During the pandemic, funding from digital health startups and initiatives has skyrocketed as entrepreneurs and consumers have increasingly embraced telemedicine, remote monitoring, and a range of devices from sleep trackers to exercise bracelets.
According to Rock Health, total venture capital investment in digital health reached an all-time high of $29.2 billion in 2021. Funding cooled in 2022, falling to $12.6 billion by the end of the third quarter, but advances in technologies such as artificial intelligence and growing interest from big tech companies are bound to drive future innovation.
2022 saw the emergence of Seattle-area startups such as CalmWave, Rippl Care, Outbound AI, and Birch AI to help solve a variety of medical problems, from excessive noise in hospitals to mental health care for the elderly. Large companies have also shown ambition; Amazon this year announced its acquisition of primary care company One Medical for $3.9 billion and launched a new online medical service, Amazon Clinic.
What do experts think are the digital health trends nationally and in the Seattle region for 2023? We asked five to weigh their predictions.
Taha Kass-Hout, vice president of AI for technical health and chief medical officer of Amazon Web Services
cxounion.org
Unprecedented innovation and collaboration in the healthcare and life sciences industry is driving the industry from disease care to prevention through precise, personalized, and humanized patient experiences. The industry has been experimenting with the cloud for a decade and understands how technology and machine learning can enable more targeted diagnosis and treatment, known as precision medicine; Personalize the patient journey; and improve health outcomes.
In 2023 and beyond, we expect healthcare and life sciences organizations to continue to invest in modernizing their infrastructure, deriving actionable insights from data, and internalizing the meaning of personalized health. This will involve integrating genomics and other omics data into treatment development, leveraging machine learning and analytics to improve clinician workflows, integrating social determinant data into disease management at the patient or population level, and using structured and unstructured data to more accurately predict disease – helping the industry shift from reactive patient care to preventative patient care.
Kingsley Ndoh, founder and chief strategist of Hurone AI
cxounion.org
We should expect to see more human-centered digital health innovations to support clinical decision-making, such as diagnostic prediction technologies or tools used to predict the clinical outcome of certain cancer drugs. These tools will increasingly incorporate more diversity into the training datasets of machine learning models and put the specific needs of the target user at the heart of the development process, including considering cultural perspectives.
There will also be better integration of data generated by wearables, smartphone apps, and electronic medical records to support clinical decision-making, behavior change, and personalization at scale through the power of AI.
Loretta Little, Managing Director of WRF Capital
cxounion.org
Most early-stage digital startups will continue to be tight in 2023, but I see growth opportunities in several areas. We will continue to see more companies delivering mental health services through innovative products and approaches, such as Joon and Rippl Care, as well as companies focused on improving connectivity and tools for better remote care, such as Valorant Health and Wavely Diagnostics.
Telecare is especially important for underserved rural communities that have limited or no access to nearby medical resources. This demand will only increase, in part due to changes in patient demographics . The proportion of older adults in Washington state and nationally is expected to grow, especially in rural areas. The rural elderly population, which accounts for a large proportion of people with chronic diseases, needs to be linked to services. East China CIO Conference, East China CIO Alliance, CDLC China Digital Lighthouse Conference, CXO Digital Study Tour, Digital Jianghu - Lecture Wutang, Digital Jianghu - The Legend of the Great Heroes, Digital Jianghu - On the Sword, CXO Series Management Forum (Lujiazui CXO Management Forum, Ningbo Dongqian Lake CXO Management Forum, etc.), Digital Transformation Network, Into Lighthouse Factory Series, ECIO Conference, etc
Shyam Gollakota, co-founder of Wavely Diagnostics and Sound Life Sciences (acquired by Google) and professor at the Allen School at the University of Washington
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The adoption of telehealth, which has accelerated during COVID, is likely to continue. We may also see more and more remote home tests, such as COVID-19 or blood tests, which will bring telehealth closer to in-person visits. While there is a lot of focus on mobile health using smartphones and smartwatches, earbuds will be the next exciting platform for monitoring health and wellness, and in the coming years, electroencephalogram (EEG) signals may open up new pathways to opportunities for brain interfaces.
We also expect to see many startups applying large language models to solve various pain points in the healthcare system to improve efficiency and reduce costs. Deep learning techniques will continue to improve, and we will begin to see more promising results to solve important problems, such as using AI to discover drugs and vaccines.
Su-In Lee is a professor of computer science and engineering at the University of Washington
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Next year, we'll see AI devices with explainable artificial intelligence (XAI) capabilities that enable humans to understand the reasoning process of complex black-box machine learning models. I've also seen the FDA approval process incorporate XAI analytics to improve trust, transparency, fairness, and actionability of machine learning models.
