Proteinuria frequency and subsequent renal dysfunction in bevacizumab-treated patients: a single center, retrospective, observational study.

BACKGROUND: Proteinuria is a common adverse event observed during treatment with antivascular endothelial growth factor (VEGF) antibodies. Proteinuria is a risk factor for renal dysfunction and cardiovascular complications in patients with chronic kidney disease. However, the association between anti-VEGF antibody-induced proteinuria and renal dysfunction or cardiovascular complications remains unclear. METHODS: This retrospective, observational study included patients with cancer that were treated with bevacizumab (BV) at Kyoto University Hospital (Kyoto, Japan) between January 2006 and March 2018. Adverse event rates were compared between patients who developed qualitative ≥ 2 + proteinuria and those who developed < 1 + proteinuria. Adverse events were defined as renal dysfunction (i.e., ≥ 57% decrease in the eGFR, compared to the rate at the initial treatment) and hospitalization due to BV-associated cardiovascular complications and other adverse events. RESULTS: In total, 734 patients were included in this analysis. Renal dysfunction was more common in patients with ≥ 2 + proteinuria than in those with < 1 + proteinuria (13/199, 6.5% vs. 12/535, 2.3%). Seven of these 13 patients with ≥ 2 + proteinuria had transient reversible renal dysfunction. Only four (2.0%) patients had BV-associated renal dysfunction. Of the 734 patients, six patients, 16 patients, and 13 patients were hospitalized because of the adverse events of cardiovascular complications, thromboembolisms, and cerebrovascular complications, respectively. No relationship was observed between these adverse events and proteinuria. CONCLUSION: BV treatment-induced proteinuria was not associated with renal dysfunction or other adverse events. Continuing BV with caution is a possible treatment option, even after proteinuria develops, in patients with cancer and a limited prognosis.

Annual trends of ophthalmic surgeries in Japan's super-aged society, 2014-2020: a national claims database study.

This study aimed to analyze the trends and factors influencing the number of ophthalmic surgeries in Japan using the open data from the National Database of Health Insurance Claims and Specific Health Checkups of Japan published by the Ministry of Health, Labour and Welfare. We calculated the number of cataract, glaucoma, and vitreoretinal surgeries, categorized by sex, age, and surgical type, for the fiscal years (FY) 2014 to 2020. The number of cataract surgeries remained stable at approximately 1.45 million cases from FY 2014 to 2018, increased to nearly 1.6 million cases in FY 2019, and decreased to 1.45 million cases in FY 2020. Among glaucoma surgeries, surgical treatments were increased 1.8 times over 7 years, from 33,000 to 60,000 cases. Laser treatment remained steady at around 55,000 cases from FY 2014 to 2017 and then increased to approximately 60,000 cases. The number of vitreoretinal surgeries was increased 1.2 times from FY 2014 to 2019, from 120,000 to 140,000, and decreased to 130,000 by FY 2020. Trends in ophthalmic surgeries over the past 7 years may be influenced by population aging, minimally invasive surgery, and the coronavirus disease pandemic. These findings have implications on surgical decision-making and resource allocation.

Reduction in X-ray Retake Rate Using the Token Economy Method.

Excessive retakes of X-ray images increase labor and material costs, as well as result in excess radiation exposure for patients and a long waiting time. In this study, we evaluated the effectiveness of the token economy method as a management method for reducing X-ray retake rate among radiology technicians. The results showed a 2.5% reduction in retake rate, indicating the effectiveness of our method. In addition, we suggest that the token-economy-based approach can be applied to other hospital management problems.

Incidence and Clinical Practice of Exudative Age-related Macular Degeneration: A Nationwide Population-Based Cohort Study.

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 and vice versa. Conclusions: This is a relatively large population-based study on the detailed epidemiology and actual treatment patterns of active exudative AMD in clinical practice. Our results can be a fundamental information source to ensure healthy eyes and promote well-being for all at all ages.

Token Economy-Based Hospital Bed Allocation to Mitigate Information Asymmetry: Proof-of-Concept Study Through Simulation Implementation.

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 concluded that a token economy-based bed allocation system has the potential to be an optimal method by mitigating information asymmetry.

Association between topical ß-blocker use and asthma attacks in glaucoma patients with asthma: a cohort study using a claims database.

