How to extract transform and load observational data?

Size: px
Start display at page:

Download "How to extract transform and load observational data?"

Transcription

1 How to extract transform and load observational data? Martijn Schuemie Janssen Research & Development Department of Pharmacology & Pharmacy, The University of Hong Kong

2 Outline Observational data & research networks OMOP Common Data Model (CDM) Transforming data to the CDM Available tools for the CDM

3 Observational data & research networks

4 Observational health data Subjective: Complaint and symptoms Medical history Objective: Observations Measurements vital signs, laboratory tests, radiology/ pathology findings Assessment and Plan: Diagnosis Treatment

5 Observational health data All captured in the medical record Medical records are increasingly being captured in EHR systems Data within the EHR are becoming increasingly structured

6 The average primary care physician sees 20 different patients a day care data

7 that s 100 visits in a week care data

8 which can reflect a panel of over 2,000 patients in a year care data

9 Databases for research Diseases Health care provided Evidence generation Electronic Health Records Insurance claims Registries Effects of treatments Differences between patients Personalized medicine

10 Key point Existing observational health care data such as Electronic Health Records and insurance claims databases have great potential for research 现 有 的 卫 生 保 健 观 测 数 据, 例 如 电 子 健 康 记 录 保 险 索 赔 数 据 库 等 具 有 巨 大 的 潜 力 为 研 究 所 用

11 Research networks Data may be at different sites Sites often cannot share data at the patient level Data can be in very different formats Patient level, identifiable information Practice Hospital Claims Registry

12 ETL ETL ETL ETL Standardized analytics Aggregate summary statistics Firewall Common Data Model Extract, Transform, Load (ETL) Patient level, identifiable information Practice Hospital Claims Registry

13 ETL ETL ETL ETL Count people on drug A and B, and the number of outcomes X Firewall Common Data Model Extract, Transform, Load (ETL) Patient level, identifiable information Practice Hospital Claims Registry

14 ETL ETL ETL ETL Count people on drug A and B, and the number of outcomes X Firewall Common Data Model Practice: 100 patients on A, 1 has X 200 patients on B, 4 have X Extract, Transform, Load (ETL) Hospital: 1000 patients Patient on A, level, 10 have identifiable X 2000 patients on B, 40 have information X Aggregated data Etc. Practice No privacy concerns Hospital Claims Registry

15 Distributed research networks Europe United States Asia AsPEN Global

16 Key point Observational data can often not be shared. Using a common data model allows analysis programs to visit the data instead. 观 测 数 据 通 常 不 是 共 享 的 而 使 用 公 共 数 据 模 型 可 以 实 现 分 析 程 序 对 不 同 来 源 数 据 的 统 一 访 问

17 OMOP Common Data Model (CDM)

18 A common structure Person - person_id - year_of_birth - month_of_birth - day_of_birth - gender_concept_id A common vocabulary Common Data Model How do we store gender? - M, F, U - 0, 1, (male), 8532 (female), 8851 (unknown gender), 8570 (ambiguous gender)

19 OMOP Common Data Model Designed for various types of data (EHR, insurance claims) in various countries Developed by the OMOP / OHDSI community Currently on 5 th version Easy to get data in, easy to get data out

20 Standardized clinical data Drug safety surveillance Device safety surveillance Vaccine safety surveillance Comparative effectiveness One model, multiple use cases Health economics Quality of care Clinical research Person Observation_period Specimen Death Standardized health system data Location Care_site Provider Standardized meta-data CDM_source Concept Visit_occurrence Procedure_occurrence Drug_exposure Device_exposure Condition_occurrence Measurement Note Observation Fact_relationship Payer_plan_period Procedure_cost Drug_era Visit_cost Drug_cost Device_cost Cohort Cohort_attribute Condition_era Dose_era Standardized health economics Standardized derived elements Vocabulary Domain Concept_class Concept_relationship Relationship Concept_synonym Concept_ancestor Source_to_concept_map Drug_strength Cohort_definition Attribute_definition Standardized vocabularies

21 Person centric CDM principles Data is split into domains (e.g. conditions, drugs, procedures) Preserve source values and map to standard values Store the source of the data Separate verbatim data from inferred data

22 OMOP Vocabulary All codes are mapped to standard coding systems Drugs: RxNorm Conditions: SNOMED Measurements: LOINC Procedures: ICD9Proc, HCPCS, CPT-4

23 Vocabulary MEDDRA Liver disorder ( ) SNOMED Injury of liver ( ) Biliary cirrhosis ( ) Primary biliary cirrhosis ( ) Source codes Biliary cirrhosis of children (J616200) CPRD Primary biliary cirrhosis (K74.3) AUSOM

24 Key point The OMOP Common Data Model provides a common structure and a common vocabulary. Different levels of precision in different coding systems are resolved using a concept hierarchy. OMOP 公 共 数 据 模 型 对 数 据 采 用 通 用 结 构 和 通 用 词 汇 ; 利 用 统 一 的 概 念 等 级 解 决 不 同 编 码 系 统 的 各 个 级 别 的 精 确 度 问 题

25 Transforming data to the CDM

26 Process to create an ETL 1. Data experts and CDM experts together design the ETL 2. People with medical knowledge create the code mappings 3. A technical person implements the ETL 4. All are involved in quality control

27 Tools to support the process 1. Data experts and CDM experts together design the ETL WHITE RABBIT RABBIT IN A HAT 2. People with medical knowledge create the code mappings 3. A technical person implements the ETL? USAGI 4. All are involved in quality control ACHILLES

28 Run Step 1. Designing the ETL WHITE RABBIT Scans the source database Creates a report describing o Tables o Fields in the tables o Values in the fields o Frequency of values

