Available data is the only thing keeping you from answering these apparently simple questions:
Assessing independent risk- and outcome predictors in cardiology is difficult because it necessitates adjustment for multiple co-morbidities that are not always readily available on file. As such, evaluating the added value of innovation and optimizing the quality of care pathways is difficult.
With the data siloed into different locations, managing a research unit with prompt access to a centralized, up-to-date real-world data platform is a costly challenge that requires lots of manual interventions.
With mobile health data streams, the extension of EHR data entry, and regulatory requirements - managing the administrative burden is often several FTEs worth of labour for the modern cardiology department.
Reviewing EHR’s with the use of NLP to determine the prognostic impact of AF and anticoagulation therapy in patients undergoing PCI.
How LynxCare is helping modern Cardiologists improve their patient care using Artificial Intelligence.
