Data Management for Clinical Data Management for Clinical TrialsTrials

(Informatics)(Informatics)

Robert Anderson, MHA, CCRA, CCRCPRobert Anderson, MHA, CCRA, CCRCPDirector, Clinical Trials AdministrationDirector, Clinical Trials AdministrationThe CRA Training Institute, HoustonThe CRA Training Institute, Houston

Clinical Research as an Clinical Research as an ActivityActivity

• Fundamental to translation of basic research Fundamental to translation of basic research to medically useful interventionsto medically useful interventions

• Big business: est. $95 B spent annually in U.S. Big business: est. $95 B spent annually in U.S. in biomedical research/drug and device testingin biomedical research/drug and device testing

• Academic centers lag behind commercial Academic centers lag behind commercial clinical trials organizations in knowledge and clinical trials organizations in knowledge and skills related to efficient and high quality skills related to efficient and high quality clinical research. clinical research. – Academic center market share of clinical trials now Academic center market share of clinical trials now

est. at 20%, was 80+% in 1990est. at 20%, was 80+% in 1990– Generally inferior performance with respect to error Generally inferior performance with respect to error

rates, missing data, timeliness of submissionrates, missing data, timeliness of submission

Importance of Importance of InformaticsInformatics to Clinical Researchto Clinical Research

• Structured observation and structured Structured observation and structured record keeping are the essence of record keeping are the essence of sciencescience

• Primary differentiation between Primary differentiation between routine clinical care and research is routine clinical care and research is how processes are controlled (i.e.., how processes are controlled (i.e.., protocol-driven) and information is protocol-driven) and information is managed to make it useful for managed to make it useful for analysisanalysis

““Classical” Data Management Classical” Data Management Flow Flow

for Clinical Researchfor Clinical Research Scientific HypothesesScientific Hypotheses

Specific Data Elements Specific Data Elements Required to Test HypothesesRequired to Test Hypotheses

Data Acquisition Instruments Data Acquisition Instruments (forms)(forms)

People and Process Development (Who People and Process Development (Who does What, When and Where)does What, When and Where)

Computer Data Model and Tool Computer Data Model and Tool Selection to Support Model and Selection to Support Model and

output to Analytical Softwareoutput to Analytical Software

Documentation: Standard Documentation: Standard Operating Policies & ProceduresOperating Policies & Procedures

Research Data Research Data Management GoalsManagement Goals

• Create processes and systems Create processes and systems that result in research data that result in research data that is:that is:– AccurateAccurate– CompleteComplete– TimelyTimely– VerifiableVerifiable– SecureSecure– Available for analysisAvailable for analysis

Regulatory Context: Regulatory Context: Good Clinical Practice Good Clinical Practice

StandardsStandards• General and uniform set of General and uniform set of

principles for conducting clinical principles for conducting clinical researchresearch

• Two themesTwo themes– Respecting rights of participantsRespecting rights of participants– Conducting research so that data is Conducting research so that data is

accurate and verifiableaccurate and verifiable• Required by FDA but a good (higher) Required by FDA but a good (higher)

standard for NIH and other standard for NIH and other sponsored researchsponsored research

GCP Standards Address...GCP Standards Address...

• Responsibilities of participating sitesResponsibilities of participating sites• Responsibilities of coordinating Responsibilities of coordinating

centers for multisite trialscenters for multisite trials• Quality Assurance methods for dataQuality Assurance methods for data• AuditsAudits• Reporting to regulatory agenciesReporting to regulatory agencies

GCP Principles of Data GCP Principles of Data ManagementManagement

• All data should be independently All data should be independently verifiableverifiable– Normally done by comparison with locally Normally done by comparison with locally

kept medical records in interventional trialskept medical records in interventional trials

• Structured approach to record keepingStructured approach to record keeping– Physical structure: tabbed participant Physical structure: tabbed participant

folders with dividers for different classes of folders with dividers for different classes of informationinformation

– Logical structure: database designs and Logical structure: database designs and tracking systemstracking systems

GCP Principles of Data GCP Principles of Data ManagementManagement

• Research records are separately Research records are separately maintained from healthcare-maintained from healthcare-related recordsrelated records

• Source document = place where Source document = place where observation first recordedobservation first recorded

