Medical Informatics

1
Medical Informatics

• Shmuel Rotenstreich

2
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

3
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

4
Computers in Medicine

• Information central to biomedical research and
  clinical practice
• Type
• integrated information-management environments
• affect on practice of medicine and biomedical
• Method
• medical computing
• medical informatics
• clinical informatics
• bioinformatics

5
Value

• Value of medical-informatics and informatics
  applications
• Computers and the Internet in biomedical
  computing
• Relation among
• medical informatics
• clinical practice
• biomedical engineering
• molecular biology
• decision support

6
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

7
Areas

• 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

8
Medical Informatics

• Medical Education
• Patient Data Collection and Recording
• Clinical Information Retrieval
• Medical Knowledge Retrieval
• Medical Decision Making

9
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

10
Example of Scientific Areas Relevant to Medical
Informatics

• Medicine/ Biology
• Mathematics
• Information Systems
• Computer Science
• Statistics
• Decision Analysis
• Economics/Health Care Policy
• Psychology

11
The Diagnostic-Therapeutic Cycle
Data collection -History -Physical
examinations -Laboratory and other tests
Information
Data
Decision making
Patient
Therapy plan
Planning
Diagnosis/assessment
12
Levels of Automated Support(Van Bemmel and
Musen, 1997)
13
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)

14
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)

15
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)

16
de Dombals System (1972)

• 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
• 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!

17
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

18
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

19
Demo - Part I

• 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

20
Case Summary

• Description bronchitis, bed-bound, venous
  thrombosis, pulmonary embolism, myocardial
  infarction, ventricular arrhythmia, hypotension,
  seizure, adult respiratory distress syndrome,
  methicillin-resistant Staph aureus

• Discharge Plan
• Where?
• What happened?
• Outpatient Follow-up
• Medications
• Laboratory
• Health Maintenance

21
Demo - Part II

• Demographic Information
• Additional Hospitalizations?
• More Discharge Summaries?
• Recent Lab Results
• Outpatient Notes

22
How Did We Do It?

• Information Science
• Standards
• Integration

23
Ambulatory Care

• Aka Primary Care, Office Medicine
• Roles (information specific)
• Patient
• Scheduling, Registration
• Nursing, Triage
• Physician
• Ancillary Services
• Radiology

24
Patient

• Able to request an appointment!
• Check meds!
• Self reported SF-36 functional
• Insurance Information!

25
Clinic Receptionist

• Appointment scheduling
• Check-in
• Insurance Information
• Billing
• Follow-up visit

26
Nurse

• Triage (certain settings)
• Chief Complaint
• Brief History
• Vital signs Initial Exam
• Pulse, BP, Respirations, Pulse Oximeter
• Psychosocial Assessment
• Discharge Instructions (Pt Education)

27
Physician

• Review Chart Data, Studies
• Document History and Physical Exam
• Dx, Tx plan (orders, follow-up)
• SOAP note
• Subjective
• Objective
• Assessment
• Plan

28
Ancillary Studies Radiology Tech

• Schedule Exam
• Review Allergies, Pregnancy
• Review Clinical Indication
• Enter Exam Data

29
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
30
Role of EMR in supporting clinical trials
Medical records systems
Clinical data repository
Clinical trial database

• Clinical trial design
• Definition of data elements
• Definition of eligibility
• Process descriptions
• Stopping criteria
• Other details of the trial

Analyses
Results
31
Networking the organization
Personnel systems
Clinical databases Electronic medical records
Enterprise network
Pharmacy
Patient workstation
Billing and financial systems
Clerical workstation
Cost accounting
Clinical workstations
Microbiology
Library resources
Research databeses
Radiology
Material management
Clinical laboratory
Data warehouse
Educational programs
Administrative systems (e.g. admissions,
discharges and transfers)
32
Moving beyond the organization
The Internet
Government health insurance programs
3rd party payers
Other hospitals and physicians
Patients
Pharmaceuticals regulators
Healthy individuals
Communicable disease agencies
Government medical research agencies
Providers in offices or clinics
Vendors of various types (e.g. pharmaceuticals com
panies
Information resources (Medline..)
Health Science Schools
33
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

34
The Heart of the Evolving Clinical Workstation

• Electronic
• Confidential
• Secure
• Acceptable to clinicians and patients.
• Integrated with non-patient-specific information

35
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.

