Five Trends in the Life Sciences Industry

Key takeaways:

• Innovations in data analytics and real-world evidence (RWE) are advancing medicine.
• Diversified and decentralized clinical trials are addressing the limitations of traditional clinical trials.
• Evolving regulations are opening new opportunities for real-world data (RWD) use.

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• Read our new e-book about how RWD, technology and innovative approaches are changing the life sciences industry.

The life sciences industry has reached a turning point and the COVID-19 pandemic accelerated challenges that were bubbling to the surface. Clinical research was reprioritized around infectious disease prevention, vaccine development and COVID-19 treatments. Simultaneously, clinical trials were impacted as people avoided unnecessary interactions with healthcare.

Life sciences companies need agility and speed to inform their business strategies and reduce risk associated with future global pandemics and events. How can these organizations keep up?

Let’s look at five life sciences trends that are changing the way medical solutions are developed and brought to market in the life sciences industry.

1. Show Me the Evidence

The depth and breadth of healthcare data to which life sciences companies have access is expanding. From billing data, insurance claims, clinical labs and imaging studies to smart watch data, there is more information collected than can be reasonably utilized in clinical development. Turning all that data into actionable RWE will be critical for advancing and transforming healthcare.

2. The Value-Based Price (VBP) is the New Normal

Life sciences companies must be strategic in developing their pricing structure by:

• Focusing on appropriateness of care for positive patient outcomes.
• Understanding how a medical therapy or drug impacts a population based on RWE.
• Knowing how their solutions fit into clinical care pathways for targeting the right patients.

VBP helps create net wins, as life sciences companies with the right data to prove efficacy and value can receive preferential selection by providers, waivers for prior authorizations from payers and overall increased utilization. This can increase access for patients and relieve the cost of care burden on health systems, providers and patients.

3. Technology is Driving Actionable Information

Advancements in artificial intelligence (AI), machine learning (ML) and natural language processing (NLP) coupled with a focus on enabling data interoperability is driving faster, more meaningful insights.

This technology will help boost research efforts. Consider how NLP has enabled the analysis of the written and spoken word by providing access to clinicians’ notes and pathology reports which consist of free flowing and unstructured text.

Clinithink’s CLiX™ unlock, a technology tool that combines NLP with ML, is one such technology that can read more than one million records per hour and utilizes clinical NLP to recognize the myriad of ways that clinicians use to describe conditions in real life.

Meaningful insights gleaned from these unstructured narratives such as characteristics of early disease stages help clinicians make connections between their patients, the patients’ symptoms, treatment and other confounding factors.

Information such as biomarkers and cancer staging data, typically not coded in electronic medical records (EMR), can have an impact on cancer patients’ journeys because some medical products are developed for use in the early cancer stages where they provide the most benefit.

NLP queries can spot these details with contextual relevance in narrative expression and rapidly scan millions of unstructured EMR notes and PDF reports, such as genomic results, in as little as an hour, helping identify these patients for early intervention, further study participation or evaluation.

These technologies make it possible for life sciences companies to mine large amounts of data and quickly turn it from information to actionable ideas that help advance the development and delivery of medical solutions.

4. Diversity in Clinical Trials

Long has there been a need for representation of racial and ethnic minority populations in clinical trials. As early as 2014, research indicated that 86 percent of clinical trial participants worldwide were white people and a 2019 study found that 79 percent of genomic data comes from people of European descent. We can and must do better at collecting data that accurately represents the populations served.

Life sciences companies will need to ensure trial participants represent the vast array of patients that will use the drug, device or therapy to help prove efficacy. One way to approach patient diversity is during the site enrollment and patient recruitment phases.

The PINC AI^™ Applied Sciences (PAS) team uses structured and unstructured data to find and recruit patients who meet the complex inclusion/exclusion (I/E) criteria of the study. This I/E criteria defined in the study protocol and developed based on a research hypothesis can consist of 10-20 eligibility criteria that participants must meet. With access to patient data, PAS can help find appropriate sites based on eligible patients as well as identify potential participants outside of a site’s known population in accordance with eligibility criteria, and desired clinical and diverse demographic characteristics of the study’s intended population.

For instance, if researchers are conducting a study and seeking patients with metastatic ovarian cancer, data can rapidly reveal minor details such as tumor type, tissue type and substages to help researchers target patient populations that can meet the disease studies’ narrow eligibility criteria. PAS’ technology-backed approach has the potential to increase the number of participants eligible for a clinical trial tenfold.

This ability to rapidly interpret large volumes of data that would otherwise be inaccessible to investigators has improved patient identification for clinical trial participation compared to traditional approaches.

5. The Decentralization Effect

The limits of traditional clinical trials were amplified during the COVID-19 pandemic with an 80 percent decrease in new patients entering trials. Researchers worked around lockdowns and stay-at-home orders with direct-to-participant (DTP) or decentralized research studies that have now become the future of medical product development.

With more than 70 percent of potential trial participants living two or more hours away from a trial site, going virtual helps keep patients enrolled and actively participating from the comfort of their home.

Patients’ uptake of digital technology has steadily increased year after year with one survey indicating 98 percent of patients were satisfied with using telemedicine making this a strong indicator that technology is here to stay.

Engagement in decentralized clinical trials that focus on the patient experience are critical to future success. Investments in this type of digital transformation can and will help improve life sciences companies’ resilience and positively impact the bottom line.

These five life sciences trends are just a few changes on the horizon as life sciences companies begin to operate in the “new normal.” Looking ahead, there will be more to come as life sciences companies expedite treatment development, uncover new uses for existing treatments and work to reach the right patients at the right time.

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