Topics in BMI: Course Objectives

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13 Δεκ 2013 (πριν από 3 χρόνια και 10 μήνες)

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Topics in BMI: Course Objectives

Prof. Steven A. Demurjian, Sr.

Computer Science & Engineering Department

The University of Connecticut

371 Fairfield Road, Box U
-
255

Storrs, CT 06269
-
2155

steve@engr.uconn.edu

http://www.engr.uconn.edu/~steve

(860) 486
-

4818

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What is Informatics?


Informatics is:


Management and Processing of Data


From Multiple Sources/Contexts


Involves Classification (Ontologies), Collection,
Storage, Analysis, Dissemination


Informatics is Multi
-
Disciplinary


Computing (Model, Store, Process Information)


Social Science (User Interactions, HCI)


Statistics (Analysis)


Informatics Can Apply to Multiple Domains:


Business, Biology, Fine Arts, Humanities


Pharmacology, Nursing, Medicine, etc.

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What is Informatics?


Heterogeneous Field


Interaction between
People, Information and
Technology


Computer Science
and Engineering


Social Science
(Human Computer
Interface)


Information Science
(Data Storage,
Retrieval and
Mining)


People

Information

Technology

Informatics

Adapted from Shortcliff textbook

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What is Biomedical Informatics (BMI)?


BMI is Information and its Usage Associated with the
Research and Practice of Medicine Including:


Clinical Informatics for Patient Care


Medical Record + Personal Health Record


Bioinformatics for Research/Biology to Bedside


From Genomics To Proteomics


Public Health Informatics (State and Federal)


Tracking Trends in Public Sector


Clinical Research Informatics


Deidentified Repositories and Databases


Facilitate Epidemiological Research and Ongong
Clinical Studies (Drug Trails, Data Analysis, etc.)

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What are Key BMI Focal Areas?


T1 Research


Transition Bench Results into


Clinical Research


Clinical Research


Applying Clinical Research Results via Trials with
Patients on Medication, Devices, Treatment Plans


T2 Research


Translating “Successful” Clinical Trials into
Practice and the Community


Clinical Practice


Tracking all of the Information Associated with a
Patient and his/her Care


Integrated and Inter
-
Disciplinary Information
Spectrum


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Where/How is BMI Utilized?

T1 Research (Bench


Clinical)


Transfer of Knowledge from Laboratory or Bench to
Clinical Trials


Move Genomic Research from Bench (Lab) to
Clinical Trial (or Genetic/Test Intervention)


Transfer in Lab/Bench Research to Pre
-
Clinical and
Early Clinical Human Subject Research


Exs:


New Genetic Test for Autism


Tested on Samples from DNA Repository


Transition to Actual Patient Population


Growing new Jaw Bone in Mice for Dental
Implants


Transition to Human Tissue


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Where/How is BMI Utilized?

Clinical Research (Trials)


Wide Range of Implications from Medical Treatment
to Medication Regime


Multi
-
Phased Process for Clinical Trials:


Phase I: First Stage


20
-
80 Healthy Patients


Phase II: Second Stage


20
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300 Patients


IIA


Dosing


How Much of Drug Should be Used


IIB


Efficacy


How Well Does Drug Work


Randomized Clinical Trials (Not all Get Drug)


Phase III: Multi
-
Center Trials


300
-
3000


Longer Term, Data Collected, Multiple Locations


Preparation of Data for Regulatory Approval (FDA)


Phase IV: Ongoing Monitoring of Drug After
Approval

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Where/How is BMI Utilized?

Clinical Research (Trials)


Differing Perspectives for Carrying out Research:


Patients: Drug, Treatment Regime, or Device


Increased Dose of Existing Drug (Safety/Effective)


Applying Drug to New Disease


Compare Two or more Treatments


Epidemiological


Study Existing Data for Trend


Against Existing Data Repositories


Patients with CHF and Diabetes Taking Statins


Tracking Communicable Disease/Outbreaks


Phases I, II, III, and IV Apply


Bad Results in IV


Pull Drug (Vioxx)

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Where/How is BMI Utilized?

