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The numbers of patients treated with hemodialysis (HD) in Japan are currently quantified by manual survey. As this method requires much effort from medical institutions and cannot achieve 100% response, a more practical method is required. We aimed to establish a novel method for determining the static and dynamic numbers of patients treated with HD.This observational study used the national medical billing database (termed NDB) of Japan, based on the records of the universal healthcare insurance system. Medical billing data registered in the NDB between April 2011 and March 2015 were analyzed. From 130 billion records, we extracted and analyzed records of patients who had undergone HD at least once per month. Patients' monthly condition was classified as newly initiated HD, chronic HD, or presumed death, using conditional expressions. We also investigated renal outcome and presumed survival in newly initiated HD patients.In the last month of the study period, 274,100 patients were identified as receiving chronic HD, which is estimated as 95% of the number of these patients identified in the manual survey so far. The monthly data showed clear seasonality in the incidence of transient HD, which increased in winter and decreased in summer.Analysis of a large national database revealed a significant increase in transient HD in winter and decrease in summer. Applied to additional epidemiological exploratory studies or clinical research, this analytical technique would enable collection of the dynamics of almost all HD patients nationwide.

BACKGROUND Telehealth using telephones or online communication is being promoted as a policy initiative in several countries. However, there is a lack of research on telehealth utilization in a country such as Japan that offers free access to medical care and regulates telehealth provision—particularly with respect to COVID-19. OBJECTIVE The present study aimed to clarify telehealth utilization, the characteristics of patients and medical institutions using telehealth, and the changes to telehealth in Japan in order to support the formulation of policy strategies for telehealth provision. METHODS Using a medical administrative claim database of the National Health Insurance and Advanced Elderly Medical Service System in Mie Prefecture, we investigated patients who used telehealth from January 2017 to September 2021. We examined telehealth utilization with respect to both patients and medical institutions, and we determined their characteristics. Using April 2020 as the reference time point for COVID-19, we conducted an interrupted time-series analysis (ITSA) to assess changes in the monthly proportion of telehealth users to beneficiaries. RESULTS The number of telehealth users before the reference time point was 13,618, and after the reference time point, it was 28,853. Several diseases and conditions were associated with an increase in telehealth utilization. Telehealth consultations were mostly conducted by telephone and for prescriptions. The ITSA results showed a sharp increase in the proportion of telehealth use to beneficiaries after the reference time point (rate ratio 2.97; 95% CI 2.14-2.31). However, no apparent change in the trend of increasing or decreasing telehealth use was evident after the reference time point (rate ratio 1.00; 95% CI 1.00-1.01). CONCLUSIONS We observed a sharp increase in telehealth utilization after April 2020, but no change in the trend of telehealth use was evident. We identified changes in the characteristics of patients and providers using telehealth.

A 1.33-Tb 4-bit/cell quadruple-level (QLC) 3-D flash memory in a 96-word-line (WL)-layer technology that achieves 8.5 Gb/mm2 has been developed. This is the biggest capacity and the highest bit density ever reported. A source-bias-negative-sense with CLK-control allows deep negative $V_{\text {th}}$ sensing while maintaining low supply voltage. A new two-step (8–16) programming method and VDD generator enhancement realized a narrow $V_{\text {th}}$ for QLC with 18% tProg improvement. Page/state-dependent word-line (WL) overdrive shortens the WL transient time by 8%. An independent plane read enables reading from different WL address from each plane in multi-plane operation. Besides, mixing selection of QLC pages (lower/middle/upper/top) in one plane and triple-level cell (TLC) pages (lower/middle/upper) or single-level cell (SLC) page in the other is also supported to provide flexibility for system implementation.

