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EMRs: Meaningful Use and Research
EMRs: Meaningful Use and Research
EMRs: Meaningful Use and Research
EMRs: Meaningful Use and Research
EMRs: Meaningful Use and Research
EMRs: Meaningful Use and Research
EMRs: Meaningful Use and Research
EMRs: Meaningful Use and Research
EMRs: Meaningful Use and Research
EMRs: Meaningful Use and Research
EMRs: Meaningful Use and Research
EMRs: Meaningful Use and Research
EMRs: Meaningful Use and Research
EMRs: Meaningful Use and Research
EMRs: Meaningful Use and Research
EMRs: Meaningful Use and Research
EMRs: Meaningful Use and Research
EMRs: Meaningful Use and Research
EMRs: Meaningful Use and Research
EMRs: Meaningful Use and Research
EMRs: Meaningful Use and Research
EMRs: Meaningful Use and Research
EMRs: Meaningful Use and Research
EMRs: Meaningful Use and Research
EMRs: Meaningful Use and Research
EMRs: Meaningful Use and Research
EMRs: Meaningful Use and Research
EMRs: Meaningful Use and Research
EMRs: Meaningful Use and Research
EMRs: Meaningful Use and Research
EMRs: Meaningful Use and Research
EMRs: Meaningful Use and Research
EMRs: Meaningful Use and Research
EMRs: Meaningful Use and Research
EMRs: Meaningful Use and Research
EMRs: Meaningful Use and Research
EMRs: Meaningful Use and Research
EMRs: Meaningful Use and Research
EMRs: Meaningful Use and Research
EMRs: Meaningful Use and Research
EMRs: Meaningful Use and Research
EMRs: Meaningful Use and Research
EMRs: Meaningful Use and Research
EMRs: Meaningful Use and Research
EMRs: Meaningful Use and Research
EMRs: Meaningful Use and Research
EMRs: Meaningful Use and Research
EMRs: Meaningful Use and Research
EMRs: Meaningful Use and Research
EMRs: Meaningful Use and Research
EMRs: Meaningful Use and Research
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EMRs: Meaningful Use and Research

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  • 1. Biomedical Informatics 1/20/12John Sharp, MSSA, PMP, FHIMSS Manager, Research Informatics Quantitative Health Sciences
  • 2. Outline1. What is an EMR/EHR? – components2. History and adoption of EMRs3. Effectiveness of EMRs4. Infrastructure - databases, warehouses5. Standards6. Meaningful Use7. Use of EMR data in Research
  • 3. Why EMRs
  • 4. What is an EHR/EMR Components
  • 5. EMR Components Lab ADTOrders EMR Radiology Notes Billing
  • 6. EMR by Workflow Check in Vitals Medical HxInsurance Nursing Symptoms Lab Orders After VisitRadiology Prescriptions Summary Results
  • 7. Inpatient Workflow ResultsAdmit Orders Flowsheet Lab ADT Images Orders D/CClinical Procedures Notes OrdersNotes Results Summary
  • 8. Brief History of EMRs And Adoption Trends
  • 9. Early History of EMRs Earliest were in the 1960s Began with lab systems and ADT (Admission, Discharge, Transfer) 1970s and 1980s – slow progress as technologies improved to include separate systems for nursing, physicians notes, OR scheduling. Epic Systems founded in 1980s 1990s – better integration of systems, first web-based systems
  • 10. EMR Adoption Hsiao et al. (2010); CDC/NCHS, National Ambulatory Medical Care Survey.
  • 11. Wiring the Health System Theoretical arguments – better coordination of care through information sharing Empirical Rationale – Using health information technology to improve quality and efficiency of care – VA and Kaiser as examples of early EMR adopters--------------------------------- David Blumenthal, MD, MPP – former director of the Office of the National Coordinator for Health IT in NEJM, 12/15/11
  • 12. Effectiveness of EMRs
  • 13. EMRsand Quality of Care
  • 14. EMR and Quality of Care Achievement of composite standards for diabetes care was 35.1 percentage points higher at EHR sites than at paper- based sites Achievement of composite standards for outcomes was 15.2 percentage points higher Across all insurance types, EHR sites were associated with significantly higher achievement of care and outcome standards and greater improvement in diabetes care Better Health Greater Cleveland
  • 15. Patricia SengstackCPOE Configuration toReduce Medication Errors,JHIM, Fall 2010 - Volume 24(4)26-32
  • 16. EMR Alert Types Clinical Decision Support Target Area of Care ExamplePreventive care Immunization, screening, disease management guidelines for secondary preventionDiagnosis Suggestions for possible diagnoses that match a patient’s signs and symptomsPlanning or implementing Treatment guidelines for specific diagnoses, drugtreatment dosage recommendations, alerts for drug-drug interactionsFollowup management Corollary orders, reminders for drug adverse event monitoringHospital, provider efficiency Care plans to minimize length of stay, order setsCost reductions and improved Duplicate testing alerts, drug formulary guidelinespatient convenience
  • 17. Unintended Consequences of Health IT A Look at Implementing CPOEPittsburgh Specific order sets designed for critical care were not created. Changes in workflow were not sufficiently predicted, resulting in a breakdown of communication between nurses and physicians. Orders for patients arriving via critical care transportation could not be written before the patients arrived at the hospital, delaying life-saving treatments. Changes, unrelated to the CPOE system, were made in the administration and dispensing of medication that further frustrated the clinical staff, for example:  At the same time the CPOE system was installed, the satellite pharmacy serving the neonatal ICU was closed and medications had to be obtained from the central pharmacy, delaying treatment.  Emergency prescriptions were required to be preapproved and all drugs were moved to the central pharmacy.
