Medical Informatics
Shmuel Rotenstreich
Friedman
“Medical Informatics is not about using
Microsoft Word to enter patient
information…”
Charles Friedman, PhD
University of Pittsburgh
at the UW Symposium, Fall 2000
Shortliffe
“ Medical informatics is the rapidly
developing scientific field that deals
with resources, devices and formalized
methods for optimizing the storage,
retrieval and management of
biomedical information for problem
solving and decision making”
Edward Shortliffe, MD, PhD
1995
Computers in Medicine
• Information central to biomedical research and
clinical practice
• Type
– integrated information-management environments
– affect on practice of medicine and biomedical
• Method
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medical computing
medical informatics
clinical informatics
bioinformatics
Value
• Value of medical-informatics and informatics
applications
• Computers and the Internet in biomedical
computing
• Relation among
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medical informatics
clinical practice
biomedical engineering
molecular biology
decision support
Difference
• information in clinical medicine and
“regular” information
• Changes in computer technology and
change in medical care and finance
• Integration of medical computing into
clinical practice and “regular” computing
integration
Areas
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Medical Decision making
Probabilistic medical reasoning
Patient care and monitoring systems
Computer aided surgery
Electronic patient records
Clinical decision support
Standards in medical informatics
Imaging
Image management systems
Telemedicine
Medical Informatics
• Medical Education
• Patient Data Collection and Recording
• Clinical Information Retrieval
• Medical Knowledge Retrieval
• Medical Decision Making
Medical Informatics is
Multidisciplinary
• Applies methodologies developed in
multiple areas of science to different tasks
• Often gives rise to new, more general
methodologies that enrich these scientific
disciplines
Example of Scientific Areas
Relevant to Medical Informatics
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Medicine/ Biology
Mathematics
Information Systems
Computer Science
Statistics
Decision Analysis
Economics/Health Care Policy
Psychology
The Diagnostic-Therapeutic Cycle
Data collection:
Data
-History
-Physical examinations
-Laboratory and other tests
Decision
making
Patient
Therapy plan
Information
Planning
Diagnosis/assessment
Levels of Automated Support
(Van Bemmel and Musen, 1997)
Medical Decision-Support Systems
• Task:
– Diagnosis/interpretation
– Therapy/management
• Scope:
– Broad (e.g., Internist-I/QMR: internal medicine
Dx; DxPlain; Iliad; EON for guideline-based
therapy)
– Narrow (e.g., a system for diagnosis of acute
abdominal pain; MYCIN: infectious diseases Dx;
ECG interpretation systems; ONCOCIN:
support of application of oncology protocols)
Types of Clinical Decision-Support
Systems
• Control level:
– Human-initiated consultation (e.g., MYCIN,
QMR)
– Data-driven reminder (e.g., MLMs)
– Closed loop systems (e.g., ICU ventilator
control)
• Interaction style:
– Prescriptive (e.g., ONCOCIN)
– Critiquing (e.g., VT Attending)
Diagnostic/Prognostic Methods
• Flow charts/clinical algorithms
• Statistical and other supervised and
nonsupervised classification methods
– Neural networks, ID3, C4.5, CART, clustering
• Bayesian/probabilistic classification
– Naïve Bayes, belief networks, influence diagrams
• Rule-based systems (MYCIN)
• “Ad hoc” heuristic systems (DxPlain)
• Cognitive-studies inspired systems (Internist I)
de Dombal’s System (1972)
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Domain: Acute abdominal pain (7 possible diagnoses)
Input: Signs and symptoms of patient
Output: Probability distribution of diagnoses
Method: Naïve Bayesian classification
Evaluation: an eight-center study involving 250 physicians
and 16,737 patients
• Results:
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Diagnostic accuracy rose from 46 to 65%
The negative laparotomy rate fell by almost half
Perforation rate among patients with appendicitis fell by half
Mortality rate fell by 22%
• Results using survey data consistently better than the
clinicians’ opinions and even the results using human
probability estimates!
