在大流行期間，随着企業家和消費者越來越多地接受遠端醫療、遠端監控以及從睡眠追蹤器到運動手環等一系列裝置，數字健康初創公司和計劃的資金激增。
根據 Rock Health的資料，2021 年數字健康領域的風險投資總額創下 292 億美元的曆史新高。資金在 2022 年降溫，到第三季度末降至 126 億美元，但人工智能等技術的進步和大型科技公司日益增長的興趣必将推動未來的創新。
2022 年出現了CalmWave、Rippl Care、Outbound AI和Birch AI等西雅圖地區的初創公司，以幫助解決從醫院過度噪音到老年人心理保健等各種醫療問題。大公司也表現出雄心壯志；亞馬遜今年宣布以 39 億美元的價格收購初級保健公司 One Medical，并推出了一項新的線上醫療服務 Amazon Clinic。
專家們認為 2023 年全國和西雅圖地區的數字健康趨勢是什麼？我們請了五位來權衡他們的預測。
Taha Kass-Hout，技術健康 AI 副總裁兼亞馬遜網絡服務首席醫療官
cxounion.org
醫療保健和生命科學行業前所未有的創新和合作正在推動該行業通過精确、個性化和人性化的患者體驗從疾病護理轉向預防。該行業已經對雲進行了十年的試驗，并且了解技術和機器學習如何能夠實作更有針對性的診斷和治療，即精準醫學；個性化患者旅程；并改善健康結果。
在 2023 年及以後，我們預計醫療保健和生命科學組織将繼續投資于基礎設施的現代化，從資料中獲得可操作的見解，并将個性化健康的意義内化。這将涉及将基因組學和其他組學資料整合到治療開發中，利用機器學習和分析來改進臨床醫生的工作流程，将社會決定因素資料整合到患者或人群層面的疾病管理中，以及使用結構化和非結構化資料更準确地預測疾病— 幫助該行業從被動的患者護理轉變為預防性的患者護理。
Kingsley Ndoh，Hurone AI創始人兼首席政策師
cxounion.org
我們應該期待看到更多以人為本的數字健康創新來支援臨床決策，例如用于預測某些抗癌藥物臨床結果的診斷預測技術或工具。這些工具将越來越多地将更多多樣性納入機器學習模型的訓練資料集，并将目标使用者的特定需求置于開發過程的核心，包括考慮文化觀點。
還将更好地整合可穿戴裝置、智能手機應用程式和電子病曆生成的資料，以通過人工智能的力量支援臨床決策、行為改變和大規模個性化。
WRF Capital董事總經理Loretta Little
cxounion.org
大多數早期數字初創公司的資金在 2023 年将繼續緊張，但我看到了幾個領域的增長機會。我們将繼續看到更多公司通過創新産品和方法提供心理健康服務，例如 Joon 和 Rippl Care，以及專注于改善連接配接性和工具以實作更好遠端護理的公司，例如 Valorant Health 和 Wavely Diagnostics。
遠端護理對于服務欠缺的農村社群尤為重要，這些社群隻能有限或無法獲得附近的醫療資源。這種需求隻會增加，部分原因 是患者人口結構的變化 。華盛頓州和全國老年人的比例 預計會增長，尤其是在農村地區。農村老年人口占慢性病患者的很大一部分，需要與服務聯系起來。華東CIO大會、華東CIO聯盟、CDLC中國數字化燈塔大會、CXO數字化研學之旅、數字化江湖-講武堂，數字化江湖-大俠傳、數字化江湖-論劍、CXO系列管理論壇（陸家嘴CXO管理論壇、甯波東錢湖CXO管理論壇等）、數字化轉型網，走進燈塔工廠系列、ECIO大會等
Shyam Gollakota，Wavely Diagnostics and Sound Life Sciences（被谷歌收購）聯合創始人，華盛頓大學艾倫學院教授
cxounion.org
在 COVID 期間加速的遠端醫療的采用很可能會持續下去。我們可能還會看到越來越多的遠端家庭測試，如 COVID-19 或血液測試，這将使遠端醫療更接近親自就診。雖然人們非常關注使用智能手機和智能手表進行移動健康，但耳塞将成為下一個令人興奮的監測健康和保健的平台，并且在未來幾年内，腦電圖 (EEG) 信号可能會開辟新的途徑大腦接口的機會。
我們也希望看到許多初創公司應用大型語言模型來解決醫療保健系統中的各種痛點，以提高效率和降低成本。深度學習技術将繼續改進，我們将開始看到更有希望的結果來解決重要問題，例如使用 AI 發現藥物和疫苗。
Su-In Lee，華盛頓大學計算機科學與工程教授
cxounion.org
明年，我們将看到具有可解釋人工智能 (XAI) 功能的人工智能裝置，使人類能夠了解複雜的黑盒機器學習模型的推理過程。我還看到 FDA 準許流程結合了 XAI 分析，以提高機器學習模型的信任度、透明度、公平性和可操作性。
保險提供商和美國醫療保險和醫療補助服務中心增加的報帳将推動 FDA 準許的人工智能裝置數量的增加。從長遠來看，醫療 AI 裝置的成功和公平将取決于 FDA 審批流程的更新程度，以反映機器學習的特定問題。例如，如果沒有要求在廣泛的膚色上評估 AI 皮膚病學裝置，那麼在深色皮膚上表現不佳的 AI 裝置似乎很可能會公開使用，并且不成比例地誤診深色皮膚的人。
原文：
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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