SlideShare une entreprise Scribd logo
1  sur  4
Télécharger pour lire hors ligne
Librarians Connecting EHR Data

   1. Good morning everyone. I would like to share with you a new project I am working on –
       using the data from our hospital EHR system as part of the curriculum of our medical
       school.
   2. With all the concern about new librarian roles for the future and how we can embed
       librarians, I thought I would first tell you about the connections that led me to this work.
       In the spring of 2010 I attended the NLM/ Wood’s Hole MBL biomedical informatics
       course. Here is half of my group after our tour of Martha’s Vineyard. I loved the course.
       On Thursday of that week, Joan Ash, a professor in the Department of Medical
       Informatics and Clinical Epidemiology at Oregon Health & Sciences University (and also a
       librarian and member of MLA) did a role playing session on the use of technology in
       hospitals, but she also mentioned that OHSU had grants (HITECH/ARRA) to get a
       graduate certificate in biomedical informatics. I immediately decided to apply, but I
       couldn’t get my old, Canadian university transcripts in time, so I had to wait a year. But I
       did get into the program, starting in Sept. 2011. After taking classes on informatics,
       evidence based practice, clinical information systems, health information management,
       statistics, SQL programming, and IT in healthcare, I had to decide on a practicum
       project.
   3. While reading a blog post http://informaticsprofessor.blogspot.com/2012/04/from-
       implementation-to-analytics-future.html , by the chair of the OHSU DMICE dept., Dr. Bill
       Hersh (who has also taught at Wood’s Hole), I became really interested in the idea of
       analytics as described in the report that was mentioned (see quote on slide). I wanted
       to do a practicum that would use analytics in some way to improve patient outcomes.
   4. After asking around at my institution, I discovered that our Center for Clinical and
       Translational Research, part of the CTSA consortium, had a Biomedical Informatics Core
       (BIC) that was working on the analytics I was interested in. They are actually setting up
       quite a few different resources, but the main focus has been on REDCap for surveys,
       i2b2 for cohort discovery, setting up a clinical data warehouse for hospital EHR data
       from Cerner, and HealthFacts, a large Cerner supplied database with data from 160
       hospitals, which can be used for research.
5. REDCap is used for collecting survey data and analyzing it, but to get the patient
    sets(which needs IRB approval) the researchers need to use i2b2 to see if there is a large
    enough cohort in the system.
6. 12b2 is another open source software for research, but it was specifically designed for
    translational research and relating patients to genomic information. At VCU,
    researchers don’t need IRB approval to use this database and they can play around with
    the patient characteristics they need for their research to find out if there are enough
    patients in our community for a study. When I learned about i2b2, I felt there must be
    more we could do with it. One Core staff member said he had taught REDCap to a
    medical informatics class so they could learn about databases, but I thought using i2b2
    would be even better.
7. So I started to learn more about the system and think of ways to use it for teaching.
    First off, there are 4 ontologies used for various parts of the system. ICD9-CM for
    Diagnoses and Procedures, LOINC for lab tests, NDC for medications, and Snomed-CT for
    microbiology. The important thing to realize about this database is that the Diagnosis
    code is for billing purposes, so there can be other diagnoses which aren’t billed and the
    numbers may not reflect what a researcher expects based on their experiences.
8. The actual interface looks like this. On the left, abox to search for terms to use. a
    workplace to share searches – especially if you need help, and a previous queries area
    that stores what I have done. On the right, a query tool for setting up terms into a
    search statement, and a query status box to show how your search is running.
9. In the navigate terms area, the ontology folder opens up to show all the areas that can
    be searched.
10. And you can keep opening the various hierarchies to get to what you need. While
    browsing is fine, it may take a while.
11. So, you can switch to the Find Terms tab and do a name or code search. You’ll notice
    though that things become a lot more complicated and specific. Luckily, you can just
    choose the base term to get the whole group. But if you needed secondary diabetes
    mellitus, you’d need to add more codes. And the terms list includes things from all
    ontologies.
12. Once the search criteria have been determined you can add date limits and run the
    search. The number of patients is +_ 3 to keep the set deidentified.
13. Once you have a set, you can switch to Analysis Tools and do basic Demographics on the
    set.
14. The basic demographics can give you some idea of the type of community you will be
    working with. So you can see, it is a not too hard a search process, but you do need to
    think about the ontologies – which is a good skill for medical students who will be using
    EHRs.
15. It is easy to say medical students should learn how to use these analytic databases, but
    another thing to convince faculty. So I’ve been combing through various objectives
    reports and finding relevant reasons to learn to use an EHR database. The Medical
    School Objectives Project of the Association of American Medical Colleges has put out
    several reports, with many objectives. This one, Physicians must be dutiful, specifically
    mentions retrieving biomedical information – not just literature.
16. This MSOP report looks at the medical informatics skills needed for various roles – Life-
    Long Learner, Clinician, Educator/Communicator, Manager, and in this objective, the
    role of Researcher.
17. And in this report, a newer one, which is nice since the MSOP reports are from 1998,
    Utilizing Informatics is a core competency for working in interprofessional teams.
18. Using i2b2 to find information to educate students about their patient populations can
    help direct their studies. Also, learning to use databases can help with future quality
    analysis efforts.
19. I2b2 can only do so much and the Provider information, especially on the hospital side
    as opposed to the clinic side, is based on the admitting provider, not necessarily the
    provider/resident who treated the patient. So I am learning about the Data Warehouse
    that is being set up at VCU. Right now Bob, the BIC specialist, is using a Business Objects
    program to run searches, similar to the i2b2 interface where you move folders and
    terms to different boxes. He almost has the more functional Data Warehouse ready. It
    will smooth out some of the variations in the EHR record, and allow searching of the
    terms entered, not just codes. And it can be accessed through any hospital terminal.
    We will create a template search for the residents so they just need to put in their name
    and then they can find out their patient loads for self-assessments.
20. I’ve touched on the problems a couple of times, but like any research work, there are
    problems with the data. As long as you are aware of the issues, you can temper your
    conclusions properly.
21. You can actually search PubMed for i2b2, clinical data warehouse, and Cerner Health
    Facts and find articles that use the databases for research. As you search, you will find
    that there are also all sorts of specialized health databases that are also being used.
    EHRs are generating huge amounts of data.
22. Which leads me to a suggestion for those of you who don’t have access to hospital data,
    learn about health data – not just health statistics. This relates well to our speaker this
    morning who discussed open data (link to a blog post about this keynote
    https://macmla.wordpress.com/2012/10/16/quad-meeting-keynote-big-data/) There
    are large amounts of open data on the web and some of it is medical. Learn about these
    resources and the tools needed to use the data. According to David Stuart, data is the
    new book. In his book Stuart paraphrases Ranganathan’s laws starting with “Every user
    his data”
23. So look for data. Data.gov is a good starting point.
24. visualizing.org has quite a few categories of health data.
25. And of course, don’t forget all the genetic data in NCBI.
26. Yesterday, when Bart Ragon, http://www.hsl.virginia.edu/bio/bart , spoke about the
    BioConnector room they have created at UVa, he ended with a slide of flowers growing
    as an analogy for their growing program. I’ve ended with flowers but for a slightly
    different reason. This piece of embroidery is a reproduction of an Elizabethan coif. It
    will be made up next year and used by interpreters at Agecroft Hall, an historical house
    in Richmond, VA. I think this coif is like the projects that I have been working on. It
    requires many people working as a team, lots of different skills, and lots of time. But in
    the end it will be a wonderful thing.

