傳統(tǒng)的醫(yī)療器械已經(jīng)徹底改變了患者的診斷和治療，并在非常廣泛的應(yīng)用范圍內(nèi)改善了患者的生活質(zhì)量。應(yīng)用軟件技術(shù)，包括數(shù)據(jù)科學(xué)、機(jī)器學(xué)習(xí)（ML）和一般人工智能（AI），涉及許多道德和監(jiān)管方面的問題。然而，在不遠(yuǎn)的將來，這些技術(shù)將推動(dòng)新一輪的創(chuàng)新，帶來積極的醫(yī)療影響。
可穿戴技術(shù)正在快速發(fā)展，隨著這種進(jìn)步，我們有機(jī)會(huì)捕捉到明顯不同于醫(yī)院記錄的連續(xù)健康數(shù)據(jù)。我們?nèi)绾螌⑵婷畹男聜鞲屑夹g(shù)與強(qiáng)大的軟件相結(jié)合，以充分利用智能、安全的個(gè)人健康監(jiān)測的機(jī)會(huì)？
Jacob Skinner是Thrive Wearables公司的首席執(zhí)行官，他利用可穿戴技術(shù)來改善醫(yī)療保健服務(wù)，并使其大眾化。他完成了實(shí)驗(yàn)物理學(xué)的博士學(xué)位，并致力于設(shè)計(jì)以人為本的技術(shù)超過10年。他在薩塞克斯大學(xué)的傳感器技術(shù)研究中心設(shè)計(jì)了商業(yè)化的電生理傳感器和應(yīng)用。
Will Berriss是一名軟件工程師，在該領(lǐng)域有超過20年的經(jīng)驗(yàn)。他是英國特許工程師（CEng）及工程與技術(shù)協(xié)會(huì)成員（MIET），并擁有醫(yī)學(xué)圖像處理的博士學(xué)位。
Q1：您對目前的醫(yī)用可穿戴設(shè)備狀況有什么看法？
Skinner：在過去，醫(yī)療設(shè)備只是在醫(yī)院和醫(yī)生辦公室使用的東西。我們現(xiàn)在看到的，是我所說的醫(yī)療設(shè)備的消費(fèi)化，在這種情況下，它們?nèi)匀唤?jīng)過醫(yī)療器械認(rèn)證，并依據(jù)嚴(yán)格的標(biāo)準(zhǔn)制造，但它們不一定是生命攸關(guān)的；而是更傾向于監(jiān)測、遠(yuǎn)程病人護(hù)理，以及支持虛擬病房。
這是行業(yè)進(jìn)步的證明，因?yàn)檫@些醫(yī)療設(shè)備可以由人們根據(jù)自己的條件來使用，它們更容易獲得。這些設(shè)備在外形上沒有那么累贅，使用起來也不那么復(fù)雜。真正有趣的是這種情況發(fā)生的方式和這個(gè)領(lǐng)域的潛力。例如，Apple Watch是一個(gè)消費(fèi)類電子設(shè)備，但在非常特殊的條件下，它也是一個(gè)醫(yī)療設(shè)備，測量特定的心電圖信號(hào)。從監(jiān)管的角度來看，哪些設(shè)備是醫(yī)療設(shè)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(dá)到。我們只做了初步嘗試，但如果應(yīng)用程序和設(shè)備能夠變得更加標(biāo)準(zhǔn)化并被采用，那就太好了。要做到這一點(diǎn)，技術(shù)可能生成的測量結(jié)果需要交付給臨床醫(yī)生，并被認(rèn)為對護(hù)理可靠。
這可能會(huì)導(dǎo)致更多的個(gè)性化醫(yī)療和治療，甚至更適合病人的情況。
Q2：您如何看待數(shù)據(jù)科學(xué)、機(jī)器學(xué)習(xí)和人工智能的使用對可穿戴技術(shù)行業(yè)的革新？
Skinner：醫(yī)療設(shè)備和人工智能之間有一種天然的不協(xié)調(diào)，從醫(yī)療風(fēng)險(xiǎn)的角度來看，這種不協(xié)調(diào)很棘手。從監(jiān)管的角度來看，使用動(dòng)態(tài)算法非常困難，因?yàn)樗鼈兛赡軐?dǎo)致結(jié)果的多樣性，所以你必須非常小心地限制模型，并確保結(jié)果在指定的范圍內(nèi)。
Berriss：在我看來，現(xiàn)在有很多數(shù)據(jù)是由私人公司采集的，但未來，部分或全部數(shù)據(jù)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?xùn)|西，比如說一個(gè)腫瘤，或者甚至與多個(gè)病人有關(guān)的大量數(shù)據(jù)進(jìn)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(jù)的決定。
Q3：這些技術(shù)能否推動(dòng)新一輪的創(chuàng)新？
