-Wisdom
(DIKW) Pyramid
Wisdom
Knowledge
Information
Data

Wisdom
Knowledge
Information
Data
Data-Information-Knowledge-Wisdom
Contextualization/
Interpretation
Processing/
Synthesis/
Organization
Judgment

Example
Wisdom
Knowledge
Information
Data
Contextualization/
Interpretation
Processing/
Synthesis/
Organization
Judgment
100,000,000
I have 100,000,000 baht
in my bank account
I am rich!!!!!
I should buy a BMW
(and a BIG house)!

Class Exercise #1: Problem A
•Patient A has a blood pressure reading of
170/100 mmHg

•Patient A has a blood pressure reading of
170/100 mmHg
• Data: 170/100
• Information: BP of Patient A = 170/100 mmHg
• Knowledge: Patient A has high blood pressure
• Wisdom:
• Patient A needs to be investigated for cause of HT
• Patient A needs to be treated with anti-hypertensives
• Patient A needs to be referred to a cardiologist
Class Exercise #1: Problem A

•Patient B is allergic to penicillin. He was
recently prescribed amoxicillin for his sore
throat.
Class Exercise #1: Problem B

• Patient B is allergic to penicillin. He was recently prescribed
amoxicillin for his sore throat.
• Data: Penicillin, amoxicillin, sore throat
• Information:
• Patient B has penicillin allergy
• Patient B was prescribed amoxicillin for his sore throat
• Knowledge:
• Patient B may have allergic reaction to his prescription
• Wisdom:
• Patient B should not take amoxicillin!!!
Class Exercise #1: Problem B

•Patient C’s plain film X-ray is as shown:
Class Exercise #1: Problem C

• Patient C’s plain film X-ray
• Data:
• Information:
• Patient C’s plain film X-ray is as seen in the image
• There is a break in the continuity of the periosteum of Patient C’s left
radius and ulna
• Knowledge:
• Patient C has fractures of left radius and ulna
• Wisdom:
• Patient C’s fractures need to be properly treated
Image Source: http://en.wikipedia.org/wiki/Bone_fracture
Class Exercise #1: Problem C

M/B/H Informatics As A Field
(Shortliffe, 2002)

Biomedical Informatics in Perspective
Basic Research
Applied Research
And Practice
Biomedical Informatics Methods,
Techniques, and Theories
Imaging
Informatics
Clinical
Informatics
Bioinformatics
Public Health
Informatics
Molecular and
Cellular
Processes
Tissues and
Organs
Individuals
(Patients)
Populations
And Society
Biomedical Informatics ≠ Health Informatics
Health Informatics
Reproduced/Adapted from American Medical Informatics Association
(http://www.amia.org/about-amia/science-informatics)

Basic Research
Applied Research
And Practice
Imaging
Informatics
Clinical
Informatics
Bioinformatics
Public Health
Informatics
Molecular and
Cellular
Processes
Tissues and
Organs
Individuals
(Patients)
Populations
And Society
Continuum with “Fuzzy” Boundaries
Biomolecular
Imaging
Consumer
Health
Pharmaco-
genomics

Basic Research
Applied Research
And Practice
Imaging
Informatics
Clinical
Informatics
Bioinformatics
Public Health
Informatics
Molecular and
Cellular
Processes
Tissues and
Organs
Individuals
(Patients)
Populations
And Society
Continuum with “Fuzzy” Boundaries
Clinical
Translational
Science

M/B/H Informatics As A Field
(Hersh, 2009)

M/B/H Informatics and Other Fields
Biomedical/
Health
Informatics
Computer &
Information
Science
Engineering
Cognitive
& Decision
Science
Social
Sciences
(Psychology,
Sociology,
Linguistics,
Law &
Ethics)
Statistics &
Research
Methods
Medical
Sciences &
Public Health
Management
Library
Science,
Information
Retrieval,
KM
And More!

