In the healthcare industry, there is a great need and use of data. These days data is stored electronically which makes it easier for health practitioners to access and work with the data. However, the sheer amount of data being collected daily and even every minute is so large that it has become an issue. In order to solve this issue, health organizations need Health Data Management. This is where the study of health data management comes in.
In carrying out the tasks of managing these data, there exist some challenges.
- In an attempt 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. Work with the owners to prioritize the work of combing through each report to document its purpose, rules, tools used, frequency, data sources, formats used, and steps are taken to produce it. This process will lead to better documentation and fewer reports that need to be touched upon system upgrade. The challenges of carrying this out are: Most organizations are surprised at how many reports have been created (and maintained) over the years. Any given system may have thousands of reports run out of the EMR. Folder security can sometimes force duplication (and probably modification) of the same reports with no audit trail for what is different. Additionally, the report filters and logic are often hidden from the report consumer, making it next to impossible to determine the rules and ultimately, the “source of truth.”
- Gather the analysts to develop a list of core competencies and a program to provide ongoing training and mentoring. Analytic excellence is tough to measure, but without certain core competencies, there is no way to guarantee high quality, consistent results.
- Assess the degree of silos and political will to improve alignment. Analytic silos breed duplication and potential waste. Although this seems obvious, the political climate often complicates remedying this risk. However, it can help inform the type of alignment needed to reduce the risk.
- Determine the current method for requesting reports and analytics. Without a formal intake, triage, prioritization, and assignment of work, the reporting and analytic environment becomes the “wild, Wild West.” Without a disciplined process, there is increased the risk that staff is not focused on the highest priority work.
- Identify current data governance processes and ownership within the organization. Current data governance might be performed through numerous, disconnected committees so dig around. Data governance focuses on managing data from initial capture in a transactional system, such as an EHR or laboratory system, through its aggregation into reporting structures and data stores and enterprise data warehouses. The intent is to make sure that each step of the health data management process is controlled and the effects of processes on the data are well documented and understood.
The true value of the data warehouse is to organize data, provide links across 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. Creating a new model that supports effective clinical data management requires an EDW to assemble and coordinate data from across the organization, providing the foundation to improve care and deliver better outcomes.
This streamlined model greatly reduces lead time, enabling analysts to provide strategic insight from the disparate data collected across various systems. Healthcare organizations that embrace the value of health data management, and harness it to create an effective clinical data management model, will be in the best position to survive and thrive in the new era of healthcare.