Performance management, value-based reimbursement models, and healthcare planning all have one thing in common — they’re better when informed by financial and clinical data.  

But with so much data available — and teams that often work in siloes — it’s difficult to drill into and find meaning in that data. By prioritizing data governance, the process of adopting roles, processes, and tools to manage data and analytics, healthcare organizations can generate actionable insights and drive change.  

This blog series highlights recent Healthcare Financial Management Association (HFMA) Finance Analytics Council discussions around how high-performing organizations structure data governance functions and improve access to information. The HFMA Financial Analytics Council, sponsored by Syntellis Performance Solutions since its inception in 2018, provides a valuable forum for members to engage in collaborative discussion and share best practices related to healthcare financial management topics.  

The council’s most recent discussions revealed that data governance in healthcare requires three components:  

  1. Adopting a leadership structure and best practices for data and analysis (part 1) 
  2. Collecting the right data and getting it into the hands of the right users 
  3. Measuring the return on data and analytics investment (part 3) 

After implementing an appropriate leadership structure, hospitals and health systems can focus on collecting meaningful data and extracting value from it.  


The Roadmap to Meaningful Analytics  

Healthcare teams frequently work in siloes, creating disparate data and analytics for finance, quality, and operations. Effective data governance requires those teams and their data to come together to collectively understand what to measure and how to turn it into actionable information. 

“If you want to simplify the idea of healthcare data governance, it’s really about getting tangible outcomes,” explains Deb Bulger, Vice President of Strategic Partners at Syntellis. “That requires getting meaningful information — not just lots of data — into the hands of people who are accountable for it.”  

Here’s how to begin that process:  

  • Start with conversations: Finance and quality teams should discuss how each team collects information and what data they gather. Each team may, for instance, define length of stay differently. Following those conversations, teams can more effectively determine how to align and collaborate to generate meaningful analytics.  
  • Identify useful data: Leaders should make strategic decisions about what information to measure to ensure the resulting data will provide accurate and useful insight. 
  • Address data quality and standardization: Health systems with multiple hospitals and care locations especially must normalize data across all facilities to be useful. 
  • Combine analytics and action: Healthcare reporting often breaks down into two categories: analytics — such as the data from the finance side — and action, such as quality initiatives. One without the other is meaningless; organizations must determine how analytics influence action and vice versa.  

With this general roadmap, healthcare leaders can begin to implement effective analytics strategies.  


Taking a Patient-Centric Approach  

Reporting generally takes a finance-oriented approach, but behind every patient interaction is a human with fears, worries, and concerns. Rather than looking solely at financial data, healthcare leaders should consider all facets of a patient experience, as that story can reveal valuable information about cost, variance, and opportunity.  

“In healthcare, when we look at financial numbers, we may look at variances by service line or department, but we need to take the lens a little deeper,” explains Bulger. “Did something happen to the patient that raised the cost? What about process variability? What about provider preferences for implants or drugs? What about the patient contribution?”  

By considering the entire patient journey, healthcare organizations can more accurately pinpoint and address variations. A patient-centric approach accounts for:  

  • Total opportunity: Is there an opportunity for cost savings? Determine this by comparing targeted or budgeted cost to actual cost. For example, if a healthcare organization budgeted $9 million for hip replacements, but actual costs were $10 million, there is a $1 million total opportunity cost.  
  • Cost variability: A deeper look into total opportunity may reveal cost discrepancies. For example, one physician may have a higher-than-average cost per case. Variability can result from provider preferences for particular tools, supplies, vendors, etc.  
  • Severity: Also consider severity of illness, as sicker patients require more services and care. Take into account factors such as clinical cohorts and patient population classifiers to see variance across patient populations.  
  • Process variability: Elements of a physician’s process can also create data variances. Surgeons that spend more time in an operating room than planned, for example, can cause delays in downstream procedures.  
  • Patient outcomes: Unexpected patient outcomes can also impact financial data. Physicians with a high percentage of patients who experience post-operative blood clots, for instance, can account for thousands of dollars in extended hospital stays and additional patient risks.  
  • Patient experience: Patient experience can directly impact revenue. A poor patient experience can dissuade individuals from seeking future care from your organization; it can also impact employee turnover. Effective analytics connect patient experience with financials and patient outcomes.  


The Right Tools for the Right User  

For most healthcare organizations, data governance requires a combination of tools and solutions, rather than a single platform. To put your framework in motion, your organization can leverage resources and strategies including:  

  • Data warehouse: A robust data management system aggregates data from disparate sources — including finance, quality, and operations teams across the organization — and supports business intelligence (BI) activities.  
  • Business intelligence strategy: A BI strategy provides a blueprint for how to use data within your organization.  
  • Data scientists: This role oversees the data structure and completes more complex analyses than a typical data analyst.  
  • Robust reporting: For optimal data access, healthcare organizations’ reporting tools should support a variety of intuitive reporting models, including visualizations, ad hoc reports, and spreadsheets. Reports should be easy to customize so each team can access the precise data they need to evaluate financial and clinical performance.  
  • Role-specific dashboards: Dashboards tailored for different users empower individuals in a variety of roles to easily view the exact information to make informed decisions. For example, management dashboards may include in-depth views of operations, financial metrics, and profitability, while clinical dashboards may focus on patient-centric metrics such as length of stay and readmissions.  


Our final blog post in this series will discuss how to measure the return on your data and analytics investment.

Axiom Enterprise Decision Support empowers healthcare organizations to drive performance and decision-making with a single, trusted source of financial and clinical performance measures. Equipped with the right data, healthcare leaders can reduce costs, optimize revenue, and improve clinical quality. 

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