Healthcare Informatics Research最新文献

Objectives: Pain is a common side effect of cancer that negatively impacts biopsychosocial well-being and quality of life. There has been increasing interest in using digital game interventions for managing pain in cancer patients. The present study aimed to consolidate and summarize knowledge regarding the role of games in reducing pain among cancer patients and enhancing their overall quality of life.

Methods: We reviewed studies published between 2000 and April 8, 2023, from databases such as PubMed, Scopus, and Web of Science. The focus was on determining the impact of health games on pain management in cancer patients.

Results: An initial search identified 2,544 studies, which were narrowed down to 10 relevant articles after removing duplicates and assessing quality. These studies examined the use of mobile and computer games across various types of cancer, including both pediatric and adult cases. The findings indicate that digital games, particularly those utilizing virtual reality technologies, can diminish pain and anxiety while enhancing treatment outcomes. Overall, the application of these technologies has the potential to improve cancer treatment.

Conclusions: Digital game interventions empower cancer patients by fostering effective communication and patient-centered approaches, which enhance perceptions, outcomes, and overall well-being. These games provide real-time feedback and facilitate interaction with healthcare professionals, which promotes self-management and boosts patient motivation and adherence to treatment protocols. As personalized educational platforms, they increase engagement through educational resources and symptom tracking, while also encouraging physical activity. Furthermore, they act as distraction tools during painful procedures, presenting new research opportunities in pain management and enhancing overall quality of life.

Objectives: This study aimed to evaluate the performance of vaccine logistics management information systems at public health facilities in Amhara Region, Ethiopia.

Methods: An institution-based concurrent explanatory mixed design was used at public health facilities in Amhara Region, Ethiopia. In total, 102 health facilities were selected using the multi-stage, stratified, random-sampling proportionate-to-size method. Data were collected using a structured data abstraction checklist from vaccine logistics management information system records. Additionally, experiences of Expanded Program for Immunization focal persons were gathered through interviewer-administered questionnaires.

Results: Among the 102 health facilities surveyed, 99 (97.1%) provided vaccination services during the study period. On the day of the visit, the average availability and utilization rates of 15 vaccine logistics management information system forms were 44.1% and 40.5%, respectively. The mean inventory accuracy for tracer vaccines stood at 58.3% ± 45.1%. The overall average accuracy percentages for urban and rural vaccine request form reports were 73.4% and 69.6%, respectively.

Conclusions: The performance of vaccine logistics management information systems in these public health facilities was suboptimal. Key issues identified include inconsistent use of recording forms, shortages of reporting forms, lack of digitalization, poor data quality, and a shortage of qualified staff. These findings underscore the urgent need for standardization of the vaccine logistics management information system and enhancement of data quality.

Objectives: This study was performed to examine the content of decision-making support and patient responses, as documented in the nursing records of individuals with cancer. These patients had received outpatient treatment at hospitals that met government requirements for providing specialized cancer care.

Methods: Nursing records from the electronic medical record system (in the subjective, objective, assessment, and plan [SOAP] format), along with data from interviews, were extracted for patients receiving outpatient care at the Department of Internal Medicine and Palliative Care and the Department of Breast Oncology. Data analysis involved simple tabulation and text mining, utilizing KH Coder version 3.beta.07d.

Results: The study included 42 patients from palliative care internal medicine and 60 from breast oncology, with mean ages of 70.5 ± 12.2 and 55.8 ± 12.2 years, respectively. Decisions most frequently regarded palliative care unit admission (25 cases) and genetic testing (24 cases). The assessment category covered keywords including (1) "pain," "treatment," "future," "recuperation," and "home," as terms related to palliative care and internal medicine, as well as (2) "treatment," "relief," and "genetics" as terms related to breast oncology. The plan category incorporated keywords such as (1) "treatment," "relaxation," and "visit" and (2) "explanation," "confirmation," and "conveyance."

Conclusions: Nurses appear crucial in evaluating patients' symptoms and treatment paths during the decision-making support process, helping them make informed choices about future treatments, care settings, and genetic testing. However, when patients cannot make a decision solely based on the information provided, clinicians must address complex psychological concepts such as disease progression and the potential genetic impact on their children. Further detailed observational studies of nurses' responses to patients' psychological reactions are warranted.

Objectives: The National Health Insurance (Jaminan Kesehatan Nasional or JKN) mobile application represents an administrative transformation of health social insurance in Indonesia. The application's user base has quadrupled in the past 2 years. The study aimed to evaluate the usability and user experience of the JKN mobile application in Indonesia.

Methods: The study administered an online survey featuring the System Usability Scale (SUS) questionnaire alongside a user experience assessment. The survey was disseminated via social media and garnered responses from 370 participants. The variables examined included usability and user experience, specifically focusing on attractiveness, perspicuity, efficiency, dependability, stimulation, and novelty. Data analysis was performed descriptively, and the results were benchmarked.

