DIGITAL HEALTH最新文献

Introduction: The COVID-19 pandemic had a significant impact on healthcare delivery worldwide. Digital tools emerged as a preferred solution for maintaining healthcare services during this crisis. This study aimed to assess the magnitude of digital health literacy among healthcare professionals in Ethiopia in 2020-2023.

Methods: A systematic review and meta-analysis were conducted following the Preferred Reporting Items for Systematic Review and Meta-Analysis (PRISMA) guidelines. Articles published from 2020 to 2023 were reviewed using various electronic databases such as Medline, PubMed, and Cochrane Library, CINAHL, HINARI, Science Direct, Google Scholar, and Global Health. Meta-analysis was performed using STATA 17, and publication bias and heterogeneity were assessed.

Results: Six studies involving a total of 2739 participants were included in the analysis. The pooled level of high digital health literacy among health professionals in Ethiopia during the pandemic was found to be 56.0% (95% CI: 55, 58). Several factors were identified as significant contributors to high digital health literacy, including internet use (AOR = 2.72, 95% CI: 1.86, 3.98), perceived ease of use (AOR = 2.79, 95% CI: 1.83, 4.25), favorable attitude (AOR = 2.49, 95% CI: 1.61, 3.85), perceived usefulness (AOR = 2.29, 95% CI: 1.65, 3.18), information-communication-technology training (AOR = 6.09, 95% CI: 1.83, 24.27), and educational level (AOR = 3.60, 95% CI: 2.96, 4.37).

Conclusion and recommendation: The study findings revealed a moderate level of high digital health literacy among Ethiopian health professionals. Factors such as internet use, favorable attitude, and information-communication-technology training were associated with high-level digital health literacy. To enhance digital health literacy, it is crucial to provide timely training and improve internet access for healthcare professionals. Additionally, promoting the perception of digital tools as useful and supporting evidence-based decision-making can further improve digital health literacy. Comprehensive information-communication-technology training programs should be implemented to equip healthcare professionals with necessary skills to effectively combat outbreaks like the COVID-19 pandemic.

Background: Children and adolescents who are overweight and obese represent a growing public health issue. The use of step-monitoring devices as an intervention tool may be a simple, cost-effective, and easily replicable solution for addressing obesity in children and adolescents. No prior systematic reviews have evaluated the effectiveness of utilizing step-monitoring devices as an intervention method for obesity in children and adolescents.

Methods: Previous studies on using step-monitoring devices to prevent and treat obesity in children and adolescents were identified in the following databases: Web of Science, EMBASE, PubMed, Cochrane Library, SPORTDiscus, and SCOPUS. The search period for each database ranged from the year of their inception to 8 March 2023 (updated in June 2024). Meta-analyses were performed for mean differences (MDs) in body mass index (BMI), BMI z-score (BMI-Z), body fat, waist circumference, and body weight.

Results: From 12,907 relevant records, 23 studies were included in this meta-analysis. The included studies were mainly at low risk of bias, except for blinding. Step-monitoring device-based interventions had significant effects in reducing BMI-Z (MD -0.06; 95% CI -0.10 to -0.02), body fat (MD -0.95%; 95% CI -1.35 to -0.54), and body weight (MD -1.23 kg; 95% CI -2.36 to -0.10). However, there was no significant effect on BMI (MD -0.16 kg/m²; 95% CI -0.55 to 0.22) and waist circumference (MD -0.33 cm; 95% CI -1.23 to 0.58). Subgroup analyses indicated that participants who were overweight or obese showed greater intervention effects on BMI and BMI-Z compared to participants with normal weight. The programs with an intervention duration of ≤6 months presented a greater intervention effect on BMI-Z than those with an intervention duration of more than 6 months. The programs that established goals had a greater intervention effect on body fat than those that did not.

Conclusions: Step-monitoring devices may be an effective and generalizable intervention tool for the prevention and treatment of obesity in children and adolescents. Future studies should further explore how to set step goals and the duration of interventions to achieve better intervention effects.

