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home > epc > summer 2011 > revolutionary trends

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European Pharmaceutical Contractor Revolutionary Trends
It is widely reported that technology can increase productivity and decrease costs in clinical development, but the shift from traditional paper techniques to electronic alternatives brings a new set of challenges that need to be overcome for effective execution. Introduced more than 10 years ago, web-based electronic data capture (EDC) systems have become mainstream in clinical research, accounting for the vast majority of data collection in trials currently being conducted. Study sponsors have come to rely on these systems to drive their clinical R&D and streamline many parts of clinical trial execution. In general, data is collected in a faster and more auditable fashion than with paper. However, many IT and clinical organisations wonder whether this mission-critical technology has delivered on its promise of less costly and more productive clinical research. Critics point out that rising clinical development costs are proof that EDC has not lived up to its billing. Achieving Success What often goes unmentioned by sceptics of EDC adoption is that productivity has dramatically increased for those organisations that have leveraged EDC as a catalyst to transform how they conduct research, improving both process and team collaboration. Forward-thinking sponsors have anticipated the impact of EDC adoption and altered processes to maximise the benefits garnered. In a prime example, using EDC for clinical data vastly increases the amount of information from which to draw conclusions about trial efficiency. This enables access to new information previously unavailable to the sponsor. Unfortunately, many sponsors don’t actually benefit from that additional data because their processes are still built around legacy systems created in the age of paper-based clinical trials. By building clinical operation processes optimised for metric review, as well as identifying opportunities for improvement, sponsors are more likely to approach the anticipated efficiency gains from moving from paper to EDC. In addition, with access to a higher volume of accurate metrics – specifically those that can be used to optimise clinical operations – sponsors are better able to diagnose performance gaps. By adopting improved processes and pursuing continued operational refinement through the analysis of more actionable data, sponsors can still realise their vision of optimised clinical operations due to EDC adoption. Process Optimisation In the days of clinical research before EDC, sponsors waited a significant amount of time before having enough trial data in their own systems to analyse performance. Typically, this took – and continues to take – place at or near the end of the study execution phase. As a result, life science firms relied on relatively simple sets of metrics to assess performance. Analysts pored over massive volumes of information but, with much of it dated even by the time they first gained access to it, only the lowest hanging fruit on the data tree was analysed with any intensity. Today, although EDC allows data to get into the hands of analysts, study managers and executives faster, the process for analysing it has hardly changed. As seen in Figures 1A and B (page 18), the overall operational refinement process is practically unchanged.While the process moves more quickly with EDC, sponsors still must wait for near completion to have enough data handy to make informed decisions about any of the four stages of the clinical life cycle. Optimising the process of analysing performance data, however, enables an entirely new paradigm for making decisions aimed at refining clinical operations.With access to data earlier in the cycle, as well as constant updates in near real-time, the possibility to ask new and deeper questions about performance arises. Figure 1C illustrates this model of continued analysis and adaptive refinement. Metrics that Matter Improved processes built around EDC and management only get sponsors part of the way to increased trial operations efficiency.Advanced reporting technology and an increased reliance on actionable metrics are also key. Although biopharmaceutical and medical device clinical R&D organisations have long developed metrics and reports to measure clinical operations performance, even when paper trials were the rule, most of the data was available too late to be actionable. Furthermore, the reports were often simple data tables and basic charts, not easily or quickly digestible by senior decision-makers. Today, with the widespread adoption of EDC, clinical operations data is inching closer to real-time. But it’s more than just timeliness that makes data actionable, the metrics gathered should be contextual, enabling apples-to-apples comparisons with other trials, and presented in a fashion that is easily and quickly reviewed.The ability to act quickly on key performance indicators (KPIs) creates a situation where speed begets speed, which in turn leads to the increased efficiency that sponsors are clamouring for with their adoption of eClinical technologies.To achieve truly actionable data, sponsors must strive for the following: Timely access to data for the most updated views of performance Contextual KPIs for apples-to-apples comparisons Engaging user experience for easy and quick review Simple implementation for shorter time-to-value The combination of these qualities allow clinical organisations to benefit from true business analytics, rather than merely reporting. Business analytics has made great strides in other industries, particularly in manufacturing and financial services, and even in other departments within life science organisations such as finance and sales operations (1). However, R&D departments have been largely left in the cold. There are a number of reasons, although the most likely factor is that the pieces were not in place until recently for clinical organisations to take full advantage of all business analytics had to offer. With the adoption of standardised metrics and improved processes to better digest KPIs, the potential exists for a hybrid analytic/operational application that also help organisations optimise their operations based on analysis of historical trends and prediction of future outcomes, while enabling automation of the execution of fact-based decisions (2). Timely Access to Data Perhaps the biggest potential impact of a shift to EDC is the elimination of the delay in accessing performance data. Previously, decision-makers would have to wait for teams of data entry professionals to convert paperbased case report forms (CRFs) to electronic databases. Valuable time was wasted, and