David A Vivero, 40Los Angeles, CA

2017023, Aug 17, 2017

Feb 12, 2016

15/043116

- San Francisco CA, US
Jorge A. Caballero - Menlo Park CA, US
David A. Vivero - San Francisco CA, US
Abhinav Chowdary Yarlagadda - San Francisco CA, US
Mary Audrey Hampden - Alameda CA, US
Sumul Mahendra Shah - Alameda CA, US
Rebecca Ackermann - San Francisco CA, US

G06F 19/00
G06F 17/30

Disclosed are methods and systems for identifying and profiling focus areas of healthcare providers. Embodiments include technology that identifies focus areas automatically from healthcare information such as diagnoses and procedures. As such, healthcare providers that frequently diagnose and/or treat patients for similar conditions may be automatically grouped into the same cluster. Expert knowledge may then be used to label a cluster as a focus area.

2017006, Mar 2, 2017

Oct 16, 2015

14/885895

- San Francisco CA, US
Sumul Mahendra Shah - Alameda CA, US
Rebecca Ackermann - San Francisco CA, US
Nicholas C. Dunkman - San Francisco CA, US
Jorge A. Caballero - Menlo Park CA, US
Abraham M. Othman - San Francisco CA, US
David A. Vivero - San Francisco CA, US

G06F 19/00

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.

2016019, Jul 7, 2016

Mar 4, 2015

14/638227

- San Francisco CA, US
David A. Vivero - San Francisco CA, US
Erica Frandsen - Alameda CA, US
Jorge A. Caballero - Menlo Park CA, US
Abhinav Chowdary Yarlagadda - San Francisco CA, US
Sumul Mahendra Shah - Alameda CA, US
Eithon Michael Galinato Cadag - Foster City CA, US
Mary Audrey Hampden - Alameda CA, US

G06F 19/00

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 health care facility that diagnosis or treats health conditions and diseases (e.g., hospital, clinic), individuals (e.g., providers, patients, care givers), 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, reconstruct timelines of healthcare events from fragmented medical data, and leverage the existing electronic health data to generate comprehensive profiles of healthcare entities.

2016019, Jul 7, 2016

Jan 7, 2016

14/990766

- San Francisco CA, US
Abhinav Chowdary Yarlagadda - San Francisco CA, US
Farid Jamshidian - South San Francisco CA, US
Kate M. Lewis - San Francisco CA, US
Eithon Michael Galinato Cadag - Foster City CA, US
James Lyon Fingal - San Francisco CA, US
Nicholas C. Dunkman - San Francisco CA, US
Kunal Dhiren Shah - San Francisco CA, US
Willard Kirk Strauser - Alameda CA, US
Michael H. Lin - Saratoga CA, US
Abraham M. Othman - San Francisco CA, US
David A. Vivero - San Francisco CA, US
Mary Audrey Hampden - Alameda CA, US
Sumul Mahendra Shah - Alameda CA, US

G06F 19/00
G06F 17/30

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, reconstruct timelines of healthcare events from fragmented medical data, and leverage the existing electronic health data to generate comprehensive profiles of healthcare entities.