Contenu connexe

Tendances

A handbook-of-statistical-analyses-using-stata-3rd-edition
A handbook-of-statistical-analyses-using-stata-3rd-editionA handbook-of-statistical-analyses-using-stata-3rd-edition
A handbook-of-statistical-analyses-using-stata-3rd-editionTriều Dương
 
Whitepaper - MedNexus for Physicians
Whitepaper - MedNexus for PhysiciansWhitepaper - MedNexus for Physicians
Whitepaper - MedNexus for PhysiciansMedNexus
 
Artificial intelligence in drug discovery and development
Artificial intelligence in drug discovery and developmentArtificial intelligence in drug discovery and development
Artificial intelligence in drug discovery and developmentNikitaSavita
 
IRJET- Heart Disease Prediction and Recommendation
IRJET-  	  Heart Disease Prediction and RecommendationIRJET-  	  Heart Disease Prediction and Recommendation
IRJET- Heart Disease Prediction and RecommendationIRJET Journal
 
MULTI MODEL DATA MINING APPROACH FOR HEART FAILURE PREDICTION
MULTI MODEL DATA MINING APPROACH FOR HEART FAILURE PREDICTIONMULTI MODEL DATA MINING APPROACH FOR HEART FAILURE PREDICTION
MULTI MODEL DATA MINING APPROACH FOR HEART FAILURE PREDICTIONIJDKP
 
Powering Scientific Discovery with the Semantic Web (VanBUG 2014)
Powering Scientific Discovery with the Semantic Web (VanBUG 2014)Powering Scientific Discovery with the Semantic Web (VanBUG 2014)
Powering Scientific Discovery with the Semantic Web (VanBUG 2014)Michel Dumontier
 
