CLINICAL TRIALS: FOCUS VS. DRIVING EFFICIENCIES BLINDLY

Transcription

1 CLINICAL TRIALS: FOCUS VS. DRIVING EFFICIENCIES BLINDLY Continuing downward pressures on healthcare economics, diminished outcomes, patent cliffs, and antiquated R&D models are among the market forces making it critical for the Bio-Pharmaceutical industry to evolve both business and operating models... Reza Zanjani

2 Clinical Trials - Focus vs. Driving Efficiencies Blindly An Overview of Practical Measures to Overcome Critical Challenges Reza Zanjani rzanjani@5amsolutions.com Continuing downward pressures on healthcare economics, diminished outcomes, patent cliffs, and antiquated R&D models are among the market forces making it critical for the Bio-Pharmaceutical industry to evolve both business and operating models. In addition to preclinical and clinical operations, shifting the operating model and adopting open innovation framework involves the outsourcing of operating functions (i.e. development). This can be an opportunity for CRO/CMO/Laboratory organizations, as well as niche services and technology companies, to establish preferred alliances for longitudinal service contracts. In turning the opportunity to advantage, however, a critical element is frequently overlooked: It is not enough for service providers merely to have competency in providing the specific service being outsourced, or for that matter, a portfolio of services. Given the pace with which the Bio- Pharmaceutical industry is evolving and challenges are emerging, service providers must also continuously apply new and innovative methods to drive cost-effective and value-added (integrated) solution design and delivery. In its most fundamental form, this is a cultural change; in more advanced forms -- where trial sponsors are encountering all-new challenges -- it requires service providers to find new ways to incorporate innovative technology solutions into highly optimized business and operating models. The challenge of creating an enterprise data environment One of the biggest hurdles to translating technology solutions into business innovation is that today s abundance of homegrown (legacy systems) and commercial software products address discrete and specific operating segments, such as Clinical Trial Management (CTMS), Clinical Data Capture and Management (i.e. EHR, EDC, CDM, e-pro, REMS), Laboratory Information Management (LIMS), Sample Management, Financial/Contracts Management, and Supply Chain Management. These independent systems perform very well in capturing and managing information pertaining to the specific segment. What s missing, however, is the integrated solution delivery and value-added service design to enable data interpretation, decision support, and propagation of data across discrete activities. Customized systems and new software development can bridge these silos to create an environment where data flows -- and can be used -- securely and seamlessly across the enterprise. The problem, however, is that customization and software development for innovative endeavors have traditionally involved a lengthy process. Because of this, immediate needs go unmet. Plus, because business and operational needs

3 are changing so quickly, protracted development carries the very real risk that the interim or end product will end up not meeting customers needs. Again, the bottom line is that amid the changes and challenges arising in today s Bio/Pharmaceutical industry, it s not enough for service providers to be competent at system engineering or software development. They must also excel in using Agile development methodologies to deliver rapid value within short iterations in order to meet immediate needs, evolving requirements and innovative objectives. What kind of innovative objectives? Let's examine a few challenges and practical solutions that would deliver benefit across the value chain. Integrated Patient Informed Consent Management Clinical trials require collection and management of patient Informed Consent Forms (ICF). This includes outlining parameters for which deidentified information, clinical/analytical data, and specimen collected (during and post clinical trial) would be used. Given variations in ICF parameters approved by different country and local Institutional Review Boards (IRBs), this could mean managing multiple versions of a Master ICF in any given clinical trial. Furthermore, each patient ICF could have optin/opt-out parameters designed to address patient concerns and increase the rate of study enrollment. Patients have the right to withdraw or amend their previously consented parameters during and after the completion of the clinical trial. Challenge: Most IT systems are focused on capturing paper-based patient ICFs. More advanced systems focus on digital capture, versioning and management of each patient ICF file relative to Master and IRB specific ICF versions. Typically, however, neither paper-based nor digital systems capture granular consented use parameters, nor do they propagate this data as minable annotations to downstream systems, including analytical testing labs and specimen biorepositories. This lack of data connectivity and information flow results in: 1. The blanket assumption by contracted labs that all samples received have been consented -- which may not be the case. 2. Extreme manual intervention (with associated delays and the risk of errors), communications, and verifications between collection sites, sponsor and labs in the event of a patient consent withdrawal or amendment. 3. Increased post-trial completion costs for determining consented use parameters of stored specimen, and limiting visibility into usability of stored assets for translational research. Benefits of an innovative IT solution: Immediate benefits can be derived from implementing an integrated ICF system that is capable of:

