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. It harnesses diverse data types, including structured and unstructured information gathered during ED visits to address the evolving needs of localized healthcare systems. METHODS: In this cross-sectional study, 80,073 ED patient records were amassed from a major southern Taiwan hospital in 2018-2019. An ensemble model incorporated structured (demographics, vital signs) and pre-processed unstructured data (chief complaints, preliminary diagnoses) using bag-of-words (BOW) and term frequency-inverse document frequency (TF-IDF). Two random forest base-learners for structured and unstructured data were employed and then complemented by a multi-layer perceptron meta-learner. RESULTS: The ensemble model demonstrates strong predictive performance for ED dispositions, achieving an area under the receiver operating characteristic curve of 0.94. The models based on unstructured data encoded with BOW and TF-IDF yield similar performance results. Among the structured features, the top five most crucial factors are age, pulse rate, systolic blood pressure, temperature, and acuity level. In contrast, the top five most important unstructured features are pneumonia, fracture, failure, suspect, and sepsis. CONCLUSIONS: Findings indicate that utilizing ensemble learning with a blend of structured and unstructured data proves to be a predictive method for determining ED dispositions.

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. This study aims to undertake a systematic review and meta-analysis of antecedents of DCT acceptance and investigate potential moderators of these antecedents. By searching multiple databases and filtering studies by using both inclusion and exclusion criteria, 76 and 25 studies were included for systematic review and meta-analysis, respectively. Random-effects models were chosen to estimate meta-analysis results since Q, I 2, and H index signified some degree of heterogeneity. Fail-safe N was used to assess publication bias. Most DCT acceptance studies have focused on DCT related factors. Included antecedents are all significant predictors of DCT acceptance except for privacy concerns and fear of COVID-19. Subgroup analysis showed that individualism/collectivism moderate the relationships between norms/privacy concerns and intention to use DCT. Based on the results, the mean effect size of antecedents of DCT acceptance and the potential moderators may be more clearly identified. Appropriate strategies for boosting the DCT acceptance rate can be proposed accordingly.

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.

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. A three-tier platform that based on cloud infrastructure with seven functionalities was developed. The survey revealed that healthcare workers with sufficient SDM knowledge have an antecedent effect on the three perceptive factors of acceptance of mobile SDM. Resistance to change and perceived ease of use show significant effect on behavioral intention. We provided a comprehensive architecture of mobile SDM and observed the implementation in a medical center. The majority of healthcare workers expressed their acceptancem; however, resistance to change still present. It is, therefore, necessary to be eliminated by continuously promoting activities that highlight the advantages of the Mobile SDM platform. In clinical practice, we validated that the mobile SDM provides patients and their families with an easy way to express their concerns to healthcare workers improving significantly their relationship with each other.

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.

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. Meanwhile, as advanced technologies bring in new communication devices, effects of different types of communications used in shared decision-making need to be addressed. OBJECTIVE: This study proposes a research framework to determine the influencing antecedents of shared decision-making, and to evaluate the effects of shared decision-making on patient outcomes when they are computer-mediated and when the decision-makers communicate face-to-face. METHODS: A cross-section survey was conducted and a total of 48 valid samples were obtained. The participants were CKD Stage III, IV, or V patients who had received medical treatment in a hospital in Taiwan. The collected data were subjected to an independent t-test and partial least squares analysis to validate the research framework. RESULTS: Doctor-patient communication (DPC) and doctor-patient relationship (DPR) have no significant direct impact on patient outcomes. Nevertheless, both DPC and DPR significantly impact shared decision-making which in turn impacts patient outcomes. Moreover, patients who use computer-mediated communication were found to have significantly higher perceptions of shared decision-making than those who did not. CONCLUSIONS: The incidence and prevalence of end-stage renal disease in Taiwan are among the highest in the world. The results of this study can serve as a reference for hospitals to improve CKD patients' outcomes. Meanwhile, during the COVID-19 pandemic, this study suggested hospitals should encourage shared decision-making with computer-mediated communication to ensure that patients receive proper treatment and have the desired outcomes.

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.

