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[Background:] Hospital bed management is an important resource allocation task in hospital management, but currently, it is a challenging task. However, acquiring an optimal solution is also difficult because intraorganizational information asymmetry exists. Signaling, as defined in the fields of economics, can be used to mitigate this problem. [Objective:] We aimed to develop an assignment process that is based on a token economy as signaling intermediary. [Methods:] We implemented a game-like simulation, representing token economy–based bed assignments, in which 3 players act as ward managers of 3 inpatient wards (1 each). As a preliminary evaluation, we recruited 9 nurse managers to play and then participate in a survey about qualitative perceptions for current and proposed methods (7-point Likert scale). We also asked them about preferred rewards for collected tokens. In addition, we quantitatively recorded participant pricing behavior. [Results:] Participants scored the token economy–method positively in staff satisfaction (3.89 points vs 2.67 points) and patient safety (4.38 points vs 3.50 points) compared to the current method, but they scored the proposed method negatively for managerial rivalry, staff employee development, and benefit for patients. The majority of participants (7 out of 9) listed human resources as the preferred reward for tokens. There were slight associations between workload information and pricing. [Conclusions:] Survey results indicate that the proposed method can improve staff satisfaction and patient safety by increasing the decision-making autonomy of staff but may also increase managerial rivalry, as expected from existing criticism for decentralized decision-making. Participant behavior indicated that token-based pricing can act as a signaling intermediary. Given responses related to rewards, a token system that is designed to incorporate human resource allocation is a promising method. Based on aforementioned discussion, we concl

Designing a deep neural network model that integrates clinical images with other electronic medical records entails various preprocessing operations. Preprocessing of clinical images often requires trimming of parts of the lesions shown in the images, whereas preprocessing of other electronic medical records requires vectorization of these records; for example, patient age is often converted into a categorical vector of 10-year intervals. Although these preprocessing operations are critical to the performance of the classification model, there is no guarantee that the preprocessing step chosen is appropriate for model training. The ability to integrate these preprocessing operations into a deep neural network model and to train the model, including the preprocessing operations, can help design a multi-modal medical classification model. This study proposes integration layers of preprocessing, both for clinical images and electronic medical records, in deep neural network models. Preprocessing of clinical images is realized by a vision transformer layer that selectively adopts the parts of the images requiring attention. The preprocessing of other medical electrical records is performed by adopting full-connection layers and normalizing these layers. These proposed preprocessing-integrated layers were verified using a posttreatment visual acuity prediction task in ophthalmology as a case study. This prediction task requires clinical images as well as patient profile data corresponding to each patient's posttreatment logMAR visual acuity. The performance of a heuristically designed prediction model was compared with the performance of the prediction model that includes the proposed preprocessing integration layers. The mean square errors between predicted and correct results were 0.051 for the heuristic model and 0.054 for the proposed model. Experimental results showed that the proposed model utilizing preprocessing integration layers achieved nearly the same perform

[Aims] The aim of this study was to elucidate the epidemiological background of central serous chorioretinopathy (CSC), including its incidence and treatment pattern. [Methods] This was a population-based longitudinal cohort study using a nationwide health insurance claims database of the Japan Ministry of Health, Labour and Welfare (MHLW). As Japan employs universal health coverage, the database covers more than 95% of claims issued in Japan. We accessed all data stored in the database with permission from the MHLW. We traced all individuals aged 30 years or older and identified individuals with new onset of CSC between January 2011 and December 2018. CSC cases were categorised by age and sex for each year, and incidence rate was calculated. We also identified major treatments for CSC to elucidate the initial treatment pattern. [Results] During the 8-year period, 247 930 incidences of CSC were identified, among which 75.9% were men. The crude incidence rate (per 100 000 person-years) in the general population aged 30 years or older was 34.0 (95% CI 33.9 to 34.2), in men was 54.2 (95% CI 53.9 to 54.4) and in women was 15.7 (95% CI 15.5 to 15.8). The mean age of onset was lower in men than in women (50.5±12.5 years vs 54.7±13.5 years). Most of the patients with newly diagnosed CSC (86.8%) did not receive major treatment. [Conclusions] The current study provides the nationwide population-based evidence to clarify the detailed epidemiology of CSC. These results could help to understand the pathogenesis and mechanisms of CSC in the future.

