cisivAligning people, process and technology in real-world studies

Payers, safety and regulatory bodies, together with patients and their supporting organisations are all seeking to assess the value of a drug in terms of health outcomes and economics. Prospective late-phase studies can help companies deliver these data and respond to the growing regulatory and financial constraints that medicine development now faces.

Most of us are familiar with the value of epidemiological data and the medical insights they have provided but the tools used to gather these data can easily be applied to broader clinical and economic scenarios. Regulators are recognising the need for clinical evidence in the real-world in order to evaluate efficacy, safety, morbidity and mortality.

The US FDA is increasing its interest in using real-world data to support decisions on approval and labelling of medical products as part of the 21st Century Cures Act. For the pharmaceutical industry, expanding the data around treatment success in real world environments is crucial to building evidence of efficacy. In addition, by providing insights to prescribing or compliance outside of the narrow, expert-led world of the interventional protocol, these data can help to refine the accuracy of cost models around medication

use. Payers and Health Technology Assessment (HTA) are requesting real-world data on effectiveness, cost-effectiveness, cost-utility, budget impact and long term outcome data.

The gathering of post-approval data is driven by the pharmaceutical industry’s reaction to the need for physicians and reimbursement authorities to increasingly assess the impact of treatments, products and services on patient outcomes and cost-effectiveness. But, with the demise of the blockbuster and a highly competitive marketplace, more proactive approaches that demonstrate greater targeting and sophistication in terms of treatment and market segmentation are needed.

For example, it is increasingly the case that new drugs coming to market are more effective and possibly safer in some patients than in others but, to maximize the benefit of these more targeted drugs, there is a need to understand patient groups and sub-groups. Similarly, to be able to demonstrate a clinical need prior to product launch involves detailed knowledge of a patient pathway, or the burden of health. Observational studies can also be used proactively to assess the on-going safety and the performance of a treatment and help to identify where a drug succeeds and the characteristics of patients where it is not successful.

Introduction

As medicines become more complex, more powerful and more expensive, recognition of the importance of real-world studies is growing.

Time for a different approach to real-world studies

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For people expert in the scientific rigour of an RCT, this can be an uncomfortable activity. Clinical trials are conducted in a far more controlled environment. They are interventional – a patient receives an unlicensed pharmaceutical product in a controlled situation – and the nature of this intervention calls for careful control both in the taking of the study drug and in external factors such as co-medication.

The exact opposite occurs in an observational study; the study sponsor cannot intervene in individual patient cases and all information is limited to that obtained from strict observation of the patient. This also has an impact on data analysis. The quality of data in observational studies should be a true representation of the area of research from where the data has been collected. Observational studies afford opportunities in the real-world setting that do not match that in the strictly controlled environment of a clinical trial. Equally, the tight data cleaning that happens in a RCT is not applicable in the real-world. If a doctor did not perform a test, the research team cannot demand that the test be carried out. They can merely observe that it was not done and ask why.

Observational studies also aim to broaden the recruitment pool to include not only more patients but also those from centres not usually included in clinical trials. The scale is therefore much greater: thousands of patients, in multiple countries and healthcare systems, can be involved over the life of the study which may last for many years.

Physicians are generally unfamiliar with clinical trial data entry processes and unaware of the ‘big picture’ regarding the value of the data they are gathering. Finally, the data sources are changing at a rapid pace and are increasingly fragmented. Real-world data can be gathered from primary or secondary care’s medical records, a patient survey, social media, wearable technology and health wearable applications.

A further challenge is to make a study truly representative of the real-world. If the site selection is too narrow, or has the wrong balance of physicians, patients or provider type, the study might well be non-interventional, but it won’t represent the real-world. According to Gary Coward, Chief Operating Officer, Synigence Research Limited, “most healthcare decision makers recognise that real-world evidence data can make a significant contribution to supporting evidence assessments but that data will only have true relevance if the study population reflects the characteristics of their treatment population. Including non-clinical trial sites in the study is a step towards recruiting a representative patient cohort but researchers should also know the profile of the treatment population prior to embarking on any study and deploy recruitment techniques that ensure they avoid recruiting a skewed study population.“

Issues in the design and delivery of real-world studies

The data sources are changing at a rapid pace and are increasingly fragmented.