The characteristics of copper (Cu) isotropic dry etching using a hydrogen-based plasma generated at 13.3 kPa (100 Torr) were improved dramatically by simply introducing a moderate amount of N2 gas into the process atmosphere. A maximum Cu etch rate of 2.4 μm/min was obtained by nitrogen addition at a H2 mixture ratio (CH2) of 0.9 and an input power of 70 W. The etch rate for the optimally N2-added plasma was 8 times higher than that for the pure H2 plasma. The Cu etch rate increased with increasing input power. The maximum etch rate reached 3.1 μm/min at an input power of 100 W and a CH2 of 0.9. The surface roughness of the etched copper decreased as a result of optimum N2 addition. Furthermore, N2 addition also improved the etch selectivity between Cu and SiO2 such that the selectivity ratio reached 190. Finally, selective etching of a trench-patterned Si wafer with an electroplated Cu layer was demonstrated.

This work demonstrates a novel 1Tb 3D Flash memory chip that has an area efficiency of 10.4Gb/mm2 in a 3b/cell technology. Using a circuit under array (CUA) design technique and over 170 word-line (WL) layers, the chip achieves 33% higher bit density than prior 3b/cell work [1], and better density than in 4b/cell technology [3]. This paper discusses the challenges advanced 3D Flash memories face: using over 100 WL layers results in large parasitic loads and decreases read/program speed, and its complicated operation increases test costs. On the other hand, as high bandwidth is also required, this chip supports a 2.0Gbps IO transfer rate, while maintaining signal integrity. This work introduces four new key technologies to address these difficulties. 1) Asynchronous independent plane read (AIPR), with a 4-plane architecture to improve system-level performance. 2) Enhanced sensing that enables faster read time $(t_{R})$. 3) IO-DCC (duty cycle correction) training for high-speed DDR operation. 4) A scan chain to improve test coverage and cost effectiveness.

The geographical imbalance of the healthcare workforce is a social problem in Japan. Except for big cities, hospitals have difficulties in securing a sufficient workforce to offer healthcare services stably. For local government, hospital service suspensions are potentially an essential indicator to figure out the capacity of the regional healthcare supply. This paper proposes an algorithm that automatically identifies and classifies hospital service suspensions from insurance claims data, based on periodicity and similarity. To verify the effectiveness, we have applied the algorithm to the insurance claim dataset, which has been provided 91 regional public insurers in Japan. The case studies have confirmed that the proposed algorithm has presented an evidential picture of hospital service suspensions, which is potentially useful to understand the actual capacity of healthcare service supply in regions.

Studies suggest that the level of muscle activation may influence force reproduction. The purpose of this study was to develop a sensorimotor-enhancing suit (SEnS)—unpowered assistive clothing without actuators or electrical devices—and to examine its efficacy for reducing voluntary muscle activation and improving force reproduction in the upper limb in healthy young adults. The SEnS was made of elastic fabric and designed to produce assistive shoulder flexion moment that can partially mitigate the required activation of the user's shoulder flexor muscles. A series of human experiments were then conducted in healthy young adults. As a proof of concept, reduction in force reproduction performance of the shoulder moment was confirmed with mitigation of the shoulder moment by using an external string. To examine the efficacy of the SEnS, voluntary muscle activation and force reproduction performance was examined with and without using the SEnS, by comparing the amount of activation in the shoulder flexor muscles and the error in force reproduction in the upper limb. The results showed that wearing SEnS reduced muscle activation and force reproduction error. These results suggest that the accuracy of force reproduction can be improved substantially by partially mitigating the shoulder moment and associated voluntary muscle activation, using the SEnS.

Medical insurance claims are useful data to offer a big-picture view and insight of a nation-wide healthcare system. Yet, formal description of the logic to analyze the claims has not been established. So far, we proposed a description scheme of analytics logic over claims database. In this paper, we propose a novel analytics framework based on the description scheme. By showing a case study, we demonstrate the effectiveness of the framework.