[Purpose] To elucidate the incidence and treatment pattern of active exudative age-related macular degeneration (AMD). [Design] A population-based cohort study conducted using the National Database of Health Insurance Claims and Specific Health Checkups of Japan (NDB), a national claims database managed by the Japanese Ministry of Health, Labour, and Welfare (MHLW). [Participants] The entire Japanese population aged 40 years or older (76 million people). [Methods] With the permission of the MHLW, we accessed the complete NDB dataset and identified patients with newly diagnosed active exudative AMD between 2011 and 2018. The incidence of active exudative AMD was categorized by age and sex per year between 2011 and 2018; moreover, details regarding first-line therapy and number of anti-vascular endothelial growth factor (VEGF) injections per elapsed year since initial treatment were obtained and changes in treatment pattern were investigated. [Main Outcome Measures] Incidence rate of active exudative AMD. [Results] During the specified 8-year period, 246 064 incident cases of active exudative AMD were identified; 61.4% of these patients were men. The overall incidence rate was 40.66 per 100 000 person-years (95% confidence interval [CI], 40.49–40.82) in the general population aged 40 years or older, 53.22 (95% CI, 52.95–53.49) in men, and 29.78 (95% CI, 29.60–29.98) in women. Mean age of onset was lower in men than in women (72.51 ± 10.50 years vs. 73.90 ± 10.46 years). Among patients with newly diagnosed active exudative AMD, 92.9% received anti-VEGF injections for initial treatments, whereas 1.8% underwent combination therapy with photodynamic therapy. The number of anti-VEGF injections in the first year (0–12 months), second year (13–24 months), and third year (25–36 months) after the initial injection was 3.66 ± 2.30, 1.39 ± 2.20, and 1.23 ± 2.19, respectively. Patients who received fewer injections in the first year received fewer injections in subsequent years a

[Introduction] Evaluating histopathology via machine learning has gained research and clinical interest, and the performance of supervised learning tasks has been described in various areas of medicine. Unsupervised learning of histological images has the advantage of reproducibility for labeling; however, the relationship between unsupervised evaluation and clinical information remains unclear in nephrology. [Methods] We propose an unsupervised approach combining convolutional neural networks (CNNs) and a visualization algorithm to cluster the histological images and calculate the score for patients. We applied the approach to the entire images or patched images of the glomerulus of kidney biopsy samples stained with hematoxylin and eosin obtained from 68 patients with immunoglobulin A nephropathy. We assessed the relationship between the obtained scores and clinical variables of urinary occult blood, urinary protein, serum creatinine (SCr), systolic blood pressure, and age. [Results] The glomeruli of the patients were classified into 12 distinct classes and 10 patches. The output of the fine-tuned CNN, which we defined as the histological scores, had significant relationships with assessed clinical variables. In addition, the clustering and visualization results suggested that the defined clusters captured important findings when evaluating renal histopathology. For the score of the patch-based cluster containing crescentic glomeruli, SCr (coefficient = 0.09, P = 0.019) had a significant relationship. [Conclusion] The proposed approach could successfully extract features that were related to the clinical variables from the kidney biopsy images along with the visualization for interpretability. The approach could aid in the quantified evaluation of renal histopathology.

Background: Acute kidney injury (AKI) occurs frequently in in-hospital patients, especially in the intensive care unit (ICU), due to various etiologies including septic shock. It is clinically important to identify high-risk patients at an early stage and perform the appropriate intervention. Methods: We proposed a system to predict AKI using one-dimensional convolutional neural networks (1D-CNN) with the real-time calculation of the probability of developing AKI, along with the visualization of the rationale behind prediction using score-weighted class activation mapping and guided backpropagation. The system was applied to predicting developing AKI based on the KDIGO guideline in time windows of 24 to 48 h using data of 0 to 24 h after admission to ICU. Results: The comparison result of multiple algorithms modeling time series data indicated that the proposed 1D-CNN model achieved higher performance compared to the other models, with the mean area under the receiver operating characteristic curve of 0.742 ± 0.010 for predicting stage 1, and 0.844 ± 0.029 for stage 2 AKI using the input of the vital signs, the demographic information, and serum creatinine values. The visualization results suggested the reasonable interpretation that time points with higher respiratory rate, lower blood pressure, as well as lower SpO2, had higher attention in terms of predicting AKI, and thus important for prediction. Conclusions: We presumed the proposed system's potential usefulness as it could be applied and transferred to almost any ICU setting that stored the time series data corresponding to vital signs.