  • 18. Reducing Unintended Consequences of Electronic Health Recordshttp://www.ucguide.org/understand-identify/understand.html
  • 19. Infrastructure databases, warehouses
  • 20. EMR Databases Relational vs. Non- relational Microsoft SQL - relational Oracle - relational MySQL – open source Intersystems Cache – Epic (object database which can handle large volumes of transactional data)
  • 21. Data Warehouses Also called Clinical Data Repositories Collection of all clinical data for reporting, research, quality improvement, clinical decision support Requires interfaces with multiple systems, data mapping and harmonization Enables data mining, extraction of data sets
  • 22. EMR Standards and VocabulariesICD9, ICD10 SNOMED-CTCPT HL7LOINC DICOM UMLS
  • 23. ICD9 – ICD10 15,000 Diagnoses Grouped by disease category Drive the Problem List in most EMRs Also used for billing Transition to ICD10 68,000 codes– by July 2013 – Cleveland Clinic using a product by IMO to ease the transition. Already in use for problem list and encounter diagnoses. https://www.cms.gov/ICD9ProviderDiagnosticCodes/ http://www.who.int/classifications/icd/en/
  • 24. ICD9 Code Categorization1. INFECTIOUS AND PARASITIC DISEASES (001-139)2. NEOPLASMS (140-239)3. ENDOCRINE, NUTRITIONAL AND METABOLIC DISEASES, AND IMMUNITY DISORDERS (240-279)4. DISEASES OF THE BLOOD AND BLOOD-FORMING ORGANS (280-289)5. MENTAL DISORDERS (290-319)6. DISEASES OF THE NERVOUS SYSTEM AND SENSE ORGANS (320-389)7. DISEASES OF THE CIRCULATORY SYSTEM (390-459)8. DISEASES OF THE RESPIRATORY SYSTEM (460-519)9. DISEASES OF THE DIGESTIVE SYSTEM (520-579)10. DISEASES OF THE GENITOURINARY SYSTEM (580-629)11. COMPLICATIONS OF PREGNANCY, CHILDBIRTH, AND THE PUERPERIUM (630- 679)12. DISEASES OF THE SKIN AND SUBCUTANEOUS TISSUE (680-709)13. DISEASES OF THE MUSCULOSKELETAL SYSTEM AND CONNECTIVE TISSUE (710- 739)14. CONGENITAL ANOMALIES (740-759)15. CERTAIN CONDITIONS ORIGINATING IN THE PERINATAL PERIOD (760-779)16. SYMPTOMS, SIGNS, AND ILL-DEFINED CONDITIONS (780-799)17. INJURY AND POISONING (800-999)
  • 25. CPT - procedures Current Procedural Terminology Includes everything from phlebotomy to major surgeries Number: 7800 Added procedures as needed Controlled by the AMA
  • 26. CPT Categories1. Evaluation and Management  Examples2. Anesthesiology 99253 Initial inpatient consultation3. Surgery 11770 Excision of pilonidal cyst or sinus;4. Radiology simple5. Pathology and Laboratory 33512 Coronary artery bypass, vein6. Medicine only, four coronary venous grafts 62270 Spinal puncture, lumbar, diagnostic 76498 Unlisted diagnostic radiographic procedures 78205 Liver imaging (SPECT) 86900 Blood typing, ABO 93010 Electrocardiogram, routine ECG with at least 12 leads; tracing only without interpretation or report
  • 27. LOINC Logical Observation Identifier Names and Codes terminology LOINC codes are intended to identify the test result or clinical observation Provides a set of universal names and ID codes for identifying laboratory and clinical test results Number: 100,000 Includes: name of the component, timing of the measurement, type of sample (serum, urine, etc.), scale of measurement Used by almost all lab systems and EMRs Managed by the Regenstrief Institute, Inc. at University of Indiana
  • 28. SNOMED-CT Systematized Nomenclature of Medicine-Clinical Terms Comprehensive clinical terminology Over 300,000 concept codes Helpful in software development to map data to medical concepts Also includes relationships between concepts, such as, knee ‘is a’ body part
  • 29. HL7 – Health Level 7 A messaging language for health care Used for real-time data transfer from one system to another - interoperability Used here for sending data from Lab system to Epic Standards that permit structured, encoded health care information of the type required to support patient care, to be exchanged between computer applications while preserving meaning HL7.org
  • 30. HL7 example - ADTMSH|^~&|GHH LAB|ELAB-3|GHH OE|BLDG4|200202150930||ORU^R01|CNTRL- 3456|P|2.4<cr>PID|||555-44-4444||EVERYWOMAN^EVE^E^^^^L|JONES|19620320|F|||153 FERNWOOD DR.^^STATESVILLE^OH^35292||(206)3345232|(206)752-121||||AC555444444||67- A4335^OH^20030520<cr>OBR|1|845439^GHH OE|1045813^GHH LAB|15545^GLUCOSE|||200202150730|||||||||555-55-5555^PRIMARY^PATRICIA P^^^^MD^^|||||||||F||||||444-44- 4444^HIPPOCRATES^HOWARD H^^^^MD<cr>OBX|1|SN|1554-5^GLUCOSE^POST 12H CFST:MCNC:PT:SER/PLAS:QN||^182|mg/dl|70_105|H|||F<cr>
