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. Despite such prevalence and its associated cost, scant proof concerning the diagnostic accuracy via Artificial Intelligence (AI) for non-melanoma skin cancer exists. This study meta-analyzes the diagnostic test accuracy of AI used to diagnose non-melanoma forms of skin cancer, and it identifies potential covariates that account for heterogeneity between extant studies. METHODS: Various electronic databases (Scopus, PubMed, ScienceDirect, SpringerLink, and Dimensions) were examined to discern eligible studies beginning from March 2022. Those AI studies predictive of non-melanoma skin cancer were included. Summary estimates of sensitivity, specificity, and area under receiver operating characteristic curves were used to evaluate diagnostic accuracy. The revised Quality Assessment of Diagnostic Studies served to assess any risk of bias. RESULTS: A literature search produced 39 eligible articles for meta-analysis. The summary sensitivity, specificity, and area under receiver operating characteristic curve of AI for diagnosing non-melanoma skin cancer was 0.78, 0.98, & 0.97, respectively. Skin cancer typology, data sources, cross validation, ensemble models, types of techniques, pre-trained models, and image augmentation became significant covariates accounting for heterogeneity in terms of both sensitivity and/or specificity. CONCLUSIONS: Meta-analysis results revealed that AI is predictive of non-melanoma with an acceptable performance, but sensitivity may become improved. Further, ensemble models and pre-trained models are employable to improve true positive rating.

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. Degree of health literacy was evaluated using the Chinese-language version of the European Health Literacy Survey Questionnaire. Most respondents had inadequate (17.6%), or problematic (49.3%), general health literacy. Multiple logistic regression analyses showed that low educational attainment was closely associated with inadequate or problematic general health literacy. Women who did not engage in regular physical activity or direct community activity were more likely to have inadequate and problematic general health literacy, respectively. Selected unhealthy behaviors (tobacco usage, alcohol consumption, betel-nut chewing) were not associated with health literacy. Low health literacy was prevalent among participants. Lower educational attainment and a lack of physical or community activity were associated with low health literacy. Health literacy should be considered during the process of delivering health information, and health education programs must enhance health literacy tailored to address individuals' lifestyles.

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. A total of 303 responses was analyzed via hierarchical regression analysis. RESULTS: The results revealed that sanction severity and sanction certainty significantly predict hospital employees' compliance intention, respectively. Further, our study found external computer monitoring significantly moderates the relationship between sanction certainty and compliance intention. CONCLUSIONS: Based on our findings, the study suggests that healthcare facilities should take proactive countermeasures, such as computer monitoring, to better protect the privacy of EMR in addition to stated privacy policy. However, the extent of computer monitoring should be kept to minimum requirements as stated by relevant regulations.

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. Eleven predictors, including gender, age, clozapine use, drug-drug interaction, dosage, duration of medication, coughing, change of leukocyte count, change of neutrophil count, change of blood sugar level, change of body weight, were used to predict the onset of pneumonia. Seven machine learning algorithms, including classification and regression tree, decision tree, k-nearest neighbors, naïve Bayes, random forest, support vector machine, and logistic regression were utilized to build predictive models used in this study. Accuracy, area under receiver operating characteristic curve, sensitivity, specificity, and kappa were used to measure overall model performance. RESULTS: Among the seven adopted machine learning algorithms, random forest and decision tree exhibited the optimal predictive accuracy versus the remaining algorithms. Further, six most important risk factors, including, dosage, clozapine use, duration of medication, change of neutrophil count, change of leukocyte count, and drug-drug interaction, were also identified. CONCLUSIONS: Although schizophrenic patients remain susceptible to the threat of pneumonia whenever treated with anti-psychotic drugs, our predictive model may serve as a useful support tool for physicians treating such patients.

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). This is done from both motivational and habitual perspectives; specifically, we investigated the mediating role of habit between motivation and continuance compliance intention with EMR privacy policy. METHODS: Data was collected from a large Taiwanese medical center by means of survey methodology. A total of 312 responses comprised of various groups of healthcare professionals was collected and analyzed via structural equation modeling. RESULTS: The results demonstrated that self-efficacy, perceived usefulness, and facilitating conditions may significantly predict hospital employees' compliance habit formation, whereas habit may significantly predict hospital employees' intention to continuance adherence to EMR privacy policy. Further, habit partially mediates the relationships between self-efficacy, perceived usefulness, facilitating conditions and continuance adherence intention. CONCLUSIONS: Based on our findings, the study suggests that healthcare facilities should take measures to promote their employees' habitualization with continuous efforts to protect EMR privacy parameters. Plausible strategies include improving employees' levels of self-efficacy, publicizing the effectiveness of on-going privacy policy, and creating a positive habit-conducive environment leading to continued compliance behaviors.

