Technical Blossom in Medical Care: The Influence of Big Data Platform on Medical Innovation

The Moderating Effects of Social Responsibility Climate and Safety Climate in Keeping Healthcare Workers' Engagement during COVID-19

OBJECTIVE

The outbreak of COVID-19 brings an overload of physical and mental demands to healthcare professionals. Keeping healthcare professionals sustainable, engaged, and performing at their highest levels becomes critical and nonetheless difficult. The objective of this research is to link the literature on organizational climates, corporate social responsibility, safety science, and work engagement, and propose a research framework that investigates the factors influencing healthcare professionals' engagement during COVID-19.

METHODOLOGY

We propose that when healthcare workers' career callings are triggered by COVID-19, it influences their perceptions of the work's meaningfulness, which ultimately enhances their work engagement. We argue that creating a social responsibility climate and a safety climate inside the hospital facilitates the process of turning healthcare workers' perceived work meaningfulness into work engagement. We collected data from 112 healthcare professionals, including nurses, doctors, and executive staff, from 16 wards in a public hospital in China to test our hypotheses.

RESULTS

Hierarchical linear regression analysis provided empirical support for our research model. We find that healthcare professionals' career callings during COVID-19 enhanced their perceived work meaningfulness, which results in increased work engagement. Moreover, a social responsibility climate and a safety climate strengthens the link between work meaningfulness and work engagement among participants.

CONCLUSIONS

Creating a social responsibility climate and a safety climate in the workplace are effective management approaches to realize healthcare workers' feelings of work meaningfulness and turn them into work engagement.

Social innovation for life expectancy extension utilizing a platform-centered system used in the Iwaki health promotion project: A protocol paper

Introduction:

We are trying to create a platform for social innovation to extend life span.

Methods:

Since 2005, health data (approximately 3000 items per person as of 2020) of approximately 1000 adults have been collected each year during the Iwaki Health Promotion Project. The industry, government, academia, and citizens have involvements in data collection, aiming to build a platform that encourages societal innovation and subsequently extends life expectancy in Aomori. The Iwaki Health Promotion Project has been supported financially by the Japanese government since it was selected as the Center of Innovation program in 2013.

Results:

Since the numbers of academia, industries, governments, and citizens involved in the Iwaki Health Promotion Project increased over the years, the big data produced during the project has become increasingly pluripotent and adaptable. It has been used to promote public health, which has also created a stronger partnership among companies and research organizations. Consequently, the amount of data collected from the project has gained attention and became more open to companies and researchers participating in the Iwaki Health Promotion Project, resulted in establishing a larger platform. It also led to the acquisition of external funding, publications of numerous research papers, creation of new health examinations, and the establishment of the Health Promotion Center (an institution for cultivating health volunteers).

Conclusion:

The Iwaki Health Promotion Project aims not only to produce a pluripotent big data but also to improve the average life expectancy of Aomori by creating a large platform in the society. Its positive impact in the future is infinite and will keep growing as long as it is maintained by the society.

A Real-Time Artificial Intelligence-Assisted System to Predict Weaning from Ventilator Immediately after Lung Resection Surgery

Assessment of risk before lung resection surgery can provide anesthesiologists with information about whether a patient can be weaned from the ventilator immediately after surgery. However, it is difficult for anesthesiologists to perform a complete integrated risk assessment in a time-limited pre-anesthetic clinic. We retrospectively collected the electronic medical records of 709 patients who underwent lung resection between 1 January 2017 and 31 July 2019. We used the obtained data to construct an artificial intelligence (AI) prediction model with seven supervised machine learning algorithms to predict whether patients could be weaned immediately after lung resection surgery. The AI model with Naïve Bayes Classifier algorithm had the best testing result and was therefore used to develop an application to evaluate risk based on patients' previous medical data, to assist anesthesiologists, and to predict patient outcomes in pre-anesthetic clinics. The individualization and digitalization characteristics of this AI application could improve the effectiveness of risk explanations and physician-patient communication to achieve better patient comprehension.

Data Analysis Model Design of Health Service Monitoring System for China's Elderly Population: The Proposal of the F-W Model Based on the Collaborative Governance Theory of Healthy Aging

In the era of artificial intelligence, big data and 5G, health care for elderly people is facing an important digital transformation. The objective of this study is to design the data analysis module of the elderly health service monitoring system (HSMS) and attempt to put forward a new healthy aging (HA) model that is applicable not only to the individual HA, but also to the regional HA system. Based on the HA theory of collaborative governance, we divided the elderly HSMS into four modules, including physical health, mental health, ability of daily activity, and social participation. Then, factors that influence HA were assessed by stepwise logistic regression to build the analysis model, using the public micro-panel data of the China Health and Retirement Longitudinal Survey (CHARLS). Age (odds ratio (OR) = 1.55 (95% confidence interval (CI): 1.06–2.27)), living in urban areas (OR = 1.57 (95% CI: 1.03–2.39)), being literate (OR = 1.51 (95% CI: 1.01–2.23)), expecting to get long-term health care in the future from their grown children (OR = 1.69 (95% CI: 1.10–2.61)) and having literate grown children (OR = 2.01 (95% CI: 0.26–0.97)) had a significant positive impact on HA of elderly people. Therefore, the F-W (factors and weighs, also family and welfare) model is proposed in this paper. The outcomes can contribute with designing HSMS for different provinces and several different regions in China and leave a door open to improve the model and algorithm application for HSMS in the future studies.