傳統的醫療器械已經徹底改變了患者的診斷和治療，并在非常廣泛的應用范圍內改善了患者的生活質量。應用軟件技術，包括數據科學、機器學習（ML）和一般人工智能（AI），涉及許多道德和監管方面的問題。然而，在不遠的將來，這些技術將推動新一輪的創新，帶來積極的醫療影響。
可穿戴技術正在快速發展，隨著這種進步，我們有機會捕捉到明顯不同于醫院記錄的連續健康數據。我們如何將奇妙的新傳感技術與強大的軟件相結合，以充分利用智能、安全的個人健康監測的機會？
Jacob Skinner是Thrive Wearables公司的首席執行官，他利用可穿戴技術來改善醫療保健服務，并使其大眾化。他完成了實驗物理學的博士學位，并致力于設計以人為本的技術超過10年。他在薩塞克斯大學的傳感器技術研究中心設計了商業化的電生理傳感器和應用。
Will Berriss是一名軟件工程師，在該領域有超過20年的經驗。他是英國特許工程師（CEng）及工程與技術協會成員（MIET），并擁有醫學圖像處理的博士學位。
Q1：您對目前的醫用可穿戴設備狀況有什么看法？
Skinner：在過去，醫療設備只是在醫院和醫生辦公室使用的東西。我們現在看到的，是我所說的醫療設備的消費化，在這種情況下，它們仍然經過醫療器械認證，并依據嚴格的標準制造，但它們不一定是生命攸關的；而是更傾向于監測、遠程病人護理，以及支持虛擬病房。
這是行業進步的證明，因為這些醫療設備可以由人們根據自己的條件來使用，它們更容易獲得。這些設備在外形上沒有那么累贅，使用起來也不那么復雜。真正有趣的是這種情況發生的方式和這個領域的潛力。例如，Apple Watch是一個消費類電子設備，但在非常特殊的條件下，它也是一個醫療設備，測量特定的心電圖信號。從監管的角度來看，哪些設備是醫療設備，哪些不是，仍然很清楚，但對用戶來說，其界限越來越寬泛。
Berriss：感覺目前的情況還沒有完全達到。我們只做了初步嘗試，但如果應用程序和設備能夠變得更加標準化并被采用，那就太好了。要做到這一點，技術可能生成的測量結果需要交付給臨床醫生，并被認為對護理可靠。
這可能會導致更多的個性化醫療和治療，甚至更適合病人的情況。
Q2：您如何看待數據科學、機器學習和人工智能的使用對可穿戴技術行業的革新？
Skinner：醫療設備和人工智能之間有一種天然的不協調，從醫療風險的角度來看，這種不協調很棘手。從監管的角度來看，使用動態算法非常困難，因為它們可能導致結果的多樣性，所以你必須非常小心地限制模型，并確保結果在指定的范圍內。
Berriss：在我看來，現在有很多數據是由私人公司采集的，但未來，部分或全部數據可以集中提供，我說的集中是指通過英國的NHS或美國的Medicare或Medicaid提供。對一樣東西，比如說一個腫瘤，或者甚至與多個病人有關的大量數據進行智能處理，可以更好地了解腫瘤的邊界，例如，它可能增長或可能不增長的速度，這最終有助于管理它的人做出有依據的決定。
Q3：這些技術能否推動新一輪的創新？
Skinner：是的。這是它的核心。這些機會的存在是因為可穿戴技術和傳感器的進步，這拓寬了醫療設備的概念。例如，腫瘤掃描一直都是數據密集型的，但現在相關數據的廣度肯定在變化。
以前沒有人知道一個人一天走了多少步；現在這是我們大多數人都能獲知的事情，而且是顯而易見的。你可以得出一些與運動有關的基本東西，然后你可以更進一步地利用這些信息。例如，你可以不斷地測量心率。如果你有患心臟病或心力衰竭的風險，但有機會讓你能夠在NHS的這些重大負擔和主要死因中更早地發現相關風險，我認為這就是更大的機遇所在。但是，知情并不總是一件好事，你不能只是給人們提供讓他們感到震驚或他們無法正確解釋的信息，但你也不能妨礙他們的知情權。歸根結底，如果你能預測一個人將會生病，那就是另一個完整的資源要求。
Berriss：當然，例如，這些技術正用于金融界以及工程和太空。隨著更多的人工智能和數據科學工作的發生，再加上日益流行的趨勢，更多的見解和改進將會產生，將會有越來越多的與健康產業相關的東西。
如果有一屋子的人，一半是健康的，一些有心臟疾病，然后你去做一些測量 - 這就是重點。真的，因為模式可能在我們尚不清楚具體位置的數據中 - 人工智能通常會發現數據中的這些差異，可能在表面上看不出來。這就是它真正有趣的地方 - 模式識別和解決人類無法解決的問題。
