去年12月，CVS提出以690億美元收購安進保險公司。今年1月，又有三大巨頭——亞馬遜、摩根大通和伯克謝爾哈撒韋表示要成立合資公司，降低共100萬左右員工的醫療成本并提高效果。之后3月，信諾保險表示將出資超過500億美元收購醫藥福利管理機構Express Scripts。

為何近來醫療領域交易如此熱鬧？猛一看你可能以為是為了追求量級。也就是管理學中常說的“規?！?。但實際上有個強大的催化劑，一種無限大又無限小的東西似乎能回答所有問題。答案就是數據。

更確切地說，是你的數據：個體生物信息、病史、健康不斷波動的情況，以及去過何處、消費習慣、睡眠狀況，飲食和排泄等等。日常生活中產生的數據，實驗室檢測結果、醫學影像、基因檔案、液體活檢、心電圖……這些都只是一小部分，但已涵蓋大量數據。再加上醫療索賠、臨床實驗、處方和學術研究等等，每天產生的數據約750萬億字節，約占全世界數據量的30%。

海量數據已存在許久，多虧現在層出不窮的新科技，先進的測量儀器，無處不在的連接和云技術，再加上人工智能，企業終于可以利用這些數據。“收集數據只是一方面?！彼箍死账寡芯克魅伟＠锟恕ね衅諣柋硎尽！暗匾氖欠治?。就在三五年前，數據還只能閑置，現在已經可以分析解讀。這是醫療領域最大的變化?！?

It Began in December, with CVS’s proposed $69 billion buyout of insurer Aetna. In January, three more corporate behemoths—Amazon JPMorgan Chase , and Berkshire Hathaway —said they were forming a joint venture aimed at reducing health care costs and improving outcomes for their combined 1 million or so employees. Then, in March, Cigna said it would buy pharmacy benefits manager Express Scripts for more than $50 billion.

What’s driving this frenzy of health care–related dealmaking? On first glance you might think it’s merely the pursuit of mass itself. Of “scale,” as management types like to say. But in truth, there’s a more powerful catalyst—one so gargantuan and infinitesimal at the same time that it sounds like the answer to a riddle. And that’s data.

More specifically, it’s your data: your individual biology, your health history and ever-fluctuating state of well-being, where you go, what you spend, how you sleep, what you put in your body and what comes out. The amount of data you slough off everyday—in lab tests, medical images, genetic profiles, liquid biopsies, electrocardiograms, to name just a few—is overwhelming by itself. Throw in the stuff from medical claims, clinical trials, prescriptions, academic research, and more, and the yield is something on the order of 750 quadrillion bytes every day—or some 30% of the world’s data production.

These massive storehouses of information have always been there. But now, thanks to a slew of novel technologies, sophisticated measuring devices, ubiquitous connectivity and the cloud, and yes, artificial intelligence, companies can harness and make sense of this data as never before. “It’s not the data,” says Eric Topol, director of the Scripps Translational Science Institute. “It’s the analytics. Up until three-to-five years ago, all that data was just sitting there. Now it’s being analyzed and interpreted. It’s the most radical change happening in health care.”

因此，獲取、分析并利用數據已成為新的淘金潮。一群科技大鱷，還有一幫熱門初創公司都在積極試水。

谷歌母公司Alphabet旗下的生物科技公司Verily就在追蹤10000名志愿者提供的生物特征信息，努力制定人類健康“基準”（流言稱其對醫療保險行業也有興趣）。蘋果剛宣布了iPhone上的新功能，可以立即對接幾家大醫療系統，上傳醫療數據，此外蘋果還與斯坦福大學進行心臟領域研究，測試可穿戴設備是否能檢測嚴重的心臟疾病。

根據醫療保險和醫療補助服務中心的數據，合理利用數據最終可以提升患者健康水平并降低醫療成本，僅2018年醫療成本就預計增長5.3％。 當然，這是遠期理想。但最起碼能帶動潛在的相關業務。BDO董事總經理大衛·弗蘭德指出，坐享海量數據的Facebook和谷歌通過廣告賺錢，他估計相關業務價值2000億美元。“醫療行業規模要大15倍，”他表示。“醫療支出達3萬億美元。理論上說，如果路走對了就能打造15個Facebook和15個谷歌。這就是競爭激烈的原因。”

也正因如此，從醫院到保險公司到福利管理機構，再到醫療和儀器制造商，如此多傳統醫療公司都在急切地重組創新，要知道相關行業體量可達經濟總量五分之一。行業重組不僅意味著企業層面重新劃定醫療行業版圖，也會影響到每個人。

埃森哲首席技術和創新官保羅·多爾蒂比較樂觀，他估計由于“信息不對稱”存在，掌握自身生物數據的病人會從中受益，享受更多紅利。

為了更好地了解未來如何實現權力均衡，也為了更深入探究目前的情況，《財富》雜志采訪了三十幾位涉及醫療行業各細分領域的高管，以及企業家、醫生、病人和其他專家。以下就是大數據革命在促進醫藥行業方面的各種說法。

數據藥丸：病患診斷新范式

雅各比八歲生日前后，林德賽·阿莫斯開始注意兒子的異常。雅各比很活潑，經常打冰球和長曲棍球，但突然變得有些懶洋洋，而且總想去洗手間。醫生測量雅各比的血糖后，通知家人趕緊送孩子去急診。路上孩子開始意識模糊。阿莫斯后來才知道，當時雅各比的血糖水平高達735毫克/分升，而健康范圍是70-140毫克/分升。幸運的是，雅各比病情沒有發展到糖尿病酮酸血癥，簡稱DKA。DKA是一種可能致命的并發癥，由于血糖水平持續升高，血液變酸性，導致器官衰竭。