36
NIH maintains a database and tools of
macromolecular 3D structures for visualization
and comparative analysisMMDB - Molecular
Modeling Database - contains experimentally
determined biopolymer structures obtained from
the Protein Data Bank
37
National Library of Medicine Medline
38
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
• 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."
• DICOM - Digital Imaging and Communications in
  Medicine

39
HL7

• A protocol for the exchange of health care
  information

HL7
7 Application
6 Presentation
5 Session
4 Transport
3 Network
2 Data Link
1 Physical
40
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

41
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

42
DICOM Application Domain
Storage, Query/Retrieve, Study Component
Print Management
Query/Retrieve Results Management
Media Exchange
Query/Retrieve, Patient Study Management
Information Management System
43
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

44
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

45
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

46
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

47
SNOMED Example

• D2-50000 SECTIONS 2-5-6 DISEASES OF THE LUNG
• D2-50100 2-501 NON-INFECTIOUS PNEUMONIAS
• D2-50100 Bronchopneumonia, NOS (T-26000)
  (M-40000)
• D2-50100 Lobular pneumonia (T-28040)
  (M-40000)
• D2-50100 Segmental pneumonia (T-280D0)
  (M-40000)
• D2-50100 Bronchial pneumonia (T-280D0)
  (M-40000)
• D2-50104 Peribronchial pneumonia
  (T-26090) (M-40000)
• D2-50110 Hemorrhagic bronchopneumonia
  (T-26000) (M-40790)
• D2-50120 Terminal bronchopneumonia
  (T-26000) (M-40000)
• D2-50130 Pleurobronchopneumonia (T-26000)
  (M-40000)
• D2-50130 Pleuropneumonia (T-26000)
  (M-40000)
• D2-50140 Pneumonia, NOS (T-28000)
  (M-40000)
• D2-50140 Pneumonitis, NOS (T-28000)
  (M-40000)
• D2-50142 Catarrhal pneumonia (T-28000)
  (M-40000)
• D2-50150 Unresolved pneumonia (T-28000)
  (M-40000)
• D2-50152 Unresolved lobar pneumonia
  (T-28770) (M-40000)
• D2-50160 Granulomatous pneumonia, NOS
  (T-28000) (M-44000)
• D2-50170 Airsacculitis, NOS (T-28850)
  (M-40000)

48
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

49
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?
50
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
Paper
Treatment
51
Monitoring

• Determine if an oncology patients 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.

52
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.

53
Display and Exploration of Time-Oriented Data
54
Temporal Abstraction

• 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.

55
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.

56
Three Basic Temporal Abstraction

• A model of three basic temporal-abstraction
  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.

57
A Temporal-Reasoning TaskTemporal 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

58
Temporal AbstractionA Bone-Marrow
Transplantation Example
PAZ protocol
BMT
Expected CGVHD
.
M0
M1
M2
M3
M1
M0
Granu-
Platelet
locyte
D
D
counts
D
D
D
counts
D
D
D
(
)
D
D
D
D
D
D
D

D



D

(
)








D
D
150K

2000



100K


1000
400
0
200
100
50
Time (days)
59
Uses of Temporal AbstractionsIn 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
60
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

61
Medical Image Processing

• Input X-Ray, CT-scan, MRI, PET, etc.
• Tasks
• Correction of multiple artifacts
• RegistrationSuperimposition to enhance
  visualization
• Segmentation Decomposition into semantically
  meaningful regions

62
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