29 Step 1. Designing the ETL Run WHITE RABBIT In an interactive session with both data experts and CDM experts, run RABBIT IN A HAT

30 RABBIT IN A HAT

31 Step 1. Designing the ETL Run WHITE RABBIT In an interactive session with both data experts and CDM experts, run RABBIT IN A HAT Output document becomes basis of ETL specification

32 Step 2. Create code mappings Need to map codes to one of the OMOP CDM standards: Drugs: RxNorm Conditions: SNOMED Lab values: LOINC Specialties: CMS

33 Prepare data Step 2. Create code mappings Codes Descriptions (translated to English if needed) Frequencies Use existing mapping information if available If ATC codes are available, use to select subsets Use (partial) mappings in UMLS Run USAGI

34 USAGI

35 Step 3. Implement the ETL No standard. Depends on the expertise of the person doing the work: SQL SAS C++ (CDMBuilder) Java (JCDMBuilder) Kettle

36 Step 4. Quality control Not yet fully defined, but we currently use: Manually compare source and CDM information on a sample of persons Use Achilles software Replicate a study already performed on the source data

37 Key point Transforming data to a common data model requires an interdisciplinary team, including clinicians, informatics specialists, and people that know the data. 实 现 数 据 到 公 共 数 据 模 型 的 转 换 需 要 一 个 跨 学 科 领 域 团 队 的 合 作, 包 括 临 床 医 生 信 息 学 专 家 以 及 了 解 数 据 内 容 的 人 员

38 Available tools for the CDM

39 Introducting OHDSI International collaborative ohdsi.org

40 Introducting OHDSI International collaborative Coordinating center in Columbia University Academia and industry Data network Research (clinical + methodology) Open source software ohdsi.org

41 ACHILLES Automated Characterization of Health Information at Large-scale Longitudinal Evidence Systems Data in CDM Aggregate statistics: - Persons - Conditions - Drugs - Lab results - Explore statistics in a web browser

42 ACHILLES >12 databases from 5 countries across 3 different platforms: Janssen (Truven, Optum, Premier, CPRD, NHANES, HCUP) Columbia University Regenstrief Institute Ajou University IMEDS Lab (Truven, GE) UPMC Nursing Home Erasmus MC Cegedim

43 Lists data quality issues Achilles Heel

44 Achilles Statistics can be explored for patterns indicating quality issues

45 Open source tools Support for ETL White Rabbit + Rabbit in a Hat to help design an ETL Usagi to help create code mappings Data exploration / study feasibility HERMES to explore the vocabulary ACHILLES to explore a database CIRCE to create cohort definitions HERACLES to explore cohort characteristics Orange: Under development Methods for performing studies Cohort method for new user cohort studies using large-scale propensity scores IC Temporal pattern discovery Self-Controlled Case Series Korean signal detection algorithms Knowledge base MedlineXmlToDatabase for loading Medline into a local database LAERTES for combining evidence from literature, labels, ontologies, etc. Black: Complete

46 Key point OHDSI is a research community that develops open source tools that run on the Common Data Model. OHDSI 是 一 个 研 究 团 体, 主 要 致 力 于 开 发 在 公 共 数 据 模 型 上 运 行 分 析 的 开 源 工 具

47 Concluding thoughts Wealth of observational data to generate wealth of evidence for health care ETL into Common Data Model can be large initial investment Common Data Model allows sharing of results, methods, software tools

48 Thank you! 谢 谢!

Learning from observational databases: Lessons from OMOP and OHDSI

Learning from observational databases: Lessons from OMOP and OHDSI Learning from observational databases: Lessons from OMOP and OHDSI Patrick Ryan Janssen Research and Development David Madigan Columbia University http://www.omop.org http://www.ohdsi.org The sole cause

More information

Open-Source Big Data Analytics in Healthcare

Open-Source Big Data Analytics in Healthcare Open-Source Big Data Analytics in Healthcare Jon Duke, George Hripcsak, Patrick Ryan www.ohdsi.org/medinfo-2015-tutorial Introduction Introducing OHDSI The Observational Health Data Sciences and Informatics

More information

Next-generation Phenotyping Using Interoperable Big Data

Next-generation Phenotyping Using Interoperable Big Data Biomedical Informatics discovery and impact Next-generation Phenotyping Using Interoperable Big Data George Hripcsak, Chunhua Weng Columbia University Medical Center Collab with Mount Sinai Medical Center

More information

Achilles a platform for exploring and visualizing clinical data summary statistics

Achilles a platform for exploring and visualizing clinical data summary statistics Biomedical Informatics discovery and impact Achilles a platform for exploring and visualizing clinical data summary statistics Mark Velez, MA Ning "Sunny" Shang, PhD Department of Biomedical Informatics,

More information

Asian Data Resources. October 24, 2014 8:30-12:30 Using pharmacoepidemiology database resources to address drug safety research

Asian Data Resources. October 24, 2014 8:30-12:30 Using pharmacoepidemiology database resources to address drug safety research Draft Asian Data Resources October 24, 2014 8:30-12:30 Using pharmacoepidemiology database resources to address drug safety research Kiyoshi Kubota MD PhD FISPE NPO Drug Safety Research Unit Japan Multiple

More information

Environmental Health Science. Brian S. Schwartz, MD, MS

Environmental Health Science. Brian S. Schwartz, MD, MS Environmental Health Science Data Streams Health Data Brian S. Schwartz, MD, MS January 10, 2013 When is a data stream not a data stream? When it is health data. EHR data = PHI of health system Data stream

More information

Scalable Architecture for Federated Therapeutic Inquiries Network (SAFTINet) ETL Specifications Document Version 4.

