Hospital dual-channel adoption decisions with telemedicine referral and misdiagnosis.

With the rapid development of telemedicine and the impact of the COVID-19 pandemic, more and more patients are now resorting to using telemedicine channels for healthcare services. However, for hospitals, there exists a lack of managerial guidance in place to help them adopt telemedicine in a practical and standardized way. This study considers a hospital operating with both telemedicine (virtual) and face-to-face (physical) medical channels, and which allocates its capacity by also taking into account the possibility of both referrals and misdiagnosis. Methodologically, we construct a game model based on a queuing framework. We first analyze equilibrium strategies for patient arrivals. Then we propose the necessary conditions for a hospital to develop a telemedicine channel and to operate both channels simultaneously. Finally, we find the optimal decisions for the service level of telemedicine, which can also be regarded as the optimal proportion of diseases treated by telemedicine, and the optimal hospital capacity allocation ratio between the two channels. We also find that hospitals in a full coverage market (e.g., for certain small-scale hospitals and community hospitals or cancer hospitals) are more difficult to adopt telemedicine than hospitals in a partial coverage market (e.g., for comprehensive large-scale hospitals with many potential patients). Small-scale hospitals are more suited to operating telemedicine as a gatekeeper to help triage patients, while large hospitals are more prone to regard telemedicine as a medical channel for providing professional medical services to patients. We also analyze the effects of the telemedicine cure rate and the cost ratio of telemedicine to the physical hospital on the overall healthcare system performance, including the physical hospital arrival rate, patients' waiting time, total profit, and social welfare. Then we compare the performance, ex ante versus ex post, the implementation of telemedicine. It is shown that when the market is partially covered, the total social welfare is always higher than it was before the implementation. However, as far as the profit goes, if the telemedicine cure rate is low and the cost ratio is high, the total hospital profit may be lower than it was prior to using telemedicine. However, the profit and social welfare of hospitals in the full coverage market are always lower than it was before the implementation. In addition, the waiting time in the hospital is always higher than that before the implementation, which means that the implementation of telemedicine will make patients who must receive treatment in the physical hospital face even worse congestion than before. More insights and results are gleaned from a series of numerical studies.

Effect of a glucagon receptor antibody (REMD-477) in type 1 diabetes: A randomized controlled trial.

The aim of the current study (Clinical trial reg. no. NCT02715193, clinicaltrials.gov) was to study the efficacy and safety of REMD-477, a glucagon receptor antagonist, in type 1 diabetes. This was a randomized controlled trial in which 21 patients with type 1 diabetes were enrolled. Glycaemic control and insulin use were evaluated in outpatient and inpatient settings, before and after a single 70-mg dose of REMD-477 (half-life 7-10 days) or placebo. Inpatient insulin use was 26% (95% CI, 47%, 4%) lower 1 day after dosing with REMD-477 than with placebo (P = .02). Continuous glucose monitoring during post-treatment days 6 to 12 showed that average daily glucose was 27 mg/dL lower (P < .001), percent time-in-target-range (70-180 mg/dL) was ~25% greater (~3.5 h/d) (P = .001), and percent time-in-hyperglycaemic-range (> 180 mg/dL) was ~40% lower (~4 h/d) (P = .001) in the REMD-477 group than in the placebo group, without a difference in percent time-in-hypoglycaemic-range (<70 mg/dL). No serious adverse events were reported. Glucagon receptor antagonism decreases insulin requirements and improves glycaemic control in patients with type 1 diabetes.

How does the hospital make a safe and stable elective surgery plan during COVID-19 pandemic?

During the COVID-19 period, randomly arrived patients flooded into the hospital, which caused staffing beds to be occupied. Then, elective surgeries could not be carried out timely. It not only affects the health of patients but also affects hospital income. The key to the above problem is how to deal with uncertainty, which is one of the most difficult problems faced in the field of optimization. Specifically, surgery duration, length of stay, the arrival time of emergency patients, and whether they are infected with the SARS-CoV-2 virus are uncertain. Therefore, we propose a bed configuration to ensure that elective patients are not affected by non-elective patients such as COVID-19 patients. More importantly, we propose a planning model based on robust optimization and fuzzy set theory, which for the first time consider different categories of uncertainty in the same healthcare system. Given that the problem is more complex than the classical surgical scheduling problem, which is NP-hard in most cases, we propose a hybrid algorithm (GA-VNS-H) based on genetic algorithm, variable neighborhood search, and heuristics for problem traits. Specifically, the heuristic for operating room allocation is used to improve the efficiency, the genetic algorithm and variable neighborhood can improve the global and local search capabilities, respectively, and the adaptive mechanism can reduce the algorithm solution time. Experiments show that the algorithm has better calculation efficiency and solution accuracy. In addition, the elective surgery planning model under the new bed configuration model can effectively cope with the uncertain environment of COVID-19.

Surgical scheduling by Fuzzy model considering inpatient beds shortage under uncertain surgery durations.

