Data is increasingly becoming one of the most valuable assets in any organization, especially in healthcare organizations. Data is now the longest lasting asset in any organization, outliving facilities, devices, and people. Hence, as a healthcare organization transitions into a more data and analytically driven industry, having Good Data Governance Practices become paramount to the survival and thriving of each health organization. Having good data governance practices starts with perfecting the data governance best practices and making them work seamlessly into how a healthcare organization is run. These best practices can be summarized under Data Quality, Data Access, Balanced and Lean Governance, Data Literacy, Data Content, Analytical, Prioritization, Master Data Management.
Data Quality, Data Access and Balanced and Lean Governance are quite common and often talked about hence a lot of health care organizations have perfected the best practices. The same cannot be said for Data Literacy, Data Content, Analytical Prioritization and Master Data Management and so they will be discussed extensively in this article.
Increasing the quality and ease of access to data serves no significant purpose of the beneficiaries or those who need the data are not educated on the interpretation and meaningful use of data as it applies to their role in the organization. Data literacy can be increased by teaching the users how to distinguish good data from bad data in the context of their decision, making environment and role in the organization; data analysis tools; process improvement techniques that are driven by data; statistical techniques that can be applied to improve decision making when data is incomplete or scarce; and 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 cases. Data governance works well when data literacy is increased.
Every Healthcare organization should have a Data Governance Committee and this data governance committee should plot a multi-year strategy for data acquisition and data provisioning, seeking to constantly expand the data ecosystem that is available for analysis in the business of healthcare delivery and health management. For example, 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 the systems to collect this data is the first step in the analytic journey and can take as long as five years to complete.
When dealing with lots of data (Big Data) mastering the art of prioritization becomes a necessary skill.The Data Governance Committee should play a major role in developing the strategic analytic plan for the C-level suite, and then play an active 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% of the organization’s analytic resources should be dedicated to top-down, centrally managed priorities, while 40% of the resources should be distributed to support the tactical requirements of departments, business units, clinical service lines, and research.
Master Data Management
As a healthcare organization progresses in analytic maturity and utilization, the Data Governance Committee will become the steward for defining, encouraging the utilization of, and resolve conflicts in master data management. This role will cover local data standards as well as regional and industry standards. In addition to coded data standards, the Data Governance Committee will also become involved in the standard use of algorithms to bind 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.