Definitions
• Medical Informatics: the science of medical
information collection and management
• Medical Decision Making: quantitative methods
for reasoning under uncertainty
• Medical Computing: computer applications for
information management
• Medical Decision Support: computer-based
information processing to help human decision
makers
Case Presentation
Description: 74 female, history of right CVA (cerebrovascular accident*) in 1989
(LLE weakness), one week of productive cough and increased debility.
Exam consistent with bronchitis, oral antibiotic prescribed, but patient had a tonic
grand mal seizure in clinic
Became flaccid, unconscious, pulseless, apneic, but upon positioning for CPR,
developed pulse and spontaneous respirations and awoke about 2 minutes
after start of episode, complaining of lower sternal chest pain.
Actions:
– Transfer to Emergency Room
– Examination
– Bloodwork
– Chest Xray
– Cardiogram
– Admission and therapy
* Of or relating to the blood vessels that supply the brain
Demo - Part I
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Lab Data: ABG and CPK/Isoenzymes
Radiology: CXR, VQ, Doppler
Cardiology: ECG, Cardiac Cath
Medications
Alerts
Discharge Summary
ABG - Arterial blood gas
CPK - blood test
CXR – Chest X-Ray
EKG: Electrocardiogram (ECG)
Cardiac Cath - Interventional heart catheterization
Case Summary
Description: bronchitis, bed-bound, venous thrombosis, pulmonary
embolism, myocardial infarction, ventricular arrhythmia, hypotension,
seizure, adult respiratory distress syndrome, methicillin-resistant Staph
aureus
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Discharge Plan
» Where?
» What happened?
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Outpatient Follow-up
» Medications
» Laboratory
» Health Maintenance
Demo - Part II
• Demographic Information
• Additional Hospitalizations?
• More Discharge Summaries?
• Recent Lab Results
• Outpatient Notes
How Did We Do It?
• Information Science
• Standards
• Integration
Ambulatory Care
• Aka Primary Care, Office Medicine…
• Roles (information specific):
– Patient
– Scheduling, Registration
– Nursing, Triage
– Physician
– Ancillary Services
• Radiology
Patient
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Able to request an appointment!
Check meds!
Self reported SF-36 functional
Insurance Information!
Clinic Receptionist
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Appointment scheduling
Check-in
Insurance Information
Billing
Follow-up visit
Nurse
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Triage (certain settings)
Chief Complaint
Brief History
Vital signs & Initial Exam
Pulse, BP, Respirations, Pulse Oximeter
Psychosocial Assessment
Discharge Instructions (Pt Education)
Physician
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Review Chart Data, Studies
Document History and Physical Exam
Dx, Tx plan (orders, follow-up)
SOAP note
– Subjective
– Objective
– Assessment
– Plan
Ancillary Studies: Radiology Tech
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Schedule Exam
Review Allergies, Pregnancy
Review Clinical Indication
Enter Exam Data
Conventional data collection for
clinical trial
Medical records
Data sheets
Clinical trial design
• Definition of data elements
•Definition of eligibility
•Process descriptions
•Stopping criteria
•Other details of the trial
Computer
database
Analyses
Results
Role of EMR in supporting clinical
trials
Medical records systems
Clinical data
repository
Clinical trial design
• Definition of data elements
•Definition of eligibility
•Process descriptions
•Stopping criteria
•Other details of the trial
Clinical trial
database
Analyses
Results
Networking the organization
Personnel
systems
Enterprise network
Pharmacy
Patient
workstation
Clerical
workstation
Research
databeses
Clinical databases
Electronic medical
records
Billing and
financial systems
Clinical
workstations
Cost
accounting
Microbiology
Library
resources
Radiology
Material
management
Clinical
Data
laboratory
warehouse
Administrative systems
(e.g. admissions, discharges and transfers)
Educational
programs
Moving beyond the organization
The Internet
3rd party
payers
Patients
Government
health insurance
programs
Other hospitals
and physicians
Pharmaceuticals
regulators
Healthy
individuals
Communicable
disease agencies
Providers
in offices
or clinics
Information
resources
(Medline..)