Q4：你如何設想這項技術被用于改善人們的健康？
Skinner：這有太多的答案了! 讓我們從大眾化開始。如果人們懂得照顧自己的健康，那么這是一個很好的起點，因為專業衛生人員不可能在任何時候都照顧到所有人。首先，這些設備可以持續測量用戶的健康狀況，將信息反饋給用戶，并在需要時提供給專業醫療人士。這就是可穿戴技術、持續數據、預測性和預防性醫療方面的實質性勝利。
因此，舉例來說，某人是否在往不對勁的方向發展，并且在兩年后就會發生心臟病？擁有這樣的洞察力將是非常有益的，以便有兩年的緩解策略，并使病人能夠掌握自己的身體健康狀態，并選擇明智的生活方式。關于虛擬病房和遠程病人監測，醫院和家庭之間的界限正變得模糊，所以我認為真正的進步將在于半導體醫療技術或監測設備，它們不屬于病危護理，卻真正擅長預測性護理。
Berriss：我認為這將通過讓人們更多地參與，來改善他們的健康。目前，你可以在家里測量血壓，去見臨床醫生時，你可以提供一個附件，上面有上周的生命體征數據。但這取決于我們是否能達到這樣的程度，即測量健康信號的某些方法可以非常標準化，以至于用戶和專業醫療人士可以對數據的有效性感到放心。這顯然需要監管，所以這些數據在醫學上是有用的，而且事后不需要復制，這就是需要彌補的差距。
Q5：如何使軟件足夠安全，使其能夠用于個人生理數據的持續監測？
Berriss：安全和保障最終要歸結為對數據進行加密，并對你分享數據的途徑以及分享數據的方式保持謹慎，特別是在持續監控方面。這方面已經有了很多進展，像蘋果公司已經對Apple Watch進行了加密，所以它不能與任何藍牙設備連接，以獲取數據。因此，在某些方面，我們所需要的已經成為可能，但反過來，這是否能在法律上得到證明。在法庭上，我們有證據證明數據沒有被篡改或落入壞人之手嗎，這能成立嗎？
Skinner：醫療軟件必須經過高度驗證和測試，因為它在連接對象、如何處理數據、誰可以訪問這些數據方面要保守得多；從本質上講，它更像是一個沙盒。因此，醫療軟件在處理生理數據時一般需要額外的成本和時間投入。我認為，要考慮的一個更關鍵的問題是，鑒于數據包含有價值的個人信息，有被濫用的風險，那如何能安全地管理和存儲數據。如果使用得當，這些數據的價值是巨大的，但也存在著巨大的風險。
通過區塊鏈或其他相關訪問協議的所有權概念很吸引人。當與加密和健康記錄相結合時，它創造了一個非常有趣的空間。我相信，這種結合在不久的將來會變得越來越重要，并產生重大影響。此外，如果個人擁有更多的數據，并希望以自己的條件訪問這些數據以獲得自己的醫療保健，他們可能需要在某些條件下獲得對其他人的匯總數據的控制性訪問，以比較他們的數據并做出結論。這意味著需要對目前的數據使用方式進行巨大的改變，目前的數據使用方式是極其自上而下的，并且存儲在大的數據庫中，訪問權限有限。轉移數據或對數據有所洞悉并不容易，而且目前數據的使用方式可能非常有用。
此外，還有可能以尊重隱私和匿名的方式購買和出售數據，這對研究目的很有用。然而，這還沒有公開或大規模地進行。
Berriss：使數據匿名的一種方法是生成分配給用戶的隨機數字代碼，而不是真實姓名。在COVID-19追蹤應用程序中，他們使用數字代碼來識別用戶，而不是他們的真實姓名，這可能是一個潛在的途徑。所以，這并非不可能，只是需要更多的努力。
Q6：其中有哪些道德和監管方面的問題？
Berriss：我想，從道德的角度來看，使用區塊鏈來保持安全性，并在個人和公司或醫療機構之間訂立合同是很重要的。許多年來，人們一直擔心自己的個人數據被用來對付自己。例如，如果你測量了某些健康指標，并發現你有可能在10年內導致你死亡的疾病，你可能不希望與你的健康保險公司分享這些信息，因為他們可能會拒絕為你提供已知或預測的疾病保險。
因此，個人在使用追蹤其健康數據的設備之前，了解其潛在的影響是至關重要的。重要的是，公司要預先披露他們可能發現的信息種類，以及他們可能與誰分享這些信息。這對那些更容易受到傷害的個人來說尤其如此，他們的健康數據可能會導致負面后果。
Skinner：是的，這是一個關鍵問題。如果我們不采取適當的措施，利用區塊鏈和其他技術保護個人數據，這些信息極有可能受到黑客的攻擊。這種漏洞的后果可能很嚴重，尤其是對NHS這樣的醫療機構。在監管方面，我認為目前存在兩個不同的問題。首先，正在被訪問以及存儲在各種數據庫中的數據量呈指數級增長。這種數據流的增加正在造成一個問題，因為有一百萬倍的數據和數百萬的不同節點。令人擔憂的是這些數據是如何被訪問和存儲的，以及與之相關的潛在風險。