• Source document verification: Source document verification: comparison of Case Report Forms comparison of Case Report Forms (CRFs) with source documents(CRFs) with source documents– corollary: CRFs are not usually corollary: CRFs are not usually

considered source documentsconsidered source documents

GCP standards example: GCP standards example: Paper Case Report FormsPaper Case Report Forms

• Follow instructionsFollow instructions• Write legiblyWrite legibly• Originals normally go to Coordinating Originals normally go to Coordinating

Center; copies localCenter; copies local• No marginalia (literally outside the box)No marginalia (literally outside the box)• Forms designed so that all variables Forms designed so that all variables

have a current value (may be code for have a current value (may be code for Pending, Missing, Missed)Pending, Missing, Missed)

• Correct units of measurement (best Correct units of measurement (best included with value as separate field)included with value as separate field)

GCP Standards for Case Report GCP Standards for Case Report Forms, cont’dForms, cont’d

• Proper methods of correctionProper methods of correction– Line through incorrect value (value still visible)Line through incorrect value (value still visible)– Correct value addedCorrect value added– Correction initialedCorrection initialed– White-out is always White-out is always redred in an auditor’s eyes - no in an auditor’s eyes - no

correction fluids or erasurescorrection fluids or erasures

• Check forms for completeness prior to Check forms for completeness prior to submissionsubmission

• Double check and verify ID info on Double check and verify ID info on CRFCRF

• Submit on timeSubmit on time

21 CFR 11:21 CFR 11:Electronic Records & Electronic Records &

SignaturesSignatures• Applies (only) to data submitted to FDA in Applies (only) to data submitted to FDA in

support of drug & device applicationssupport of drug & device applications• Address issues related to paperless data Address issues related to paperless data

management systems where there is no management systems where there is no source document for verificationsource document for verification

• Subpart C relates to digital signaturesSubpart C relates to digital signatures• Full compliance requires formal software Full compliance requires formal software

validation testing and certificationvalidation testing and certification• To date, has paradoxically impeded rather To date, has paradoxically impeded rather

than advanced use of electronic research than advanced use of electronic research data management systemsdata management systems

General Forms Design General Forms Design PrinciplesPrinciples

• Have definitions of all data to be Have definitions of all data to be collected in hand before starting collected in hand before starting the studythe study– Avoids unnecessary forms revisions Avoids unnecessary forms revisions

that often confuse Clinical Research that often confuse Clinical Research Associates (CRA’s), participants, and Associates (CRA’s), participants, and creates statistical complexitiescreates statistical complexities

– Avoids ‘fishing expedition’ approach Avoids ‘fishing expedition’ approach to iterative protocol modificationto iterative protocol modification

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

retrievalretrieval• StrengthsStrengths

– Deploy to any location on the InternetDeploy to any location on the Internet– Platform independent (sort of… be careful and test Platform independent (sort of… be careful and test

all software on all potential clients)all software on all potential clients)– No software to install or license on user’s machinesNo software to install or license on user’s machines

• WeaknessesWeaknesses– Less efficient (compact interface)Less efficient (compact interface)– Fewer controls availableFewer controls available– Limited repertoire of ‘widgets’ (buttons, lists, etc.)Limited repertoire of ‘widgets’ (buttons, lists, etc.)– SlowerSlower– Dependent upon Internet connectivityDependent upon Internet connectivity

Specialized Software for Specialized Software for Clinical TrialsClinical Trials

• RegistrationRegistration• RandomizationRandomization• Participant trackingParticipant tracking• Site communicationsSite communications

– Transaction or batch upload of local Transaction or batch upload of local data to coordinating centerdata to coordinating center

– Websites for protocols, forms, Websites for protocols, forms, administrative infoadministrative info

Specialized Software for Specialized Software for Clinical Trials, cont’dClinical Trials, cont’d

• Performance measuresPerformance measures– Site actual vs. projected accrualSite actual vs. projected accrual– Data completenessData completeness– Data accuracyData accuracy– Data timelinessData timeliness

• Usually displayed as trends over timeUsually displayed as trends over time• Performance measures should include Performance measures should include

reference values for performance at reference values for performance at all sites combinedall sites combined

Data Acquisition Data Acquisition TechnologiesTechnologies

Data acquisition Technologies:Data acquisition Technologies:

