Health systems that aim to improve patient care without increasing operating costs are called value-based systems. In radiology, the high volume of medical images is considered a high cost to health systems with the number of images produced being the gold standard for measuring the productivity of a radiological center. However, this practice goes against a value-based system and opens the discussion between value-based and volume-based systems that can only be solved by increasing research and innovation for best practices. Eventually, radiologists are in the middle of this discussion where it is only economically recognized by radiological centers when a lot of medical images are produced without regard to the value to the patient.

A situation of a volume-based system occurs when producing clinical studies only to avoid a legal consequence due to malpractice or medical negligence, but without considering the expense involved or the ionizing radiation to which a patient can be exposed without need. On the other hand, in a value-based system, a key event would be to implement radiologist-patient communication to improve diagnosis by including clinical symptoms along with the information provided by the clinical study which improves the patient’s experience and treatment response times. The cost to health systems of specialized medical imaging is the cause of using low-quality images for medical diagnoses, which concludes in a lack of clinical information for a correct diagnosis, this practice increases costs considerably but decreases the value. A valuable solution is to implement artificial intelligence algorithms to get the most out information of from a conventional clinical study.

The complexity and volume of diagnostic images have been increasing recently but the financial cut in health systems generates erroneous diagnoses due to work overload and exhaustion of radiologists, as well as lack of time and monetary incentives, making it difficult to implement value-based processes.

Increased researching and validation technologies or processes are needed to add value to radiological care. However, health personnel can resist the use of new technologies or processes that change their work routines, do not using expensive medical studies can bring ethical consequences and delays in the medical treatment to the patient. Investigating and evaluating the efficiency of current radiological images and processes to incentivize the use of value-based systems can greatly improve quality rather than quantity in diagnostic services provided in radiological centers.

Sources:

Kwee, T. C., Yakar, D., Pennings, J. P., & Kasalak, Ö. (2022). Value-based radiology cannot thrive without reforms and research. European Radiology, 1-3.

Sheethal, M. S., Amudha, P., & Sivakumari, S. (2022). An Intelligent Survey on Deep Learning-Based Strategies for Multi-Grade Brain Tumor Classification. In Data Intelligence and Cognitive Informatics (pp. 787-794). Springer, Singapore.