Anonymization of medical data helps protect patient identities. However, with conventional anonymized personal identifiers it is difficult to trace patients, which hinders longitudinal analyses in insurance claim database. Herein, we describe the development of a method to identify unique patients by using partial equivalence relationships of multiple anonymized personal identifiers. By using two conventional anonymized personal identifiers, we have developed virtual patient identifiers (vPIDs) to indicate unique patients. To verify the effectiveness of the developed identifiers, we have applied vPIDs to a six-year dataset of national-level Japanese insurance claims dataset and a prefectural-level insurance claims dataset with enrollee master data. In addition, we have applied vPIDs to practical analyses of medical expenditures and doctor consultations. vPID has enabled the continued tracing of patients throughout the six-year study period, and demonstrated the validity of our method. Therefore, the proposed method can be used to improve patient traceability in insurance claims database.

Medical insurance claims data is one of the most useful data sources that can offer a big-picture view of a nation-wide healthcare system. Form the viewpoint of medical policy planning, Japan's Ministry of Health, Labour and Welfare has been continuously collecting claims data. However, claims data in Japan has an ordered nested tuple format, and a method for describing the logic to analyze this form in a simple and clear manner has not been established yet. In the present work, we construct a novel analytics framework based on previous analyses that we conducted with medical researchers and design a UI that facilitates the construction of the processing logic in a simple and clear manner. By showing the execution result of typical analyses of claim data, we demonstrate the effectiveness of the developed tool.

A 75 mm $^{2}$ low power 64 Gb MLC NAND flash memory capable of 30 MB/s program throughput and 533 MB/s data transfer rate at 1.8 V supply voltage is developed in 15 nm CMOS technology. 36% power reduction from 3.3 V design is achieved by a new pumping scheme. New low current peak features reduce a multi-die concurrent programming peak by 65% for 4-die case, and an erase verifying peak by 40%, respectively. Nanoscale transistors reducing bit-line discharge time by 70% is introduced to improve performance.

The first multi-layer stacked 3D Flash memory was proposed as BiCS FLASH in 2007 [1]. Since then, memory bit density has grown rapidly due to the increase in the number of stacked layers from continuous 3D technology innovations. On the other hand, the multi-level-cell technology, which was initially proposed for 2D Flash, has also been adopted to 3D Flash memories. The first 3b/cell 32-layer Flash was presented in 2015 [2], followed by a 48-layer one in 2016 [3], and a 64-layer one in 2017 [4,5]. This paper describes a 512Gb 3b/cell 3D Flash memory in a 96-word-line-layer BiCS FLASH technology. This work implements three key technologies to improve performance: (1) a string based start bias control scheme achieves a 7% shorter program time; (2) a smart V t -tracking read improves read retry performance by minimizing the tracking time and supporting a program suspend read function, and; (3) a low-pre-charge sense-amplifier bus scheme reduces both the power consumption and the data-transfer time between the sense amplifier (SA) and the data cache by half. Figure 20.1.1 shows the die micrograph and the summary of the key features of the chip.

Background Heart failure is one of the leading causes of hospitalization in the United States. Advances in big data solutions allow for storage, management, and mining of large volumes of structured and semi-structured data, such as complex healthcare data. Applying these advances to complex healthcare data has led to the development of risk prediction models to help identify patients who would benefit most from disease management programs in an effort to reduce readmissions and healthcare cost, but the results of these efforts have been varied. The primary aim of this study was to develop a 30-day readmission risk prediction model for heart failure patients discharged from a hospital admission. Methods We used longitudinal electronic medical record data of heart failure patients admitted within a large healthcare system. Feature vectors included structured demographic, utilization, and clinical data, as well as selected extracts of un-structured data from clinician-authored notes. The risk prediction model was developed using deep unified networks (DUNs), a new mesh-like network structure of deep learning designed to avoid over-fitting. The model was validated with 10-fold cross-validation and results compared to models based on logistic regression, gradient boosting, and maxout networks. Overall model performance was assessed using concordance statistic. We also selected a discrimination threshold based on maximum projected cost saving to the Partners Healthcare system. Results Data from 11,510 patients with 27,334 admissions and 6369 30-day readmissions were used to train the model. After data processing, the final model included 3512 variables. The DUNs model had the best performance after 10-fold cross-validation. AUCs for prediction models were 0.664 ± 0.015, 0.650 ± 0.011, 0.695 ± 0.016 and 0.705 ± 0.015 for logistic regression, gradient boosting, maxout networks, and DUNs respectively. The DUNs model had an accuracy of 76.4% at the classification threshold that corresponded with maximum cost saving to the hospital. Conclusions Deep learning techniques performed better than other traditional techniques in developing this EMR-based prediction model for 30-day readmissions in heart failure patients. Such models can be used to identify heart failure patients with impending hospitalization, enabling care teams to target interventions at their most high-risk patients and improving overall clinical outcomes. Electronic supplementary material The online version of this article (10.1186/s12911-018-0620-z) contains supplementary material, which is available to authorized users.

