Error loading player: No playable sources found

IThA3

Using Predictive Analytics to Reduce No-Shows

Date
October 24, 2019

Other industries have tackled scheduling problems in sophisticated ways, it is time that the health care industry follows suit – increasing patient access, building a reliable schedule for clinicians, and improving productivity. Machine Learning is a very effective tool to predict no-shows, but then you need to put the operational processes and procedures in place to take advantage of this new insight. Learn how seasoned health care executives are modifying their operations to take advantage of this new technology. Presenters will take you through the steps of Machine Learning and explain how to integrate it into your solution set.

Speakers

Speaker Image for Joseph Caruso
President and CEO, COMPASS Family and Community Service
Speaker Image for Quentin Fisher
Chief Executive Officer, Health Care Analytics LLC
Speaker Image for Jonathan Lee
Chief Executive Officer, Signature Health Inc.

Tracks

Related Products

Thumbnail for Leveraging Alternative Care Delivery Models, Data, and Technology to Maintain Access to Care
Leveraging Alternative Care Delivery Models, Data, and Technology to Maintain Access to Care
COVID-19 changed how health centers approach providing access to primary and specialty care for communities disproportionately affected by COVID-19…
Thumbnail for The Search for an Electronic Patient Engagement Tool That Meets Health Center’s Needs
The Search for an Electronic Patient Engagement Tool That Meets Health Center’s Needs
Health Centers have seen a boom of available electronic patient engagement tools promising everything from decreasing no shows to improving patient self-registration and screening…
Thumbnail for Reply “CARE:” How Big Data Kept Our Patients Connected to Care During the COVID-19 Pandemic
Reply “CARE:” How Big Data Kept Our Patients Connected to Care During the COVID-19 Pandemic
A deadly pandemic. Business shut down and health center closure. Shelter in place. Financial stress. Racial Injustice. A Perilous Future…
Thumbnail for Making the Shots: A Data-Driven Approach to Improving Rates of Child and Adolescent Immunizations
Making the Shots: A Data-Driven Approach to Improving Rates of Child and Adolescent Immunizations
Childhood immunizations is a UDS measure on which is notoriously difficult to improve. The change in the age cut-off for the measure from 3 to 2 years along with persistent cultural and social factors surrounding vaccinations has made this measure a challenge for community health centers…

Privacy Policy Update: We value your privacy and want you to understand how your information is being used. To make sure you have current and accurate information about this sites privacy practices please visit the privacy center by clicking here.