Jason Jones, PhD

Chief Analytics and Data Science Officer, General Manager, Data and Analytics Platform

Jason Jones is passionate about achieving the Quadruple Aim through better and easier use of data in healthcare, including helping organizations to find analytic focus; helping providers feel that the systems they work for have their backs; and helping people to understand and have their goals and preferences respected for the hopefully brief periods during which they are “patients.” Previously, Jones served as Vice President, Information Support for Care Transformation, at Kaiser Permanente (KP). In that capacity, he brought together and co-led the national Hospital and Healthplan Quality and Finance analytic functions and led development of national quality strategy and care delivery IT investments. Prior to that, he was KP’s Executive Director of Clinical Intelligence and Decision Support and a Research Scientist in KP’s Southern California region. Before joining KP, Jones was a Senior Medical Informaticist for Intermountain Healthcare. He also held analytic and marketing positions at Bayer Healthcare in Wayne, N.J., and Ingenix (now Optum) Pharmaceutical Information Products in Salt Lake City, where he developed a model for converting United Healthcare data into a saleable asset for external customers conducting outcomes research. Throughout his career, Jones has taught graduate courses in statistics to medical informaticists at the University of Southern California and at the University of Utah. He has published dozens of peer-reviewed papers in medicine, predictive modeling, and outcomes improvement.

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How to Use AI in Healthcare to Unlock Four Secrets in Your Data

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.

Is your organization’s leadership part of the 4% that can correctly identify these four points in data presented over time? If not, your data and analytics may be doing more harm than good.

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.

Part 2: How AI and Data Can Streamline Healthcare

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 […]

Part 1: How AI and Data Can Streamline Healthcare

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 […]

Five Ways Healthcare AI Gives You Superpowers

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.

With Healthcare.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.

Hospital Capacity Management: How to Prepare for COVID-19 Patient Surges

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.

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