Scalable Architecture for Federated Therapeutic Inquiries Network (SAFTINet) ETL Specifications Document Version 4. Scalable Architecture for Federated Therapeutic Inquiries Network (SAFTINet) ETL Specifications Document Version 4.0 March 3rd, 2013 SAFTINet ETL Specifications Document Page 1 LICENSE 2011 Foundation

More information

Bootstrapping Adoption of a Universal Exchange Language for Health Information Exchange

Bootstrapping Adoption of a Universal Exchange Language for Health Information Exchange Bootstrapping Adoption of a Universal Exchange Language for Health Information Exchange Speakers: Tajh L. Taylor, Lowell Vizenor OMG SOA in Healthcare Conference July 15, 2011 Agenda The Health Information

More information

Meaningful use. Meaningful data. Meaningful care. The 3M Healthcare Data Dictionary: Standardizing lab data to LOINC for meaningful use

Meaningful use. Meaningful data. Meaningful care. The 3M Healthcare Data Dictionary: Standardizing lab data to LOINC for meaningful use Meaningful use. Meaningful data. Meaningful care. The 3M Healthcare Data Dictionary: Standardizing lab data to LOINC for meaningful use Executive summary By using standard terminologies to report on core

More information

Disclosures. Real World Data Sources

Disclosures. Real World Data Sources Disclosures Real World Data Sources Christopher M. Blanchette, PhD, MBA Director, HEOR, Otsuka America Pharmaceutical Inc. Research Associate Professor, Public Health Sciences, University of North Carolina,

More information

From Fishing to Attracting Chicks

From Fishing to Attracting Chicks The Greater Plains Collaborative: a PCORNet Clinical Data Research Network s Strategies for Creating an Interoperable Architecture From Fishing to Attracting Chicks Russ Waitman, PhD Associate Professor,

More information

Find the signal in the noise

Find the signal in the noise Find the signal in the noise Electronic Health Records: The challenge The adoption of Electronic Health Records (EHRs) in the USA is rapidly increasing, due to the Health Information Technology and Clinical

More information

HL7 and Meaningful Use

HL7 and Meaningful Use HL7 and Meaningful Use Grant M. Wood HL7 Ambassador HIMSS14 2012 Health Level Seven International. All Rights Reserved. HL7 and Health Level Seven are registered trademarks of Health Level Seven International.

More information

Secondary Uses of Data for Comparative Effectiveness Research

Secondary Uses of Data for Comparative Effectiveness Research Secondary Uses of Data for Comparative Effectiveness Research Paul Wallace MD Director, Center for Comparative Effectiveness Research The Lewin Group Paul.Wallace@lewin.com Disclosure/Perspectives Training:

More information

Frequently Asked Questions About Quality Data Reporting

Frequently Asked Questions About Quality Data Reporting Why am I being asked to submit claims for all of my patients if SQCN does not have any payer contracts? SQCN is a Clinical Integration (CI) network. The success of our network will depend upon our CI program

More information

Standardized Terminologies Used in the Learning Health System

Standardized Terminologies Used in the Learning Health System Standardized Terminologies Used in the Learning Health System Judith J. Warren, PhD, RN, BC, FAAN, FACMI Christine A. Hartley Centennial Professor University of Kansas School of Nursing 1 Learning Objectives

More information

Stage 2 EHR Certification: NLM Vocabulary Update. Betsy Humphreys Deputy Director, NLM

Stage 2 EHR Certification: NLM Vocabulary Update. Betsy Humphreys Deputy Director, NLM Stage 2 EHR Certification: NLM Vocabulary Update Betsy Humphreys Deputy Director, NLM Proposed Stage 2 certification criteria **SNOMED CT problem list *LOINC laboratory tests *RxNorm medications, med.

More information

Eligible Professionals please see the document: MEDITECH Prepares You for Stage 2 of Meaningful Use: Eligible Professionals.

Eligible Professionals please see the document: MEDITECH Prepares You for Stage 2 of Meaningful Use: Eligible Professionals. s Preparing for Meaningful Use in 2014 MEDITECH (Updated December 2013) Professionals please see the document: MEDITECH Prepares You for Stage 2 of Meaningful Use: Professionals. Congratulations to our

More information

Distributed Networking

Distributed Networking Distributed Networking Millions of people. Strong collaborations. Privacy first. Jeffrey Brown, Lesley Curtis, Richard Platt Harvard Pilgrim Health Care Institute and Harvard Medical School Duke Medical

More information

Problem-Centered Care Delivery

Problem-Centered Care Delivery HOW INTERFACE TERMINOLOGY MAKES STANDARDIZED HEALTH INFORMATION POSSIBLE Terminologies ensure that the languages of medicine can be understood by both humans and machines. by June Bronnert, RHIA, CCS,

More information

Health Care Information System Standards

Health Care Information System Standards Health Care Information System Standards 1 Standards Development Process Four Methods (Hammond & Cimino, 2001) Ad hoc no formal adoption process De facto vendor or other has a very large segment of the

More information

IBM Watson and Medical Records Text Analytics HIMSS Presentation

IBM Watson and Medical Records Text Analytics HIMSS Presentation IBM Watson and Medical Records Text Analytics HIMSS Presentation Thomas Giles, IBM Industry Solutions - Healthcare Randall Wilcox, IBM Industry Solutions - Emerging Technology jstart The Next Grand Challenge

More information

MED 2400 MEDICAL INFORMATICS FUNDAMENTALS

MED 2400 MEDICAL INFORMATICS FUNDAMENTALS MED 2400 MEDICAL INFORMATICS FUNDAMENTALS NEW YORK CITY COLLEGE OF TECHNOLOGY The City University Of New York School of Arts and Sciences Biological Sciences Department Course title: Course code: MED 2400

