開始時刻
前のセッションの直後
この勉強会は終了しました。ご参加ありがとうございました。
As healthcare providers have transitioned from paper to electronic health records they have gained access to increasingly sophisticated documentation aids such as custom note templates. However, little is known about how providers use these aids. To address this gap, we examine how 48 ophthalmologists and their staff create and use content-importing phrases a customizable and composable form of note template to document office visits across two years. In this case study, we find 1) content-importing phrases were used to document the vast majority of visits (95%), 2) most content imported by these phrases was structured data imported by data-links rather than boilerplate text, and 3) providers primarily used phrases they had created while staff largely used phrases created by other people. We conclude by discussing how framing clinical documentation as end-user programming can inform the design of electronic health records and other documentation systems mixing data and narrative text.
Deep learning algorithms promise to improve clinician workflows and patient outcomes. However, these gains have yet to be fully demonstrated in real world clinical settings. In this paper, we describe a human-centered study of a deep learning system used in clinics for the detection of diabetic eye disease. From interviews and observation across eleven clinics in Thailand, we characterize current eye-screening workflows, user expectations for an AI-assisted screening process, and post-deployment experiences. Our findings indicate that several socio-environmental factors impact model performance, nursing workflows, and the patient experience. We draw on these findings to reflect on the value of conducting human-centered evaluative research alongside prospective evaluations of model accuracy.
Artificial intelligence (AI) assistants for clinical decision making show increasing promise in medicine. However, medical assessments can be contentious, leading to expert disagreement. This raises the question of how AI assistants should be designed to handle the classification of ambiguous cases. Our study compared two AI assistants that provide classification labels for medical time series data along with quantitative uncertainty estimates: conventional vs. ambiguity-aware. We simulated our ambiguity-aware AI based on real-world expert discussions to highlight cases likely to lead to expert disagreement, and to present arguments for conflicting classification choices. Our results demonstrate that ambiguity-aware AI can alter expert workflows by significantly increasing the proportion of contentious cases reviewed. We also found that the relevance of AI-provided arguments (selected from guidelines either randomly or by experts) affected experts' accuracy at revising AI-suggested labels. Our work contributes a novel perspective on the design of AI for contentious clinical assessments.
Implementation of new health information systems such as Electronic Health Records (EHR) is expected to reap many benefits. However, the transition from one information system to another is often associated with inefficiency, ineffectiveness, and patient safety hazards. These negative consequences are difficult to predict and avoid before system transitions take place. The changed physical form of information remains an unexamined facet of healthcare system transitions. Using ethnographic methods in two clinical sites, we discovered a recurrent set of problems that emerged due to physical disconnections between information and practice predicated on implementation of new information systems. "Physical misalignments" are instances where workers cannot bring information sources to hand in the precise time and place in which they are needed. We identify three types of physical misalignments, then discuss how physical misalignments can be proactively identified and corrected before, during, and after implementation of new health information systems.
Online Ask the Doctor (AtD) services allow access to health professionals anytime anywhere beyond existing patient-provider relationships. Recently, many free-market AtD platforms have emerged and been adopted by a large scale of users. However, it is still unclear how people make use of these AtD platforms in practice. In this paper, we present an interview study with 12 patients/caregivers who had experience using AtD in China, highlighting patient agency in seeking more reliable and cost-effective healthcare beyond clinic settings. Specifically, we illustrate how they make strategic choices online on AtD platforms, and how they strategically integrate online and offline services together for healthcare. This paper contributes an empirical study of the use of large-scale AtD platforms in practice, demonstrates patient agency for healthcare beyond clinic settings, and recommends design implications for online healthcare services.