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. The purpose of this study was to explore factors which may tend to inhibit nurses' intentions to violate privacy policy concerning EMR based upon the deterrence theory perspective. Utilizing survey methodology, 262 responses were analyzed via structural equation modeling. Results revealed that punishment certainty, detection certainty, and subjective norm would most certainly and significantly reduce nurses' intentions to violate established EMR privacy policy. With these findings, recommendations for health administrators in planning and designing effective strategies which may potentially inhibit nurses from violating EMR privacy policy are discussed.

The influence of telemedicine experience on physicians' perceptions regarding adoption.

BACKGROUND: The purposes of this study were to explore the factors influencing physicians' intention of adopting telemedicine and to conduct a multigroup analysis comparing the perceptions about telemedicine adoption between experienced and inexperienced physicians. Based on the Theory of Planned Behavior, we conducted a cross-sectional survey to collect data from 15 hospitals in Taiwan. MATERIALS AND METHODS: In total, 106 valid questionnaires were returned. We used structural equation modeling to analyze the collected data. RESULTS: Attitude (AT), subjective norm (SN), and perceived behavioral control (PBC) were found to be positively related to behavioral intention (BI) for combined data. Moreover, the relationships between AT→BI, SN→BI, and PBC→BI varied significantly between experienced and inexperienced physicians. Experienced physicians held stronger beliefs about the relationship between AT→BI than inexperienced physicians. CONCLUSIONS: According to the results, our study suggests that differing strategies for experienced and inexperienced physicians must be formulated to substantially boost the adoption of telemedicine technology.

Do ePortfolios Contribute to Learners' Reflective Thinking Activities? : A Preliminary Study of Nursing Staff Users.

The purposes of this study are threefold: 1) to find out what characteristics are required for the successful use of ePortfolios; 2) to discover what activities best represent reflective thinking during the use of ePortfolios; and, 3) to investigate the interrelationship between nursing staff users' perceived success levels with ePortfolios and with their reflective thinking activities. Survey methodology was used to gather responses from 78 nurses from a medical center located in southern Taiwan via questionnaires. Factor analysis and canonical correlation analysis were used to analyze the collected data. The results demonstrated that system quality, information quality, and user satisfaction are important variables in successful ePortfolio usage; while habitual action, understanding, reflection, and critical reflection are major variables of reflective thinking. Further, we found a significant relationship exists between the relative success of ePortfolios and reflective thinking activities of ePortfolios users. The subject hospital should pay special attention to important characteristics including system quality, information quality, and user satisfaction when employing ePortfolios to help nursing staff users to achieve their learning goals through this form of reflective thinking.

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. We have therefore undertaken a meta-analysis to quantify the diagnostic performance of machine learning models facilitating the prediction of COVID-19. MATERIALS AND METHODS: A comprehensive electronic database search for the period between January 1st, 2021 and December 3rd, 2021 was undertaken in order to identify eligible studies relevant to this meta-analysis. Summary sensitivity, specificity, and the area under receiver operating characteristic curves were used to assess potential diagnostic accuracy. Risk of bias was assessed by means of a revised Quality Assessment of Diagnostic Studies. RESULTS: A total of 30 studies, including 34 models, met all of the inclusion criteria. Summary sensitivity, specificity, and area under receiver operating characteristic curves were 0.86, 0.86, and 0.91, respectively. The purpose of machine learning models, class imbalance, and feature selection are significant covariates useful in explaining the between-study heterogeneity, in terms of both sensitivity and specificity. CONCLUSIONS: Our study findings show that non-image data can be used to predict COVID-19 with an acceptable performance. Further, class imbalance and feature selection are suggested to be incorporated whenever building models for the prediction of COVID-19, thus improving further diagnostic performance.

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. This practice is done in order to better allocate limited healthcare resources and to improve overall patient safety. Methods: Four hundred and forty-six patients who experienced fall events at a large Taiwanese hospital were referenced. Eight predictors were used to ascertain the severity of patient falls solely based on the above study population. Multinomial logistic regression, Naïve Bayes, random forest, support vector machine, eXtreme gradient boosting, deep learning, and ensemble learning were adopted to establish predictive models. Accuracy, F1 score, precision, and recall were utilized to assess the models' performance. Results: Compared to other learners, random forest exhibited satisfying predictive performance in terms of all metrics (accuracy: 0.844, F1 score: 0.850, precision: 0.839, and recall: 0.875 for the test dataset), and it was adopted as the base learner for a severity-level predictive system which is web-based. Furthermore, age, ability of independent activity, patient sources, use of assistive devices, and fall history within the past 12 months were deemed the top five important risk factors for evaluating fall severity. Conclusions: The application of machine learning techniques for predicting the severity level of patient falls may result in some benefits to monitor fall severity and to better allocate limited healthcare resources.