More information

Clinical Decision Support Systems An Open Source Perspective

Clinical Decision Support Systems An Open Source Perspective Decision Support Systems An Open Source Perspective John McKim CTO, Knowledge Analytics Incorporated john@knowledgeanalytics.com http://www.knowledgeanaytics.com OSEHRA Open Source Summit 2014 Agenda CDS

More information

Meaningful Use Stage 2 Update: Deploying SNOMED CT to provide decision support in the EHR

Meaningful Use Stage 2 Update: Deploying SNOMED CT to provide decision support in the EHR Meaningful Use Stage 2 Update: Deploying SNOMED CT to provide decision support in the EHR James R. Campbell MD University of Nebraska Medical Center Implementation Showcase October 31, 2014 Disclosure

More information

HIMSS Electronic Health Record Definitional Model Version 1.0

HIMSS Electronic Health Record Definitional Model Version 1.0 HIMSS Electronic Health Record Definitional Model Version 1.0 Prepared by HIMSS Electronic Health Record Committee Thomas Handler, MD. Research Director, Gartner Rick Holtmeier, President, Berdy Systems

More information

ICD-9-CM to MedDRA Mapping How Well Do the. Disclaimer

ICD-9-CM to MedDRA Mapping How Well Do the. Disclaimer ICD-9-CM to MedDRA Mapping How Well Do the Two Terminologies Correlate Anna Zhao-Wong, MD, PhD Deputy Director MedDRA MSSO Disclaimer The views and opinions expressed in the following PowerPoint slides

More information

Beacon User Stories Version 1.0

Beacon User Stories Version 1.0 Table of Contents 1. Introduction... 2 2. User Stories... 2 2.1 Update Clinical Data Repository and Disease Registry... 2 2.1.1 Beacon Context... 2 2.1.2 Actors... 2 2.1.3 Preconditions... 3 2.1.4 Story

More information

Overview of ONC HIT Standards Committee Vocabulary Recommendations

Overview of ONC HIT Standards Committee Vocabulary Recommendations Overview of ONC HIT Standards Committee Vocabulary Recommendations Marjorie Rallins, DPM, Director, Specifications, Standards & Informatics, AMA, Physician Consortium for Performance Improvement Floyd

More information

Electronic Health Records - An Overview - Martin C. Were, MD MS March 24, 2010

Electronic Health Records - An Overview - Martin C. Were, MD MS March 24, 2010 Electronic Health Records - An Overview - Martin C. Were, MD MS March 24, 2010 Why Electronic Health Records (EHRs) EHRs vs. Paper Components of EHRs Characteristics of a good EHRs A Kenyan EHRs implementation

More information

AHIMA Curriculum Map Health Information Management Baccalaureate Degree Approved by AHIMA Education Strategy Committee February 2011

AHIMA Curriculum Map Health Information Management Baccalaureate Degree Approved by AHIMA Education Strategy Committee February 2011 HIM Baccalaureate Degree Entry Level Competencies (Student Learning Outcomes) I. Domain: Health Data Management I. A. Subdomain: Health Data Structure, Content and Standards 1. Manage health data (such

More information

Extracting Value from Secondary Uses of Electronic Health Data

Extracting Value from Secondary Uses of Electronic Health Data Extracting Value from Secondary Uses of Electronic Health Data The Regenstrief Institute, Indianapolis Indiana Thomas S. Inui, ScM, MD President and CEO, Regenstrief Institute Associate Dean for Health

More information

SEMANTIC DATA PLATFORM FOR HEALTHCARE. Dr. Philipp Daumke

SEMANTIC DATA PLATFORM FOR HEALTHCARE. Dr. Philipp Daumke SEMANTIC DATA PLATFORM FOR HEALTHCARE Dr. Philipp Daumke ABOUT AVERBIS Founded: 2007 Location: Focus: Languages: Current Sectors: Freiburg, Germany Terminology Management, Text Mining, Search multilingual

More information

Beyond the EMR: Disease Registries 3/5/2010

Beyond the EMR: Disease Registries 3/5/2010 Beyond the EMR: Disease Registries Better Health Greater Cleveland Learning Collaborative March 5, 2010 Anil Jain, MD, FACP Senior IT Executive, Information Technology, Cleveland Clinic Managing Director,

More information

Electronic Health Record. Standards, Coding Systems, Frameworks, and Infrastructures

Electronic Health Record. Standards, Coding Systems, Frameworks, and Infrastructures Brochure More information from http://www.researchandmarkets.com/reports/2178436/ Electronic Health Record. Standards, Coding Systems, Frameworks, and Infrastructures Description: Discover How Electronic

More information

Meaningful use. Meaningful data. Meaningful care. The 3M Healthcare Data Dictionary: Enabling effective health information exchange

Meaningful use. Meaningful data. Meaningful care. The 3M Healthcare Data Dictionary: Enabling effective health information exchange Meaningful use. Meaningful data. Meaningful care. The 3M Healthcare Data Dictionary: Enabling effective health information exchange Understanding health information exchanges (HIEs) What is the goal of

More information

TEST INSTRUCTIONS FOR CROSS VENDOR EXCHANGE TABLE OF CONTENTS

TEST INSTRUCTIONS FOR CROSS VENDOR EXCHANGE TABLE OF CONTENTS TEST INSTRUCTIONS FOR CROSS VENDOR EXCHANGE TABLE OF CONTENTS Introduction...2 Meaningful Use Objectives and Measures... 3 Eligible Professionals 495.6(j)(14)... 3 Eligible Hospitals and Critical Access

More information

Shelly Spiro, Executive Director, Pharmacy HIT Collaborative reports no relevant financial relationships.