T2 Research (Clinical Research


Practice/Community)


Practice
-
Oriented Translation Research


Results: Clinical Trails


Clinical Practice


Strategies for Establishing/Implementing New
Technologies


Improvements in Practice


New Evidence
-
Based Guidelines


New Care Models


Phase III Success Translated to Health Providers


Examples



Statin Drugs (Lipitor) and Exercise


New Treatment Regime for Chronic Disease

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Where/How is BMI Utilized?

Clinical Practice


Dealing with Patients


Direct Medical Care


Hospital or Clinic


Physician’s Office


Testing Facility


Insurance/Reimbursement


Tracking All Data Associated with Patients


Medical Record


Medical Tests (Lab, Diagnostic, Scans, etc.)


Prescriptions


Stringent Data Protection (HIPAA)


Distributed Repositories, Inability to Access Data in
Emergent Situations, Competition, etc.

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What is Medical Informatics?


Clinical Informatics, Pharmacy Informatics


Public Health Informatics


Consumer Health Informatics


Nursing Informatics


Systems and People Issues


Intended to Improve Clinical outcomes,
Satisfaction and Efficiency


Workflow Changes, Business Implications,
Implementation, etc…


Patient Centered


Personal Health Record and
Medical Home


Care Centered


Pay for Performance, Improving
Treatment Compliance

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What is Bionformatics?


Focused on Research Tools for T1:


Genomic and Proteomic Tools, Evaluation
Methods, Computing And Database Needs


Information Retrieval and Manipulation of Large
Distributed (caBIG) Data Sets
(cabig.cancer.gov/index.asp)


Often Requires Grid Computing


Includes Cancer and Immunology Research


Increasing Need to Tie These Separate Types of
Systems Together = Personalized Medicine


Biology and the Bedside (
www.i2b2.org
)


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Where is Data/How is it Used?


Medical And Administrative Data Found in Clinical
Information Systems (CIS) Such As:


Hospital Info. Systems Electronic Medical Records


Personal Health Records such as Google Health
and Microsoft Healthvault


Pharmacy, Nursing, Picture Archiving Systems


Complex Data Storage and Retrieval


Many
Different Systems


T1 Research Increasingly Reliant on CIS


T2 Research is Reliant on:


End Systems for Embedding EBM (Evidence
-
Based Medicine) Guidelines


Measuring Outcomes, Looking at Policy

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What are Major Informatics Challenges?


Shortage of Trained People Nationally


Slows adoption of Health Information Technology


Results in Poor Planning and Coordination,
Duplication of Efforts and Incomplete Evaluation


What are Critical Needs?


Dually Trained Clinicians or Researchers in
Leadership of some Initiatives


Connect all folks with Informatics Roles across
Institutions to Improve Efficiency


Multi
-
Disciplinary: CSE, Statistics, Biology,
Medicine, Nursing, Pharmacy, etc.


Emerging Standards for Information Modeling and
Exchange (
www.hl7.org
) based on XML

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What is UConn Doing in this Area?


NIH’s CTSA Program: Transform the Way Clinical
and Translational Science Research is Conducted


From Bench to Clinical Research to Translational
Research to the Bedside and Back Again


45+ Academic Medical Centers Awarded to Date

see: http://www.ctsaweb.org/


Under President Mike Hogan’s Leadership


UConn Submitted a CTSA Proposal in Oct 2008


Formed CICaTS: Connecticut Institute for Clinical
and Translational Science (Sept. 29
th

09)


University Initiative with Partners


John Dempsey, St. Francis, Hartford Hospital, CCMC,
Hospital for Central CT, Institute for Living, etc.


http://cicats.uconn.edu/

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CICATS


Official Launching:


Tuesday September 29, 10:30am
-
1:30pm


UConn Global Business Learning Center, Hartford


Speakers Include: Pres. M. Hogan, Provost P.
Nichols, and Dean Cato Laurencin (Med School)


