Informatics for Clinical and Translational Research Daniel R' Masys, MD Professor and Chair Departme

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Informatics for Clinical and Translational
Research Daniel R. Masys, MDProfessor and
ChairDepartment of Biomedical InformaticsProfess
or of MedicineVanderbilt University School of
Medicine
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Session Outline

• Goals for this session
• Size and scope of clinical research ie., why this
  topic is important
• Role of informatics in clinical and translational
  research
• Informatics-related topics in the design and
  execution of clinical research studies
• Regulatory context Good Clinical Practice
  standards and 21 CFR 11
• Design of forms and databases
• Data security and HIPAA compliance
• Specialized technologies
• Putting it all together

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Premise and Goals for this session

• This week is a teach the teachers session for
  change agents
• You might want (or be asked) to teach some of the
  material covered here
• The topics touched upon during this session are
  taught in a 20 credit hour Masters course, and
  all PowerPoint, readings, quizzes and course
  outlines are available for your use.
• This session is a sampler of topics from the
  course

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Full Course website http//dbmichair.mc.vanderbil
t.edu/courses/crestdata/
Educational objectives Session outlines Readings P
owerPoint flies
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Widely used text Second Edition 2007
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Clinical Research as an activity

• Fundamental to translation of basic research to
  medically useful interventions
• Big business est. 95 B spent annually in U.S.
  in biomedical research/drug and device testing
• Academic centers lag behind commercial clinical
  trials organizations in knowledge and skills
  related to efficient and high quality clinical
  research.
• Academic center market share of clinical trials
  now est. at 20, was 80 in 1990
• Generally inferior performance with respect to
  error rates, missing data, timeliness of
  submission

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National Context

• NIH Roadmap and Clinical Translational Science
  Awards (CTSA) envision networks of clinical
  researchers
• FDA rules now being applied to NIH sponsored
  research
• Clinical research training has historically
  included biostatistics and trial design, but
  little regarding informatics and data management

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Barriers to Research

• Administrative bottlenecks
• Poor integration of translational resources
• Delay in the completion of clinical studies
• Difficulties in human subject recruitment
• Little investment in methodologic research
• Insufficient bi-directional information flow
• Increasingly complex resources needed
• Inadequate models of human disease
• Reduced financial margins
• Difficulty recruiting, training, mentoring
  scientists

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CTSA A roadmap initiative
It is the responsibility of those of us involved
in todays biomedical research enterprise to
translate the remarkable scientific innovations
we are witnessing into health gains for the
nation.
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NIH CTSAs Home for Clinical and Translational
Science
Clinical Research Ethics
Trial Design
Advanced Degree-Granting Programs
Biomedical Informatics
CTSA HOME
Industry
Participant Community Involvement
Clinical Resources
Biostatistics
Regulatory Support
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Re-engineering Clinical Research
Interdisciplinary Research Innovator Award
Public-Private Partnerships
Bench
Bedside
Practice
Building Blocks and Pathways Molecular
Libraries Bioinformatics Computational
Biology Nanomedicine
Integrated Research Networks Clinical Research
Informatics NIH Clinical Research
Associates Clinical outcomes Harmonization Trainin
g
Translational Research Initiatives
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Role of Informatics in Clinical and
Translational Research

• Structured observation and structured record
  keeping are the essence of science
• Primary differentiation between routine clinical
  care and research is how processes are controlled
  (ie., protocol-driven) and information is managed
  to make it useful for analysis

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Classical Data Management Flow for Clinical
Research
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Research Data Management Goals

• Create processes and systems that result in
  research data that is
• Accurate
• Complete
• Timely
• Verifiable
• Secure
• Available for analysis

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Regulatory ContextGood Clinical Practiceand21
CFR 11Electronic Records Signatures
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Regulatory Context Good Clinical Practice
standards

• General and uniform set of principles for
  conducting clinical research
• Two themes
• Respecting rights of participants
• Conducting research so that data is accurate and
  verifiable
• Required by FDA but a good (higher) standard for
  NIH and other sponsored research

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http//www.fda.gov/oc/gcp/
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GCP Standards address...

