Additionally, such tools as FRAX require a clinician to physically find the necessary information in a patient’s chart and manually enter these data into the online form. The FREM tool and others are being evaluated for integration directly into EHR systems and nationwide patient information registries. This feature will automate the process, not relying on individual human effort. The possibility of discovering new fracture risk factors by combining big data repositories also exists and, to this point, has not been used to evaluate fracture risk in a systematic way.
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Explore This IssueSeptember 2019
On the subject of automated assistance in identifying patients at risk for recurrent fracture, Prof. Abrahamsen noted that neural networks are already being evaluated to recognize incidental radiographic findings that may quite dramatically change a patient’s risk estimate for future fracture. In 2017, Israeli researchers used an automated method to detect spine compression fractures in computed tomography (CT) scans. This research was based on the established concept that the presence of a vertebral compression fracture is indicative of osteoporosis and represents a robust predictor for development of a second osteoporotic fracture in the spine or elsewhere. Such a system could easily be integrated into radiology reports in which such fractures are not commented on because they were not the indication for the CT study.6
Opportunity & Challenges Ahead
The question-and-answer segment of this clinical practice session highlighted the opportunities and potential challenges faced by the enactment of such automated systems in the real world. Example: Even if the algorithms presented by Prof. Abrahamsen and colleagues use open-source software, the vendors who operate EHR systems may choose not to incorporate these algorithms into their programs, because doing so will cost time and money.
The question of how patients will view this fairly proactive means of health data evaluation also remains. Concerns include giving the impression patient health information is being reviewed without necessary consent, as well as the problem of providing information about a low, but not negligible, risk of fracture that may induce anxiety and uncertainty in a patient, causing them to second-guess the next, best therapeutic steps.
Ultimately, similar to many aspects of medicine, the integration of new technology and the use of data analytics are likely to continue to increase. It is up to physicians, administrators, patients and all stakeholders to determine how best to implement these new solutions and deal with the issues they may create.
Jason Liebowitz, MD, recently completed his fellowship in rheumatology at Johns Hopkins University, Baltimore, where he also earned his MD. He is currently in practice with Arthritis, Rheumatic, and Back Disease Associates, New Jersey.