Skinner：是的。這是它的核心。這些機(jī)會(huì)的存在是因?yàn)榭纱┐骷夹g(shù)和傳感器的進(jìn)步，這拓寬了醫(yī)療設(shè)備的概念。例如，腫瘤掃描一直都是數(shù)據(jù)密集型的，但現(xiàn)在相關(guān)數(shù)據(jù)的廣度肯定在變化。
以前沒有人知道一個(gè)人一天走了多少步；現(xiàn)在這是我們大多數(shù)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(yùn)動(dòng)有關(guān)的基本東西，然后你可以更進(jìn)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(fēng)險(xiǎn)，但有機(jī)會(huì)讓你能夠在NHS的這些重大負(fù)擔(dān)和主要死因中更早地發(fā)現(xiàn)相關(guān)風(fēng)險(xiǎn)，我認(rèn)為這就是更大的機(jī)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(quán)。歸根結(jié)底，如果你能預(yù)測一個(gè)人將會(huì)生病，那就是另一個(gè)完整的資源要求。
Berriss：當(dāng)然，例如，這些技術(shù)正用于金融界以及工程和太空。隨著更多的人工智能和數(shù)據(jù)科學(xué)工作的發(fā)生，再加上日益流行的趨勢，更多的見解和改進(jìn)將會(huì)產(chǎn)生，將會(huì)有越來越多的與健康產(chǎn)業(yè)相關(guān)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(diǎn)。真的，因?yàn)槟Ｊ娇赡茉谖覀兩胁磺宄唧w位置的數(shù)據(jù)中 - 人工智能通常會(huì)發(fā)現(xiàn)數(shù)據(jù)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(shí)別和解決人類無法解決的問題。
Q4：你如何設(shè)想這項(xiàng)技術(shù)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(gè)很好的起點(diǎn)，因?yàn)閷I(yè)衛(wèi)生人員不可能在任何時(shí)候都照顧到所有人。首先，這些設(shè)備可以持續(xù)測量用戶的健康狀況，將信息反饋給用戶，并在需要時(shí)提供給專業(yè)醫(yī)療人士。這就是可穿戴技術(shù)、持續(xù)數(shù)據(jù)、預(yù)測性和預(yù)防性醫(yī)療方面的實(shí)質(zhì)性勝利。
因此，舉例來說，某人是否在往不對勁的方向發(fā)展，并且在兩年后就會(huì)發(fā)生心臟?。繐碛羞@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(tài)，并選擇明智的生活方式。關(guān)于虛擬病房和遠(yuǎn)程病人監(jiān)測，醫(yī)院和家庭之間的界限正變得模糊，所以我認(rèn)為真正的進(jìn)步將在于半導(dǎo)體醫(yī)療技術(shù)或監(jiān)測設(shè)備，它們不屬于病危護(hù)理，卻真正擅長預(yù)測性護(hù)理。
Berriss：我認(rèn)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(yī)生時(shí)，你可以提供一個(gè)附件，上面有上周的生命體征數(shù)據(jù)。但這取決于我們是否能達(dá)到這樣的程度，即測量健康信號(hào)的某些方法可以非常標(biāo)準(zhǔn)化，以至于用戶和專業(yè)醫(yī)療人士可以對數(shù)據(jù)的有效性感到放心。這顯然需要監(jiān)管，所以這些數(shù)據(jù)在醫(yī)學(xué)上是有用的，而且事后不需要復(fù)制，這就是需要彌補(bǔ)的差距。
Q5：如何使軟件足夠安全，使其能夠用于個(gè)人生理數(shù)據(jù)的持續(xù)監(jiān)測？
Berriss：安全和保障最終要?dú)w結(jié)為對數(shù)據(jù)進(jìn)行加密，并對你分享數(shù)據(jù)的途徑以及分享數(shù)據(jù)的方式保持謹(jǐn)慎，特別是在持續(xù)監(jiān)控方面。這方面已經(jīng)有了很多進(jìn)展，像蘋果公司已經(jīng)對Apple Watch進(jìn)行了加密，所以它不能與任何藍(lán)牙設(shè)備連接，以獲取數(shù)據(jù)。因此，在某些方面，我們所需要的已經(jīng)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(jù)證明數(shù)據(jù)沒有被篡改或落入壞人之手嗎，這能成立嗎？
