In order to understand clinical data management, it is helpful to first understand what data management is. Data management is usually done with data management systems. Data management systems are computer programs designed to manage a database, a large set of structured data, and run operations on the data requested by numerous users. In order to make sense of the vast quantities of data that enterprises are gathering, analyzing, and storing today, companies turn to data management solutions and platforms. Data management solutions make processing, validation, and other essential functions simpler and less time-intensive.
Clinical data management is a critical phase in clinical research, which leads to the generation of high-quality, reliable, and statistically sound data from clinical trials. Clinical data management assures collection, integration, and availability of data at appropriate quality and cost. Clinical data management has been around for quite some time and as it continues to be useful, health researchers continue to seek new ways to improve the process and make it more efficient. For starters, the standard and generally accepted approach to clinical data management have some challenges. One challenge has to do with the use of spreadsheets and the other is the changing and dynamic analytics roles and tools.
The Spreadsheet Challenge
In order to deal with the abundance and continuous increase of data, many health organizations and hospital departments, develop their own tools to help. They end up using multiple spreadsheets. These spreadsheets often contain information that may be inaccurate or in conflict with corporate data, which leads teams to focus on the wrong priorities or miss improvement opportunities altogether. Source data once identified and captured, often become shadow systems with one spreadsheet linking to another and then another. This spreadsheet mania further complicates the process and introduces an unnecessary risk of error.
Changing Analytics Roles and Tools
As mentioned above there are lots of gaps in the data structure which leads to inaccuracies or missed opportunities. In today’s value-based care environment, data analysts must be equipped with the right tools to identify performance gaps in the organization and to create actionable recommendations that drive improved outcomes. Providing the proper infrastructure mitigates the risk of compromising data during capture or transfer. Accurate and timely data is crucial to engaging clinicians and gaining their trust.
Data analysts spend roughly 80 percent of their time hunting and gathering data as opposed to performing high-value work, such as interpreting data and making recommendations about how to improve the outcomes. Most analysts would rather conduct strategic analyses and contribute to organizational decision making. Today, the approach to clinical data management focuses most analytic resources on the task of hunting and gathering.
Creating a process that is efficient, effective, and reliable will enable analysts to spend more time on gleaning insight from the data. Most data analysts and hospital management staff dislike the current approach and with good reason. The multiple pressures of improving care, reducing cost, and planning for the future require near-real-time data. Submitting a request, being placed in a queue, and waiting weeks for a response is no longer an option.
There have been proposed strategies to help improve clinical Data Management some of the best solutions are listed below
Identify the Analysts in the Organization
In order to align the analysts, a good first step is to simply identify the current analyst pool sprinkled throughout the organization. Finding all the analysts can sometimes be a challenge, as many are in different roles with different job titles spread across the organization. One way is by working with HR to get a list of positions with names similar to “analyst,” “specialist,” etc
Assess Analytic Improvement Opportunities in the Organization
Once the analyst pool is determined, elect a core analyst team responsible for assessing the risk within the organization. Some of the team’s new duties, along with the risks they are certain to uncover, include the tasks in the table below:
Create an Enterprise Data Warehouse (EDW) as the Framework for Clinical Data Management
Create or purchase an enterprise data warehouse (EDW) as a critical step toward 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 significant and long-term.