An ensemble model for predicting dispositions of emergency department patients.

Objective: The healthcare challenge driven by an aging population and rising demand is one of the most pressing issues leading to emergency department (ED) overcrowding. An emerging solution lies in machine learning's potential to predict ED dispositions, thus leading to promising substantial benefits. This study's objective is to create a predictive model for ED patient dispositions by employing ensemble learning.

Antecedents predicting digital contact tracing acceptance: a systematic review and meta-analysis.

An awareness of antecedents of acceptance of digital contact tracing (DCT) can enable healthcare authorities to design appropriate strategies for fighting COVID-19 or other infectious diseases that may emerge in the future. However, mixed results about these antecedents are frequently reported. Most prior DCT acceptance review studies lack statistical synthesis of their results.

The accuracy of artificial intelligence used for non-melanoma skin cancer diagnoses: a meta-analysis.

Background: With rising incidence of skin cancer and relatively increased mortality rates, an improved diagnosis of such a potentially fatal disease is of vital importance. Although frequently curable, it nevertheless places a considerable burden upon healthcare systems. Among the various types of skin cancers, non-melanoma skin cancer is most prevalent.

Antecedents, Consequences, and the Role of Third Parties in the Trust Repair Process: Evidence Taken from Orthodontics.

Orthodontic treatment has popularized in Taiwan. Healthcare institutions can be responsive in their coping strategies and determine whether third-party intervention should take place involving medical disputes related to orthodontics in order to repair patient trust. This study draws on orthodontic treatment to explore the effect of various trust repair strategies employed by healthcare institutions and third-party involvement positively affecting outcomes related to trust repair.

Antecedents and consequences of shared decision making for patients with chronic kidney diseases: A cross-sectional survey.

Background: Chronic kidney disease (CKD) has a strong negative impact on patients. Finding ways to improve CKD patients' conditions by shared decision-making is receiving much attention. However, little attention has been paid to influencing antecedents and effects of shared decision-making.

Using Healthcare Resources Wisely: A Predictive Support System Regarding the Severity of Patient Falls.

Background: An injurious fall is one of the main indicators of care quality in healthcare facilities. Despite several fall screen tools being widely used to evaluate a patient's fall risk, they are frequently unable to reveal the severity level of patient falls. The purpose of this study is to build a practical system useful to predict the severity level of in-hospital falls.

Why would you use medical chatbots? interview and survey.

Background: Chatbots, empowered by artificial intelligence, are becoming increasingly popular in many fields and have much potential for application in real life situations. However, little attention has been paid to medical chatbots and most existing evidence focuses on technical issues while behavioral research is still lacking.

Objective: This study seeks to examine the key factors that can motivate individuals to use medical chatbots.

The accuracy of machine learning approaches using non-image data for the prediction of COVID-19: A meta-analysis.

Objective: COVID-19 is a novel, severely contagious disease with enormous negative impact on humanity as well as the world economy. An expeditious, feasible tool for detecting COVID-19 remains yet elusive. Recently, there has been a surge of interest in applying machine learning techniques to predict COVID-19 using non-image data.

Implementation of a patient-centered mobile shared decision making platform and healthcare workers' evaluation: a case in a medical center.

Shared decision making is a patient-centered clinical decision-making process that allows healthcare workers to share the existing empirical medical outcomes with patients before making critical decisions. This study aims to explore a project in a medical center of developing a mobile SDM in Taiwan. Chi Mei Medical Center developed the mobile SDM platform and conducted a survey of evaluation from healthcare workers.

Hospital Staff's Adherence to Information Security Policy: A Quest for the Antecedents of Deterrence Variables.

Information security has come to the forefront as an organizational priority since information systems are considered as some of the most important assets for achieving competitive advantages. Despite huge capital expenditures devoted to information security, the occurrence of security breaches is still very much on the rise. More studies are thus required to inform organizations with a better insight on how to adequately promote information security.

The Critical Factors Affecting the Deployment and Scaling of Healthcare AI: Viewpoint from an Experienced Medical Center.

Healthcare Artificial Intelligence (AI) has the greatest opportunity for development. Since healthcare and technology are two of Taiwan's most competitive industries, the development of healthcare AI is an excellent chance for Taiwan to improve its health-related services. From the perspective of economic development, promoting healthcare AI must be a top priority.

Does Online Community Participation Contribute to Medication Adherence? An Empirical Study of Patients with Chronic Diseases.

Effectively improving the medication adherence of patients is crucial. Past studies focused on treatment-related factors, but little attention has been paid to factors concerning human beliefs such as trust or self-efficacy. The purpose of this study is to explore the following aspects of patients with chronic diseases: (1) The relationship between emotional support, informational support, self-efficacy, and trust; (2) the relationship between self-efficacy, trust, and medication adherence; and, (3) whether chronic patients' participation in different types of online communities brings about significant statistical differences in the relationships between the abovementioned variables.

Health literacy and cancer screening behaviors among community-dwelling female adults in Taiwan.

This study was designed to explore the association among health literacy and cancer screening behaviors in Taiwanese females. A total of 353 community-dwelling females were recruited in this cross-sectional study from February to October 2015. Demographic, socioeconomic and personal behavior variables including physical activity, community activity, smoking, alcohol consumption, and betel nut chewing were recorded.

A multi-class classification model for supporting the diagnosis of type II diabetes mellitus.