阿莫斯一家住在丹佛郊區，可怕事件發生后醫生給出的應對方案卻極為隨意，也不明確。她的家人上了速成課程學習1型糖尿病，了解高血糖和低血糖如何危及生命。他們得知要計算雅各比的糖分攝入，一天里要多次檢查血糖，刺破手指用糖尿病測試條，然后手寫記錄在日志中。

壓力，訓練，胰島素，各種食物……再加上各項的影響因素相互作用，導致血糖水平出了名的難掌控，要把雅各比的血糖水平保持在健康范圍內讓人精疲力盡也很可怕。阿莫斯處處小心計算，但效果不佳，雅各比的血糖就像坐過山車，時而飆升到極高水平（他會感覺疲勞），時而又會降到極低（他會頭暈）。

但雅各比的生死就看這些數字。阿莫斯希望能時刻了解血糖水平，雅各比診斷后幾個星期，她用盡一切努力測試兒子的血糖，一天能測20多次，遠遠超過保險覆蓋的次數。

The quest to retrieve, analyze, and leverage that data has become the new gold rush. And a vanguard of tech titans—not to mention a bevy of hot startups—are on the hunt for it.

Alphabet life sciences arm Verily is aiming to create a “baseline” of human health by tracking all kinds of biometric information from 10,000 volunteers (and is rumored to have an interest in the health insurance business). Apple just released an iPhone feature offering users in several big health systems instant access to their own medical record—an effort that joins its ongoing heart study with Stanford, testing if wearables can detect serious cardiac conditions.

Tapping this reservoir, say many, will ultimately improve patient health and decrease medical costs, which are projected to rise 5.3% in 2018 alone, according to the Centers for Medicare & Medicaid Services. That’s a noble aspiration, certainly. But not lost on anyone is that it’s sure to make for a potentially blockbuster business too. David Friend, managing director at BDO, points out that data-rich Facebook and Google make their money on advertising—a business worth $200 billion, he estimates. “Health care is 15 times bigger than that,” he says. “We spend $3 trillion. In theory, if this is done right, you’ll have 15 Facebooks and 15 Googles. That’s what’s up for grabs.”

Which is why so many old-guard health care companies, from hospitals and insurers to benefits managers and drug and device makers—which together account for one-fifth of the economy—are hastily recombining and reinventing themselves. The realignment promises not only to drastically reshape the health care landscape for companies overall, but for you as well.

Optimists, like Accenture’s chief technology and innovation officer Paul Daugherty, predict that the “information asymmetry” will soon favor patients whose ownership of their own biological data will give them new power.

To see what the new balance of power will look like in the coming years—and what it looks like right now—Fortune interviewed more than three dozen executives at companies across the health care continuum, along with entrepreneurs, doctors, patients, and other experts. Here’s how the big-data revolution is—and isn’t—transforming medicine.

The Data Pill: A New Paradigm for Patients

Right around his eighth birthday, Lindsay Amos noticed her son Jacoby seemed not quite himself. The usually active boy who played hockey and lacrosse was sluggish, and he seemed to be going to the bathroom a lot. When the boy’s doctor took a reading of Jacoby’s blood sugar, the family was told to rush to the ER. On the drive there, the eight-year-old drifted in and out of consciousness. Jacoby’s blood sugar, Amos later learned, was 735 mg/dL, compared with a healthy range of 70–140 mg/dL. The boy was lucky not to have developed diabetic ketoacidosis, or DKA, a sometimes fatal complication in which, owing to prolonged elevated glucose levels, the blood becomes acidic, and organs begin to shut down.

The resolution to those terrifying events struck Amos, who lives in the Denver suburbs, as startlingly casual and vague. Her family received a crash course on Type 1 diabetes, and the life-threatening consequences of both dangerously high and low blood sugar. They were told to count Jacoby’s carbohydrates and check his blood sugar throughout the day—a process that involves pricking a finger and using a diabetes test strip—and to record this all manually in a logbook.

The dance between stress, exercise, insulin, various kinds of food—and what impact all those factors and more have on blood sugar—is notoriously hard to master, and trying to keep Jacoby in a healthy range was both exhausting and frightening. She’d dutifully do the math, but it didn’t seem to matter. Jacoby was on a roller coaster: His glucose level swinging unpredictably to alarming highs (when he’d feel fatigued) to dangerous lows (when he’d feel dizzy).

His life depended on this number. Amos wanted to have an idea of where it stood at all times and for the first few weeks after Jacoby’s diagnosis, she did her best—testing her son’s glucose levels 20 or so times a day, far exceeding the number of test strips their insurance provider covered.

