Hawai'i-Pacific Chapter
A quarterly e-newsletter for the Hawai'i Pacific Chapter of ACHE Spring 2017
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Messages from Chapter Leadership
Message from your ACHE Regent, Spring 2017
Message from the Chapter President, Spring 2017
Recent Events
2017 Annual New Member Breakfast
Original Articles By ACHE Members
Taking Action to Reduce Health Care Disparities
Turning Insight into Action: The future of data driven decision-making
Calendar of Events, Spring 2017
Calendar of Educational Events, Spring 2017
News & Committee Updates
Upcoming Event - Spring Social Mixer
News from the Education Committee
Membership Report: New Fellows, Members, and Recertified Fellows
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Financial Report, Spring 2017
ACHE National News, Spring 2017
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Original Articles By ACHE Members
Turning Insight into Action: The future of data driven decision-making
Ms. Kelly Wheeler, Process Improvement Specialist, Army Regional Health Command-Pacific

The views expressed in this article are those of the author and do not reflect the official policy or position of the Department of the Army, Department of Defense, or the United States Government.

The Military Health System (MHS), like civilian healthcare organizations, faces pressures to reduce costs while providing improved safety, quality, outcomes and being more patient centric. The Fiscal Year 2017 National Defense Authorization Act (NDAA), signed into law in December 2016, provisions for healthcare reforms that simplify TRICARE options, expand access to care, improve beneficiary experience, ensure quality health care, and strengthen the readiness of military healthcare professionals and Soldiers. The complex issues confronting MHS leaders require insights into system performance that increase value, the perceived outcomes achieved in relation to the cost required by system stakeholders. Employing analytics competencies that harness the big data available to the MHS is providing the insight needed to guide the organization and its leaders to data driven decision-making (D3M).  Data driven decision-making, when coupled with deliberate process improvement yields improved quality, desired outcomes, and increased value.

Organizations operating in a myriad of industries are embracing analytics and the power of big data. Thomas Davenport, in “Competing on Analytics,” identified characteristics shared by analytics competitors.Characteristics included widespread use of modeling and optimization, an enterprise approach, senior executive advocates, the right focus, culture, people, the right technology, a data strategy, business intelligence software, and computing hardware. Companies such as Amazon, UPS, Capital One, and Proctor & Gamble are among analytics competitors that are transforming their organizations through utilization of analytics to create a strategic advantage. Andrew Mcafee and Erik Byrnjolfsson conducted structured interviews with 330 companies to determine if there was evidence that the intelligent use of big data actually improved business performance.2 They found a broad array of attitudes and approaches in all industries specifically identified that “the more companies characterized themselves as data-driven, the better they performed on object measures of financial and operational results.”   

Despite advantages of the MHS’s access to big data resulting from the use of electronic health records since 2003, barriers exist that stymie the adoption of robust D3M. In a 2010 study conducted for an IBM Institute of Business Value executive report, 130 healthcare executives around the world were surveyed on a variety of questions around the use of big data. In response to what are barriers to adoption, "organizational barriers" were considered the greatest. The top three barriers included the ability to get the data, a culture that does not encourage sharing of information, and lack of understanding on how to use analytics to improve business. Additional organizational barriers included the lack of management bandwidth due to competing priorities, absence of executive sponsorship, lack of internal skills, and not knowing where to start the quest for analytics excellence.3

The Army Medical Command (MEDCOM) and the Army Regional Health Command-Pacific (RHC-P) are poised to mitigate the aforementioned organizational barriers. In January 2016, RHC-P partnered with MEDCOM to pilot a structure for its Program Analysis and Evaluation (PAE), the primary analysis arm of the organization. The structure includes a senior MEDCOM decision scientist as its Chief (civilian sector vice president equivalent) and regional analysts assigned to support Medical Treatment Facilities (MTFs) that span the Pacific from Alaska, Washington State, Hawaii, to Japan and Korea.  The structure provides expert mentorship and reach-back capability to MEDCOM senior decision scientists. This model for organizational structure, alongside the adoption of business intelligence platforms and mentorship to develop enterprise performance management dashboards, allow beginning to mid-career decision scientists to develop necessary skills to support D3M. It provides a means to have improved access and knowledge of disparate data systems, understanding on how to utilize data to garner insight on performance, and to build competencies in analytics that will sustain and transform the organization for years to come.

