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Dale Sanders

Strategic Advisor

Dale has been one of the most influential leaders in healthcare analytics and data warehousing since his earliest days in the industry, starting at Intermountain Healthcare from 1997-2005, where he was the chief architect for the enterprise data warehouse (EDW) and regional director of medical informatics at LDS Hospital. In 2001, he founded the Healthcare Data Warehousing Association. From 2005-2009, he was the CIO for Northwestern University’s physicians’ group and the chief architect of the Northwestern Medical EDW. From 2009-2012, he served as the CIO for the national health system of the Cayman Islands where he helped lead the implementation of new care delivery processes that are now associated with accountable care in the US. Prior to his healthcare experience, Dale had a diverse 14-year career that included duties as a CIO on Looking Glass airborne command posts in the US Air Force; IT support for the Reagan/Gorbachev summits; nuclear threat assessment for the National Security Agency and START Treaty; chief architect for the Intel Corp’s Integrated Logistics Data Warehouse; and co-founder of Information Technology International. As a systems engineer at TRW, Dale and his team developed the largest Oracle data warehouse in the world at that time (1995), using an innovative design principle now known as a late binding architecture. He holds a BS degree in chemistry and minor in biology from Ft. Lewis College, Durango Colorado, and is a graduate of the US Air Force Information Systems Engineering program.

See content from Dale Sanders

Academic Medical Centers: A Triple Threat Approach to Leveraging Healthcare Analytics

Academic medical centers (AMCs) are a triple threat on the healthcare court with their combined medical center, education, and research sections.
With a unique set of resources, AMCs have the ability to take a  comprehensive, holistic approach to patient care.
However, one of the challenges they still face is utilizing healthcare analytics effectively within the patient care setting. With the Healthcare Analytics Adoption Model and other data expertise, AMCs can learn how to merge siloed data, while improving operations, and delivering the highest quality of care to each patient.

Bridging the Data and Trust Gaps: Why Health Catalyst Entered the Life Sciences Market

Why would a healthcare data warehousing and analytics company partner with the life sciences industry? Because trust and collaboration across the industry—between life sciences, healthcare delivery systems, and insurance—is the only path to real healthcare transformation.

Health Catalyst recognizes an industrywide improvement opportunity in collaborating with life sciences to build mutual trust, integrate data, and leverage analytics insights for a common interest (i.e., patient outcomes). By aligning themselves around human health fulfillment, Health Catalyst, their provider partners, and life sciences will advance important healthcare goals:

1. Improving clinical trial design and execution.
2. Stimulating clinical innovation.
3. Supporting population health.
4. Reducing pharmaceutical costs.
5. Improving drug safety and pharmacovigilance.

The Homegrown Versus Commercial Digital Health Platform: Scalability and Other Reasons to Go with a Commercial Solution

Public cloud offerings are making homegrown digital platforms look easier and more affordable to health system CTOs and CIOs. Initial architecture and cost, however, may be the only real benefit of a do-it-yourself approach. These homegrown systems can’t scale at the level of commercial vendor systems when it comes to long-term performance and expense, leaving organizations with a potentially costly and undereffective platform for years to come.

Over his 25 years as a health system CIO, Dale Sanders, President of Technology for Health Catalyst, has observed both the tremendous value of healthcare-specific vendor platforms, as well as the shortcomings of homegrown solutions. He shares his insights in a question-and-answer session that addresses pressing issues in today’s digital healthcare market.

Critical Healthcare M&A Strategies: A Data-driven Approach

Historically technology and talent were primary assets used to weigh the value of M&A activity, but data is an equal pillar. Buyers (the acquiring organizations) face enormous responsibility and risk with M&A transactions. C-suite leaders have a lot to consider—enterprise-wide technology, finances, operations, facilities, talent, processes, workflows, etc.—during the due diligence process. But attention is often heavily weighted toward time-honored balance sheet and facility assets rather than next-generation assets with the long-term strategic value in the M&A process: data. The model for conducting due diligence around data involves four disciplines:

• Establish the strategic objectives of the M&A with the leadership team.
• Prioritize data along with the standardization of solutions and the design of a new IT organization (i.e., a co-equal effort for data, tools, and talent).
• Identify the near-term data strategic priorities, stakeholders, and tools.
• Assess the talent and consider creating an analytics center of excellence (ACOE) to harness organizational capabilities.