Trendspotting: Decentralized Trials Maintain Momentum, Additional Remote Solutions, AI and Reducing Bias

January 10, 2022 | We spoke with clinical research leaders to gain insights into expectations for the coming year. We heard predictions for a return to some of the pre-pandemic normalcy in the clinical trial world. “The rapid increase of Direct-to-Patient (DtP) drug shipments and remote solutions to support patients at home during the pandemic is expected to return to the same levels as prior to the Pandemic,” said Dave Kelleher of 4G Clinical. “With the industry still adjusting to new approaches to study start-up and recruitment, regulators will continue to provide wide berths to companies implementing new methods of decentralized trials,” added Henry McNamara of Oracle Health Sciences.

Other notable predictions were made around increased cell and gene therapy (C&GT) research. “The logistical and scheduling requirements of C&GT trials are the most complex aspects of running C&GT trials. The success of scaling these trials and treatment opportunities will depend on technological advancements to support the processes, products, and patients, replacing the current manual processes,” said Kelleher. Kelleher also predicts increased real-time drug tracking. “Tracking solution companies can now offer real-time tracking solutions, which include multi-modal tracking capabilities, reliable and appropriate battery life, and advanced communication capabilities, which make real-time tracking for clinical trial shipments a reality.”

Researchers expect other pandemic developments to stay. “Methods and technology that were being explored pre-pandemic—from remote monitoring to video visits to phone visits, eConsent, and EHR—have come front and center during the past 18 months and will remain,” said McNamara. Oracle-Informa research found that 97% of companies implementing new clinical trial methods during the pandemic will continue using at least one of these methods and that 92% of respondents were equally or more confident in the data collected from these methods, according to McNamara. McNamara also predicts that community sites will provide opportunities for expanded patient recruitment. “Communal sites such as retailers like CVS and Walgreens will become more popular as trial hosts. By partnering with these locations, sponsors will have expanded opportunities for patient recruitment because they will be offering patients the convenience and comfort of visiting sites they are familiar with,” he said.

This can also help to limit trial bias, another topic of concern in our community. Mark Day of iRhythm predicts that “improved clinical study design will foster more heterogeneous and representative patient populations, resulting in algorithms that reduce bias.” “We believe the universe of evidence will expand to seamlessly integrate clinical, real-world, and historic trial data to advance hybrid approaches to evidence generation,” added Sastry Chilukuri of Medidata.

Here are the full trends and predictions including additional forecasts for direct-to-patient drug shipments, supply chain digitization, payviders, and AI with wearables and other clinical data. –the Editors

 

Dave Kelleher, Founder & CEO, 4G Clinical

DCT trial designs will grow steadily: Decentralized Clinical Trials (DCTs) have been around for decades but didn’t really grow in application. Access to patients not proximate to investigational sites, diversified patient populations, providing patients choices (patient centricity), and accelerating enrollment are key advantages of DCT designs. The rapid increase of Direct-to-Patient (DtP) drug shipments and remote solutions to support patients at home during the pandemic is expected to return to the same levels as prior to the Pandemic. Clarifying regulatory guidance, best-in-class integrated technology platforms, and successful use-cases will accelerate the adoption of DCT trial designs.

Digitization of clinical supply chains: Questions such as “Where is my drug”, “What’s the drug status”, and “When do I need to produce or ship” often require supply chain experts to consult multiple systems, are time consuming, and are mostly single-study centric. Manufacturing capacity constraints and increased clinical supplies costs from new drug innovations drive the need to focus on integral and balanced cost-risk management approaches. Smart supply strategies such as drug-pooling, free-picking, and just-in-time packaging/labeling are already being applied in response, but moving the needle requires fully integrated supply chain management solutions.

Smart Packaging concepts will gain momentum: When an investigational drug requires self-administration, patients typically register the date and time of the drug intake in diaries. Recently regulators started questioning the accuracy of this registration and as such are questioning drug adherence and validity of study data. Technology and smart packaging concepts remove the human element and digitize drug adherence registration.

Cell & Gene Therapy research will grow rapidly: However, the acceleration of technology solutions and special conditioned resource capacities are required in the entire value chain, not only for clinical research but even more for commercial growth. The logistical and scheduling requirements of C&GT trials are the most complex aspects of running C&GT trials. The success of scaling these trials and treatment opportunities will depend on technological advancements to support the processes, products, and patients, replacing the current manual processes. Fully integrated technology platforms will be able to seamlessly automate the C&GT value chain, allowing for the scaling required to support the widespread availability of personalized medicine.

