medical informatics
DESCRIPTION
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. - PowerPoint PPT PresentationTRANSCRIPT
Medical Informatics
Shmuel Rotenstreich
Friedman
“Medical Informatics is not about usingMicrosoft Word to enter patientinformation…”
Charles Friedman, PhDUniversity of Pittsburghat 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, PhD1995
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
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
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• 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
• Medicine/ Biology• Mathematics• Information Systems• Computer Science• Statistics• Decision Analysis• Economics/Health Care Policy• Psychology
The Diagnostic-Therapeutic Cycle
Patient
Data collection:-History-Physical examinations-Laboratory and other tests
Decisionmaking
Planning
Information
Diagnosis/assessmentTherapy plan
Data
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)• 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!
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 PresentationDescription: 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• 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-RayEKG: Electrocardiogram (ECG) Cardiac Cath - Interventional heart catheterization
Case SummaryDescription: 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
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
• Able to request an appointment!• Check meds!• Self reported SF-36 functional• Insurance Information!
Clinic Receptionist
• Appointment scheduling• Check-in• Insurance Information• Billing• Follow-up visit
Nurse
• Triage (certain settings)• Chief Complaint• Brief History• Vital signs & Initial Exam• Pulse, BP, Respirations, Pulse Oximeter• Psychosocial Assessment• Discharge Instructions (Pt Education)
Physician
• 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
• Schedule Exam• Review Allergies, Pregnancy• Review Clinical Indication• Enter Exam Data
Conventional data collection for clinical trial
Clinical trial design• Definition of data elements•Definition of eligibility•Process descriptions•Stopping criteria•Other details of the trial
Data sheets
Computer database
Analyses
Results
Medical records
Role of EMR in supporting clinical trials
Clinical trial design• Definition of data elements•Definition of eligibility•Process descriptions•Stopping criteria•Other details of the trial
Clinical trial database
Analyses
Results
Medical records systems
Clinical datarepository
Networking the organization
Enterprise network
Patientworkstation
Clinical workstations
Clerical workstation
Researchdatabeses
Administrative systems(e.g. admissions, discharges and transfers)
Libraryresources
Radiology
Billing andfinancial systems
Costaccounting
Microbiology
Pharmacy
Clinical databasesElectronic medical
records
Personnelsystems
Materialmanagement
Educationalprograms
Clinicallaboratory
Datawarehouse
Moving beyond the organization
Patients
Healthyindividuals
Providersin officesor clinics
Informationresources(Medline..)
Governmentmedical research
agencies
3rd partypayers
The Internet Governmenthealth insurance
programsOther hospitalsand physicians
Pharmaceuticalsregulators
Communicabledisease agencies
Health ScienceSchools
Vendorsof various types
(e.g. pharmaceuticalscompanies
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
• 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
• 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
A protocol for the exchange of health care information
1 Physical2 Data Link3 Network4 Transport5 Session6 Presentation7 Application
HL7
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
MAGN
ETOM
Information Management System
Storage, Query/Retrieve, Storage, Query/Retrieve, Study ComponentStudy Component
Query/Retrieve, Query/Retrieve, Patient & Study ManagementPatient & Study Management
Query/RetrieveQuery/RetrieveResults ManagementResults Management
Print ManagementPrint Management
Media ExchangeMedia Exchange
LiteBox
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 Example003 Other Salmonella Infections
003.0 Salmonella Gastroenteritis003.1 Salmonella Septicemia003.2 Localized Salmonella Infections003.20 Localized Salmonella Infection, Unspecified003.21 Salmonella Meningitis003.22 Salmonella Pneumonia003.23 Salmonella Arthritis003.24 Salmonella Osteomyelitis003.29 Other Localized Salmonella Infection003.8 Other specified salmonella infections003.9 Salmonella infection, unspecified
DRG Example75 - 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 ExampleRespiratory Tract Diseases
Lung DiseasesPneumoniaBronchopneumoniaPneumonia, AspirationPneumonia, LipidPneumonia, LobarPneumonia, MycoplasmaPneumonia, Pneumocystis CariniiPneumonia, RickettsialPneumonia, StaphylococcalPneumonia, ViralLung Diseases, FungalPneumonia, Pneumocystis Carinii
SNOMED ExampleD2-50000 SECTIONS 2-5-6 DISEASES OF THE LUNG
D2-50100 2-501 NON-INFECTIOUS PNEUMONIASD2-50100 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)
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.”
Paper
Aura
Treatment Headache
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
• 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 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.
A Temporal-Reasoning Task:
Temporal Abstraction• Input: time-stamped clinical data and relevant events• Output: interval-based abstractions• Identifies past and present trends and statesSupports 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.
•
0 40020010050
•
1000
2000
( )
100K150K( )
•••
• • • •
•• •
•••
Granu-locytecounts
• • •
•
Time (days)
Plateletcounts
PAZ protocol
M[0] M[1]M[2]M[3] M[1] M[0]
BMT
Expected CGVHD
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
Clinicaldecision-supportapplication
TM TR DB
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