DIGITAL HEALTH最新文献

Objectives: The aim of the study is to describe the user experiences of a nationwide digital decision support system (DDSS).

Summary of background data: DDSSs have the potential to improve the quality and safety of healthcare services by supporting clinical decision-making with evidence-based recommendations. Due to a lack of knowledge, it is difficult to assess whether DDSSs are fulfilling their purpose. In Estonia, a nationwide DDSS for general practitioners (GPs) was implemented in 2020. To understand the impact of DDSS on the quality of care in the Estonian context and meet the demands of healthcare, it is necessary to gather information about the experiences of the users. This is the first study that examines the experiences of GPs on the use of DDSS nationwide.

Methods: A qualitative descriptive study was conducted based on snowball sampling. Semi-structured interviews were performed in February-March 2022 with nine GPs. Data were analyzed by thematic analysis. A total of six themes and 16 subthemes emerged from the data.

Results: A total of six themes and 16 subthemes emerged from the data. The following themes were identified: user-friendliness, DDSS use in clinical practice, benefits of the DDSS, and the impact of the DDSS on GPs' work, barriers to using the DDSS, and suggestions for improving the user experience. The results of the study are important, as they address and contribute to the relevant aspects of digital health in primary care.

Conclusion: GPs shared their individual user experiences, including user-perceived barriers and enabling factors that influence the implementation and use of a decision support system in primary care settings. It is revealed that GPs have different benefits and barriers depending on the topic discussed. Future research should evaluate the functioning of the DDSS and the quality of the decisions it provides by observing and evaluating patient records. Systematic user experiences need to be collected and examined to ensure the usability and sustainability of the DDSS.

Objective: A ballistocardiogram (BCG) is a vibration signal generated by the ejection of the blood in each cardiac cycle. The BCG has significant variability in amplitude, temporal aspects, and the deficiency of waveform components, attributed to individual differences, instantaneous heart rate, and the posture of the person being measured. This variability may make methods of extracting J-waves, the most distinct components of BCG less generalizable so that the J-waves could not be precisely localized, and further analysis is difficult. This study is dedicated to solving the variability of BCG to achieve accurate feature extraction.

Methods: Inspired by the generation mechanism of the BCG, we proposed an original method based on a profile of second-order derivative of BCG waveform (2ndD-P) to capture the nature of vibration and solve the variability, thereby accurately localizing the components especially when the J-wave is not prominent.

Results: In this study, 51 recordings of resting state and 11 recordings of high-heart-rate from 24 participants were used to validate the algorithm. Each recording lasts about 3 min. For resting state data, the sensitivity and positive predictivity of proposed method are: 98.29% and 98.64%, respectively. For high-heart-rate data, the proposed method achieved a performance comparable to those of low-heart-rate: 97.14% and 99.01% for sensitivity and positive predictivity, respectively.

Conclusion: Our proposed method can detect the peaks of the J-wave more accurately than conventional extraction methods, under the presence of different types of variability. Higher performance was achieved for BCG with non-prominent J-waves, in both low- and high-heart-rate cases.

Objective: Most dengue cases are managed in an outpatient setting, where patients are advised to return to the clinic daily for monitoring. Some patients can develop severe dengue at home and fail to recognise the deterioration. An application called DengueAid was designed as a self-monitoring tool for patients to reduce delay in seeking timely treatment. This study aimed to assess the feasibility of conducting a randomised controlled trial to determine the effectiveness of the DengueAid application.

Methods: Dengue patients were recruited from a public health clinic in Malaysia and randomised to either use the DengueAid application plus standard care for dengue or receive only the standard care. The outcomes evaluated were the (1) feasibility of recruitment, data collection and follow-up procedures; (2) preliminary clinical outcome measures; and (3) acceptability of DengueAid. Qualitative interviews were conducted for participants in the intervention arm to assess the acceptability of DengueAid.

Results: Thirty-seven patients were recruited with 97% (n = 36) retention rates. The recruitment rate was low (63% refusal rate, n = 62/99) with difficulty in data collection and follow-up due to the variable interval of care for dengue in an outpatient setting. DengueAid application was acceptable to the participants, but preliminary clinical outcomes and qualitative data suggested limited utility of the application. Unwell conditions of patients and limited access to healthcare are important factors impacting the application's utility.

Conclusion: The feasibility trial uncovered issues with recruitment, data collection and follow-up processes. Further research and modification to the application are needed to improve its utility and usability.

Background: Health services are undergoing digitalization and applying new digital tools. These changes may provide healthcare managers with opportunities to exercise digital leadership. However, managers' attitudes may influence the extent to which they demonstrate digital leadership. This study explores the attitudes of Norwegian healthcare managers towards: (1) digital tools and change and (2) to what extent digital tools are applicable to various tasks of managers.

Methods: Cross-sectional study including 154 managers in hospitals and municipal health services in a Norwegian county. The questionnaire was about management and digital tools, and the data was analyzed by descriptive statistics, correlations, and content analysis.