Some Areas of Popular Interests
• Health IT applications & implementation
• Electronic Health Records (EHRs)
• Computerized Physician Order Entry (CPOE)
• Clinical Decision Support Systems (CDSSs)
• Picture Archiving and Communication Systems (PACS)
• Other hospital IT (nursing, pharmacy, lab, etc.)
• Personal Health Records (PHRs)
• Telemedicine & Telehealth
• eHealth, mHealth, Health Information Exchange (HIE)
• Health IT adoption and use, public policy
• People & organizational (POI), ethical-legal-social (ELSI)
• Consumer health
• Knowledge representation & discovery, NLP
• Standards & Interoperability
• Workforce building & education

“Bible” of Biomedical/Health Informatics
Shortliffe EH, Cimino JJ, Chiang MF, editors. Biomedical
Informatics: Computer Applications in Health Care and
Biomedicine. 5th ed. New York: Springer; 2021. 1195 p.

Information is Everywhere in Medicine
Shortliffe EH. Biomedical informatics in the education of physicians. JAMA. 2010 Sep 15;304(11):1227-8.

WHO Six Building Blocks of Health System
28
WHO (2009)

Health IT
Use of information and communications
technology (ICT) in health & healthcare
settings
Source: The Health Resources and Services Administration, Department of
Health and Human Service, USA
Slide adapted from: Dr. Boonchai Kijsanayotin

eHealth
Use of information and communications
technology (ICT) for health; Including
• Treating patients
• Conducting research
• Educating the health workforce
• Tracking diseases
• Monitoring public health.
Sources: 1) WHO Global Observatory of eHealth (GOe) (www.who.int/goe)
2) World Health Assembly, 2005. Resolution WHA58.28
Slide adapted from: Mark Landry, WHO WPRO & Dr. Boonchai Kijsanayotin

eHealth & Health IT
eHealth  Health IT
Slide adapted from: Dr. Boonchai Kijsanayotin

The Anatomy of Health IT
Health
Information
Technology
Goal
Value-Add
Means

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

Still Many Other Forms of Health IT
m-Health
Health Information
Exchange (HIE)
Biosurveillance
Information Retrieval
Telemedicine &
Telehealth
Images from Apple Inc., Geekzone.co.nz, Google, Microsoft, PubMed.gov, and American Telecare, Inc.
Personal Health Records
(PHRs)

High Quality Care
• 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.

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

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

To Err is Human 3: Cognition
• Cognitive Errors - Example: Decoy Pricing
The Economist Purchase Options
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C. Print & web subscription $125
Ariely (2008)
16
0
84
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68
32
# of
People
# of
People

Common Errors
• Medication Errors
• Drug Allergies
• Drug Interactions
• Ineffective or inappropriate treatment
• Redundant orders
• Failure to follow clinical practice guidelines

Unintended Consequences of Health IT
Some risks
• Alert fatigue

Success of IT Implementation
DeLone & McLean (1992)

Documented Benefits of Health IT
• Literature suggests improvement through
• Guideline adherence (Shiffman et al, 1999;Chaudhry et al, 2006)
• Better documentation (Shiffman et al, 1999)
• Practitioner decision making or process of care
(Balas et al, 1996;Kaushal et al, 2003;Garg et al, 2005)
• Medication safety
(Kaushal et al, 2003;Chaudhry et al, 2006;van Rosse et al, 2009)
• Patient surveillance & monitoring (Chaudhry et al, 2006)
• Patient education/reminder (Balas et al, 1996)
• Cost savings and better financial performance
(Parente & Dunbar, 2001;Chaudhry et al, 2006;Amarasingham et al, 2009;
Borzekowski, 2009)

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

More CDS Examples
•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)

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

Other CDS Examples
•Pre-defined documents
• Order sets, personalized “favorites”
• Templates for clinical notes
• Checklists
• Forms
•Can be either computer-based or
paper-based

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

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

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

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

External Memory
Knowledge Data
Long Term Memory
Knowledge Data
Inference
DECISION
PATIENT
Perception
Attention
Working
Memory
CLINICIAN
Abnormal lab
highlights