Background: Numerous studies have utilized machine-learning techniques to predict the early onset of type 2 diabetes mellitus. However, fewer studies have been conducted to predict an appropriate diagnosis code for the type 2 diabetes mellitus condition. Further, ensemble techniques such as bagging and boosting have likewise been utilized to an even lesser extent.

Deterrence approach on the compliance with electronic medical records privacy policy: the moderating role of computer monitoring.

Background: This study explored the possible antecedents that will motivate hospital employees' compliance with privacy policy related to electronic medical records (EMR) from a deterrence perspective. Further, we also investigated the moderating effect of computer monitoring on relationships among the antecedents and the level of hospital employees' compliance intention.

Methods: Data was collected from a large Taiwanese medical center using survey methodology.

Development of a clinical support system for identifying social frailty.

Objective: Recognizing frailty, also known as clinical geriatric syndrome in the elderly that is characterized by high vulnerability and low resilience, and its extensive influence in clinical practice is challenging. This study aims to develop a social frailty prediction system based on machine learning approaches in order to identify the social frailty status of the elders in order to advance appropriate social services provision.

Materials And Methods: This cross-sectional study enrolled and collected information from 595 community-dwelling seniors aged 65+.

Factors associated with low health literacy among community-dwelling women in Taiwan.

The present study investigated factors associated with health literacy in community-dwelling Taiwanese women, particularly focusing on those associated with prevalent unhealthy behaviors. This cross-sectional study recruited 353 community-dwelling women aged 39-89 years from February to October 2015 in urban, suburban, and rural areas. Variables investigated included physical activity, community activity, tobacco usage, alcohol consumption, and betel-nut chewing.

The impact of health literacy on knowledge, Attitude and decision towards hospice care among community-dwelling seniors.

The aim of this study was to investigate the relationships between health literacy and hospice knowledge, attitude and decision in community-dwelling elderly participants. This cross-sectional study enrolled 990 community-dwelling elderly participants in three residential areas, with a mean age of 71.53 ± 7.

Predicting hospital-acquired pneumonia among schizophrenic patients: a machine learning approach.

Background: Medications are frequently used for treating schizophrenia, however, anti-psychotic drug use is known to lead to cases of pneumonia. The purpose of our study is to build a model for predicting hospital-acquired pneumonia among schizophrenic patients by adopting machine learning techniques.

Methods: Data related to a total of 185 schizophrenic in-patients at a Taiwanese district mental hospital diagnosed with pneumonia between 2013 ~ 2018 were gathered.

Continuance compliance of privacy policy of electronic medical records: the roles of both motivation and habit.

Background: Hospitals have increasingly realized that wholesale adoption of electronic medical records (EMR) may introduce differential tangible/intangible benefits to them, including improved quality-of-care, reduced medical errors, reduced costs, and allowable instant access to relevant patient information by healthcare professionals without the limitations of time/space. However, an increased reliance on EMR has also led to a corresponding increase in the negative impact exerted via EMR breaches possibly leading to unexpected damage for both hospitals and patients. This study investigated the possible antecedents that will influence hospital employees' continuance compliance with privacy policy of Electronic Medical Records (EMR).

Impact of Health Literacy on Frailty among Community-Dwelling Seniors.

Health literacy has been reported to have effects on health behavior change and health-related outcomes, but few studies have explored the association between health literacy and frailty. The aim of our study is to investigate the relationships between health literacy and frailty among community-dwelling seniors. This cross-sectional study enrolled 603 community-dwelling older adults (307 women) in residential areas, with a mean age of 70.

Strategic Improvement for Quality and Satisfaction of Hospital Information Systems.

The purpose of our study aimed to identify attributes capable of improving physicians' satisfaction levels with the use of a hospital information system (HIS). A model inclusive of system quality, information quality, and service quality related to an HIS is used to form antecedents of user satisfaction. Survey methodology was used to collect an attributive set representing the system quality, information quality, and service quality made available from 150 physicians at a large health-care system in southern Taiwan.

A Deterrence Approach to Regulate Nurses' Compliance with Electronic Medical Records Privacy Policy.

Hospitals have become increasingly aware that electronic medical records (EMR) may bring about tangible/intangible benefits to managing institutions, including reduced medical errors, improved quality-of-care, curtailed costs, and allowed access to patient information by healthcare professionals regardless of limitations. However, increased dependence on EMR has led to a corresponding increase in the influence of EMR breaches. Such incursions, which have been significantly facilitated by the introduction of mobile devices for accessing EMR, may induce tangible/intangible damage to both hospitals and concerned individuals.

How can hospitals better protect the privacy of electronic medical records? Perspectives from staff members of health information management departments.

Purpose: The adoption of electronic medical records (EMR) is expected to better improve overall healthcare quality and to offset the financial pressure of excessive administrative burden. However, safeguarding EMR against potentially hostile security breaches from both inside and outside healthcare facilities has created increased patients' privacy concerns from all sides. The aim of our study was to examine the influencing factors of privacy protection for EMR by healthcare professionals.

Does information overload prevent chronic patients from reading self-management educational materials?

Background: Self-care management is becoming an important part of care for chronic patients. However, various kinds of self-management educational materials which government or healthcare institutions provide for patients may not achieve the expected outcome. One of the critical reasons affecting patients' use intention could be patients' perceived information overload regarding the self-management educational materials.