Clinical Trials have always driven discovery and innovation in the healthcare industry. They produce a huge amount of valuable information through data collected from routine practices, insurance, electronic medical charts, visits to doctors, purchasing over the counter medicines or even the FitBit worn provides information. And this data has the potential to predict valuable insights, customers’ deepest wants and needs, as well which treatment or medicines works best for us.
But, much of the research data gathered is observational in nature and based on statistical models with many assumptions which makes it hard to say whether they hold true or not.
‘Big data’ has the real potential to transform real-world evidence in clinical trials, medicine and healthcare.
How organizations can create and implement Big Data analytics-driven approach to clinical trial for feasibility and for success? With these vital building blocks, enterprises can build evidence-based and entirely feasible decisions:
1. Understanding the true patient and the landscape of epidemiology
Big Data can be used for distinct purposes in order to identify the highest concentrations for the patients. With epidemiology data, enterprises can build heat maps and develop an insightful report of where the eligible patient might be located. It shows which physicians and clinical sites are treating them. The actual data can be used to simulate the symptoms and medical events while identifying patients with rare diseases. The data derived from social media serves in deploying automated social listening techniques and also supplementing RWD to recognize eligible patients. It also helps in recognizing the undiagnosed and underdiagnosed patients with diseases.
2. Render competitive intelligence and landscape assessments
A competitive intelligent assessment using Big Data helps in choosing the correct investigators who bear the capacity to participate in complementary trials rather than that of competitive trials. Along with this, competitive landscape assessment is beneficial in forecasting important metrics such as the number of investigators, sites, and patients available for your trial. It also shows the enrollment rates at a country and site level. An in-depth understanding of a specific indication can help in planning the trial taking all the other interventions, be it investigational or marketed along with their treatment guidelines into account.
3. Developing an efficient enrollment prediction
Enterprises typically make critical errors in the area of enrollment prediction. An aspirational enrollment timeline that is not supported by data can significantly impact trial budgets. One good solution to figure out the availability of the clinical trial patients on their eligibility criteria and then mapping the sites and investigator-to-patient ratios. After that, integrate all the gathered data to forecast enrollment rates. There are some most commonly used methods for forecasting enrollment curves. Accurate budget forecasting using Big Data is the result of accurate enrollment forecasting. Completing these activities with acute precision result in more efficient spending and optimal utilization of internal resources.
4. Measure Regional Potential
Enterprises can determine which regions or countries are best for patient enrollment by identifying eligible patients utilizing real-world data. Organizations can gain local clinical trial history either by their own internal data, or open data sets or ideally by combining both of them. On trial costs or average startup times, data can actually help an organization to decide where to conduct a specific trial based on budget and timelines.
5. Data driving the global site and investigator selection
Along with internal data and external benchmarking data on historical site performance, organizations also have data based on investigator experience and site quality. This can be factored into the decisions of site selection like protocol deviations, average query resolution times, etc. To get a deeper understanding of how your enterprise’s metrics compare to competitors, this data can be compared with historical benchmarks using big data analytics.
6. Initiate KOL mapping
It is vital to deeply understand the KOL landscape for site selection. As influential physicians are spread out around geography, they can gain referrals from the community to the treatment centers. Beyond conventional KOL mapping methods, organizations can use AI and machine learning to recognize influencers that are based on the authorship of clinical trial participation, editorial & leadership positions with journals, papers & practice guidelines, as well as professional organizations.
7. Development Strategies: Use of Global Drug & Asset Profiles
It is important to analyze drug development data for precise studies. It helps in knowing where comparable drugs are approved, what their approved label information is, how much they cost, and much more. Having a look at the competitive world, branded agents’ and generics’ approval status and market size can help in building an effective development strategy.