Twenty Years of IAIMS: The Columbia University/ New York Presbyterian Hospital Clinical Data Repository James J. Cimino Department of Biomedical Informatics Columbia University College of Physicians and Surgeons IAIMS Consortium Annual Meeting Boston, MA April 10, 2005 Overview • History and evolution (or creation) • Where we are today • What we learned History and Evolution (or Creation) • 1983-1986: IAIMS Planning Grant – Rachel Anderson – Organizational • 1986-1988: IAIMS Demonstration Grant – Paul Clayton and Rachel Anderson – Center for Medical Informatics – Vision History and Evolution (or Creation) • 1983-1986: IAIMS Planning Grant – Rachel Anderson – Organizational • 1986-1988: IAIMS Demonstration Grant – Paul Clayton – Center for Medical Informatics – Vision – “$6M Demo” • 1988-1993: IAIMS Implementation Grant – Funding from NLM, IBM, CU, Presbyterian Hospital – Network – Clinical data architecture Clinical Data Architecture • Central repository to collect data from myriad sources • Myriad users of data - some not yet imagined New York Presbyterian Hospital Clinical Information Systems Architecture Medical Logic Modules Clinical Database Alerts & Reminders Database Monitor Results Review Database Interface Medical Entities Dictionary Administrative Research Reformatter ... Radiology Reformatter Discharge Summaries Reformatter Laboratory ... Clinical Data Architecture • • • • • Central repository to collect data from myriad sources Myriad users of data - some not yet imagined Patient-oriented, not visit oriented, database Relational, not hierarchical, model Entity-attribute-value model Entity-Attribute-Value Clinical Data Repository Clinical Data Architecture • • • • • • • Central repository to collect data from myriad sources Myriad users of data - some not yet imagined Patient-oriented, not visit oriented, database Relational, not hierarchical, model Entity-attribute-value model Coded data wherever possible Unify terminology Medical Entities Dictionary: A Central Terminology Repository Communicating Terminology Changes K#1 = 4.2 K#1 = 3.3 K#2 = 3.2 K#1 = 3.0 K#3 = 2.6 K#1 K#2 K#3 Solution: Hierarchical Integration K#1 = 4.2 K#1 = 3.3 K#2 = 3.2 K#1 = 3.0 K#3 = 2.6 K K#1 K#2 K#3 MED Structure Medical Entity Substance Chemical Laboratory Specimen Anatomic Substance Plasma Carbohydrate Bioactive Substance Plasma Specimen Event Diagnostic Procedure Laboratory Test Plasma Glucose Glucose Laboratory Procedure CHEM-7 Part of Where We Are Today - Repository • Patients: 2.6 million • Visits: >10 million since 1996 with archives going back to 1979 • Visit diagnoses, locations, procedures, providers, insurance • Lab procedures: 16 million with 130 million results (to 1989) • Radiology procedures reports: 5.7 million • Pathology: 1.4 million • Cardiology procedures: 1.5 million • Resident signout notes:760,000 • Operative Notes: 426,000 • Clinical Notes: 400,000 • Discharge Summaries: 420000 • Medication orders: >60 million • ObGyn Procedure Reports: 241,000 • GI Procedure Reports: 101,000 • Neurology Procedure Reports: 54,000 • Ideatel BP’s: 215,000 • Ideatel Glucose: 650,000 • Consult Events: 18000 • HEENT Events:13000 • Hospitalist Notes:30000 • PFT: 25000 • Provider profiles 11000 • IDX 1.4 million • East Campus Where We Are Today - MED • Domains: – – – – – – – – – HP lab terms Misys lab terms Cerner lab terms Misys Radiology Digimedix drugs Cerner Drugs ICD9-based problem list terms Other applications Knowledge terms • Size: – – – – – – Concept-based (95,641) Multiple hierarchy (141,306) Synonyms (239,581) Translations (141,717) Semantic links (225,698) Attributes (210,456) Where We Are Today - Outputs • 7000 Users • Clinical information systems [LPRO]->(AE)->[ANTB]->(DS)->[PFUN]->(PO)->[ORGM]<-(PP)<-[OATT] A procedure assesses the effect of an antibiotic which disrupts a physiologic function which is a process of an organism which has an attribute (sensitive/resistant). Where We Are Today - Outputs • 7000 Users • Clinical information systems • MedLEE MedLEE Problems present: diarrhea discomfort (abdomen ) vomiting lightheaded unconscious Problems absent: pain (chest ) seizure Findings present: demo ( 67 year ) History: syncope HISTORY OF PRESENT ILLNESS: This 67 year old with a history of syncope in 1987 and 1989. She reported that she was evaluated both times and the work up was negative for any specific etiology. On the day of admission she reports having one episode of severe diarrhea and she was having increasing abdominal discomfort with flatulence and one episode of vomiting. When she returned to the bath room to move her bowels again she felt light headed and called for a family member. The family member reported that the patient was unconscious at that time and was placed in bed and recovered within 1-2 minutes. there was no history of any precipitating shortness of breath, chest pain or any seizure activity. At the time the patient was seen in the hospital she already felt fine. Where We Are Today - Outputs • • • • 7000 Users Clinical information systems MedLEE Decision support systems – Vigilens: TB, Freq Admit, Lab Vals Where We Are Today - Outputs • • • • 7000 Users Clinical information systems MedLEE Decision support systems – Vigilens: TB, Freq Admit, Lab Vals – Infobuttons Where We Are Today - Outputs • • • • 7000 Users Clinical information systems MedLEE Decision support systems – Vigilens: TB, Freq Admit, Lab Vals – Infobuttons • Clinical data warehouse select patient_id , time = primary_time from visit2004_diagnosis where diagnosis_icd9_code like '410%' and b.primary_time between '01/01/2000' and '01/01/2005' and b.comp_code = 30366 MI MI+Beta 2000 2001 2002 2003 2004 Where We Are Today - Outputs • 7000 Users • Clinical information systems • Decision support systems – Vigilens: TB, Freq Admit, Lab Vals – Infobuttons • Clinical data warehouse • Other clinical systems – – – – – – Infection Control CPOE Marconi IDEATel Data Mining Bioinformatics Lessons Learned • The repository architecture paid off • Model the data, not the applications • Write once, read many times • Pay attention to your terminology • You will reuse data • You can’t predict how you will reuse it Acknowledgements • • • • • New York Presbyterian Hospital Columbia University National Library of Medicine IBM People: – – – – – – – – Tom Morris Henrik Bendixen Rachel Anderson Paul Clayton Steve Johnson George Hripcsak Bob Sideli Somitra (Sen) Sengupta