A Japanese retrospective study of non-tuberculous mycobacterial infection in children, adolescents, and young adult patients with hematologic-oncologic diseases.

Non-tuberculous mycobacterial infection (NTM) is rare in healthy children, with lymphadenitis being the most common presentation. Immunocompromised populations are known to be at high risk, but the clinical picture of NTM infection in pediatric hematology/oncology patients is unclear. In this nationwide retrospective analysis of patients under the age of 40 treated in Japanese pediatric hematology/oncology departments who developed NTM infection between January 2010 and December 2020, 36 patients (21 patients with hematopoietic stem cell transplantation (HSCT) and 15 nontransplant patients) were identified. Post-transplant patients were infected with NTM at 24 sites, including the lungs (n = 12), skin and soft tissues (n = 6), bloodstream (n = 4), and others (n = 2). Nine of twelve patients with pulmonary NTM infection had a history of pulmonary graft-versus-host disease (GVHD), and rapid-growing mycobacteria (RGM) were isolated from five of them. In nontransplant patients, the primary diseases were acute lymphoblastic leukemia (ALL; n = 5), inborn errors of immunity (IEI; n = 6), and others (n = 4). All cases of ALL had bloodstream infections with RGM, whereas all cases of IEI were infected with slow-growing mycobacteria (SGM). In summary, three typical clinical scenarios for pediatric hematology/oncology patients have been established: RGM-induced pulmonary disease in patients with pulmonary GVHD, RGM bloodstream infection in patients with ALL, and SGM infection in patients with IEI. Our findings suggest that NTM must be regarded as a pathogen for infections in these high-risk patients, especially those with pulmonary GVHD, who may require active screening for NTM.

In-Hospital Cancer Mortality Prediction by Multimodal Learning of Non-English Clinical Texts.

Predicting important outcomes in patients with complex medical conditions using multimodal electronic medical records remains challenge. We trained a machine learning model to predict the inpatient prognosis of cancer patients using EMR data with Japanese clinical text records, which has been considered difficult due to its high context. We confirmed high accuracy of the mortality prediction model using clinical text in addition to other clinical data, suggesting applicability of this method to cancer.

Prognostic Factors for Covid-19 on Admission Profile and Air Pollutants.

It has been reported that the severity and lethality of Covid-19 are associated with coexisting underlying diseases (hypertension, diabetes, etc.) and cardiovascular diseases (coronary artery disease, atrial fibrillation, heart failure, etc.) that increase with age, but environmental exposure such as air pollutants may also be a risk factor for mortality. In this study, we investigated patient characteristics at admission and prognostic factors of air pollutants in Covid-19 patients using a machine learning (random forest) prediction model. Age, Photochemical oxidant concentration one month prior to admission, and level of care required were shown to be highly important for the characteristics, while the cumulative concentrations of air pollutants SPM, NO2, and PM2.5 one year prior to admission were the most important characteristics for patients aged 65 years and older, suggesting the influence of long-term exposure.

Smart hospital infrastructure: geomagnetic in-hospital medical worker tracking.

PURPOSE: Location visualization is essential for locating people/objects, improving efficiency, and preventing accidents. In hospitals, Wi-Fi, Bluetooth low energy (BLE) Beacon, indoor messaging system, and similar methods have generally been used for tracking, with Wi-Fi and BLE being the most common. Recently, nurses are increasingly using mobile devices, such as smartphones and tablets, while shifting. The accuracy when using Wi-Fi or BLE may be affected by interference or multipath propagation. In this research, we evaluated the positioning accuracy of geomagnetic indoor positioning in hospitals. MATERIALS AND METHODS: We compared the position measurement accuracy of a geomagnetic method alone, Wi-Fi alone, BLE beacons alone, geomagnetic plus Wi-Fi, and geomagnetic plus BLE in a general inpatient ward, using a geomagnetic positioning algorithm by GiPStech. The existing Wi-Fi infrastructure was used, and 20 additional BLE beacons were installed. Our first experiment compared these methods' accuracy for 8 test routes, while the second experiment verified a combined geomagnetic/BLE beacon method using 3 routes based on actual daily activities. RESULTS: The experimental results demonstrated that the most accurate method was geomagnetic/BLE, followed by geomagnetic/Wi-Fi, and then geomagnetic alone. DISCUSSION: The geomagnetic method's positioning accuracy varied widely, but combining it with BLE beacons reduced the average position error to approximately 1.2 m, and the positioning accuracy could be improved further. We believe this could effectively target humans (patients) where errors of up to 3 m can generally be tolerated. CONCLUSION: In conjunction with BLE beacons, geomagnetic positioning could be sufficiently effective for many in-hospital localization tasks.

Use of in-Hospital Geomagnetic Fingerprinting Localization.

Recently, visualizing location of people and things in a hospital has become an issue particularly for improving work efficiency and incident prevention. Although radio frequency waves such as Wi-Fi and Bluetooth are commonly used in indoor positioning, they have several limitations owing to their physical characteristics. We proposed in-hospital hybrid positioning technique, involving a combination of radio waves and geomagnetic fingerprinting techniques. We compared accuracy of proposed technique with that of Wi-Fi- and BLE-based techniques.

An Analysis of the Factor Model on the Workload of Nursing Staff Using a Hospital Management Tool.

We developed a tool that allows a medical facility to offer efficient nursing care with limited human resources by optimizing the distribution of hospital ward nursing tasks. The use of information and communications technology to visualize daily workloads and make use of quantified workload data is important for identifying management elements that allow the efficient allocation of personnel and tasks. The goal of this study was to utilize data from the ward management tool that we developed to consider workflow processes for nursing staff and the relationships between the nursing competence of the nursing staff and the patients' conditions and how these impact on workloads. We found a correlation between workload and staff competence. With respect to the teamwork index and patient condition, structural equation modeling analysis using the intensity of nursing care needs and degree of independent daily living showed that patient condition had a meaningful effect on workload.

Prototyping context-aware nursing support mobile system.

A context aware nursing support system to push right information to the right person at the right moment is the key to increase clinical safety under a computerized hospital. We prototyped a system which obtains context from positions of nurses and list of expected clinical procedures. A WoZ test showed that the proposed approach has potential to decrease incidents caused by information delivery error.