Keyboard Data EntryKeyboard Data Entry

• Average keystroke error rates will be Average keystroke error rates will be 0.1% to 1%, depending upon data type0.1% to 1%, depending upon data type

• Improve accuracy over baseline by:Improve accuracy over baseline by:– Double entry and file comparison (‘gold Double entry and file comparison (‘gold

standard’ but inefficient and expensive)standard’ but inefficient and expensive)– Special technologies for referential integrity Special technologies for referential integrity

items (e.g., barcode visit and participant ID)items (e.g., barcode visit and participant ID)– Event-driven auditing and source document Event-driven auditing and source document

verification of scientifically important verification of scientifically important variablesvariables

Data acquisition Technologies:Data acquisition Technologies:

Double keyingDouble keying

• Common “best practice”: forms entered Common “best practice”: forms entered by two different data entry operatorsby two different data entry operators

• Computer generates difference (diff) fileComputer generates difference (diff) file• Third person (usually data manager with Third person (usually data manager with

clinical expertise) reviews and resolves clinical expertise) reviews and resolves differencesdifferences

• Increases personnel costs by factor of 2 - Increases personnel costs by factor of 2 - 2.5 over single entry plus sample-based 2.5 over single entry plus sample-based auditingauditing

Data acquisition Technologies:Data acquisition Technologies: BarcodingBarcoding

• ApplicationsApplications– Referential integrity items: identifiers for Referential integrity items: identifiers for

participant, study, site, protocol, event/visitparticipant, study, site, protocol, event/visit– Physical object tracking: e.g., tissue Physical object tracking: e.g., tissue

specimens, freezer inventory management specimens, freezer inventory management systemssystems

• System-generated barcode labelsSystem-generated barcode labels– Various barcode standards: 3-of-9 generally Various barcode standards: 3-of-9 generally

used for scientific applicationsused for scientific applications– Produced by TrueType fonts or dedicated Produced by TrueType fonts or dedicated

barcode printersbarcode printers

Data acquisition Technologies:Data acquisition Technologies: Barcoding, cont’dBarcoding, cont’d

• Barcode readersBarcode readers– ““Keyboard wedge” - wand or Keyboard wedge” - wand or

handheld scanner plugged between handheld scanner plugged between keyboard and computerkeyboard and computer

– Self-contained scanners with infrared Self-contained scanners with infrared or USB bulk data upload (derived or USB bulk data upload (derived from warehouse inventory systems)from warehouse inventory systems)

Data acquisition Technologies:Data acquisition Technologies: Mark-sensing TechnologiesMark-sensing Technologies

• Example: Scantron Example: Scantron (www.scantronforms.com)(www.scantronforms.com)

• StrengthsStrengths– Mature technologyMature technology– Efficient for re-usable form scanningEfficient for re-usable form scanning

• WeaknessesWeaknesses– Low information density: poor for most biomedical Low information density: poor for most biomedical

usesuses– Susceptible to “frame shift” errors by usersSusceptible to “frame shift” errors by users– Requires forms printingRequires forms printing– Cost effective at level of ~ 100K formsCost effective at level of ~ 100K forms

Mark sensing technologies

Data acquisition Technologies:Data acquisition Technologies: POF: Plain Old FaxPOF: Plain Old Fax

• Design issuesDesign issues– Include signature or initials on faxable formsInclude signature or initials on faxable forms

• StrengthsStrengths– Widely used surrogate for paperWidely used surrogate for paper

• WeaknessesWeaknesses– Not considered a source documentNot considered a source document– LegibilityLegibility– Requires additional effort to enter data into Requires additional effort to enter data into

computable formcomputable form

Data acquisition Technologies:Data acquisition Technologies: Fax + Optical Character RecognitionFax + Optical Character Recognition

• Example: Teleform (www.cardiff.com)Example: Teleform (www.cardiff.com)• StrengthsStrengths

– Can substitute for data entry staffCan substitute for data entry staff– Includes design, recognition, and verification Includes design, recognition, and verification

functionalityfunctionality– 90+% recognition accuracy depending upon data type90+% recognition accuracy depending upon data type

