大數(shù)據(jù)將有助于提高醫(yī)療保健行業(yè)的效率，促進(jìn)在該行業(yè)推行問(wèn)責(zé)制。然而到目前為止，其他行業(yè)在這方面要成功得多：通過(guò)對(duì)多種數(shù)據(jù)源進(jìn)行大規(guī)模的整合和分析，獲得了實(shí)用價(jià)值。

那些成功行業(yè)弄明白了一個(gè)問(wèn)題，那就是：當(dāng)不同的數(shù)據(jù)集在具體某個(gè)人的層面上連接起來(lái)時(shí)，大數(shù)據(jù)就會(huì)產(chǎn)生變革性的價(jià)值。相比之下，生物醫(yī)學(xué)大數(shù)據(jù)分散在研究機(jī)構(gòu)中，而且被特意地隔離起來(lái)，目的是為了保護(hù)病人的隱私。連接這些分散的數(shù)據(jù)，既有技術(shù)方面的挑戰(zhàn)，也有社會(huì)方面的挑戰(zhàn)。只有迎接兩個(gè)方面的挑戰(zhàn)，才能使生物醫(yī)學(xué)大數(shù)據(jù)對(duì)醫(yī)療保健行業(yè)發(fā)揮充分的作用。在今天的“觀點(diǎn)”欄目中，我們要著重分析這種連接所帶來(lái)的挑戰(zhàn)。CDA數(shù)據(jù)分析師：http://cda.pinggu.org/

競(jìng)選活動(dòng)、政府和企業(yè)利用大數(shù)據(jù)盡可能更多地了解選民或客戶情況，然后利用先進(jìn)的估算方法來(lái)制定策略。2012年奧巴馬競(jìng)選的時(shí)候，把來(lái)自臉譜網(wǎng)（Facebook）、人口普查、選民列表以及積極推廣等多種渠道的數(shù)據(jù)綜合起來(lái)，以確認(rèn)、接近和影響那些猶豫不決的選民。國(guó)家安全局通過(guò)電話公司和互聯(lián)網(wǎng)公司來(lái)確認(rèn)恐怖分子。CDA數(shù)據(jù)分析師：http://cda.pinggu.org/

通過(guò)用戶的上網(wǎng)歷史和地理環(huán)境，谷歌公司將每個(gè)人的搜索結(jié)果進(jìn)行了個(gè)性化處理。在所有的這些事例中，關(guān)鍵是已經(jīng)超出了綜合數(shù)據(jù)的范圍，將信息連接到了具體的人。知道在某個(gè)行政區(qū)域內(nèi)有很多猶豫不決的選民是有所幫助的，但是跟這些具體的人們獲得聯(lián)系可能有助于贏得一場(chǎng)競(jìng)選。

獲得大數(shù)據(jù)可能會(huì)使醫(yī)生和研究人員驗(yàn)證新的假設(shè)，并確認(rèn)那些可能遭受干預(yù)的領(lǐng)域。例如，通過(guò)從不同地區(qū)的商店所獲得的雜貨購(gòu)買模式，能否預(yù)測(cè)出公共衛(wèi)生數(shù)據(jù)庫(kù)中肥胖癥和2型糖尿病的患病率呢？能否像配藥時(shí)對(duì)后續(xù)配方進(jìn)行測(cè)量那樣，將家庭監(jiān)視裝置所記錄的運(yùn)動(dòng)量跟降膽固醇藥物的療效相互關(guān)聯(lián)起來(lái)呢？病人的臉譜網(wǎng)網(wǎng)友在多大程度上能夠影響他們對(duì)生活方式的選擇和對(duì)醫(yī)學(xué)治療的依從呢？至于這些相互關(guān)聯(lián)的推斷是否真正地存在于大數(shù)據(jù)中，以及醫(yī)生們將如何利用這些信息，這些情況都還不清楚。

然而，將數(shù)據(jù)連接到具體病人的層面上來(lái)，是探索這些可能性的先決條件。

在有效利用生物醫(yī)學(xué)大數(shù)據(jù)方面，首要的挑戰(zhàn)就是要確定衛(wèi)生保健信息的潛在來(lái)源是什么，以及確定將這些數(shù)據(jù)連接起來(lái)之后所帶來(lái)的價(jià)值如何。將數(shù)據(jù)集按照“大小”從不同的方面進(jìn)行條理化，這個(gè)大數(shù)據(jù)就會(huì)提供解決問(wèn)題的潛在方案。