In addition, the organizational structure facilitates processes that are being employed. These processes include the development and utilization of standardized analyses and standard work such as quarterly MTF comprehensive performance assessments, monthly MTF Review and Analyses - a forum for deeper discussion and insight on key performance indicators with pre-selected topics, and the regional MTF support analysts who work directly with local MTF analysts on deep dives into management questions and assist in problem solving.

"Changes in the delivery of healthcare are necessary to keep up with advances in evidence-based care, quality of standards, and practice guidelines," states Col. Scott Avery, RHC-P Chief of Staff. "Understanding that change can be emotional for those experiencing it, the systems and tools developed by the Regional Health Command-Pacific's Program Analysis and Evaluation team provides empirical evidence on safety, productivity, and satisfaction that allows for informed, data driven, analytical decisions in the management and delivery of healthcare; impacting choices made in the exam room to the board room."  The RHC-P regional analysts immerse themselves not only in the data for supported MTFs, but the organization as a whole. Erin McGlothlin, RHC-P regional analyst states, “Numbers are just numbers without the context to apply them. We seek to foster relationships with our MTF customers, to understand their needs, their concerns and do what we can to remove the organizational barriers which can prevent us from using evidence-based practices and being a data-driven system of health.”

Unique to the RHC-P structure are embedded process improvement experts with analysis backgrounds who are tied directly to decision scientists and into the PAE structure. Process improvement experts take insight from analytics to identify and guide improvement efforts at MTFs that have further potential to move the organization toward goal achievement and creating value for patients and stakeholders. In its more nascent stages of re-development, the Strategy and Innovation cell has brought process improvement skills to over 100 military and civilian employees at RHC-P organizations in Fiscal Year 2016 and anticipated 120 more in Fiscal Year 2017.  The provision of expert mentorship in utilizing data guides process and performance improvement efforts at the tactical level.

The successful partnership between MEDCOM and RHC-P has provided proof that the concept can be replicated across the enterprise. This has spurred the MEDCOM “PAE 2.0” initiative, standardizing Army Regional Health Command structure and processes throughout the enterprise. Sherry Van Patten, MEDCOM Senior Decision Scientist affirms, “There has historically been a strong partnership between Regional Health Commands, however the concept has proven that having an embedded Chief who is also a MEDCOM member allows for more stake in the results.” 

Already the first year of implementation of the new structure, leadership engagement, standardized analysis, and performance is showing the success of efforts. Over the past 12 months the region has decreased appointment time to the third next available 24 hour appointment from a 12-month average of 1.20 days to .82 days, indicating increased availability of primary care appointments for beneficiaries. As an enterprise priority, the team utilized bi-weekly analysis provided by regional MTF analysts, working directly with MTF staff using standardized tools and templates, to identify root causes and employ a variety of solutions.

This represents the beginning of a journey to increase analytic capabilities to further automate processes previously used to merge disparate data sources facilitating a common operating picture of performance across the enterprise to drive improved performance and outcomes.  The structure and processes employed by RHC-P and MEDCOM, utilizing the insights from analytics to drive leadership discussion provides a great opportunity to take our organizations from good to exceptional outcomes.  The RHC-P PAE structure and processes, although primarily administrative in nature, are designed to not only overcome challenges to the use of analytics and big data, but to provide the back bone for  decision makers to apply D3M that supports improved outcomes and increased value.


1. Davenport, T. H. (2006, January). Competing on Analytics - Harvard Business Review. Retrieved March 23, 2017, from https://hbr.org/2006/01/competing-on-analytics

2. McAfee, A., & Brynjolfsson, E. (2012, October). Big Data: The Management Revolution - hbr.org. Retrieved March 23, 2017, from https://hbr.org/2012/10/big-data-the-management-revolution

3. Cortada, J. W., Gordon, D., & Lenihan, B. (n.d.). IBM The value of analytics in healthcare - United States. Retrieved March 23, 2017, from https://public.dhe.ibm.com/common/ssi/ecm/gb/en/gbe03473usen/GBE03473USEN.PDF

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