External Memory
Knowledge Data
Long Term Memory
Knowledge Data
Inference
DECISION
PATIENT
Perception
Attention
Working
Memory
CLINICIAN
Drug-Allergy
Checks

External Memory
Knowledge Data
Long Term Memory
Knowledge Data
Inference
DECISION
PATIENT
Perception
Attention
Working
Memory
CLINICIAN
Drug-Drug
Interaction
Checks

External Memory
Knowledge Data
Long Term Memory
Knowledge Data
Inference
DECISION
PATIENT
Perception
Attention
Working
Memory
CLINICIAN
Clinical
Practice
Guideline
Reminders

External Memory
Knowledge Data
Long Term Memory
Knowledge Data
Inference
DECISION
PATIENT
Perception
Attention
Working
Memory
CLINICIAN
Diagnostic/Treatment
Expert Systems

Proper Roles of CDS
• 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

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

Areas of Health Informatics
Patients &
Consumers
Providers &
Patients
Healthcare
Managers, Policy-
Makers, Payers,
Epidemiologists,
Researchers
Clinical
Informatics
Public
Health
Informatics
Consumer
Health
Informatics

Incarnations of Health IT
Clinical
Informatics
Public
Health
Informatics
Consumer
Health
Informatics
HIS/CIS
EHRs
Computerized Physician
Order Entry (CPOE)
Clinical Decision
Support Systems
(CDS) (including AI)
Closed Loop
Medication
PACS/RIS
LIS
Nursing
Apps
Disease Surveillance
(Active/Passive)
Business
Intelligence &
Dashboards
Telemedicine
Real-time Syndromic
Surveillance
mHealth for Public
Health Workers &
Volunteers
PHRs
Health Information
Exchange (HIE)
eReferral
mHealth for
Consumers
Wearable
Devices
Social
Media

Where We Are Today...
Clinical
Informatics
Public
Health
Informatics
Consumer
Health
Informatics
Technology that
focuses on the sick,
not the healthy
Silos of data
within hospital
Poor/unstructured
data quality
Lack of health data
outside hospital
Poor data
integration across
hospitals/clinics
Poor data integration
for monitoring &
evaluation
Poor data quality (GIGO)
Finance leads
clinical outcomes
Poor IT change
management
Cybersecurity
& privacy risks
Few real examples
of precision
medicine
Little access
to own
health data
Poor patient
engagement
Poor accuracy
of wearables Lack of evidence
for health values
Health literacy
Information 
Behavioral
change
Few standards
Lack of health IT
governance

Slide reproduced from Dr. Oraluck Pattanaprateep

What if each data used different language

Or same language: Apple

Data standards
• Structure (data architecture) – Arrangement
of data
• Semantics (data context) – Concept and
relationships

Structure : Text
Concept : Fruit
Relationship : Is A
Structure : Text
Concept : Hardware
Relationship : Is A

Standard term for information

What is a Standard?
▪is a definition, set of rules or guidelines, format,
or document that establishes uniform
engineering or technical specifications, criteria,
methods, processes, or practices
▪approves by a recognized standard
development organization (SDO), or have been
accepted by the industry
▪de facto standards VS de jure standards
From: Public Health data standards consortium http://www.phdsc.org/default.asp 2011

What is Standardization?
▪is the process of agreeing on standards
▪ represent the common language
▪ allows the exchange of data between disparate
data systems.
▪ Goals are
▪ to achieve comparability, compatibility, and
interoperability between independent systems
▪ to ensure compatibility of data
▪ to reduce duplication of effort and redundancies.

