Informatics for Health & Social Care最新文献

Background: Many individuals with depression are not being linked to treatment by their primary care providers. Electronic health records (EHRs) are common in medicine, but their impact on depression treatment is mixed. Because EHRs are diverse, differences may be attributable to differences in functionality. This study examines the relationship between EHR functions, and patterns of depression treatment in primary care.

Methods: secondary analyses from the 2013-2016 National Ambulatory Medical Care Survey examined adult primary care patients with new or acute depression (n = 5,368). Bivariate comparisons examined patterns of depression treatment by general EHR use, and logistic regression examined the impact of individual EHR functions on treatment receipt.

Results: Half the sample (57%; N = 3,034) was linked to depression treatment. Of this, 98.5% (n = 2,985) were prescribed antidepressants, while 4.3% (n = 130) were linked to mental health. EHR use did not impact mental health linkages, but EHR functions did affect antidepressant prescribing. Medication reconciliation decreased the odds of receiving an antidepressant (OR = .60, p < .05), while contraindication warnings increased the likelihood of an antidepressant prescription (OR = 1.91, p < .001).

Conclusions: EHR systems did not impact mental health linkages but improved rates of antidepressant prescribing. Optimizing the use of contraindication warnings may be a key mechanism to encourage antidepressant treatment.

To examine residents' subjective mental workload when they enter prescriptions in a computerized physician order entry (CPOE) system. Twenty-two residents completed six prescribing tasks in which two factors were manipulated: numerical input method and level of urgency. Data on demographic characteristics, familiarity with CPOE, and pretest performance were collected. The subjective mental workload was measured by the National Aeronautics and Space Administration-Task Load Index (NASA-TLX). Temporal demand (Mean = 34.48) contributed most to residents' workload on the CPOE task, followed by Performance (Mean = 29.23). No significant associations were found between workload and demographic characteristics, CPOE familiarity, or pretest CPOE performance (p's > .05). A 3 × 2 repeated-measures ANOVA yielded main effects of numerical input method [F (2, 19) = 88.358, p < .001, η² = .900] and level of urgency [F (1, 21) = 169.654, p < .001, η² = .890], and interaction of input method and urgency [F (2, 20) = 87.427, p < .001, η² = .900]. Residents' major sources of workload during the CPOE prescription were temporal demand and performance. Prescriptions entered by the row of numbers exhibited the highest workload. Workload increased with higher level of urgency. It is necessary to emphasize the negative impact of subjective workload, especially in prescription task under urgent situation. Further researches focus on medical staff's workload are encouraged to ensure patient safety.

Type 2 diabetes is a chronic, costly disease and is a serious global population health problem. Yet, the disease is well manageable and preventable if there is an early warning. This study aims to apply supervised machine learning algorithms for developing predictive models for type 2 diabetes using administrative claim data. Following guidelines from the Elixhauser Comorbidity Index, 31 variables were considered. Five supervised machine learning algorithms were used for developing type 2 diabetes prediction models. Principal component analysis was applied to rank variables' importance in predictive models. Random forest (RF) showed the highest accuracy (85.06%) among the algorithms, closely followed by the k-nearest neighbor (84.48%). The analysis further revealed RF as a high performing algorithm irrespective of data imbalance. As revealed by the principal component analysis, patient age is the most important predictor for type 2 diabetes, followed by a comorbid condition (i.e., solid tumor without metastasis). This study's finding of RF as the best performing classifier is consistent with the promise of tree-based algorithms for public data in other works. Thus, the outcome can guide in designing automated surveillance of patients at risk of forming diabetes from administrative claim information and will be useful to health regulators and insurers.

The successful implementation of a Computerized Provider Order Entry (CPOE) system is a challenging process for any healthcare organization. It requires a dramatic change not only to the way the care is provided but also to the way clinicians work. Because of the required change complexity, organizations must consider key factors of clinicians' acceptance to avoid resistance and maximize chances of success. This paper aims to identify the different factors that affect clinicians' acceptance of CPOE systems and their relation to existing change management models. A systematic literature review was conducted to identify barriers and recommendations to the clinicians' acceptance of CPOE systems. Then, a comparative analysis was used to explain the relationship between the discovered factors and change management, with a focus on Kotter's model. The review included 23 articles. A total of 28 barriers and 25 recommendations have been identified. In conclusion, factors of clinicians' acceptance fall into two categories: one related to the used implementation strategy and the other related to how the system was designed. Most of the factors are similar to change management principles. The systematic incorporation of change management principles during CPOE implementation would likely improve clinicians' acceptance of the system.

