Journal of Medical Systems
ISSN / EISSN : 0148-5598 / 1573-689X
Published by: Springer Science and Business Media LLC (10.1007)
Total articles ≅ 3,976
Latest articles in this journal
Journal of Medical Systems, Volume 45, pp 1-2; doi:10.1007/s10916-021-01759-y
Journal of Medical Systems, Volume 45, pp 1-11; doi:10.1007/s10916-021-01757-0
COVID-19, caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), spread rapidly and affected most of the world since its outbreak in Wuhan, China, which presents a major challenge to the emergency response mechanism for sudden public health events and epidemic prevention and control in all countries. In the face of the severe situation of epidemic prevention and control and the arduous task of social management, the tremendous power of science and technology in prevention and control has emerged. The new generation of information technology, represented by big data and artificial intelligence (AI) technology, has been widely used in the prevention, diagnosis, treatment and management of COVID-19 as an important basic support. Although the technology has developed, there are still challenges with respect to epidemic surveillance, accurate prevention and control, effective diagnosis and treatment, and timely judgement. The prevention and control of sudden infectious diseases usually depend on the control of infection sources, interruption of transmission channels and vaccine development. Big data and AI are effective technologies to identify the source of infection and have an irreplaceable role in distinguishing close contacts and suspicious populations. Advanced computational analysis is beneficial to accelerate the speed of vaccine research and development and to improve the quality of vaccines. AI provides support in automatically processing relevant data from medical images and clinical features, tests and examination findings; predicting disease progression and prognosis; and even recommending treatment plans and strategies. This paper reviews the application of big data and AI in the COVID-19 prevention, diagnosis, treatment and management decisions in China to explain how to apply big data and AI technology to address the common problems in the COVID-19 pandemic. Although the findings regarding the application of big data and AI technologies in sudden public health events lack validation of repeatability and universality, current studies in China have shown that the application of big data and AI is feasible in response to the COVID-19 pandemic. These studies concluded that the application of big data and AI technology can contribute to prevention, diagnosis, treatment and management decision making regarding sudden public health events in the future.
Journal of Medical Systems, Volume 45, pp 1-8; doi:10.1007/s10916-021-01758-z
The American Society of Anesthesiologists (ASA) Physical Status Classification System has been used to assess pre-anesthesia comorbid conditions for over 60 years. However, the ASA Physical Status Classification System has been criticized for its subjective nature. In this study, we aimed to assess the correlation between the ASA physical status assignment and more objective measures of overall illness. This is a single medical center, retrospective cohort study of adult patients who underwent surgery between November 2, 2017 and April 22, 2020. A multivariable ordinal logistic regression model was developed to examine the relationship between the ASA physical status and Elixhauser comorbidity groups. A secondary analysis was then conducted to evaluate the capability of the model to predict 30-day postoperative mortality. A total of 56,820 cases meeting inclusion criteria were analyzed. Twenty-seven Elixhauser comorbidities were independently associated with ASA physical status. Older patient (adjusted odds ratio, 1.39 [per 10 years of age]; 95% CI 1.37 to 1.41), male patient (adjusted odds ratio, 1.24; 95% CI 1.20 to 1.29), higher body weight (adjusted odds ratio, 1.08 [per 10 kg]; 95% CI 1.07 to 1.09), and ASA emergency status (adjusted odds ratio, 2.11; 95% CI 2.00 to 2.23) were also independently associated with higher ASA physical status assignments. Furthermore, the model derived from the primary analysis was a better predictor of 30-day mortality than the models including either single ASA physical status or comorbidity indices in isolation (p < 0.001). We found significant correlation between ASA physical status and 27 of the 31 Elixhauser comorbidities, as well other demographic characteristics. This demonstrates the reliability of ASA scoring and its potential ability to predict postoperative outcomes. Additionally, compared to ASA physical status and individual comorbidity indices, the derived model offered better predictive power in terms of short-term postoperative mortality.