• WeaknessesWeaknesses– Error rates equivalent to single entry, higher than Error rates equivalent to single entry, higher than

double entrydouble entry– Cost vs. person hours becomes favorable only at large Cost vs. person hours becomes favorable only at large

numbers of forms (50-100K)numbers of forms (50-100K)

Data acquisition Technologies:Data acquisition Technologies: Direct Computer Entry by Direct Computer Entry by

ParticipantsParticipants• Can use thin client (HTML forms) or ‘thick client’ Can use thin client (HTML forms) or ‘thick client’

i.e., workstation forms (e.g., MS Access)i.e., workstation forms (e.g., MS Access)• StrengthsStrengths

– If well designed, eliminates data entry stepIf well designed, eliminates data entry step– Can add multimedia explanations and tutorialsCan add multimedia explanations and tutorials– Can be more enjoyable for study participants than paper Can be more enjoyable for study participants than paper

formsforms

• WeaknessesWeaknesses– Requires basic computer skills (mouse +/- keyboard)Requires basic computer skills (mouse +/- keyboard)– Requires literacy skillsRequires literacy skills– Requires staff assistance and verificationRequires staff assistance and verification

Data acquisition Technologies:Data acquisition Technologies: Computer to Computer MessagingComputer to Computer Messaging

• Example: import lab results from lab system directly into Example: import lab results from lab system directly into research database for study participants.research database for study participants.

• StrengthsStrengths– If well designed, eliminates data entry stepIf well designed, eliminates data entry step– TimelinessTimeliness– AccuracyAccuracy

• WeaknessesWeaknesses– Requires specialized computer programming expertiseRequires specialized computer programming expertise– Requires standards for representing clinical data (most Requires standards for representing clinical data (most

widely used = HL-7) widely used = HL-7) – Requires willingness of systems managers at source of data Requires willingness of systems managers at source of data

(e.g., medical center Information Services) to allow data (e.g., medical center Information Services) to allow data connectionsconnections

Data acquisition Technologies:Data acquisition Technologies: PDA’sPDA’s

• Example: Pendragon software Example: Pendragon software • StrengthsStrengths

– Portable, relatively low costPortable, relatively low cost– Nonprogrammer interfaces to MSAccessNonprogrammer interfaces to MSAccess

• WeaknessesWeaknesses– Limited screen size and navigation speedLimited screen size and navigation speed– Not suitable for text entryNot suitable for text entry– Security: lost or stolen PDASecurity: lost or stolen PDA

Data Archiving and Data Archiving and Database DesignDatabase Design

Commonly used data Commonly used data archiving and analysis archiving and analysis

softwaresoftware• Single investigator, simple trial:Single investigator, simple trial:

– Spreadsheet (MS Excel)Spreadsheet (MS Excel)• Beware using spreadsheets for HIPAA-regulated data – Beware using spreadsheets for HIPAA-regulated data –

no audit trail capabilityno audit trail capability

– Workgroup-capable database management Workgroup-capable database management software (MS Access, Filemaker Pro, 4th software (MS Access, Filemaker Pro, 4th Dimension, MS Visual FoxPro)Dimension, MS Visual FoxPro)

• Data Center, multiple studiesData Center, multiple studies– Enterprise relational database systemEnterprise relational database system

• Sybase, Oracle, MS SQL ServerSybase, Oracle, MS SQL Server

– Dedicated statistical analysis packagesDedicated statistical analysis packages• SAS, BMDP, SPSS, S Plus, JMPSAS, BMDP, SPSS, S Plus, JMP

Commonly used data Commonly used data archiving and analysis archiving and analysis

software, cont’dsoftware, cont’d• Pharmaceutical companies - Pharmaceutical companies -

multiple drugs, multiple sites, multiple drugs, multiple sites, multiple studies, FDA auditsmultiple studies, FDA audits– Dedicated clinical trials software (e.g., Dedicated clinical trials software (e.g.,

BBN ClinTrials, Oracle Clinical)BBN ClinTrials, Oracle Clinical)

Study_Data

*ParticipantIDDateAnswer1Answer2Answer3Answer4Last_updateUpdate_by

Person (Participant)

* ParticipantID [primary key]Last_nameFirst_nameAddressCityStateZipPhoneFaxE-mailMRNBirthdateSSNGenderLast_updateUpdate_by