第二個問題與醫療標準的放寬有關。雖然這種放寬可能有很好的理由，但也有相關的風險。美國食品藥品監督管理局（FDA）通過了許多通常不會批準的技術。這方面的例子包括由于移動電話的興起而出現的許多數字解決方案。這些數字系統通常被歸入更嚴格的醫療設備法規之外（根據所謂的510(k)提交），以及無數的I類和II類設備，它們基本上只是以合格的方式感應和傳遞信息。如果把這一點做到極致，就會出現通常被冠以“健康”之名的設備，它們（在市場上）被定位為非醫療性質。這為醫療效果以及醫療技術的可信度定下了一個移動的目標。很難知道界線在哪里，而且很有可能技術進步和監管在大多數時候都不能很好地保持一致。這是一個細微的問題，但重要的是要意識到快速發展的技術在醫療領域的潛在影響。
Q7：將這些技術整合到可穿戴設備及其應用程序時，您認為有哪些挑戰？
Berriss：我認為與我們在技術方面討論過的任何東西進行整合的關鍵困難在于所涉及的數據量太大。首先，如果你想把數據傳輸到其他地方，就需要維持網絡帶寬問題，如果你不這樣做，那么你就需要在本地處理，這就帶來了一系列的挑戰。傳輸如此大量的數據往往是不可行的。
如果集成到一個移動應用中，雖然手機能夠處理復雜的任務，但仍有挑戰，比如說，你要進行什么處理，什么數據會被傳輸。還可能存在一個問題，即用戶對如此強大的設備有什么興趣，這也可能影響到如何創建這樣的設備，并確定它的形式。
Skinner：我相信主要的挑戰在于證明可穿戴技術的價值。將使用技術的價值與它所產生的沖突聯系起來的等式是公認的。從本質上講，如果有人從使用一項技術中獲得很多價值，他們就更有可能采用它。然而，需要近距離接觸的以人為本的技術可能對個人空間有相當大的侵犯性，因此很難克服這一障礙。因此，為了鼓勵技術的采用，該技術的價值必須被證明是非常高的。
即使在它可能意味著生與死的區別的情況下，比如可以檢測潛在心臟病發作的可穿戴技術，如果感覺太過笨重或礙眼，人們仍然可能會抵制使用它。此外，即使該技術提醒用戶有潛在的健康問題，他們也可能不會采取行動來解決這個問題。簡而言之，最大的挑戰是通過提高可穿戴技術的價值，使其超越它所產生的沖突，從而改善可穿戴技術的采用。
Q8：這個領域下一步會發生什么？請向我們分享您的預測。
Skinner：本質上，差不多是我們已經討論過的內容。我們談到的主題都有時間表。這些包括醫療保健的大眾化，利用預防和預測措施讓人們更好地了解和處理自己的健康狀態，并通過這些措施減少醫院就診的次數。此外，通過利用虛擬病房和遠程監控幫助人們早點回家護理，是我的主要預測。
Berriss：根據我的經驗，我發現當你在媒體或這個期間聽到它作為一個反復討論的話題時，往往會知道什么會成為下一件大事。例如，我已經看到許多關于心率和心率變異性的討論。如果你問我緊接著是什么，我會認為是這個領域的東西。
英文原文：
A conversation with Jacob Skinner and Will Berriss, Thrive Wearables
Conventional medical devices have revolutionized patient diagnostics and treatments and improved quality of life across a staggering breadth of applications. Applying software techniques, including data science, machine learning (ML), and general artificial intelligence (AI), has many ethical and regulatory dimensions. However, the future is heading rapidly toward a point where these techniques are driving a new wave of innovation and positive health impacts.