Operating Room (OR) management has been among the mainstream of hospital management research, as ORs are commonly considered as one of the most critical and expensive resources. The complicated connection and interplay between ORs and their upstream and downstream units has recently attracted research attention to focus more on allocating medical resources efficiently for the sake of a balanced coordination. As a critical step, surgical scheduling in the presence of uncertain surgery durations is pivotal but rather challenging since a patient cannot be hospitalized if a recovery bed will not be available to accommodate the admission. To tackle the challenge, we propose an overflow strategy that allows patients to be assigned to an undesignated department if the designated one is full. It has been proved that overflow strategy can successfully alleviate the imbalance of capacity utilization. However, some studies indicate that implementation of the overflow strategy exacerbates the readmission rate as well as the length of stay (LOS). To rigorously examine the overflow strategy and explore its optimal solution, we propose a Fuzzy model for surgical scheduling by explicitly considering downstream shortage, as well as the uncertainty of surgery duration and patient LOS. To solve the Fuzzy model, a hybrid algorithm (so-called GA-P) is developed, stemming from Genetic Algorithm (GA). Extensive numerical results demonstrate the plausible efficiency of the GA-P algorithm, especially for large-scale scheduling problems (e.g., comprehensive hospitals). Additionally, it is shown that the overflow cost plays a critical role in determining the efficiency of the overflow strategy; viz., benefits from the overflow strategy can be reduced as the overflow cost increases, and eventually almost vanishes when the cost becomes sufficiently large. Finally, the Fuzzy model is tested to be effective in terms of simplicity and reliability, yet without cannibalizing the patient admission rate.

Decision Tree-Based Body Constitution Diagnosis System for Traditional Chinese Medicine.

This study aimed to establish a method for fast and accurate determination of body constitution types from the body constitution questionnaire (BCQ) by employing a decision tree model. The model was trained for 4 classes, namely, Yin-Xu, Yang-Xu, Phlegm and Blood Stasis, and Normal, and it achieved 67% accuracy for the testing dataset. The model also reduced the required number of BCQ questions from 44 to 3-6, depending on the responses. Lastly, we developed the Traditional Chinese Medicine (TCM) body constitution online diagnosis system using our model to collect data digitally and use it more practically and efficiently. This system can assist doctors to improve the diagnosis and treatment in TCM practice.

A new technique to characterize CT scanner bow-tie filter attenuation and applications in human cadaver dosimetry simulations.

PURPOSE: To present a noninvasive technique for directly measuring the CT bow-tie filter attenuation with a linear array x-ray detector. METHODS: A scintillator based x-ray detector of 384 pixels, 307 mm active length, and fast data acquisition (model X-Scan 0.8c4-307, Detection Technology, FI-91100 Ii, Finland) was used to simultaneously detect radiation levels across a scan field-of-view. The sampling time was as short as 0.24 ms. To measure the body bow-tie attenuation on a GE Lightspeed Pro 16 CT scanner, the x-ray tube was parked at the 12 o'clock position, and the detector was centered in the scan field at the isocenter height. Two radiation exposures were made with and without the bow-tie in the beam path. Each readout signal was corrected for the detector background offset and signal-level related nonlinear gain, and the ratio of the two exposures gave the bow-tie attenuation. The results were used in the geant4 based simulations of the point doses measured using six thimble chambers placed in a human cadaver with abdomen/pelvis CT scans at 100 or 120 kV, helical pitch at 1.375, constant or variable tube current, and distinct x-ray tube starting angles. RESULTS: Absolute attenuation was measured with the body bow-tie scanned at 80-140 kV. For 24 doses measured in six organs of the cadaver, the median or maximum difference between the simulation results and the measurements on the CT scanner was 8.9% or 25.9%, respectively. CONCLUSIONS: The described method allows fast and accurate bow-tie filter characterization.

In vitro dose measurements in a human cadaver with abdomen/pelvis CT scans.

PURPOSE: To present a study of radiation dose measurements with a human cadaver scanned on a clinical CT scanner. METHODS: Multiple point dose measurements were obtained with high-accuracy Thimble ionization chambers placed inside the stomach, liver, paravertebral gutter, ascending colon, left kidney, and urinary bladder of a human cadaver (183 cm in height and 67.5 kg in weight) whose abdomen/pelvis region was scanned repeatedly with a multidetector row CT. The flat energy response and precision of the dosimeters were verified, and the slight differences in each dosimeter's response were evaluated and corrected to attain high accuracy. In addition, skin doses were measured for radiosensitive organs outside the scanned region with OSL dosimeters: the right eye, thyroid, both nipples, and the right testicle. Three scan protocols were used, which shared most scan parameters but had different kVp and mA settings: 120-kVp automA, 120-kVp 300 mA, and 100-kVp 300 mA. For each protocol three repeated scans were performed. RESULTS: The tube starting angle (TSA) was found to randomly vary around two major conditions, which caused large fluctuations in the repeated point dose measurements: for the 120-kVp 300 mA protocol this angle changed from approximately 110° to 290°, and caused 8%-25% difference in the point dose measured at the stomach, liver, colon, and urinary bladder. When the fluctuations of the TSA were small (within 5°), the maximum coefficient of variance was approximately 3.3%. The soft tissue absorbed doses averaged from four locations near the center of the scanned region were 27.2±3.3 and 16.5±2.7 mGy for the 120 and 100-kVp fixed-mA scans, respectively. These values were consistent with the corresponding size specific dose estimates within 4%. The comparison of the per-100-mAs tissue doses from the three protocols revealed that: (1) dose levels at nonsuperficial locations in the TCM scans could not be accurately deduced by simply scaling the fix-mA doses with local mA values; (2) the general power law relationship between dose and kVp varied from location to location, with the power index ranged between 2.7 and 3.5. The averaged dose measurements at both nipples, which were about 0.6 cm outside the prescribed scan region, ranged from 23 to 27 mGy at the left nipple, and varied from 3 to 20 mGy at the right nipple over the three scan protocols. Large fluctuations over repeated scans were also observed, as a combined result of helical scans of large pitch (1.375) and small active areas of the skin dosimeters. In addition, the averaged skin dose fell off drastically with the distance to the nearest boundary of the scanned region. CONCLUSIONS: This study revealed the complexity of CT dose fluctuation and variation with a human cadaver.