Shelly Spiro, Executive Director, Pharmacy HIT Collaborative reports no relevant financial relationships. Shelly Spiro, Executive Director, Pharmacy HIT Collaborative reports no relevant financial relationships. 1. Discuss the vision, mission, and goals of the Pharmacy HIT Collaborative as it relates to informatics,

More information

Bench to Bedside Clinical Decision Support:

Bench to Bedside Clinical Decision Support: Bench to Bedside Clinical Decision Support: The Role of Semantic Web Technologies in Clinical and Translational Medicine Tonya Hongsermeier, MD, MBA Corporate Manager, Clinical Knowledge Management and

More information

Briefing on ehr Content Hong Kong Clinical Terminology Table (HKCTT) By Maggie LAU Health Informatician, ehriso

Briefing on ehr Content Hong Kong Clinical Terminology Table (HKCTT) By Maggie LAU Health Informatician, ehriso Briefing on ehr Content Hong Kong Clinical Terminology Table (HKCTT) By Maggie LAU Health Informatician, ehriso Agenda Background Application Adoption Management RecognisedTerminologies in ehr Registered

More information

ehr Sharable Data Vicky Fung Senior Health Informatician ehr Information Standards Office

ehr Sharable Data Vicky Fung Senior Health Informatician ehr Information Standards Office ehr Sharable Data Vicky Fung Senior Health Informatician ehr Information Standards Office ehr - Vision DH HA epr Private Private Hospit als Hospitals EHR Repository Access Portal CMS onramp PPP Clinics

More information

A leader in the development and application of information technology to prevent and treat disease.

A leader in the development and application of information technology to prevent and treat disease. A leader in the development and application of information technology to prevent and treat disease. About MOLECULAR HEALTH Molecular Health was founded in 2004 with the vision of changing healthcare. Today

More information

3M Health Information Systems

3M Health Information Systems 3M Health Information Systems 1 Data Governance Disparate Systems Interoperability Information Exchange Reporting Public Health Quality Metrics Research Data Warehousing Data Standards What is the 3M Healthcare

More information

Healthcare Data: Secondary Use through Interoperability

Healthcare Data: Secondary Use through Interoperability Healthcare Data: Secondary Use through Interoperability Floyd Eisenberg MD MPH July 18, 2007 NCVHS Agenda Policies, Enablers, Restrictions Date Re-Use Landscape Sources of Data for Quality Measurement,

More information

Meaningful Use Stage 2 Certification: A Guide for EHR Product Managers

Meaningful Use Stage 2 Certification: A Guide for EHR Product Managers Meaningful Use Stage 2 Certification: A Guide for EHR Product Managers Terminology Management is a foundational element to satisfying the Meaningful Use Stage 2 criteria and due to its complexity, and

More information

HL7 and Meaningful Use

HL7 and Meaningful Use HL7 and Meaningful Use HIMSS Las Vegas February 23, 2012 Grant M. Wood Intermountain Healthcare Clinical Genetics Institute Meaningful Use What Does It Mean? HITECH rewards the Meaningful Use of health

More information

Using Health Information Technology to Improve Quality of Care: Clinical Decision Support

Using Health Information Technology to Improve Quality of Care: Clinical Decision Support Using Health Information Technology to Improve Quality of Care: Clinical Decision Support Vince Fonseca, MD, MPH Director of Medical Informatics Intellica Corporation Objectives Describe the 5 health priorities

More information

What is your level of coding experience?

What is your level of coding experience? The TrustHCS Academy is dedicated to growing new coders to enter the field of medical records coding while also increasing the ICD-10 skills of seasoned coding professionals. A unique combination of on-line

More information

Natural Language Processing Supporting Clinical Decision Support

Natural Language Processing Supporting Clinical Decision Support Natural Language Processing Supporting Clinical Decision Support Applications for Enhancing Clinical Decision Making NIH Worksop; Bethesda, MD, April 24, 2012 Stephane M. Meystre, MD, PhD Department of

More information

Terminology Services in Support of Healthcare Interoperability

Terminology Services in Support of Healthcare Interoperability Terminology Services in Support of Healthcare Russell Hamm Informatics Consultant Apelon, Inc. Co-chair HL7 Vocabulary Workgroup Outline Why Terminology Importance of Terminologies Terminologies in Healthcare

More information

The ecosystem of the OpenClinic GA open source hospital information management software

The ecosystem of the OpenClinic GA open source hospital information management software The ecosystem of the OpenClinic GA open source hospital information management software HEALTH FACILITY INFORMATION SYSTEMS AND INTEROPERABILITY FRANK VERBEKE, VRIJE UNIVERSITEIT BRUSSEL OpenClinic login

More information

Custom Report Data Elements: 2012 IT Database Fields. Source: American Hospital Association IT Survey

Custom Report Data Elements: 2012 IT Database Fields. Source: American Hospital Association IT Survey Custom Report Data Elements: 2012 IT Database Fields Source: American Hospital Association IT Survey COMPUTERIZED SYSTEM IMPLEMENTATION 3 Bar Coding 3 Computerized Provider Order Entry 3 Decision Support

More information

Healthcare Professional. Driving to the Future 11 March 7, 2011

Healthcare Professional. Driving to the Future 11 March 7, 2011 Clinical Analytics for the Practicing Healthcare Professional Driving to the Future 11 March 7, 2011 Michael O. Bice Agenda Clinical informatics as context for clinical analytics Uniqueness of medical

More information

A.4.2. Challenges in the Deployment of Healthcare Information Systems and Technology

A.4.2. Challenges in the Deployment of Healthcare Information Systems and Technology A.4.2. Challenges in the Deployment of Healthcare Information Systems and Technology In order to support its constituent enterprise in Latin America and the Caribbean and deliver appropriate solutions,

More information

Overview of FDA s active surveillance programs and epidemiologic studies for vaccines