Mission:


to educate and nurture new scientists


to

increase

clinical and translational
research

being conducted at UCHC, regional
hospitals, UConn Storrs, and healthcare
organizations throughout greater Hartford



to work collaboratively with regional stakeholders
to

combat the leading causes of morbidity,
mortality, disability, and health disparities


CICATS will have Biomedical Informatics Center

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Biomedical Informatics in CICATS

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Summary of Web Sites of Note:


AMIA (www.amia.org)


IHE (http://www.ihe.net/)


Smartplatform (http://www.smartplatforms.org/)


Mysis MOSS (
http://www.misys.com/OpenSource
)


NSF Clinical and Translational Science Program


http://www.ctsaweb.org/


Emerging Patient Data Standard


http://www.hl7.org/


Informatics for Integrating Biology & the Bedside.


https://www.i2b2.org/


Cancer Biomedical Informatics Grid


http://cabig.cancer.gov/index.asp

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Semester Topics (weeks)


Four Core Topics:


Semester and Course Overview (0.5)


Informatics/Information Engineering (1.5)


Software Architectures (2)


Security and Dynamic Coalition Problem (2)


Service Based Computing (2)


CORBA, JINI, .NET, Interoperability, Web


Security


Discussion of Semester Project (0.5)


Presentations by Outside Speakers (2.5)


Student Presentations on Biomedical Informatics
Materials (3)

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Planned Speakers


Dr. L. Fagan, Co
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Director, Stanford Biomedical
Informatics Training Program, March 31


Dr. M. Smith, Pharmacy Practice, UConn, April 5


Dr. T. Shortliffe, President, AMIA, April 28


Others to be Scheduled:


Dr. Thomas Agresta


Dr. Michael Blechner


Dr. Xiaoyan Wang


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Class Materials, Textbook, Projects, etc.


Course Web Site:
http://www.engr.uconn.edu/~steve/Cse300/cse300.html


Reading List


Constant Updates and Changes


Textbook


Biomedical Informatics: Computer Applications in
Health Care and Biomedicine (Health Informatics),
Edward H. Shortliffe (Editor), James J. Cimino
(Editor), ISBN
-
10: 0387289860


Project 1


Due in 2 weeks


Project 2


Out in 2 weeks


Team Project


Out in 2 weeks as well


Questions? Comments? Suggestions?


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Course Projects and Exam (???) …


Individual/Team Course Project(s) Throughout the
Semester


Individual Projects have two Goals


Increase Student Knowledge on BMI


Assist in Creating Courseware


Project will be the Entire Class


Explore and Learn about BMI Technologies


Span Subset of: T1 Research
-

Clinical Research
-

T2
Research
-

Clinical Practice


Explore Open Source and Other Solutions


Develop Extensible Plug and Play Framework


Exam


At MOST Final Exam (Still open to debate!)

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Individual Semester Projects


Readings, Readings, and More Readings


Project 1: Annotated Bibliography


Accumulate Web/Hard Links on T1 Research
-

Clinical Research
-

T2 Research
-

Clinical Practice


Read 7 Papers on Clinical & Translational Science


Project 2: Courseware Materials


Choose two Different Areas for Indepth
Examination


Topics include (but not Limited to):


HIE I2b2


Standards (HL7, Common Data Architecture CDA)


caBIG


BIRN (Biomedical Informatics Research Network)


Another NIH Computing Initiative

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Semester Project


Still Evolving


Possible Projects Include:


Usage of SmartPlatform


Utilization of Personal Health Records (PHR) Such
as Google Health and/or MS Healthvault in New or
Extended Context


Interoperability with EMR


Google Health Hibernate API Available


XML (HL7/CDA) to i2b2 DB Translation


Supervised by M. Blechner (UCHC BMI Faculty)


Extending Cell Phone Applications (iphone,
blackberry, and android) for


Maintaining Prescriptions


Observations of Daily Living


Prior Work by Undergraduate Teams (with Source)

