Healthcare Data Management

Healthcare Data Management can be defined as the process of linking identity data and reference data across multiple IT systems into a single, consistent point of reference. That single point of reference could be a patient, or it could be a procedure code. Healthcare organizations constantly seek ways and strategies to implement that would improve their healthcare data management systems and procedures. Some steps these organizations can take to make these improvements are outlined below.

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. 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 include 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 ongoing 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.

The main benefit of using an enterprise data warehouse is to master data is that it is a very achievable solution to the problem. The main drawback of this approach, however, is that the mastered data is only available for analytics. An enterprise data warehouse will not solve master data challenges at the level of transactional systems.

The following are the main instances when it is best to use an enterprise data warehouse for master data management:

When an organization needs to do analytics but doesn’t have another MDM solution in place. To survive in the healthcare industry of today, every health system needs to implement analytics that helps drive higher-quality, lower-cost care. A system shouldn’t put off analytics just because it doesn’t have an MDM solution already in place. An EDW can fill MDM needs in a smooth manner so that the organization can move forward with analytics to eliminate waste, improve margins, and successfully participate in value-based initiatives.


This also applies to situations where a third-party MDM solution initiative falters or fails. We’ve mentioned how risky these upstream MDM implementations are. The EDW can step in and resolve MDM problems for analytics purposes while the health system sorts out what to do about transactional data matching. When an organization inevitably starts integrating data sources from outside its consolidated infrastructure or its EMPI.

As soon as a health system encounters an important data source that isn’t integrated into its chosen MDM solution, the organization may need an EDW to handle that integration. We can cite several examples of health systems with MDM solutions in place who have subsequently needed to integrate data from a source outside of their consolidated systems or EMPI.