The demand for high-throughput NAND Flash memory systems for mobile applications such as smart phones, tablets, and laptop PCs with solid-state drives (SSDs) has been growing recently. To obtain higher throughput, systems employ multiple NAND Flash memories operating simultaneously in parallel. The available power for a mobile device is severely restricted and the peak total operating current may be high enough to cause large supply-voltage drop or even an unexpected system shutdown. Therefore it is important for NAND Flash memories to reduce operating power and peak operating current.

In this study, we examined (1) weight discrimination capability of human subjects with different body postures, and (2) sensorimotor capability of human subjects when using a muscle assistive equipment. According to previous studies, humans can sense the intensity of external stimulus more accurately when voluntary muscle activation is less. We developed a three-dimensional musculoskeletal model based on an upper extremity model, and calculated the muscle activity required to keep a posture. We also conducted human experiments and revealed that the weight discrimination capability improves as voluntary muscle activation is less. Based on the experimental results, we developed a muscle assistive equipment that unloads the weight of one's upper limb and evaluated the improvement in the sensorimotor capability when using the equipment. The results show that assisting the muscle load is effective to improve the sensorimotor performance.

The models of motion and perception characteristics of humans are helpful to evaluate subjective efforts associated with intuitive, safe, and easy-to-use products. Traditionally, the muscle activity and/or the joint torque estimated by using a musculoskeletal model have been evaluated, whereas there are few studies that consider human's perception characteristics. In this paper, the perception characteristics of force are investigated based on the estimation of the muscle activities. Experimental analysis of human force perception characteristics in a steering wheel operation has revealed that the perceived force depends on the angle of the steering wheel and the direction of the load. This paper proposes the estimation model of the force perceived by a subject that considers human's posture- and direction-dependent perception characteristics in the steering operation. We estimate the muscle activity during the steering wheel operation by using a three-dimensional musculoskeletal model, develops a perception conversion model that follows the Weber-Fechner law, and describes the experimental results that confirm the feasibility of the proposed force perception estimation.

Copper (Cu) dry etching is demonstrated using a narrow-gap hydrogen plasma generated at 13.3 kPa (100 Torr) for applications in the Cu wiring technology of integrated circuits. A localized hydrogen plasma is generated around the apex of a fine pipe electrode. The Cu etching can be observed only when the process gas contains hydrogen, and the etching rates decrease with decreased hydrogen concentration. The plasma heating effect owing to plasma localization is negligible for the Cu etching because no etching occurs in the presence of pure N2 plasma whose volume is almost equal to that of the pure H2 plasma. Furthermore, the influences of physical sputtering and vacuum ultraviolet irradiation on the Cu etching are confirmed to be insignificant by exposing the samples to rare-gas plasma. The maximum Cu etching rate of 500 nm/min can be achieved at a stage temperature of 0 °C. However, the Cu etching rate has no obvious dependence on the stage temperature in a range from −20 to 330 °C. In contrast, the etchin...