by the time the data from the paper CRFs was accessible to clinical operations decision makers, the window to implement any positive changes in these trials had closed. Unfortunately, most sponsors do not yet rely on a reporting infrastructure that takes full advantage of the wealth of data available as they are captured from eCRFs. At a minimum, however, access to data earlier in the trial life cycle can make a large difference to both tactical and strategic decisions. Contextual KPIs Optimising eClinical performance is an end-to-end proposition, therefore the improved perspective gained by enhanced reporting and analysis must be applied at the study and protocol design stage of the clinical trial life cycle. To do this, processes for the analysis and review of clinical performance must be adopted to ensure consistent application of findings for the refinement of existing processes. Study and portfolio managers need to be truly confident in decisions about alterations in their processes, so metrics must not only be available in close to real-time but also provide as much context as possible. The ability to view performance at both macro and granular levels is perhaps the most significant part of context. Viewing clinical operations metrics at an organisational or portfolio level can provide senior decision-makers with an understanding of relative performance across phases, therapeutic areas and geographies. Sometimes, however, an analysis of study- or even site-specific data is more important. Reporting tools that deliver both perspectives are vital to improved insight into KPIs. Optimal contextual reporting metrics also require a standardised set of data. Defining metrics at a company or industry level can simplify performance comparisons. Additionally, sponsors are increasingly seeking industry benchmarks for accurate comparisons to their peers, enabling operations executives to establish aggressive yet realistic improvement goals and study managers to have an instant understanding of their performance. Engaging User Experience Sponsors have tried for years to improve the metrics captured during clinical research. As discussed, better context is a key part of increased comprehension. However, truly actionable data must also be easily accessed and understood. Advances in data visualisation herald a new era for data interpretation. Unlike legacy reporting tools that simply show large volumes of raw data in table format or export basic charts and graphs, data visualisation allows for truly dynamic graphics, with embedded metadata and data cues, allowing for a much more holistic view of the data. Additionally, utilising an analytics tool with visualisation not only provides a better overall view, but also improves the user experience for decision-makers. Visualisation technology enables one to focus more clearly on the data that is of most importance, facilitating adoption. Simple Implementation Generally viewed as a cost centre in life science organisations, R&D departments are often asked to demonstrate a return on technology investments earlier than profit centres must. Adoption of business analytics to drive better operational decisionmaking is under similar scrutiny and requires a similar organisational commitment to succeed. Innovative clinical trial sponsors looking to maximise the impact of a business analytics implementation are likely to reap benefits more quickly using software-as-a-service (SaaS) tools. Companies deploying an onpremise business analytics system from scratch should think hard about whether they can justify the high costs of hardware, software and services, plus the 20 to 25 per cent in annual support and maintenance fees of traditional software (3). However, a SaaS business analytics solution that is seamlessly integrated into the EDC software already being used can minimise those costs and,more importantly, greatly reduce the time needed to implement a solution. Conclusion Clinical operations departments are under more pressure than ever before. They are expected to deliver safe, effective new drugs or indications to market quickly in a complicated regulatory environment and in the midst of concerns around the patent cliff.Many are looking to technology to help. Despite the widespread adoption of EDC over the past decade, broad productivity gains have been hard to come by for some research organisations.While regulatory guidelines have caused some of the hurdles in maximising the benefits of EDC usage, in many cases, life science firms have failed to adopt processes that better leverage EDC’s advantages over paper-based data capture. The few who have done so, though, have seen the efficiency gains promised with the introduction of EDC in the late 1990s. Even among those that have altered their processes, because of outdated operational analysis processes,many often fail to develop and analyse the metrics that could best provide the insights needed to make confident operational decisions. While other industries have flocked to business intelligence and analytics to drive efficiency gains across complicated workflows and processes – with many already seeing dramatic gains – biopharmaceutical and medical device companies have not been able to truly benefit from a clinical business analytics solution until recently. Unfortunately, many in-house implementations have disappointed. Industry analysts have found that between 70 and 80 per cent of business analytics projects, regardless of the industry, fail to deliver in a timely fashion (4). The reasons are many, but given the cost and time required, customers should expect more. Adopting a SaaS business analytics solution can alleviate customer concerns about extended implementations and uncontrollable risk. In summary, sponsors are faced with a variety of challenges in achieving the efficiency gains promised with the shift to EDC from paper-based trials. Life science organisations seeking to maximise the impact of EDC adoption would be wise to revisit their internal processes to ensure that they are in a position to truly benefit from the speed and potential of EDC. Furthermore, they must think beyond mere reporting in order to make more informed decisions about their clinical performance. Just as clinical data capture has evolved from paper, so too has reporting evolved to business analytics, delivering unprecedented timeliness, context, user experience and, when combined with a seamless SaaS solution, ease of implementation. References IDC, Business analytics and the path to better decisions, September 2010 IDC, The case for investing in business analytics technology, February 2009 Computerworld, Self-service BI catches on, December 2010 Gartner Group, Business Intelligence Summit 2011, January 2011
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Revolutionary Trends