每年都有數百萬美國家庭遇到類似疾病沖擊；但2015年開始，新出現的智能手機技術部分緩解了擔憂。加州一家名叫Dexcom的公司將血糖持續監測儀（已經問世十多年）與智能手機（或智能手表）無線連接，用戶可以監測，繪制并分享血糖數據，每隔五分鐘即可獲得最新數據，全天待命，血糖達到危險水平時還能發出警報。

一些專家認為，隨時在線的設備會告訴病人太多信息，但阿莫斯半開玩笑地介紹自己如何“跟蹤”兒子血糖水平時說，這種儀器堪稱救生員。她說，雅各比現在可以正常地讀小學三年級，只要注意看看Apple Watch上的血糖數據就可以，不用再一天跑很多趟校醫院。（阿摩斯也在iPhone上隨時盯著。）而且現在雅各比可以安心睡整晚，不用隔幾個小時就起床扎手指測血糖。

設備提供的還不僅僅是安心，其收集的數據確實可以幫阿莫斯和雅各比了解糖尿病情況以及如何控制。他們能發現哪些食物導致血糖飆升，以及何時注射胰島素能更好地控制比薩之類復雜碳水化合物。是的，他的糖尿病沒有治愈。但現在至少可以預測，很少出現意外。很明顯，這項技術已經完全融入他們的日常生活?！案嚢踩珟Ш妥孕熊囶^盔沒有區別?！卑⒛贡硎?。

這只是智能手機和聯網設備改變病人與健康數據關系的方式之一，過程中也能幫助病人改善健康狀況。

Virta和Omada Health之類數字糖尿病預防和治療平臺還連接起社區和健康教練，教練可以遠程監控體重、血糖、飲食和服藥等情況。現在還有Proteus Digital Health可食入傳感器，都值得《黑鏡》拍一集了，這種傳感器可以幫助患者（以及醫生和家屬）隨時查看是否服藥。Froedtert醫院及威斯康星醫療網絡醫學院首席創新和數字官邁克爾·安德烈斯介紹，每粒藥片接觸胃酸時都會啟動應用程序，傳感器把整個過程顯示得像游戲一樣。根據Froedtert測試，丙肝患者服用Gilead Harvoni等昂貴藥物期間，配合傳感器按時服藥比例達98.6％。按時服藥不僅有利于治愈疾病，還能省下不少錢，因為多吃一個月藥費用會多出數萬美元。這就是Froedtert（而不是患者）支付傳感器費用的原因。

This everyday trauma affected millions of American families each year; then, in 2015, a bit of smartphone technology took away some of the worry. A California company called Dexcom connected a continuous glucose monitor (a device that had been around for more than a decade) wirelessly to a smartphone (or smart watch), allowing the user to read, plot, and share blood sugar levels with anyone, at five-minute intervals, all day long—and sending an alert when patients were at risk.

While some experts believe such always-on devices can leave patients with too much information, Amos—who half-jokingly speaks of “stalking” her son’s glucose levels—says it’s a lifesaver. Jacoby is now just another normal third-grader, she says. Rather than leaving class to go to the nurse’s office multiple times a day, he discreetly monitors his blood sugar on his Apple Watch. (Amos watches too, on her iPhone.) And instead of waking every few hours to have his finger pricked, he sleeps through the night.

But the device offers more than just peace of mind. The data it generates has actually helped Amos and Jacoby understand his diabetes and how to manage it. It lets them see what foods make his blood sugar soar and how best to time his insulin shots around complex carbs like pizza. No, his diabetes isn’t cured. But his blood sugar is now predictable and rarely triggers an alarm. It’s remarkable how unremarkable the technology now is in their lives. “It’s no different than a seat belt or a bicycle helmet,” she says.

This is but one way in which smartphones and connected devices are changing the relationship between patients and their health data—and enabling them to improve their health in the process.

Digital diabetes prevention and treatment platforms such as Virta and Omada Health connect users with support communities and health coaches—who can remotely monitor things like weight, blood sugar, diet, and medicine intake. Then there’s Proteus Digital Health’s ingestible sensor, which—with a technology worthy of an episode of Black Mirror—helps patients (and, if they want, their doctors and family members) keep tabs on whether or not they’re taking their meds. By pinging an app every time a pill hits stomach acid, the sensor gamifies the prescription process, as Michael Anderes, the chief innovation and digital officer at Froedtert and the Medical College of Wisconsin Health Network, puts it. Hepatitis C patients on expensive drugs like Gilead’s Harvoni were 98.6% compliant in taking the medicine at the right time when using the sensors, in Froedtert’s experience. That’s not just critical for curing the disease, it’s also a major money saver because an extra month of medication would cost tens of thousands of dollars. Which is why Froedtert (not its patients) foots the bill for the sensor.

流行的Apple Watch或Android系統可穿戴健康設備已經普及，提醒各種健康事項，從睡眠呼吸暫停到高血壓，甚至是嚴重心律失常。人們越發關注自己的基因組，希望預測患上某些疾?。ㄈ绨┌Y和阿爾茨海默病）的風險，有些暫時還無法實現，有些技術尚不完備（并且有爭議）。消費者開始利用Colour Genomics和23andMe等公司非常便宜又方便的基因檢測試劑盒，檢測結果會提示罹患某些疾病幾率較高。支持者認為，了解風險有時可以幫人們采取相應預防措施，從而降低風險。

突然之間，類似家庭基因測試變成必需品：最近的黑色星期五，23andMe的標準DNA測試銷量躋身亞馬遜前五，差點趕上亞馬遜自家出品的智能音箱Echo Dot和多功能高壓鍋“Instant Pot”。

所有革新技術的賣點都很簡單：消費者主導。

一些大型保險公司甚至發現，讓患者參與數據有助于改善結果和控制成本。這也是安泰保險首席執行官個馬克·貝爾托里尼下的賭注。貝爾托里尼正爭取與CVS達成合作，他認為，如果企業能明確告知消費者享受的益處，消費者就可能甚至會積極共享數據?！拔覀冎贫烁鞣N規則保護數據，”貝爾托里尼表示，“但如果換個角度告訴客戶，‘如果我們能掌握有關你的信息，服務起來更方便，’他或她就會愿意提供數據。這也是社交媒體作用很大的原因。”

掌握個性化數據后，公司可以與患者共同制定健康計劃，貝爾托里尼說：“我們會告訴患者，如果一起制定計劃就可以免除共同支付金額，也不必再授權，就因為計劃是一起制定的?！?

