Freed Associates

As seen in Becker’s Hospital Review.

Problem to Solve

As health care organizations grapple with complex data and analytics, their business units often rely on multiple siloed, domain-specific and, in some cases, home-grown applications to manage their operations. This hinders transparency into the end-to-end enterprise data life cycle, leading to delayed, incomplete or inaccurate data for executive strategy-setting and decision-making.

A large complex health system was facing such challenges with its financial data. These issues arose from the sheer number of business units and departments gathering, analyzing and reporting the organization’s financial and clinical data. Data producers and consumers did not fully understand underlying business terms and methodologies. As a result, the organization’s reporting cycle was inefficient with re-work due to data inaccuracies, conflicts and missing values.

Recognizing the need for a durable solution to its systemic data issues, the health system instituted a new phased data governance approach for its financial data. Its overarching vision was to improve reporting accuracy and efficiency by establishing a single source of truth for key data elements and metrics. Freed Associates (Freed) was engaged to operationalize the strategy and start a new data governance program.

Strategy & Tactics

A four-phased delivery approach was implemented to mobilize the organization and deliver quick wins in the following sequence:

• Phase 1: Discover data issues. Through interviews with finance executive leaders and functional leads, a need for robust change management processes, better controls and decision authority was identified. Establishing a common understanding of underlying business terms across the enterprise emerged as an overriding priority. A core set of business-critical financial and clinical metrics were identified for standardization.

• Phase 2: Institute a data governance infrastructure. A governance council of senior leaders was created to provide data oversight; the council in turn operationalized four working groups, including subject matter experts, to translate their data expertise and knowledge into business rule documentation that would enable data transparency.

• Phase 3: Defining metadata. An end-to-end metric and measurement definition process involved examining existing nomenclatures and definitions across the enterprise. It also included understanding logical processes for metric data generation and consumption across business entities. This effort was augmented by researching external, industry-standard, regulatory, and compliance definitions and directives, and reconciling any conflicting metric definitions. These data elements served as inputs to construct a single, organization-wide metric definition. To drive data quality accountability and ownership, the working groups recommended formal data owners, data stewards, and data custodians for each metric. The data council approved a metric recommendation packet consisting of the core definition and assigned data stewards.

• Phase 4: Implementing change management strategy. With data governance council approval, IT and business stakeholders developed formal roles and responsibilities for data stewardship. This helped define governance processes for resolving data quality issues and bringing in new data under updated governance standards.

Results                                                                                                            

Through this effort, the health system gained standardized business terms and definitions for more than 140 clinical and financial performance metrics and measures, enabling the organization to make business decisions far more efficiently and accurately. The metrics and measures addressed covered such areas as:

• Volume and statistics – This included key metrics such as average daily census, adjusted discharges, case mix index, adjusted occupied bed and outpatient factor
• Financial performance – For example, income statement and net position measures such as days cash on hand, gross patient revenue and unrestricted cash
• Other key financial information – Other performance measures including labor/productivity, occupancy rate and pharmaceutical expense

This work also codified a single source of truth for data elements obtained and transformed through various disparate applications and systems, and standardized rules governing calculations and derivations for metrics. The health system was able to assign data stewards to formally drive accountability for managing information resources on behalf of others. This facilitated publishing governed metrics in a web-based, metadata catalog readily accessible to all data consumers across the enterprise.

Conclusion

By instituting new financial data governance standards, the health system ensured its data is accurate and well-documented, as well as accessible and secure. With these data improvements, the organization has recalibrated its reporting processes, introduced operational efficiencies, and demystified methodologies driving the values for common metrics and measures. Establishing standard definitions for business-critical metrics and measures will help the organization launch complementary data initiatives rooted in accurate and reliable data such as data profiling and data visualization.