Identification of Meaningful Data for Providing Real-Time Intraoperative Feedback in Laparoscopic Surgery Using Delphi Analysis.
Marilou JansenEsther Z BarsomAnne-Sophie H M van DalenPatricia J ZondervanMarlies P SchijvenPublished in: Surgical innovation (2020)
Background. Surgeons are at risk of being overwhelmed with information while performing surgery. Initiatives focusing on the use of medical data in the operating room are on the rise. Currently, these initiatives require postprocessing of data. Although highly informative, data cannot be used to influence preventable error in real time. Ideally, feedback is provided preemptive. Aims. First, to identify which information is considered to be relevant for real-time feedback during laparoscopic surgery according to surgeons. Second, to identify the optimal routing for providing such feedback, and third, to decide on optimal timing for feedback to alarm users during laparoscopic surgery. Methods. A Delphi study of 3 iterations was conducted within the Amsterdam UMC, location AMC. A total of 25 surgeons and surgical residents performing laparoscopy were surveyed using 5-point Likert scales. Consensus was obtained when 80% of answers fitted the same answering category. Results. Delphi round 1 resulted in 198 unique ideas within 5 scenarios. After round 3, consensus was obtained on 102 items. Feedback most relevant during laparoscopic surgery refers to equipment like the gas insufflator, diathermy, and suction device. Feedback should be delivered via an additional monitor. Surgeons want to be instantly alarmed about aberrations in patients' vital parameters or combinations of vital parameters, preferably via a designated section on a monitor in their field of vision. Conclusions. Surgeons performing laparoscopy are uniform in their opinion that they need to be alarmed immediately when patients' vital parameters are becoming aberrant. Surgeons state that information regarding supporting equipment is best displayed on an additional monitor.
Keyphrases
- laparoscopic surgery
- quality improvement
- end stage renal disease
- electronic health record
- thoracic surgery
- ejection fraction
- newly diagnosed
- chronic kidney disease
- big data
- prognostic factors
- gene expression
- climate change
- emergency department
- machine learning
- acute coronary syndrome
- health information
- patients undergoing
- coronary artery bypass
- room temperature
- coronary artery disease
- artificial intelligence
- drug induced
- genome wide