Informatics for Health & Social Care最新文献

Human-centered design methods should be implemented throughout the client information system (CIS) development process to understand social welfare professionals' needs, tasks, and contexts of use. The aim of this study was to examine Finnish social welfare professionals' experiences of participating in CIS development.A national cross-sectional web-based survey on the CIS experiences of social welfare professionals (1145 respondents) was conducted in Finland in spring 2019. This study focused on statements concerning the experiences of end users with CIS development and participation. The results are reported by professional and age groups.Half (50%) of the 1145 respondents had participated in CIS development. Half (56%) knew to whom and how to send feedback to software developers, but most (87%) indicated that changes and corrections were not made according to suggestions and quickly enough. The most preferred methods of participation were telling a person in charge of information systems development about usage problems (53%) and showing developers on site how professionals work (34%); 19% were not interested in participating.Social welfare professionals are willing to participate in CIS development, but vendors and social welfare provider organizations are underutilizing this resource. Social welfare informaticists are needed to interpret the needs of end users to software developers.

We derived machine learning models utilizing features generated by natural language processing (NLP) of free-text data from an ambulance services provider to identify fall cases. The data comprised samples of electronic patient care records care records (ePCRs) from St John Western Australia (WA), the sole ambulance services provider in most of WA. We manually labeled fall cases by reviewing the free-text summary. The models used features including case characteristics (e.g., age) and text frequency-inverse document frequency (tf-idf) of each word of the free-text generated by NLP. Support vector machine (SVM) and random forest were used as classifiers. We compared the performance of the models against the manual identification of falls by recall, precision, and F-measure. A total of 9,447 cases (1%) were randomly sampled, of which 1,648 (17%) were labeled as fall. The best model was an SVM model using case characteristics and tf-idf's of the first 100 words of free-text, with recall of 0.84, precision of 0.86, and F-measure of 0.85. This performance was better than an SVM model with only case characteristics. Machine-learning models incorporated with features generated by NLP improved the performance of classifying fall cases compared with models without such features. Scope remains for further improvement.

(a) Objective: Obstructive sleep apnea syndrome (OSAS) is typically diagnosed through polysomnography (PSG). However, PSG incurs high medical costs. This study developed new models for screening the risk of moderate-to-severe OSAS (apnea-hypopnea index, AHI ≥15) and severe OSAS (AHI ≥30) in various age groups and sexes by using anthropometric features in the Taiwan population.(b) Participants: Data were derived from 10,391 northern Taiwan patients who underwent PSG.(c) Methods: Patients' characteristics - namely age, sex, body mass index (BMI), neck circumference, and waist circumference - was obtained. To develop an age- and sex-independent model, various approaches - namely logistic regression, k-nearest neighbor, naive Bayes, random forest (RF), and support vector machine - were trained for four groups based on sex and age (men or women; aged <50 or ≥50 years). Dataset was separated independently (training:70%; validation: 10%; testing: 20%) and Cross-validated grid search was applied for model optimization. Models demonstrating the highest overall accuracy in validation outcomes for the four groups were used to predict the testing dataset.(d) Results: The RF models showed the highest overall accuracy. BMI was the most influential parameter in both types of OSAS severity screening models.(e) Conclusion: The established models can be applied to screen OSAS risk in the Taiwan population and those with similar craniofacial features.

The rate of antidepressant prescriptions is globally increasing. A large portion of patients stop their medications, which could lead to many side effects including relapse, and anxiety. The aim of this was to develop a drug-continuity prediction model and identify the factors associated with drug-continuity using online patient forums. We retrieved 982 antidepressant drug reviews from the online patient's forum AskaPatient.com. We followed the Analytical Framework Method to extract structured data from unstructured data. Using the structured data, we examined the factors associated with antidepressant discontinuity and developed a predictive model using multiple machine learning techniques. We tested multiple machine learning techniques which resulted in different performances ranging from accuracy of 65% to 82%. We found that Random Forest algorithm provides the highest prediction method with 82% Accuracy, 78% Precision, 88.03% Recall, and 84.2% F1-Score. The factors associated with drug discontinuity the most were: withdrawal symptoms, effectiveness-ineffectiveness, perceived-distress-adverse drug reaction, rating, and perceiveddistress related to withdrawal symptoms. Although the nature of data available at online forums differ from data collected through surveys, we found that online patients forum can be a valuable source of data for drug continuity prediction and understanding patients experience. The factors identified through our techniques were consistent with the findings of prior studies that used surveys.

Community pharmacies have made significant advances in digital technology; however, mobile systems are only emerging in this sector and mostly focusing patient-centric connections. This study reveals a case of digital transformation in a mobile connected pharmacy, balancing efficient pharmaceutical services and digital innovation. A mobile connected pharmacy solution (mPharmaCare) is developed for a community of near 100.000. The first stage includes a bibliometric analysis and a structured literature review of the mobile connected pharmacy. In the second stage, action research was conducted to evaluate mPharmaCare adoption. A dual organizational structure was tested to cope with innovation and efficient exploration of pharmacy services. Community Pharmacy 5.0 is an inspiring vision that will take advantage of mobility. However, there are tensions between the core pharmacy business and the new technology layers of community connections. Community pharmacies require both client-centric and community-centric approaches to achieve individualization of patient care and horizontal and end-to-end digital integration of pharmacy data. Digital transformation can remove silos in the community pharmacy. Creating an - internal or outsourced - innovation division may be suitable for medium and large community pharmacies. Moreover, pharmacies must consider shifting to a product-service system offer, deploying synchronization mechanisms with different stakeholders.

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.