  • 31.  For imaging Designed to ensure the interoperability of systems Used to: Produce, Store, Display, Process, Send, Retrieve, Query or Print medical images and derived structured documents as well as to manage related workflow. http://medical.nema.org/
  • 32. # 0x44 - Item 1: > (0x00080100, SH, "mV") # 0x2 - Code Value OK > (0x00080102, SH,DICOM "UCUM") # 0x4 - Coding SchemeCode Designator OK > (0x00080103, SH, "1.4") # 0x4 - Concept group revision OK > (0x00080104, LO, "millivolt") # 0xA - Code Meaning OK > (0x003A0212, DS, "1") # 0x2 - Sensitivity correction factor OK > (0x003A0213, DS, "0") # 0x2 - Channel baseline OK > (0x003A0214, DS, "0") # 0x2 - Channel Time skew OK > (0x003A021A, US, 0x0010) # 0x2 - Bits per sample OK > (0x003A0220, DS, ".05") # 0x4 - Filter low frequency OK > (0x003A0221, DS, "100") # 0x4 - filter high frequency OK
  • 33. UMLS Unified Medical Language System Integrates and distributes key terminology, classification and coding standards to promote more effective and interoperable biomedical information systems and services, including electronic health records 100 source vocabularies in the UMLS Metathesaurus Includes SNOMED-CT, LOINC, others From the National Library of Medicine
  • 34. Meaningful Use Of EMRs
  • 35. EMR Incentives $44,000 over five years for eligible professionals Must show meaningful use Must be an approved EMR Program to assist small practices -REC Most health systems have or are in process
  • 36. Meaningful Use
  • 37. Meaningful Use Eligible Hospital Meaningful Use Table of Contents Core and Menu Set Objectives https://www.cms.gov/EHRIncentivePrograms/Downloads/ Hosp_CAH_MU-TOC.pdf
  • 38. Use of EMRs in Research
  • 39. Basis for Research Integrating research workflow into the EMR  Clinical trial patient calendar A rich source of clinical data – data mining Data is from real clinical situations, unlike highly controlled clinical trials But is messy – not always easy to compare groups, clinical events are not in a standard sequence Missing data
  • 40. How to Begin Research question Define cohort – inclusion, exclusion criteria Data elements to be included Statistical tests to be utilized – descriptive statistics or more Modify cohort or data elements Analyze results
  • 41. Retrospective Cohort Studies Descriptive Typically utilizes discrete data elements in the EHR Internal validation recommended – comparing a random sample of patients in the database with what is documented in the front end of the EHR Example: Development and Validation of an Electronic Health Record–Based Chronic Kidney Disease Registry
  • 42. Prospective Cohort Studies Prospective in the sense that measurements are taken from the EMR at specific time points Time points need to be within a given range, for instance, 1 year after time zero plus or minus one month Missing data may eliminate patients from the cohort Example: Underdiagnosis of Hypertension in Children and Adolescents
  • 43. Prospective Studies Begin collecting data from the EMR at a specific time point May also include manual data collection Example – biomarker for infection in the ICU
  • 44. EMR Data in Research Example Chronic Kidney Disease Registry Established 2009 60,000 patients from the health system Cohort – Adults with two eGFRs less than 60 within 3 months, outpatient results only, or diagnosis of CKD http://www.chrp.org/pdf/HSR_12022011_Slides.pdf
  • 45. Registry Validation
  • 46. Validation Results Our dataset’s agreement with EHR-extracted data for documentation of the presence and absence of comorbid conditions, ranged from substantial to near perfect agreement. Hypertension and coronary artery disease were exceptions 65% sensitivity 50% negative predictive value
  • 47. Registry Results 2011 5 out of 5 abstracts accepted to American Society of Nephrology annual meeting Three papers accepted to nephrology journals NIH grant Partnerships with other research centers
  • 48. Upcoming Publication Book chapter on eResearch Editor, Rob Hoyt, University of West Florida http://www.uwf.edu/sahls/medicalinformatics/

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