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Improving Data Management

Data management can be defined as the development and execution of policies, practices, architectures, and procedures to properly manage full data lifecycle needs of an enterprise. Data management is essentially important in the healthcare industry because of the magnitude of data that it produces and needs daily. Every area of the healthcare industry relies on an endless stream of data flowing in order for the system to function. Data management evolved with the advent of technology which ushered in electronic records. Data previously was stored in paper records, files, and boxes which made it almost impossible to manage. Issues like missing document, misfiling of a document or illegible handwritings served as a hindrance to data scientists who could use data from patient history to predict the future of patient care.

Data Management

Electronic data management is objectively better than old systems of data management. However, with more and more data being produced daily, the healthcare industry constantly seeks ways to improve data management and make it more efficient. These improvements are done to ensure that the right data is found quickly when needed and that the right people can access the data. There are strategies https://www.healthcatalyst.com/improve-clinical-data-management-healthcare-reduce-waste/  that can be carried out to improve on clinical data management.

Strategies to Improve Clinical Data Management

  1. Locate the Data Analysts in your Organization: As simple as this sounds, it can be quite a difficult challenge to identify the analysts in an organization as they usually are scattered around the organization. A good way to locate the analysts is to work with the human resources department of the organization to get a list of anyone with “analyst,” “specialist,” or “informaticist” in their job title. This way you can identify the data handlers and start the process of improving the system. In some organizations, some data analysts don’t even realize how important for the organization and far reaching their job function is.
  2. Assess Analytic Improvement Opportunities in the Organization: After all the data analysts in the organization have been identified, a core analyst team should be selected from this pool. This elected pool will be responsible for assessing the risk within the organization. The duties and tasks of this team includes but not limited to
  • Create a reported inventory and find the logical “owners” of each report. It is most practical to start with recent reports, pulled during the last year. Reports that haven’t been run in over a year are candidates for archiving. Working with the owners, prioritize the work of combing through each report to document the report’s purpose, rules, tools used, frequency, data sources, formats used, and steps taken to produce it. This process will lead to better documentation and a reduction in the number of reports that need to be touched upon system upgrade.
  • Bring your analysts together to develop a list of core competencies and a program to provide on-going training and mentoring.
  • Assess the degree of silos and political will to improve alignment.
  • Determine the current method for requesting reports and analytics.
  • Identify current data governance processes and ownership within your organization. Current data governance might be performed through numerous, disconnected committees so you will need to dig around.

It can be difficult to implement this model as it can be incredibly disruptive to the organization. However, with the right political capital, it can be done.

3. Champion the Creation of an EDW as a Foundation for Clinical Data Management: Creating or purchasing an enterprise data warehouse (EDW) is a critical step in creating a robust analytic infrastructure. The EDW becomes a safe, central repository of data that is organized and optimized for measurement, analysis, and reporting. Sure, this is a large effort, but the payoff is huge–and lasting.

The true value of the data warehouse is to organize data, provide links to disparate data sources (so the analysts don’t have to), and provide access so analysts and clinicians can “fish for themselves.” Aligning the analysts and developing clear clinical data governance and management policies will strengthen the entire analytics environment.