Ziad Obermeyer

Bio: Ziad Obermeyer is an academic researcher from University of California, Berkeley. The author has contributed to research in topics: Population & Health care. The author has an hindex of 31, co-authored 75 publications receiving 5362 citations. Previous affiliations of Ziad Obermeyer include Institute for Health Metrics and Evaluation & Brigham and Women's Hospital.

Dissecting racial bias in an algorithm used to manage the health of populations

Abstract: Health systems rely on commercial prediction algorithms to identify and help patients with complex health needs. We show that a widely used algorithm, typical of this industry-wide approach and affecting millions of patients, exhibits significant racial bias: At a given risk score, Black patients are considerably sicker than White patients, as evidenced by signs of uncontrolled illnesses. Remedying this disparity would increase the percentage of Black patients receiving additional help from 17.7 to 46.5%. The bias arises because the algorithm predicts health care costs rather than illness, but unequal access to care means that we spend less money caring for Black patients than for White patients. Thus, despite health care cost appearing to be an effective proxy for health by some measures of predictive accuracy, large racial biases arise. We suggest that the choice of convenient, seemingly effective proxies for ground truth can be an important source of algorithmic bias in many contexts.

Predicting the Future - Big Data, Machine Learning, and Clinical Medicine.

Abstract: The algorithms of machine learning, which can sift through vast numbers of variables looking for combinations that reliably predict outcomes, will improve prognosis, displace much of the work of radiologists and anatomical pathologists, and improve diagnostic accuracy.

Coverage of cervical cancer screening in 57 countries: low average levels and large inequalities.

Abstract: Emmanuela Gakidou and colleagues find that coverage of cervical cancer screening in developing countries is on average 19% compared to 63% in developed countries.

Prediction Policy Problems.

Abstract: Empirical policy research often focuses on causal inference. Since policy choices seem to depend on understanding the counterfactual–what happens with and without a policy–this tight link of causality and policy seems natural. While this link holds in many cases, we argue that there are also many policy applications where causal inference is not central, or even necessary. Consider two toy examples. One policy maker facing a drought must decide whether to invest in a rain dance to increase the chance of rain. Another seeing clouds must deciding whether to take an umbrella to work to avoid getting wet on the way home? Both decisions could benefit from an empirical duty of rain. But each has different requirements of the estimator. One requires causality: do rain dances cause rain? The other does not, needing only prediction: is the chance of rain high enough to merit an umbrella? We often focus on rain dance like policy problems. But there are many important policy problems umbrella-like. Not only are these prediction problems neglected, machine learning can help us solve them more effectively. In this paper, we (i) provide a simple framework that clarifies the distinction between causation and prediction; (ii) explain how machine learning adds value over traditional regression approaches in solving prediction problems; (iii) provide an empirical example from health policy to illustrate how improved predictions can generate large social impact; (iv) illustrate how “umbrella” problems are common and important in many important policy domains; and (v) argue that solving these problems produces not just policy impact but also theoretical and economic insights.1

Contributions of risk factors and medical care to cardiovascular mortality trends.

Abstract: Ischaemic heart disease, stroke, and other cardiovascular diseases (CVDs) lead to 17.5 million deaths worldwide per year. Taking into account population ageing, CVD death rates are decreasing steadily both in regions with reliable trend data and globally. The declines in high-income countries and some Latin American countries have been ongoing for decades without slowing. These positive trends have broadly coincided with, and benefited from, declines in smoking and physiological risk factors, such as blood pressure and serum cholesterol levels. These declines have also coincided with, and benefited from, improvements in medical care, including primary prevention, diagnosis, and treatment of acute CVDs, as well as post-hospital care, especially in the past 40 years. These variables, however, explain neither why the decline began when it did, nor the similarities and differences in the start time and rate of the decline between countries and sexes. In Russia and some other former Soviet countries, changes in volume and patterns of alcohol consumption have caused sharp rises in CVD mortality since the early 1990s. An important challenge in reaching firm conclusions about the drivers of these remarkable international trends is the paucity of time-trend data on CVD incidence, risk factors throughout the life-course, and clinical care.

High-performance medicine: the convergence of human and artificial intelligence

Abstract: The use of artificial intelligence, and the deep-learning subtype in particular, has been enabled by the use of labeled big data, along with markedly enhanced computing power and cloud storage, across all sectors. In medicine, this is beginning to have an impact at three levels: for clinicians, predominantly via rapid, accurate image interpretation; for health systems, by improving workflow and the potential for reducing medical errors; and for patients, by enabling them to process their own data to promote health. The current limitations, including bias, privacy and security, and lack of transparency, along with the future directions of these applications will be discussed in this article. Over time, marked improvements in accuracy, productivity, and workflow will likely be actualized, but whether that will be used to improve the patient-doctor relationship or facilitate its erosion remains to be seen.