Cause a path to pragmatic innovation in clinical trials

A model quickly emerged after talking about clinical data leaders at events in Basel, New York, London and Copenhagen. Although thousands of kilometers from each other, the emphasis on the simplification and standardization of the data was clear. This common thread is the result of increasing complexity in the clinical landscape and more and more companies adopt pragmatic innovation to rationalize the execution of the study.

The FDA recently published advice that encourages practical clinical trials for specific situations. By adding design elements in a study similar to the clinical routine practice, more patients (including those of various populations) can access participating, registering and contributing to clinical research.

The ideas of clinical leaders have helped to surface five transition trends to pragmatic innovation that will shape the future of clinical data management.

Prioritize the RBQM in your strategy

Although regulatory organizations recommend risks based on risks for some time, many organizations have always been looking for the security of complete examination models and source data verification (SDV). However, clinical leaders believe that risk -based quality management (RBQM) can offer value quickly in trials and take measures to collect the advantages. Some already add advanced solutions and increase clinical data managers to accelerate the transition from data science data checks.

A global biopharma combines risk -based checks with technology to allow clinical research partners (CRAS) to see the SDV requirements without downloading a report or apply macros to a spreadsheet. This can eliminate thousands of patient visits and hours of clinical data work.

An emerging risk -based approach uses historical trend data for proactive management management. Data analysis can show trends and how they evolve and determine how the problems are resolved. This requires early entry and alignment between functions and teams, and attenuation plans and procedures in place to manage risks. The objective is that when a new trial begins, the teams will have access to the examination and monitoring of the data to identify the inconsistencies and share the signals.

The application of risk -based approaches has the potential to provide measurable value to clinical trials. Detection of proactive problems can provide higher data quality, centralized data journals can improve the efficiency of resources and faster database locking time can speed up marketing time.

Go from data management to data science

The Society for Clinical Data Management (SCDM) noted the need for biopharmacy companies to adopt a more scientific approach to clinical data and to transform clinical data management teams to the application of scientifically data. Companies take advantage of automation, the role of the data manager goes from the collection and cleaning to the delivery of information and to forecasting the results. However, the transition to data science has challenges, in particular the need for clean and harmonized data.

To allow data science, data management and other functions such as clinical operations and pharmacovigilance can work together to rationalize data flow. Especially with the ever -increasing number of data sources in tests, allowing data managers to prioritize high value activities to stimulate data sciences can have a significant impact on productivity.

Although the transition from data management to data science is underway, it is necessary to establish clear KPIs and performance objectives at the start and at the end of each study while retaining the highest quality levels. There are also additional development areas, including optimization of patient data flows, integration of data quality and examination, using AI, ML and advanced analysis, and allowing digitized and automated analysis, to allow data science. The adoption of this change will require that data managers focus more on analysis and interpretation and less on the realization of a control list.

Go all in Smart Automation

Smart Automation Research The best approach – be it an AI, one rule or another – to optimize efficiency and manage risks for each use case. It simply focuses on the delivery of the value, not on media threshing.

By adopting an approach based on automation rules, human surveillance is not necessary. More and more companies are investing in automation to add capacities that offer profits quickly while creating a foundation. This may include feedback loops and high -speed API integration for AI use cases which can be applied in the future. Another example is to use automation based on rules to speed up cleaning, transformation and data reports. The approach helps increase confidence in data and reduce manual work for data managers.

Today, biopharmas use automation for data cleaning to speed up the database locking times. Rules -based automation provides the most important cost of cost and efficiency in the medium term. In the long term, many leaders plan that Genai will be the co -pilot during clinical studies. AI can potentially deliver caused suggestions, identify fraud or predict compliance membership. The establishment of a clean data foundation, fueled by intelligent automation, will improve quality and provide the useful data necessary to supply cases of use of AI in the future.

Focus MDR and data standards on what matters

With metadata solutions based on the benchmark (MDR), clinical data teams bring together study design, data collection, analysis and submission. As the electronic data capture (EDC) has become the main application used in data collection, increasing perception was that all data collection metadata (or almost all) should be stored in a system to automate study builds.

The truth is that data collection in a repository has proven to be difficult for organizations on the scale of metadata management. This is probably due to the dependence on the spreadsheets.

An emerging strategy that has proven to be more effective is to focus MDR on the things that matter: study metadata that are common, shared and essential to data management and statistics. For example, when assessing common design metadata between data collection and data analysis, there may be as little as 25 properties (over more than 1,000) EDC metadata which affect programming and downstream analysis.

Alternatively, the design of the study can start with MDR and during the data collection stage, the teams confirm the standardized data definitions. This allows data management and statistics to operate in parallel to provide the same definition. Eastering the approach from a global MDR to simplified standards can accelerate the path of the construction of the study to the locking of the database. Adopting this more pragmatic approach means that clinical teams can offer value faster.

Make patient optionality a reality

Only 3% of American doctors and patients participate in clinical trials for new therapies. One of the results of the low participation is that almost 80% of studies do not meet registration times, causing expensive delays.

The rise of decentralized clinical trials (DCT) has led to discussions and debates on the place where the trials take place, and not the impact on the overall experience of patients, research sites, regulators, data managers, etc. The industry changes. Instead of focusing on the location, clinical chiefs focus on patients’ optionality. Significant development since decentralized tools are a standard way to function where patients decide how they participate in a study – whether at home, a site or a clinic – to manage research in timely and effective.

Sponsors are considering a more holistic approach to test experience, ensuring that patients are not overwhelmed by the number of devices and tools. The establishment of clear policies “Bring your own device” (byod) can provide convenience while maintaining the quality and security of data during a test.

Clinical data leaders are also starting to alleviate the charge of patients by asking participants to study less data. This is established during the protocol design phase. It begins by thinking about the tangible advantages for patients before introducing new applications (for example, ecsente) and taking advantage of surveys to acquire a more in -depth understanding of the patient experience and identify improvements.

Be pragmatic to simplify and standardize clinical trials

With the growing complexity of clinical trials, the life sciences increasingly applies pragmatic innovation. The adoption of a pragmatic approach means that agile clinical teams will go beyond inherited practices without risking quality. To do this effectively, the commitment of the research site will be more tailor -made to understand and support their objectives to deal with patients while making reality data.

Priority to risk -based management, data science, intelligent automation, standards and patients’ options are essential for industry to be followed by market changes. The recent indications of the FDA which encourage “pragmatic tests” in specific scenarios are a movement in the right direction. Sponsors and CROs can start by designing elements that closely reflect standard clinical practice, preparing a future where more patients join and participate in clinical research.

Photo: Deidre Blackman, Getty Images

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