Government
medical research
agencies
Vendors
of various types
(e.g. pharmaceuticals
companies
Health Science
Schools
Healthcare institutes Needs
• Healthcare institutes are seeking Integrated
clinical work stations that will assist with clinical
matters by:
– Reporting results of tests
– Allowing direct entry of orders
– Facilitating access to transcribed reports
– Supporting telemedicine applications
– Supporting decision-support functions
The Heart of the Evolving Clinical
Workstation
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Electronic
Confidential
Secure
Acceptable to clinicians and patients.
Integrated with non-patient-specific
information
Bioinformatics vs. Clinical
• Bioinformatics - The study of how information
is represented and transmitted in biological
systems, starting at the molecular level.
• Clinical informatics deals with the
management of information related to the
delivery of health care
• Bioinformatics focuses on the management of
information related to the underlying basic
biological sciences.
NIH maintains a database and tools of macromolecular 3D structures for
visualization and comparative analysis
MMDB - Molecular Modeling Database - contains experimentally determined
biopolymer structures obtained from the Protein Data Bank
National Library of Medicine
Medline
Medical Informatics Standards
• Medical Information Bus - IEEE 1073
– Standard for connecting up to 255 medical devices
– Not all devices compatible
– Decreases errors in data capture
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HL-7 Health Level 7
– Domain: clinical and administrative data.
– Mission: "provide standards for the exchange, management and
integration of data that support clinical patient care and the
management, delivery and evaluation of healthcare services.
Specifically, to create flexible, cost effective approaches,
standards, guidelines, methodologies, and related services for
interoperability between healthcare information systems."
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DICOM - Digital Imaging and Communications in
Medicine
HL7
A protocol for the exchange of health care
information
7 Application
6 Presentation
5 Session
4 Transport
3 Network
2 Data Link
1 Physical
Medical Information Bus IEEE 1073
• Standard for medical device communication
• A family of standards for providing
interconnection and interoperability of medical
devices and computerized healthcare
information systems.
• Medical devices include a broad range of clinical
monitoring, diagnostic, therapeutic equipment
• Computerized healthcare information systems
include broad range of clinical data management
systems, patient care systems and hospital
information systems
THE DICOM STANDARD
• applicable to a networked environment.
• applicable to an off-line media
environment.
• specifies how devices claiming
conformance to the Standard react to
commands and data being exchanged.
• specifies levels of conformance
DICOM Application
Domain
LiteBox
Storage, Query/Retrieve,
Study Component
MAGN
ETOM
Print Management
Query/Retrieve
Results Management
Media Exchange
Query/Retrieve,
Patient & Study Management
Information Management System
Standards for Vocabulary
• International Classification of Diseases, 9th
Edition, with Clinical Modifications (ICD9-CM)
• Diagnosis-Related Groups (DRGs)
• Medical Subject Headings (MeSH)
• Unified Medical language System (UMLS)
• Systematized Nomenclature of Medicine
(SNOMED)
• Read Codes
• Knowledge-Based Vocabularies
ICD9- CM Example
003 Other Salmonella Infections
003.0 Salmonella Gastroenteritis
003.1 Salmonella Septicemia
003.2 Localized Salmonella Infections
003.20 Localized Salmonella Infection, Unspecified
003.21 Salmonella Meningitis
003.22 Salmonella Pneumonia
003.23 Salmonella Arthritis
003.24 Salmonella Osteomyelitis
003.29 Other Localized Salmonella Infection
003.8 Other specified salmonella infections