• Responsibilities of participating sites
• Responsibilities of coordinating centers for
  multisite trials
• Quality Assurance methods for data
• Audits
• Reporting to regulatory agencies

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GCP Principles of Data Management

• All data should be independently verifiable
• Normally done by comparison with locally kept
  medical records in interventional trials
• Structured approach to record keeping
• Physical structure tabbed participant folders
  with dividers for different classes of
  information
• Logical structure database designs and tracking
  systems

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GCP Principles of Data Management

• Research records are separately maintained from
  healthcare-related records
• Source document place where observation first
  recorded
• Source document verification comparison of Case
  Report Forms (CRFs) with source documents
• corollary CRFs are not usually considered source
  documents

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GCP standards example Paper Case Report Forms

• Follow instructions
• Write legibly
• Originals normally go to Coordinating Center
  copies local
• No marginalia (literally outside the box)
• Forms designed so that all variables have a
  current value (may be code for Pending, Missing,
  Missed)
• Correct units of measurement (best included with
  value as separate field)

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GCP standards for Case Report Forms, contd

• Proper methods of correction
• Line through incorrect value (value still
  visible)
• Correct value added
• Correction initialed
• White-out is always red in an auditors eyes - no
  correction fluids or erasures
• Check forms for completeness prior to submission
• Double check and verify ID info on CRF
• Submit on time

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21 CFR 11Electronic Records Signatures

• Applies (only) to data submitted to FDA in
  support of drug device applications
• Address issues related to paperless data
  management systems where there is no source
  document for verification
• Subpart C relates to digital signatures
• Full compliance requires formal software
  validation testing and certification
• To date, has paradoxically impeded rather than
  advanced use of electronic research data
  management systems

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Forms Design
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General Forms Design Principles

• Have definitions of all data to be collected in
  hand before starting the study
• Avoids unnecesary forms revisions that often
  confuse Clinical Research Associates (CRAs),
  participants, and creates statistical
  complexities
• Avoids fishing expedition approach to iterative
  protocol modification

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Forms Design

• Think of forms as chronologically ordered data
  containers
• Define container size and content on the basis of
  who, when, where
• Who fills out the form
• When the data becomes available in the course of
  the study
• Where the data will be collected

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General Forms Principles, contd

• Design forms for ease of use
• this can be difficult since same form often
  serves different user groups
• Strive to be clear and unambiguous
• only achievable when others review the forms
• Create forms that make it more likely to do the
  right thing than to make mistakes
• amount of training required is an indicator
• Get draft forms early enough in the planning of
  the trial that they can be pilot tested prior to
  study launch

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Forms Design principles, contd

• Format should anticipate three purposes
• Data acquisition (recording on paper forms or
  entering data into electronic forms)
• Data entry from paper to computer
• Data retrieval (inspection, QA, inference)

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Forms Design principles, contd

• Top three considerations
• Consistency, consistency, consistency
• Includes consistency of overall layout, and
  consistency of coding
• Use standardized form header for referential
  integrity items
• Study ID
• Participant ID
• Event ID and/or Date
• Site ID
• Consider systems approach to ID data barcoded
  labels or barcoded unique forms

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Web browser (thin client) electronic forms for
data entry and retrieval

• Strengths
• Deploy to any location on the Internet
• Platform independent (sort of be careful and
  test all software on all potential clients)
• No software to install or license on users
  machines
• Weaknesses
• Less efficient (compact interface)
• Fewer controls available
• Limited repertoire of widgets (buttons, lists,
  etc.)
• Slower
• Dependent upon Internet connectivity

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Specialized Software
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Specialized Software for Clinical Trials

• Registration
• Randomization
• Participant tracking
• Site communications
• Transaction or batch upload of local data to
  coordinating center
• Websites for protocols, forms, administrative info

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Specialized Software for Clinical Trials, contd

