Healthcare Analytics

Getting Started with Healthcare Data Analytics

Put the Right Team in Place

Creating a healthcare analytics culture capable of transformational analysis of clinical and cost data is critical to the success of organizations that are taking on risk by adopting value-based reimbursement models. The complexity of the clinical and financial information involved necessitates that in addition to IT, an enterprise-level, cross-functional team of C-level executives, clinicians, quality experts, and finance professionals must be key stakeholders in this process. In July 2014, the HealthLeaders Media Council found that more than half of healthcare leaders agree.

A graph that shows responses to the question which model best describes how your organization will assign responsibility for healthcare data analytics?
Source: Healthcare Analytics Buzz Survey, HealthLeaders Media Council, July 2014

Establish a Data Governance Process

4 Essential Data Governance Priorities

  • Appoint a workgroup of key stakeholders including C-suite sponsorship to define required data points
  • Establish user rules for data quality with input from clinical and financial stakeholders because IT definitions may be inadequate
  • Inventory data source systems and understand their uses and limitations
  • Share goals throughout the organization, create clear targets, assign ownership, and create accountability checkpoints

Data governance in healthcare is the foundation for a healthcare data analytics framework. It allows stakeholders to provide vision and strategy, not just for technology, but also for overall adoption and outcomes. While governance is extremely beneficial, it requires a significant investment in effort and time. In fact, only 19% of health systems have already established an enterprise-wide data governance process, according to McKesson focus group data. Data governance creates accountability and assigns stewardship for information assets by addressing:

  • Data quality
  • Hygiene
  • Maintenance
  • Policies
  • Processes
  • Compliance
  • Security
  • Risk management

2 Key Data Governance Components

Data Stewardship
Data stewards are responsible for carefully managing data components. They deeply understand the data for which they are accountable and may have clinical, financial, operational, or IT roles. Stewardship lets organizations answer questions such as: If a medication error emerges, who owns and understands how the information is stored, accessed, and updated?
A Single Source of Truth
Data often lives in multiple systems and has different meanings to different users. It needs to be standardized, validated, and given a universal definition in order to effectively power healthcare data analytics. Through data aggregation and governance, a single source of truth can be used across the enterprise to inform and guide all decisions. This single source can stop decision makers from simply shopping for data that supports their view

Transition Economics and Healthcare Data Analytics Initiatives

The transition from fee-for-service to value-based reimbursement requires healthcare organizations to analyze different types of data. A fee-for-service model is episodic in nature, focusing on standard financial and operational metrics with internal and external benchmarks. In contrast, care quality, and population health analytics are much more complex and need to produce insight that can drive desired clinical and/or financial outcomes. These advanced healthcare data analytics must be able to:

  • Understand patient populations
  • Intervene with at-risk patients
  • Measure quality and coordinate care across all network providers
  • Manage risk, variability of care, and resource utilization for populations who place the organization at financial risk

Managing transition economics requires healthcare organizations to leverage existing information to optimize current operations, while concurrently developing and deploying new financial and operating models. Organizations must be aware of the competing agendas that are inherent in transition economics. These different agendas can undermine a nascent analytics culture by competing for resources and misaligning strategies. Ultimately, organizations should look to balance competing priorities by focusing on both incremental and transformational improvements.

Are you searching for a healthcare data analytics solution? Learn more about McKesson Performance Analytics™.

Next: Accelerate Performance Improvement in Healthcare

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