Objectives: To assess the quality and alignment of ChatGPT's cancer treatment recommendations (RECs) with National Comprehensive Cancer Network (NCCN) guidelines and expert opinions.

Methods: Three urologists performed quantitative and qualitative assessments in October 2023 analyzing responses from ChatGPT-4 and ChatGPT-3.5 to 108 prostate, kidney, and bladder cancer prompts using two zero-shot prompt templates. Performance evaluation involved calculating five ratios: expert-approved/expert-disagreed and NCCN-aligned RECs against total ChatGPT RECs plus coverage and adherence rates to NCCN. Experts rated the response's quality on a 1-5 scale considering correctness, comprehensiveness, specificity, and appropriateness.

Results: ChatGPT-4 outperformed ChatGPT-3.5 in prostate cancer inquiries, with an average word count of 317.3 versus 124.4 (p < 0.001) and 6.1 versus 3.9 RECs (p < 0.001). Its rater-approved REC ratio (96.1% vs. 89.4%) and alignment with NCCN guidelines (76.8% vs. 49.1%, p = 0.001) were superior and scored significantly better on all quality dimensions. Across 108 prompts covering three cancers, ChatGPT-4 produced an average of 6.0 RECs per case, with an 88.5% approval rate from raters, 86.7% NCCN concordance, and only a 9.5% disagreement rate. It achieved high marks in correctness (4.5), comprehensiveness (4.4), specificity (4.0), and appropriateness (4.4). Subgroup analyses across cancer types, disease statuses, and different prompt templates were reported.

Conclusions: ChatGPT-4 demonstrated significant improvement in providing accurate and detailed treatment recommendations for urological cancers in line with clinical guidelines and expert opinion. However, it is vital to recognize that AI tools are not without flaws and should be utilized with caution. ChatGPT could supplement, but not replace, personalized advice from healthcare professionals.

Background: Precision Public Health (PPH) is a newly emerging field in public health medicine. The application of various types of data allows PPH to deliver more tailored interventions to a specific population within a specific timeframe. However, the application of PPH possesses several challenges and limitations that need to be addressed.

Objective: We aim to provide evidence of the various use of PPH in outbreak management, the types of data that could be used in PPH application, and the limitations and barriers in the application of the PPH approach.

Methods and analysis: Articles were searched in PubMed, Web of Science, and Science Direct. Our selection of articles was based on the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) for Scoping Review guidelines. The outcome of the evidence assessment was presented in narrative format instead of quantitative.

Results: A total of 27 articles were included in the scoping review. Most of the articles (74.1%) focused on PPH applications in performing disease surveillance and signal detection. Furthermore, the data type mostly used in the studies was surveillance (51.9%), environment (44.4), and Internet query data. Most of the articles emphasized data quality and availability (81.5%) as the main barriers in PPH applications followed by data integration and interoperability (29.6%).

Conclusions: PPH applications in outbreak management utilize a wide range of data sources and analytical techniques to enhance disease surveillance, investigation, modeling, and prediction. By leveraging these tools and approaches, PPH contributes to more effective and efficient outbreak management, ultimately reducing the burden of infectious diseases on populations. The limitation and challenges in the application of PPH approaches in outbreak management emphasize the need to strengthen the surveillance systems, promote data sharing and collaboration among relevant stakeholders, and standardize data collection methods while upholding privacy and ethical principles.

Objective: This study investigated the effectiveness of remote administration of speech audiometry, an essential tool for diagnosing hearing loss and determining its severity. Utilizing two software tools for remote testing, the research aimed to compare these digital methods with traditional, in-person speech audiometry to evaluate their feasibility and accuracy.

Design: Participants underwent the Cantonese Hearing in Noise Test (CHINT) under three listening conditions-quiet, noise from the front, and noise from the right side-using three different administration methods: the conventional in-person approach, video conferencing software, and remote access software.