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. Patients were recruited among those who have undergone orthodontic treatments, and 353 valid scenario-based questionnaires were collected through an online survey. Results revealed that: (1) the affective and informational repair strategies positively impacted trust repair while the functional repair strategy did not; (2) trust repair positively impacted patient satisfaction/word-of-mouth and mediated between repair strategies and satisfaction/word-of-mouth; and (3) third-party involvement moderated the relationship between trust repair and word-of-mouth. The findings suggest that rather than receiving monetary compensation, patients usually prefer that healthcare institutions acknowledge their fault, offer apologies, and engage in active communications to clarify the causes of medical dispute. Further, an objective third party should be involved to mediate the medical disputes to afford satisfaction all around.

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. To address this issue, this study investigates important factors influencing hospital staff's adherence to Information Security Policy (ISP). Deterrence theory is adopted as the theoretical underpinning, in which punishment severity and punishment certainty are recognized as the most significant predictors of ISP adherence. Further, this study attempts to identify the antecedents of punishment severity and punishment certainty by drawing from upper echelon theory and well-acknowledged international standards of IS security practices. A survey approach was used to collect 299 valid responses from a large Taiwanese healthcare system, and hypotheses were tested by applying partial least squares-based structural equation modeling. Our empirical results show that Security Education, Training, and Awareness (SETA) programs, combined with internal auditing effectiveness are significant predictors of punishment severity and punishment certainty, while top management support is not. Further, punishment severity and punishment certainty are significant predictors of hospital staff's ISP adherence intention. Our study highlights the importance of SETA programs and internal auditing for reinforcing hospital staff's perceptions on punishment concerning ISP violation, hospitals can thus propose better internal strategies to improve their staff's ISP compliance intention accordingly.

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. A questionnaire survey was conducted in this study, with 452 valid questionnaires collected from chronic patients previously participating in online community activities. Partial Least Squares-Structural Equation Modeling analysis showed that emotional support and informational support positively predict self-efficacy and trust, respectively, and consequently, self-efficacy and trust positively predict medication adherence. In addition, three relationships including the influence of emotional support on trust, the influence of trust on medication adherence, and the influence of self-efficacy on medication adherence, the types of online communities result in significant statistical differences. Based on the findings, this research suggests healthcare professionals can enhance patients' self-efficacy in self-care by providing necessary health information via face-to-face or online communities, and assuring patients of demonstrable support. As such, patients' levels of trust in healthcare professionals can be established, which in turn improves their medication adherence.

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+. Fourteen predictors established from questionnaires and electronic medical records were used to predict the social frailty of participants. Bagged classification and regression trees, model average neural network, random forest, C5.0, eXtreme gradient boosting, and stochastic gradient boosting were used to build the predictive model in use. Performance was compared using accuracy, kappa, area under receiver operating characteristic curve, sensitivity, and specificity. The frailty predictive system was web-based and built upon representational state transfer application program interfaces. RESULTS: C5.0 achieved the best overall performance than remaining learners, and was adopted as the base learner for the social frailty prediction system. In terms of the area under receiver operating characteristic curve (AUC), health literacy (AUC = 0.68) was found to be the most important variable for predicting one's social frailty, followed by comorbidity (AUC = 0.67), religious participation (AUC = 0.67), physical activity (AUC = 0.66), and geriatric depression score (AUC = 0.62). CONCLUSIONS: Results suggest that a combination of such data that is both available and unavailable from electronic medical records is predictive of the social frailty of an elderly population.

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. Responses were segmented into low and high satisfaction and analyzed with partial least squares and importance-performance analysis. The results reveal that system quality, information quality, and service quality may be used to significantly predict physicians' satisfaction. Two system quality attributes (reliability and response time) were identified as the highest priorities for intervention by low- and high-satisfaction users. Low-satisfaction users further expect improvement of the HIS service quality to take place. The subject health-care system should produce coping interventions for those high priorities to enhance the satisfaction of physicians.

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. METHOD: We used survey methodology to collect questionnaire responses from staff members in health information management departments among nine Taiwanese hospitals active in EMR utilisation. A total of 209 valid responses were collected in 2014. We used partial least squares for analysing the collected data. RESULTS: Perceived benefits, perceived barriers, self-efficacy and cues to action were found to have a significant association with intention to protect EMR privacy, while perceived susceptibility and perceived severity were not. CONCLUSION: Based on the findings obtained, we suggest that hospitals should provide continuous ethics awareness training to relevant staff and design more effective strategies for improving the protection of EMR privacy in their charge. Further practical and research implications are also discussed.