Prediction of Heart Disease using Machine Learning Algorithms: A Survey
Prediction of Heart Disease using Machine Learning Algorithms: A SurveyPrediction of Heart Disease using Machine Learning Algorithms: A Survey
Prediction of Heart Disease using Machine Learning Algorithms: A Surveyrahulmonikasharma
 
IRJET- Develop Futuristic Prediction Regarding Details of Health System for H...
IRJET- Develop Futuristic Prediction Regarding Details of Health System for H...IRJET- Develop Futuristic Prediction Regarding Details of Health System for H...
IRJET- Develop Futuristic Prediction Regarding Details of Health System for H...IRJET Journal
 
Uses of computer ln
Uses of computer lnUses of computer ln
Uses of computer lnPreet Sweet
 
Smart health disease prediction python django
Smart health disease prediction python djangoSmart health disease prediction python django
Smart health disease prediction python djangoShaikSalman28
 
IRJET- Analyse Big Data Electronic Health Records Database using Hadoop Cluster
IRJET- Analyse Big Data Electronic Health Records Database using Hadoop ClusterIRJET- Analyse Big Data Electronic Health Records Database using Hadoop Cluster
IRJET- Analyse Big Data Electronic Health Records Database using Hadoop ClusterIRJET Journal
 
archenaa2015-survey-big-data-government.pdf
archenaa2015-survey-big-data-government.pdfarchenaa2015-survey-big-data-government.pdf
archenaa2015-survey-big-data-government.pdfAkuhuruf
 
Using NLP and curation to make clinical data available for research
Using NLP and curation to make clinical data available for researchUsing NLP and curation to make clinical data available for research
Using NLP and curation to make clinical data available for researchWarren Kibbe
 
MSBI library orientation
MSBI library orientationMSBI library orientation
MSBI library orientationKristin Kroger
 
How many medline platforms on the web?
How many medline platforms on the web?How many medline platforms on the web?
How many medline platforms on the web?Basset Hervé
 

Tendances (19)

A handbook-of-statistical-analyses-using-stata-3rd-edition
A handbook-of-statistical-analyses-using-stata-3rd-editionA handbook-of-statistical-analyses-using-stata-3rd-edition
A handbook-of-statistical-analyses-using-stata-3rd-edition
 
Updatedpowerpoint
UpdatedpowerpointUpdatedpowerpoint
Updatedpowerpoint
 
Whitepaper - MedNexus for Physicians
Whitepaper - MedNexus for PhysiciansWhitepaper - MedNexus for Physicians
Whitepaper - MedNexus for Physicians
 
Artificial intelligence in drug discovery and development
Artificial intelligence in drug discovery and developmentArtificial intelligence in drug discovery and development
Artificial intelligence in drug discovery and development
 
IRJET- Heart Disease Prediction and Recommendation
IRJET-  	  Heart Disease Prediction and RecommendationIRJET-  	  Heart Disease Prediction and Recommendation
IRJET- Heart Disease Prediction and Recommendation
 
Some Early Thoughts
Some Early ThoughtsSome Early Thoughts
Some Early Thoughts
 
MULTI MODEL DATA MINING APPROACH FOR HEART FAILURE PREDICTION
MULTI MODEL DATA MINING APPROACH FOR HEART FAILURE PREDICTIONMULTI MODEL DATA MINING APPROACH FOR HEART FAILURE PREDICTION
MULTI MODEL DATA MINING APPROACH FOR HEART FAILURE PREDICTION
 
Powering Scientific Discovery with the Semantic Web (VanBUG 2014)
Powering Scientific Discovery with the Semantic Web (VanBUG 2014)Powering Scientific Discovery with the Semantic Web (VanBUG 2014)
Powering Scientific Discovery with the Semantic Web (VanBUG 2014)
 
Prediction of Heart Disease using Machine Learning Algorithms: A Survey
Prediction of Heart Disease using Machine Learning Algorithms: A SurveyPrediction of Heart Disease using Machine Learning Algorithms: A Survey
Prediction of Heart Disease using Machine Learning Algorithms: A Survey
 
IRJET- Develop Futuristic Prediction Regarding Details of Health System for H...
IRJET- Develop Futuristic Prediction Regarding Details of Health System for H...IRJET- Develop Futuristic Prediction Regarding Details of Health System for H...
IRJET- Develop Futuristic Prediction Regarding Details of Health System for H...
 
