Informatics for Health & Social Care最新文献

The goal of the Foundation Healthcare Group (FHG) Vanguard model was to develop a sustainable local hospital model between two National Health Service (NHS) Trusts (a London Teaching Hospital Trust and a District General Hospital Trust) that makes best use of scarce resources and can be replicated across the NHS, UK. The aim of this study was to evaluate the provision, use, and implementation of the IT infrastructure based on qualitative interviews focused mainly on the perspectives of the IT staff and the clinicians' perspectives.

Methods: In total, 24 interview transcripts, along with 'Acute Care Collaboration' questionnaire responses, were analyzed using a thematic framework for IT infrastructure, sharing themes across the vascular, pediatric, and cardiovascular strands of the FHG programme.

Results: Findings indicated that Skype for Business had been an innovative and helpful development widely available to be used between the two Trusts. Clinicians initially reported lack of IT support and infrastructure expected at the outset for a national Vanguard project but later appreciated that remote access to most clinical applications including scans between the two Trusts became operational. The Local Care Record (LCR), an IT project was perceived to have been delivered successfully in South London. Shared technology reduced patient traveling time by providing locally based shared care.

Conclusion: Lesson learnt is that ensuring patient benefit and priorities is a strong driver to implementation and one needs to identify IT rate-limiting steps at an early stage and on a regular basis and then focus on rapid implementation of solutions. In fact, future work may also assess how the IT infrastructure developed by FHG vanguard project might have helped/boosted the 'digital health' practice during the COVID-19 times. Spreading and scaling-up innovations from the Vanguard sites was the aspiration and challenge for system leaders. After COVID-19, the use of IT is scaled up and now, the challenges in the use of IT are much less compared to the pre-COVID-19 time when this project was evaluated.

To evaluate the fibromyalgia (FM) content in YouTube videos and verify if American College of Rheumatology (ACR) guidelines are being met. The videos were searched with the keyword "Fibromyalgia." Two independent researchers evaluated and coded specific characteristics of the videos. The popularity of the videos, the presentation properties, and content related to FM according to the ACR criteria were analyzed. Of the 200 videos included, the majority were presented by health professionals, 61.5%. Most videos covered more than one subject, 38.5%. The videos presented by health professionals were the most viewed. Following the ACR guidelines, 38% defined FM, 24% described the etiology, 19.5% described the diagnostic criteria and 52% presented recommended management strategies. The results indicate that users mainly watch videos published by health professionals. Most of the published videos do not follow the information recommended by the ACR guidelines. Therefore, videos should be interpreted with caution, not being the most appropriate resource for health education for patients with FM. Most of the videos published on YouTube about FM do not meet the ACR guidelines for FM.

This study aimed to provide an integrated model that examines the determinants of older adults' intention to use mobile registration applications (apps) based on UTAUT, and the role of aging factors including perceived physical condition, technology anxiety, inertia, and self-actualization needs. The proposed model was tested by PLS (Partial Least Squares) with data collected from 361 older adults. Results indicated that three variables derived from UTAUT, namely performance expectancy, social influence, and facilitating conditions, influence mobile registration app usage intention. Additionally, the aging factors of inertia and self-actualization needs have significant impacts on older adults' usage intentions. Results further demonstrated that smart phone usage experience had a moderator effect on the relationship between usage intention and three antecedents (performance expectancy, effort expectancy, facilitating condition), but not social influence. Findings provide valuable theoretical contributions for researchers, and practical implications for hospitals developing mobile registration apps in Taiwan.

Continuous positive airway pressure (CPAP) is the "gold-standard" therapy for obstructive sleep apnea (OSA), but the main problem is the poor adherence. Therefore, we have searched for the causes of poor adherence to CPAP therapy by applying predictive machine learning (ML) methods. The study was conducted on OSAs in nighttime therapy with CPAP. An outpatient follow-up was planned at 3, 6, 12 months. We collected several parameters at the baseline visit and after dividing all patients into two groups (Adherent and Non-adherent) according to therapy adherence, we compared them. Statistical differences between the two groups were not found according to baseline characteristics, except gender (P< .01). Therefore, we applied ML to predict CPAP adherence, and these predictive models showed an accuracy and sensitivity of 68.6% and an AUC (area under the curve) of 72.9% through the SVM (support vector machine) classification method. The identification of factors predictive of long-term CPAP adherence is complex, but our proof of concept seems to demonstrate the utility of ML to identify subjects poorly adherent to therapy. Therefore, application of these models to larger samples could aid in the careful identification of these subjects and result in important savings in healthcare spending.

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.