Hypertension is a heterogeneous disease for which role sharing in treatment between specialized facilities and small clinics is needed for efficient healthcare provision. However, the Japanese healthcare system has a "free access" attribute; therefore, nobody can control treatment resource allocation. We aimed to describe the current situation of role sharing by comparing antihypertensive therapies among different types of medical facilities. We analyzed 1% sampled Japanese medical insurance claims data related to outpatient care as of October 2014. We divided the target patients into four groups according to the size of the facilities that issued the insurance claim for them. Among these groups, we compared the number of antihypertensive drugs and proportion of difficult-to-treat hypertensive cases and performed a stratified analysis. The proportion of patients with hypertension and diabetes mellitus receiving renin-angiotensin-aldosterone system inhibitors (RAASis) as the first-choice drug was also compared. We identified 3465, 1797, 2323, and 34, 734 claims issued from large, medium-sized, small hospitals, and clinics, respectively. The mean number of hypertensive drugs was 1.96, 1.87, 1.81, and 1.69, respectively, and the proportion of difficult-to-treat hypertensive cases was 18.9, 17.0, 14.3, and 12.0%, respectively, with both showing significant differences. Stratified analysis showed similar results. The proportion of patients with hypertension and diabetes mellitus receiving RAASis as the first-choice drug was higher in large hospitals than in clinics. In conclusion, facility size is positively associated with the number of antihypertensive drugs and proportions of difficult-to-treat hypertensive cases. This finding describes the current role sharing situation of hypertension therapy in the Japanese healthcare system with a "free-access" attribute.

Background: Pervasive games aim to create more fun and engaging experiences by mixing elements from the real world into the game world. Because they intermingle with players’ lives and naturally promote more casual gameplay, they could be a powerful strategy to stimulate physical activity among older adults. However, to use these games more effectively, it is necessary to understand how design elements of the game affect player behavior. Objective: The aim of this study was to evaluate how the presence of a specific design element, namely social interaction, would affect levels of physical activity. Methods: Participants were recruited offline and randomly assigned to control and intervention groups in a single-blind design. Over 4 weeks, two variations of the same pervasive game were compared: with social interaction (intervention group) and with no social interaction (control group). In both versions, players had to walk to physical locations and collect virtual cards, but the social interaction version allowed people to collaborate to obtain more cards. Changes in the weekly step counts were used to evaluate the effect on each group, and the number of places visited was used as an indicator of play activity. Results: A total of 20 participants were recruited (no social interaction group, n=10; social interaction group, n=10); 18 participants remained active until the end of the study (no social interaction group, n=9; social interaction group, n=9). Step counts during the first week were used as the baseline level of physical activity (no social interaction group: mean 46, 697.2, SE 7905.4; social interaction group: mean 45, 967.3, SE 8260.7). For the subsequent weeks, changes to individual baseline values (absolute/proportional) for the no social interaction group were as follows: 1583.3 (SE 3108.3)/4.6% (SE 7.2%) (week 2), 591.5 (SE 2414.5)/2.4% (SE 4.7%) (week 3), and −1041.8 (SE 1992.7)/0.6% (SE 4.4%) (week 4). For the social interaction group, changes to ind

We present the design process and evaluation of a pervasive, location-based mobile game created to act as an experiment system and allow evaluation of how different design elements can influence player behaviour, using social interaction as a study case. A feasibility study with a group of community dwelling elderly volunteers from the city of Kyoto, Japan, was performed to evaluate the system. Results showed that the choice of theme and overall design of game was adequate, and that elderly people could understand the game rules and their goals while playing. Points of improvement included reducing the complexity of game controls and changing social interaction mechanics to account for situations when there are only a few players active or players are too far apart.