医生八成工作将由科技代劳
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今天的医疗保健往往靠的还是“医术”，而不是“医学”。 拿治疗发烧为例。150年来，医生一般都是开布洛芬等退烧药帮助退烧。但在2005年，迈阿密大学（the University of Miami）的研究人员对82名病危患者进行了一项研究。82名病人被随机分成两组，其中一组的病人在体温超过摄氏38.5度时便服用退烧药（即“标准疗法”），另一组只有在体温达到摄氏40度时才服用退烧药。结果随着试验的开展，有7名接受标准疗法的病人死亡了，而没有服用退烧药的那一组中，只有一名病人死亡。但就在这时，试验由于伦理问题被终止了，因为研究团队认为再让更多病人接受标准疗法是不道德的。 像退烧这样基本的病症的治疗都难以避免地带有“医术”的痕迹，而且这种情况持续了100多年都没有改观，我们不禁要问：还有哪些疗法是依托于传统，而不是依托于科学？ 今天的诊断方式部分根据患者的病史，部分根据患者的症状（不过患者一般都不擅长描述症状）。甚至大多数时候，医生是根据医药广告，以及多年前在医学院学到的知识做出诊断。且不说医学院讲授的内容好多已经过时，而且时间一久，医生自己也忘了许多课本上的知识，何况光是从医学院学来的知识也难免存在认识偏差、经验编差以及其它人为错误。许多时候，三个医生诊断同一个病症，可能会得出三种不同的诊断和三种不同的疗法。 结果是患者不仅诊疗效果不理想，而且还花了大笔冤枉钱。约翰霍普金斯大学（Johns Hopkins University）的一项研究发现，美国每年都有40,500余名患者因为误诊而死亡，几乎与死于乳腺癌的人数相当。另一份研究则发现，有65%的误诊病例都与处理不当、团队合作不协调、沟通不畅等所谓的“系统相关因素”有关。而75%的误诊病例都存在所谓的“认识因素”，而其中最主要的原因，是由于医生坚持一开始的错误诊断，忽视了其它合理的可能。这种误诊也增加了医疗支出，每次误诊索赔的平均金额为30万美元。 医疗诊断应该更多运用数据演绎的方法，减少“摸着石头过河”的成分。而这个目标离开现代科技则很难实现，因为如今可使用的数据和研究方法越来越多。新一代的医疗技术将用到更多、更复杂的生理模型，并且使用更多的传感器数据，从而提供个性化的诊疗，这些数据可能不是光靠一个人类医生就能理解得了的。每次诊断都将依托于成千上万个基线数据和多个组学数据点，以及更加全面的病史和患者行为等要素。 日益完善的对话管理系统将有助于对病人进行更为有效和更为全面的数据捕捉及探测。其中的关键就是数据科学。最终，它将有助于降低医疗成本，减少医生的工作量，提高患者的医疗水平。 |
Healthcare today is often really the "practice of medicine" rather than the "science of medicine." Take fever as an example. For 150 years, doctors have routinely prescribed antipyretics like ibuprofen to help reduce fever. But in 2005, researchers at the University of Miami, Florida, ran a studyof 82 intensive care patients. The patients were randomly assigned to receive antipyretics either if their temperature rose beyond 101.3°F ("standard treatment") or only if their temperature reached 104°F. As the trial progressed, seven people getting the standard treatment died, while there was only one death in the group of patients allowed to have a higher fever. At this point, the trial was stopped because the team felt it would be unethical to allow any more patients to get the standard treatment. So when something as basic as fever reduction is a hallmark of the "practice of medicine" and hasn't been challenged for 100+ years, we have to ask: What else might be practiced due to tradition rather than science? Today's diagnoses are partially informed by patients' medical histories and partially by symptoms (but patients are bad at communicating what's really going on). They are mostly informed by advertising and the doctor's half-remembered and potentially obsolete lessons from medical school (which are laden with cognitive biases, recency biases, and other human errors). Many times, if you ask three doctors to look at the same problem, you'll get three different diagnoses and three different treatment plans. The net effect is patient outcomes that are inferior to and more expensive than what they should be. A Johns Hopkins study found that as many as 40,500 patients die in an ICU in the U.S. each year due to misdiagnosis, rivaling the number of deaths from breast cancer. Yet another studyfound that 'system-related factors', e.g. poor processes, teamwork, and communication, were involved in 65% of studied diagnostic error cases. 'Cognitive factors' were involved in 75%, with 'premature closure' (sticking with the initial diagnosis and ignoring reasonable alternatives) as the most common cause. These types of diagnostic errors also add to rising healthcare expenditures, costing $300,000 per malpractice claim. Healthcare should become more about data-driven deduction and less about trial-and-error. That's hard to pull off without technology, because of the increasing amount of data and research available. Next-generation medicine will utilize more complex models of physiology, and more sensor data than a human MD could comprehend, to suggest personalized diagnosis. Thousands of baseline and multi-omic data points, more integrative history, and demeanor will inform each diagnosis. Ever-improving dialog manager systems will help make data capture and exploration from patients more accurate and comprehensive. Data science will be key to this. In the end, it will reduce costs, reduce physician workloads, and improve patient care. |