一些大數(shù)據(jù)，如電子健康記錄（EHRs），提供詳盡資料，包括病人接受診斷時(shí)的多種資料（如：圖片、診斷記錄等）。盡管如此，其他大數(shù)據(jù)，如保險(xiǎn)理賠數(shù)據(jù)，提供縱深資料——顧及病人在很長(zhǎng)一段時(shí)間里、在某個(gè)狹窄的疾病類型范圍內(nèi)所經(jīng)歷的病史。當(dāng)連接數(shù)據(jù)有助于填補(bǔ)空白的時(shí)候，這些大數(shù)據(jù)才會(huì)增加價(jià)值。CDA數(shù)據(jù)分析師：http://cda.pinggu.org/

只有記住這些，才能更容易明白如何將衛(wèi)生保健系統(tǒng)之外非傳統(tǒng)來(lái)源的生物醫(yī)學(xué)數(shù)據(jù)融入這些情況之中。盡管數(shù)據(jù)的質(zhì)量有所不同，但社會(huì)媒體、信用卡購(gòu)物、人口普查記錄以及大量其他類型的數(shù)據(jù)，都會(huì)有助于收集一個(gè)病人的歷史資料，特別是有助于揭示可能對(duì)健康產(chǎn)生影響的社會(huì)因素和環(huán)境因素。

英語(yǔ)原文：

Finding the Missing Link for Big Biomedical Data

It has been argued that big data will enable efficiencies and accountability in health care. However, to date, other industries have been far more successful at obtaining value from large-scale integration and analysis of heterogeneous data sources. What these industries have figured out is that big data becomes transformative when disparate data sets can be linked at the individual person level. In contrast, big biomedical data are scattered across institutions and intentionally isolated to protect patient privacy. Both technical and social challenges to linking these data must be addressed before big biomedical data can have their full influence on health care. It is this linkage challenge that we address in this Viewpoint.

Political campaigns, government, and businesses use big data to learn everything possible about their constituents or customers, and then apply advanced computation to hone strategy. The 2012 Obama campaign identified, approached, and influenced swing voters using data fused from Facebook, census, voter lists, and active outreach. The National Security Agency employs massive data on individuals from phone and Internet companies to identify terrorists. Google personalizes search results with the user’s web history and geographic context. In all these examples, the key has been to go beyond aggregate data and link information to individual people. Knowing that there are many swing voters in a zip code is helpful, but contacting those specific individuals may help to win an election.

Linking big data will enable physicians and researchers to test new hypotheses and identify areas of possible intervention. For example, do grocery shopping patterns obtained from stores in various areas predict rates of obesity and type 2 diabetes in public health databases? Does level of exercise recorded by home monitoring devices correlate with response rates of cholesterol-lowering drugs, as measured by continued refills at the pharmacy? Does increased physical distance from patients’ homes to hospitals and pharmacies affect utilization of health care and result in distinct patterns in claims data? To what extent do patients’ Facebook friends influence lifestyle choices and compliance with medical treatments? It is unknown whether these types of correlative inferences will really be found in big data and how physicians would use that information. However, being able to link data at the patient level is a prerequisite to exploring the possibilities.

The first challenge in using big biomedical data effectively is to identify what the potential sources of health care information are and to determine the value of linking these together. The Figure presents a potential way of approaching this problem by organizing data sets along different dimensions of “bigness.” Although some big data, such as electronic health records (EHRs), provide depth by including multiple types of data (eg, images, notes, etc) about individual patient encounters, others, such as claims data provide longitudinality—a view of a patient’s medical history over an extended period for a narrow range of categories. Linking data adds value when they help fill in the gaps. With this in mind, it becomes easier to see how nontraditional sources of biomedical data outside of the health care system fit into the picture. Social media, credit card purchases, census records, and numerous other types of data, despite varying degrees of quality, can help assemble a holistic view of a patient, and, in particular, shed light on social and environmental factors that may be influencing health.

CDA數(shù)據(jù)分析師：http://cda.pinggu.org/