Data is now one of the most valuable assets in any healthcare organization, especially as we transition into a more analytically driven industry. Data is the longest lasting asset in any organization, outliving facilities, devices and people.
In the past few years, as the value and longevity of data have become better realized, the term ‘data governance’ has emerged to describe the concept of managing and influencing the collection and utilization of data in an organization. The adoption and creation of ACOs is motivated as much by the acquisition of more data to manage risk and understand outcomes, as it is by the acquisition of clinicians, patients, and facilities. If we accept the assertion that healthcare is a knowledge delivery industry, it is our obligation to exploit the data assets in our environment to augment and optimize that knowledge.
While information and data security is a long-standing body of practice and knowledge in corporations, data governance is less mature, especially in healthcare. As a result, there is a tendency to operate in extremes, with either too much governance or too little. Over time, as data and analytic maturity increases, the healthcare industry will find a natural equilibrium. For example, in the Healthcare Analytic Adoption Model, a robust data governance function is required in order to achieve Level 5 maturity.
A new body of knowledge can be a ripe ground for confusion and over-complication; and there are many vendors and consultants that have an inclination to benefit unfairly from this complexity in these formative stages. Below are the seven simple practices of data governance that can be used as a self-guided tour through the maze of puzzling advice.
1. Balanced, Lean Governance
The Data Governance Committee should practice a cultural philosophy that believes in governing data to the least extent necessary to achieve the greatest common good. Quite often, organizations will either over-apply data governance in their enthusiasm for the new function, or under-apply it due to their lack of experience. The best approach is to start off with a broad vision and framework — but limited application — and expand the governance function incrementally (only as needed, and no more). The Data Governance Committee should be a subcommittee to an existing governance structure, with the influence necessary to institute changes to workflows, resolve data quality conflicts, and develop complex data acquisition strategies to support the strategic clinical and financial optimization of the organization.
The Committee should also enlist front-line employees as Data Stewards who are knowledgeable about the collection of data in source transaction systems such as the EMR, cost accounting, scheduling, registration, and materials management systems. CIOs who function horizontally, across business lines, at the application and data content layers of the IT stack (as opposed to those who operate primarily at the infrastructure layers) are a natural fit for facilitating and leading the Data Governance Committee.
When in doubt, govern less, not more. Keep it lean. Grow slowly and carefully into the need for more.
2. Data Quality
Overseeing and ensuring data quality is probably the single most important function of data governance. When low quality data has a negative impact on the accuracy or timeliness of the organization’s decision making, the Data Governance Committee must be capable of quickly reacting to these issues and enforcing the changes required in source data systems (not the analytic systems) and workflows that are necessary for raising data quality. Simply defined, Data Quality = Completeness of Data x Validity of Data x Timeliness of Data. The Committee must make each of these variables in the data quality equation a leadership priority.
3. Data Access
Increasing access to data across all members of the enterprise, including external stakeholders, members of the community, and especially patients, is a critical function of the Committee. While the information security committee tends to protect data and restrict access to data, the Data Governance Committee should create a productive tension in the opposite direction. In the most effective organizations, the data governance and information security committees are combined, thus forcing the members to balance the tension internally and streamlining what can otherwise be lengthy decision making and reconciliation between the two committees.
4. Data Literacy
It serves no purpose to increase the quality of or access to data if the intended beneficiaries are not educated on the interpretation of data as it applies to their role in the organization. Data literacy can be increased by: (1) teaching the users how to distinguish good data from bad data in the context of their decision-making environment and role in the organization; (2) data analysis tools; (3) process improvement techniques that are driven by data; (4) statistical techniques that can be applied to improve decision making when data is incomplete or scarce; and (5) the very deliberate collection and dissemination of metadata, especially that which is associated with enterprise data warehouse (EDW) content. The Data Governance Committee should champion the cause of data-driven decision making and data transparency around quality and cost. These campaigns should include the use of slogans, spokespeople, role models and other attributes of successful causes.
5. Data Content
The Data Governance Committee should plot a multi-year strategy for data acquisition and provisioning, seeking to constantly expand the data ecosystem. Activity-based costing data, genetic and familial data, bedside devices data, and patient-reported observations and outcomes data are all critically important to the evolution of analytics in the industry. Building and acquiring systems to collect this data is the first step in the analytic journey and can take as long as five years to complete. All of the aforementioned data sources are required to progress through the Healthcare Analytic Adoption Model.
6. Analytic Prioritization
The Data Governance Committee should play a major role in developing the strategic and analytic plan for the C-level suite, and play a key role in ensuring the requirements of that plan are implemented. Inevitably, there will be more demand for analytic services than there are resources available to meet that demand. The Data Governance Committee cannot resolve every priority, but it can balance top-down corporate priorities with bottom-up requests from the clinical and business units by advocating a resource allocation of 60/40 between centralized and decentralized analytic resources — that is, 60 percent of the organization’s analytic resources should be dedicated to top-down, centrally managed priorities, while 40 percent of the resources should be distributed to support the tactical requirements of departments, business units, clinical service lines, and research.
7. Master Data Management
As the organization progresses in analytic maturity and utilization, the Data Governance Committee will become the steward for defining, encouraging the utilization of, and resolving conflicts in master data management. This role will cover local data standards (facility codes, department codes, etc.), as well as regional and industry standards (CPT, ICD, SNOMED, LOINC, etc.). In addition to coded data standards, the Committee will also become involved in binding data into analytic algorithms that should be consistently used throughout the organization, such as calculating length of stay, defining readmission criteria, defining patient cohorts, and attributing patients to providers in accountable care arrangements.
If you are struggling to understand and implement a data governance function in your organization, following these seven simple practices will help you avoid all of the major pitfalls of either under-governing or over-governing. Of utmost importance, a lean and balanced data governance function will help your organization maximize the value of your data to deliver the best possible care at the lowest price.
Great post, thanks for sharing. Data quality and data-driven decision making is certainly making an impact in the world of healthcare, as seen with the implementation of ICD-10. There is so much great work being done with data analysis and data linkage tools for the future of healthcare.