Journal of Medical Systems, Volume 45, pp 1-7; doi:10.1007/s10916-021-01754-3
In this retrospective cohort study we sought to evaluate the association between the etiology and timing of rapid response team (RRT) activations in postoperative patients at a tertiary care hospital in the southeastern United States. From 2010 to 2016, there were 2,390 adult surgical inpatients with RRT activations within seven days of surgery. Using multivariable linear regression, we modeled the correlation between etiology of RRT and timing of the RRT call, as measured from the conclusion of the surgical procedure. We found that respiratory triggers were associated with an increase in time after surgical procedure to RRT of 10.6 h compared to activations due to general concern (95% CI 3.9 – 17.3) (p = 0.002). These findings may have an impact on monitoring of postoperative patients, as well as focusing interventions to better respond to clinically deteriorating patients.
Journal of Medical Systems, Volume 45, pp 1-9; doi:10.1007/s10916-021-01756-1
Patient wait time can negatively impact treatment quality in a proton therapy center, where multiple treatment rooms share one proton beam. Wait time increases patient discomfort that can lead to patient motion, dissatisfaction, and longer treatment delay. This study was to develop a patient call-back model that reduced patient wait while efficiently utilizing the proton beam. A “Gatekeeper” logic allowing therapists to adjust the time of a patient’s call-back to the treatment room was developed. It uses a two-pronged approach to minimize overlap of long treatment and the possibility of excessive wait in the queue to receive the proton beam. The goal was to reduce the maximum wait time to less than eight minutes per field for a four-room facility. The effectiveness of this logic was evaluated through simulation, and five scenarios were compared. Four scenarios implementing various levels of gatekeeper logic were compared with the original scenario without the logic. The best performing model provided a reduction of the maximum field wait by 26% and met the predefined goal. Adjusting call-back extended the treatment day length by an average of 6 min and a maximum of 12 min in total. The use of this gatekeeper logic significantly reduces patient field wait with minimal impact on treatment day length for a four-room proton facility. A sample interface that adopts this logic for therapists to make informed decision on patient call-back time is demonstrated.
Journal of Medical Systems, Volume 45, pp 1-10; doi:10.1007/s10916-021-01751-6
Medical image segmentation has seen positive developments in recent years but remains challenging with many practical obstacles to overcome. The applications of this task are wide-ranging in many fields of medicine, and used in several imaging modalities which usually require tailored solutions. Deep learning models have gained much attention and have been lately recognized as the most successful for automated segmentation. In this work we show the versatility of this technique by means of a single deep learning architecture capable of successfully performing segmentation on two very different types of imaging: computed tomography and magnetic resonance. The developed model is fully convolutional with an encoder-decoder structure and high-resolution pathways which can process whole three-dimensional volumes at once, and learn directly from the data to find which voxels belong to the regions of interest and localize those against the background. The model was applied to two publicly available datasets achieving equivalent results for both imaging modalities, as well as performing segmentation of different organs in different anatomic regions with comparable success.
Journal of Medical Systems, Volume 45; doi:10.1007/s10916-021-01753-4
Surgical trays contain unused instruments which generate wasted resources from unnecessary reprocessing/replacement costs. We implemented a quality improvement initiative to optimize surgical trays for common otolaryngology procedures, and examined the impact on costs, operating room (OR) efficiency, and patient safety.We studied five common otolaryngology procedures over a 10-month period at a single community hospital. We compared pre- and post-intervention outcome measures including instrument utilization, tray set up time, tray rebuilding time, and balancing measures (operative time, instrument recall, patient safety). We estimated cost-savings from an institutional perspective over 1- and 10-year time horizons. Costs were expressed in 2017 Canadian dollars and modeled as a function of surgical volume, labor costs, instrument depreciation, and indirect costs.A total of 238 procedures by six surgeons were observed. At baseline, only 35% of instruments were utilized. We achieved an average instrument reduction of 26%, yielding 1-year cost savings of $9,010 CDN and 10-year cost savings of $69,576 CDN. Tray optimization reduced average OR tray setup time by 2.5 ± 0.4 min (p = 0.03) and average tray rebuilding time by 1.4 ± 0.2 min (p = 0.06). There was minimal impact on balancing measures such as OR time, stakeholder perception of patient safety and trainee education, and only a single case of instrument recall.Surgical tray optimization is a simple, effective, and scalable strategy for reducing costs and improving OR efficiency without compromising patient safety.
