Healthcare Analytics

We recently caught up with two Huntzinger data analytics experts to discuss their approach in applying and enhancing analytics in healthcare organizations.

While healthcare’s roots are in the care of patients, healthcare organizations must operate like a business in order to provide that humanitarian mission.  We all likely have heard the adage that Sister Irene Kraus professed “no margin, no mission” and how it explains why hospitals must earn enough to sustain their purpose.  

In every industry, there is a lot of data that is generated as a byproduct of automated processes and workflows, but healthcare is arguably one of the most data-intensive industries. Most data-intensive industries, recognizing data and information as a strategic asset, invest heavily in leveraging that data both tactically (improving business performance) and strategically (understanding and making decisions about new opportunities). Over the last two decades, healthcare has invested in automating its key process in the form of electronic health records (EHR) systems and enterprise resource planning (ERP) systems. However, it continues to lag in leveraging the data that these healthcare applications generate as a byproduct of their daily operations. 

Healthcare analytics, or business intelligence (BI), is the practice of leveraging that data for the benefit of the healthcare organization and the patients it serves. 

When healthcare organizations become aware of the need to leverage data more strategically, they do so by creating a strategic analytics plan or roadmap. When building a strategic analytics plan, it’s vital to get an understanding of the healthcare organization’s current state with respect to analytics. Key areas to assess include but are not limited to:

  • A review of the healthcare organization’s overall current strategic plan/objectives
  • A full inventory of all major applications
  • Data access/use policies
  • Inventory of all report writing/BI tools in use
  • Inventory of any/all free standing data marts or data warehouses in use
  • An inventory of all staff engaged as report writers/data analysts (even if just partially)
  • BI/Analytics organization (if any)
  • Formal data governance approaches
  • Stakeholder data decision rights
  • Locus of sponsorship for analytics/BI

While performing a current state assessment, it’s common for unique characteristics to be uncovered that are crucial in creating a successful analytics roadmap. For instance, an organization may have recently acquired a hospital or merged with another healthcare system. In this case, it’s important to understand the organizational priorities and human resource bandwidth for a successful alignment of the analytics initiative.

After identifying an organization’s current state, there are several key strategic options that an organization must consider and gain consensus around when creating a future state vision for analytics. Some of these options include but are not limited to:

  • Data warehousing approach (physical vs. logical vs. hybrid model) 
  • Enterprise analytics organization (decentralized vs. centralized vs. federated vs. consulting model)
  • Metric reporting/visualization tools
  • Predictive/prescriptive modeling and machine learning/AI approaches
  • Governance of the analytics program
  • Enterprise data governance
  • Education and training 

Each of these must be considered in the individual context of an organization’s current state to have a greater chance at achieving the envisioned future state.

Moving from the planning stage to executing the strategic analytics roadmap requires a high degree of diligence. The greatest obstacle in successfully executing a strategic analytics roadmap is the organization’s own culture. Frequently, organizations manage their data analytics in silos with limited data sharing and integration. For an organization to gain maximum benefit out of its business analytics, the data must be democratized.

When executing a strategic analytics roadmap, it’s important to have a keen awareness and a deliberate approach to overcome this genuine fear that once the data is out there, then it can be misused.  However, for many organizations, shifting the culture to adopt these analytic approaches is a big hurdle to overcome. Luckily, the awareness and the recognition of data analytics has greatly increased in recent years. Hospitals want to improve the quality of the data, and healthcare executives are putting a stake in the ground and wanting their data integration and data governance protocols to bring their organization to a place where data’s value is truly utilized.