Uses of computer ln
Uses of computer lnUses of computer ln
Uses of computer ln
 
Smart health disease prediction python django
Smart health disease prediction python djangoSmart health disease prediction python django
Smart health disease prediction python django
 
IRJET- Analyse Big Data Electronic Health Records Database using Hadoop Cluster
IRJET- Analyse Big Data Electronic Health Records Database using Hadoop ClusterIRJET- Analyse Big Data Electronic Health Records Database using Hadoop Cluster
IRJET- Analyse Big Data Electronic Health Records Database using Hadoop Cluster
 
archenaa2015-survey-big-data-government.pdf
archenaa2015-survey-big-data-government.pdfarchenaa2015-survey-big-data-government.pdf
archenaa2015-survey-big-data-government.pdf
 
Using NLP and curation to make clinical data available for research
Using NLP and curation to make clinical data available for researchUsing NLP and curation to make clinical data available for research
Using NLP and curation to make clinical data available for research
 
cpoe
cpoecpoe
cpoe
 
MSBI library orientation
MSBI library orientationMSBI library orientation
MSBI library orientation
 
Final ppt
Final pptFinal ppt
Final ppt
 
How many medline platforms on the web?
How many medline platforms on the web?How many medline platforms on the web?
How many medline platforms on the web?
 

En vedette

BB Citizenship 2 Project script
BB Citizenship 2 Project scriptBB Citizenship 2 Project script
BB Citizenship 2 Project scriptKutsuzawa
 
Teen pregnancy issue
Teen pregnancy issueTeen pregnancy issue
Teen pregnancy issuevanialundina
 
Teenage pregnancy
Teenage pregnancyTeenage pregnancy
Teenage pregnancyTra Etty
 
Kabanata 31 ng el filibusterismo
Kabanata 31 ng el filibusterismoKabanata 31 ng el filibusterismo
Kabanata 31 ng el filibusterismoIanPaul2097
 
Social Studies SBA template on teenage pregnancy
Social Studies SBA template on teenage pregnancySocial Studies SBA template on teenage pregnancy
Social Studies SBA template on teenage pregnancyAugustine Ferdinand
 
Script For Perfect Presentation
Script For Perfect PresentationScript For Perfect Presentation
Script For Perfect PresentationAlan Doherty
 
abortion ppt
abortion pptabortion ppt
abortion pptAmeenah
 

En vedette (7)

BB Citizenship 2 Project script
BB Citizenship 2 Project scriptBB Citizenship 2 Project script
BB Citizenship 2 Project script
 
Teen pregnancy issue
Teen pregnancy issueTeen pregnancy issue
Teen pregnancy issue
 
Teenage pregnancy
Teenage pregnancyTeenage pregnancy
Teenage pregnancy
 
Kabanata 31 ng el filibusterismo
Kabanata 31 ng el filibusterismoKabanata 31 ng el filibusterismo
Kabanata 31 ng el filibusterismo
 
Social Studies SBA template on teenage pregnancy
Social Studies SBA template on teenage pregnancySocial Studies SBA template on teenage pregnancy
Social Studies SBA template on teenage pregnancy
 
Script For Perfect Presentation
Script For Perfect PresentationScript For Perfect Presentation
Script For Perfect Presentation
 
abortion ppt
abortion pptabortion ppt
abortion ppt
 

Similaire à Ehr presentation script for blog

PhRMA Some Early Thoughts
PhRMA Some Early ThoughtsPhRMA Some Early Thoughts
PhRMA Some Early ThoughtsPhilip Bourne
 
Script for MIS webinar 2016 - RDM for Clinical Trials and Quality Improvement
Script for MIS webinar 2016 - RDM for Clinical Trials and Quality ImprovementScript for MIS webinar 2016 - RDM for Clinical Trials and Quality Improvement
Script for MIS webinar 2016 - RDM for Clinical Trials and Quality ImprovementMargaret Henderson
 
Biomedical Research as Part of the Digital Enterprise
Biomedical Research as Part of the Digital EnterpriseBiomedical Research as Part of the Digital Enterprise
Biomedical Research as Part of the Digital EnterprisePhilip Bourne
 
Data at the NIH: Some Early Thoughts
Data at the NIH: Some Early ThoughtsData at the NIH: Some Early Thoughts
Data at the NIH: Some Early ThoughtsPhilip Bourne
 
Wisdom Continuum Paper.docx
Wisdom Continuum Paper.docxWisdom Continuum Paper.docx
Wisdom Continuum Paper.docxwrite22
 
Submit your paper in the following order format.I. Author a bi.docx
Submit your paper in the following order  format.I. Author a bi.docxSubmit your paper in the following order  format.I. Author a bi.docx
Submit your paper in the following order format.I. Author a bi.docxdeanmtaylor1545
 
IJERD (www.ijerd.com) International Journal of Engineering Research and Devel...
IJERD (www.ijerd.com) International Journal of Engineering Research and Devel...IJERD (www.ijerd.com) International Journal of Engineering Research and Devel...
IJERD (www.ijerd.com) International Journal of Engineering Research and Devel...IJERD Editor
 