• Performance measures
• Site actual vs. projected accrual
• Data completeness
• Data accuracy
• Data timeliness
• Usually displayed as trends over time
• Performance measures should include reference
  values for performance at all sites combined

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Data Acquisition Technologies
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Data acquisition TechnologiesKeyboard Data Entry

• Average keystroke error rates will be 0.1 to 1,
  depending upon data type
• Improve accuracy over baseline by
• Double entry and file comparison (gold standard
  but inefficient and expensive)
• Special technologies for referential integrity
  items (e.g., barcode visit and participant ID)
• Event-driven auditing and source document
  verification of scientifically important
  variables

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Data acquisition TechnologiesDouble keying

• Common best practice forms entered by two
  different data entry operators
• Computer generates difference (diff) file
• Third person (usually data manager with clinical
  expertise) reviews and resolves differences
• Increases personnel costs by factor of 2 - 2.5
  over single entry plus sample-based auditing

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Data acquisition Technologies Barcoding

• Applications
• Referential integrity items identifiers for
  participant, study, site, protocol, event/visit
• Physical object tracking e.g., tissue specimens,
  freezer inventory management systems
• System-generated barcode labels
• Various barcode standards 3-of-9 generally used
  for scientific applications
• Produced by TrueType fonts or dedicated barcode
  printers

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Data acquisition Technologies Barcoding, contd

• Barcode readers
• Keyboard wedge - wand or handheld scanner
  plugged between keyboard and computer
• Self-contained scanners with infrared or USB bulk
  data upload (derived from warehouse inventory
  systems)

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Barcodes and reading devices
Workstation accessories
Code 39 (3-of-9) with and without readable
text Note without text is not a security measure
and increases errors
Self-contained Reader unit
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Data acquisition Technologies Mark-sensing
Technologies

• Example Scantron (www.scantronforms.com)
• Strengths
• Mature technology
• Efficient for re-usable form scanning
• Weaknesses
• Low information density poor for most biomedical
  uses
• Susceptible to frame shift errors by users
• Requires forms printing
• Cost effective at level of 100K forms

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Mark sensing technologies
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Data acquisition Technologies POF Plain Old Fax

• Design issues
• Include signature or initials on faxable forms
• Strengths
• Widely used surrogate for paper
• Weaknesses
• Not considered a source document
• Legibility
• Requires additional effort to enter data into
  computable form

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Data acquisition Technologies Fax Optical
Character Recognition

• Example Teleform (www.cardiff.com)
• Strengths
• Can substitute for data entry staff
• Includes design, recognition, and verification
  functionality
• 90 recognition accuracy depending upon data
  type
• Weaknesses
• Error rates equivalent to single entry, higher
  than double entry
• Cost vs. person hours becomes favorable only at
  large numbers of forms (50-100K)

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Data acquisition Technologies Direct Computer
Entry by Participants

• Can use thin client (HTML forms) or thick
  client ie., workstation forms (e.g., MS Access)
• Strengths
• If well designed, eliminates data entry step
• Can add multimedia explanations and tutorials
• Can be more enjoyable for study participants than
  paper forms
• Weaknesses
• Requires basic computer skills (mouse /-
  keyboard)
• Requires literacy skills
• Requires staff assistance and verification

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Data acquisition Technologies Computer to
Computer Messaging

• Example import lab results from lab system
  directly into research database for study
  participants.
• Strengths
• If well designed, eliminates data entry step
• Timeliness
• Accuracy
• Weaknesses
• Requires specialized computer programming
  expertise
• Requires standards for representing clinical data
  (most widely used HL-7)
• Requires willingness of systems managers at
  source of data (e.g., medical center Information
  Services) to allow data connections

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Data acquisition Technologies PDAs

• Example Pendragon software
• Strengths
• Portable, relatively low cost
• Nonprogrammer interfaces to MSAccess
• Weaknesses
• Limited screen size and navigation speed
• Not suitable for text entry
• Security lost or stolen PDA