Health chatbots interview patients and collect health data. This process makes demands on data security and data privacy. To identify how and to what extent security and privacy are considered in current health chatbots. We conducted a scoping review by searching three bibliographic databases (PubMed, ACM Digital Library, IEEExplore) for papers reporting on chatbots in healthcare. We extracted which, how, and where data is stored by health chatbots and identified which external services have access to the data. Out of 1026 retrieved papers, we included 70 studies in the qualitative synthesis. Most papers report on chatbots that collect and process personal health data, usually in the context of mental health coaching applications. The majority did not provide any information regarding security or privacy aspects. We were able to determine limitations in literature and identified concrete challenges, including data access and usage of (third-party) services, data storage, data security methods, use case peculiarities and data privacy, as well as legal requirements. Data privacy and security in health chatbots are still underresearched and related information is underrepresented in scientific literature. By addressing the five key challenges in future, the transfer of theoretical solutions into practice can be facilitated.

Contact tracing for infectious diseases can be partially automated using mobile applications. However, the success of these tools is dependent on significant uptake and frequent use by the public. This study explored the barriers and facilitators to the New Zealand (NZ) general public's use of the COVID-19 contact NZ COVID Tracer app. Adults (≥18 years, N = 373) in NZ. Qualitative and quantitative data were gathered from a nation-wide online survey. App use and frequency of use were presented as descriptive statistics. Qualitative data were analyzed thematically. 31% reported using the app frequently, 24% used it sometimes, 21% had installed but not used it, and 24% had not installed it. Barriers to use include technical issues, privacy and security concerns, forgetfulness and a lack of support from businesses. The perceived risk of contracting COVID-19, government recommendations and communications, and the importance of contact tracing facilitated use. Technical, user, business, and government factors influenced the public's use of a COVID-19 contact tracing app. The development of apps requiring minimal user effort and initial user testing may improve uptake. Enabling environments and better risk communication may improve uptake of similar community-driven contact tracing apps during future pandemics.

Electronic clinical pathways (ECPs) strongly encourage the standardization of medical treatment and the sharing of information among medical staff. The goal of this study was to determine the influence of ECPs on information sharing among nurses in a university hospital. Four experienced nurses, selected based on ECP composing and operation experience, were recruited from the department with the most frequent users in the first-round interview, 132 nurses' questionnaire answers were analyzed, and eight nurses participated in the second-round interview. This study conducted a mixed-method (interview-questionnaire-interview) investigation to extract the behavioral signs of unintended errors in information sharing after the ethical approval was obtained. On the basis of ANOVA and t-test for the questionnaire and constant comparison for interview, this study found that the greater extent of user dependency on convenient ECPs in the frequent-use group led to mistakes under hectic conditions. This study also found evidence of poor management of ECPs when problems occurred. The immature design of ECPs provoked inappropriate behaviors among nurses even though they brought about some benefits such as mitigation of the burden of daily recording tasks. The findings empirically showed the ECP user's behavioral changes regarding the technology-induced error.

Data visualization tools have the potential to support decision-making for public health professionals. This review summarizes the science and evidence regarding data visualization and its impact on decision-making behavior as informed by cognitive processes such as understanding, attitude, or perception.An electronic literature search was conducted using six databases, including reference list reviews. Search terms were pre-defined based on research questions.Sixteen studies were included in the final analysis. Data visualization interventions in this review were found to impact attitude, perception, and decision-making compared to controls. These relationships between the interventions and outcomes appear to be explained by mediating factors such as perceived trustworthiness and quality, domain-specific knowledge, basic beliefs shared by social groups, and political beliefs.Visualization appears to bring advantages by increasing the amount of information delivered and decreasing the cognitive and intellectual burden to interpret information for decision-making. However, understanding data visualization interventions specific to public health leaders' decision-making is lacking, and there is little guidance for understanding a participant's characteristics and tasks. The evidence from this review suggests positive effects of data visualization can be identified, depending on the control of confounding factors on attitude, perception, and decision-making.

This study examined the association between caregivers' burdens and their individual characteristics and identified characteristics that are useful for predicting the level of caregiver burden. We successfully surveyed 387 family caregivers, having them complete the caregiver burden inventory scale (CBI) and an individual characteristic questionnaire. When we compared the average CBI scores between groups with a particular individual characteristic (including caring for older adult(s), educational level, employment status, place of birth, marital status, financial status, need for family support, need for friend support, and need for nonprofit organizational support), we found a significant difference in the average scores. From a logistic regression model, with burden level as the outcome, we found that caring for older adult(s), educational level, employment status, place of birth, financial situation, and need for nonprofit organizational support were significant predictors of the burden level of caregivers. The research findings suggest that certain individual characteristics can be adopted for identifying and quantifying caregivers who may have a higher level of burden. The findings are useful to uncover caregivers who may need prompt support and social care.

The Help2Care e-Health platform was developed in order to capacitate informal caregivers with digital, multimedia training materials. Health professionals select these materials according to the needs of the homebound patients under the supervision of these caregivers. In turn, caregiver can then use their smartphones to consult and apply the care procedures illustrated by these materials. In this paper, we present the results of performed usability tests for both web and mobile software applications of the Help2Care platform. These indicate an overall positive outcome, revealing less usable aspects such as the navigation flow in the web application and some design elements in the mobile application. Important written feedback was also collected, which we took into consideration to improve the software features of the platform.