Drug tracking will transition from passive to real-time tracking as a standard: Hardware and communication technology advancements have seen significant improvements in recent years which will begin to yield expectations far beyond the industry standards of the past. Tracking solution companies can now offer real-time tracking solutions, which include multi-modal tracking capabilities, reliable and appropriate battery life, and advanced communication capabilities, which make real-time tracking for clinical trial shipments a reality. The adoption of real-time tracking will inevitably lead to additional regulatory expectations for both shipment tracking and site storage, providing further opportunity for technological advancements by best-in-class integrated technology platforms. The resulting oversight opportunities will help ensure drug efficacy and patient safety.

 

Henry McNamara, SVP and General Manager, Oracle Health Sciences

Newly adopted clinical trial methods will become permanent: At the onset of the pandemic, the industry had to quickly adapt to keep trials afloat. This forced change helped researchers understand when and how to implement alternative approaches to improve clinical research. Methods and technology that were being explored pre-pandemic—from remote monitoring to video visits to phone visits, eConsent, and EHR—have come front and center during the past 18 months and will remain. In fact, according to Oracle-Informa 2021 research, 97% of companies who implemented new clinical trial methods during the pandemic indicated their organization will continue using at least one of these new methods. 

Confidence in the data generated from newly adopted clinical trial approaches will continue to grow: Oracle-Informa’s recent study found that 92% of respondents who implemented new clinical trial methods during the pandemic are equally or more confident in the data collected from these methods, compared to data collected via pre-pandemic methods. This confidence will continue to build as comfort with, and expansion of, newly adopted technologies and methods continues to grow. 

Community sites will provide opportunities for expanded patient recruitment: In order to attract new trial participants, it’s critical for pharmaceutical companies to make it easier for patients to access trial locations that are convenient for them. To this end, communal sites such as retailers like CVS and Walgreens will become more popular as trial hosts. By partnering with these locations, sponsors will have expanded opportunities for patient recruitment because they will be offering patients the convenience and comfort of visiting sites they are familiar with. 

Regulators’ will continue flexibility with hybrid clinical trials: With the industry still adjusting to new approaches to study start-up and recruitment, regulators will continue to provide wide berths to companies implementing new methods of decentralized trials. While flexibility will be key, regulators won’t be central to further decentralized adoption; that will be driven by the industry. 

 

Sastry Chilukuri, Co-CEO, Medidata, a Dassault Systèmes company

Data management becomes data scienceBringing disparate high-resolution data (e.g., multi-omic, imaging, sensors, labs, and clinical) together in a compliant manner unlocks new insights around patient response, biomarkers, safety, and dosing. Too often valuable data is scattered across multiple sources, which makes it difficult to realize their full value. In addition, the data collected by individual sponsors may not be large enough to get meaningful scientific insights. Pooling data from multiple sponsors allows innovators to expand their data size. We believe as the data management function takes on more data science responsibilities, the data curation capabilities will evolve rapidly.

Decentralized study execution is the new normAs patients, investigators, and sponsors continue to adapt to work in a hybrid environment of remote and in-person interactions, the technology ecosystem around them is evolving to support these needs. Analytics are moving to real-time in order to respond to our rapidly changing world. 

Expanded body of evidence: COVID-19 demonstrated the value of real-world data and the need to integrate it with clinical evidence. We believe the universe of evidence will expand to seamlessly integrate clinical, real-world, and historic trial data to advance hybrid approaches to evidence generation.

 

Mark Day, EVP R&D, iRhythm

Shift from retrospective to predictive analysis: In the near future, we expect AI innovation in healthcare to shift focus from retrospective analysis to predictive insight. To reach this milestone, health wearables must become both proficient and validated in determining who needs preventive care before symptoms and associated outcome risks manifest. At its core, digital health is meant to streamline complex processes and bring preventative care to high-risk populations. Predictive AI will help to deliver on this potential.

Bias in AI: Within the next year, AI companies will continue to improve data collection methods and develop processes that avoid bias in algorithm training and, in turn, performance in the intended population. Specifically, improved clinical study design will foster more heterogeneous and representative patient populations, resulting in algorithms that reduce bias. On the technical side, methods will develop to provide greater insight into the “black box” of AI algorithm decisions, which will guide understanding into whether these decisions represent bias based on factors including race, gender, and age.

 

Josh Gluck, Vice President Global Healthcare Technology Strategy at Pure Storage

A voracious appetite for faster-time-to-science is here to stay: The appetite for faster time to science is voracious and will likely continue. The world’s scientific community continues to break records in the fight against COVID-19—leveraging massive information sharing that is leading to a more accurate picture of COVID-19 and accelerated development and testing of vaccines and therapeutic treatment candidates. We’ve seen what can be done faster than ever imagined. Health sciences organizations across the board seek to build on this momentum safely and effectively to further accelerate the pace of personalized medicine. Genomics and artificial intelligence (AI) are key to this quest. To realize AI at scale, however, requires liquid data and modern data infrastructure that re-imagines the role of data and how it is used.