指揮中心：通過數據實現更好決策

疼痛來襲時，羅德尼·M正在給妻子挑選生日禮物。這位52歲的公關公司首席執行官抓緊胸口，掙扎著做在椅子上，腰部以上都失去了知覺，只感到麻木。他感覺就像“被卡車撞了一樣”。

兩年前羅德尼剛治療過癌癥，又患上一種新重病——為身體輸送血液的關鍵動脈撕裂導致的“主動脈夾層”。

他還算幸運，急救車及時趕到把他送到馬里蘭州哥倫比亞市的霍華德縣總醫院，五分之一罹患動脈夾層的病人送到醫院之前就去世了。這還沒折騰完，幾分鐘內羅德尼又被飛機送到巴爾的摩的約翰霍普金斯醫院，經過七個小時的復雜手術終于活了下來。

當時他并不知道，其實是霍普金斯醫院人工智能驅動的數據“指揮中心”救了他的命?！?1分鐘。從監控系統報警到飛機從醫院起飛只用了41分鐘?！笔紫姓偌贰な鎮惐硎?，他負責醫療中心不斷擴大的急診部門。

指揮中心從十幾個數據流中實時提取信息，包括病歷、緊急調度服務更新、實驗檢測結果，以及在特定時間醫院有多少張病床。然后通過人工改進的算法，系統可以瞬間完成患者分級并根據需要進行安排，例如在羅德尼的案例中提前安排好外科手術團隊。

對于醫院來說，數據管理產生的經濟效益無可爭議?！盎羝战鹚贯t院指揮中心2016年啟動以來，收治復雜癌癥患者的能力增加了約60％，急診室等床時間（等待住院病床）減少了25％以上，而手術室的等候時間減少了60％，“霍普金斯指揮中心設計方——通用電氣醫療該項目負責人杰夫·特里說?；羝战鹚贯t院的舒倫表示，應用該技術后，相當于醫院不用實際投入，而收治能力增加15到16張病床。今年通用電氣計劃宣布新成立10個指揮中心，覆蓋30家不同的醫院。特里稱，未來五年里醫療中心的投資回報率可達近4比1。

電子病歷（EHR）也是近期大數據最新突破，可能沒那么吸睛，但同樣具有革新性。

電子病歷是醫療圈少有人人討厭的技術。醫生抱怨稱浪費的時間太多，跟其他醫療記錄系統也沒法順利協作，并且其中大部分患者都讀不懂。2015年以來，至少有593篇學術論文和一條說唱視頻吐槽過該技術導致醫生耗盡精力。如果大數據真能革新行業，機會可能就在這，應該將電子病歷從耗時工作轉變為可行的研究工具。

密歇根州西南部一個非盈利社區衛生系統Lakeland Health里，大數據確實發揮了作用。2012年，Lakeland開始啟用電子病歷系統，但基本由紙和筆組成。護士先在圖表里記錄每個病人的生命體征，回到電腦邊再手動重新輸入醫院的電子系統，這一轉錄過程需要15到20分鐘，并且經常出現錯誤。2016年中，醫院啟動全新流程，通過患者的腕帶自動上傳，或護士利用手持設備的床邊輸入數據。

Lakeland首席護理信息官阿瑟·巴拉奇表示，從一開始就有個明顯變化：護士們花在輸入數據上的時間減少，有更多時間照顧患者。但更徹底也令人驚訝的變化是“藍色警報”下降：即患者心臟和呼吸停止。自英荷健康巨頭飛利浦開發的新系統于2016年6月推出后，藍色警報已下降56％。為什么？部分原因是警報系統嵌入了人工智能技術，不僅能檢測生命體征的細微變化，還能根據病情打出風險評分，方便護士優先照顧病情更危急的病人。

“重要的是誠實地認識現狀。”飛利浦戰略與創新首席醫療官羅伊·史麥斯表示。許多新數字和數據工具關注的重點不是護理，而是如何使護理更高效、更智能、更精確地實現。

史麥斯跟《財富》雜志采訪的許多位專家觀點一致，即使是對數字健康極感興趣的人。他們都警告不要過度使用新醫療技術。

“我們過度承諾，真正實現的不多。”西達斯西奈醫學中心醫生兼健康服務研究主任布倫南·斯皮格爾表示。“我認為自己是對技術保持懷疑的技術狂。但硅谷這個“回聲室”里有太多人從來沒接觸過病人，也不理解數字醫療多么困難?！八蛊じ駹栠€在加州洛杉磯分校擔任醫學和公共健康教授，他舉了親身經歷的失敗案例為證，包括2015年西達斯西奈想通過Fitbit、Apple Watch、Withings等可穿戴設備將病人與電子病歷連接起來，結果一敗涂地?！拔覀儧]能給病人適合的信息，也沒誠摯邀請他們參與項目。”潛在參與者沒什么理由參與連接和項目，因為項目沒有明確的價值主張?！皵底纸】挡皇怯嬎銠C科學或工程科學，而是社會科學和行為科學?！?

