Jason Jones, PhD

Today’s healthcare leaders want their organizations to be data driven. However, many leaders incorrectly interpret data and analytic visualizations. Without accurate interpretation of analytics, healthcare leaders don’t have real data-driven insight to know how to strategically improve and measure wins and losses.

Concerningly, only 4% of analysts, department leaders, executives, and board members can correctly identify the following four pivotal pieces of information in a line chart:

1. Baseline performance.
2. Extreme historical points.
3. Persistent shifts in performance.
4. Forecasted performance in six months.

随着时间的推移，您的组织的领导部分是否属于能够正确识别数据中这四点的4% ?否则，你的数据和分析可能弊大于利。

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.

In his second episode of the Health Catalyst podcast, Owning the Future of Healthcare, Jason Jones, Chief Analytics and Data Science Officer, Senior Vice President, and General Manager of the Platform Business Unit at Health Catalyst, continues his explanation of augmented intelligence (AI) in healthcare. Jones addressed AI’s burgeoning role in healthcare in his first […]

In the first ever episode of Owning the Future of Healthcare, a Health Catalyst podcast, Chief Analytics and Data Science Officer, Senior Vice President, and General Manager of the Platform Business Unit at Health Catalyst, Jason Jones, tells listeners and viewers the critical differences between augmented and artificial intelligence (AI). He expounds on the use […]

As healthcare decisions, data points, and options increase, time, resources, and margin of error decrease. To succeed in this environment, leaders and analysts must know where to focus and how to allocate resources and set accountability targets.

与医疗保健。AI™, five super-powered assistive augmented intelligence capabilities help healthcare leaders and analysts determine values, understand context, and provide data-driven motivation to transform healthcare:

1. Enhancing humans’ natural visual pattern recognition.
2. Calculating trajectories.
3. Accelerating the pace at which analysts produce and experiment with how to present the insights.
4. Producing high-caliber, high-quality analytic results.
5. Building trust by enabling immediate, visual, and transparent results.

Health system resource strain became an urgent concern early in the COVID-19 pandemic. Hard-hit areas exhausted their hospital beds, ventilators, personal protective equipment, staffing, and other life-saving essentials, while other regions scrambled to prepare for inevitable surges.
These resource concerns heightened the need for accurate, localized hospital capacity planning. With additional waves of infection in the summer months following the initial spring 2020 crisis, health systems must continue to forecast resource demands for the foreseeable future.
An accurate capacity planning tool uses population demographics, governmental policies, local culture, and the physical environment to predict healthcare resource needs and help health systems prepare for surges in patient demand.