Overview of FDA s active surveillance programs and epidemiologic studies for vaccines Overview of FDA s active surveillance programs and epidemiologic studies for vaccines David Martin, M.D., M.P.H. Director, Division of Epidemiology Center for Biologics Evaluation and Research Application

More information

NQF health information technology glossary. a guide to HIT jargon

NQF health information technology glossary. a guide to HIT jargon NQF health information technology glossary a guide to HIT jargon June 2015 Health Information Technology Glossary Accountable Care Organization An organization of health care providers that agrees to be

More information

HIT Standards Committee Transitional Vocabulary Task Force Wednesday October 14th, 2015, 10:30 A.M. 12:00 P.M. Et

HIT Standards Committee Transitional Vocabulary Task Force Wednesday October 14th, 2015, 10:30 A.M. 12:00 P.M. Et HIT Standards Committee Transitional Vocabulary Task Force Wednesday October 14th, 2015, 10:30 A.M. 12:00 P.M. Et Agenda Overview Of The Transitional Vocabularies Task Force Workgroup Charge And Workplan

More information

AHIMA Curriculum Map Health Information Management Associate Degree Approved by AHIMA Education Strategy Committee February 2011

AHIMA Curriculum Map Health Information Management Associate Degree Approved by AHIMA Education Strategy Committee February 2011 HIM Associate Degree Entry Level Competencies (Student Learning Outcomes) I. Domain: Health Data Management A. Subdomain: Health Data Structure, Content and Standards 1. Collect and maintain health data

More information

HL7 & Meaningful Use. Charles Jaffe, MD, PhD CEO Health Level Seven International. HIMSS 11 Orlando February 23, 2011

HL7 & Meaningful Use. Charles Jaffe, MD, PhD CEO Health Level Seven International. HIMSS 11 Orlando February 23, 2011 HL7 & Meaningful Use Charles Jaffe, MD, PhD CEO Health Level Seven International HIMSS 11 Orlando February 23, 2011 Overview Overview of Meaningful Use HIT Standards and Meaningful Use Overview HL7 Standards

More information

Health Informatics Development in the Hospital Authority

Health Informatics Development in the Hospital Authority Health Informatics Development in the Hospital Authority Dr CP Wong Chairman, Clinical Informatics Program Executive Group Co-Chairman, Clinical Informatics Program Steering Group Begin to take off in

More information

THE HEALTH INFORMATION TECHNOLOGY SUMMIT

THE HEALTH INFORMATION TECHNOLOGY SUMMIT THE HEALTH INFORMATION TECHNOLOGY SUMMIT 1.05 Community-Based Collaborations: HIT Community-Based Collaborations 101 October 22 10:15 AM 60 minutes Frisse and me A Short History of the J. Marc Overhage,

More information

MEANINGFUL USE. Community Center Readiness Guide Additional Resource #13 Meaningful Use Implementation Tracking Tool (Template) CONTENTS:

MEANINGFUL USE. Community Center Readiness Guide Additional Resource #13 Meaningful Use Implementation Tracking Tool (Template) CONTENTS: Community Center Readiness Guide Additional Resource #13 Meaningful Use Implementation Tracking Tool (Template) MEANINGFUL USE HITECH s goal is not adoption alone but meaningful use of EHRs that is, their

More information

Utility of Common Data Models for EHR and Registry Data Integration: Use in Automated Surveillance

Utility of Common Data Models for EHR and Registry Data Integration: Use in Automated Surveillance Utility of Common Data Models for EHR and Registry Data Integration: Use in Automated Surveillance Frederic S. Resnic, MS, MSc Chairman, Department of Cardiovascular Medicine Co-Director, Comparative Effectiveness

More information

Singapore s National Electronic Health Record

Singapore s National Electronic Health Record Singapore s National Electronic Health Record The Roadmap to 2010 Dr Sarah Christine Muttitt Chief Information Officer Information Systems Division 17 th July, 2009 Taking the Next Step (MSM April 2008)

More information

IMPROPER USE OF MEDICAL INFORMATION

IMPROPER USE OF MEDICAL INFORMATION IMPROPER USE OF MEDICAL INFORMATION ehealth PRIVACY & SECURITY Presented at 5th Annual National Conference on Healthcare Leadership INNOVATION 2011, Bangalore 26th Jan 2011 Dr Pankaj Gupta ehealth Business

More information

Meaningful Use. Michael L. Brody, DPM FACFAOM CCHIT Ambulatory Workgroup HITSP Physician Perspective Technical Committee NYeHC

Meaningful Use. Michael L. Brody, DPM FACFAOM CCHIT Ambulatory Workgroup HITSP Physician Perspective Technical Committee NYeHC Meaningful Use Michael L. Brody, DPM FACFAOM CCHIT Ambulatory Workgroup HITSP Physician Perspective Technical Committee NYeHC What is Meaningful Use? Meaningful use is a term defined by CMS and describes

More information

Guide To Meaningful Use

Guide To Meaningful Use Guide To Meaningful Use Volume 1 Collecting the Data Contents INTRODUCTION... 3 CORE SET... 4 1. DEMOGRAPHICS... 5 2. VITAL SIGNS... 6 3. PROBLEM LIST... 8 4. MAINTAIN ACTIVE MEDICATIONS LIST... 9 5. MEDICATION

More information

Automated Problem List Generation from Electronic Medical Records in IBM Watson

Automated Problem List Generation from Electronic Medical Records in IBM Watson Proceedings of the Twenty-Seventh Conference on Innovative Applications of Artificial Intelligence Automated Problem List Generation from Electronic Medical Records in IBM Watson Murthy Devarakonda, Ching-Huei

More information

Understanding Diagnosis Assignment from Billing Systems Relative to Electronic Health Records for Clinical Research Cohort Identification

