Dealing with challenges in quality evidence generation with a real-time analytical framework that makes clinical sense for innovators

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Evidence linking interventions with health outcomes is vital for healthcare decision-making. Making sound choices about healthcare requires the best possible and quality evidence from clinical research. However, some of the decisions currently made during the drug development process are not supported by high-quality evidence.  As such, making informed decisions for allocating adequate resources to guide clinical development becomes challenging. At mid-stage clinical development, the challenge entails in determining the specific indication, if there are multiple potential indications. Moreover, evidence that is complete for some individuals or groups may be incomplete for others, leading to inefficiencies in decision-making.

Evidence generation strategies are especially important at Phase III and Phase IV trials to allow for effective navigation through competitive and regulatory hurdles, while it may be difficult to effectively communicate potentially attractive product attributes to the stakeholders, especially when it has no clear advantage over comparators. Stakeholders also lack the evidence needed to make real-world decisions on approval, coverage and use of treatments as most current processes used in evidence generation focus narrowly on the safety and efficacy of treatment. 

Datasets to inform real-time decision making 

The traditional demarcation between pre- and post-approval phases does not fit many medical products, as regulatory decisions could be informed by the same evidence that informs the use and coverage decisions, though the criteria for decisions should not be the same for both cases. Validated tools, based on large datasets to help inform real-time decision making are invaluable, yet they are currently limited. When new treatments are approved, healthcare payers and those who participate in shared savings base coverage determination on their value which is calculated by the evidence of benefit and net costs. The incorporation of real-world data (RWD) and patient-reported outcomes (PRO) into the evidence generation process could assist in making coverage determinations by rendering clinical evidence and research more immediately translatable to the beneficiary population. 

Real-world data (RWD) and real-world evidence (RWE)

Additionally, large differences usually exist between the evidence required for initial adopters, such as surveys and studies, and that required for most prospective randomized control trials (RCTs). While the healthcare community uses RWD and RWE to develop decision support tools for use in clinical practices, medical product developers use these data to support clinical trial designs and observational studies to generate innovative treatment approaches. FDA uses RWE and RWD to monitor adverse events, post-market safety of the drug, and to make regulatory decisions. While RWD can be collected from various sources such as electronic health records (EHRs) and product and disease registries, RWE can be generated by different study designs including observational studies and randomized trials.  

Aligning stakeholders for evidence generation 

Aligning stakeholders is another big challenge of evidence generation as different stakeholders will have their own perspectives on uncertainties throughout the drug development lifecycle. Regulators may have different views as to what is acceptable to that of the patient. As such, it remains an industry-wide challenge to provide credible evidence for clinical research to innovators and investigators. Challenges exist for healthcare innovators to keep up to date with compliance and regulations about evidence generation as regulatory space evolves fast.  

Because pharmaceutical companies tend to delegate evidence generation to individual departments that are often siloed, the process occurs sequentially, resulting in delays in crucial milestones such as getting regulatory approval before initiating an outcomes-based study. 

https://www.futuremedicine.com/doi/10.2217/cer-2017-0073

An analytical framework model that makes clinical sense 

There is a pressing need for high-quality evidence generation as regulators and payers seek more long-term data on product safety and effectiveness. As such, more efficient methodologies for generating evidence are required for decision-making, and to enhance clinical evidence collection and interpretation. An analytical framework model makes clinical sense as an evidentiary pathway, however, the challenge for investigators in evidence gathering is to fill out the framework. If the study design is weak, then the link in the evidence chain is also weak. Studies need to be carefully and prospectively designed, and opportunities exist to add well-designed studies into current practices. Study teams and researchers should consider how to most effectively translate diagnostic tests into practice needs within clinical settings. 

Quality clinical evidence of safety and efficacy 

The Jeeva™ eClinical Cloud platform (Jeeva™) provides clinical decision-makers with a modular and integrated approach to evidence planning and generation. From a single dashboard, study leaders can monitor data in real time to track safety and efficacy in representative patient populations across vast distances. The Jeeva™ eClinical Cloud is designed for efficient, remote long-term follow-up, natural history and other observational studies as well as interventional clinical trials regardless of therapeutic area. Jeeva™ enables quality clinical evidence generation to evaluate treatment safety and efficacy and tracks patients’ adherence to medications, in compliance with regulatory agencies such as Institutional Review Boards, EMA, FDA, and GDPR. 

Digital-first approach to evidence generation 

Study teams, innovators, drug developers, biopharmaceutical sponsors, clinical researchers, hospital sites and contract research organizations (CROs) face challenges to overcome the “no evidence, no implementation—no implementation, no evidence” paradox. Jeeva™ provides a new, digital-first, patient-centric approach to evidence generation that considers patients as partners for clinical trials, not merely subjects.  

The Jeeva™ eClinical Cloud is user-designed software-as-a-service (SaaS) platform that allows volunteers to conveniently complete clinical trials wherever they are. The flexible and modular bring-your-own-device (BYOD) solution works on any browser-enabled mobile device and cuts out 70% of logistical burdens for study teams and patients. The modular and flexible Software as a Service (SaaS) subscription-based model is enriched with many features such as automated enrollment workflows, electronic patient-reported outcomes, 2-way email and SMS communication, uploading of lab reports, and more that are designed to encourage innovators to undertake research activities, rather than be intimidated by the complexity, logistical burdens, duration and costs of the traditional evidence generation approaches. 

 

Quickly setup clinical studies of any scale or duration 

Jeeva™ applies an innovative approach to remote screening, eConsent, patient registries and natural history studies can enable the generation of higher-quality, low-cost and more timely evidence generation for clinical trials. Jeeva™  offers a cost-effective solution to quickly set up and conduct clinical studies, of any scale or duration, with or without patient travel involved (e.g. hybrid or fully decentralized clinical trial protocols). Jeeva™ provides a more effective clinical trial design in terms of evidence generation, accelerating patient recruitment, site feasibility and endpoints that bring unmatched efficiencies in terms of the quality of evidence, time, and costs. 

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