斯科利普斯研究所的埃里克·托普爾同時也是一位著名的心臟病專家，他也提出了類似警告?！俺兄Z很多，但大部分沒實現。”他表示原因是存在各種系統性障礙。挑戰之一便是美國僵化又“長期存在”的醫療機構。美國醫生有半數超過50歲，他們抗拒改變，“除非獲得更多補償。”

對抗“故障問題”

按照安進公司的說法，大數據已然顛覆加州生物醫藥研發過程，還大大影響了研發階段新藥。事情是2011年開始的，時任研發主管的肖恩·哈珀前往冰島旅行。他的目的是解決公司面臨的“故障問題”，也是行業的問題。簡單來說，90%的研發新藥都沒法進入市場。

研發新藥非常昂貴而且低效。企業經常投資數十億美元，花很多年努力驗證可能有希望的科學假設。藥品科學家渴望化學試驗時突降好運，實際上并沒完全理解努力追求的生物復雜性，也不明白為何某些藥在白鼠身上作用明顯，對人類卻不起作用。

對哈珀來說，冰島似乎可以提供一些獨特的醫療數據。數據由冰島政府提供，包括16萬公民的基因排序信息，還有醫療和家譜，數據由總部位于雷克雅未克的人類基因設備公司deCode存儲分析。該公司1996年成立，一直在苦苦經營。

盡管償付能力存在問題，但deCode在基因探索領域成果豐碩。其擁有的大量數據可以用來挖掘遺傳變異人群，并將變異與癌癥到精神分裂癥等疾病的臨床結果聯系起來。隨著計算機處理能力提升，測序成本大幅下降，哈珀發現該公司對藥物研發來說是價值低估的資產，于是2012年安進公司以4.15億美元將其收入囊中。

此次收購徹底改變了安進的研發過程。收購之前，安進候選分子中只有15％針對特定的遺傳目標進行了驗證。收購完成后，安進開始利用deCode的數據庫評估所有候選藥物。審核發現了一些明顯不起作用的藥物。證據顯示，有5％的候選分子沒有療效。管理層立刻關停相關項目（包括一種非常受期待的冠狀動脈藥物，而且即將進入人體試驗階段），轉而優先考慮基因靶點明確的藥物。經deCode基因數據庫確認后，安進也通過了十多種藥物。

哈珀表示，現在安進公司四分之三的研發產品都會參考基因數據，大部分都來自deCode數據庫，如今收購成本早已收回。

盡管對藥物靶點進行基因驗證并不能保證最后成功，但科學家們還是得弄清楚如何安全有效地給藥，應對大量生物學上的挑戰，確實開了個好頭。哈珀表示，“如果回報率能提高50％，變化已經很大。”

生物科技公司再生元也曾想與deCode合作，跟安進公司收購deCode時間差不多，其策略也跟安進類似。最終再生元沒有尋求收購，而是以再生元遺傳學中心（RGC）的形式建立自己的研究中心，花費四年給大量外顯組完成測序（蛋白質編碼部分的基因組）盡可能與病歷配對，并加速藥物研發。

再生元基因組生物信息學負責人杰夫·里德表示，很多人在討論藥品研發時利用基因技術，“卻沒有提前設計樣本流。”簡單來說：他們沒有數據。后來里德得知再生元與蓋辛格公司有合作，便加入了再生元（參見《基礎護理》），蓋辛格是位于賓夕法尼亞州的健康管理系統，計劃從10萬名病歷完整的患者中收集樣本并對完成測序?！八麄兿Ｍ@得數據后能切實改善對患者的護理?！痹偕z傳學中心負責人阿里斯·巴拉斯說。

如今再生元合作方已超過60個，包括已招募50萬名參與者的英國生物銀行。巴拉斯和里德說，其掌握的數據規模和多樣性都不斷發展，內部研究數據能力也不斷增強，這點至關重要。迄今為止已啟動50個靶點生物學項目。

隱藏的數字：病歷未盡之用

腫瘤科醫生和前杜克大學教授艾米·阿伯內西認為，雜亂的健康信息沒什么意義，除非符合兩個關鍵標準：質量和背景。“不了解醫學實踐核心的人都不知道到底多么混亂?！彼哪昵皳蜦latiron Health首席醫療官的阿伯內西表示，這家創業公司由谷歌風投（GV）投資。

以Flatiron專長的癌癥病歷為例。腫瘤學電子病歷中的許多重要信息（實際上約一半）可能都在醫生的筆記中，但筆記沒法組成特定的數據字段。各種觀察結果沒法按類整理在表格中。

“過去電子病歷只是收費和收藏工具，醫生按規矩撰寫才能保住工作?！碧锛{西腫瘤中心的醫生兼首席執行官杰弗里·巴頓解釋說。田納西腫瘤中心是基于社區的醫療機構，收治了該州大部分癌癥患者，也是數百個使用Flatiron系統的社區癌癥中心之一。

諷刺的是，Flatiron的真正賣點是人類。遇到此類數據，人類往往能發現計算機系統可能錯過的細節。阿伯內西說，真正的挑戰不是收集數據，而是要“清理干凈”。“如果不理解相關背景真的很難做到。”

Flatiron現在掌握美國20％主動治療的癌癥患者數據，“數據結構很清晰。”羅氏制藥公司首席執行官丹尼爾·奧蒂表示，今年2月羅氏出資19億美元收購了Flatiron公司?！癋latiron特別之處在于能整理出符合監管要求的真實數據?！眾W蒂告訴《財富》。他表示，Flatiron的數據非常完備，“理論上可以取代羅氏為癌癥免疫治療藥物Tecentriq設置的臨床試驗‘對照組’之一”。

理論上，Flatiron的數據系統可能對臨床試驗招募產生更廣泛的影響。數十年來，癌癥藥物研究中病人招募都是最大挑戰之一。舉例來說，如果招募順利就更容易根據病人情況匹配適合的藥物。