Study sample: Fifty-six Cantonese-speaking adults residing in Hong Kong participated in this study.

Results: Analysis revealed no significant differences in CHINT scores among the three administration methods, indicating the potential for remote administration to yield results comparable to those of conventional methods.

Conclusions: The findings supported the feasibility of remote speech audiometry using the investigated digital tools. This study paved the way for the wider adoption of tele-audiology practices, particularly in situations where in-person assessments are not possible.

Background: Maternal education is often linked to improved awareness. This study aimed to determine the impact of complementary feeding education for mothers using mobile phone applications on the anthropometric indices of Iranian infants.

Methods: This quasi-experiment study involved 86 eligible women divided into two groups-intervention (n = 43) and control (n = 43)-using a multistage sampling method. A researcher-designed questionnaire collected demographic data from parents and infants. Education was delivered through a mobile phone application. Infant anthropometric indices (weight-for-age, length-for-age, and weight-for-length) were measured before and 3 months after the intervention. Statistical analysis included independent t-tests, paired t-tests, chi-square tests (or Cochran-Armitage tests), and analysis of covariance.

Results: The mean weight-for-age Z-scores of the infants in the intervention group were significantly different before (0.07 ± 0.52) and after the intervention (0.37 ± 0.53) (p < .001), while this difference was not significant in the control group. The mean infant weight-for-length Z score in the intervention group was significantly different before (0.09 ± 0.72) and after the intervention (0.29 ± 0.63) (p = .015); however, in the control group, the difference was not statistically significant. The mean length-for-age Z score in the intervention group was significantly different before (0.12 ± 0.68) and after the intervention (0.40 ± 0.76) (p = .006). In contrast, in the control group, the mean length-for-age Z score after the intervention (-0.03 ± 0.84) decreased compared to that before the intervention (0.38 ± 0.75) (p < .001).

Conclusion: The study demonstrated that educating mothers on complementary feeding through mobile phone applications positively impacted infant anthropometric indices. Healthcare providers are encouraged to use this educational approach to prevent infant growth disorders.

Objective: Unlocking the potential of routine medical data for clinical research requires the analysis of data from multiple healthcare institutions. However, according to German data protection regulations, data can often not leave the individual institutions and decentralized approaches are needed. Decentralized studies face challenges regarding coordination, technical infrastructure, interoperability and regulatory compliance. Rare diseases are an important prototype research focus for decentralized data analyses, as patients are rare by definition and adequate cohort sizes can only be reached if data from multiple sites is combined.

Methods: Within the project "Collaboration on Rare Diseases", decentralized studies focusing on four rare diseases (cystic fibrosis, phenylketonuria, Kawasaki disease, multisystem inflammatory syndrome in children) were conducted at 17 German university hospitals. Therefore, a data management process for decentralized studies was developed by an interdisciplinary team of experts from medicine, public health and data science. Along the process, lessons learned were formulated and discussed.

Results: The process consists of eight steps and includes sub-processes for the definition of medical use cases, script development and data management. The lessons learned include on the one hand the organization and administration of the studies (collaboration of experts, use of standardized forms and publication of project information), and on the other hand the development of scripts and analysis (dependency on the database, use of standards and open source tools, feedback loops, anonymization).

Conclusions: This work captures central challenges and describes possible solutions and can hence serve as a solid basis for the implementation and conduction of similar decentralized studies.

Background: The Inpatient Telehealth Program permits family to remotely communicate with the patient and care team through secure, live video. We aimed to assess the implementation of this program for family engagement from the perspectives of patients, families, and providers.

Methods: We used a convergent mixed methods design. The quantitative component was a cross-sectional analysis of surveys assessing patient, family, and provider experience. The qualitative component used thematic analysis of patient, family, and provider interviews plus survey free text responses. We performed memo-writing and coding. We developed hypotheses about relationships among categories and identified analytic themes. We used data transformation and narrative discussion to report the integrated findings.