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Data Archiving and Database design
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Commonly used data archiving and analysis software

• Single investigator, simple trial
• Spreadsheet (MS Excel)
• Beware using spreadsheets for HIPAA-regulated
  data no audit trail capability
• Workgroup-capable database management software
  (MS Access, Filemaker Pro, 4th Dimension, MS
  Visual FoxPro)
• Data Center, multiple studies
• Enterprise relational database system
• Sybase, Oracle, MS SQL Server
• Dedicated statistical analysis packages
• SAS, BMDP, SPSS, S Plus, JMP

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Commonly used data archiving and analysis
software, contd

• Pharmaceutical companies - multiple drugs,
  multiple sites, multiple studies, FDA audits
• Dedicated clinical trials software (e.g., BBN
  ClinTrials, Oracle Clinical)

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Sample data model for one-time administration of
a survey
one
Person (Participant) ParticipantID primary
key Last_name First_name Address City State Zip P
hone Fax E-mail MRN Birthdate SSN Gender Last_upda
te Update_by
one
Study_Data ParticipantID Date Answer1 Answer2 An
swer3 Answer4 Last_update Update_by
Best practices store Person table on removable
media with physical security OR store Person
encrypted by private key
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Simple clinical study with a variable number of
identical repeat visits
one
Person (Participant) ParticipantID Last_name Fi
rst_name Address City State Zip Phone Fax E-mail M
RN Birthdate SSN Gender Last_update Update_by
many
Study_Data ParticipantID VisitID VisitDate BPsys
tolic BPdiastolic Weight Sodium Potassium Chloride
Bicarb BUN Creatinine Last_update Update_by
Note In best pactice, primary key of Study_Data
is the combination of Participant ID and the
study visit, which defines a unique protocol
event. VisitDate is the calendar date that event
occurs.
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Clinical study with a baseline evaluation
followed by variable number of identical repeat
visits
one
Baseline ParticipantID VisitDate DataItem1 DataI
tem2 DataItem3 Last_update Update_by
Person (Participant) ParticipantID Last_name Fi
rst_name Address City State Zip Phone Fax E-mail M
RN Birthdate SSN Gender Last_update Update_by
one
many
Follow_Up ParticipantID VisitID VisitDate BPsyst
olic BPdiastolic Weight Sodium Potassium Chloride
Bicarb BUN Creatinine Last_update Update_by
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Data Security
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Information Security Elements

• Availability - when and where needed
• Authentication -a person or system is who they
  purport to be (preceded by Identification)
• Access Control - only authorized persons, for
  authorized uses
• Confidentiality - no unauthorized information
  disclosure
• Integrity - Information content not alterable
  except under authorized circumstances
• Attribution/non-repudiation - actions taken are
  reliably traceable

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Research Records Security,General Principles

• Physical Security
• Locked file storage for physical files
• Programmable locks best
• Change combination on a regular basis (common
  practice twice a year)
• Person-identifiable data
• Keep separate from other study data
• Consider additional protections such as two
  person access requirements

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Research Records Security, contd

• Electronic Security
• No workstations viewable from public areas
• Password-protected login
• Screensaver timeouts
• Separate login and password for database access
• Store demographics data separately and encrypted
  if feasible
• Regular backups and offsite backup storage

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Research Records Security, contd

• Network Security
• Safest but least useful disconnect workstations
  with research data from network
• Keep all workstations and servers patched with
  latest security updates
• Run antivirus software on all machines
• Consider firewall computer to protect Internet
  access point, and/or workstation firewall software

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Security Rule Basic Concepts

• Applies security principles well established in
  other industries
• Like Privacy Rule, affects Covered Entities that
  create, store, use or disclose Protected Health
  Information (PHI)
• Unlike the Privacy Rule, affects only PHI in
  electronic format (not oral or paper-based)
• Like the Privacy Rule, written for health care
  research not the principal focus
• Scalable burden relative to size and complexity
  of organization

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Three Categories of Standards