IBM沃森在該領域初見成效。梅奧診所今年3月報告稱，使用IBM先進的認知計算系統后，乳腺癌臨床試驗參與人數增加了80％。 “沃森幫我們更快更準確地匹配患者與潛在的臨床試驗，以往腫瘤醫生很難做到?！泵穵W診所首席信息官克里斯托弗·羅斯接受貿易出版物MobiHealthNews采訪時表示。

即使是以前很少有機會接觸患者的機構，例如藥房福利管理結構，由于掌握海量數據，也可能為改善人類健康和降低成本貢獻力量。

以密蘇里州的藥房福利管理結構Express Scripts為例，今年 3月剛剛由信諾保險收購。Express Scripts每年為1億美國人管理14億個處方，人們什么時候不按時吃藥都知道。不遵醫囑服藥每年導致的成本達1000億美元至3000億美元，數字差異是由于測算方式不同。成本出現的原因是患者不遵醫囑出現并發癥，引起后續治療。

Express Scripts首席數據官湯姆·亨利表示，已經發現300種可能讓病人放棄照處方配藥的因素。各項因素包括從基本的人口統計數據（收入水平和郵政編碼）到行為數據（根據患者不取處方藥后的調查問卷，判斷患者健忘程度和拖延傾向）再到直觀性較低的因素，如開藥者和患者的性別（接受女性醫生診治的男性不遵從醫囑可能性更大）。該公司稱，算法準確率達到94％，可以用算法為患者風險評分，并采取不同的提醒服務。亨利稱提醒服藥的方式“比較溫和，不會強迫”。總之，Express Scripts稱不遵醫囑服藥現象減少了37％，為客戶節省了1.8億美元。

與其他領域的革命一樣，許多人沒考慮清楚下一步，也沒準備好承擔相應后果便紛紛涌入。在醫療大數據社交實驗中問題也有多方面，從病人隱私到警告人們有些風險無法避免時的道德困境等。對許多數字醫療支持者來說，大數據仿佛是苦苦等待的一?！澳g子彈”。問題是這顆子彈究竟往哪里打。（財富中文）

本文另一版本發表于2018年4月《財富》雜志，標題為《當大數據遇上生物學》。

譯者：Pessy

審校：夏林

Wearable health trackers like the popular Apple Watch or Android-based devices are now alerting their owners to everything from sleep apnea to hypertension to even serious cardiac arrhythmias. And increasingly, that self-awareness is drilling into our own genomes, helping people—if for now, imperfectly (and controversially)—gauge their risk of developing certain diseases, such as cancer and Alzheimer’s. Consumers are turning to ever-cheaper, spit-and-send genetic test kits offered by companies like Color Genomics and 23andMe, to forewarn them of specific genetic susceptibilities—an awareness that, boosters say, can sometimes enable individuals to take preventive action that may mitigate those risks.

Out of the blue, such at-home gene tests have become consumer must-haves: On the most recent Black Friday, 23andMe’s standard DNA test was one of Amazon’s top five sellers—barely trailing Amazon’s own Echo Dot and the “Instant Pot.”

The selling point for all of these transformative technologies is a simple one: The consumer is in the driver’s seat.

Some big insurers are even discovering that engaging patients with their data is a good way to improve outcomes and control costs. That’s what Mark Bertolini is betting on. Bertolini, the CEO of Aetna, which is looking to combine forces with CVS , believes that consumers can be—and would actively want to be—data-sharing partners, if companies can demonstrate how consumers can benefit from that cooperation. “We have all these rules about protecting data,” says Bertolini. “But if you turn it around and say to the customer, ‘If we have this information about you, we can make this a lot more convenient for you,’ he or she will give you the data. That’s why social media works the way it does.”

With that personalized data, then, the company can build a health plan in concert with those patients, says Bertolini: “We want to say to them, if we build a plan together, there are no copays and there are no authorizations because we built it together.”

Command Central: Better Decisions Through Data

Rodney M. was picking up a birthday present for his wife when the pain hit. The 52-year-old CEO of a communications firm clutched his chest and scrambled to a seat. He felt numb, paralyzed from the waist down. It felt like he’d been “hit by a truck.”

Already a cancer survivor in remission for two years, Rodney had just suffered a whole new kind of medical nightmare—an “aortic dissection,” caused by a tear in the critical artery that supplies the body’s lifeblood.

He was lucky when the ambulance arrived quickly to take him to Howard County General Hospital, in Columbia, Md.—one in five aortic dissection patients dies before reaching a hospital. But his journey didn’t end there: Within minutes, Rodney was being airlifted to Johns Hopkins in Baltimore—and after a complex, seven-hour surgery, survived.

He didn’t know it at the time, but an artificial intelligence-powered data “command center” at Hopkins helped save his life. “Forty-one minutes. That’s how long it took to get wheels up from the hospital,” says Jim Scheulen, the chief administrative officer tasked with overseeing the medical center’s sprawling emergency medicine unit.

The command center pulls in information from more than a dozen data streams in real time, including patient health records, emergency dispatch service updates, lab results, and tabs on how many hospital beds are available at any given time. Then, through its human-trained algorithms, it makes split-second decisions on triaging patients and getting them where they need to go—prepping the surgical team ahead of time in cases like Rodney’s.

