A growing of health-care organizations are in the process of modifying their clinical information systems (CIS) to support browser-based access. Consequently, care-providers are expected to modify their workflow to take advantage of the new technology. Intuitive interfaces, fast response and new functionality are few of the features used to promote endorsement of the change. In parallel, administrators are required to constantly assess user compliance and intervene where necessary to prevent rejection. Such monitoring translates to frequent surveys, analysis of logs and prudent utilization of user-groups. These methods tend to further burden users, suffer from "post-hoc" temporality and are difficult to maintain. In this paper we suggest an alternative approach to such data acquisition. "CareQuest" is an interactive Web-based service that can be woven into clinical applications without coding. It acquires information from the clinician at the relevant point in her workflow. It allows extensive interaction customization, data-driven response, real-time Web-based data-analysis, and full Web-based administration.
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Singapore Med J
March 2024
Tsinghua Medicine, School of Medicine, Tsinghua University, Beijing, China.
With the rise of generative artificial intelligence (AI) and AI-powered chatbots, the landscape of medicine and healthcare is on the brink of significant transformation. This perspective delves into the prospective influence of AI on medical education, residency training and the continuing education of attending physicians or consultants. We begin by highlighting the constraints of the current education model, challenges in limited faculty, uniformity amidst burgeoning medical knowledge and the limitations in 'traditional' linear knowledge acquisition.
View Article and Find Full Text PDFLaryngoscope Investig Otolaryngol
April 2022
Department of Otolaryngology-Head and Neck Surgery, Sean Parker Institute for the Voice Weill Cornell Medicine New York New York USA.
Objective: This study aims to develop and validate a convolutional neural network (CNN)-based algorithm for automatic selection of informative frames in flexible laryngoscopic videos. The classifier has the potential to aid in the development of computer-aided diagnosis systems and reduce data processing time for clinician-computer scientist teams.
Methods: A dataset of 22,132 laryngoscopic frames was extracted from 137 flexible laryngostroboscopic videos from 115 patients.
J Med Internet Res
May 2019
Division of General Internal Medicine, Department of Medicine at Zuckerberg San Francisco General Hospital, University of California San Francisco, San Francisco, CA, United States.
Background: Safety-net systems serve patients with limited health literacy and limited English proficiency (LEP) who face communication barriers. However, little is known about how diverse safety-net patients feel about increasing clinician electronic health record (EHR) use.
Objective: The aim of this study was to better understand how safety-net patients, including those with LEP, view clinician EHR use.
J Am Med Inform Assoc
January 2017
Division of General Internal Medicine, University of California, San Francisco.
Objective: Patients with limited health literacy (LHL) and limited English proficiency (LEP) experience suboptimal communication and health outcomes. Electronic health record implementation in safety net clinics may affect communication with LHL and LEP patients.We investigated the associations between safety net clinician computer use and patient-provider communication for patients with LEP and LHL.
View Article and Find Full Text PDFJAMA Intern Med
January 2016
Division of General Internal Medicine, University of California, San Francisco (UCSF)2UCSF Center for Vulnerable Populations at San Francisco General Hospital, San Francisco.
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