Skinner：醫(yī)療軟件必須經(jīng)過高度驗(yàn)證和測試，因?yàn)樗谶B接對象、如何處理數(shù)據(jù)、誰可以訪問這些數(shù)據(jù)方面要保守得多；從本質(zhì)上講，它更像是一個(gè)沙盒。因此，醫(yī)療軟件在處理生理數(shù)據(jù)時(shí)一般需要額外的成本和時(shí)間投入。我認(rèn)為，要考慮的一個(gè)更關(guān)鍵的問題是，鑒于數(shù)據(jù)包含有價(jià)值的個(gè)人信息，有被濫用的風(fēng)險(xiǎn)，那如何能安全地管理和存儲(chǔ)數(shù)據(jù)。如果使用得當(dāng)，這些數(shù)據(jù)的價(jià)值是巨大的，但也存在著巨大的風(fēng)險(xiǎn)。
通過區(qū)塊鏈或其他相關(guān)訪問協(xié)議的所有權(quán)概念很吸引人。當(dāng)與加密和健康記錄相結(jié)合時(shí)，它創(chuàng)造了一個(gè)非常有趣的空間。我相信，這種結(jié)合在不久的將來會(huì)變得越來越重要，并產(chǎn)生重大影響。此外，如果個(gè)人擁有更多的數(shù)據(jù)，并希望以自己的條件訪問這些數(shù)據(jù)以獲得自己的醫(yī)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(shù)據(jù)的控制性訪問，以比較他們的數(shù)據(jù)并做出結(jié)論。這意味著需要對目前的數(shù)據(jù)使用方式進(jìn)行巨大的改變，目前的數(shù)據(jù)使用方式是極其自上而下的，并且存儲(chǔ)在大的數(shù)據(jù)庫中，訪問權(quán)限有限。轉(zhuǎn)移數(shù)據(jù)或?qū)?shù)據(jù)有所洞悉并不容易，而且目前數(shù)據(jù)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(shù)據(jù)，這對研究目的很有用。然而，這還沒有公開或大規(guī)模地進(jìn)行。
Berriss：使數(shù)據(jù)匿名的一種方法是生成分配給用戶的隨機(jī)數(shù)字代碼，而不是真實(shí)姓名。在COVID-19追蹤應(yīng)用程序中，他們使用數(shù)字代碼來識(shí)別用戶，而不是他們的真實(shí)姓名，這可能是一個(gè)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(jiān)管方面的問題？
Berriss：我想，從道德的角度來看，使用區(qū)塊鏈來保持安全性，并在個(gè)人和公司或醫(yī)療機(jī)構(gòu)之間訂立合同是很重要的。許多年來，人們一直擔(dān)心自己的個(gè)人數(shù)據(jù)被用來對付自己。例如，如果你測量了某些健康指標(biāo)，并發(fā)現(xiàn)你有可能在10年內(nèi)導(dǎo)致你死亡的疾病，你可能不希望與你的健康保險(xiǎn)公司分享這些信息，因?yàn)樗麄兛赡軙?huì)拒絕為你提供已知或預(yù)測的疾病保險(xiǎn)。
因此，個(gè)人在使用追蹤其健康數(shù)據(jù)的設(shè)備之前，了解其潛在的影響是至關(guān)重要的。重要的是，公司要預(yù)先披露他們可能發(fā)現(xiàn)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(gè)人來說尤其如此，他們的健康數(shù)據(jù)可能會(huì)導(dǎo)致負(fù)面后果。
Skinner：是的，這是一個(gè)關(guān)鍵問題。如果我們不采取適當(dāng)?shù)拇胧?，利用區(qū)塊鏈和其他技術(shù)保護(hù)個(gè)人數(shù)據(jù)，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(yán)重，尤其是對NHS這樣的醫(yī)療機(jī)構(gòu)。在監(jiān)管方面，我認(rèn)為目前存在兩個(gè)不同的問題。首先，正在被訪問以及存儲(chǔ)在各種數(shù)據(jù)庫中的數(shù)據(jù)量呈指數(shù)級(jí)增長。這種數(shù)據(jù)流的增加正在造成一個(gè)問題，因?yàn)橛幸话偃f倍的數(shù)據(jù)和數(shù)百萬的不同節(jié)點(diǎn)。令人擔(dān)憂的是這些數(shù)據(jù)是如何被訪問和存儲(chǔ)的，以及與之相關(guān)的潛在風(fēng)險(xiǎn)。
