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Volume 39, Issue 6
Repurposing existing technology to alleviate traditional ‘pain points’ in ensuring clinical investigator payments transparency
Site payments comprise approximately half of each clinical trial budget. That makes them an obvious target for managing study risks, costs, and timelines. It also means life sciences financial managers need reliable payment forecasting for setting development priorities and helping to ensure their business’ success. Given the complexities of clinical trials, however, accurate updating of actual (incurred to date or inception-to-date) and forecasted site payments for real-time reporting remains one of the largest challenges in medical product development.
Many factors confound payments transparency. Clinical trials evolve continuously, and each adjustment to the protocol, work order, or vendor mix requires new budgeting and forecasting. The larger and more complicated the study, the greater the variance in contracts, transparency requirements, procedure fees, currencies, and more exists across sites and countries. Patient enrollment, enrollment timing, site performance, and activity costs vary
by site, often quite markedly. Much of the input that determines payment status, such as ad hoc invoices from the sites or approvals from the project team, depends on human schedules and discipline. As suggested by the “challenge boxes” in this article, an infinite range of activities beyond the financial manager’s expectations or control can affect payment projections and cash flow.
Even though thousands of data points are generated in clinical studies, investigator payment forecasting currently relies on highly simplified inputs. That is, financial and project managers-whether with the biopharmaceutical sponsor or contract research organization (CRO)-primarily work with budgeted, in process, and actual payment amounts.
Adding to the challenge is that sponsors, CROs, and other payments vendors manage payments and forecasts via manual systems. Whether using spreadsheets or online fill-in-the-blank software, these are time and labor intensive processes, highly prone to errors, and inflexible. Delayed, incorrect, or confusing payments can alienate study investigators and site staff and increase the risk of missed timelines and budgets. Unhappy investigators also are less likely to participate in future studies or, possibly, to recommend the launched product to patients or peers.
Manual tracking also makes payment forecasting time consuming and, thus, costly. Syneos Health estimates that manual forecasting takes four to 20 hours per study depending on the forecast granularity and the study complexity (e.g., number of countries, sites, and patients; number and variance of ad hoc costs; screen failure
rates; therapeutic area; etc.).
Despite the challenges, the stakes could not be higher, especially for small pharmaceutical or biotechnology companies with limited resources. Unexpected fluctuations in costs or costly mistakes in payments can severely hamper such companies’ cash flow. Perhaps more devastating are delays in information that may enable the financial manager or project management team to resolve study risks ahead of delays or cost overruns.
The need for a more timely and precise site payment solution is driving development efforts among sponsors, CROs, and niche software providers. Their collective goal is a solution that will automate the payments process to manage complexities such as currency exchange, transparency reporting (e.g., the US-mandated Sunshine Act), financial reporting across borders, contracting party issues, and value-added taxes. Automating as much of the process as possible will reduce manual effort and minimize human errors.
Using technology that is already available, the ideal forecasting solution would harness the full power of each study’s data. The tool would draw critical information from all of the study’s or portfolio’s data systems-at the CRO and, where possible, from the sponsor. This “big data” or “data lake” would be agnostic to data sources and repurpose information already ingested and integrated as a common data model/dataset.
As noted, accurate payment forecasts depend on input from the full project team and clinical trial landscape, rather than just the invoices in the processing queue. A tool that enables financial managers to adapt quickly to all factors affecting site payments could significantly reduce study costs, timelines, surprises, and “re-work.” Such factors include change orders, protocol amendments, delays or surges in enrollment, partial data entry, manual invoices, and more.
A forecasting tool that processes granular site-level detail and uses the actual negotiated clinical trial agreement (CTA) rates for each site could enable financial managers to dial in on exact costs. This contrasts with the use of blended country rates in manually computed forecasts. Beyond delivering highly accurate budgets, this development could enable financial managers to shift funds to initiate or expand other studies in the sponsor’s pipeline.
Similarly, with the addition of an ad hoc cost management functionality, forecasters could capture the limits or maximum amounts that sites can invoice for ad hoc costs. Managing these costs would facilitate more predictive modeling, especially for worst-case scenarios.
Streamlining use of the data lake for current payments and forecasting would enhance accuracy in revising budgets and forecasts as the study evolves. It also could shorten forecasting time from up to 20 hours in highly complex studies for manual forecasts to minutes with the forecasting tool.
Beyond keeping the payments forecasts current with changing study parameters, an ideal forecasting tool would enable sponsors or CROs to identify potential financial and operational gaps in study performance. Further, it would combine enterprise-wide data for modeling future estimates. Stated another way, this forecasting solution would be a vehicle for continuous improvement from study concept through closeout.
This article has focused on payments forecasting for current or planned drug development studies. But what does the future hold for this capability? A more advanced forecasting tool opens several new horizons. It could be expanded for full-spend forecasting for each study or program. It might be used post-study to analyze site cost and performance data, for example, to inform future site selection, site mix, and contracting decisions.
Using that information, sponsors could maximize enrollment at the lowest-cost sites.
By incorporating artificial intelligence (AI) technology, payments forecasting would evolve to power predictive modeling. That is, pharma and biotech executives would be able to harness vast historical study phase and therapeutic area data to quickly compare and contrast a broad range of potential development pathways. By quantifying the actual and opportunity costs of each possible combination of studies toward product approval in competing regions, financial officers will help drive enterprise success through the ideal forecasting solution.
As mentioned, the acute need for better forecasting is spurring innovation from stakeholders across the life sciences industry, including CROs. Companies in this space are well positioned to design and deliver the payments forecasting solution that fully supports sponsors’ business goals. They can bring process and clinical development expertise not offered by specialty software developers. At the same time, CROs are acutely accountable for delivering high quality clinical study data on time and on budget. They are able to leverage project team, study, and industry data, and portfolio governance for more comprehensive forecasting and project insights.
CROs also offer the tools and motivation to develop the most pragmatic and consistent payment-forecasting solution.
It is important for drug developers to look to CROs that prioritize investigator payments within their full-service and functional service provider (FSP) operations. For example, is the CRO’s investigator payments team a standalone business unit? Does it have dedicated staff specializing in site payments, transparency reporting, and payment forecasting? Will each study have a designated investigator payments specialist as part of the CRO’s project team, and will that individual interact with the sponsor from project award through study duration?
Another critical filter is whether the CRO’s site payments function supports the organization’s mission. A CRO that focuses on and is known for its site relationships will make site payments a critical component of its operations. Such a CRO will have a better chance at delivering the solution sponsors need, because it shares the same pain points and its success is dependent on accurate and timely forecasting, too.
Investigator payments and payment forecasting have long been a pain point for biopharma companies, sites, and CROs. The industry has the technology and tools to deliver payments and payments forecasting accurately and on time. Once put in place, the ideal forecasting tool will enable financial executives to manage strong performance in the near term-and to deploy powerful and effective predictive modeling capabilities going forward.
Shaun Williams is Executive Director, Investigator Management Solutions, Syneos Health