Best practices: store Person table on removable media with physical security OR store Person encrypted by private key

one one

Sample data model for one-time administration of a surveySample data model for one-time administration of a survey

Study_Data

*ParticipantIDVisitIDVisitDateBPsystolicBPdiastolicWeightSodiumPotassiumChlorideBicarbBUNCreatinineLast_updateUpdate_by

Person (Participant)

* ParticipantIDLast_nameFirst_nameAddressCityStateZipPhoneFaxE-mailMRNBirthdateSSNGenderLast_updateUpdate_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.

many one

Simple clinical study with a variable number of identical repeat visitsSimple clinical study with a variable number of identical repeat visits

Follow_Up

*ParticipantIDVisitIDVisitDateBPsystolicBPdiastolicWeightSodiumPotassiumChlorideBicarbBUNCreatinineLast_updateUpdate_by

Person (Participant)

* ParticipantIDLast_nameFirst_nameAddressCityStateZipPhoneFaxE-mailMRNBirthdateSSNGenderLast_updateUpdate_by

many

one

Clinical study with a baseline evaluation followed by variable number of identical repeat visits

Baseline

*ParticipantIDVisitDateDataItem1DataItem2DataItem3Last_updateUpdate_by

one

Data SecurityData Security

Information Security Information Security ElementsElements

• AvailabilityAvailability - when and where needed- when and where needed• AuthenticationAuthentication -a person or system is who they -a person or system is who they

purport to be (preceded by Identification)purport to be (preceded by Identification)• Access ControlAccess Control - only authorized persons, for - only authorized persons, for

authorized usesauthorized uses• ConfidentialityConfidentiality - no unauthorized information - no unauthorized information

disclosuredisclosure• IntegrityIntegrity - Information content not alterable except - Information content not alterable except

under authorized circumstancesunder authorized circumstances• Attribution/non-repudiationAttribution/non-repudiation - actions taken are - actions taken are

reliably traceablereliably traceable

Research Records Research Records Security,Security,

General PrinciplesGeneral Principles• Physical SecurityPhysical Security

– Locked file storage for physical filesLocked file storage for physical files• Programmable locks bestProgrammable locks best• Change combination on a regular basis Change combination on a regular basis

(common practice: twice a year)(common practice: twice a year)

– Person-identifiable data Person-identifiable data • Keep separate from other study dataKeep separate from other study data• Consider additional protections such as Consider additional protections such as

two person access requirementstwo person access requirements

• Electronic SecurityElectronic Security– No workstations viewable from public areasNo workstations viewable from public areas– Password-protected loginPassword-protected login– Screensaver timeoutsScreensaver timeouts– Separate login and password for database Separate login and password for database

accessaccess– Store demographics data separately and Store demographics data separately and

encrypted if feasibleencrypted if feasible– Regular backups and offsite backup storageRegular backups and offsite backup storage

Research Records Security, Research Records Security, cont’dcont’d

Research Records Security, Research Records Security, cont’dcont’d

• Network SecurityNetwork Security– Safest but least useful: disconnect Safest but least useful: disconnect

workstations with research data from workstations with research data from networknetwork

– Keep all workstations and servers Keep all workstations and servers patched with latest security updatespatched with latest security updates

– Run antivirus software on all machinesRun antivirus software on all machines– Consider firewall computer to protect Consider firewall computer to protect

Internet access point, and/or workstation Internet access point, and/or workstation firewall softwarefirewall software

Information Security Information Security ElementsElements

• AvailabilityAvailability - when and where needed- when and where needed• AuthenticationAuthentication -a person or system is who they -a person or system is who they

purport to be (preceded by Identification)purport to be (preceded by Identification)• Access ControlAccess Control - only authorized persons, for - only authorized persons, for

authorized usesauthorized uses• ConfidentialityConfidentiality - no unauthorized information - no unauthorized information

disclosuredisclosure• IntegrityIntegrity - Information content not alterable except - Information content not alterable except

under authorized circumstancesunder authorized circumstances• Attribution/non-repudiationAttribution/non-repudiation - actions taken are - actions taken are

reliably traceablereliably traceable

Security Rule: Basic Security Rule: Basic ConceptsConcepts

• Applies security principles well established Applies security principles well established in other industriesin other industries