Wearable technology is advancing at a rapid pace and with this advancement comes the opportunity to capture a very different kind of continuous health data than that recorded in a hospital setting. How do we combine incredible new sensing technologies with robust software to take full advantage of the opportunity for intelligent, safe personal health monitoring?
Jacob Skinner is the CEO of Thrive Wearables, where he uses wearable technology to improve healthcare and democratize access to it. He completed a D.Phil. in experimental physics and has designed human-centered technology for over 10 years. He has designed commercially available electrophysiology sensors and applications at the University of Sussex's Sensor Technology Research Centre.
Will Berriss is a software engineer with over 20 years of experience in the field. He is a Chartered Engineer (CEng) and member of the Institution of Engineering and Technology (MIET) and has a Ph.D. in medical image processing.
What is your view on the current medical wearable device landscape?
Skinner: In the past, medical devices were things that we just used in hospitals and in doctors’ offices. What we see now is what I call the consumerization of medical devices, in which they are still medical device certified and built within strict standards, but they are not necessarily life critical; they are more geared to monitoring, remote patient care, and in supporting virtual wards.
It’s a testament to advances in the industry, because these medical devices can be used by people on their own terms and they are much more accessible. They're not as cumbersome physically or as complex to use. What is really interesting as well is the way in which this is happening and the potential in this space. For example, the Apple Watch is a consumer electronics device, but under very particular conditions it's also a medical device, measuring specific ECG signals. It's still clear from a regulatory point of view which devices are medical and which are not, but for the user the boundaries are increasingly broad.
Berriss: It feels like the current landscape is not quite there yet. We're sort of dipping a toe in the water, but it would be great if apps and devices could become more heavily standardized and adopted. For this to happen, the measurements that tech could generate would need to be delivered to clinicians and be considered reliable for care.
This could result in more personalized healthcare and treatment that is even more tailored to a patient.
How do you see the use of data science, machine learning, and AI revolutionizing the wearable tech industry?
Skinner: There's a natural dissonance between medical devices and artificial intelligence that is tricky to navigate from a medical risk point of view. From a regulatory perspective, it's very hard to use dynamic algorithms because of the diversity of outcomes they might lead to, so you have to constrain the models very carefully and ensure outcomes are within specified boundaries.
Berriss: The way I see things, there is a lot of data being captured by private companies but, going forward, some or all of that data could be made available centrally, and by centrally I mean the NHS in the U.K or, perhaps Medicare or Medicaid in the USA. A huge amount of data processed intelligently about one thing, say a tumor, or even in relation to multiple patients, could give a much better understanding of the boundary of the tumor, and, for example, how quickly it may or may not grow, which ultimately helps those managing it to make informed decisions.
Could these techniques drive a new wave of innovation?
Skinner: Yes. That's the core of it. These opportunities exist because of wearable technology and advances in sensors, which broaden the concept of medical devices. For example, tumor scanning has always been data heavy, but the breadth of what data is relevant now is definitely changing.