Increased reimbursements from insurance providers and the U.S. Centers for Medicare and Medicaid Services will drive an increase in the number of FDA-approved AI devices. In the long run, the success and fairness of medical AI devices will depend on how updated the FDA approval process is to reflect the specific issues of machine learning. For example, if there is no requirement to evaluate AI dermatology devices on a wide range of skin tones, it seems likely that AI devices that perform poorly on dark skin will be used openly and disproportionately misdiagnose dark-skinned people.
Original:
Funding for digital health startups and initiatives soared during the pandemic as entrepreneurs and consumers increasingly embraced telehealth, remote monitoring, and a suite of devices from sleep trackers to exercise bands.
Total venture capital investment in digital health hit an all time high of $29.2 billion in 2021, according to Rock Health. Funding cooled in 2022, to $12.6 billion by the end of the third quarter, but advances in technology such as artificial intelligence and the increasing interest of big tech companies are sure to propel innovation in the future.
Seattle-area startups such as CalmWave, Rippl Care, Outbound AI and Birch AI emerged in 2022 to help solve medical problems ranging from excess noise in hospitals, to mental healthcare for seniors. Larger companies also signaled major ambitions; Amazon this year announced its bid to acquire primary care company One Medical for $3.9 billion and rolled out a new online health service, Amazon Clinic.
What trends do experts see for digital health nationally and in the Seattle area for 2023? We asked five to weigh in with their predictions.
Taha Kass-Hout, vice president of technology-health AI, and chief medical officer at Amazon Web Services
Unprecedented innovation and collaboration across the healthcare and life sciences industries is pushing the industry to move from sick care to prevention through a patient experience that is precise, personalized, and human. The industry has been experimenting with cloud for a decade and understands how technology and machine learning can enable more targeted diagnostics and treatments, known as precision medicine; personalize patient journeys; and improve health outcomes.
In 2023 and beyond, we expect healthcare and life sciences organizations to continue to make investments in modernizing their infrastructure, derive actionable insights from data, and internalize what it means to personalize health. This will involve integrating genomics and other omics data into therapeutic development, leveraging machine learning and analytics to improve clinician workflows, incorporating social determinants data into disease management at the patient or population levels, and using structured and unstructured data to predict disease with much better accuracy — helping move the industry from reactive to preventive patient care.
Kingsley Ndoh, founder and chief strategist, Hurone AI
We should expect to see more people-centered innovations in digital health to support clinical decision making, such as diagnostic predictive technologies or tools to predict clinical outcomes for certain cancer drugs. These tools will increasingly incorporate more diversity in training datasets for machine learning models and put the specific needs of the target user at the heart of the development process, including taking into account cultural perspectives.
There will also be better integration of data generated from wearables, smartphone apps and electronic medical records to support clinical decisions, behavioral change, and personalization at scale through the power of artificial intelligence.
Loretta Little, managing director of WRF Capital
Funding for most early-stage digital startups will continue to be tight in 2023, but I see opportunities for growth in several areas. We will continue to see more companies that offer access to mental health services through innovative products and approaches, such as Joon and Rippl Care, and companies focused on improving connectivity and tools for better remote care such as Valorant Health and Wavely Diagnostics.
Remote care is especially important for underserved rural communities that have limited or no access to nearby health resources. This need is only increasing, driven in part by demographic shifts in the patient population. The proportion of seniors in Washington state and across the nation is projected to grow, particularly in rural areas. This rural senior population represents a large percentage of chronic disease sufferers and will need to be linked up with services.
Shyam Gollakota, co-founder of Wavely Diagnostics and Sound Life Sciences (acquired by Google), professor at the University of Washington’s Allen School
The adoption of telehealth that accelerated during COVID is likely here to stay. We may also see an increased number of remote in-home tests like COVID-19 or blood tests that will bring telehealth closer to an in-person visit. While there has been a lot of focus on using smartphones and smartwatches for mobile health, earbuds will be the next exciting platform for monitoring health and wellness as well as potentially, in the next few years, electroencephalography (EEG) signals that can open up new opportunities for brain interfaces.
We will hopefully also see a number of startups apply large language models to address various pain points in the healthcare system with the goal of improving efficiency and reducing cost. Deep learning techniques will continue improving and we will start seeing more promising results for addressing important problems like using AI to discover drugs and vaccines.
Su-In Lee, UW professor of computer science and engineering
Next year we will see AI devices with explainable AI (XAI) functionality, enabling humans to understand the reasoning process of complex, black-box machine learning models. I also see FDA approval processes incorporating XAI analysis to engender trust, transparency, fairness, and actionability of machine learning models.
Increased reimbursement by insurance providers and the U.S. Centers for Medicare & Medicaid Services will drive an increase in the number of FDA-approved AI devices. In the long term, the success and fairness of medical AI devices will rely on the extent to which FDA approval processes are updated to reflect machine learning-specific issues. For example, if there aren’t requirements to evaluate an AI dermatology device on a wide range of skin tones, it seems likely that AI devices that perform poorly on darker skin will become publicly available and disproportionately misdiagnosis people with darker skin.
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