• Like Privacy Rule, affects Covered Entities Like Privacy Rule, affects Covered Entities that create, store, use or disclose that create, store, use or disclose Protected Health Information (PHI)Protected Health Information (PHI)

• Unlike the Privacy Rule, affects only PHI in Unlike the Privacy Rule, affects only PHI in electronic format (not oral or paper-electronic format (not oral or paper-based)based)

• Like the Privacy Rule, written for health Like the Privacy Rule, written for health care; research not the principal focus care; research not the principal focus

• Scalable: burden relative to size and Scalable: burden relative to size and complexity of organizationcomplexity of organization

Two types of Rule Two types of Rule elementselements

1.1. Required standardsRequired standards2.2. ““Addressable” standardsAddressable” standards

– CE must decide whether the standard CE must decide whether the standard is is reasonable and appropriatereasonable and appropriate to the to the local setting, and cost to implementlocal setting, and cost to implement

– Can eitherCan either1.1. Implement the standard as publishedImplement the standard as published2.2. Implement some alternative (and Implement some alternative (and

document why)document why)3.3. Not implement the standard at all (and Not implement the standard at all (and

document why)document why)

Three Categories of Three Categories of StandardsStandards

• Administrative safeguardsAdministrative safeguards– Policies and procedures to prevent, detect, Policies and procedures to prevent, detect,

contain and correct information security contain and correct information security violationsviolations

• Physical SafeguardsPhysical Safeguards– IT equipment and media protectionsIT equipment and media protections

• Technical SafeguardsTechnical Safeguards– Controls (mostly software) for access, Controls (mostly software) for access,

information integrity, audit trailsinformation integrity, audit trails

Administrative SafeguardsAdministrative Safeguards• Required Required

1.1. Risk AnalysisRisk Analysis

2.2. Risk Management PlanRisk Management Plan

3.3. Sanctions PolicySanctions Policy

4.4. Information System Activity Review (audits)Information System Activity Review (audits)

5.5. Security Incident Response & ReportingSecurity Incident Response & Reporting

6.6. Data Backup PlanData Backup Plan

7.7. Disaster Recovery PlanDisaster Recovery Plan

8.8. Emergency Mode OperationsEmergency Mode Operations

9.9. Periodic Evaluations of Standards CompliancePeriodic Evaluations of Standards Compliance

Physical SafeguardsPhysical Safeguards

• RequiredRequired1.1. Workstation Use AnalysisWorkstation Use Analysis

2.2. Workstation SecurityWorkstation Security

3.3. Disposal of mediaDisposal of media– deletion of PHI prior to disposal, ordeletion of PHI prior to disposal, or– Secure disposal so data nonrecoverableSecure disposal so data nonrecoverable

4.4. Media Reuse Media Reuse – Deletion of PHI prior to re-useDeletion of PHI prior to re-use

Technical SafeguardsTechnical Safeguards

• RequiredRequired1.1. Unique User IdentificationUnique User Identification

– No shared loginsNo shared logins

2.2. Emergency access proceduresEmergency access procedures

3.3. Audit controlsAudit controls– Logs of who created, edited or viewed PHILogs of who created, edited or viewed PHI

4.4. Person and/or Entity AuthenticationPerson and/or Entity Authentication– No systems without access controlNo systems without access control

Implications for ResearchImplications for Research

• Avoid HIPAA Security Rule Avoid HIPAA Security Rule entanglements if possible by:entanglements if possible by:– Thoughtful definition of Covered Entity Thoughtful definition of Covered Entity

with respect to research activitieswith respect to research activities• E.g., Vanderbilt is Hybrid Covered Entity; E.g., Vanderbilt is Hybrid Covered Entity;

research not a covered function except for research not a covered function except for research that uses or creates medical research that uses or creates medical recordsrecords

– Use of de-identified data and/or Limited Use of de-identified data and/or Limited Data Sets wherever possibleData Sets wherever possible

– Not storing PHI in electronic format in Not storing PHI in electronic format in research settingsresearch settings

If a research project If a research project maintains e-PHI…maintains e-PHI…

• Responsible group must designate a Security Responsible group must designate a Security Officer who has responsibility for implementing Officer who has responsibility for implementing HIPAA-compliant policies and procedures for HIPAA-compliant policies and procedures for research use of e-PHIresearch use of e-PHI

