A Drone Logistic Model for Transporting the Complete Analytic Volume of a Large-Scale University Laboratory

Abstract

We present a model for drone transport of the complete annual analytic volume of 6.5 million analyses— (routine and emergency) between two inner-city university laboratories at Oslo University Hospital located 1.8 km apart and with a time restriction for the analyses of no more than 60 min. The total laboratory activity was analyzed per min for the complete year of 2018. The time from the clinical ordering of tests to the loading of the drone, drone transport time, and analysis time after the sample arrived at the analyzing laboratory were assessed using the lead time of emergency analyses of C-reactive protein, troponin, and the international normalized ratio. The activity had characteristic diurnal patterns, with the most intensive traffic between 8 and 12 a.m. on weekdays and there being considerably less traffic for the rest of the day, at night and on weekends. Drone schedules with departures 15–60 min apart were simulated. A maximum of 15 min between flights was required to meet the emergency demand for the analyses being completed within 60 min. The required drone weight capacity was below 3.5 kg at all times. In multiple simulations, the drone times were appropriate, whereas variations in the clinic- and laboratory-related time intervals caused violations of the allowed time 50% of the time. Drone transport with regular schedules may potentially improve the transport time compared with traditional ground transport and allow the merging of large laboratories, even when the demand for emergency analyses restricts the maximum transport time. Comprehensive economic evaluations and robust drone technology are needed before such solutions can be ready for implementation.

A Socio-Analytical Approach to the Integration of Drones into Health Care Systems

“…Emerging technologies are usually influenced by the existing institutional solutions (in our context, hospitals), which operate in a stable configuration until a new technology emerges and creates an interplay of multiple technologies [37]. Compliance with legacy systems, as well as ethical standards and standards of clinical and laboratory processes, are crucial in such processes [44].…”

“…Johannessen et al [44] concluded that it would require extensive research to understand how best to engage clinical and laboratory leaders and managers in actively facilitating long-term improvement processes to optimize drone transport solutions. They highlighted the relevance of well-known specialized methods that focus on looking for waste in production processes to improve workflows and create more value with less effort: the LEAN method (originating from the Toyota car manufacturing system) and Six Sigma (originating from Motorola).…”

“…Therefore, the TOE framework may offer factors affecting the adoption of technologies to improve health care logistics processes when applied in a health care logistics setting. The TOE dimension related to drone implementation was discussed in the context of a logistic drone model by Johannessen et al [44].…”

A Conceptual Approach to Time Savings and Cost Competitiveness Assessments for Drone Transport of Biologic Samples with Unmanned Aerial Systems (Drones)

“…Both clinical procedure times and existing laboratory processes may be a function of transport mode. It should be expected that such internal processing times may influence the overall time perspective from acquiring biologic samples from the patient to final analysis [20].…”

“…Regarding biologic transport, a previous study at Oslo University Hospital (OUS) found that drone transport could theoretically enable the merger of two of the largest laboratories, achieving potential annual savings of NOK 100 million (EUR 9.5 million) [20]. Although such radical solutions may be uncommon, promising reductions in cost duplications may be achieved if laboratories can be networked.…”