Nobody used to know how many steps they took in a day; now it’s something most of us are aware of and can easily find out, and you can derive some basic stuff relating to exercise, but then you can take that so much further. For example, you could be measuring heart rate constantly. If you're at risk of heart disease or heart failure and there was a chance that you might be able to detect it just that little bit earlier, I think that's where the bigger opportunities are, in these big burdens on the NHS and big causes of death. But knowledge isn’t always a good thing, and you can’t just give people information that alarms them or that they can’t interpret properly, but you also can’t shield them from their rights to be informed. Ultimately, if you can predict that someone is going to get ill, that’s a whole other resource requirement.
Berriss: Definitely, these techniques are being used in the financial world and in engineering and space, for example. As more AI and data science work happens, and it becomes more popular, more of those insights and improvements will get generated and there will be more and more that is relevant to the health industry.
If you had a room full of people and half were healthy and some had a heart condition and then you go and take some measurements – and that’s the whole point, really, because the pattern could be in the data somewhere we don’t know about already – AI would typically spot those differences in the data that might not be visible on the face of it. And that’s where it becomes really interesting – pattern recognition and solving things that humans can’t.
How do you envisage this technology being used to improve people’s health?
Skinner: There are so many answers here! Let's start with democratization. If people are looking after their own health, then that's a great starting point, because health professionals cannot look after everyone at all times. First, these devices could start constantly measuring users’ health and feeding information back to users and potentially escalating to medical professionals as needed. That's kind of the bread and butter win in terms of wearable tech, constant data, and predictive and preventive healthcare.
So, for example, is somebody moving in the wrong direction and is two years away from a heart attack? Having that kind of insight would be incredibly beneficial in order to have two years’ worth of a mitigation strategy and to empower patients to take ownership of their physical health and make informed lifestyle choices. In regard to virtual wards and remote patient monitoring, it’s blurring the boundary between hospitals and homes, so I think the real advances will be in semi-medical technologies or monitoring devices that are not critical care but are really good at predictive care.
Berriss: I think it would improve people's health by involving them more. Currently, you can take your blood pressure at home, and when you go to see a clinician you could provide an attachment with vitals data from the last week. But it depends on whether we can get to a point where certain approaches to measuring health signals can be so standardized that users and medical professionals can feel confident in the validity of the data. This obviously needs regulating, so this data is medically useful and doesn’t need to be replicated afterward, and that’s the gap that needs closing.
How can software be made safe and secure enough for it to be used in the continuous monitoring of personal physiological data?
Berriss: Safety and security ultimately comes down to encrypting data and being careful with the way in which you share it and also how you share it, especially with continuous monitoring. There is a lot of progress in this already happening, with companies like Apple, which has encrypted the Apple Watch so it cannot connect with just any Bluetooth device to retrieve data from it. So, in some respects, what we need is already possible, but on the flip side is whether this can be legally proven. Would it hold up in a court of law that we have proof the data hasn't been tampered with or been placed into the wrong hands?
Skinner: Medical software has to be highly validated and tested, as it's much more conservative in terms of what it's connecting to, how it's processing data, who's got access to it; essentially, it's all much more of a sandbox. So, there's a general additional cost and time investment required in medical software processing physiological data. I believe that an even more crucial question to consider is how data can be managed and stored securely, given that it contains valuable personal information that is at risk of being misused. The value of this data is enormous if it is used appropriately, but there is also a significant risk.
The concept of ownership through a blockchain or other associated access protocols is fascinating. When combined with encryption and health records, it creates a very interesting space. I believe that this combination will become increasingly important and have a significant impact in the near future. In addition, if individuals have more data and want to access it on their own terms for their own healthcare reasons, they may need to be given controlled access to other people's aggregated data on certain terms to compare their data and make conclusions. This represents a need for a sea change in how data is currently used, which is extremely top-down and stored in big databases with limited access. It is not easy to shift data around or gain insights from it, and it is not currently being used in a way that could be incredibly useful.
Furthermore, there is the potential for buying and selling of data in a way that respects privacy and anonymity, which could be useful for research purposes. However, this is not being done openly or on a large scale yet.
