Machine Learning for Clinical Management: From the Lab to the Hospital

摘要

Population aging, increasing social demands, and rising costs of treatments are stressing healthcare systems to the point of risking the sustainability of universal and accessible healthcare. A hope in this dismal panorama is that there are large inefficiencies, and so opportunities for getting more from the same resources. To name a few, avoidable hospitalizations, unnecessary medication and tests, and lack of coordination among healthcare agents are estimated to cost several hundred billion euros per year in the EU. Technology can be useful for locating and reducing these inefficiencies, and within technology, the full exploitation of the data that the system collects to record its activity. In this talk, I will review the case for activity data analytics in healthcare, with two main considerations: 1) The need to include information about resources and costs in the models, in addition to clinical knowledge and patient outcomes, and 2) the need to use mostly data that healthcare organizations already collect and is not locked and distributed in silos. Fortunately, data collected for administrative and billing purposes, even though imperfect, partial, and low resolution, can be used to improve efficiency and safety, as well as fairness and equity. I will focus on the work carried out at Amalfi Analytics, a spin-off of my research group at UPC in Barcelona. On the one hand, we have addressed predictive management in hospitals, from influx to the emergency room to availability of surgical areas, beds, and staff. Anticipating activity, needs, and resource availability lets managers improve critical KPIs, e.g. waiting times, but also reduce staff stress, which leads to fewer medical errors and accidents. On the other hand, we have developed a patient cohort analyzer, based mostly on a recent clustering algorithm, that gives experts a fresh view of their patient population and lets them refine protocols and identify high-risk patient groups. This tool has also been used to support territorial planning and resource allocation. These problems have been extensively addressed in the past, but actual penetration of solutions in hospitals is smaller than one could expect. For example, one can find hundreds of papers on predicting influx to emergency rooms or bed demands, but many of them conclude after producing an AUC figure, and even fewer describe a working system that can be exported from the hospital where they were developed to others at an affordable cost. I will describe the approach taken at Amalfi so that hospitals can have such a solution up and running in a few days of work for their IT departments, in what I think is an interesting combination of software engineering and automatic Machine Learning.