January 10, 2022 | When we spoke with the vendor community of Diagnostics World to gather predictions for the coming year, it was gratifying not to be inundated with pathogen surveillance plans. COVID-19 remains an issue, of course, but the discussion has moved on to the lessons learned: how can we develop and bring a technology to market in record time and even how technologies can continue to solve ancillary staffing issues. “The therapeutics industry was able to demonstrate what speed and efficiency could achieve with the incredibly rapid release of COVID -19 vaccines, and we need to continue to ensure those researchers have the right tools at their disposal to keep that pace,” says Kevin J. Knopp of 908 Devices.
Moving away from the pandemic, we heard that metabolomics and proteomics represent the next impending wave in understanding biology. “These fields are going to completely change the degree of visibility we have into both state and trajectory in biological systems,” said Christopher D. Brown of 908 Devices. “Look for protein sequencing to take center stage as the next breakout biotechnology,” added Malay Gandhi of Benchling.
AI and its capacity to improve healthcare and diagnostics is a common refrain, especially as it is used with wearable sensor, ‘omics, and imaging. Mark Day of iRhythm espouses digital wearables and their influence on predictive diagnostics. With these, he says, “we expect AI innovation in healthcare to shift focus from retrospective analysis to predictive insight.” Similarly, Ed Ikeguchi of AiCure says that, “video and audio-based digital biomarkers can catch subtle cues of a patient’s disease progression and how a treatment impacts their daily life.” Sastry Chilukuri of Medidata adds that “bringing disparate high-resolution data (e.g., multi-omic, imaging, sensors, labs, and clinical) together in a compliant manner” will unlock “new insights around patient response, biomarkers, safety, and dosing.”
Here are the full trends and predictions including additional forecasts for imaging data infrastructures, staffing shortage remedies, SaaS medical devices, and avoiding bias in AI algorithms. –the Editors
Sastry Chilukuri, Co-CEO, Medidata
Data management becomes data science: Bringing 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 norm: As 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.
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.
Kevin J. Knopp, CEO and co-founder of 908 Devices
Keeping Up with Diverse Biotherapeutics Pipelines: In the biotech and life sciences space, the venture-backed small biotechs are chasing an incredibly diverse biotherapeutics pipeline. These very well-funded entities are moving at lightning pace and exploiting cutting edge biology. Life sciences tools must innovate to keep pace with the speed of these new/emerging therapeutic classes.
Unlocking Rapid Discovery with Accessibility to High-Fidelity Tools: The accessibility of high-fidelity tools to understand these biological systems and processes is key to driving rapid discovery. The therapeutics industry was able to demonstrate what speed and efficiency could achieve with the incredibly rapid release of COVID-19 vaccines, and we need to continue to ensure those researchers have the right tools at their disposal to keep that pace, and even accelerate further.
Christopher D. Brown, CTO and co-founder of 908 Devices
How ‘Omics’ Will Impact Life Sciences: The last 20 years have seen an explosion of the “omics” areas of knowledge discovery. It started with genomics and is now rapidly shifting to proteomics, metabolomics, and beyond. The promise of genomic information improving the understanding of healthy and aberrant biology led to the creation of an entire industry segment in tools, and that is happening again now with proteomic and metabolomic developments. These fields are going to completely change the degree of visibility we have into both state and trajectory in biological systems.
Extracting Valuable Insights from Mountains of Data: Life science researchers have incredible volumes of data coming at them daily. Many folks describe “drowning in data but starving for information.” We really need to do a much better job of extracting insights from these extremely multi-variable data sets in ways that the non-expert can quickly exploit, and at the same time ensuring that researchers have the data-oriented tools to improve the design and gathering of that data in the first place. Data science has exploded in visibility, and while there will always be a need for specialists in that field, the data gathering systems/tools/devices themselves need to put in a lot more effort to close the loop for the researcher, rather than just tossing them a lot of numbers.
Susan Collins, global head of healthcare at Twilio
Healthcare burnout from COVID will be the tipping point that drives the structural change needed for widespread AI adoption by the end of 2023. We have less human capacity to deliver healthcare than we did before the pandemic, and the only way to avoid further burnout for healthcare staff is to adopt AI and tech that reduces friction and opens up clinicians’ time for meaningful care.
Trishan Panch, co-founder of Wellframe:
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.
Mark Day, EVP R&D, iRhythm
Retrospective to Predictive Analysis Shift: 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.
Ed Ikeguchi, CEO of AiCure
Urgency to address bias in AI: In 2022, we will need to work together across healthcare to enhance the equitability of the AI that our patients’ care increasingly relies on. AI is only as strong as the data it is fed, but today’s developers are often left to train algorithms on single-source data with limited diversity. We’ve learned that the resulting biases can impact the ability to capture accurate data around a drug’s safety, ultimately impacting patients’ health. By equipping developers with diverse, high-quality training data sets and executing more rigorous evaluations of AI’s performance, these tools can reach their full potential to truly optimize drug development and patient care.
