Interview with Lee Pierce of Sirius Computer Solutions

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Lee Pierce, BS, MIS, is the Healthcare Chief Data Officer for Sirius. Lee was the chief data officer at Intermountain Healthcare for 5 years, and was involved leading and building data and analytics capabilities at Intermountain for over 20 years. He has been accountable for the development of Intermountain’s data and analytics strategy, infrastructure, data governance efforts, business intelligence development, and coordination of analytics and data science services throughout the Intermountain enterprise. Lee has over 22 years of experience in healthcare IT, specializing in organizing enterprise efforts to leverage data as an enterprise asset to by developing data and analytics strategy and competencies. Read his full bio.

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Interview with Lee Pierce of Sirius Computer Solutions

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[et_pb_accordion_item title=”Q: What is the state of big data and analytics in healthcare, and how to best use the reams of data available?”]

A: More than ever, Healthcare organizations are achieving measurable value through use of their data and analytics assets. There is more raw material available than ever to create value. This raw material is the data flowing from internal systems and applications and also from devices and systems external to healthcare organizations. The high-level steps required to turn that raw material into insight and action have remained consistent for a long time. Unfortunately, so have the challenges remained consistent. But progress is being made across healthcare to achieve real, measurable value with data and analytics.

The challenges to achieve the value desired from data assets and analytics span all areas of technology, people and processes. When it comes to technology, it’s certainly not a lack of choices and vendors trying to help, but this causes more confusion to most healthcare organizations rather than contributing value in a system wide, coordinated way. Many organizations look to their EMR vendor to provide the technologies needed to organize and analyze data. This is an important step and is certainly part of an ecosystem of tools and technologies needed to achieve value. But to even discuss the technologies needed, there must be a strategy for providing capabilities for each category of data users, from basic consumers to the most advanced producers of analytic solutions. This requires a set of capabilities, leveraging a combination of tools and technologies, typically not just from one technology vendor.

There are also significant people and process challenges in healthcare that must be addressed to achieve value. There is no “easy button” to organize, analyze and implement insights in healthcare, especially without having an understanding of the people and processes required to achieve value. It requires a holistic approach of people, process and technology. It requires business and clinical leaders who are committed to managing their part of healthcare business with data and analytics insights, committed to take insights and use them to change the decisions and processes they manage. This requires data literacy, governance, and a commitment that is lacking in many healthcare organizations today. But even with the challenges that exist, there is a well-established path to healthcare value from data/analytics and hope for healthcare organizations to make significant advancements with their data/analytics capabilities.

[/et_pb_accordion_item][et_pb_accordion_item title=”Q: What are the benefits of using big data and analytics for patients?”]

A: The benefits to healthcare organizations to develop data and analytics solutions and capabilities are significant. Patients, people, individuals are the ultimate benefactor of data/analytics being done right. At an individual level, this is allowing people to live as healthy as possible, as they understand their role in preventing disease, helping them make better daily health decisions. At a provider and health system level, data and analytics value is available across all business and clinical areas. In clinical use cases, this is measured at a high level by having the best clinical care being provided at the lowest overall cost possible. When understood and applied correctly to clinical practice, these insights benefit individual patients one by one as they are implemented, and better care is provided at an individual level. In operational areas of healthcare, analytics are contributing to significant financial and operational savings. Even these savings to the healthcare system should translate to lower costs for individual patients.

[/et_pb_accordion_item][et_pb_accordion_item title=”Q: What are the barriers to healthcare organizations achieving the value and vision of personalized analytics?”]

A: Personalized analytics requires an analytics maturity level that most healthcare organizations have not yet achieved. Most healthcare organizations are still working through the decisions and processes for achieving basic to moderate analytic competencies, to provide macro level analytic insights and value at a departmental and system level, with a hope that personalized analytics will come soon after. Personalized analytics also requires a data profile at an individual level that is only partially available today. Healthcare systems need more data at an individual person level, that is not available in an EMR system, that is only available when a holistic strategy and view of analytics and data assets is taken. Some of this is due to our continued lack of data standards in healthcare, even with the many efforts over many decades to define and implement these standards. Yet there still is hope and progress being made, but we must make it easier than it is today.

[/et_pb_accordion_item][et_pb_accordion_item title=”Q: What are the challenges of incorporating genomic data into the healthcare system?”]

A: There are technology and process challenges with incorporating genomic data into healthcare systems. On the technology side, there is data storage and sheer computational power limitations that can be overcome, but most organizations do not have at their disposal today. The ability to then integrate this genomic data and insights with other data assets then becomes an additional step required, and is a challenge most healthcare organizations are facing today. On the process side, most healthcare organizations are not ready to use genomic data and incorporate that data into their clinical practice, even if it was stored and analyzed and insights available for use. Of course, there are exceptions to this and genomic data is being used at organizations like Intermountain Healthcare for oncology use cases, but there is so much more to be achieved. We need to address the basic blocking and tackling of data challenges described above first, then we can better incorporate genomic data as an additional data source for achieving the next level of healthcare value from data and analytics.

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