Results: The JKN mobile application had an average SUS score of 60.9 ± 13.47 (adjective rating "good," Grade "F," acceptability rating of "low marginal," and a percentile range of 34). User experience testing yielded the following results: attractiveness (1.09 ± 1.09), perspicuity (1.05 ± 1.17), efficiency (1.07 ± 1.20), dependability (1.03 ± 1.12), stimulation (0.88 ± 1.20), and novel-ty (0.58 ± 1.09). The benchmark for the JKN mobile application placed the user experience in the "below average" category.

Conclusions: The evaluation of the usability and user experience of the JKN mobile application was generally positive. However, there is a need to improve the usability and all parameters of the user experience. Broader-scale research is needed to explore the factors that influence usage and user experience to promote the adoption of the JKN application and increase user satisfaction.

Objectives: With the growing importance of monitoring cancer patients' internet usage, there is an increasing need for technology that expands access to relevant information through text mining. This study analyzed internet articles from portal sites in 2023 to identify trends in the information available to cancer patients and to derive meaningful insights.

Methods: This study analyzed 19,578 news articles published on Naver, a major Korean portal site, from January 1, 2023, to December 31, 2023. Natural language processing, text mining, network analysis, and word cloud analysis were employed. The search term "am" (Korean for "cancer") was used to identify keywords related to cancer.

Results: In 2023, an average of 1,631 cancer-related articles were published monthly, with a peak of 1,946 in September and a low of 1,371 in February. A total of 132,456 keywords were extracted, with "cure" (2,218 occurrences), "lung cancer" (1,652), and "breast cancer" (1,235) being the most frequent. Term frequency-inverse document frequency analysis ranked "struggle" (1064.172) as the most significant keyword, followed by "lung cancer" (839.988) and "breast cancer" (744.840). Network analysis revealed four distinct clusters focusing on treatment, celebrity-related issues, major cancer types, and cancer-causing factors.

Conclusions: The analysis of cancer-related keywords in 2023 indicates that news articles often prioritize gossip over essential information. These findings provide foundational data for future policy directions and strategies to address misinformation. This study underscores the importance of understanding the nature of cancer-related information consumed by the public and offers insights to guide official policies and healthcare practices.

Objectives: Online consultation scheduling is increasingly common in health services across various countries. This paper reviews articles published in the past five years and reflects on the risks and benefits of this practice, linking it to a recent Portuguese pilot project.

Methods: A search for articles from Web of Science and Scopus published since 2018 was conducted using the terms "online scheduling," "online booking," and "consultations." This search was completed in the last week of 2023.

Results: Out of 64 articles retrieved, 26 were relevant to the topic. These articles were reviewed, and their main findings, along with those from other relevant sources, were discussed.

Conclusions: Several limitations of online consultations were identified, encompassing ethical, clinical, and economic aspects. While these consultations tend to be less expensive, their accessibility varies based on factors such as the users' age, whether they reside in rural or urban areas, and the technological capabilities of different countries, indicating that access disparities may continue to widen. Confidentiality concerns also arise, varying by medical specialty, along with issues related to payment. Overall, however, both users and health professionals view the advent of online consultation booking positively. In conclusion, despite the risks identified, online consultation booking has the potential to enhance user access to health services, provided that usage limitations and technological disparities are addressed. Research production has not kept pace with rapid technological advancements.

Objectives: Sepsis is a leading global cause of mortality, and predicting its outcomes is vital for improving patient care. This study explored the capabilities of ChatGPT, a state-of-the-art natural language processing model, in predicting in-hospital mortality for sepsis patients.

Methods: This study utilized data from the Korean Sepsis Alliance (KSA) database, collected between 2019 and 2021, focusing on adult intensive care unit (ICU) patients and aiming to determine whether ChatGPT could predict all-cause mortality after ICU admission at 7 and 30 days. Structured prompts enabled ChatGPT to engage in in-context learning, with the number of patient examples varying from zero to six. The predictive capabilities of ChatGPT-3.5-turbo and ChatGPT-4 were then compared against a gradient boosting model (GBM) using various performance metrics.

Results: From the KSA database, 4,786 patients formed the 7-day mortality prediction dataset, of whom 718 died, and 4,025 patients formed the 30-day dataset, with 1,368 deaths. Age and clinical markers (e.g., Sequential Organ Failure Assessment score and lactic acid levels) showed significant differences between survivors and non-survivors in both datasets. For 7-day mortality predictions, the area under the receiver operating characteristic curve (AUROC) was 0.70-0.83 for GPT-4, 0.51-0.70 for GPT-3.5, and 0.79 for GBM. The AUROC for 30-day mortality was 0.51-0.59 for GPT-4, 0.47-0.57 for GPT-3.5, and 0.76 for GBM. Zero-shot predictions using GPT-4 for mortality from ICU admission to day 30 showed AUROCs from the mid-0.60s to 0.75 for GPT-4 and mainly from 0.47 to 0.63 for GPT-3.5.

Conclusions: GPT-4 demonstrated potential in predicting short-term in-hospital mortality, although its performance varied across different evaluation metrics.