Designing and delivering observational studies effectively and efficiently requires a different mind-set to the one applied to the established methodologies of clinical trials.

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A common reaction to designing a real-world study is to use Standard Operating Procedures (SOPs) designed for managing clinical trials, an approach that is not only costly and inefficient, but also likely to lead to frustration, poor data quality and endless changes. The trend in the pharmaceutical industry has been to outsource an increasing number of observational studies to organisations that are experts in clinical trials and have the reach required. The astute and experienced CROs are creating new Standard Operating Procedures (SOPs) for observational studies and are challenging the use of standards transferred from clinical trials. Some trail blazing CROs lead the way and propose new processes. However, for many there remains a challenging conflict between the embedded clinical trials approaches and the need to innovate to meet the demand for real-world evidence.

From post-marketing safety studies through to registries and health outcomes studies, all tend to follow Good Pharmacoepidemiology Practice (GPP), which is generally regarded as the foundational set of guidelines for non-interventional studies. Whilst not as long or as detailed as Good Clinical Practice (GCP) guidelines and, because the study observes rather than intervenes, most activities are covered by current medical practice or existing regulations, such as the Helsinki Declaration, or the country’s laws or data protection legislation. However, some of the challenges cited included the set up and maintenance can be burdensome from economical and organisational points of view, and there is often much wasted resource because there is poor experience sharing.

Accessing real-world data requires a paradigm shift in thinking around the study design and implementation, the achievement of rapid yet accurate data collection and recognition of the need for complete rather than ‘perfect’ data. One of the biggest challenges is recognition of the key differences between non-interventional studies and clinical trials and taking advantage of them. Asking the right questions and coping with the scale and flexibility required for observational studies drives the need for more specialist platforms. Even though Electronic Data Capture (EDC) systems designed for clinical trials are equipped to manage sophisticated cross-checking and data verification, they are often too rigid for observational studies.

They do not easily migrate to an environment where data is captured in different way and can require considerable user training and infrastructure support. As a result, health care professionals (HCPs) can see the data collection and the EDC as complex, time consuming and of little value.With observational studies, the number of people involved within hospitals and clinics is much higher than in Phase II and III clinical trials, and the HCPs running the studies do not have the same motivation and interest as clinical investigators. As studies often run for several years, the use of over-complex software that demotivates HCPs can lead to failure to meet recruitment targets which, in turn, can generate additional expense and the possibility of failure to meet reporting requirements. But why is a new approach so difficult? Like many other industries, clinical research is undergoing transformation resulting from the availability of information technology that can automate, simplify and streamline many of the manual routine tasks performed in the past.

However, the eClinical technologies that currently dominate the industry are built around clinical trials processes and are embedded in practice. Changing the process to meet the requirements of non-interventional studies is therefore more challenging.

A recent benchmarking survey conducted by the Deloitte Center for Health Solutions (DCHS) reported that life sciences companies are making some progress in strengthening their real-world evidence (RWE) capabilities but highlight that there is still room for improvement. Survey participants cited lack of access to both data and internal data science expertise as two of the biggest implementation challenges. According to DCHS, the survey findings point to a need for life science companies to consider the bigger picture when responding to the need for healthcare data, and adopt ‘a holistic approach that spans traditional siloes and considers talent, technologies, governance, operating model, and external partnerships’ to respond and act on the proliferation of RWE.3

The need for a different approach

A common reaction to designing a real-world study is to use Standard Operating Procedures (SOPs) designed for managing clinical trials, an approach that is not only costly and inefficient, but also likely to lead to frustration, poor data quality and endless changes.