003.9 Salmonella infection, unspecified
DRG Example
75 - Respiratory disease with major chest operating room procedure, no
major complication or comorbidity
76 - Respiratory disease with major chest operating room procedure, minor
complication or comorbidity
77 - Respiratory disease with other respiratory system operating procedure,
no complication or comorbidity
79 - Respiratory infection with minor complication, age greater than 17
80 - Respiratory infection with no minor complication, age greater than 17
89 - Simple Pneumonia with minor complication, age greater than 17
90 - Simple Pneumonia with no minor complication, age greater than 17
475- Respiratory disease with ventilator support
538 - Respiratory disease with major chest operating room procedure and
major complication or comorbidity
MeSH Example
Respiratory Tract Diseases
Lung Diseases
Pneumonia
Bronchopneumonia
Pneumonia, Aspiration
Pneumonia, Lipid
Pneumonia, Lobar
Pneumonia, Mycoplasma
Pneumonia, Pneumocystis Carinii
Pneumonia, Rickettsial
Pneumonia, Staphylococcal
Pneumonia, Viral
Lung Diseases, Fungal
Pneumonia, Pneumocystis Carinii
SNOMED Example
D2-50000
D2-50100
D2-50100
D2-50100
D2-50100
D2-50100
D2-50104
D2-50110
D2-50120
D2-50130
D2-50130
D2-50140
D2-50140
D2-50142
D2-50150
D2-50152
D2-50160
D2-50170
SECTIONS 2-5-6 DISEASES OF THE LUNG
2-501 NON-INFECTIOUS PNEUMONIAS
Bronchopneumonia, NOS
(T-26000) (M-40000)
Lobular pneumonia
(T-28040) (M-40000)
Segmental pneumonia
(T-280D0) (M-40000)
Bronchial pneumonia
(T-280D0) (M-40000)
Peribronchial pneumonia
(T-26090) (M-40000)
Hemorrhagic bronchopneumonia
(T-26000) (M-40790)
Terminal bronchopneumonia
(T-26000) (M-40000)
Pleurobronchopneumonia
(T-26000) (M-40000)
Pleuropneumonia
(T-26000) (M-40000)
Pneumonia, NOS
(T-28000) (M-40000)
Pneumonitis, NOS
(T-28000) (M-40000)
Catarrhal pneumonia
(T-28000) (M-40000)
Unresolved pneumonia
(T-28000) (M-40000)
Unresolved lobar pneumonia
(T-28770) (M-40000)
Granulomatous pneumonia, NOS
(T-28000) (M-44000)
Airsacculitis, NOS
(T-28850) (M-40000)
Temporal Reasoning and
Planning in Medicine
• Almost all medical data are time
stamped or time oriented (e.g., patient
measurements, therapy interventions)
• It is virtually impossible to plan therapy,
apply the therapy plan, monitor its
execution, and assess the quality of the
application or its results without the
concept of time
Time in Natural Language
From—
“Mr. Jones was alive after Dr. Smith operated on him”
Does it follow that—
“Dr. Smith operated on Mr. Jones before Mr. Jones was
alive?”
Is Before the inverse of After?
Understanding a Narrative
• List all, find at least one, or prove the
impossibility of a legal scenario for the following
statements:
– John had a headache after the treatment
– While receiving treatment, John read a paper
– before the headache, John experienced a visual aura
• One legitimate scenario (among many) is:
– “John read the paper from the very beginning of the
treatment until some point before its end; after
reading the paper, he experienced a visual aura that
started during treatment and ended after it; then he
had a headache.” Aura
Headache
Treatment
Paper
Monitoring
Determine if an oncology patient’s record
indicates a second episode that has been
lasting for more than 3 weeks, of Grade II
bone-marrow toxicity (as derived from the
results of several different types of blood
tests), due to a specific chemotherapy
drug.
Planning and Execution
If the patient develops sever anemia for
more than 2 weeks, reduce the
chemotherapy dose by 25% for the next 3
weeks and in parallel monitor the
hemoglobin level every day.
Display and Exploration of Time-Oriented Data
Temporal Abstraction
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Many clinical tasks require a great deal of [time-oriented] patient data of multiple
types to be measured and captured for interpretation, often using electronic media.
