lsh clinical data management whitepaper · clinical data management ... regulatory approvals...

Simplifying ClinicalData ManagementJourney to EnableCustomer Satisfaction

AUTHOR:DR. NIDHI BAJPAI

WHITEPAPER December 2015

2

TABLE OF CONTENTS

EXECUTIVE SUMMARY 3

PROPOSED SOLUTION 3-4

COMPREHENSIVE CDM SOLUTION:MITIGATION OF PROCEDURAL GAPS

4-8

Collection 4-5Capture

5-6Cleaning

7Collation7-8

OUTPUT ENSURED 8

LIST OF ACRONYMS 9

REFERENCE 9

Simplifying Clinical Data Management Journey to Enable Customer Satisfaction | december 2015

© 2015, HCL TECHNOLOGIES. REPRODUCTION PROHIBITED. THIS DOCUMENT IS PROTECTED UNDER COPYRIGHT BY THE AUTHOR, ALL RIGHTS RESERVED.

Executive SummaryClinical Data Management (CDM) is an important segment of clinical research, with an aim to generate high-grade, accurate, credible, and reliable clinical trials data which can be easily analyzed by the biostatistician. Trial data serves as a foundation for the entire drug development cycle. Usually the challenges associated with CDM involve skill-based practices which are time consuming and laborious. These steps are to be undertaken with extreme precision so that the regulatory requirements are not infringed. In clinical trials, the focused approach for patient safety is the prime concern. Therefore, CDM tasks have to be performed in a manner that leaves no scope for fraudulent, duplicate, redundant, and biased outcomes. Well managed data will facilitate the support of therapeutic and/or prophylactic outcome for a particular condition or disease. Failure to appropriately organize and manage study data can result in delayed regulatory approvals leading to extended timelines to reach market for a promising compound.At HCL, we provide quick fix solutions to identify the gaps in the CDM procedures along with technology integration to mobile and other similar study devices. HCL experts will work to enhance the CDM portfolio to strive for the highest quality data by establishing robust procedures. Suitable application of simple techniques of predictive analysis and on-time corrective action on the procedural gap(s) can help shrink the drug development time and study budget. This paper provides glimpses of pilot study conducted in-house as a proof of concept.

Proposed SolutionProcesses in CDM are broadly defined as CRF designing, CRF annotation, database designing, Data-entry, Data validation, Discrepancy Management, Medical Coding, Data Extraction, and Database Locking1. These activities can be further classified into the following phases:

3© 2015, HCL TECHNOLOGIES. REPRODUCTION PROHIBITED. THIS DOCUMENT IS PROTECTED UNDER COPYRIGHT BY THE AUTHOR, ALL RIGHTS RESERVED.

CD

M P

hase

s

Commencement (Startup)

Conduct

Closed

Figure 1: CDM Phases



Dynamic and rapidly evolving technology for CDM can leave the end users in a dilemma with respect to cost of software license, its validation, and subsequent implementation. Often the existing software is not easy to operate thereby requiring additional lead time to accomplish the task. To overcome this we support quick action in a cost effective manner. Considering that most of the CDM personnel are from non-IT background, the proposed solution is simple to operate as it involves the use of drag and drop menu, based on study level metadata to identify and fix-up procedural gaps.For each of the above mentioned CDM phases, the solution can be doctored, tailored, and customized to appropriately meet the requirements of individual pharmaceutical companies/CROs/projects to address the following aspects of data management:

The section below provides a glimpse into the recommended approach for identifying gaps with an objective to improve work efficiency, and decrease processing time to overcome budgetary restrictions with quality output in compliance with regulations.