• Administrative safeguards
• Policies and procedures to prevent, detect,
  contain and correct information security
  violations
• Physical Safeguards
• IT equipment and media protections
• Technical Safeguards
• Controls (mostly software) for access,
  information integrity, audit trails

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Administrative Safeguards

• Required
• Risk Analysis
• Risk Management Plan
• Sanctions Policy
• Information System Activity Review (audits)
• Security Incident Response Reporting
• Data Backup Plan
• Disaster Recovery Plan
• Emergency Mode Operations
• Periodic Evaluations of Standards Compliance

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Physical Safeguards

• Required
• Workstation Use Analysis
• Workstation Security
• Disposal of media
• deletion of PHI prior to disposal, or
• Secure disposal so data nonrecoverable
• Media Reuse
• Deletion of PHI prior to re-use

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Technical Safeguards

• Required
• Unique User Identification
• No shared logins
• Emergency access procedures
• Audit controls
• Logs of who created, edited or viewed PHI
• Person and/or Entity Authentication
• No systems without access control

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If a research project maintains e-PHI

• Responsible group must designate a Security
  Officer who has responsibility for implementing
  HIPAA-compliant policies and procedures for
  research use of e-PHI
• Must do and document a risk analysis
• Must create risk management plan based on the
  risk analysis
• Must create and keep current a HIPAA Security
  Rule compliance document that includes
  description of how 17 Required elements are met,
  and decisions regarding Addressable elements

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Widespread current research practices that dont
meet the standard

• Research workgroups that create or use PHI in
  electronic format but have no written security
  procedures, policies or training
• Workstations with no login security (e.g.,
  Windows98)
• Data management and analysis applications used to
  store PHI that have no ability to generate audit
  trails
• E.g., Excel spreadsheets with PHI in them

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Using the Internetfor Clinical Research
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Internet Functionality for Clinical Research

• E-mail
• Avoid putting HIPAA PHI in e-mail
• Study participant recruitment

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Internet Functionality for Clinical Research,
contd

• E-mail
• Avoid putting PHI in e-mail
• Study participant recruitment
• Private FTP site as drop box for study related
  file communications
• encrypt files if they contain PHI
• Data submission and reporting
• Multi-site coordination and administration

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Approved Internet Technologies relevant to
Clinical Research
1containing person-identifiable i.e., HIPAA PHI
2 must be encrypted to HCFA/CMS std
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Sample Project administration website for
multi-center study
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Putting it All TogetherResearch Data Management

• An artful selection of physical and electronic
  management methods
• Signed informed consent documents
• Paper forms
• Regulatory and project management binders
• Data models and databases
• Data acquisition and display technologies
• Communications technologies for project
  management as well as data management

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Attributes of Successful Data Management

• Attention to detail
• Explicit structure and process
• Robust designs
• Anticipate failures, lapses and mistakes
• Design systems that identify and correct them
• Mechanisms for verification
• Well documented

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Lessons Learned about Data Management in Clinical
and Translational Research

• Effective data management is a continuous
  process, not a point in time analysis
• Historically, health care organizations and
  providers have invested suboptimally in
  information systems and this provides an uneven
  infrastructure for clinical research
• In health care organizations, data management and
  information systems implementation is 20
  technology and 80 sociology (R. Gardner) plan
  accordingly

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Research Data Trends

• Data Tsunamis
• Genome, proteome, regulome,new forms of imaging
• High dimensionality variables gtgt subjects

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Premise and Goals for this session

• This week is a teach the teachers session for
  change agents
• You might want (or be asked) to teach some of the
  material covered here
• The topics touched upon during this session are
  taught in a 20 credit hour Masters course, and
  all PowerPoint, readings, quizzes and course
  outlines are available for your use.
• This session is a sampler of topics from the
  course

79
Educational objectives Session outlines Readings P
owerPoint flies
Full Course website http//dbmichair.mc.vanderbil
t.edu/courses/crestdata/
80
(No Transcript)
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Questions?