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Semester Project Objectives


Objective


Wide Scale Open Source Framework


Envision Plug and Play Architecture


High Reliance on Open Source Solutions for PHR and
EMR


Support Interoperability to Components via XML and
Standards


Develop Complete, Integrated, and Extensible
Framework

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SmartPlatform


Substitutable Medical Apps, reusable technology


(
http://www.smartplatforms.org/
)


NSF/NIH Funded SHARP Proposal at Harvard


Intended to:

“A platform with substitutable apps constructed
around core services is a promising approach to
driving down healthcare costs, supporting standards
evolution, accommodating differences in care
workflow, fostering competition in the market, and
accelerating innovation”


Likely Led by Timo Ziminski

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Personal Health Records


Google Health


Detailed Hibernate API to Allow Programmatic
Transfer of Information to/From Google Health


Utilized in Web
-
Based Application


Utilized by Cell Phone Projects (see later slides)


Existing Platform Available for Future Design,
Development, and Usage


Explore EMR/PHR Interoperability

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TMR Architecture

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XML (HL7/CDA) to i2b2 DB Translation


Work with Dr. Michael Blechner (UCHC BMI Faculty
Member)


Explore a Prototype that can take:


HL7/CDA Data (Simulated from an EMR)


Store in a i2b2 Compatible Database


Utilization of Standards, New Technologies, etc.


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Cell Phone Applications


RWJ Project Health Design


Observations of Daily Living and PHRs


Passive


Once Initiated, Collects Data


Accelerometer


Pedometer


Pill Bottle that Sends a Time Stamp Message (over
Bluetooth?) to SmartPhone


Active


Patient Initiated


Providing Information via Smartphone on:


Diabetes (Glucose, Weight, Insulin)


Asthma (Peak Flow, use of Inhaler)


Heart Disease (Pulse, BP, Diet)


Pain, Functional status, Fatigue, etc.


http://www.engr.uconn.edu/~steve/Cse4904/cse4904.html

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Focus of Grant


Management of Two Diseases in Women of Color



Obesity and Osteoarthritis


Team


TRIPP (Crowell, Fifield) and AHFP (Agresta)


SisterTalk (Headley) and CHCAT (Granger)


UConn Storrs (Demurjian) and Netsoft (Collins)

Microsoft
HealthVault
Patient Demographics
and
ODLs
Figure 1: Architecture Diagram of the Proposed System.
Patients
Providers
Researchers
Lifelines
Repository
Web/Application
Server
SQL Server
Database
Client Side Technologies
https, html, Ajax, XML
Server Side Technologies
Java, JSP, Hibernate,
Relational Database, XML
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CSE4904


Spring 2010


Smartphone Projects on ODLs and Other Medical
Data Tracking and Alerts


Three Platforms:


Google’s Android (Java)


Blackberry (Java)


iPhone (Objective C)


Three Teams of Three Students Each

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Blackberry Team


Ability to Track Information on ODLs and
Prescriptions


Login Screen


Connection to Google Health


Health Screen to Track ODLs


Charting of ODLs over Time


Loading Scripts from Google Health


Prescription Alarms


Adam Siena, Kristopher Collins, William Fidrych

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Screen Shots

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Android Team


Similar Capabilities to Blackberry Project


Wellness Diary and Medication Alarm


Integration with Google Health


Much Improved ODL Screens


Male and Female Faces


Change “Face” Based on Value


Tracking Prescriptions and Alarms


Reports via. Google Charts


Ishmael Smyrnow, Kevin Morillo, James Redway

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Screen Shots

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iPhone Team


Similar Capabilities to Blackberry Project


Tracking of Conditions, Medications, and
Allergies


ODLs for:


Blood
-
Glucose, Peak
-
Flow, and Hypertension


Generation of Reports


Synchronization with Google Health


Brendan Heckman, Ryan McGivern, Matthew Fusaro

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Questions?