PURPOSE: To investigate the association between the use of topical ß-blockers and subsequent asthma attacks in glaucoma patients with asthma. METHODS: This was a retrospective longitudinal cohort study using an administrative claims database. All patients aged 20 years or older who were registered in the health insurance claims database updated and managed by JMDC Inc. (Tokyo, Japan). Patients who were newly prescribed eye drops for glaucoma treatment were identified between 2011 and 2017. The patients with glaucoma were divided into two groups: ß-blocker users and non-ß-blocker users, based on the presence of a ß-blocker in the prescribed eye drops. We investigated whether the incidence of asthma attacks in patients with previously treated asthma differed between the two groups. RESULTS: We categorized 17,666 patients in the ß-blocker-user group and 12,609 patients in the non-ß-blocker-user group. A total of 580 patients in the ß-blocker group (3.28%) and 847 in the non-ß-blocker group (6.72%) underwent asthma treatment before the prescription of anti-glaucoma eye drops (P < 0.001). Furthermore, 94 patients in the ß-blocker-user group (0.53%) and 278 in the non-ß-blocker user group (2.20%) were undergoing current treatment for asthma (P < 0.001). The adjusted hazard ratios of asthma attacks were 0.73 (95% confidence interval, 0.46-1.16, P = 0.18) in patients with a history of asthma treatment and 1.22 (95% confidence interval, 0.56-2.70, P = 0.62) in patients with current asthma treatment, compared to the non-ß-blocker-user group. CONCLUSION: Our results clarified that several patients with asthma were prescribed topical ß-blockers for glaucoma treatment. However, asthma attacks may not be significantly attributed to topical ß-blockers, even in glaucoma patients under current asthma treatment. The administration of topical ß-blockers to asthma patients could be a treatment option in the absence of other treatment options, if adequate informed consent is obtained. Further studies are needed to draw a firm conclusion on this clinical question.

Incidence of central serous chorioretinopathy (2011-2018): a nationwide population-based cohort study of Japan.

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.

Evaluation of Kidney Histological Images Using Unsupervised Deep Learning.

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.

[Promotion of Diversity in the Academic Field of Urology : Analysis of 10,000 Articles Published in Acta Urologica Japonica during a 65-Year Period].

Gender equality is one of the most important issues in the promotion of diversity. The participation status of female urologists in academic activities has not been clarified. In the present study, we analyzed a total of 10,288 articles published by 58,914 authors in Acta Urologica Japonica since the first issue in 1955 to the present. The author's gender was determined by an application program interface for gender estimation in combination with independent manual confirmation by two researchers. The increasing rate (⊿person/⊿year) of female authors was as low as 0.067 in 1955-79, but increased to 0.400 in 1980-2000 and 0.814 in 2001-20. Over the time periods, the annual total numbers of female authors (person/year) showed an increasing trend from 3.2 in 1955-79 to 16.3 in 1980-2000 and 26.0 in 2001-20. The numbers of female author individuals, the ratio of female authors to all authors and the ratio of publications by female first author to all publications also showed similar trends. These results suggest that gender equality is becoming more prevalent in the academic field of urology. The methods and data of this study are considered to be useful for the promotion of gender equality in the academic field of urology for the future.

Prediction and visualization of acute kidney injury in intensive care unit using one-dimensional convolutional neural networks based on routinely collected data.

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.

Validation study of the claims-based definition for age-related macular degeneration at a single university hospital in Japan.

PURPOSE: To evaluate the efficacy of the claims'-based definition of age-related macular degeneration (AMD) in detecting clinically-diagnosed AMD. STUDY DESIGN: A validation study using cross-sectional data. METHODS: Seven hundred clinically-diagnosed AMD patients and seven hundred non-AMD individuals were randomly selected from patients at the Kyoto University Hospital's ophthalmology outpatient clinic between January 2011 and December 2017. We evaluated the sensitivity, specificity, and positive/negative likelihood ratio of eight different claims'-based definitions of AMD for detecting clinically-diagnosed AMD. These definitions consist of the diagnosis name (AMD) in combination with (1) fluorescent fundus angiography, and/or (2) treatment of AMD, and (3) the exclusion of patients who had a diagnosis of central serous chorioretinopathy (CSC) or myopic choroidal neovascularization. RESULTS: Defining by the diagnosis name AMD in the claims' data showed the highest accuracy (sensitivity 94.9%, specificity 92.6%, accuracy 93.7%). Combining the diagnosis name AMD with fluorescence fundus angiography and/or anti-vascular endothelial growth factor (anti VEGF) treatment increased the specificity at the expense of sensitivity. Notably, the combination with AMD treatment achieved a specificity of 98.3%. CONCLUSION: The current validation study elucidated the high accuracy of the disease name (AMD) in the claims' data for identifying clinically-diagnosed AMD at a single university hospital. Although drawing wider conclusions may be limited, the results of this study contribute to creating real-world evidence in ophthalmology, based on the National Database of Health Insurance Claims in Japan.