What is Interoperability?
▪ "the ability of two or more systems or components to
exchange information and to use the information that has
been exchanged". [The Institute of Electrical and Electronics
Engineers (IEEE, USA)]
▪ "In healthcare, interoperability is the ability of different
information technology systems and software applications
to communicate, to exchange data accurately, effectively,
and consistently, and to use the information that has been
exchanged. " [The National Alliance for Health Information
Technology (NAHIT, USA) ]

Interoperability and Standards
From: Tim Benson: Principle of Health Interoperability HL7 & SNOMED

76
Standards: Why?
• The Large N Problem
N = 2, Interface = 1
# Interfaces = N(N-1)/2
N = 100, Interface = 4,950

77
Objectives
• Interoperability
• Inter-operable
systems
Ultimate Goals
• Continuity of Care
• Quality
▪ Safety
▪ Timeliness
▪ Effectiveness
▪ Equity
▪ Patient-Centeredness
▪ Efficiency
Why Health Information Standards?

Health Data Standards
Interoperability needs STANDARDs
• Core data sets standards
• Semantics standards
▪ Coding : ICDs, National Drug Codes
▪ Medical Terminology: SNOMED-CT, LOINC
• Syntactic standards : HL7 messaging standards, HL7-CDA
(Clinical Document Architecture)
• Security and Privacy standards

79
Various Kinds of Standards
• Unique Identifiers
• Standard Data Sets
• Vocabularies & Terminologies
• Exchange Standards
– Message Exchange
– Document Exchange
• Functional Standards
• Technical Standards: Data Communications,
Encryption, Security

80
Levels of Interoperability
Functional
Semantic
Syntactic

81
Necessary Standards in Health IT
Functional
Semantic
Syntactic
Technical Standards
(TCP/IP, encryption,
security)
Exchange Standards (HL7 V2,
HL7 V3 Messaging, HL7 CDA,
HL7 FHIR, DICOM)
Vocabularies, Terminologies,
Coding Systems (ICD-10, ICD-9,
CPT, SNOMED CT, LOINC)
Information Models (HL7 V3 RIM,
ASTM CCR, HL7 CCD)
Standard Data Sets
Functional Standards (HL7 EHR
Functional Specifications)
Some may be hybrid: e.g. HL7 V3, HL7 CCD
Unique ID

Without Terminology
Standards……
•Health data is non-comparable
•Health systems cannot interchange data
•Secondary uses (Research, Efficiency) is
not possible
•Linkage to decision support resources not
possible

Ontology
Controlled terminology invoking formal semantic
relationships between and among concepts,
manifested as a type of description logic, which is a
subset of first-order predicate logic, chosen to
accommodate computational tractability. A common
example is OWL (Web Ontology Language;
www.w3.org/OWL/).

TPU
Trade Product
Use
TPP
Trade Product
Pack
GP
Generic
Product
TP
Trade Product
GPU
Generic
Product Use
VTM
Virtual Therapeutic
Moiety
GPP
Generic
Product Pack
Dosage
Form
Strength
Unit of
Uses
Pack
content
Manufacturer
Thai Medicines Terminology (TMT)
Data Model

86
TMT Example
IS_
A
IS_A IS_A
IS_A
IS_A
IS_A
IS_A
IS_A
ibuprofen 200 mg +
paracetamol 500 mg tab,
oral
(GP) TMTID:125623*
Ibuprofen + paracetamol
(VTM) TMTID:123456*
PARAFEN (ibuprofen 200
mg + paracetamol 500 mg)
tab, oral
(TP) TMTID:134562*
ibuprofen 200 mg +
oral, 1 tab
(GPU) TMTID:135462*
tab, oral, 1 tab
(TPU) TMTID: 127260*
ibuprofen 200 mg +
oral, 10 x 1 tab
(GPP) TMTID: 145678*
tab,
oral, 10 x 1 tab
(TPP) TMTID: 156832*

SNOMED
1205543012: diarrhoeal disorder
181114011: is a
302652014: drug-induced gastrointestinaldisturbance
2472589016: drug or medicament
367784012: causativeagent
373249005: anti-infectiveagent
76644002: antipyretic
372555007: antiallergic
373231005: musculoskeletalsystem agent
372701006: antiviralagent
372528003: retroviral protease inhibitor
387067003: lopinavir
TMT
87

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