Results: The healthcare managers perceived that digital tools facilitated a positive change in organizational work processes aligned with values and goals. Digital tools supported administrative tasks such as gaining control over responsibilities. However, 76 managers stated that certain tasks, including interactions with employees (e.g. performance appraisals and sick leave follow-up) and the building of an organizational culture, should not be performed using digital tools or using them only to a limited extent; for these tasks, they preferred in-person meetings.

Discussion: Norwegian healthcare managers' attitudes toward digital tools are generally positive, but there are areas where they find the tools less suitable.

Conclusions: The results provide new insights into healthcare by indicating that many managers may have positive attitudes toward digital tools. However, digital leadership may not be applicable equally in all areas of healthcare managers' work. This raises the question of whether digital leadership can or should be exercised uniformly in every area of health services.

Background: The electronic health record (EHR) is integral to improving healthcare efficiency and quality. Its successful implementation hinges on patient willingness to use it, particularly in Germany where concerns about data security and privacy significantly influence usage intention. Little is known about how specific characteristics of medical data influence patients' intention to use the EHR.

Objective: This study aims to validate the privacy calculus model (PCM) regarding EHRs and to assess how personal and disease characteristics, namely disease-related stigma and disease time course, affect PCM predictions.

Methods: An online survey was conducted to empirically validate the PCM for EHR, incorporating a case vignette varying in disease-related stigma (high/low) and time course (acute/chronic), with N = 241 participants, aged 18 years and older residing in Germany with no previous experience with the diseases mentioned in the respective medical reports. Participants were randomized (single-blinded) into four groups in parallel: high stigma and acute time course (n = 74), high stigma and chronic time course (n = 56), low stigma and acute time course (n = 62) and low stigma and chronic time course (n = 49). The data were analyzed using structural equation modeling with partial least squares.

Results: The model explains R² = 71.8% of the variance in intention to use. The intention to use is influenced by perceived benefits, data privacy concerns, trust in the provider, and social norms. However, only the disease's time course, not stigma, affects this intention. For acute diseases, perceived benefits and social norms are influential, whereas for chronic diseases, perceived benefits, privacy concerns, and trust in the provider influence intention.

Conclusions: The PCM validation for EHRs reveals that personal and disease characteristics shape usage intention in Germany. The need for tailored EHR adoption strategies that address specific needs and concerns of patients with different disease types. Such strategies could lead to a more successful and widespread implementation of EHRs, especially in privacy-conscious contexts.

Background: Telemedicine has emerged as a novel healthcare service model that plays a vital role in addressing the unequal distribution of medical resources. Telemedicine has recently gained significant traction in economically prosperous cities such as Beijing, Shanghai, and Guangzhou in China. However, Xinyang City in Henan Province is an economically less developed city, and telemedicine is still in its early stages. By exploring the views of medical staff and administrators at Xinyang City's tertiary A-level hospital on telemedicine, this study aims to determine the development status of telemedicine services in Xinyang City. The objective was to identify the challenges in the construction process and formulate corresponding strategies for advancing telemedicine in Xinyang City.

Methods: Face-to-face personal interviews were conducted with 16 medical staff and administrators from a tertiary A-level hospital in Xinyang City, Henan Province, and the data were collected and analyzed using grounded theory. In the process of data analysis, NVivo12 software was used to encode and organize the data line by line.

Results: The development of telemedicine in Xinyang City has the potential to benefit residents, hospitals, and healthcare personnel despite several challenges, including the absence of laws and regulations, inadequate policy support, limited hospital cooperation, and low resident awareness, which must be addressed to unlock the full potential of telemedicine.

Conclusion: The telemedicine system in Xinyang City is currently in a coordinated developmental phase, and several areas require further improvement. The development of standardized telemedicine in Xinyang City requires government support, better training for general practitioners, public awareness campaigns, and improved technology while ensuring reasonable work schedules and motivating medical personnel.

Objective: The objective of this study was to assess the attitudes toward the Colposcopic Artificial Intelligence Auxiliary Diagnostic System (CAIADS) of colposcopists working in mainland China.

Methods: A questionnaire was developed to collect participants' sociodemographic information and assess their awareness, attitudes, and acceptance toward the CAIADS.

Results: There were 284 respondents from 24 provinces across mainland China, with 55% working in primary care institutions. Participant data were divided into two subgroups based on their colposcopy case load per year (i.e. ≥50 cases; <50 cases). The analysis showed that participants with higher loads had more experience working with CAIADS and were more knowledgeable about CAIADS and AI systems. Overall, in both groups, about half of the participants understood the potential applications of big data and AI-assisted diagnostic systems in medicine. Although less than one-third of the participants were knowledgeable about CAIADS and its latest developments, more than 90% of the participants were open with the idea of using CAIADS.

Conclusions: While a related lack of acknowledgement of CAIADS exists, the participants in general had an open attitude toward CAIADS. Practical experience with colposcopy or CAIADS contributed to participants' awareness and positive attitudes. The promotion of AI tools like CAIADS could help address regional health inequities to improve women's well-being, especially in low- and middle-income countries.

