Big Data in home healthcare: A new frontier in personalized medicine. Medical emergency services and prediction of hypertension risks

The Role of Informatics in Advancing Emergency Medicine: A Comprehensive Review

Emergency Medicine Informatics (EMI) is a rapidly advancing field that utilizes information technology to enhance the delivery of emergency medical services. This comprehensive literature review explores the key components, benefits, challenges, and future directions of EMI. By integrating Electronic Health Records, Clinical Decision Support Systems, telemedicine, data analytics, interoperability, and patient monitoring systems, EMI has the potential to significantly improve patient outcomes and operational efficiency in emergency departments. However, the implementation of these technologies faces several obstacles, including interoperability issues, data security concerns, usability challenges, and high costs. This review highlights how these technologies are transforming emergency care, discusses the barriers to their implementation, and provides perspectives on potential solutions and future progress in the field.

How can big data analytics be used for healthcare organization management? Literary framework and future research from a systematic review

Background

Multiple attempts aimed at highlighting the relationship between big data analytics and benefits for healthcare organizations have been raised in the literature. The big data impact on health organization management is still not clear due to the relationship’s multi-disciplinary nature. This study aims to answer three research questions: a) What is the state of art of big data analytics adopted by healthcare organizations? b) What about the benefits for both health managers and healthcare organizations? c) What about future directions on big data analytics research in healthcare?

Methods

Through a systematic literature review the impact of big data analytics on healthcare management has been examined. The study aims to map extant literature and present a framework for future scholars to further build on, and executives to be guided by.

Results

The positive relationship between big data analytics and healthcare organization management has emerged. To find out common elements in the studies reviewed, 16 studies have been selected and clustered into 4 research areas: 1) Potentialities of big data analytics. 2) Resource management. 3) Big data analytics and management of health surveillance systems. 4) Big data analytics and technology for healthcare organization.

Conclusions

In conclusion is identified how the big data analytics solutions are considered a milestone for managerial studies applied to healthcare organizations, although scientific research needs to investigate standardization and integration of the devices as well as the protocol in data analysis to improve the performance of the healthcare organization.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12913-022-08167-z.

Analysis of Inducing Factors of Chronic Pulmonary Heart Disease Caused by Chronic Obstructive Pulmonary Disease at High Altitude through Epidemiological Investigation under Intelligent Medicine and Big Data

This study explores the risk factors of chronic pulmonary heart disease (CPHD) induced by plateau chronic obstructive pulmonary disease (COPD) based on intelligent medical treatment and big data of electrocardiogram (ECG) signal. Based on GPU, a wavelet algorithm is introduced to extract features of ECG signal, and it was combined with generalized regression neural network (GRNN) to improve classification accuracy. From June 2018 to December 2020, 10,185 patients diagnosed with COPD in the plateau area by pulmonary function testing, ECG, and chest X-ray at X Hospital are taken as the research objects to evaluate the distribution of CPHD incidence at different ages and altitudes. The running time of GTX780Ti is about 15 times shorter than that of CPU. The accuracy of N detection based on the GPU-accelerated neural network model reached 98.06%. Accuracy (Acc), sensitivity (Se), specificity (Sp), and positive rate (PR) of V were 99.03%, 89.17%, 98.92%, and 93.18%, respectively. The Acc, Se, Sp, and PR of S were 99.54%, 86.22%, 99.74%, and 92.56%, respectively. The GRNN classification accuracy was up to 98%. 19% of COPD patients were diagnosed with CPHD, including 1,409 males (72.82%) and 526 females (36.24%). The highest prevalence of CPHD was 64.60% when the altitude was 1,900-2,499 m, and the prevalence was only 2.43% when the altitude was ≥3,500 m. The highest prevalence of CPHD was 63.77% at the age of 61-70 years, and the lowest prevalence at the age of 15∼20 years was only 0.26%. Therefore, the GPU-based neural network model improved the classification accuracy of ECG signals. Age and altitude were risk factors for CPHD induced by high-altitude COPD, which provided a reference for the prevention, diagnosis, and treatment of CPHD in high-altitude areas.

Using an Individual-Centered Approach to Gain Insights From Wearable Data in the Quantified Flu Platform: Netnography Study

BACKGROUND

Wearables have been used widely for monitoring health in general, and recent research results show that they can be used to predict infections based on physiological symptoms. To date, evidence has been generated in large, population-based settings. In contrast, the Quantified Self and Personal Science communities are composed of people who are interested in learning about themselves individually by using their own data, which are often gathered via wearable devices.

OBJECTIVE

This study aims to explore how a cocreation process involving a heterogeneous community of personal science practitioners can develop a collective self-tracking system for monitoring symptoms of infection alongside wearable sensor data.

METHODS

We engaged in a cocreation and design process with an existing community of personal science practitioners to jointly develop a working prototype of a web-based tool for symptom tracking. In addition to the iterative creation of the prototype (started on March 16, 2020), we performed a netnographic analysis to investigate the process of how this prototype was created in a decentralized and iterative fashion.

RESULTS

The Quantified Flu prototype allowed users to perform daily symptom reporting and was capable of presenting symptom reports on a timeline together with resting heart rates, body temperature data, and respiratory rates measured by wearable devices. We observed a high level of engagement; over half of the users (52/92, 56%) who engaged in symptom tracking became regular users and reported over 3 months of data each. Furthermore, our netnographic analysis highlighted how the current Quantified Flu prototype was a result of an iterative and continuous cocreation process in which new prototype releases sparked further discussions of features and vice versa.

CONCLUSIONS

As shown by the high level of user engagement and iterative development process, an open cocreation process can be successfully used to develop a tool that is tailored to individual needs, thereby decreasing dropout rates.

The Role of the Internet of Things in Healthcare: Future Trends and Challenges

BACKGROUND AND OBJECTIVE

With the recent advances in the Internet of Things (IoT), the field has become more and more developed in healthcare. The Internet of things will help physicians and hospital staff perform their duties comfortably and intelligently. With the latest advanced technologies, most of the challenges of using IoT have been resolved, and this technology can be a great revolution and has many benefits in the future of digital. Healthcare is one of the most useful areas for IoT use. The most important application of IoT is to monitor and make quick decisions in critical situations. Thanks to this technology-based treatment approach, there is an unprecedented opportunity to better the quality and productivity of treatments and better the patient's well-being and better government funding.

METHODS

In this paper, we provide a comprehensive overview of the primary uses of IoT in healthcare. We used the Systematic Literature Review (SLR) method to analyze and comparison articles published in this field between 2015 and March 2020.

RESULTS

A comprehensive taxonomy is presented based on the contents of the articles under study. In this article, a brief overview of selected articles based on research questions is given and highlights the most critical challenges and case studies for the future use of IoT in healthcare.

CONCLUSIONS

According to a detailed study of the 89 articles and a glimpse into about 208 articles, challenges and future trends in healthcare have been identified.