Big data makes a difference at Penn Medicine

The team of clinicians and medical informatics experts led by Mike Draugelis, chief data scientist at Penn Medicine in Philadelphia, is busy these days. Using insights from a massively parallel computer cluster that stores a huge volume of data, the team is building prototypes of new care pathways, testing them out with patients and feeding the results back into algorithms so that the computer can learn from its mistakes.

提高护理质量这个大数据的做法已经产生了一个显著的成功：宾夕法尼亚团队提高临床医生预测哪些患者是在发展败血症，高度危险状态的风险的能力，它现在能够确定这些患者24小时早于它可能引入算法之前。

Draugelis and his colleagues work in the hospital of the University of Pennsylvania. On the academic research side, the university's medical school has launched anInstitute of Biomedical Informatics(IBI) to do basic research using big data techniques. Announced in 2013, IBI is now coalescing a few months after naming Jason Moore, Ph.D., who founded a similar institute at Dartmouth, as its director. IBI will focus its efforts on precision medicine, a hot field that is starting to take off as genomic sequencing costs drop.

The effort to link genomic differences with "phenotypes" – the variations in patients’ characteristics and diseases – has been underway for five years, says C. William Hanson III, M.D., chief medical information officer and vice president of Penn Medicine and a member of IBI. But he sees this kind of research quickly accelerating.

史蒂芬Steinhubl，医学博士，在数字医学主任Scripps Translational Science Institute拉霍亚，加利福尼亚州，同意。“我们仍然对我们打算从大数据学习什么的曲线的上升部分，”他说。“这是快速增长，但它会更加快像他们已经收集数据的宾夕法尼亚大学乘虚而入大型医疗中心，并在此基础之上添加基因组学”。

改变临床路径

Draugelis' team at Penn Medicine is using algorithms to tweak the guidelines that doctors and nurses follow in diagnosing and treating particular conditions. When a protocol changes, he explains, the clinical team must develop a new care pathway that specifies each step in the workflow of clinicians. It is very intensive work, and so is coding the changes that must be made in the algorithm to adjust to the feedback from the frontline of patient care.

“我们在2周冲刺，其中临床医师调整自己的途径正在努力，我们调整算法来他们的需求，” Draugelis笔记。

The team builds a prototype of a new pathway for a particular condition about once every six months. Currently, it is focusing on finding a better way to predict which patients have congestive heart failure and which are likely to be readmitted after discharge from the hospital. In addition, the team is working on acute conditions such as maternal deterioration after delivery and severe sepsis.

“我们正在创造机的基础上成千上万的变量学习预测模型，” Draugelis说。“我们看他们在实时，但我们培训他们在数以百万计的患者记录。”

在败血症的情况下，球队开始与称为SIRS专家模型（系统性炎症反应综合征），使用温度，心脏，呼吸速率和白细胞计数的特定阈值败血症风险的关键指标。在所有可用数据的加载在一个病人后，包括electronic health record(EHR) data, the computer uses the algorithm to determine how closely a patient's characteristics match those of patients who developed sepsis in the past. When a patient matches that profile, the clinician caring for the patient receives an alert, acts on it or doesn't, and feeds his or her reaction back to the algorithm to improve it.

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宾夕法尼亚医学院的床边监护仪不断跟踪生命体征，并在电子病历记录他们。这种自动化的生命体征文档中没有发生五年前，汉森笔记。它仍然是不是重症监护病房外普遍认为Steinhubl，但是当它成为例行公事，他补充说，这将大大推动那种的工作，Draugelis的团队做的。

Dean Sittig, Ph.D., a professor at theUniversity of Texas Health School of Biomedical Informaticsin Houston, likes the idea of continuous monitoring and feeding data into computer algorithms. In contrast to the average floor nurse, who can only watch a patient 20 percent of the time if she has five patients, "The computer can be looking at every minute, and the idea of continuous monitoring and surveillance is very powerful," he says. "If you can teach the computer what the nurse would be looking for, the computer can be much more vigilant [than the nurse]."

使决策支持警报有用,然而r, the staff has to be ready to spring into action, especially with a condition like sepsis, Sittig says. In addition, the alerts that the algorithm triggers must be fairly accurate. "As a rule of thumb, if the computer is right more than half the time – especially with something serious like sepsis – clinicians will pay attention to it. But if it's only right 10 percent of the time, it starts to be a bother."

Precision medicine

两个重要的发展已经走到了一起，使可能的那种精密医学研究的潘恩医学的IBI在做什么。首先，电子病历在过去几年变得普遍：大多数医院和80％以上的医生现在有这些系统。其次，基因组测序的成本已经下降至约$ 1,000完整的基因组。部分基因组或基因组测序的成本小于。由于这些趋势的结果，基因型和表型相关的想法变来发现疾病和药物的个体反应，现在可行的。

为了进行这方面的研究，宾州医学院创造，到目前为止，已经存储了大约20,000与患者的权限基因组样品一个专门的“生物银行”布莱恩·韦尔斯，医疗技术的助理副总裁和学术计算为医疗保健说，系统。个性化诊断的独立中心拥有测序肿瘤基因组超过5000名患者，他指出。

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The sheer volume of genomic data is staggering. For example, Penn Medicine has two petabytes of disk space in its high performing computer cluster, and it plans to expand that, says Wells.

“One researcher told us that in the next few years, he might go from five to 30 petabytes of space related to neuroscience sequencing. So we're prepared to add to that as we need to," he notes.

挑战CMIOs和CIO

The biggest challenges that Hanson faces as Penn Medicine grapples with its big data projects, he says, is the lack of interoperability among EHRs and the need for good, clean, structured data. Currently, Penn has different EHRs in its hospital, ER, ICU and ambulatory practices, but it is moving to a single system. Structured clinical data is harder to deliver, however, because "clinicians tend to document in an unstructured way," he says.

Penn intends to use natural language processing (NLP) to mine unstructured data in EHRs and convert it into structured information, Wells notes. "That’s for retrospective analysis rather than clinical decision support, because you can't rely it on it 100 percent of the time," he adds.

目前大数据的方法是足够的处理基因组数据的大洪水，但生物信息学谁知道如何工作，这个数据是供不应求，Steinhubl说。他预测，瓶颈将在数据处理和存储开发时医疗保健机构开始审查，预计在从移动设备和便携的传感器对流动的生理数据。

Nevertheless, Steinhubl is very excited about the promise of big data in fields like precision medicine and clinical quality improvement. "Eventually, it's going to completely change medicine and the way we treat common chronic conditions,” he says.

For example, he notes, most cases of hypertension are defined as a single disease. "So we put them all in one basket and treat them the same way. With these tools, we'll be able to refine their phenotype and their genotype and better treat these individuals. Right now, it's mostly trial and error."

汉森李文大数据与一些清醒的思考寄予厚望。首先，他指出，这将是一段时间以前，大多数供应商都准备在远程监测数据来拉，因为它必须预先筛分是在病人护理可用。其次，虽然精度医学是一个伟大的想法，大多数人还没有被测序，“我们没有解释他们的基因型数据，使之作为行动的一致的方式。”

While oncologists are increasingly using information about the genetic differences among individual cancer patients, it will be a while, Hanson says, before this approach filters down to primary care physicians. However, precision medicine research is moving fast at Penn Medicine and other leading academic medical centers. "We're on the verge of an explosive development," he says.

This story, "Big data makes a difference at Penn Medicine" was originally published byCIO .