Promoting Physical Activity in Japanese Older Adults Using a Social Pervasive Game: Randomized Controlled Trial.

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 individual baseline values were as follows: 11520.0 (SE 3941.5)/28.0% (SE 8.7%) (week 2), 9567.3 (SE 2631.5)/23.0% (SE 5.1%) (week 3), and 7648.7 (SE 3900.9)/13.9% (SE 8.0%) (week 4). The result of the analysis of the group effect was significant (absolute change: η2=0.31, P=.04; proportional change: η2=0.30, P=.03). Correlations between both absolute and proportional change and the play activity were significant (absolute change: r=0.59, 95% CI 0.32 to 0.77; proportional change: r=0.39, 95% CI 0.08 to 0.64). CONCLUSIONS: The presence of social interaction design elements in pervasive games appears to have a positive effect on levels of physical activity. TRIAL REGISTRATION: Japan Medical Association Clinical Trial Registration Number JMA-IIA00314; https://tinyurl.com/y5nh6ylr (Archived by WebCite at http://www.webcitation.org/761a6MVAy).

Association between the size of healthcare facilities and the intensity of hypertension therapy: a cross-sectional comparison of prescription data from insurance claims data.

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.

Evaluation of the Quality of Life and Health-Related Quality of Life of Patients With End-Stage Kidney Disease Resulting From Kidney Transplantation Using the Kidney Disease Quality of Life-Short Form and EuroQOL-5 Dimension-5 Level Questionnaires.

BACKGROUND: Renal transplantation improves the quality of life (QOL) of end-stage renal disease (ESRD) patients with renal failure. However, it remains unclear which renal disease-specific QOL aspects determine general health-related QOL of ESRD patients. This study aimed to identify these QOL items by examining the QOL of ESRD patients using the Kidney Disease Quality of Life-Short Form (KDQOL-SF), version 1.3, and EuroQoL-5 dimension-5 levels (EQ-5D-5L) questionnaires. METHODS: We conducted QOL surveys with 67 renal transplant recipients at our hospital. EQ-5D-5L, which evaluates general health-related QOL, was the response variable, and KDQOL-SF, which includes the renal disease-specific instrument and general health-related QOL SF-36 instrument, was the explanatory variable. We analyzed the effects of each KDQOL-SF domain on EQ-5D-5L using Pearson correlation coefficient. RESULTS: Regarding the general health-related QOL assessed by SF-36, physical health aspects, such as physical functioning (R = 0.749) and daily functioning physical (R = 0.603), showed a strong correlation with EQ-5D-5L, and the domains related to the psychological and social aspects of QOL showed a limited correlation. Regarding kidney disease-specific scales, symptoms/problems related to physical function showed a good correlation (R = 0.691) with EQ-5D-5L, whereas other scales, including burden of kidney disease (R = 0.168), quality of social interaction (R = 0.284), and those related to the mental and social aspects of QOL showed a low correlation with EQ-5D-5L. CONCLUSION: Among kidney transplant recipients, the physical health aspects of QOL, such as symptoms/problems, were the major factors influencing overall QOL as assessed by EQ-5D-5L.

Nationwide incidence of central retinal artery occlusion in Japan: an exploratory descriptive study using the National Database of Health Insurance Claims (2011-2015).

AIMS: The latest evidence in the incidence of central retinal artery occlusion (CRAO) is needed to support the development of novel treatments as orphan drugs. However, up-to-date information on the incidence of CRAO in the ageing or aged population is limited. We aimed to investigate the nationwide epidemiological and clinical characteristics of CRAO in Japan, using nationwide health insurance claims data. METHODS: We analysed a total of 16 069 762 claims data in the sampling dataset of the National Database of Health Insurance Claims and Specific Health Checkups of Japan (NDB), which is the nationwide health insurance claims database of 127 million whole Japanese individuals. CRAO was identified using the International Classification of Diseases 10th edition diagnostic code H34.1. The crude incidence rates and age-standardised incidence rates of CRAO, according to the standard age-structure population of the WHO, were calculated. RESULTS: The crude incidence rate of CRAO in Japan was 5.84 (95% CI, 5.71 to 5.97) per 100 000 person-years. With respect to the sex-related incidence, the rate was higher 1.40 times in men than in women (6.85 (95% CI, 6.65 to 7.06) vs 4.88 (95% CI, 4.71 to 5.05), p<0.001). The age-standardised incidence rate was 2.53 (95% CI, 2.29 to 2.76) per 100 000 person-years. CONCLUSIONS: The crude incidence rate of CRAO was higher in Japan than in other countries, as reported previously, reflecting the Japanese population structure as a super-aged society. These findings can be helpful for the development of appropriate healthcare policies to address the increasing incidence of CRAO with the ageing population.

