This article is based on an expert dialogue series: Data and Analytics presented by Health Catalyst and industry leaders from UnityPoint, Stanford, Hospital Sisters Health System, and Community Health Network.
Data and analytics have become a significant component of game-changing strategies for healthcare organizations. The sophistication of these tools provides an advantage in meeting population health, accountable and value-based care, and health equity goals. Tackling community-driven initiatives through analytics requires strategic alignment and planning across the organization to solve clinical challenges. Here are four top strategies that will build a successful data and analytics foundation and improve the health of patient populations and communities.
Defining a data governance structure is critically important to success, which often begins with engaging the senior most leaders within the organization. Organizational stakeholders in strategy, quality, and c-suite roles should be involved in organizing, supporting, and advancing the data governance structure.
Establishing data governance is a gateway to defining a standard set of key performance indicators (KPIs) that are tracked and measured with analytics. Healthcare leaders should implement the following best practices for data governance.
An effective data governance strategy must be tied to a network strategy, or the organization will be misaligned with overarching goals. Gaining executive support and focusing on data literacy will determine how successful data and analytics will impact positive change for patient populations.
Analytics is a team sport. An effective analytics strategy requires participation and input from across the organization to build trust with the data and reports. Coordinating efforts with people and departments can reduce data literacy gaps while hiring mission-driven people to lead those efforts will fuel a collaborative approach to achieving program success.
Leading discussions with analytics is also a way to challenge healthcare leaders to avoid decision-making that is siloed or lacks data proof. The right people must be active in the discussion to identify KPI measures, including representatives from HR, quality, nursing, financial, surgery, and pharmacy, to name a few.
An analytics strategy fuels collaborative work for people and teams, advancing the organization’s mission. The effort to align stakeholders will build trust across departments and emphasize how data and analytics can solve unique organizational problems.
While dashboards and data summaries are beneficial, publishing data and analytics without context or a complete understanding of the department’s goals can cause a setback in trust and data literacy. Incorporate people and feedback throughout the process to define how information is reported and if it is usable and accessible at decision points. For example, if an organization is looking to reduce readmissions, knowing the diagnosis that drives readmissions is important to capture and analyze so that a proper outreach program can be developed for patients. Analytics can be further built around the targeted interventions to determine program outcomes, successes, and results.
To optimize data and analytics processes,
Building analytics for each program or department requires time and effort in digging deeper into the clinical problem. Ask questions to help come to a common understanding so that trust, data governance, and data literacy all become the foundation for a successful data and analytics strategy.
Behind the data and analytics are people who need to access, analyze, and understand the information to improve patient and community health. Remember the necessary checkpoints to maintain a continuous improvement mindset when rolling out an analytics strategy.
Enabling a culture within the organization where people aren’t afraid to ask questions and make suggestions is a meaningful part of the analytics strategy. For example, if a healthcare system operates across multiple states, looking at data such as average length of stay (LOS) may cause concern if one state’s average is disproportionate to another. Data and analytics can be used to further assess average LOS compared to the individual hospital market or region when looking at factors such as the COVID-19 pandemic. This detailed analysis is beneficial in many ways.
An emphasis must begin with launching a data and analytics strategy to effectively solve clinical problems. An enterprise-wide platform allows clinicians, program managers, and patient care teams to interpret data, reports, or dashboards and take action for healthier lives. A data foundation supported by the right governance structure will help achieve KPIs and other clinical goals. Establishing trust between people and departments is needed so people and teams can analyze department data and collaborate for optimal data literacy. In today’s healthcare environment, where there is an abundance of data, a strategic approach to analytics accelerates positive outcomes and produces a data-driven organization with healthier patient populations.