Journal of Medical Systems, Volume 45, pp 1-10; doi:10.1007/s10916-021-01750-7
Upgraded network technology presents an advanced technological platform for telecare medicine information systems (TMIS) for patients. However, TMIS generally suffers various attacks since the information being shared through the insecure channel. Recently, many authentication techniques have been proposed relying on the chaotic map. However, many of these designs are not secure against the known attacks. In spite of the fact that some of the constructions attain low computation overhead, they cannot establish an anonymous communication and many of them fail to ensure forward secrecy. In this work, our aim is to present authentication and key agreement protocol for TMIS utilizing a chaotic map to achieve both security and efficiency. The underlying security assumptions are chaotic theory assumptions. This scheme supports forward secrecy and a secure session is established with just two messages of exchange. Moreover, we present a comparative analysis of related authentication techniques.
Journal of Medical Systems, Volume 45, pp 1-9; doi:10.1007/s10916-021-01752-5
Quantitative data on the sensory environment of intensive care unit (ICU) patients and its potential link to increased risk of delirium is limited. We examined whether higher average sound and light levels in ICU environments are associated with delirium incidence. Over 111 million sound and light measurements from 143 patient stays in the surgical and trauma ICUs were collected using Quietyme® (Neshkoro, Wisconsin) sensors from May to July 2018 and analyzed. Sensory data were grouped into time of day, then normalized against their ICU environments, with Confusion Assessment Method (CAM-ICU) scores measured each shift. We then performed logistic regression analysis, adjusting for possible confounding variables. Lower morning sound averages (8 am-12 pm) (OR = 0.835, 95% OR CI = [0.746, 0.934], p = 0.002) and higher daytime sound averages (12 pm–6 pm) (OR = 1.157, 95% OR CI = [1.036, 1.292], p = 0.011) were associated with an increased odds of delirium incidence, while nighttime sound averages (10 pm-8 am) (OR = 0.990, 95% OR CI = [0.804, 1.221], p = 0.928) and the ICU light environment did not show statistical significance. Our results suggest an association between the ICU soundscape and the odds of developing delirium. This creates a future paradigm for studies of the ICU soundscape and lightscape.
Journal of Medical Systems, Volume 45, pp 1-10; doi:10.1007/s10916-021-01745-4
Coronavirus disease 2019 (COVID-19) is an infectious disease with first symptoms similar to the flu. COVID-19 appeared first in China and very quickly spreads to the rest of the world, causing then the 2019-20 coronavirus pandemic. In many cases, this disease causes pneumonia. Since pulmonary infections can be observed through radiography images, this paper investigates deep learning methods for automatically analyzing query chest X-ray images with the hope to bring precision tools to health professionals towards screening the COVID-19 and diagnosing confirmed patients. In this context, training datasets, deep learning architectures and analysis strategies have been experimented from publicly open sets of chest X-ray images. Tailored deep learning models are proposed to detect pneumonia infection cases, notably viral cases. It is assumed that viral pneumonia cases detected during an epidemic COVID-19 context have a high probability to presume COVID-19 infections. Moreover, easy-to-apply health indicators are proposed for estimating infection status and predicting patient status from the detected pneumonia cases. Experimental results show possibilities of training deep learning models over publicly open sets of chest X-ray images towards screening viral pneumonia. Chest X-ray test images of COVID-19 infected patients are successfully diagnosed through detection models retained for their performances. The efficiency of proposed health indicators is highlighted through simulated scenarios of patients presenting infections and health problems by combining real and synthetic health data.