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 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!

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 companies 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. A protocol for the exchange of health care information HL7 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. LiteBox MAGN ETOM 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 Treatment Paper