Results: Surveys from 214 individuals (33 patients, 145 family, 36 providers) were evaluated. Mean (standard deviation) experience ratings (1-poor, 5-best) were 4.0 (1.5) for patients, 4.6 (0.8) for family, and 4.0 (1.4) for providers. We received 134 free text responses and conducted 21 interviews. Three themes emerged: (1) inpatient telehealth enhanced patient and family experience through strengthened relationships and increased support; (2) inpatient telehealth enhanced patient care through improved information sharing and engagement; (3) low awareness of the program limited adoption. Quantitative and qualitative data aligned in that participants perceived inpatient telehealth to be valuable; however, surveys revealed that patients and providers have relatively lower satisfaction with the program.

Conclusion: Inpatient telehealth for family engagement was perceived to improve family-centeredness of care. Future work is needed to overcome implementation challenges and to increase awareness of this resource among patients and families.

Background: Patients access relevant information concerning their orthopaedic surgery resources through multiple information channels before presenting for clinical treatment. Recently, artificial intelligence (AI)-powered chatbots have become another source of information for patients. The currently developed AI chat technology ChatGPT (OpenAI LP) is an application for such purposes and it has been rapidly gaining popularity, including for patient education. This study sought to evaluate whether ChatGPT can correctly answer frequently asked questions (FAQ) regarding periprosthetic joint infection (PJI).

Methods: Twelve FAQs about PJI after hip and knee arthroplasty were identified from the websites of fifteen international clinical expert centres. ChatGPT was confronted with these questions and its responses were analysed for their accuracy using an evidence-based approach by a multidisciplinary team. Responses were categorised in four groups: (1) Excellent response that did not require additional improvement; (2) Satisfactory responses that required a small amount of improvement; (3) Satisfactory responses that required moderate improvement; and (4) Unsatisfactory responses that required a large amount of improvement.

Results: From the analysis of the responses given by the chatbot, no reply received an 'unsatisfactory' rating; one did not require any correction; and the majority of the responses required low (7 out of 12) or moderate (4 out of 12) clarification. Although a few responses required minimal clarification, the chatbot responses were generally unbiased and evidence-based, even when asked controversial questions.

Conclusions: The AI-chatbot ChatGPT was able to effectively answer the FAQs of patients seeking information around PJI diagnosis and treatment. The given information was also written in a manner that can be assumed to be understandable by patients. The chatbot could be a valuable clinical tool for patient education and understanding around PJI treatment in the future. Further studies should evaluate its use and acceptance by patients with PJI.

Background: Coordination mechanisms based on information and communication technologies (ICTs) are gaining attention, especially since the pandemic, due to their potential to improve communication between health professionals. However, their impact on cross-level clinical coordination remains unclear. The aim is to synthesize the evidence on the impact of ICT-based coordination mechanisms on clinical coordination between primary care and secondary care (SC) doctors and to identify knowledge gaps.

Methods: A scoping review was conducted by searching for original articles in six electronic databases and a manual search, with no restrictions regarding time, area, or methodology. Titles and abstracts were screened. Full texts of the selected articles were reviewed and analysed to assess the impact of each mechanism, according to the cross-level clinical coordination conceptual framework.

Results: Of the 6555 articles identified, 30 met the inclusion criteria. All had been conducted in high-income countries, most (n = 26) evaluated the impact of a single mechanism - asynchronous electronic consultations via electronic health records (EHR) - and were limited in terms of design and types and dimensions of cross-level clinical coordination analysed. The evaluation of electronic consultations showed positive impacts on the appropriateness of referrals and accessibility to SC, yet the qualitative studies also highlighted potential risks. Studies on other mechanisms were scarce (shared EHR, email consultations) or non-existent (videoconferencing, mobile applications).

Conclusions: Evidence of the impact of ICT-based mechanisms on clinical coordination between levels is limited. Rigorous evaluations are needed to inform policies and strategies for improving coordination between healthcare levels, thus contributing to high-quality, efficient healthcare.