Case Retrieval using Bhattacharya Coefficient with Particle Swarm Optimization
Case Retrieval using Bhattacharya Coefficient with Particle Swarm OptimizationCase Retrieval using Bhattacharya Coefficient with Particle Swarm Optimization
Case Retrieval using Bhattacharya Coefficient with Particle Swarm Optimizationrahulmonikasharma
 
List Of Figures And Functions Requirements
List Of Figures And Functions RequirementsList Of Figures And Functions Requirements
List Of Figures And Functions RequirementsLeslie Lee
 
Centralized BI in Healthcare
Centralized BI in HealthcareCentralized BI in Healthcare
Centralized BI in HealthcarePanorama Software
 
Use of Academic Research Databases Discussion.docx
Use of Academic Research Databases Discussion.docxUse of Academic Research Databases Discussion.docx
Use of Academic Research Databases Discussion.docxwrite4
 
The Dual Nature of InformaticsInformatics can be used for impr.docx
The Dual Nature of InformaticsInformatics can be used for impr.docxThe Dual Nature of InformaticsInformatics can be used for impr.docx
The Dual Nature of InformaticsInformatics can be used for impr.docxhe45mcurnow
 
Sun==big data analytics for health care
Sun==big data analytics for health careSun==big data analytics for health care
Sun==big data analytics for health careAravindharamanan S
 
Secinaro et al-2021-bmc_medical_informatics_and_decision_making
Secinaro et al-2021-bmc_medical_informatics_and_decision_makingSecinaro et al-2021-bmc_medical_informatics_and_decision_making
Secinaro et al-2021-bmc_medical_informatics_and_decision_makingNethminiWijesinghe
 
Understanding the Need of Data Integration in E Healthcare
Understanding the Need of Data Integration in E HealthcareUnderstanding the Need of Data Integration in E Healthcare
Understanding the Need of Data Integration in E Healthcareijtsrd
 
2016 CRI Year-in-Review
2016 CRI Year-in-Review2016 CRI Year-in-Review
2016 CRI Year-in-ReviewPeter Embi
 
Nursing informatics: Internet Tools and NI abroad
Nursing informatics: Internet Tools and NI abroadNursing informatics: Internet Tools and NI abroad
Nursing informatics: Internet Tools and NI abroadjhonee balmeo
 

Similaire à Ehr presentation script for blog (20)

PhRMA Some Early Thoughts
PhRMA Some Early ThoughtsPhRMA Some Early Thoughts
PhRMA Some Early Thoughts
 
Script for MIS webinar 2016 - RDM for Clinical Trials and Quality Improvement
Script for MIS webinar 2016 - RDM for Clinical Trials and Quality ImprovementScript for MIS webinar 2016 - RDM for Clinical Trials and Quality Improvement
Script for MIS webinar 2016 - RDM for Clinical Trials and Quality Improvement
 
Connecting eh rdataquad12
Connecting eh rdataquad12Connecting eh rdataquad12
Connecting eh rdataquad12
 
Biomedical Research as Part of the Digital Enterprise
Biomedical Research as Part of the Digital EnterpriseBiomedical Research as Part of the Digital Enterprise
Biomedical Research as Part of the Digital Enterprise
 
Data at the NIH: Some Early Thoughts
Data at the NIH: Some Early ThoughtsData at the NIH: Some Early Thoughts
Data at the NIH: Some Early Thoughts
 
Wisdom Continuum Paper.docx
Wisdom Continuum Paper.docxWisdom Continuum Paper.docx
Wisdom Continuum Paper.docx
 
Nursing informatics
Nursing informaticsNursing informatics
Nursing informatics
 
Nursing informatics 2011
Nursing informatics 2011Nursing informatics 2011
Nursing informatics 2011
 
Submit your paper in the following order format.I. Author a bi.docx
Submit your paper in the following order  format.I. Author a bi.docxSubmit your paper in the following order  format.I. Author a bi.docx
Submit your paper in the following order format.I. Author a bi.docx
 
IJERD (www.ijerd.com) International Journal of Engineering Research and Devel...
IJERD (www.ijerd.com) International Journal of Engineering Research and Devel...IJERD (www.ijerd.com) International Journal of Engineering Research and Devel...
IJERD (www.ijerd.com) International Journal of Engineering Research and Devel...
 