第二個(gè)問題與醫(yī)療標(biāo)準(zhǔn)的放寬有關(guān)。雖然這種放寬可能有很好的理由，但也有相關(guān)的風(fēng)險(xiǎn)。美國食品藥品監(jiān)督管理局（FDA）通過了許多通常不會(huì)批準(zhǔn)的技術(shù)。這方面的例子包括由于移動(dòng)電話的興起而出現(xiàn)的許多數(shù)字解決方案。這些數(shù)字系統(tǒng)通常被歸入更嚴(yán)格的醫(yī)療設(shè)備法規(guī)之外（根據(jù)所謂的510(k)提交），以及無數(shù)的I類和II類設(shè)備，它們基本上只是以合格的方式感應(yīng)和傳遞信息。如果把這一點(diǎn)做到極致，就會(huì)出現(xiàn)通常被冠以“健康”之名的設(shè)備，它們（在市場上）被定位為非醫(yī)療性質(zhì)。這為醫(yī)療效果以及醫(yī)療技術(shù)的可信度定下了一個(gè)移動(dòng)的目標(biāo)。很難知道界線在哪里，而且很有可能技術(shù)進(jìn)步和監(jiān)管在大多數(shù)時(shí)候都不能很好地保持一致。這是一個(gè)細(xì)微的問題，但重要的是要意識(shí)到快速發(fā)展的技術(shù)在醫(yī)療領(lǐng)域的潛在影響。
Q7：將這些技術(shù)整合到可穿戴設(shè)備及其應(yīng)用程序時(shí)，您認(rèn)為有哪些挑戰(zhàn)？
Berriss：我認(rèn)為與我們在技術(shù)方面討論過的任何東西進(jìn)行整合的關(guān)鍵困難在于所涉及的數(shù)據(jù)量太大。首先，如果你想把數(shù)據(jù)傳輸?shù)狡渌胤?，就需要維持網(wǎng)絡(luò)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(zhàn)。傳輸如此大量的數(shù)據(jù)往往是不可行的。
如果集成到一個(gè)移動(dòng)應(yīng)用中，雖然手機(jī)能夠處理復(fù)雜的任務(wù)，但仍有挑戰(zhàn)，比如說，你要進(jìn)行什么處理，什么數(shù)據(jù)會(huì)被傳輸。還可能存在一個(gè)問題，即用戶對如此強(qiáng)大的設(shè)備有什么興趣，這也可能影響到如何創(chuàng)建這樣的設(shè)備，并確定它的形式。
Skinner：我相信主要的挑戰(zhàn)在于證明可穿戴技術(shù)的價(jià)值。將使用技術(shù)的價(jià)值與它所產(chǎn)生的沖突聯(lián)系起來的等式是公認(rèn)的。從本質(zhì)上講，如果有人從使用一項(xiàng)技術(shù)中獲得很多價(jià)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(shù)可能對個(gè)人空間有相當(dāng)大的侵犯性，因此很難克服這一障礙。因此，為了鼓勵(lì)技術(shù)的采用，該技術(shù)的價(jià)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(qū)別的情況下，比如可以檢測潛在心臟病發(fā)作的可穿戴技術(shù)，如果感覺太過笨重或礙眼，人們?nèi)匀豢赡軙?huì)抵制使用它。此外，即使該技術(shù)提醒用戶有潛在的健康問題，他們也可能不會(huì)采取行動(dòng)來解決這個(gè)問題。簡而言之，最大的挑戰(zhàn)是通過提高可穿戴技術(shù)的價(jià)值，使其超越它所產(chǎn)生的沖突，從而改善可穿戴技術(shù)的采用。
Q8：這個(gè)領(lǐng)域下一步會(huì)發(fā)生什么？請向我們分享您的預(yù)測。
Skinner：本質(zhì)上，差不多是我們已經(jīng)討論過的內(nèi)容。我們談到的主題都有時(shí)間表。這些包括醫(yī)療保健的大眾化，利用預(yù)防和預(yù)測措施讓人們更好地了解和處理自己的健康狀態(tài)，并通過這些措施減少醫(yī)院就診的次數(shù)。此外，通過利用虛擬病房和遠(yuǎn)程監(jiān)控幫助人們早點(diǎn)回家護(hù)理，是我的主要預(yù)測。
Berriss：根據(jù)我的經(jīng)驗(yàn)，我發(fā)現(xiàn)當(dāng)你在媒體或這個(gè)期間聽到它作為一個(gè)反復(fù)討論的話題時(shí)，往往會(huì)知道什么會(huì)成為下一件大事。例如，我已經(jīng)看到許多關(guān)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(huì)認(rèn)為是這個(gè)領(lǐng)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.
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