Berriss: One way to anonymize the data could be by generating random number codes assigned to users instead of real names. In the COVID-19 tracking apps, they used a number code to identify people instead of their real names, and this could be potentially one avenue to follow. So, it’s not impossible, it just needs more work.
What are the ethical and regulatory dimensions at play here?
Berriss: I suppose using a blockchain to keep things secure and establish contracts between individuals and companies or medical bodies is important from an ethical perspective. For many years, people have been concerned about their personal data being used against them. For example, if you measure certain health metrics and discover that you are predisposed to a condition that could lead to your death in 10 years, you may not want this information to be shared with your health insurance company because they could deny you coverage for known or predicted conditions.
Therefore, it is crucial for individuals to understand the potential implications before using devices that track their health data. It is important for companies to disclose up front what kind of information they may discover and with whom they may share this information. This is especially true for individuals who are more vulnerable and may have health data that could lead to negative consequences.
Skinner: Yes, this is a critical issue. If we don't implement proper measures to secure personal data using a blockchain and other technologies, it is highly likely that this information will be vulnerable to hacking. The consequences of such breaches could be severe, especially for healthcare agencies like the NHS. In terms of regulation, I think there are two different issues taking place. The first is the exponential increase in the amount of data that is being accessed and stored in various databases. This increase in data flow is causing a problem, as there is a million times more data and millions of different nodes. The concern is how this data is being accessed and stored and the potential risks associated with it.
The second issue is related to the relaxation of medical standards. While there may be good reasons for this relaxation, there is a risk associated with it. The FDA is allowing many technologies to pass that traditionally wouldn't have. Examples could include many digital solutions that have come into being due to the emergence of mobile phones. These digital systems are usually classified outside of the more stringent medical device regulations (under what is called a 510k submission), as well as a myriad of Class I and II devices, which are essentially just sensing and passing on the information in a qualified way. Taking this to the extreme leads to what are often termed “wellness” devices, which are very much positioned (in the market) as non-medical in nature. This creates a moving target in terms of what medical efficacy is and what medical technology credibility is. It's hard to know quite where the line is, and there's a strong chance that technology advances and regulation will not be well aligned most of the time. This is a nuanced discussion, but it's important to be aware of the potential implications of fast-tracked technologies in the medical field.
What challenges do you see in the integration of these technologies into wearable devices and their apps?
Berriss: I think the key difficulties with integrating with anything we've discussed on the technical side lies in the sheer amount of data involved. First, there are network bandwidth issues that need to be maintained if you want to transmit the data elsewhere, and if you don't, then you'll need to process it locally, which presents its own set of challenges. It's often not feasible to transmit such large amounts of data.
If you integrate into a mobile application, while mobile phones are capable of handling complex tasks, there are still challenges with what works, for example, what processing you do and what data will be transmitted. There also may be a question about what appetite users have for a device that's so powerful, which could also impact how such a device is created and define what form it takes.
Skinner: I believe the main challenge lies in proving the value of wearable technology. The equation that relates the value of using the technology to the friction it creates is well recognized. Essentially, if someone perceives a lot of value from using a piece of technology, they are more likely to adopt it. However, human-centered technologies that require close proximity can be quite invasive to personal space, making it difficult to overcome this barrier. Therefore, in order to encourage adoption, the value of the technology must be proven to be very high.
Even in cases where it could mean the difference between life and death, such as with wearable technology that could detect a potential heart attack, people may still resist using it if it feels too bulky or obtrusive. Additionally, even if the technology alerts them to a potential health issue, they may not take action to address it. In short, the biggest challenge is improving the adoption of wearable technology by increasing its value beyond the friction it creates.
What’s next in this area? Please give us your predictions.
Skinner: Essentially, I would say more of what we've already discussed. The themes we talked about all have timelines. These include the democratization of healthcare, using preventive and predictive measures for people to better understand and engage with their health, and reducing the number of hospital visits through these measures. Additionally, helping people go home sooner by utilizing virtual wards and remote monitoring are the key predictions.
Berriss: In my experience, I find you tend to know what's going to be the next big thing when you hear it as a recurring topic in the media or in this space. For example, I’ve seen many discussions about heart rate and heart rate variability. If you ask me what's immediately next, I would say something in that space.