Case Retrieval using Bhattacharya Coefficient with Particle Swarm Optimization
Case Retrieval using Bhattacharya Coefficient with Particle Swarm OptimizationCase Retrieval using Bhattacharya Coefficient with Particle Swarm Optimization
Case Retrieval using Bhattacharya Coefficient with Particle Swarm Optimization
 
List Of Figures And Functions Requirements
List Of Figures And Functions RequirementsList Of Figures And Functions Requirements
List Of Figures And Functions Requirements
 
Centralized BI in Healthcare
Centralized BI in HealthcareCentralized BI in Healthcare
Centralized BI in Healthcare
 
Use of Academic Research Databases Discussion.docx
Use of Academic Research Databases Discussion.docxUse of Academic Research Databases Discussion.docx
Use of Academic Research Databases Discussion.docx
 
The Dual Nature of InformaticsInformatics can be used for impr.docx
The Dual Nature of InformaticsInformatics can be used for impr.docxThe Dual Nature of InformaticsInformatics can be used for impr.docx
The Dual Nature of InformaticsInformatics can be used for impr.docx
 
Sun==big data analytics for health care
Sun==big data analytics for health careSun==big data analytics for health care
Sun==big data analytics for health care
 
Secinaro et al-2021-bmc_medical_informatics_and_decision_making
Secinaro et al-2021-bmc_medical_informatics_and_decision_makingSecinaro et al-2021-bmc_medical_informatics_and_decision_making
Secinaro et al-2021-bmc_medical_informatics_and_decision_making
 
Understanding the Need of Data Integration in E Healthcare
Understanding the Need of Data Integration in E HealthcareUnderstanding the Need of Data Integration in E Healthcare
Understanding the Need of Data Integration in E Healthcare
 
2016 CRI Year-in-Review
2016 CRI Year-in-Review2016 CRI Year-in-Review
2016 CRI Year-in-Review
 
Nursing informatics: Internet Tools and NI abroad
Nursing informatics: Internet Tools and NI abroadNursing informatics: Internet Tools and NI abroad
Nursing informatics: Internet Tools and NI abroad
 

Plus de Margaret Henderson

Final long version notes for Preparing Health Sciences Students for Real Worl...
Final long version notes for Preparing Health Sciences Students for Real Worl...Final long version notes for Preparing Health Sciences Students for Real Worl...
Final long version notes for Preparing Health Sciences Students for Real Worl...Margaret Henderson
 
Preparing Health Sciences Students for Real World Information Gathering Using...
Preparing Health Sciences Students for Real World Information Gathering Using...Preparing Health Sciences Students for Real World Information Gathering Using...
Preparing Health Sciences Students for Real World Information Gathering Using...Margaret Henderson
 
NNLM SEA webinar June 2018 script
NNLM SEA webinar June 2018 scriptNNLM SEA webinar June 2018 script
NNLM SEA webinar June 2018 scriptMargaret Henderson
 
Research Data Management for Clinical Trials and Quality Improvement
Research Data Management for Clinical Trials and Quality ImprovementResearch Data Management for Clinical Trials and Quality Improvement
Research Data Management for Clinical Trials and Quality ImprovementMargaret Henderson
 
Compliance: Data Management Plans and Public Access to Data
Compliance: Data Management Plans and Public Access to DataCompliance: Data Management Plans and Public Access to Data
Compliance: Data Management Plans and Public Access to DataMargaret Henderson
 
How to Comply with Grants: Writing Data Management Plans and Providing Public...
How to Comply with Grants: Writing Data Management Plans and Providing Public...How to Comply with Grants: Writing Data Management Plans and Providing Public...
How to Comply with Grants: Writing Data Management Plans and Providing Public...Margaret Henderson
 
Inroads into Data: Getting Involved in Data at Your Institution
Inroads into Data: Getting Involved in Data at Your InstitutionInroads into Data: Getting Involved in Data at Your Institution
Inroads into Data: Getting Involved in Data at Your InstitutionMargaret Henderson
 
Ostp memo henderson_reznik-zellen_april2015
Ostp memo henderson_reznik-zellen_april2015Ostp memo henderson_reznik-zellen_april2015
Ostp memo henderson_reznik-zellen_april2015Margaret Henderson
 
NSF Data Requirements and Changing Federal Requirements for Research
NSF Data Requirements and Changing Federal Requirements for ResearchNSF Data Requirements and Changing Federal Requirements for Research
NSF Data Requirements and Changing Federal Requirements for ResearchMargaret Henderson
 

Plus de Margaret Henderson (17)

Final long version notes for Preparing Health Sciences Students for Real Worl...
Final long version notes for Preparing Health Sciences Students for Real Worl...Final long version notes for Preparing Health Sciences Students for Real Worl...
Final long version notes for Preparing Health Sciences Students for Real Worl...
 
Preparing Health Sciences Students for Real World Information Gathering Using...
Preparing Health Sciences Students for Real World Information Gathering Using...Preparing Health Sciences Students for Real World Information Gathering Using...
Preparing Health Sciences Students for Real World Information Gathering Using...
 
