Learn: Leadership, Culture, Governance, and Equity
March 22, 2023
2023 Healthcare Trends: What Leaders Need to Know About the Latest Emerging Market and Policy Issues
March 8, 2023
Alleviate Provider Burnout: How Digital Care Technology Can Help
February 16, 2023
HIE Adopt Data Quality Standards for Long-Term Success
February 8, 2023
Healthcare Leaders Share Three Best Practices to Improve the Quality of Care When Implementing a Data Analytics Platform
January 6, 2023
Health Catalyst Announces a New, External Leadership Assignment for Paul Horstmeier
December 29, 2022
Two Ways ACOS Can Optimize Data Sharing for Better Care Coordination
Healthcare Analytics Summit
HAS 21 Virtual in Review: Soaring Satisfaction Rates, Attendee Profiles, and More
Even virtually, transporting 3,000-plus healthcare leaders and activists across three international destinations is no small feat. Add world-class data and analytics insight and inspiration from healthcare and beyond to the voyage and you have a three-day journey of a lifetime—otherwise known as the Healthcare Analytics Summit™ (HAS) 21 Virtual. The 2021 edition of healthcare’s premier analytics summit once again gathered innovators and heroes from around the globe to explore multi-domain analytics as the framework for a winning team approach to healthcare transformation.
Advancing Health Equity: A Data-Driven Approach Closes the Gap Between Intent and Action
Improving health equity is gaining traction as a healthcare delivery imperative. Yet, while equity is indivisible from healthcare quality, many initiatives targeting disparities fall short. Organizations too often rely solely on leader and stakeholder passion and perseverance without sufficiently leveraging data and analytics to understand, measure, and support equity improvement efforts. It’s time for the industry to pursue equitable care with the same resources it uses in other key dimensions, such as safety and efficacy—by leveraging data. A data-driven approach to equity opens health system’s most advanced predictive resources to equity efforts, thereby driving massive, measurable, data-informed improvement that benefits all.
Using Data Science for Effective COVID-19 Capacity Planning
COVID-19 is causing many hospitals and health systems to face resource and capacity restrictions, making the accurate estimation of COVID-19 requirements crucial. Carle Health needed the ability to anticipate the impact COVID-19 would have on its organization and community. After analyzing national COVID-19 capacity planning resources, Carle chose a model that was customized for its organization. Carle leveraged its analytics platform, using local data and infection rates to forecast the impact of COVID-19 locally. The organization now has critical insight into when surges will occur and can determine if it has enough available resources.
Understanding Population Health Management: A Diabetes Example
Managing individual cases of diabetes require actively involving patients in their care plan, enabling each patient to monitor and understand key data, such as A1c readings, and adjust lifestyle or other factors affecting overall health. Managing diabetes across larger populations, however, is best done through the use of a data and analytics platform that can aggregate data from multiple sources and provide actionable insights. Specifically, a data platform can identify patients who aren’t up to date on tests and those at high risk for other complications, uncover variations in diabetes care across an organization, and more.
AI Can Advance Health Equity
Health technology and augmented intelligence (AI) can significantly improve or worsen health equity. Recently, there has been a growing concern that AI is increasing disparity. ChristianaCare set a goal to reduce avoidable health disparities. The organization faced many challenges, including inconsistent collection, storage, and use of personal characteristics such as race, ethnicity, and language. Using its data platform and Healthcare.AI™, ChristianaCare now has a single “source of truth” for personal characteristics data. By treating health equity as a goal with the same commitment and focus as it would for other clinical, operational, or financial improvement efforts, the organization is purposefully using AI to achieve health equity.