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Can Clinical Trials Research and Business Intelligence Coexist?

Originally published May 22, 2007

BioPharmas ensure the safety and efficacy of drugs being discovered, developed and approved for the market, enabling drug candidates to be accelerated through the discovery and development cycle and “killing” underperforming candidates from either entering clinical programs or continuing in the cycle. They do this through enhancing decision-making capabilities for managerial processes (e.g., planning, budgeting, controlling, assessing, measuring and monitoring) and ensuring that critical information is being exploited in a timely manner.

From data management to statistical programming, to the exchange of data between companies in the BioPharma ecosystem, the value of business intelligence (BI) and data warehousing is becoming more of a priority – the biopharmaceutical industry is seeing a 13% to 15% annual increase in its business intelligence and data warehousing spending.

For more than three decades, software and supportive technologies have been used in pharmaceutical research settings for data management, analytics and reporting. Traditionally, these roles were staffed by the programmer and the statistician. At the same time, dramatic shifts in the research informatics ecosystem have occurred with advances in standards, such as CDISC, ICH and HL7, and the convergence of clinical data management systems and modernization strategies, such as electronic data capture and 21 CFR Part 11.

So what do modern approaches to reporting, querying, OLAP, business activity monitoring, dashboarding and scorecards have to do with clinical research? Can business intelligence really coexist with the stalwarts of clinical informatics, such as SAS, SPSS, S-PLUS and the myriad of specialty systems which support clinical trials research?

The Need for BI and Data Integration in Pharmaceuticals
As Tim Furey outlines in “Spending Trends Demonstrate Value of Business Intelligence and Data Warehousing,” the value of incremental improvements efficiency – the empowerment of management to make better, faster, higher quality decisions – has a huge potential impact on the bottom line. With enhanced decision-making capabilities, and the ability to respond to health authority requests and inquiries in a timelier manner, the concerted organizational effort leading up to NDA review/approval is being accelerated by as many as five days – leading to earlier revenue recognition. Revenue is being estimated at $1,000,000 per day each day the review or approval is accelerated.

Drug Killers
With enhanced decision-making capabilities, in addition to the ability to utilize all drug discovery and development data in a consistent, accurate and trusted manner, there is potential to “kill” at least one more drug per year earlier in the cycle and/or prevent one from entering a clinical program.

Note: 9,999 out of 10,000 drug candidates fail to make it to the market; and for the one that makes it, the research and development costs are almost $750 million. The cost avoidance is estimated to be in the millions.

Pharmaceutical companies make a tremendous investment in getting drugs or devices to market. Given the industry’s intense focus on efficiency and making fact-based decisions, should business intelligence provide a better mousetrap or even a completely different paradigm for managing those fact-based frameworks? For a variety of reasons we will explore here, business intelligence has not made it into the laboratories and onto the desks of medical reviewers for the exploration of primary research. But first, let’s take a step back and clarify what we mean by business intelligence and data integration specifically in the context of clinical trials research.

Business Intelligence in Clinical Trials Research
Business intelligence is a broad category of application programs and technologies for gathering, storing, analyzing and providing access to data to help enterprise users make better business decisions. BI applications include the activities of decision support, query and reporting, online analytical processing (OLAP), statistical analysis, forecasting and data mining.

BI is a set of concepts and methods used to improve business decision making through fact-based support systems.

In clinical trials, we typically see data flow from case report forms to submissions – all with an eye toward the conservative, unflinching attention to data, its quality and accuracy. Countless hours are spent on checking and rechecking the values coming out of the clinician’s shop into the final report. While there have been advances, there certainly have not been major shifts in the way that we process data in the last 30 years. Electronic data capture, now relatively mainstream, has helped in the data acquisition, CDISC models have supported interchange between companies in the BioPharma ecosystem, and evolving standards in safety reporting and submission standards have improved the way that we think about the process. But as we lift the covers on the process, those performing the work of tables, figures and listings for clinical trial submissions have not fundamentally changed the way they do work in more than three decades.

Data Integration in Clinical Trials Research
Data integration is traditionally known as “data management.” Data integration involves combining data from several different locations, formats and structures to create a single, credible version of the “truth.”

Data integration is about beating data into submission – getting the data right before it’s used.

As a practical matter, data integration is seen in a number of usage areas, such as those found in statistical programming where traditional programming techniques support the generation of derived data sets and analysis data sets might be used. Over the last few years, a tremendous uptake in the adoption of CDISC standards and the support from vendors such as SAS to aid in the management of the standards process with new generation of tools to support CDISC had been noted. Finally, there are also a number of classic data warehousing strategies that employ the use of data integration tools, such as the development of clinical or patient data warehouses, being used.


Clinical Trials Research Life Cycle
The clinical research objectives are to provide clinical information useful in the making of business and economic decisions and to provide understandable information that will aid stakeholders in predicting the safety and efficacy of a compound.

The qualitative characteristics of clinical reporting include relevance (timeliness, predictive value and feedback value), reliability (verifiability, neutrality, representational faithfulness) and comparability (including consistency).

There are a number of core functions that are areas for focus when it comes to enabling technologies. These include,

  • Protocol Design and Study Start-Up

  • Patient and Investigator Recruitment

  • Clinical Trial Management

  • Clinical Data Management

  • Data Analysis

  • Clinical Supplies

  • Regulatory and Safety

It should be noted that business intelligence has only played on the periphery of these areas – primarily as a decision support framework rather than the tactical or operational role that many of these areas require. Examples include dashboards for trial management or business activity monitoring for safety reporting.

