We are fully immersed in the digital age of healthcare, where Artificial Intelligence (AI) is beginning to impact the industry. A recent survey on AI in health care reveals that proper implementation of AI remains a priority for many industry executives, given its potential contribution to automating workflows, achieving health equity, improving patient care and gaining tangible cost savings.
The implementation of AI in healthcare will take many years, thus, there are still areas of the industry that have significant opportunities to be realized through automation. One of these areas is clinical data management. The standard process of manually mining clinical data is extremely time-intensive for clinical experts, who are already working against years’ worth of backlogs due to the ongoing health care labor shortage. This also has implications for the quality of the clinical data, as the farther it’s removed from the patient interaction, the less relevant it becomes for medical research.
The integration of AI into our existing technology, however, may serve as a solution: this enhanced technology can augment clinical experts’ capabilities, ultimately improving efficiency and data integrity.
The role of artificial intelligence in clinical data management Today, there is a great deal of rich clinical information that could be used to inform medical decisions trapped in large pools of unstructured data. For example, doctor’s notes must be extracted and interpreted to capture the valuable insights that can be used to advance precision medicine, support regulatory decision making and understand real-world outcomes of certain therapies. To be used in these real world data applications, which also serves as a source of revenue for hospitals, this valuable clinical information requires a copious amount of time for clinicians to uncover without automation.
This inefficiency is precisely where AI can help.
AI can assist clinical data experts in identifying recurring themes and trends through topic modeling and natural language processing, greatly reducing the amount of work that the experts must do themselves. This tool allows individuals working through the data to use their expertise as an auditor versus the abstractor. Thanks to a more streamlined process, clinical experts can spend their time more efficiently, interrogating only the data the AI has flagged as important information.
As a result, AI is poised to serve as an invaluable tool that can drive deep exploration of data to unearth patterns and trends that comprise true health care intelligence.