EDC, CTMS, IRT, eTMF, are all acronyms of now-classic analytics software used in clinical trials. Each tool can complete a primary task, with some of these tasks being very complex. These specialties software were created to replace locally-stored paper with multi-flow input/output pathways, now accessible globally via the cloud. Many sponsors initially made their own analytical tools using Excel spreadsheets, but they were often limited in scope, and proprietary formats prevented sharing the content with their vendors.

Linking some of these tools together has long been a common theme in reducing operational time and expenses. CTMS + EDC were often touted as ‘integrated’ for Clinical Management, with some newer versions adding in IRT. [1,2] Likewise, there are integrated back-end tools for data warehousing and visualization/computing.[3,4,5]

The Value of Implemented Analytical Tools

Senior management, in part, evaluates the return-on-investment of tools by reducing the clinical trial metrics. Common metrics are: time from protocol draft to first patient, time of patient treatment phase, and time from last patient last visit to study report. For each tool added, improvement in one of these metrics is likely a focal point, even if the tool’s primary implementation purpose is elsewhere. For example, Clinical can certainly tout that the integrated IRT and EDC allows auto-reordering of drug supply when a patient continues the trial after reaching a milestone. IRT’s primary value to Clinical is drug supply and drug accountability, but management support may be gained by demonstrating prevention of study delays or patient dropouts, both which negatively affect the study metrics.

The perceived value of the implemented analytical tools may be viewed for “process” as well as for function. Process is a series of actions that produce something or that lead to a particular result.[6] Electronic Data Capture (EDC), as an innovation example, was a tool provided to Investigators to enable entry at the site. Initially this was just considered as “electronic paper” since the data were still recorded first on paper or the hospital health record, with subsequent manual transcription by the site coordinator. But when steps in the old data collection process were eliminated (i.e. double keyed entry and reconciliation by the sponsor), process benefits were noted with shorter overall time until evaluable data.[7,8,9]

Adding enhanced innovations, such as laptop or tablet or cellphone portals for eSource collection, enable data entry to be completed bedside contemporaneously with the clinical observations.[10,11] Now the data cleaning process, formally a separate subsequent activity, could occur simultaneously with data entry by computer generated range-checks ‘queries’ issued, and resolved, while the Investigator is still with the patient. This example shows that the adoption of a single versatile tool eliminated unnecessary processes, streamlining the activities and reducing the metrics.

Use of A Multiuse Tool Alone not Necessarily the Answer

Implementation of a versatile tool is not necessarily a panacea. In the above example, the investigator entry provides cleaned data to the sponsor faster, but it is not sufficient on its own to substantially improve the data collection and cleaning cycle. The next step in the process, monitoring, also needs to be timely. The tool, in this case EDC, can aid monitoring by establishing remote review, issuance of queries, AE coding, and incorporation of external lab results. But, the metric is still dependent on the capacity and capability of the individual monitor to be available in near-real-time to complete the actions.

Clinical Trial Management Systems (CTMS) are also ubiquitous since they were designed to be the central hub of a trial, with outreach to EDC, IRT, and study close-out components (eTMF, submission compilation tools, and regulatory submission tracking). One claim for adding the new study startup tools, is that CTMS is a centralized source of study maintenance and logistics, but the study startup tools look at end-to-end improvements in clinical trials.[12,13].  This introduces the process overview early into the trial conduct, and the use of analytical tools for site selection, contracts, and IRB review, can be reflected by a 33 percent improvement in time to the first patient treated.[14]

Instead of using independent software in parallel to the existing integrated set, vendors have consolidated [15,16], formed partnerships and/or taken steps to enable “interoperability.”  Interoperability is the extent to which systems and devices can exchange data and interpret that shared data [HIMSS]. We may naively believe that software can ‘talk’ to each other or share data since they can import/export in common formats, such as XLSX (Excel) or XML (Extensible Markup Language). But interoperability is a challenge, as observed with the years of implementation issues with the Federally-mandated Electronic Health Records (EHRs). While they all use HL7 messaging, different EHRs still do not communicate, requiring new interfaces and the HL7 FHIR initiative, to create a patch.[18,19,20]

A Well Designed Data Analytics Platform can Reduce Risk

With a focus on quality manufacturing process, the FDA issued a guideline on Process Analytical Technology [21] to set a framework for an “integrated systems approach to regulating pharmaceutical product quality.” The guideline covers both human and veterinary products, and includes an expectation of continuous real time quality assurance, and multivariate tools for design, data acquisition and analysis. For clinical trials, combining the host of individual tools into a single integrated suite would logically follow the FDA footsteps in manufacturing. Given the complicated and evolving backdrop of clinical trials, using a well-designed data analytics platform to consolidate relevant information into one access point can reduce risk, increase the success and practicality of sponsor oversight and contribute to the sustainability of the overall business.[22] Convenience and operating smoothness are elements for the list of features when selecting such a system, and the single-point platform would eliminate all transfer points, which is a time saver plus an advance in demonstrating data integrity.

