Amino Corporation

Google Inc.

Rebecca Ackermann

Abraham M Othman

Mary Audrey Hampden

Nicholas C Dunkman

Jorge A Caballero

David A Vivero

Sumul Mahendra Shah

Xavier Benavides Palos

Abhinav Yarlagadda

Scott McKinney

Quang Duong

Kunal Dhiren Shah

Rory Abbott Sayres

Eithon Michael Galinato Cadag

William Chen

James Lyon Fingal

Thad Hughes

Teagan Daly

Jacob Stimes

Eric Lindley

Cristhian Cruz

Willard Kirk Strauser

Beverly Freeman

Michael H Lin

Farid Jamshidian

A set of user interface tools is described facilitating asynchronous adjudication of one or more regions-of-interest in a medical image by a group of two or more graders, each of which has access to the set of tools in a workstation environment. The set of tools include (1) a feature for enabling the graders to assess the medical image and manually delineate one or more specific regions-of-interest (ROI) in the medical image, (2) a feature for assessing the ROI(s) delineated by other graders, including display of the ROI delineated by other graders; (3) dialog features for explaining and discussing the assessments, including a text feature for discussing the assessments. The dialog features and the ROIs delineated by all the graders are visible to all the graders on the workstation display as they collectively adjudicate the medical image in a round-robin manner. The set of tools further include (4) a feature for manually verifying grader agreement with the other graders' assessments.

Asynchronous region-of-interest adjudication for medical images

A computer-implemented method includes displaying a number of questions on a stack of overlapping graphical objects that dynamically indicates a status of the number of remaining questions, determining an output based on answers input by a user, and displaying an indication of the output.

Gathering information from a healthcare consumer using context-based questions, and progressively presenting information associated with a ranked list of suggested healthcare providers

Disclosed are systems and techniques for providing a master health entity index and a data analysis mechanism designed for entity cohort discovery and entity profiling. The entity can be a healthcare facility that diagnoses or treats health conditions and diseases (e.g., hospital, clinic), individuals (e.g., providers, patients, caregivers), healthcare data (e.g., medical conditions, treatments, diagnostic studies, health outcomes), etc. For example, a data analysis mechanism may identify distinctive patient cohorts based on what happened to patients in a hospital and why the occurrence happened, glean insight from the activities of the distinctive patient cohorts, and assist with additional patients with the gleaned insight.

Data analysis mechanism for generating statistics, reports and measurements for healthcare decisions

Introduced are methods and systems for gathering information from a healthcare consumer using context-based questions, and progressively presenting information associated with a ranked list of suggested healthcare providers. First, the server gathers information associated with the healthcare consumer using context-based questions. Each question is formulated based on the answers the healthcare consumer has provided to previous questions, so that minimal and necessary questions are asked. Second, the server progressively sends to a healthcare consumer device, information associated with a ranked list of suggested healthcare providers. The first information tier sent to the healthcare consumer device includes a graphic representing the match strength between the healthcare consumer and the healthcare provider, and the number of similar healthcare consumers the healthcare provider has treated. Based on the healthcare consumer interest, the server displays additional information to the healthcare consumer.

A method includes: presenting, on a device, a view of at least part of an augmented reality (AR) environment, the AR environment including a first AR location corresponding to a first physical location in a physical environment; initiating a download to the device of first data representing a first AR object associated with the first AR location; assigning a size parameter to a first loading indicator for the first AR object based on a size of the first AR object; determining a spatial relationship between the view and the first physical location; and presenting the first loading indicator in the view, the first loading indicator having a size based on the assigned size parameter and being presented at a second AR location based on the determined spatial relationship.

Loading indicator in augmented reality environment

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San Francisco, CA, United States of America

Rebecca Ackermann