Classification of glomerular pathological findings using deep learning and nephrologist-AI collective intelligence approach.

BACKGROUND: Automated classification of glomerular pathological findings is potentially beneficial in establishing an efficient and objective diagnosis in renal pathology. While previous studies have verified the artificial intelligence (AI) models for the classification of global sclerosis and glomerular cell proliferation, there are several other glomerular pathological findings required for diagnosis, and the comprehensive models for the classification of these major findings have not yet been reported. Whether the cooperation between these AI models and clinicians improves diagnostic performance also remains unknown. Here, we developed AI models to classify glomerular images for major findings required for pathological diagnosis and investigated whether those models could improve the diagnostic performance of nephrologists. METHODS: We used a dataset of 283 kidney biopsy cases comprising 15,888 glomerular images that were annotated by a total of 25 nephrologists. AI models to classify seven pathological findings: global sclerosis, segmental sclerosis, endocapillary proliferation, mesangial matrix accumulation, mesangial cell proliferation, crescent, and basement membrane structural changes, were constructed using deep learning by fine-tuning of InceptionV3 convolutional neural network. Subsequently, we compared the agreement to truth labels between majority decision among nephrologists with or without the AI model as a voter. RESULTS: Our model for global sclerosis showed high performance (area under the curve: periodic acid-Schiff, 0.986; periodic acid methenamine silver, 0.983); the models for the other findings also showed performance close to those of nephrologists. By adding the AI model output to majority decision among nephrologists, out of the 14 constructed models, the results of the majority decision showed improvement in sensitivity for 10 models (four of them were statistically significant) and specificity for eight models (five significant). CONCLUSION: Our study showed a proof-of-concept for the classification of multiple glomerular findings in a comprehensive method of deep learning and suggested its potential effectiveness in improving diagnostic accuracy of clinicians.

Detecting Severe Incidents from Electronic Medical Records Using Machine Learning Methods.

The goal of this research was to design a solution to detect non-reported incidents, especially severe incidents. To achieve this goal, we proposed a method to process electronic medical records and automatically extract clinical notes describing severe incidents. To evaluate the proposed method, we implemented a system and used the system. The system successfully detected a non-reported incident to the safety management department.

Preliminary Evaluation of Market Mechanism-Based Bed Allocation System.

Effective bed management is important for hospital management. Until now, bed allocation process is generally controlled by administrative staffs in centralized manner but it is not always effective. In the present study, we proposed and evaluated new method for bed allocation applying market mechanism via token. Evaluation was performed with newly-developed game-type simulation. Nurse managers as research participants played it and answered for survey. The result showed that the proposed method can be useful with appropriate operational design.

Prioritizing Features to Redesign in an EMR System.

Redesigning Electronic Medical Record (EMR) systems is needed to improve their usefulness and usability. For user-centered redesign, designers should consider which EMR features are the most important to the users. However, prioritizing the EMR features is complicated because: (i) EMR systems involve multiple users with different, and sometimes conflicting, priorities and (ii) targeting one feature will affect other features of the EMR system. In this work, we propose a method for prioritizing the features to target when redesigning an EMR system. The method takes into consideration the different priorities of the users and the relationships between the different features. We illustrate the method through a case study on redesigning EMR systems in Japanese antenatal care settings. Our results show the importance of considering the different types of EMR users and the relationships between different EMR features. Designers could use the proposed method as a decision-aid tool in EMR redesign projects.

Early Nephrosis Detection Based on Deep Learning with Clinical Time-Series Data.

Nephrosis is disease characterized by abnormal protein loss from impaired kidney. We constructed early prediction model using machine learning from clinical time series data, that can predict onset of nephrosis for more than one month. Long short-term memory capable of recognizing temporal sequential data patterns, was adopted as early prediction model for nephrosis. We verified our proposed prediction model has higher accuracy compared with those of baseline classifiers by 5-fold cross validation.