4 Associating use parameter tags within content of all ICF versions (Master, Country, IRB and patient specific) Capturing parameters of signed patient ICF as data annotations that are detailed down to the individual specimen collected Propagating these annotations (and any updates) to downstream systems Such an integrated ICF would deliver 1) operating efficiency, 2) higher quality, 3) active adherence to regulatory compliance, and 4) data mining for better use of stored specimen for development and research collaborations and selection of potential patient cohorts for targeted studies relative to their consent. Central Analytics (Labs/Testing) Portal In clinical trials, sample collection, sample testing, and results reporting can involve entities ranging from individual sites to central labs, local labs, specialty labs, reference labs, and third-party contracted labs. To interpret results according to specific test reference ranges and ensure accuracy in associating results with the correct patients and visits, it is necessary for participating labs to have accurate and up to date sample information and patient clinical demographics. Today, this information flow typically relies on a paper-based requisition form and manual data entry and verification. Similarly, practices that participant labs use to report results for inclusion in centralized Clinical Data Management Systems (CDMS) range from fax to excel spreadsheets. In more structured practices, data is imported according to CDISC standards into the CDMS. Challenge: 1. Due to manual errors, significant time is lost making data queries to sites and labs to clear up conflicting sample information or patient demographics from visit to visit. 2. Updated and corrected information is not automatically synchronized across all parties. This results in repetitive datacleaning at various reporting labs which raises the potential for two problems: applying incorrect reference range and result outcome; and reported data for specific patient demographics and sample information that conflicts with data residing in the CDMS. 3. Clinical database lock delays (interim or final) due to significant data reconciliation time and lack of proactive status tracking and visibility into outstanding tests at various labs. Benefits of an innovative IT solution: Time and cost associated with data reconciliations across the participating entities could be significantly reduced by implementing a Central Analytics (lab) portal which enables updated, synchronized, bidirectional flow of both sample information and patient demographics, and lab results associated with specific patient/visit samples. A key element in the

5 success of the solution s design and implementation is achieving flexible integration with minimal disruption to the existing clinical site (EDC/CTMS) and lab (LIMS) systems. In addition to streamlining data reconciliations, the portal would: Provide visibility into the status of outstanding tests and support proactive actions to avoid data base lock delays Improve time-to-regulatory-filing and time-to-market by facilitating automated results reporting, incorporating specific reference ranges, and providing a validated mechanism for integrating data into the CDMS. Cerebral - Management Decision Support Having an integrated view of cross-functional Key Performance Indicators (KPIs), and leveraging data patterns and relationships are critical for making timely decisions, driving change, gaining efficiencies, and measuring progress. Most often, however, these critical elements are locked within discrete operating functions and KPIs. This siloed decisionmaking and organizational culture is a disadvantage in today s environment. In projecting revenue, for example, central labs commonly use the number of Patient Visit Collection Kits shipped to sites as a KPI. While it does provide rough percentages of outstanding kits in the field arriving over a period of time, this KPI lacks quantitative measures for factors, such as the aggregate rate of patient enrollment and visit schedules across all clinical studies. Nor does it take into account individual-site ordering habits or situations in which numbers are inflated due to excessive kits being shipped or re-orders caused by expired kit components. Challenge: 1. In order to employ cross-functional KPIs which account for quantitative variables, data correlation, and pattern analysis, it is necessary to create a cerebral system where robust data visualization and decision support systems harness data across various siloed systems and present it a meaningful manner. 2. While each operating division is incentivised and tracks their respective throughput and KPIs, the integrated service and product delivery model cannot be realized because it cannot be measured effectively and quantitatively. 3. Instead of targeting the root cause of issues, innovation can focus only on implementing solutions that target the symptoms. Benefits of an innovative IT solution: Investing in a robust management data visualization and decision support system can make the difference in leading and innovating vs. reacting to business and market needs. Establishing cross-functional KPIs makes it

6 possible to implement early deviation detection mechanisms at the root source, even though the trigger may had been presented in another functional division. Bottom line: a system that includes the functionality to verify the accuracy of defined services, detect variances from expectation and rely on real-time dashboards to correct root causes, frees labs from repetitive reactive damage control. No more driving efficiencies blindly The stakes continue to rise for conducting faster, more efficient and higher-quality clinical trials. Labs, CROs and trial sponsors must do more than simply drive efficiencies blindly, targeting symptoms rather than root causes. Plus, instead of just focusing on efficiencies, the breakthroughs come in focusing on innovation and targeting that innovation on the continuously evolving priorities and possibilities that will get successful trials completed more cost-effectively, and new products to market more quickly. Creating these breakthrough business and operating models requires the right systems and software. This means working with service providers who not only understand the technical sophistication required for a crossfunctional, cerebral system, but who have the agile, iterative processes to develop smarter software and systems that answer immediate needs while keeping the end-product sharply focused on priorities -- no matter how quickly they re evolving.