Reducing the throughput time of the diagnostic track involving CT scanning with computer simulation

Introduction\ud To examine the use of computer simulation to reduce the time between the CT request and the consult in which the CT report is discussed (diagnostic track) while restricting idle time and overtime.\ud \ud Methods\ud After a pre implementation analysis in our case study hospital, by computer simulation three scenarios were evaluated on access time, overtime and idle time of the CT; after implementation these same aspects were evaluated again. Effects on throughput time were measured for outpatient short-term and urgent requests only.\ud \ud Conclusion\ud The pre implementation analysis showed an average CT access time of 9.8 operating days and an average diagnostic track of 14.5 operating days. Based on the outcomes of the simulation, management changed the capacity for the different patient groups to facilitate a diagnostic track of 10 operating days, with a CT access time of 7 days. After the implementation of changes, the average diagnostic track duration was 12.6 days with an average CT access time of 7.3 days. The fraction of patients with a total throughput time within 10 days increased from 29% to 44% while the utilization remained equal with 82%, the idle time increased by 11% and the overtime decreased by 82%.\ud \ud The fraction of patients that completed the diagnostic track within 10 days improved with 52%. Computer simulation proved useful for studying the effects of proposed scenarios in radiology management. Besides the tangible effects, the simulation increased the awareness that optimizing capacity allocation can reduce access times.\ud \u

Reducing the throughput time of the diagnostic track involving CT scanning with computer simulation

Introduction To examine the use of computer simulation to reduce the time between the CT request and the consult in which the CT report is discussed (diagnostic track) while restricting idle time and overtime. Methods After a pre implementation analysis in our case study hospital, by computer simulation three scenarios were evaluated on access time, overtime and idle time of the CT; after implementation these same aspects were evaluated again. Effects on throughput time were measured for outpatient short-term and urgent requests only. Conclusion The pre implementation analysis showed an average CT access time of 9.8 operating days and an average diagnostic track of 14.5 operating days. Based on the outcomes of the simulation, management changed the capacity for the different patient groups to facilitate a diagnostic track of 10 operating days, with a CT access time of 7 days. After the implementation of changes, the average diagnostic track duration was 12.6 days with an average CT access time of 7.3 days. The fraction of patients with a total throughput time within 10 days increased from 29% to 44% while the utilization remained equal with 82%, the idle time increased by 11% and the overtime decreased by 82%. The fraction of patients that completed the diagnostic track within 10 days improved with 52%. Computer simulation proved useful for studying the effects of proposed scenarios in radiology management. Besides the tangible effects, the simulation increased the awareness that optimizing capacity allocation can reduce access times

Three-Dimensional Tumor Margin Demarcation Using the Hybrid Tracer Indocyanine Green-Tc-99m-Nanocolloid: A Proof-of-Concept Study in Tongue Cancer Patients Scheduled for Sentinel Node Biopsy

For radical resection of squamous cell carcinoma of the oral cavity, a tumor-free margin of at least 5 mm is required. Unfortunately, establishing in-depth margins is a surgical conundrum. Knowing that the hybrid sentinel node (SN) tracer indocyanine green (ICG)-Tc-99m-nanocolloid generates temporary tattoolike markings at the site of administration, we studied the ability to apply this tracer for tumor margin demarcation combined with SN biopsy. Methods: Nineteen patients with clinical T1-T2 oral tongue tumors received the traditional superficial 3 or 4 deposits of ICG-Tc-99m-nanocolloid (0.1 mL each), and in 12 patients additional deposits were placed deeply using ultrasound guidance (total of 6; 0.07 mL each). SN mapping was performed using lymphoscintigraphy and SPECT/CT. Before and directly after tumor excision, fluorescence imaging was performed to monitor the tracer deposits in the patient (fluorescent deposits were not used to guide the surgical excision). At pathologic examination, primary tumor samples were studied in detail. Results: The number of tracer depositions did not induce a significant difference in the number of SNs visualized (P = 0.836). Reproducible and deep tracer deposition proved to be challenging. The fluorescent nature of ICG-Tc-99m-nanocolloid supported in vivo and ex vivo identification of the tracer deposits surrounding the tumor. Pathologic examination indicated that in 66.7% (8/12), all fluorescence was observed within the resection margins. Conclusion: This study indicates that tumor margin demarcation combined with SN identification has potential but that some practical challenges need to be overcome if this technique is to mature as a surgical guidance concept. Future studies need to define whether the technology can improve the radical nature of the resections.Imaging- and therapeutic targets in neoplastic and musculoskeletal inflammatory diseas