Understanding Diagnosis Assignment from Billing Systems Relative to Electronic Health Records for Clinical Research Cohort Identification Understanding Diagnosis Assignment from Billing Systems Relative to Electronic Health Records for Clinical Research Cohort Identification Russ Waitman Kelly Gerard Daniel W. Connolly Gregory A. Ator Division

More information

The FDA s Mini- Sen*nel Program and the Learning Health System

The FDA s Mini- Sen*nel Program and the Learning Health System info@mini- sen*nel.org 1 The FDA s Mini- Sen*nel Program and the Learning Health System Richard PlaB, MD, MS Harvard Pilgrim Health Care Ins*tute Harvard Medical School October 1, 2014 Vision We seek the

More information

Results of a Peer Mentoring Intervention in Older Patients with Diabetes: The Care Companion Program

Results of a Peer Mentoring Intervention in Older Patients with Diabetes: The Care Companion Program Results of a Peer Mentoring Intervention in Older Patients with Diabetes: The Care Companion Program Deborah Graham, MSPH AAFP National Research Network Cynthia Henderson, RN, CCM WellMed Medical Management

More information

Employing SNOMED CT and LOINC to make EHR data sensible and interoperable for clinical research

Employing SNOMED CT and LOINC to make EHR data sensible and interoperable for clinical research Employing SNOMED CT and LOINC to make EHR data sensible and interoperable for clinical research James R. Campbell MD W. Scott Campbell PhD Hubert Hickman MS James McClay MD Implementation Showcase October

More information

PUBLIC HEALTH MU OBJECTIVES

PUBLIC HEALTH MU OBJECTIVES PUBLIC HEALTH MU OBJECTIVES STAGE 2 3/14/2013 Specific to Stage 2 MU Public Health s, the capability to submit data for Immunizations, Reportable Laboratory Results and Syndromic Surveillance are all in

More information

Electronic Health Record (EHR) Standards Survey

Electronic Health Record (EHR) Standards Survey Electronic Health Record (EHR) Standards Survey Compiled by: Simona Cohen, Amnon Shabo Date: August 1st, 2001 This report is a short survey about the main emerging standards that relate to EHR - Electronic

More information

Patient Similarity-guided Decision Support

Patient Similarity-guided Decision Support Patient Similarity-guided Decision Support Tanveer Syeda-Mahmood, PhD IBM Almaden Research Center May 2014 2014 IBM Corporation What is clinical decision support? Rule-based expert systems curated by people,

More information

RESULTS OF HOSPITAL SURVEY

RESULTS OF HOSPITAL SURVEY RESULTS OF HOSPITAL SURVEY n=37 Georges De Moor EuroRec, Ghent University Electronic Health Records for Clinical Research 1 Which of the following best describes your organisation? Electronic Health Records

More information

Electronic health records to study population health: opportunities and challenges

Electronic health records to study population health: opportunities and challenges Electronic health records to study population health: opportunities and challenges Caroline A. Thompson, PhD, MPH Assistant Professor of Epidemiology San Diego State University Caroline.Thompson@mail.sdsu.edu

More information

BI en Salud: Registro de Salud Electrónico, Estado del Arte!

BI en Salud: Registro de Salud Electrónico, Estado del Arte! BI en Salud: Registro de Salud Electrónico, Estado del Arte! Manuel Graña Romay! ENGINE Centre, Wrocław University of Technology! Grupo de Inteligencia Computacional (GIC); UPV/EHU; www.ehu.es/ ccwintco!

More information

International HL7 Interoperability Conference - IHIC 2010

International HL7 Interoperability Conference - IHIC 2010 International HL7 Interoperability Conference - IHIC 2010 National ehealth Initiatives: Global Health Information Technology Standards Serving Local Needs Building Interoperability across many localities

More information

ADVANCING MEASUREMENT OF PATIENT- CENTERED OUTCOMES AND QUALITY METRICS WITH ELECTRONIC HEALTH RECORDS

ADVANCING MEASUREMENT OF PATIENT- CENTERED OUTCOMES AND QUALITY METRICS WITH ELECTRONIC HEALTH RECORDS ADVANCING MEASUREMENT OF PATIENT- CENTERED OUTCOMES AND QUALITY METRICS WITH ELECTRONIC HEALTH RECORDS Tina Hernandez-Boussard, PhD, MPH, MS Director, Surgical Health Services Research Unit Assistant Professor

More information

Electronic Health Record Sharing Benefits and Challenges

Electronic Health Record Sharing Benefits and Challenges Electronic Health Record Sharing Benefits and Challenges ehealth Record Office Food and Health Bureau www.ehealth.gov.hk Slide 1 Objectives Develop a territory-wide patient oriented electronic health record

More information

Mona Osman MD, MPH, MBA

Mona Osman MD, MPH, MBA Mona Osman MD, MPH, MBA Objectives To define an Electronic Medical Record (EMR) To demonstrate the benefits of EMR To introduce the Lebanese Society of Family Medicine- EMR Reality Check The healthcare

More information

Medicare and Medicaid Programs; EHR Incentive Programs

Medicare and Medicaid Programs; EHR Incentive Programs Medicare and Medicaid Programs; EHR Incentive Programs Background The American Recovery and Reinvestment Act of 2009 establishes incentive payments under the Medicare and Medicaid programs for certain

More information

7/31/2015. Shelly Spiro, Executive Director, Pharmacy HIT Collaborative reports no relevant financial relationships.

7/31/2015. Shelly Spiro, Executive Director, Pharmacy HIT Collaborative reports no relevant financial relationships. Pharmacy Health Information Technology Collaborative Shelly Spiro, Executive Director, Pharmacy HIT Collaborative reports no relevant financial relationships. 1. This session will describe the struggles

More information

Georgia Department of Education Career Pathway Descriptions

Georgia Department of Education Career Pathway Descriptions Health Science Cluster Planning, managing, and providing therapeutic services, diagnostic services, health informatics, support services, and biotechnology research and development. Diagnostic Services

More information

How Electronic Health Records Might Change Birth Defects Surveillance (and what to do about it!)