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. To that end, we have extended the theory of planned behavior by incorporating pertinent constructs derived after interviews with users. A research model and hypotheses are then proposed and tested. METHODS: Interviews were first conducted to collect qualitative data from 20 participants based on purposive sampling. Content analysis was then used to find evidence supporting important constructs for a research model. A survey methodology based on convenience sampling was then used to collect data. Totally, 205 valid responses were gathered and analyzed by using partial least squares structural equation modeling to validate the research model. RESULTS: Health consciousness and perceived convenience were found positively associated with individuals' attitudes towards the use of medical chatbots. Moreover, attitude and subjective norm were found to be significantly and positively related to individuals' intentions to use medical chatbots. CONCLUSIONS: The proposed model with the extended theory of planned behavior is able to predict individuals' intention to use medical chatbots well. Hospital managers can formulate strategies to improve individuals' health consciousness and perceptions of convenience to develop the desired attitudes among individuals, using medical chatbots. Further, strategies to improve patients' awareness of medical chatbots should also be formulated.

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.

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.

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. However, despite having many breakthroughs in research and pilot projects, healthcare AI is still considered rare and is broadly used in the healthcare setting. Based on a medical center in Taiwan that has introduced a variety of healthcare AI into practice, this study discussed and analyzed the issues and concerns in the development and scaling of medical AIs from the perspective of various stakeholders in the healthcare setting, including the government, healthcare institutions, users (healthcare workers), and AI providers. The present study also identified critical influential factors for the deployment and scaling of healthcare AI. It is hoped that this paper can serve as an important reference for the advancement of healthcare AI not only in Taiwan but also in other countries.

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.

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. Health literacy was evaluated using the Mandarin version of the European Health Literacy Survey Questionnaire. Data on screening behaviors for cervical, breast and colorectal cancers were confirmed by the Taiwanese National eHealth Database. Most respondents with inadequate or problematic general health literacy had no or irregular screening behaviors for cervical, breast and colorectal cancers. In multivariable regression analysis, women with inadequate health literacy were at a greater risk (Odds ratio = 5.71; 95% CI: 1.40-23.26) of having no previous Pap smear screening or >3 years screening interval regardless of education level. However, this association was not detected for breast or colorectal cancer. Women with inadequate health literacy were more likely to have irregular cervical cancer screening, however no associations among health literacy and breast or colorectal cancer were detected. The impact of health literacy on cancer screening behavior warrants further attention and research.

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. The present study aims to identify appropriate diagnosis codes for type 2 diabetes mellitus patients by means of building a multi-class prediction model which is both parsimonious and possessing minimum features. In addition, the importance of features for predicting diagnose code is provided. METHODS: This study included 149 patients who have contracted type 2 diabetes mellitus. The sample was collected from a large hospital in Taiwan from November, 2017 to May, 2018. Machine learning algorithms including instance-based, decision trees, deep neural network, and ensemble algorithms were all used to build the predictive models utilized in this study. Average accuracy, area under receiver operating characteristic curve, Matthew correlation coefficient, macro-precision, recall, weighted average of precision and recall, and model process time were subsequently used to assess the performance of the built models. Information gain and gain ratio were used in order to demonstrate feature importance. RESULTS: The results showed that most algorithms, except for deep neural network, performed well in terms of all performance indices regardless of either the training or testing dataset that were used. Ten features and their importance to determine the diagnosis code of type 2 diabetes mellitus were identified. Our proposed predictive model can be further developed into a clinical diagnosis support system or integrated into existing healthcare information systems. Both methods of application can effectively support physicians whenever they are diagnosing type 2 diabetes mellitus patients in order to foster better patient-care planning.

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.

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.

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 ± 7.22 years. Health literacy was assessed using the Mandarin version of the European Health Literacy Survey Questionnaire. Knowledge, attitude and decision towards hospice care were assessed using an interviewer-administered questionnaire. Partial least squares were used for data analysis. More than half of the respondents had sufficient knowledge of hospice care (60.7%) and a positive attitude (77.3%) and positive decision (85%) towards hospice care. In the structural equation model, general health literacy positively predicted knowledge (ß = 0.73, p <0.001), attitude (ß = 0.06, p = 0.038) and decision (ß = 0.14, p < 0.001) towards hospice care. General health literacy had a greater overall effect on hospice decision (ß = 0.57) than hospice knowledge (ß = 0.54). In addition, disease prevention health literacy also demonstrated a higher level of influence on hospice decision (ß = 0.59) than hospice knowledge (ß = 0.53). Health literacy was associated with hospice knowledge, attitude and decision. Incorporating health literacy interventions into hospice promotion strategies is recommended.

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.