Driving Accountability in Healthcare Through Effective Data Governance

Maximize analytics to improve outcomes and lower costs

The increased emphasis on accountability in healthcare comes as no surprise as the industry continues to move toward a more value-based healthcare system that delivers better quality care at a more affordable cost.

Whether it’s accountability to care and outcomes, fiscal improvement, patient security and privacy, or regulatory requirements, every stakeholder – clinicians, hospitals, suppliers, payers, regulators, and even researchers – is affected and driven by accountability. In light of this, it’s imperative that these stakeholders find a way to accurately measure and evaluate performance across both the clinical and non-clinical sides of the business.

The Heart of Accountability

Data lies at the heart of accountability in healthcare, as well as the industry’s transformation to a value-based model. In fact, data is more than an asset; it is essential to helping us improve care, control costs, identify trends and evaluate performance.

Multiple information systems feed massive amounts of data into decision support systems and meaningful use dashboards that we use on a daily basis. While most healthcare organizations today have basic data management in place — storage, cleansing, consolidation, and reporting — typically the control and enablement processes around the data are strung together through spreadsheets, meeting notes and local, onsite experts.

The reality is that most healthcare data lacks any true, ongoing governance. A recent report by Chilmark Research finds that the majority of healthcare organizations have rudimentary governance structures at best, which results in some major repercussions for the overall industry, not just providers.

Trusting Data

If data is going to work for healthcare, we need to ensure its integrity, lineage and transparency. Data governance gives us a framework by which to trust our data. A good data governance framework creates a common set of rules, definitions, and processes to help determine who actually owns, manages, stewards, and is allowed to use the organization’s data.

In doing so, we can establish context and confidence for that data and understand how data flows across systems, reports and analytical models. This allows us to make timely decisions based on accurate information. When we trust the data and outcomes it reflects, we can maximize our analytics to make continuous improvements.

Strategies for Creating Flexible Policies

The real challenge for any organization, not just in healthcare, is where to get started with their data governance program. In planning out a program, it’s important to keep in mind that “Rome wasn’t built in a day” and the same will hold true for a data governance program.

However, following six simple steps will help ensure that you create data governance policies and process that are highly flexible, adaptive and agile.

  1. Define and align principles. Create a set of statements – no more than 10 – that clearly set forth the expectations and goals for all aspects of data governance. These statements will become the foundation for the organization’s data policy, and should reflect the requirements for HIPAA compliance, the HITECH Act, as well as other industry regulations. These statements should include details about how the organization will define, manage, control, provide access to and ensure quality of critical data elements.
  2. Establish an organizational structure. Create a data governance model that can scale to support all divisions and operational groups, and includes resources from across the organization yet is flexible to change with the rapid change of the industry. Identify and define roles such as content owners, data stewards, platform owners and data consumers. Engage the different parts of the organization as you rollout your data governance program.
  3. Define critical data elements. Tightly define data inputs, sources, definitions and accountability for all critical data elements and build a common “data language” that will make it easier to establish authority and accountability as well as manage sources, controls and flows. Remember not all data is of equal value and therefore not all data is equally critical. Start with what is truly critical today.
  4. Define core business rules. Set clear rules about what the organization does with data, and who does it, to make decisions or perform an activity. Document and communicate these rules in context of the data and how it is used.
  5. Expect and prepare for data issues. In large, complex environments, such as a healthcare organization, where the data sources are diverse (clinical, financial, to name a few), there are bound to be ongoing issues and questions about data quality, terminology, glossary, use, authority and reporting. Develop sound workflow procedures to triage and mitigate issues.
  6. Create feedback loops. Ensure that you have processes and technologies that allow you to easily and continuously measure success. To ease the burden of monitoring performance, find and deploy an automated platform that can collect, aggregate and report on metrics; track progress and issues; and be self-documenting and automated wherever possible.

Research firm IDC estimates that the volume of healthcare data is growing by more than 48% annually, which outpaces the remarkable 40% annual growth across all other industries.

Given this, there has never been a more opportune time to implement a sound data governance program, particularly in healthcare where the stakes surrounding the ability to analyze massive amounts of data for improved outcomes, both financial and clinical, has never been greater.

About The Author