Institutional Affiliation: University of California at Berkeley
|Group Testing in a Pandemic: The Role of Frequent Testing, Correlated Risk, and Machine Learning|
with , , : w27457
Group testing increases efficiency by pooling patient specimens and clearing the entire group with one negative test. Optimal grouping strategy is well studied in one-off testing scenarios with reasonably well-known prevalence rates and no correlations in risk. We discuss how the strategy changes in a pandemic environment with repeated testing, rapid local infection spread, and highly uncertain risk. First, repeated testing mechanically lowers prevalence at the time of the next test. This increases testing efficiency, such that increasing frequency by x times only increases expected tests by around √x rather than x. However, this calculation omits a further benefit of frequent testing: infected people are quickly removed from the population, which lowers prevalence and generates further eﬃ...
|A Machine Learning Approach to Low-Value Health Care: Wasted Tests, Missed Heart Attacks and Mis-Predictions|
with : w26168
We use machine learning to better characterize low-value health care and the decisions that produce it. We focus on costly tests, specifically for heart attack (acute coronary syndromes). A test is only useful if it yields new information, so efficient testing is grounded in accurate prediction of test outcomes. Physician testing decisions can therefore be benchmarked against tailored algorithmic predictions, which provide a more precise way to study low-value care than the usual approach—looking at average test yield. Implemented in a large national sample, this procedure reveals significant over-testing: 52.6% of high-cost tests for heart attack are wasted. At the same time, it also reveals significant under-testing: many patients with predictably high risk go untested, then experience f...