Use of Real World Data (RWD) to generate Real World Evidence (RWE) in gynecologic surgery

摘要

From a historic point of view the most reliable evidence generated in medicine has been from randomized clinical trials (RCT). Such data are, however, difficult to generate and takes time due to ethical concerns, data protecting issues, and inclusion of patients who have to accept the study based on informed consent. Furthermore, such trials are expensive. Thus, other options have been evaluated such as cohort studies or other similar designs. Although there may be some claiming that RCT may not represent the “real world” the design compensates for this, including bias due to lack of demographic variability, selection of specific age groups etc. Another option is the use of Real World Data (RWD) generated from many sources to generate Real World Evidence (RWE). However, clear definitions of RWD and RWE are still lacking. Makady et al.1 defined RWD as data collected in a non-randomized controlled trial setting but a significant number of authors and stakeholders did not have an official, institutional definition for RWD. Recently, FDA defined RWE as the “clinical evidence about the usage and potential benefits or risks of a medical product derived from analysis of RWD” and RWD as “data relating to patient health status and/or the delivery of healthcare routinely collected from a variety of sources”.2 Thus, different perception of RWD may add to the confusion. Furthermore, there are several barriers to the use of RDW including a general lack of knowledge and systems that are built to use RWE as well as mistrust among health professionals, including gynecological surgeons, about RWE compared to evidence generated by RCT. However, the benefit of using RWD and RWE is becoming more evident with the introduction of new option for data monitoring using advanced electronic equipment continuously generating data from monitoring patients, patient surveys, patient reported outcome (PROdata), electronic patient records and internet resources. It should also be noted that RWD applications can aggregate clinical information across institutions and countries thereby increasing opportunities to achieve fast insight into complex systems or rare cases. This, however, depends very much on health providers´ input in electronic medical records, their understanding of diseases, correct coding etc. The main issue is how to collect high-quality data, and therefore specific conditions need to be met for the data to be consistent, reproducible, and accessible. One way to use RWD has been identification of RWD and RWE as a pathway for fast-tracking drug approval for clinical use. This is in accordance with the Food and Drug Administration (FDA) that has mandated to expand the role of RWE in support of drug approval thereby including data outside clinical trials.3 Accordingly, coupling of data generating equipment with advances in computational analyses and new legislative changes have shed more light on RWD in healthcare and thus RWE as a pathway for drug approval. RWD have been already available for clinicians. Due to the fast introduction of highly advanced electronic systems in surgery such data are continuously generated. In relation to gynecological surgery, these data include the frequency of use, type of equipment used, and the time spent using the equipment etc. Currently, robotic systems and similar equipments generate data report to the companies reporting the number of procedures performed by each robot, the number of robotic arms used, the failure rate etc. This report is also available for clinicians, but in many cases the understanding of this information and how to use it is problematic. Also, a barrier is legislation in order to protect data related to individuals. Thus, for RWD to be useful, integration of electronic equipment data with PROdata, electronic patents records and ICD10 codes is needed. It could enable the clinicians to generate new evidence fast. This may also be relevant in the evaluation of standard operating procedures an