Comprehensive CDM Solution: Mitigation of Procedural GapsHCL has developed a practical and practicable strategy. The underlined technology will be flexible enough to ensure reusability, scalability, and seamless integration with the existing system. A brief description of our model is given below:CollectionTrial data is collected in the Case Report Form (CRF) which may be paper-based or an electronic instrument. Thus, the quality of data collected


CostControl

ClinicalTrial Data

Collation

Collection

CaptureCleaning

Correct

Compliant

Credible

Figure 2: CDM Tasks


from the trial will depend on the quality of instrument. Following are the key focus areas to handle this aspect:� Use of standards: Standard pages will be developed to enrich the

organizational global library based on the principles of CDISC (CDASH) to facilitate submission and analysis.

� Duplicate data: Depending upon the type of CRF/trial, pages should be designed in a manner to avoid collection of same information repetitively, for example,

o Enrollment ID may be collected more than once in case of paper CRF but for eCRF it just have to be collected once.

o Usually, weight of the subject is not collected in every visit, unless the study is related with obesity (primary end point) or dose titration has to be done based on weight or investigational product is expected to have an effect on the weight (secondary end point)

� Redundant data: The instrument will be refrained from collecting extraneous information, for example no (or very less) use of question which will have free text as a reply

� Study-specific pages: These pages will be designed as per the protocol requirements. Later these will be categorized based on therapeutic segments, phase of study, and type of investigational product. Categorization would be done to facilitate reusability.

� Field level checks: Some examples are: o Use of correct length for the fields to accommodate patient data o Use of correct units in standard format o Use of correct and consistent code list/ controlled terminology

The process flow adopted may be customized as outlined below:

CaptureIt is imperative that to support the ecosystem of CDM methodology, a study database has to be designed appropriately to enter data for further processing. Business processes demand that the task should be accomplished with incorporation of correct validation (edit checks) programming to deliver high efficiency within cut-throat project timelines.


Study Detail

Based on protocol review, segregation of standard, and study-specific pages

HCL Experts

Experienced experts of HCL from concerned teams (Medical writing, CDM, Biostatistician, QA, IT, etc.) to provide input

Final CRF

Quality check to ensure compliance with Protocol, CDISC (CDASH), applicable regulations , SOPs , etc.

Figure 3: Data Collection, Mitigation of procedural gaps

Database design (to be done preferably with standard pages)

QC of database before moving the same into production (with standard check-list as applicable)

Creation of dashboards depicting gaps

Evolution to the said job can be witnessed through the timely input of the gaps, for example improper cursor movement, mandatory fields not flagged, hard coding not incorporated2, etc. The process can be depicted as follows:

Once the study database programming is complete, data entry gets initiated. The data entry is applicable for paper and eCRF both, with a difference in case the EDC and RDC the entry is done at the site, without the need to perform double data entry.

Unless data is entered correctly, the analysis may not give accurate outcome. As obvious, the timely completion of data entry is important for a timely lock.

Below are a few examples of suggested reports which could be used to improve the procedural efficiency:� Site with maximum data entry errors � Data entry operator making

maximum errors � List of systematic entry errors by the

site/ entry operator

The figure (5) provides an example of one such report where data entry status has been compared between the study sites vs. actual/ planned data entry task:

Figure 4: Data Capture and Mitigation of procedural gaps for database design

Data Capture: Mitigation of Gaps

| Protocol ID Search

Current Date/ Time

Data Entry Status

29-Jan-2015 13:05:32:37

Dashboard

Date From : Date To :

29-Jan-2014

29-Jan-2013

Email this report

Country CRA Details Site Patient ID

Select option(s)

Report

Non Comparative Report Comparative Report

Select option(s) for comparison based on site, country, etc.


Figure 5: Data capture and mitigationof procedural gaps for data entry


CleaningData cleaning is done with programmed validation checks as per approved edit specification document. These programs are created to check the adherence of the study data as per the requirements of the protocol. Elimination of procedural gaps can help drastically reduce the time to database lock. This may include, not limited to, the lack of application of standard logics for standard pages; creation of duplicated discrepancies; high number of protocol deviations for a particular site; high TAT/ discrepancy aging for the site(s) monitored by particular CRA, etc.