Aim: To optimize gastric cancer screening score and reduce screening costs using machine learning models.

Methods: This study included 228,634 patients from the Taizhou Gastric Cancer Screening Program. We used three machine learning models to optimize Li's gastric cancer screening score: Gradient Boosting Machine (GBM), Distributed Random Forest (DRF), and Deep Learning (DL). The performance of the binary classification models was evaluated using the area under the curve (AUC) and area under the precision-recall curve (AUCPR).

Results: In the binary classification model used to distinguish low-risk and moderate- to high-risk patients, the AUC in the GBM, DRF, and DL full models were 0.9994, 0.9982, and 0.9974, respectively, and the AUCPR was 0.9982, 0.9949, and 0.9918, respectively. Excluding Helicobacter pylori IgG antibody, pepsinogen I, and pepsinogen II, the AUC in the GBM, DRF, and DL models were 0.9932, 0.9879, and 0.9900, respectively, and the AUCPR was 0.9835, 0.9716, and 0.9752, respectively. Remodel after removing variables IgG, PGI, PGII, and G-17, the AUC in GBM, DRF, and DL was 0.8524, 0.8482, 0.8477, and AUCPR was 0.6068, 0.6008, and 0.5890, respectively. When constructing a tri-classification model, we discovered that none of the three machine learning models could effectively distinguish between patients at intermediate and high risk for gastric cancer (F1 scores in the GBM model for the low, medium and high risk: 0.9750, 0.9193, 0.5334, respectively; F1 scores in the DRF model for low, medium, and high risks: 0.9888, 0.9479, 0.6694, respectively; F1 scores in the DL model for low, medium, and high risks: 0.9812, 0.9216, 0.6394, respectively).

Conclusion: We concluded that gastric cancer screening indicators could be optimized when distinguishing low-risk and moderate to high-risk populations, and detecting gastrin-17 alone can achieve a good discriminative effect, thus saving huge expenditures.

The adoption of e-healthcare services is critical for improving healthcare accessibility and efficiency, particularly in regions with diverse populations, such as Malaysia. Although e-healthcare services offer numerous advantages, their adoption is considerably low and requires a thorough understanding of the key factors that influence their use. This study investigated the determinants and dynamics of e-healthcare adoption among adults over 40 years by extending the unified theory of acceptance and use of technology. We employed a quantitative research approach, specifically a cross-sectional design. Data were collected from 393 Malaysian respondents through a structured survey questionnaire, using convenience sampling. They were analyzed using partial least-squares-structural equation modeling. The findings revealed that performance expectancy, effort expectancy, social influence, and perceived product value significantly influenced individuals' intentions to use e-healthcare services. Meanwhile, perceived risk had an insignificant negative effect. Facilitating conditions significantly influenced individuals' intentions and actual usage of e-healthcare services. Furthermore, individuals' intentions to use e-healthcare services significantly affected their actual use of these services. Additionally, the intention to use e-healthcare services mediated the relationship between the factors and usage of e-healthcare services, except for perceived risk. Surprisingly, perceived service accuracy had no significant moderating effect on the relationship between individuals' intention to use and their actual use of e-healthcare services. This study offers valuable insights for educators, practitioners, and policymakers, enriching the scholarly discourse in this field. For education, integrating e-healthcare topics into curricula can enhance digital health literacy. In practice, healthcare providers should focus on improving user experience and addressing barriers to technology adoption. For policy making, developing supportive policies, and infrastructure to facilitate e-healthcare adoption is crucial to enhancing public health outcomes.

Introduction: ChatGPT can serve as an adjunct informational tool for ophthalmologists and their patients. However, the reliability and readability of its responses to myopia-related queries in the Chinese language remain underexplored.

Purpose: This study aimed to evaluate the ability of ChatGPT to address frequently asked questions (FAQs) about myopia by parents and caregivers.

Method: Myopia-related FAQs were input three times into fresh ChatGPT sessions, and the responses were evaluated by 10 ophthalmologists using a Likert scale for appropriateness, usability, and clarity. The Chinese Readability Index Explorer (CRIE) was used to evaluate the readability of each response. Inter-rater reliability among the reviewers was examined using Cohen's kappa coefficient, and Spearman's rank correlation analysis and one-way analysis of variance were used to investigate the relationship between CRIE scores and each criterion.

Results: Forty-five percent of the responses of ChatGPT in Chinese language were appropriate and usable and only 35% met all the set criteria. The CRIE scores for 20 ChatGPT responses ranged from 7.29 to 12.09, indicating that the readability level was equivalent to a middle-to-high school level. Responses about the treatment efficacy and side effects were deficient for all three criteria.

Conclusions: The performance of ChatGPT in addressing pediatric myopia-related questions is currently suboptimal. As parents increasingly utilize digital resources to obtain health information, it has become crucial for eye care professionals to familiarize themselves with artificial intelligence-driven information on pediatric myopia.