Ps rwebinar january2019final
Ps rwebinar january2019finalPs rwebinar january2019final
Ps rwebinar january2019final
 
NNLM SEA webinar June 2018 script
NNLM SEA webinar June 2018 scriptNNLM SEA webinar June 2018 script
NNLM SEA webinar June 2018 script
 
Research Data Management for Clinical Trials and Quality Improvement
Research Data Management for Clinical Trials and Quality ImprovementResearch Data Management for Clinical Trials and Quality Improvement
Research Data Management for Clinical Trials and Quality Improvement
 
Compliance: Data Management Plans and Public Access to Data
Compliance: Data Management Plans and Public Access to DataCompliance: Data Management Plans and Public Access to Data
Compliance: Data Management Plans and Public Access to Data
 
How to Comply with Grants: Writing Data Management Plans and Providing Public...
How to Comply with Grants: Writing Data Management Plans and Providing Public...How to Comply with Grants: Writing Data Management Plans and Providing Public...
How to Comply with Grants: Writing Data Management Plans and Providing Public...
 
Notes for Inroads into Data
Notes for Inroads into DataNotes for Inroads into Data
Notes for Inroads into Data
 
Inroads into Data: Getting Involved in Data at Your Institution
Inroads into Data: Getting Involved in Data at Your InstitutionInroads into Data: Getting Involved in Data at Your Institution
Inroads into Data: Getting Involved in Data at Your Institution
 
Al aposter mhenderson2015
Al aposter mhenderson2015Al aposter mhenderson2015
Al aposter mhenderson2015
 
Racm april29 ostp
Racm april29 ostpRacm april29 ostp
Racm april29 ostp
 
Ostp memo henderson_reznik-zellen_april2015
Ostp memo henderson_reznik-zellen_april2015Ostp memo henderson_reznik-zellen_april2015
Ostp memo henderson_reznik-zellen_april2015
 
Rdap panel script
Rdap panel scriptRdap panel script
Rdap panel script
 
M henderson rdap2014
M henderson rdap2014M henderson rdap2014
M henderson rdap2014
 
NSF Data Requirements and Changing Federal Requirements for Research
NSF Data Requirements and Changing Federal Requirements for ResearchNSF Data Requirements and Changing Federal Requirements for Research
NSF Data Requirements and Changing Federal Requirements for Research
 