For the hospital, the financial benefits of all this data management are unmistakable. “At Hopkins, there’s been about a 60% increase in the ability to accept complex cancer patients, an over 25% reduction in emergency room boarding [those waiting for an inpatient bed], and a 60% reduction in operating room holds with the command center since its launch in February 2016,” says Jeff Terry, who oversees such projects for GEHealthcare, which built the Hopkins command center. Hopkins’s Scheulen says the technology functionally expanded the hospital’s capacity by 15 or 16 beds without the need to add, well, actual beds. This year, GE plans to announce 10 new command centers covering 30 different hospitals—which, over five years, Terry claims, should yield those medical centers a roughly 4 to 1 return on their investment.

Far less whizbang, but likely more transformative, are recent upgrades to last generation’s big-data breakthrough: the electronic health record (EHR).

It’s hard to find a medical technology more universally hated than the EHR. Doctors complain that it consumes too much of their time, that they don’t work well with other medical records systems, and that they’re still largely indecipherable to patients. The technology’s role in physician burnout has been explored in no less than 593 scholarly articles and one rap video since 2015. But if the big-data mission has found a worthy calling, it is here—transforming electronic health records from a time suck to a viable research tool.

That’s what happened at Lakeland Health, a not-for-profit community health system in southwest Michigan. Lakeland got an EHR system in 2012—but it might as well have been composed of paper and pen. Nurses there recorded vital signs of every patient on charts—and once back at their workstation, would manually reenter the stats into the hospital’s electronic system, a transcription process that ate up 15 to 20 minutes and often resulted in errors. Then, in mid-2016, they switched to a new process that enabled data to be automatically uploaded from patient wristbands or entered by nurses at their bedside on handheld devices.

One change was obvious from the start: Nurses spent less time on data entry and more time tending to patients, says Arthur Bairagee, Lakeland’s chief nursing informatics officer. But more radical—and surprising—was the drop in “code blues”: the warnings that patients were in cardiac and respiratory arrest. They’ve dropped a mammoth 56% since the new system, developed by Anglo-Dutch health giant Philips, was introduced in June 2016. Why? In part, the A.I.-driven warning system built into the monitoring technology, which not only picks up on even subtle changes in vital signs, but also assigns patients risk scores that help nurses prioritize their attention.

“It’s important to be honest and pragmatic about where we are now,” says Roy Smythe, chief medical officer for strategy and innovation at Philips. Many of the new digital and data tools aren’t so much about providing care, but rather about making that care more efficient, smarter, and precisely delivered.

Like so many experts Fortune interviewed—even those who are gung ho about digital health—Smythe cautions against overhyping the new med tech.

“We have overpromised and under–delivered,” says Brennan Spiegel, a physician and director of health services research at the Cedars-Sinai health system. “I consider myself a techno-skeptical techno-philiac. But there are way too many people in the Silicon Valley echo chamber who have never touched hands on a patient and don’t understand how hard digital health is.” Spiegel, who is also a professor of medicine and public health at UCLA, points to high-profile failures he’s personally experienced in the field—including a 2015 Cedars-Sinai project to connect patients, through wearables like Fitbit, Apple Watch, Withings, and others, to electronic health records, which flopped spectacularly. “We didn’t give patients the optimal messaging, and we didn’t invite them in the most compelling way.” So potential participants had little personal rationale to connect and stay engaged with the program because it didn’t present a tangible value proposition. “Digital health is not a computer science or an engineering science; it’s a social science and a behavioral science.”

Eric Topol, at Scripps, who’s also a renowned cardiologist, sounds a similar cautionary note. “There’s a tremendous amount of promise, but so much is unfulfilled,” he says, owing to a variety of systemic roadblocks. Among the challenges, he says, are America’s rigid and “long in the tooth” medical establishment—half of U.S. doctors are over 50—which is resistant to changing its ways “unless it’s going to lead to higher compensation.”

Fighting the “Failure Problem”

To hear the folks at Amgen tell it, big data has upended the California biotech’s drug development process and significantly reshaped its pipeline. That story begins in 2011, when R&D chief Sean Harper, started making trips to Iceland. He was trying to solve his company’s—which is also the industry’s—“failure problem,” which is summed up by the fact that 90% of drug candidates fail to make it to market.

Discovering new medicines is a wildly expensive and inefficient endeavor. Companies will often invest billions of dollars and many years chasing a promising scientific hypothesis. Pharmaceutical scientists hope for a moment of chemical serendipity, despite often not fully understanding the complexity of the biological mechanisms they’re targeting—or why something might fail in humans when it works so neatly in a mouse model.

For Harper, Iceland seemed to offer an unparalleled pool of health-related data. The collection of that data—the genetic sequences of 160,000 citizens, along with their medical and genealogical records—was made possible by the Icelandic government, and the storage and analysis of that data was overseen by deCode, a Reykjavík-based human genetics outfit that, since its founding in 1996, had struggled to stay afloat financially.

Despite its solvency issues, deCode had become a prolific publisher of genetic discovery. Its trove of data allowed the company to mine the population for genetic variants and connect those variants to clinical outcomes in diseases ranging from cancer to schizophrenia. As the cost of sequencing plummeted in sync with the rise of computer processing power, Harper saw an undervalued asset for drug discovery: Amgen bought the company in 2012 for $415 million.

That purchase has utterly transformed Amgen’s R&D process. Prior to the deCode acquisition, only 15% of Amgen candidate molecules had been validated against specific genetic targets. After the purchase, Amgen began evaluating all of its drug candidates against deCode’s database. The review exposed some clear losers; in the case of 5% of its candidate molecules, there was evidence the agent wouldn’t work. Managers killed those programs (including one highly anticipated drug aimed at coronary disorders that was about to head into human trials) and prioritized others where there was a clear genetic target for the drug. Amgen also green-lighted more than a dozen drugs for which it found confirmation in deCode’s genetic data.

