Clinical data registries are playing a crucial role in enhancing health care and reducing the overall costs. Clinical data registries are implemented with the aim of monitoring quality patient care and defining treatment patterns.
It helps to record information regarding the patient’s medical status and the quality of healthcare they receive over a period of time. It lets health care professionals determine what treatments are available and how patients with varying diseases and attributes respond to treatments.
The health care professionals use clinical trials software to record patient information and recommend the best course of treatments to improve their health in the future.
This information is further leveraged to compare the services of health care providers pertaining to their quality performance, outcomes, and utilization of resources.
Usually, patients approach to different organizations for various health care services over time. Each time a patient visits their health care professionals or is hospitalized; detailed information regarding the care they received as well as their health status is recorded.
This information is stored in an encrypted form and the health care professionals send this information to clinical data registries through an electronic health record or a secure web portal.
When the data is entered into the clinical data registry system, it goes through a quality check process that ensures the accuracy and completeness of the data.
Though clinical data registries allow to match the right patients with right health care, and target potential research areas, there are certain challenges that imperative to consider. In this blog, we’ve outlined the following key challenges of mastering clinical data registries.
Understand Data Sources
Data comes in all shapes and sizes. The more data you can accumulate, the better the insights you avail. But managing a myriad range of data, its sources and types can be a daunting challenge.
You need to have a patient registry database software in place that is capable of handling varied data components differently from those that are more identical and stable.
For instance, data models that define the measurement outputs (such as data collection forms and devices that produce measurement data files), and the data produced by the instruments, needs to be managed differently as compared to the research operations data (such as grants, studies, and research staff).
These measurement outputs may likely include over hundreds and thousands of scientific variables as well as operational workflows with many tables having numerous columns.
In 2014, Doug Laney, a Garnet Analyst, said that “No greater barrier to effective data management will exist than the variety of incompatible data formats, non-aligned data structures, and inconsistent data semantics.”
This holds true even today. APIs (Application Programming Interfaces), conversion tools and universal standards are aiding to pave a way to efficient data utilization and integration.
Data Sources Evolve Overtime
Health care related data is updated quite frequently. This makes it important to identify the relevance of data over a period of time.
This challenge is critical especially in multidisciplinary behavioral and mental health research where tens and thousands of columns are involved, new data models for experimental purposes involve several associated tables, and models transform in the course of a typical project.
It is imperative to consider the way a clinic trials software will transform and adapt to the changes in health care data, the data metrics it will include, and the amount of time it will require to store data prior to archiving or deleting it.
Understand Protocol Variability
Every health care research project differs greatly in terms of accumulating, curating, and sharing researched patient data. In fact, within the same research center, it is often unfeasible to implement or standardize a single research workflow across various projects.
The challenge here is that the systems need to be operated throughout centers or sites and this challenge eventually grows.
Therefore, it’s important to be able to customize workflows to meet local research requirements, ensuring maximum expandability and flexibility.
But to attain expandability and flexibility in a clinical data registry, research operation workflows should be configured by the clinical research staff to manage variability across research domains, sites, and studies.
This means the non-technical staff should have the ability to configure those research domains, sites, and studies.
This, in turn, aids in addressing the speed and cost issues with adding a new form, studies, and research assets.
Maintain Research Data
Research data is a fundamental base for health care activities. Health care professional utilizes the data acquired and stored over a period of time or the course of patient treatment for providing enhanced patient experience.
This makes it essential to maintain the patient registry database software that support such complex research activities.
This patient registry database software should be comparatively easy to use. Unexpected changes to the environment should be taken care of by the in-house or external staff, especially when the prime delivery team is unavailable.
The system must also be capable of storing data to assure longevity of the valuable research assets, without having to worry about the future technological complexities or changes.
Share the Research Data
The accumulation and re-utilization of the research data necessitate the ability to import and change data from a range of data sources.
After the research data is acquired, clinical data registries should be able to reorganize and transform the research data for a variety of uses.
From the output perspective, clinical data registries should facilitate both manual and programmatic techniques to query and export the research data. At the same time, it is essential to identify the data that is non-sharable.
Hence, view your data as a consistently, accumulating asset and share it widely with your collaborators.
Which other challenges you think are important to address when it comes to mastering the clinical data registries? We’d like you to supplement your thoughts and views in the comments area below.