Data interoperability will reach a tipping point: After years of discussion and debate, compliance deadlines for the healthcare interoperability rule and related requirements are here. The requirements, which are designed to support seamless and secure access to patient electronic health information, offer the push needed to standardize patient records and modernize legacy systems. As digitized and standardized records become available through the rule’s payer-to-payer exchange requirements, which must be met by January 2022, payers will have the ability to dig deeper into some of the social determinants of health and chronic disease that can open doors to greater patient engagement. Real-time analytics will be essential to this vision though and can only be achieved if the right foundation is in place. To realize the full potential of the interoperability rules, health care organizations need an infrastructure that is not only built for the new requirements and standards but also offers the capability to secure, process, analyze, and scale the influx of data quickly—often in real time—and in a cost-effective way.

“Payviders” expand reach and see data as key to success: The “payvider” model in healthcare is taking hold, with organizations like CVS/Aetna, Cambia Health Plan in Washington, and Healthfirst in Florida finding success with this model, which offers a shared financial risk and reward compensation that incentivizes providers to offer value-based care, shifting the focus from quantity of care to quality of outcomes. While the model has proven better for patient satisfaction, to maintain momentum and improve the patient experience, payviders need to manage and analyze the influx of data from both the payer and provider side of the organization to realize the full benefits of being a payvider. With the right data storage and analytics tools, payviders can offer:

  • Faster clinical decisions made at the point of care
  • Better prescription management, ensuring the patient is prescribed the right medication as indicated without fear of being rejected by a payer
  • Improved patient follow-up—providers can see if the patient filled their prescription or received their refills and follow-up as needed

Healthcare organizations are playing a dangerous game of cybersecurity roulette: RBC has projected that the healthcare industry will generate 36% of the world’s data by 2025. That’s 10% faster than the data-driven financial services industry and 11% faster than the media and entertainment sector. While this is great news and opens the door to the possibility of better outcomes and lower costs, there is another side to the story. As data acquisition and importance has grown exponentially, it has become a pillar for business continuity. Health care organizations simply cannot operate without it. At the same time, however, most healthcare organizations’ cybersecurity strategy and infrastructure have not kept pace. In an era of data-driven care and unprecedented cyber threats, this reality places patients and healthcare providers at risk.

Dr. John Frownfelter, Chief Medical Officer at Jvion

Data Will Drive Action to Address Health Inequities: AI will help analyze data on social determinants of health to determine their impact on both individual patients and their communities. This will help healthcare organizations pinpoint the resources that will have the greatest impact on reducing health disparities in their communities.

AI Will Fill the Gaps Left by the “Great Resignation”: As more healthcare workers burned out by the pandemic leave the profession, AI will help health systems make the most of their increasingly scarce human resources, automating what can be automated and guiding care teams on how to best prioritize their time and resources with patients who need it the most.

Value-Based Care Will Becomes the Norm: The shift from episodic care to long-term care management will depend on preventative care and early interventions. Prescriptive analytics will help look at each individual patient holistically and identify not only the most vulnerable but also their greatest risk drivers and the interventions that will best address them.

AI Will Enable the Continued Growth of Hospital at Home: The home care model took off during the pandemic as a safer alternative to hospitals. AI will help triage patients for home care, identify potential barriers, and ingest data from monitoring devices to predict avoidable hospitalizations and recommend interventions to prevent them.

 

Trishan Panch, co-founder of Wellframe

AI for Healthcare and Insurance: Health providers and insurers will use AI in a wide variety of contexts in 2022. They will see the best results in a hybrid context, where they can leverage the human ability to generate hypotheses and collaborate by combining it with AI’s ability to analyze large volumes of data to optimize for specific, well-defined criteria. Health systems should not look at AI like a medical device, but more as a resource for information. To properly apply AI within clinical workflows, health systems will need to hire AI specialists or clinicians to maintain its quality and safety. 

 

Hari Prasad, CEO of Yosi Health

Increased focus on mental health diagnostic tools: The pandemic has worsened both the mental health and opioid crisis, urging the demand for new technologies to help identify, combat and treat at-risk patients as well as modify treatment plans.

In person care paradigm shift to virtual visits will become solidified: Health facilities pivoted quickly offering invaluable telehealth services during the pandemic months. While in-person visits have returned and will continue to increase, the accessibility of virtual visits will continue indefinitely.

AI will continue to dominate the healthcare industry: While artificial intelligence has been at the forefront of medical innovation, we will continue to see the influence of AI in proper diagnosis and processing health care data to reducing back-end errors and improving overall care.

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