AI-powered precision medicine: AI-powered tools will increasingly take on an important role in precisely and accurately understanding a patient’s response to treatment. Rather than relying on subjective data such as patient-reported outcomes or one-off check-ins, video and audio-based digital biomarkers can catch subtle cues of a patient’s disease progression and how a treatment impacts their daily life. The sensitivity and objectivity of these novel assessments can not only inform personalized, proactive support for a patient, but also improve a sponsor’s understanding of the right patient for their specific drug.
Reliance on open-source data platforms to advance AI: In 2022 and beyond, we will see a push to build trust in these novel measurements in the public domain through peer review. Today’s algorithms are mostly proprietary, meaning researchers cannot access them to exercise and validate them on their own data sets, limiting their potential. Open-source platforms for algorithms can allow the scientific community to collaborate and jointly contribute to novel assessments, helping them become a more widely adopted means to objectively assess patient response.
Andrew Kasarskis, Chief Data Officer at Sema4
ML and AI Advances: We continue to see great advances in machine learning (ML) and artificial intelligence (AI) applied to large information-rich data sources in fields such as image analysis and natural language processing, and I don’t expect that will slow down at all. Some of these algorithms are already being successfully applied to biomedical data and are great at grouping and classifying data and entities represented by vectors, matrices, or cubes of data.
Efficient allocation of data curation resources: This is a need for technological and process innovation that I’d love to exist but don’t yet see happening. When obtaining those large corpuses of well-labeled data to train the AI, some human manual and semi-manual work is inevitably needed. This work is always expensive, never scales well, and frequently takes experts with esoteric knowledge away from important value-generating activities. Figuring out the most efficient way to allocate manual curation work seems, to me, like a significant unmet need that impedes progress in the use of data technology, particularly in biomedicine.
Continued focus on data equity: Societal biases and inequities can be present whenever data is used. I expect individuals and organizations to continue discovering errors, omissions, and blunders in their data where biases in collection and storage led to incorrect, misleading, and harmful outcomes. Continued focus on identifying and resolving these issues is important for both accuracy of conclusions and equity in data use.”
Malay Gandhi, Head of Strategy at Benchling
Look for protein sequencing to take center stage as the next breakout biotechnology. Technology advances will follow the successful path of single-cell DNA and RNA sequencing to unlock major advances in drug discovery.
Record biotech funding and exits will expand the hottest job market in America. Much like we’ve seen a talent crunch for software, look for a sizzling IPO market to drive demand and salaries across biotech.
Kimberly Powell, Vice President of Healthcare, NVIDIA
AI Generates Million X Drug Discovery: Simultaneous breakthroughs of AlphaFold and RoseTTAFold, creating a thousand-fold explosion of known protein structures, and AI that can generate a thousand more potential chemical compounds has increased the opportunity to discover drugs by a million times. Molecular simulations help to model target and drug interactions completely in silico. To keep up with the million times opportunity, AI is helping to introduce a new class of molecular simulations from system size and timescale to quantum accuracy.
AI Creates SaaS Medical Devices: The medical device industry has a game-changing opportunity enabled by AI to miniaturize and reduce cost, to automate and increase accessibility, and to continuously deliver innovation over the life of the product. This is creating a new business model to enable medical device companies to evolve from hardware solutions into software-as-a-service solutions that can be upgraded remotely and keep devices state of the art years after deployment.
Multimodal AI: Understanding disease is still our biggest grand challenge with over 10 thousand diseases without a therapy. Whether discovering drugs or treating patients, the use of multiple sources of health data is required. In order to leverage the world’s largest data sources with the most diversity, multimodal AI will bring us to that new frontier in discovering disease pathways as well as personalizing the treatment and prognosis of patients.
AI 2.0 with Federated Learning: To help AI application developers industrialize their AI technology and expand the application’s business benefit, AI must be trained and validated on data that resides outside the possession of their group, institution, and geography. Federated learning is the key to enable such collaboration of building robust AI models and validating models in the wild without sharing sensitive data. Federated learning will be needed at the far edges of every industry to facilitate the continuous learning and evaluation of AI.
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.
XNAT and self-tuning data infrastructures will transform imaging analytics to drive faster discovery and more accurate diagnostics: As healthcare and life sciences teams look to become agile with their data, the ability to automate medical imaging analytics and deploy machine learning algorithms on imaging data is critical. As a result, IT leaders at these organizations are reconsidering their data infrastructure—both storage and compute—as they rethink how to make the most of their imaging data at hand. Many of these organizations are turning to XNAT, an open-source imaging-informatics platform that helps import, archive, process, and securely distribute imaging data. Unlike PACS, XNAT has increased support for machine learning and annotation workflows. That means researchers and physicians can extend their capabilities when diagnosing diseases based on radiology. In recent years, XNAT has presented some formidable bottlenecks related to latency in data ingestion from clinical PACS to XNAT. Clinical studies on the imaging side tend to vary in their data sets, and traditional HPC storage systems are not optimized for this application. Another potential bottleneck is the learning curve in the tuning necessary to start bringing in various file types from other imaging modalities because research environments typically need to be tuned for specific data sets. The emergence of self-tuning data infrastructures accelerates data loading as well as the learning curve, unlocking the true power of XNAT to drive faster discovery and more accurate diagnostics.
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.