Va sla nov 15 final
Va sla nov 15 finalVa sla nov 15 final
Va sla nov 15 final
 
Open data oct 2013
Open data oct 2013Open data oct 2013
Open data oct 2013
 

Ehr presentation script for blog

  • 1. Librarians Connecting EHR Data 1. Good morning everyone. I would like to share with you a new project I am working on – using the data from our hospital EHR system as part of the curriculum of our medical school. 2. With all the concern about new librarian roles for the future and how we can embed librarians, I thought I would first tell you about the connections that led me to this work. In the spring of 2010 I attended the NLM/ Wood’s Hole MBL biomedical informatics course. Here is half of my group after our tour of Martha’s Vineyard. I loved the course. On Thursday of that week, Joan Ash, a professor in the Department of Medical Informatics and Clinical Epidemiology at Oregon Health & Sciences University (and also a librarian and member of MLA) did a role playing session on the use of technology in hospitals, but she also mentioned that OHSU had grants (HITECH/ARRA) to get a graduate certificate in biomedical informatics. I immediately decided to apply, but I couldn’t get my old, Canadian university transcripts in time, so I had to wait a year. But I did get into the program, starting in Sept. 2011. After taking classes on informatics, evidence based practice, clinical information systems, health information management, statistics, SQL programming, and IT in healthcare, I had to decide on a practicum project. 3. While reading a blog post http://informaticsprofessor.blogspot.com/2012/04/from- implementation-to-analytics-future.html , by the chair of the OHSU DMICE dept., Dr. Bill Hersh (who has also taught at Wood’s Hole), I became really interested in the idea of analytics as described in the report that was mentioned (see quote on slide). I wanted to do a practicum that would use analytics in some way to improve patient outcomes. 4. After asking around at my institution, I discovered that our Center for Clinical and Translational Research, part of the CTSA consortium, had a Biomedical Informatics Core (BIC) that was working on the analytics I was interested in. They are actually setting up quite a few different resources, but the main focus has been on REDCap for surveys, i2b2 for cohort discovery, setting up a clinical data warehouse for hospital EHR data from Cerner, and HealthFacts, a large Cerner supplied database with data from 160 hospitals, which can be used for research.
  • 2. 5. REDCap is used for collecting survey data and analyzing it, but to get the patient sets(which needs IRB approval) the researchers need to use i2b2 to see if there is a large enough cohort in the system. 6. 12b2 is another open source software for research, but it was specifically designed for translational research and relating patients to genomic information. At VCU, researchers don’t need IRB approval to use this database and they can play around with the patient characteristics they need for their research to find out if there are enough patients in our community for a study. When I learned about i2b2, I felt there must be more we could do with it. One Core staff member said he had taught REDCap to a medical informatics class so they could learn about databases, but I thought using i2b2 would be even better. 7. So I started to learn more about the system and think of ways to use it for teaching. First off, there are 4 ontologies used for various parts of the system. ICD9-CM for Diagnoses and Procedures, LOINC for lab tests, NDC for medications, and Snomed-CT for microbiology. The important thing to realize about this database is that the Diagnosis code is for billing purposes, so there can be other diagnoses which aren’t billed and the numbers may not reflect what a researcher expects based on their experiences. 8. The actual interface looks like this. On the left, abox to search for terms to use. a workplace to share searches – especially if you need help, and a previous queries area that stores what I have done. On the right, a query tool for setting up terms into a search statement, and a query status box to show how your search is running. 9. In the navigate terms area, the ontology folder opens up to show all the areas that can be searched. 10. And you can keep opening the various hierarchies to get to what you need. While browsing is fine, it may take a while. 11. So, you can switch to the Find Terms tab and do a name or code search. You’ll notice though that things become a lot more complicated and specific. Luckily, you can just choose the base term to get the whole group. But if you needed secondary diabetes mellitus, you’d need to add more codes. And the terms list includes things from all ontologies. 12. Once the search criteria have been determined you can add date limits and run the search. The number of patients is +_ 3 to keep the set deidentified.
  • 3. 13. Once you have a set, you can switch to Analysis Tools and do basic Demographics on the set. 14. The basic demographics can give you some idea of the type of community you will be working with. So you can see, it is a not too hard a search process, but you do need to think about the ontologies – which is a good skill for medical students who will be using EHRs. 15. It is easy to say medical students should learn how to use these analytic databases, but another thing to convince faculty. So I’ve been combing through various objectives reports and finding relevant reasons to learn to use an EHR database. The Medical School Objectives Project of the Association of American Medical Colleges has put out several reports, with many objectives. This one, Physicians must be dutiful, specifically mentions retrieving biomedical information – not just literature. 16. This MSOP report looks at the medical informatics skills needed for various roles – Life- Long Learner, Clinician, Educator/Communicator, Manager, and in this objective, the role of Researcher. 17. And in this report, a newer one, which is nice since the MSOP reports are from 1998, Utilizing Informatics is a core competency for working in interprofessional teams. 18. Using i2b2 to find information to educate students about their patient populations can help direct their studies. Also, learning to use databases can help with future quality analysis efforts. 19. I2b2 can only do so much and the Provider information, especially on the hospital side as opposed to the clinic side, is based on the admitting provider, not necessarily the provider/resident who treated the patient. So I am learning about the Data Warehouse that is being set up at VCU. Right now Bob, the BIC specialist, is using a Business Objects program to run searches, similar to the i2b2 interface where you move folders and terms to different boxes. He almost has the more functional Data Warehouse ready. It will smooth out some of the variations in the EHR record, and allow searching of the terms entered, not just codes. And it can be accessed through any hospital terminal. We will create a template search for the residents so they just need to put in their name and then they can find out their patient loads for self-assessments.
  • 4. 20. I’ve touched on the problems a couple of times, but like any research work, there are problems with the data. As long as you are aware of the issues, you can temper your conclusions properly. 21. You can actually search PubMed for i2b2, clinical data warehouse, and Cerner Health Facts and find articles that use the databases for research. As you search, you will find that there are also all sorts of specialized health databases that are also being used. EHRs are generating huge amounts of data. 22. Which leads me to a suggestion for those of you who don’t have access to hospital data, learn about health data – not just health statistics. This relates well to our speaker this morning who discussed open data (link to a blog post about this keynote https://macmla.wordpress.com/2012/10/16/quad-meeting-keynote-big-data/) There are large amounts of open data on the web and some of it is medical. Learn about these resources and the tools needed to use the data. According to David Stuart, data is the new book. In his book Stuart paraphrases Ranganathan’s laws starting with “Every user his data” 23. So look for data. Data.gov is a good starting point. 24. visualizing.org has quite a few categories of health data. 25. And of course, don’t forget all the genetic data in NCBI. 26. Yesterday, when Bart Ragon, http://www.hsl.virginia.edu/bio/bart , spoke about the BioConnector room they have created at UVa, he ended with a slide of flowers growing as an analogy for their growing program. I’ve ended with flowers but for a slightly different reason. This piece of embroidery is a reproduction of an Elizabethan coif. It will be made up next year and used by interpreters at Agecroft Hall, an historical house in Richmond, VA. I think this coif is like the projects that I have been working on. It requires many people working as a team, lots of different skills, and lots of time. But in the end it will be a wonderful thing.