Accessing real-world data requires a paradigm shift in thinking around the study design and implementation

Cisiv’s platform, Baseline Plus, uses a lightweight user interface, more in line with the consumer experience of e-commerce and online banking, which reduces the need for training. Baseline Plus offers an intelligent and adaptive workflow which enables the data to be >95% clean and complete at the point of entry. This lightens the workload for data management, reducing costs by as much as 60% when compared with standard methodologies as well as reducing the HCP burden around query resolution.

Cisiv is an innovative company providing novel technology solutions to global pharmaceutical, biotech companies, research organisations and academia. With extensive expertise in real-world research, Cisiv is passionate about advancing technology for its implementation. For the study sponsor, Cisiv’s approach can help to deliver complete data of a high quality and suitable for epidemiological study, regulatory approval, or health economics and outcomes research, across multiple countries and healthcare settings.

Sponsors can access this data in real time and monitor progress, patient enrolment and physician activity.

For the physicians, well-designed real-world studies can offer high usability, with simple, fast, effective data entry at site and provide insights into how results will impact on their patients and clinical practice. The case for broad-based real-world research is now undeniable but designing appropriate studies and managing data collection to meet demands requires a change of approach. Cisiv is committed to challenging the assumption that the acquisition of RWE can be supported and managed using the same technologies and SOPs as clinical trials.

It offers bespoke technology to support the capture of large-scale real world data within a user-friendly framework and meet the growing demand for high-quality RWE in increasingly complex global healthcare environments.

How can Cisiv help?

Cisiv can help life science companies meet these RWE challenges. Cisiv’s new generation of EDC technology is designed to support the unique requirements of non-interventional studies.

Case Studies

www.cisiv.com/case-studies

Dominic Farmer is Cisiv’s Chief Executive Officer and a founder of the company. He has led the organisation (formerly IES) through successful rapid growth and financing, recognised by a Deloitte Fast 50 award and supported by the SEEDA High Growth Business programme. Dominic has overseen the design, development and go-to-market strategy for Baseline Plus, Cisiv’s proprietary software platform for EDC in non-interventional studies, as well as Baseline Designer, the design tool licensed out to clients who wish to design their own studies.

Dominic has over 25 years’ experience in healthcare technology and real-world research with a keen focus on knowledge sharing and data management systems. He founded Cisiv to develop and provide technologies for use in real world research following his extensive experience with leading global Pharma companies in both software development and implementation of large-scale global studies.

Florence Barkats is Cisiv’s Commercial Director She leads the go-to-market strategy and the sales & marketing team. Florence has over 15 years’ experience in Healthcare technology. Prior to Cisiv, Florence started as a researcher in Immunology, before moving to commercial roles at Johnson & Johnson.

In the last 5 years, she led a European business unit at GE Healthcare for clinical software. Florence brings commercial vision and the ability to build partnerships that create value for the biotech and pharmaceutical clients as well as for patients. She holds a BSc. from Johns Hopkins University (Maryland, USA) in Molecular Biology and an MBA from London Business School.

CISIV has worked closely with leading pharmaceutical companies for over 10 years developing collaborative web platforms for use in post-approval activities. Baseline Plus was developed to meet the need to capture essential real world data in an international setting. Its aim was to keep data quality levels high whilst keeping data entry easy and intuitive, essential in large, long-term non-interventional studies with low levels of monitoring.

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About Cisiv

Contributors

1. Draft guidance: Use of Real-World Evidence to Support Regulatory Decision-Making for Medical Devices Accessed at: https://www.fda.gov/downloads/medicaldevices/deviceregulationandguidance/guidancedocuments/ucm513027.pdf2. In the context of a real-world study, “investigators” should be referred to as healthcare professionals. In some instance, a sponsor might place a dedicated person to collect the data. More often, physicians, nurses and research associates who deliver patient care are also the ones providing data. 3.Getting real with real-world evidence. 2017 RWE Benchmark Survey. The Deloitte Center for Health Solutions (DCHS) surveyed 15 of the top life sciences companies by revenue globally. The survey was conducted over the phone and results were recorded in an online survey tool. All results were blinded during analysis and results were aggregated. The survey was conducted Q3-Q4 of 2016.4. 60% is the average, depending on the complexity of the study.