Starting and sustaining an enterprise data warehouse (EDW) for a sizeable healthcare organization might seem as challenging as, say, forming a new country. While it is an arduous undertaking, there are plenty who have gone before. In this article, one EDW operations manager shares five steps for success:
1. Start with a Leadership Commitment to Outcomes Improvement
2. Build the Right Team
3. Establish Effective Partnerships with IT
4. Develop Interest and Gain Buy-In
5. Pivot Toward Maintaining Success
Successfully implementing and sustaining EDW operations is about establishing and managing priorities and understanding the enterprise-wide implications.
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A healthcare enterprise data warehouse (EDW) is a fundamental component of a robust analytics platform. It brings together data from multiple sources in a healthcare system: EMR, claims, supply chain, cost accounting systems, and more. It allows healthcare organizations to comprehensively and systematically measure many different disease conditions, care delivery processes and operations, and then produce analytics that lead to decisions for improvement.
Once established, a considerable challenge for any healthcare data warehouse operations team is maintaining the performance of the analytics platform while simultaneously building new content to satisfy constantly evolving needs in outcomes improvement.
Just having a robust EDW in the healthcare organization doesn’t automatically guarantee success. Getting to an optimal state of ongoing outcomes improvement is a multi-stage process executed by effective partnerships between the various business units, subject matter experts, analysts, information services, and the data warehousing team.
I have the privilege of managing the EDW for a large not-for-profit healthcare system that handles more than 8.5 million clinic visits, and hospital inpatient and outpatient admissions annually. All this activity generates a lot of data. I’ve served multiple roles on our EDW team over the past 11 years; first as an employee of the health system and continuing as a Health Catalyst® team member since 2015. We work with Health Catalyst’s EDW and analytics platform, which offers a unique perspective on the EDW implementation lifecycle and the lessons learned through its many maturity levels. This perspective can be distilled into a five-step guide for successfully implementing and sustaining an EDW operations unit:
A successful EDW begins with a sustained commitment by leadership to outcomes improvement. Ours benefited from the vision of a former Medical Director, who is now the CEO, to place the EDW team within a department of our organization focused on creating clinical outcomes improvement. Our leadership team made the critical decision to team with Health Catalyst to develop and implement the very first version of its Catalyst Analytics Platform (CAP). Health Catalyst founders’ experience in data warehousing and outcomes improvement helped us to quickly implement core data content and the organizational structure necessary to translate our data assets into actionable information.
After getting our start with Health Catalyst, we’ve come back together again. As part of a 10-year partnership, we’re implementing the latest version of CAP, discovering new ways to challenge what the technical platform can do, and making sure it continues to meet its promises of performance and extensibility. This type of long-term commitment is typical between Health Catalyst and its healthcare system partners.
Implementing and sustaining a successful EDW platform likewise requires a talented and committed team of professionals. Our team consists of Health Catalyst and health system team members who occupy the following roles:
To implement the Health Catalyst platform, the first step for our data architects is building a robust set of source data marts containing varied clinical and operational content from across the health system. Source data marts provide the foundation for integrating content into focused subject areas, which are leveraged by business intelligence developers and analysts to create analytic tools used to drive outcomes improvement. Our team benefits from using the CAP tools Source Mart Designer and SAM (Subject Area Mart) Designer to speed the implementation of both source and integrated SAM data content. Starting with a foundation from Health Catalyst, our data architects likewise establish and maintain standards for both design and implementation so that content in our EDW is sustainable over time. Once the CAP is established, architects retain responsibility for managing daily loads, monitoring platform performance, conducting design reviews for new content being added to the EDW, and making sure that existing content is consistently available to users.
Our business intelligence (BI) developers share similar responsibilities to the data architect in that both roles involve integrating source data into SAMs. However, the BI developer’s specialty is in leveraging SAMs to build dashboards and other visualization tools, transforming data to actionable information for a diverse audience ranging from analysts with focused clinical expertise to executive-level leadership. The tools our BI developers create are critical drivers for both clinical and operational improvement initiatives across the organization. Moreover, our BI developers require a diverse set of skills; they’re equal parts technician, analyst, and project manager.
Data stewards govern access to content in the data warehouse and work in concert with subject matter experts to ensure the appropriate use and quality of data. We have approximately 30 people serving in the capacity of data steward with perhaps three to four times as many subject matter experts supporting them. Subject matter experts are essential for helping data architects, BI developers, and analysts properly use data from the EDW in their respective roles; they typically have expertise in specific areas, like finance or pharmacy data. Many of our subject matter experts are clinical analysts and report developers working within the same outcomes improvement department as our data architects and BI developers.
Our team serves our health system’s collection of 160 clinics, rehab locations, pharmacy locations, and hospitals, as well as over 25,000 employees and physicians. We are located onsite at the health system’s corporate office. If our daily EDW load is running late in the morning, or if there are data issues of a technical nature or that require triaging with a data steward, we are accountable and available to our customers.
EDW operations could not be sustained without strong partners in information services including systems administrators to manage software and tools, operations programmers to maintain the network and servers, and a database administrator to keep our data infrastructure performing efficiently. Our data warehouse benefits from a responsive and technically proficient team of IT professionals. Through consistent communication and processes, our IT team has developed a solid understanding operational needs so that technology can be applied in a pragmatic and effective manner.
In 2008, we were retiring a 10-year-old data warehouse platform with a smaller user base than what we have today—primarily analysts working with financial and claims data. As our health system transitioned to an EMR, it presented new opportunities to augment our EDW with a significantly greater depth and breadth of clinical data. Fortunately, our organization benefited from a mature and dedicated user base from the previous data warehouse, providing a significant head start on user engagement. But to realize the potential of our current platform, we had to generate interest and gain buy-in among a new set of clinical data users. We met this challenge by integrating our new clinical data along with other sources, broadening our analytic toolset to include dashboards, and providing education to help our users put these new tools into action. Combined with training and ongoing support, we have developed an audience of over 1,000 distinct users.
In the early years of a new platform, success can be marked by developing robust content and tools, finding influential early adopters, and establishing a dedicated user base. Ironically, these same markers can sometimes leave an EDW team feeling like the victim of its own success during the platform’s middle years. As content expands, the same morning deadline for refreshing data that had been easily achieved becomes harder to meet and sometimes missed. There are more source data marts, SAMs, and analytic tools that need enhancements, upgrades, and other routine maintenance.
The EDW team faces challenges in balancing priorities between maintaining existing and adding new data content. Unintended consequences from decisions made in the early years may start rearing their head. It’s essential to channel the trials of this experience toward future success by placing greater focus on prioritization of work, performance monitoring, and sustainable, supportable design and controls to avoid repeating past mistakes. Now, as we are migrating to a new version of CAP we are benefitting from previous lessons learned. Though the tools and capabilities we’re working with have advanced significantly, experience has taught us to foresee the challenges of growth and manage expectations appropriately.
Going through the different stages of maturity in building an EDW creates evolving challenges and tensions, and they are moving targets. The tension stems from prioritizing initiatives and content within the EDW, and then keeping it operationally sound and performing well daily. But knowing what to expect at the outset and managing the expectations of others goes a long way toward successfully implementing an EDW and turning it loose to massively improve clinical, financial, and operational outcomes.
Developing and sustaining an effective EDW operations unit is a substantial effort and long-term commitment, but hopefully the steps outlined in this article create a blueprint for facilitating a speedy, and ultimately successful, implementation.
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