Clinical trials cost breakdown
With cost estimates ranging from $48 million to $225 million for a pivotal clinical trial, there is plenty of opportunity to reduce expenditure. A literature review reveals four key areas where operational inefficiencies drive cost and where a thoughtful application of digital technologies can bring significant improvements and innovation. By adopting data- and patient-centric strategies and deploying digital, data-driven solutions, organizations can significantly improve clinical trial timelines and mitigate spending in the four main areas of opportunity:
- Protocol design
- Patient identification and recruitment
- Patient retention and engagement
- Data management
This article examines the main contributors to the cost of clinical trials based on literature review and key technological developments that promise to reduce clinical trial timelines and costs through digital innovation in each area of opportunity.
Key drivers of cost in clinical trials
A Tufts study published in 2016 calculated the total cost of bringing a new drug to market at approximately $2.6 billion, with out-of-pocket cost per approved new compound amounting to nearly $1.4 billion. An average cost of a phase III clinical trial was estimated at $255 million. Six years later, a study by Moore et al. published in the British Medical Journal showed lower numbers, with the median cost for a pivotal (usually a phase III) clinical trial at $48 million and an interquartile range of $20 million to $102 million. The same study calculated the average patient cost to be $41,413 in pivotal clinical trials.
While estimates vary, there is no question that clinical trial costs are significant. And as leaders look for ways to reduce costs, four areas come to mind as key opportunities to mitigate spending:
- Protocol design: In 2016, Tufts CSDD reported that 57% of protocols had at least one substantial amendment, 45% of those were avoidable, and the median direct cost to implement a substantial amendment was $535,000 for a phase III protocol.
- Patient identification and recruitment: According to a study commissioned by the U.S. Department of Health and Human Services, patient recruitment costs 1.7% to 2.7% of the total trial budget. The hidden cost lies in delays caused by failure to meet enrollment goals on time. That cost to sponsors has been estimated to be between $600,000 and $8 million lost each day that a product’s development and launch are delayed.
- Patient retention and engagement: An average patient dropout rate in clinical trials in 2019 was about 19%. And 30% and 50% non-adherence rates require 50% and 200% increases in sample size, respectively. This leads to added timelines and costs for every churned patient who must be replaced and, due to the increase in the sample size, to mitigate inadmissible data from patients not adhering to treatment in clinical trials.
- Data management: On-site monitoring with the primary purpose of source data verification (SDV) can account for up to 25% of the total clinical trial budget. Additionally, a lack of established data governance and management best practices results in 80% of data scientists’ time spent assembling data and only 20% of their time analyzing it.
A thoughtful application of digital technologies is vital to mitigating clinical trial costs in these four areas. Let’s take a look.
A protocol that leads to as few costly amendments as possible can be designed with data, artificial intelligence (AI), and machine learning (ML). De-identified RWD – electronic health records (EHRs), claims, prescription databases, etc. – combined with historical research data can feed analytical statistical or AI/ML models to optimize protocol design. Example applications of data and AI/ML that offer the potential to increase the success rate of clinical trials include:
- Predictions of the probability of clinical trial success,
- Optimization of inclusion and exclusion criteria,
- Identification of patient subpopulations based on predicted response to treatment,
- Optimization of the sample size,
- Prediction of optimal drug dosage,
- Generation of an external control arm from EHR data.
Patient identification and recruitment
Finding patients eligible for clinical trials can be difficult and time-consuming. RWD is a promising enabler that helps match eligible patients to study protocols. By analyzing de-identified healthcare data from multiple EHRs or other sources of RWD, organizations can identify healthcare systems and physicians who are more likely to have access to eligible patients. The RWD-derived geolocation of patient populations with a higher probability of matching a clinical trial protocol can guide a more targeted deployment of unbranded awareness campaigns that allow patients to opt-in for information about clinical trials. Other promising solutions that support patient identification and recruitment include:
- EHR system add-ons that match patients to clinical trials and alert physicians when a match is found,
- Chatbots that pre-screen potential candidates with a series of qualifying questions,
- Patient-facing experiences within patient portals or patient communities that help patients find clinical trials relevant to them,
- Patient-facing applications that match consented patients to specific studies based on shared healthcare records.
Patient retention and engagement
The burden of participation in clinical trials causes many patients to drop out. This leads to lost opportunities for patients to benefit from experimental treatments and contributes to the loss of valuable data and the overall cost of research. A thoughtful application of technology can help patients understand what participation in a clinical trial entails and support them throughout clinical trials to encourage adherence to treatment and retention. Patient-centric solutions that show promise and continue to gain adoption include:
- Digital clinical trial platforms (supporting both decentralized and hybrid clinical trials),
- Electronic informed consent (eConsent) experiences, inclusive of modalities that promote comprehension and retention of information (e.g., audio, video, interactive experiences, metaverse),
- Patient-facing experiences and mobile applications that enable televisits, schedule tracking, medication adherence, monitoring of health status and outcomes, and remote assessments.
Data management relies on data governance as a foundation (including best practices and processes that govern data strategy, quality, use, and control), and it requires data interoperability as an enabler. A comprehensive approach to data management can reduce or eliminate manual processes and redundant data, introduce efficiencies, and make data readily available for analysis. Some of the exciting developments that contribute to better data management include:
- Growing adoption of the FHIR standard to enable the exchange of healthcare data across multiple systems, e.g., between EHR and electronic data capture (EDC),
- Semantic integration of data across heterogeneous sources through implementation of ontologies and knowledge graphs,
- API-enabled, composable enterprise architecture of clinical trials digital systems that supports seamless data transfer and exchange.
Improving the timelines and reducing spending in clinical research takes a holistic strategy that is both data- and patient-centric and encourages a thoughtful application of technologies to decrease rather than exacerbate operational burdens. A good starting point is examining how to capitalize on the four areas of opportunity – protocol design, patient identification and recruitment, patient retention and engagement, and data management – to optimize clinical research operations.
Developing a digital strategy that leads to an interoperable ecosystem where data exchange is seamless, manual processes and redundant data are eliminated, and digital technologies support patient engagement and retention will facilitate faster time to market for life-saving treatments and ultimately improve patient outcomes.
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