Much of the AI work in healthcare is focused around disease prediction in clinical settings, which is an important application that has yet to deliver in earnest. However, there are other fundamental aspects like helping patients and care teams interact and communicate in efficient and meaningful ways, which could deliver quadruple-aim improvements. After heart disease and cancer, preventable medical errors are the third leading cause of death in the United States. The largest subset of medical errors is medication error. Providing the right treatment plan for patients includes knowledge about their current medications and drug allergies, an often challenging task. The widespread growth of prescribing and consuming medications has increased the need for applications that support medication reconciliation. We show a deep-learning application that can help reduce avoidable errors with their attendant risk, i.e., correctly identifying prescription medication, which is currently a tedious and error-prone task. We demonstrate prescription-pill identification from mobile images in the NIH NLM Pill Image Recognition Challenge dataset. Our application recognizes the correct pill within the top-5 results at 94% accuracy, which compares favorably to the original competition winner at 83.3% for top-5 under comparable, though not identical configurations. The Institute of Medicine claims that better use of information technology can be an important step in reducing medication errors. Therefore, we believe that a more immediate impact of AI in healthcare will occur with a seamless integration of AI into clinical workflows, readily addressing the quadruple aim of healthcare.
The field of biomedical informatics experienced a productive 2015 in terms of research. In order to highlight the accomplishments of that research, elicit trends, and identify shortcomings at a macro level, a 19-person team conducted an extensive review of the literature in clinical and consumer informatics. The result of this process included a year-in-review presentation at the American Medical Informatics Association Annual Symposium and a written report (see supplemental data). Key findings are detailed in the report and summarized here. This article organizes the clinical and consumer health informatics research from 2015 under 3 themes: the electronic health record (EHR), the learning health system (LHS), and consumer engagement. Key findings include the following: (1) There are significant advances in establishing policies for EHR feature implementation, but increased interoperability is necessary for these to gain traction. (2) Decision support systems improve practice behaviors, but evidence of their impact on clinical outcomes is still lacking. (3) Progress in natural language processing (NLP) suggests that we are approaching but have not yet achieved truly interactive NLP systems. (4) Prediction models are becoming more robust but remain hampered by the lack of interoperable clinical data records. (5) Consumers can and will use mobile applications for improved engagement, yet EHR integration remains elusive.
Zillow is undergoing a major evolution, transitioning from serving as the world's largest digital marketplace for real estate advertising into an end‐to‐end platform to support customers across the phases of buying, selling, and renting homes. As Zillow expands into more transactional spaces, the company has recognized the need to develop a clear and actionable understanding of users and their experiences as they interact with our products and services. To address this need, we set out to establish an Experience Measurement program to provide organization‐wide visibility into how well our Zillow experiences meet users’ needs and expectations as they progress through their real estate journey. This program will enable teams to gain insights at the intersection of attitudinal and behavioral experience data and lead us to our end goal of empowering informed decision‐making across all levels of the experience and organization.
In this paper, we provide an overview of our approach to establishing an Experience Measurement program at Zillow. We focus on a small subset of Experience Outcomes (XOs) in an initial feasibility study to develop a program that would scale and drive impact in assessment and decision making across lines of business. Finally, we share lessons learned throughout the process of developing and validating the framework and discuss the impact of this work on current and future organizational outcomes.
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