Today, three-quarters of Amgen’s pipeline is based on genetic insights largely gleaned from the database, says Harper, and the company has more than earned its investment back.

While having genetic validation for a target is no guarantee of success—scientists still have to figure out how to drug the target safely and effectively, and meet myriad other biological challenges—it does offer a head start. Says Harper, “If you can increase your rate of return by 50%, that’s enormous.”

Regeneron, a biotech that was looking to partner with deCode around the same time Amgen bought it, has a similar strategy. But rather than buying up a genetic research outfit, it decided to build its own in the form of the Regeneron Genetics Center (RGC), an ambitious four-year old effort to sequence as many exomes (the protein-encoding part of the genome) as possible, pair them with medical records, and accelerate drug development.

Lots of people talk about the promise of using genetics in pharma R&D, says Jeff Reid, Regeneron’s chief of genome bioinformatics, “but they don’t have a vision for sample flow.” Translation: They don’t have the data. Reid joined Regeneron when he learned the company had partnered with Geisinger (see “Keystone Care”), a Pennsylvania-based health care system with which it planned to collect and sequence samples from 100,000 consenting patients who also have comprehensive medical records. “They wanted to take this data and actually improve the care of patients,” says Aris Baras, who heads up the RGC.

The company has since partnered with more than 60 other sources including the UK Biobank, which has recruited 500,000 participants. Baras and Reid say the scale and diversity of its growing data set, along with the ability to make all of these discoveries in-house, are critical. The work has so far spawned 50 target biology programs.

Hidden Figures: The Untapped Value of Medical Records

A random assortment of health information doesn’t mean much if it doesn’t meet at least two critical criteria, says oncologist and former Duke professor Amy Abernethy: quality and context. “Anyone who doesn’t understand the core aspects of practicing medicine can’t understand how messy it is,” says Abernethy, who four years ago became the chief medical officer at Flatiron Health, a startup backed by Google Ventures (GV).

Take cancer records—Flatiron’s specialty—as an example. Many of the most useful nuggets in an oncology EHR (about half, in fact) may reside in doctors’ notes that aren’t structured into specific data fields. These are the sorts of observations that can’t be neatly packaged into categories on a form.

“Historically, these electronic records are billing and collection tools, documentation we have to comply with to get paid,” explains Jeffrey Patton, a physician and CEO of Tennessee Oncology, a community-based health system that treats the largest number of cancer patients in the state and is one of hundreds of community cancer centers that now uses Flatiron’s system.

Flatiron’s selling point, ironically, is humans. When it comes to this type of data, it seems, people can figure out things a purely computer-driven system might miss. The real challenge isn’t to gather the data, but to “clean it up,” says Abernethy. “And that’s really hard without an understanding of context.”

Flatiron now has data from 20% of active cancer patients in the U.S., “and it’s extremely well structured,” says Daniel O’Day, CEO of Roche Pharmaceuticals, a unit of Roche Holding AG, which snapped up Flatiron in a $1.9 billion deal announced in February. “What set Flatiron apart was that it was able to create regulatory grade, real-world data,” O’Day tells Fortune—data that O’Day claims is so well curated, that it “could have theoretically replaced the ‘control’ arm” of one of Roche’s own clinical trials for the cancer immunotherapy drug Tecentriq.

In theory, Flatiron’s system could have a broader impact on clinical trials accrual—for generations, one of the most stubborn challenges in cancer drug research—making it easier, for example, to match up specific patients with appropriate drug studies.

That, indeed, is an area where IBM Watson has already found some success. In March, the Mayo Clinic reported that using Big Blue’s advanced cognitive computing system increased enrollment in clinical trials for breast cancer by 80%. “Watson is able to give us faster, better matching of patients to potential clinical trials that our oncologists wouldn’t have otherwise been able to see,” Mayo Clinic CIO Christopher Ross told the trade publication MobiHealthNews.

Even organizations which previously only had distant relationships with patients—pharmacy benefit managers, for instance—are, because of the vast kingdoms of data they oversee, well positioned to draw insights that might improve population health and lower costs.

Consider Express Scripts, the Missouri-based PBM that just announced its sale to insurer Cigna in March. Express Scripts administers 1.4 billion prescriptions for 100 million Americans each year—and it knows when you don’t take your meds. Such nonadherence costs between $100 billion and $300 billion a year, depending on which estimate one believes. That cost comes when patients suffer complications from not following the doctor’s orders.

The company has identified 300 different factors that can help determine the likelihood that a patient will not fill a prescription says Express Scripts’ chief data officer Tom Henry. They range from that basic demographic data (income level and zip code) to behavioral data (one’s level of forgetfulness and tendency to procrastinate, gleaned from surveys the company does after patients fail to pick up their prescriptions) to less intuitive things like the genders of the prescriber and patient (men with a woman physician are more likely to not follow orders). The company uses the algorithm, which it says is validated and 94% accurate, to assign risk scores to patients and target them with varying modes of outreach—Henry says those efforts are “soft touch, nothing Orwellian.” Nonetheless, Express Scripts claims this work has reduced nonadherence by 37% and saved its clients more than $180 million.

As with any revolution, many rush into action without considering weighty questions about what comes next and what unintended consequences may arise. In this social experiment, those questions cover everything from patient privacy to the ethical dilemma of warning someone about a risk they can’t avoid. To many digital health evangelists, big data is the “magic bullet” we’ve been waiting for. The question is, where exactly that bullet strikes.

A version of this article appears in the April 2018 issue of Fortune with the headline “Big Data Meets Biology.”