For pharma to have the most opportunistic reduction in metrics, the tools must be interoperable, and preferably, seamless. Conceptually, the ideal world is a full-scale combined platform [13, 23, 24]   The advantage of combined platforms include: no logging into different software, interoperability built in, and immediate access to the multiple sources of data. However, this leads to trade-offs since each component of a suite may not fulfill the needs of the Sponsor as well as separate tools. [25]  While process and functional advantages are many, the cost of a full platform will be a budget issue, especially for upstart companies with a single molecule and few trials. Some of these combined suites allow the sponsor to pick and choose which elements to purchase as their needs grow.

Reduced Metrics

Implementing individual analytical tools, or even a full suite of tools, are much easier if the sponsor establishes standards. International data standards, such as CDISC/CDASH, should be the basis for the initial data collection.  This can reduce or eliminate most of the end-mapping process to submission-validated-SDTM. A library of standard eCRFs (from the Study Start-up tool) aids in a faster EDC build, and re-use of standard edit checks and standard templates allow acceleration of the user acceptance testing (UAT).

Those standard eCRFs and databases can be leveraged by a standard extraction process, and then read by a standard statistical analysis tool to produce standard tables/listings/figures. Reuse of standards reduces or eliminates the one-off database design, reduces the on-study risk-management tool programming, and the final statistical programming for the CSR. Standards, combined with a common analytical platform and a single source of data, results in an efficiently linked process that is bound to be reflected in reduced metrics.


Terry Katz

Director, Global Data Management and Statistics

Merck Animal Health



1) Eclipse Clinical Technology http://www.eclipsesol.com/clinical-technology/interactive-response-technology

2) Medidata, The Unified Experience, https://www.mdsol.com/sites/default/files/RAVE_IRT-EDC-Unified_20131030_Medidata_White-Paper.pdf

3) Liaison Unified Platform, https://www.liaison.com/wp-content/uploads/2015/11/The-Liaison-Difference-Unified-Platform.pdf

4) Oracle Data Integration, https://www.oracle.com/solutions/business-analytics/data-visualization.html

5) IBM Watson Data Platform, https://www.ibm.com/analytics/us/en/watson-data-platform/

6) Merriam-Webster on-Line Dictionary: https://www.merriam-webster.com/dictionary/process

7) Medidata, “Capturing the Value of EDC”, 10/2013 White Paper, https://www.mdsol.com/sites/default/files/RAVE_Capturing-Value-EDC_20131130_Medidata_White-Paper.pdf

8) Gannon, William E., Jr , “The Value of EDC in Early-stage Clinical Trials”, http://www.capcitytek.com/files/2213/1981/0504/Gannon-The_Value_of_EDC_in_Early_Stage_Clinical_Trials.pdf

9) Applied Clinical Trials Editors, Realize Maximum Value When Implementing Electronic Data Capture, Feb 01, 2002, http://www.appliedclinicaltrialsonline.com/realize-maximum-value-when-implementing-electronic-data-capture

10) Katz, Terry, “Adaptive EDC at Field, Patient and Study Arm Levels,” Arena Clinical Trials Yearbook 2015, 2nd Edition, (Dec 2014).

11) Katz, Terry, “Improve Data Flow with Proven Data Capture Techniques and Transfer Protocols,” Endpoint Adjudication Forum, (Oct 2016)

12) goBalto, https://www.gobalto.com/

13) Vault Clinical Suite, https://www.veeva.com/products/vault-clinical/

14) goBalto, “Using eClinical systems to speed clinical trials”,  https://www.gobalto.com/etmf-ctms-study-startup-how-to-use-eclinical-systems-to-speed-up-clinical-trials-process

15) Waban bought by Phase, http://www.clinpage.com/article/waban_bought_by_phase/C11

16) Oracle Buys Phase Forward, http://www.oracle.com/us/corporate/press/068204

17) HIMSS, “What is Interoperability?”,  http://www.himss.org/library/interoperability-standards/what-is-interoperability

18) Infodesk, “EHR Interoperability in 2017 and Beyond: 10 Key Resources for Healthcare IT Professionals”, https://www.modmed.com/ehr-interoperability/

19) Pennic, Fred, “4 Challenges of Establishing EHR Interoperability” (10/02/2015) http://hitconsultant.net/2015/10/02/4-challenges-of-establishing-ehr-interoperability/

20) Milstein, Julia Adler, “Moving past the EHR Interoperabiity Blame Game (July 18, 2017), https://catalyst.nejm.org/ehr-interoperability-blame-game/

21) US Food and Drug Administration, Guidance for Industry: PAT — A Framework for Innovative Pharmaceutical Development, Manufacturing, and Quality Assurance (2004)  https://www.fda.gov/downloads/drugs/guidances/ucm070305.pdf

22) DePasquale, Rachel, “A Transformative Landscape: Clinical Analytics Systems as the Future Tool for Clinical Trial Management”, ClinicalTrialsArena, (May 4, 2017), https://ind-dev-kgi-verdict-network.pantheonsite.io/news/data/a-transformative-landscape-clinical-analytics-systems-as-the-future-tool-for-clinical-trial-management-5802107

23) Medidata Clinical Cloud: https://www.mdsol.com/en/clinical-cloud

24) Covance Xcellerate Informatics Suite, https://www.covance.com/industry-solutions/drug-development/by-phase/xcellerate-clinical-trial-optimization.html

25) Warnock, Neil, “The Benefits Of Using Integrated Technology With Clinical Trials,” https://www.clinicalleader.com/doc/the-benefits-of-using-integrated-technology-0001