Challenges
So why haven’t we seen the adoption rates one would expect in an industry demanding more efficient processes? This is an industry wrought with regulations designed to ensure that drugs that make it to the market are safe and effective. Therefore, the technology challenges of auditability, security, repeatability, manageability and compliance are ever-present.

Perhaps one of the most significant regulatory requirements is CFR 21 Part 11, which has to do with security, audit trail and version control. This FDA guidance requires the following characteristics for computerized systems used to support medically based decisions:

  • Validation – that is, accuracy, reliability, and consistent performance, and the means to discern invalid or altered records

  • Restriction of system access to only authorized individuals

  • Secure, computer-generated, time-stamped audit trails to record operator entries and actions to create, modify and delete electronic records that must be retained and available for agency review and copying for a required period of time 

  • Operational system checks to enforce permitted sequencing of steps and events as appropriate

  • Authority checks for use, e-signature, access of input and output device, altering a record and performing operation at hand

In addition, there are other standards that help deliver safe and effective drugs to the marketplace. These include ICH and CDISC.


ICH
The International Conference on Harmonization (ICH) has been compiling a series of guidelines for the preparation, design, conduct and reporting of clinical trials with an aim to harmonize the interpretation and application of technical guidelines and requirements for product registration.

CDISC
Clinical Data Interchange Standards Consortium (CDISC) is primarily concerned with developing standards that aid in the exchange of information between companies in the BioPharma ecosystems. These include the following models:

  • Operational Data Model (ODM) – operational support of data collection

  • Study Data Tabulation Model (SDTM) – data tabulation data sets

  • Case Report Tabulation Data Definition Specification (CRTDDS – aka define.xml)

  • Laboratory Data Model (Lab)

  • Standard for Exchange of Nonclinical Data (SEND)


  • BRIDG – Protocol Representation

  • Analysis Data Model (ADaM) – analysis data structures

  • And others… (e.g., LAB, SEND)


Taken together, these standards and guidelines represent challenges of supporting the clinical research process, and traditional business intelligence offerings are not ready to tackle these fundamental challenges. So, where is the gap between what traditional BI can offer (ease of use, commercial grade software, reuse, validated software models) and where the current state of clinical trials information management and delivery systems are today?

With these in mind, let’s explore what business intelligence offers us in terms of solutions.

Use of Business Intelligence in BioPharma
In the BioPharma Channel on the Business Intelligence Network, we will explore how business intelligence can be used to solve a wide variety of business challenges. In this context, let’s highlight just a few of the successes we have seen.

Nonclinical Use
There are a number of ways in which business intelligence as a technology platform can be used to support the pharmaceutical value chain. There is ample evidence to show how business intelligence has been used successfully in a number of areas including:

  • Sales and Marketing
  • Manufacturing
  • Finance 
  • Human Resources 
  • Information Services 
  • Executive and Portfolio Management


Clinical Use
Within clinical research, the strongest use of technology is in pre-clinical research, clinical, statistical programming and supporting other groups such as:

  • Data management (patient profiles)
  • Medical writing
  • Finance
  • Project management
  • Patient registries and post-marketing surveillance

Looking across these uses from an architectural perspective, it becomes more apparent that a centralized data and business intelligence infrastructure is the key to effectively managing the metadata, data, people, processes, and technology services. Where most business intelligence offerings fall short, however, is integration and “reproducibility.” Integration of data information and metadata should flow from the study design to the patient data warehouse, which holds information from multiple protocols. The issue of reproducibility is salient in today’s world where most BI offerings focus on the point-and-click interface for generating reports, but have no audit trail or code that can be run in a non-interactive mode.

But it’s not just about centralized management. In a perfect world, the platform should capture intelligence through a submission process that can be easily reused from study to study. A statistician who creates a new method for analyzing adverse events can add this to the metadata so other statisticians can easily reuse it with little to no modification.

Summary
The clinical trials research processes seems to be one of the areas that is ripe for opportunity for improvement. Having seen firsthand most of the major pharmaceutical companies' technical environments for how they support clinical trials stat programming, it’s clear that something must change in the fundamental way that we use and reuse information. There are regulatory hurdles that make this challenging, but the business intelligence vendors need to step up and support this community with better, more efficient techniques. But lest we blame the vendors too quickly, we must look beyond inadequate software – it has been my experience that culture, process change, data uniformity, data quality, corporate priorities, governance, and other issues play a much more significant role in the successful uptake of BI initiatives than software maturity or functionality.

We certainly have seen widespread adoption of business intelligence and data integration strategies in sales and marketing, finance and even in manufacturing – but relative silence in the “black-box” world of clinical trials research. The opportunity exists for both IT and the business to step up and examine how we can not just marginally but revolutionarily improve.

Please send your “thots” on how business intelligence can be used and where you’ve seen it used successfully and where it has failed to greg@thotwave.com.

  • Greg NelsonGreg Nelson

    Greg Nelson is the Founder and Chief Executive Officer of ThotWave Technologies, the health and life sciences business intelligence company. Greg provides professional services to healthcare, biopharma as well as government and academic researchers. Greg has served as the Director of Technology for the largest, privately held CRO, Director of Application Development for the Gallup Organization and a director at the University of Georgia’s computer center. He has published and presented more than 150 professional papers in the United States and Europe.  

    While Greg has been a practitioner for the past 23 years, his academic roots began with a BA in Psychology from the University of California at Santa Cruz, in addition to doctoral level work in Social Psychology and Quantitative methods at the University of Georgia. Greg also holds a Project Management Professional Certificate. Greg can be reached at greg@thotwave.com.

    Editor's note: More articles, resources, news and events are available in the BeyeNETWORK's Health & Life Sciences Channel. Be sure to visit today!

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