CollationWith an impending mandate to submit the trial data in an electronic format, it is important to correctly collate and create submission datasets in the required format for multiple regulatory authorities. A meticulous approach to mitigate the procedural gaps can help sustain automation of processes without validation failure, preferably as per CDISC requirement(s).

A widely acknowledged and used data submission model for trials is defined by CDISC in the SDTM document, which hardly requires an introduction. The following conditions (gaps), if prevailing in the organization,

| Protocol ID Search

Current Date/ Time

Average Protocol Deviation

29-Jan-2015 13:05:32:37

Dashboard

Date From : Date To :

29-Jan-2014

29-Jan-2013

Email this report

Country CRA Details Site Patient ID

Report

Non Comparative Report Comparative Report

Select option(s)

Select option(s) for comparison based on site, country, etc.

Figure 7: Data collation and creation of submission datasets

Flexible andReusableMetadataRepositories

Lab data uploadTrial Data

Data Extracted:Submission dataset



Figure 6: Data cleaning and mitigation ofprocedural gaps, an example

can be easily avoided with the use of HCL’s strategy, implementation of CDASH (during start-up phase) and SDTM standards:� Non-uniform implementation of CDASH standards during data capture � Non-uniform execution of SDTM standards� Use of different controlled terminologies for similar information� Use of different domains to capture similar information for non-standard

pages� Non-validated library resulting into more time for UAT

Output EnsuredTo conclude, it can be said that innovative technological advancements will be leveraged and customized as per the need, which is foreseen to logically drive evidence-based schemes for mitigating CDM procedural gaps. The task would be accomplished with inputs from our experienced SMEs to ensure hassle-free delivery and full customer satisfaction.



List of AcronymsCDASH - Clinical Data Acquisition Standards HarmonizationCDISC - Clinical Data Interchange Standards ConsortiumCDM - Clinical Data Management CRA - Clinical Research Associate (Study Monitor)CRF - Case Repot FormEDC - Electronic Data CaptureRDC - Remote Data CaptureSDTM - Study Data Tabulation ModelTAT - Turnaround TimeUAT - User Acceptance TestingSME - Subject Matter Expertise

Reference 1. Krishnankutty B., Bellary S., Kumar N.B.R., and Moodahadu L.S.,

“Data management in clinical research: An overview”, Indian Journal of Pharmacology, vol. 44, no. 2, pp. 168–172, 2012.

2. Bajpai N., Dang S., Sharma S. K., “Clinical Data Management Operational Model for the Conduct of Myfive™ Vaccine Study”. International Research Journal of Humanities, Engineering & Pharmaceutical Sciences, vol. 01, no. 07, pp. 52-62, 2014.



Dr. Nidhi has over 15+ Years of experience in Clinical Research in the Pharma Industry and has worked for all the stages of Drug development. Prior to joining us, she was Heading Clinical Data Management Department for Panacea Biotec Ltd, New Delhi. She has also worked with organizations like Novartis and Quintiles in Mumbai. Her qualifications are PhD, M.Phil., M.Sc., P.G.D.M. and holds Bachelor ’s degree in Pharmacy (B.Pharma).



DR. NIDHI BAJPAI


Please feel free to write to us at [email protected]

HCL Life Sciences & HealthcareHCL is a leading provider of Life Sciences and Healthcare Business and Technology services. We are the chosen service provider for enabling new growth drivers for our clients, providing them with industry leading best practices, taking care of their compliance needs and ensuring goldstandard process cycle times. Our clientele includes seven of the top ten global pharmaceutical companies, seven of the top ten medical devices companies, six of the top ten health plans, three of the top five CRO’s and two of the top three data providers. Equipped with certified technologyexperts and domain specialists, HCL offers services in critical areas of the life sciences and healthcare eco system such as drug discovery, clinical development, drug safety, regulatory compliance, manufacturing and plant automation, commercial, Healthcare analytics, Population Health Management [PHM], mHealth, member experience management [MEM], fraud, waste and abuse management [FWA].

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