• Must do and document a risk analysisMust do and document a risk analysis• Must create risk management plan based on the Must create risk management plan based on the

risk analysisrisk analysis• Must create and keep current a HIPAA Security Must create and keep current a HIPAA Security

Rule compliance document that includes Rule compliance document that includes description of how 17 Required elements are met, description of how 17 Required elements are met, and decisions regarding Addressable elementsand decisions regarding Addressable elements

Widespread current Widespread current research practices that research practices that

don’t meet the standarddon’t meet the standard• Research workgroups that create or use Research workgroups that create or use

PHI in electronic format but have no PHI in electronic format but have no written security procedures, policies or written security procedures, policies or trainingtraining

• Workstations with no login security (e.g., Workstations with no login security (e.g., Windows98)Windows98)

• Data management and analysis Data management and analysis applications used to store PHI that have applications used to store PHI that have no ability to generate audit trailsno ability to generate audit trails– E.g., Excel spreadsheets with PHI in themE.g., Excel spreadsheets with PHI in them

Using the InternetUsing the Internetfor Clinical Researchfor Clinical Research

Internet Functionality for Clinical Internet Functionality for Clinical ResearchResearch

• E-mailE-mail– Avoid putting HIPAA PHI in e-mailAvoid putting HIPAA PHI in e-mail

• Study participant recruitmentStudy participant recruitment

Internet Functionality for Clinical Internet Functionality for Clinical Research, cont’dResearch, cont’d

• E-mailE-mail– Avoid putting PHI in e-mailAvoid putting PHI in e-mail

• Study participant recruitmentStudy participant recruitment• Private FTP site as ‘drop box’ for study Private FTP site as ‘drop box’ for study

related file communicationsrelated file communications– encrypt files if they contain PHIencrypt files if they contain PHI

• Data submission and reportingData submission and reporting• Multi-site coordination and Multi-site coordination and

administrationadministration

Approved Internet Technologies Approved Internet Technologies relevant to Clinical Researchrelevant to Clinical Research

Trial Trial AdministratioAdministrationn11

Data Data ReportingReporting11

Data Data submission submission 11

n.a.n.a.AdvertiseAdvertise

servicesservices

File Transfer File Transfer ProtocolProtocol22

smtpsmtp

E-mailE-mail128 bit SSL128 bit SSL

Secure WebSecure WebStdStd

WebWebFunctionFunction

11containing person-identifiable containing person-identifiable i.e., HIPAA PHIi.e., HIPAA PHI

22 must be encrypted to HCFA/CMS must be encrypted to HCFA/CMS stdstd

Sample Project administration website for multi-center study

Putting it All Together:Putting it All Together:Research Data Research Data ManagementManagement

• An artful selection of physical and An artful selection of physical and electronic management methodselectronic management methods– Signed informed consent documentsSigned informed consent documents– Paper formsPaper forms– Regulatory and project management Regulatory and project management

bindersbinders– Data models and databasesData models and databases– Data acquisition and display technologiesData acquisition and display technologies– Communications technologies for project Communications technologies for project

management as well as data managementmanagement as well as data management

Attributes of Successful Attributes of Successful Data ManagementData Management

• Attention to detailAttention to detail• Explicit structure and processExplicit structure and process• Robust designsRobust designs

– Anticipate failures, lapses and Anticipate failures, lapses and mistakesmistakes

– Design systems that identify and Design systems that identify and correct themcorrect them

• Mechanisms for verificationMechanisms for verification• Well documentedWell documented

Lessons Learned about Data Lessons Learned about Data Management in Clinical Management in Clinical

ResearchResearch• Effective data management is a continuous Effective data management is a continuous

process, not a point in time analysisprocess, not a point in time analysis• Historically, health care organizations and Historically, health care organizations and

providers have invested suboptimally in providers have invested suboptimally in information systems and this provides an information systems and this provides an uneven infrastructure for clinical researchuneven infrastructure for clinical research

• In health care organizations, data In health care organizations, data management and information systems management and information systems implementation is “20% technology and implementation is “20% technology and 80% sociology” (R. Gardner) – plan 80% sociology” (R. Gardner) – plan accordinglyaccordingly