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3 Phases of Healthcare Data Governance in AnalyticsBy Mike Doyle
Healthcare Data Governance Healthcare data governance. It sounds like it could be a dry topic, right? The truth is that data governance is a very exciting and complicated challenge in healthcare analytics. The purpose of this commentary is to: Show the challenges involved with adaptive healthcare data governance Demonstrate how effective data governance evolves throughout the lifecycle of a health system’s analytics program Show the challenges involved with adaptive healthcare data governance
What Data Governance Is Data governance is: The scope of data governance includes data stewardship, storage, and technical roles and responsibilities. It also requires leadership and processes to get the most out of an investment in analytics.
What Data Governance Is Because leadership buy-in is so important, the focus here will be on the important role a core group of leaders — the key stakeholders who form the healthcare data governance committee — will play in setting, growing, and sustaining a successful analytics program.
What Data Governance Is Stakeholders must understand how important it is to ensure the right group of leaders is engaged in the governance of the BI program as a whole. The best data governance committees drive efficient, equitable use of a health system’s information, and enable the organization to achieve its goals of higher-quality, lower-cost care.
Effective Healthcare Data Governance Is Adaptive Few health systems claim to have figured out effective healthcare data governance. One difficulty is that no single template for data governance can be applied to every organization. Data governance approached as a rigid, idealized plan often ends up being scrapped.
Effective Healthcare Data Governance Is Adaptive In General there are three phases of adaptive data governance that characterize successful analytics implementations. 1. The Early Stage of Healthcare Data Governance 2. The Mid-Term Stage of Healthcare Data Governance 3. The Steady State of Healthcare Data Governance
The Early Stage of Healthcare Data Governance Committee Makeup In the early stages of planning sponsors of the health system’s data governance initiative must have some decision-making authority. Ideally, these leaders have a passion for using data, and are generally described as being both exciting and decisive. They may be executives, directors, influential managers in the quality department, nurses, or physicians.
The Early Stage of Healthcare Data Governance Committee Focus Some of the most important questions this group will address are: Where should the initiative begin? Where should resources focus? The committee’s first role will be to keep the peace, and ensure everyone impacted understands the priority. They must protect the integrity of the initiative to drive real quality and cost improvement.
The Mid-Term Stage of Healthcare Data Governance Committee Makeup As your implementation of analytics continues, the nature of the decisions made by the data governance committee will change. It’s possible some of the initial committee members will lose interest at this stage, and miss meetings. They may view program maintenance as more of an operational problem. It’s okay to let them walk away. Their replacements may bring new energy to the initiative.
The Mid-Term Stage of Healthcare Data Governance Committee Focus At this phase the committee will begin to implement the solution in new areas of the enterprise. You may take the lessons learned in Cardiology to branch out and serve Labor and Delivery. The group will also be responsible for monitoring the progress of existing initiatives. They will ask questions like: How are things going? Who is using the system? What additional training or tools are needed to increase utilization? What should we keep doing/stop doing/do more of?
The Steady State of Healthcare Data Governance Council Makeup In this stage, the data governance committee may want to start calling itself something more interesting, like a governance council. Let it happen. Only a few of the initial leaders will still be involved, but the council itself will continue to evolve.
The Steady State of Healthcare Data Governance Council Makeup The most effective agents of governance at this point are those who are the “ethical politicians” and have a talent for peacekeeping and consensus building. They will have a natural, one-on-one leadership style that fosters team communication, in or out of the meeting. They won’t be solving big problems in real time; rather, they’ll be addressing frequently heard common concerns.
The Steady State of Healthcare Data Governance Council Focus If the analytics effort is going well, there will be dissatisfied customers. Some will believe they haven’t received as much support as other areas. Leaders’ role will be to hear these customers’ concerns, make their voices heard, help them feel there’s hope for improvement, and then keep them in the loop until their concerns are addressed.
The Steady State of Healthcare Data Governance Council Focus At this point the group must stay the course in spite of vocal resistance from other departments. If the initiative is working for most of the organization, the council will have to actively engage the disenfranchised and bring them around. Finally the team will continue to be responsible for monitoring the success of the initiative and prioritizing efforts.
How to Know If Healthcare Data Governance is Working To know if data governance is working, explore using data to measure the effectiveness of your data governances if you can. Here are some of the key metrics to study: # of users # of requests # of queries, reports and page views # of success stories from your users! # of key stakeholders who are aware of your group and what you do If governance is successful, these metrics will trend up: User/customer satisfaction. Technology/analytics team satisfaction Time and resources it takes to answer common analytic questions # of requests you get to evaluate competing analytic systems These metrics should hopefully trend down: These metrics should stay solid, or trend upwards in rare cases:
How to Know If Healthcare Data Governance is Working At various points in the lifecycle of the project, you may be tempted by the simplicity of a “one size fits all” framework for data governance. Healthcare data governance is most effective when it is allowed flexibility to adapt and change. If you are prepared to allow your data governance strategy to evolve as analytic maturity develops, your organization will be well positioned for success.
More about this topic 7 Essential Practices for Data Governance in Healthcare Dale Sanders, Senior VP of Strategy Why a Data Governance Is One of the Six Reasons for Data Warehouse Failures Steve Barlow, Co-Founder and Senior VP of Client Ops The Best Organizational Structure for Healthcare Analytics John Wadsworth, VP of Client Technical Operations Keys to a Successful Analytics ImplementationJared Crapo, VP; Eric Just, VP of Technology; and Dan Lidgard, VP Demystifying Healthcare Data GovernanceDale Sanders, Senior VP of Strategy
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Other Clinical Quality Improvement Resources Click to read additional information at www.healthcatalyst.com Mike Doyle joined Health Catalyst in May of 2013 as a Vice President. He has been connected with the Health Catalyst senior leadership team since 2006. Prior to Health Catalyst, Mike led the Enterprise Data Warehouse (EDW) program at Allina Health as Director of Healthcare Intelligence. He helped Allina grow its EDW program from a nascent clinical improvement initiative to an enterprise-wide strategic asset, in heavy demand by thousands of users across all of Allina’s 11 hospitals and 100+ clinics. Prior to his work with Allina, Mike was employed on the Northwestern Medicine campus in Chicago, beginning as a Systems Administrator at the Medical School and eventually leading the Analytics and Systems Integration team at Northwestern Medical Faculty Foundation. In addition to his experience building strong technology teams, Mike has experience in technical roles such as database administrator, web programmer, data architect, and business intelligence developer. Mike holds a Master of Music degree from Northwestern University and a Bachelor of Fine Arts degree from Carnegie Mellon University.