This is particularly true in the management of patients with chronic conditions.
Diagnostic or therapeutic decisions depend on context sensitive interpretation of
these data.
Most stored data include a time stamp at which a particular datum is valid.
Temporal trends and patterns in clinical data add significant insights to static analysis.
Thus it is desirable automatically to create abstractions (short, informative, and
context-sensitive interpretations*) of time-oriented clinical data, and to be able to
answer queries about these abstractions.
The provision of this capability would benefit both a physician and a decision support
tool (e.g., for patient management, quality assessment and clinical research).
To be of optimum use, a summary should not only use time points such as dates
when data were collected; it should also be capable of aggregating significant
features over intervals of time.
Temporal Abstraction
• Clinical tasks require time-oriented patient data of multiple types to
be measured and captured for interpretation.
– Particularly true in the management of patients with chronic conditions.
• Diagnostic or therapeutic decisions depend on context sensitive
interpretation of these data.
• Most stored data include a time stamp at which a particular datum is
valid.
• Temporal trends and patterns in clinical data add significant insights
to static analysis.
• Desirable automatically create abstractions (short, informative, and
context-sensitive interpretations*) of time-oriented clinical data, and
to be able to answer queries about these abstractions.
• The provision of this capability would benefit both a physician and a
decision support tool (e.g., for patient management, quality
assessment and clinical research).
• Of optimum use, a summary should not only use time points such as
dates when data were collected; it should also be capable of
aggregating significant features over intervals of time.
Three Basic Temporal Abstraction
• A model of three basic temporalabstraction mechanisms:
– Point temporal abstraction - a mechanism
for abstracting the values of several
parameters into a value of another parameter;
– Temporal inference, a mechanism for
inferring sound logical conclusions over a
single interval or two meeting intervals; and
– Temporal interpolation, a mechanism for
bridging non-meeting temporal intervals.
A Temporal-Reasoning Task:
Temporal Abstraction
• Input:
time-stamped clinical data and relevant
events
• Output: interval-based abstractions
• Identifies past and present trends and states
Supports decisions based on temporal patterns
“modify therapy if the patient has a second
episode of Grade II bone-marrow toxicity lasting
more than 3 weeks
• Focuses on interpretation, rather than on
forecasting
Temporal Abstraction:
A Bone-Marrow Transplantation Example
PAZ protocol
BMT
Expected CGVHD
M[0]
Platelet
counts
(• )
D D
• •
150K
D
•
D
•
D D D D
• •
• •
M[1]M[2]M[3] M[1]
D
•
100K
0
50
100
.
D
D D
•
•
•
200
Time (days)
D
•
D
•
D
•
M[0]
Granulocyte
counts
(D)
D D D
• • •
2000
1000
400
Uses of Temporal Abstractions
In Medical Domains
• Planning therapy and monitoring patients over time
• Creating high-level summaries of time-oriented
patient records
• Supporting explanation in medical decision-support
systems
• Representing the intentions of therapy guidelines
• Visualization and exploration of time-oriented
medical data
Temporal Reasoning Versus Temporal
Maintenance
• Temporal reasoning supports inference tasks
involving time-oriented data; often connected
with artificial-intelligence methods
• Temporal data maintenance deals with storage
and retrieval of data that has multiple temporal
dimensions; often connected with database
systems
• Both require temporal data modeling
DB
TM
Clinical
decision-support
application
TR
Medical Image Processing
• Input: X-Ray, CT-scan, MRI, PET, etc.
• Tasks:
– Correction of multiple artifacts
– Registration:Superimposition to enhance
visualization
– Segmentation: Decomposition into
semantically meaningful regions
Conclusion
• Multidisciplinary research, development, and
application
– inspired by and benefits underlying core
scientific/engineering areas
• Medical Decision support systems:
– Tasks: Diagnosis, therapy
– Mode: Human initiated, data driven, closed loop
– Interaction style: Prescriptive, critiquing
• Multiple diagnostic/therapeutic methodologies