How Electronic Health Records Might Change Birth Defects Surveillance (and what to do about it!) How Electronic Health Records Might Change Birth Defects Surveillance (and what to do about it!) Seth Foldy, MD MPH FAAFP Senior Advisor Public Health Informatics and Technology Program Office Office of

More information

THE STIMULUS AND STANDARDS. John D. Halamka MD

THE STIMULUS AND STANDARDS. John D. Halamka MD THE STIMULUS AND STANDARDS John D. Halamka MD THE ONC STRATEGY Grants - Accelerating Adoption Standards - Interim Final Rule Meaningful Use - Notice of Proposed Rulemaking Certification - Notice of Proposed

More information

West Virginia Information Technology Summit. November 4, 2009

West Virginia Information Technology Summit. November 4, 2009 West Virginia Information Technology Summit November 4, 2009 WVHIN Background Enabled by W. Va. Code seq. (2006) 16-29G-1, et Managed by 17-member public/private Board of Directors Charged to design, implement

More information

Medication Therapy Management Services Clinical Documentation: Using a Structured Coding System SNOMED CT

Medication Therapy Management Services Clinical Documentation: Using a Structured Coding System SNOMED CT Medication Therapy Management Services Clinical Documentation: Using a Structured Coding System SNOMED CT Medication Therapy Management Services Clinical Documentation: Using a Structured Coding System

More information

Coding Certificate Program Competencies

Coding Certificate Program Competencies Coding Certificate Program Competencies A significant change in approach is noted with this release of the curricula. The emphasis and measurement of success is with attainment of the Bloom s taxonomy

More information

University of Minnesota Health Informatics Graduate Program: Education, Research and Practice

University of Minnesota Health Informatics Graduate Program: Education, Research and Practice University of Minnesota Health Informatics Graduate Program: Education, Research and Practice Terrence Adam MD, PhD Assistant Professor, University of Minnesota Pharmaceutical Care and Health Systems Director

More information

A Semantic Foundation for Achieving HIE Interoperability

A Semantic Foundation for Achieving HIE Interoperability A Semantic Foundation for Achieving HIE Interoperability Introduction Interoperability of health IT systems within and across organizational boundaries has long been the holy grail of healthcare technologists.

More information

AHIMA Curriculum Map Health Information Management Associate Degree Approved by AHIMA Education Strategy Committee February 2011

AHIMA Curriculum Map Health Information Management Associate Degree Approved by AHIMA Education Strategy Committee February 2011 AHIMA Curriculum Map Health Information Management Associate Degree Approved by AHIMA Education Strategy Committee February 2011 HIM Associate Degree Entry-Level Competencies (Student Learning Outcomes)

More information

DENOMINATOR: All female patients aged 65 years and older with a diagnosis of urinary incontinence

DENOMINATOR: All female patients aged 65 years and older with a diagnosis of urinary incontinence Measure #50: Urinary Incontinence: Plan of Care for Urinary Incontinence in Women Aged 65 Years and Older National Quality Strategy Domain: Person and Caregiver-Centered Experience and Outcomes 2016 PQRS

More information

FDA s Sentinel Initiative: Current Status and Future Plans. Janet Woodcock M.D. Director, CDER, FDA

FDA s Sentinel Initiative: Current Status and Future Plans. Janet Woodcock M.D. Director, CDER, FDA FDA s Sentinel Initiative: Current Status and Future Plans Janet Woodcock M.D. Director, CDER, FDA FDA Amendments Act of 2007 Section 905: Active Postmarket Risk Identification and Analysis Establish a

More information

Version 1.0. HEAL NY Phase 5 Health IT & Public Health Team. Version Released 1.0. HEAL NY Phase 5 Health

Version 1.0. HEAL NY Phase 5 Health IT & Public Health Team. Version Released 1.0. HEAL NY Phase 5 Health Statewide Health Information Network for New York (SHIN-NY) Health Information Exchange (HIE) for Public Health Use Case (Patient Visit, Hospitalization, Lab Result and Hospital Resources Data) Version

More information

Qualifying for Medicare Incentive Payments with Crystal Practice Management. Version 4.1.25

Qualifying for Medicare Incentive Payments with Crystal Practice Management. Version 4.1.25 Qualifying for Medicare Incentive Payments with Crystal Practice Management Version 4.1.25 01/01/ Table of Contents Qualifying for Medicare Incentive Payments with... 1 General Information... 3 Links to

More information

The Big Data Dividend

The Big Data Dividend The Big Data Dividend Enhancing Revenue in an Era of Change May 7, 2015 Agenda Big Data Sample Healthcare Big Data Sets Healthcare Applications of Big Data Revenue Enhancement Opportunities Rate Benchmarking/Rate

More information

Single Presentation Layer: Design Directions for iehr Graphical User Interface

Single Presentation Layer: Design Directions for iehr Graphical User Interface Single Presentation Layer: Design Directions for iehr Graphical User Interface CAPT Michael Weiner, MC, USN DoD/VA Interagency Program Office Dr. Jonathan Nebeker Veterans Health Administration, Office

More information

Completeness of Physician Billing Claims for Diabetes Prevalence Estimation

Completeness of Physician Billing Claims for Diabetes Prevalence Estimation Completeness of Physician Billing Claims for Diabetes Prevalence Estimation Lisa M. Lix 1, John Paul Kuwornu 1, George Kephart 2, Khokan Sikdar 3, Hude Quan 4 1 University of Manitoba; 2 Dalhousie University;

More information