Healthcare Informatics Research最新文献

Objectives: This study examined the adoption and utilization of personal health records (PHR) across Korean medical institutions using data from the 2020 National Health and Medical Informatization Survey.

Methods: Spearheaded by the Ministry of Health and Welfare and prominent academic societies, this study surveyed PHR utilization in 574 medical institutions.

Results: Among these institutions, 84.9% (487 hospitals) maintained medical portals. However, just 14.1% (81 hospitals) had web-based or mobile PHRs, with 66.7% (28 of 42) of tertiary care hospitals adopting them. Tertiary hospitals led in PHR services: 87.8% offered certification issuance, 51.2% provided educational information, 63.4% supported online payment, and 95.1% managed appointment reservations. In contrast, general and smaller hospitals had lower rates. Online medical information viewing was prominent in tertiary hospitals (64.3%). Most patients accessed test results via PHRs, but other data types were less frequent, and only a few allowed downloads. Despite the widespread access to medical data through PHRs, integration with wearables and biometric data transfers to electronic medical records remained low, with limited plans for expansion in the coming three years.

Conclusions: Approximately two-thirds of the surveyed medical institutions provided PHRs, but hospitals and clinics in charge of community care had very limited PHR implementation. Government-led leadership is required to invigorate the use of PHRs in medical institutions.

Objectives: Systematic evaluations of the benefits of health information technology (HIT) play an essential role in enhancing healthcare quality by improving outcomes. However, there is limited empirical evidence regarding the benefits of IT adoption in healthcare settings. This study aimed to review the benefits of artificial intelligence (AI), the internet of things (IoT), and personal health records (PHR), based on scientific evidence.

Methods: The literature published in peer-reviewed journals between 2016 and 2022 was searched for systematic reviews and meta-analysis studies using the PubMed, Cochrane, and Embase databases. Manual searches were also performed using the reference lists of systematic reviews and eligible studies from major health informatics journals. The benefits of each HIT were assessed from multiple perspectives across four outcome domains.

Results: Twenty-four systematic review or meta-analysis studies on AI, IoT, and PHR were identified. The benefits of each HIT were assessed and summarized from a multifaceted perspective, focusing on four outcome domains: clinical, psycho-behavioral, managerial, and socioeconomic. The benefits varied depending on the nature of each type of HIT and the diseases to which they were applied.

Conclusions: Overall, our review indicates that AI and PHR can positively impact clinical outcomes, while IoT holds potential for improving managerial efficiency. Despite ongoing research into the benefits of health IT in line with advances in healthcare, the existing evidence is limited in both volume and scope. The findings of our study can help identify areas for further investigation.

Objectives: The benefits of physical activity (PA) for breast cancer (BC) patients and survivors are well documented. With the widespread use of the internet and mobile phones, along with the recent coronavirus disease 2019 pandemic, there has been a growing interest in digital health interventions. This study conducted a systematic review and meta-analysis to evaluate the effects of digital PA interventions for BC patients and survivors in improving PA and quality of life (QoL).

Methods: We searched eight databases, including PubMed, CINAHL, Embase, Scopus, Web of Science, Cochrane Central Register of Controlled Trials in the Cochrane Library, RISS, and DBpia. Studies were included if they provided digital PA interventions, assessed PA and QoL among BC patients and survivors, and were published from inception to December 31, 2022.

Results: In total, 18 studies were identified. The meta-analysis showed significant improvement in the total PA duration (five studies; standardized mean difference [SMD] = 0.71; 95% confidence interval [CI], 0.25-1.18; I2 = 86.64%), functional capacity (three studies; SMD = 0.38; 95% CI, 0.10-0.66; I2 = 14.36%), and QoL (nine studies; SMD = 0.45; 95% CI, 0.22-0.69; I2 = 65.55%).

Conclusions: Digital PA interventions for BC patients and survivors may significantly improve PA, functional capacity, and QoL. Future research should focus on the long-term effects of digital PA interventions, using objective outcome measures.

Objectives: A substantial portion of the data contained in Electronic Health Records (EHR) is unstructured, often appearing as free text. This format restricts its potential utility in clinical decision-making. Named entity recognition (NER) methods address the challenge of extracting pertinent information from unstructured text. The aim of this study was to outline the current NER methods and trace their evolution from 2011 to 2022.

Methods: We conducted a methodological literature review of NER methods, with a focus on distinguishing the classification models, the types of tagging systems, and the languages employed in various corpora.

Results: Several methods have been documented for automatically extracting relevant information from EHRs using natural language processing techniques such as NER and relation extraction (RE). These methods can automatically extract concepts, events, attributes, and other data, as well as the relationships between them. Most NER studies conducted thus far have utilized corpora in English or Chinese. Additionally, the bidirectional encoder representation from transformers using the BIO tagging system architecture is the most frequently reported classification scheme. We discovered a limited number of papers on the implementation of NER or RE tasks in EHRs within a specific clinical domain.

Conclusions: EHRs play a pivotal role in gathering clinical information and could serve as the primary source for automated clinical decision support systems. However, the creation of new corpora from EHRs in specific clinical domains is essential to facilitate the swift development of NER and RE models applied to EHRs for use in clinical practice.

Objectives: Artificial intelligence (AI) technologies are developing very rapidly in the medical field, but have yet to be actively used in actual clinical settings. Ensuring reliability is essential to disseminating technologies, necessitating a wide range of research and subsequent social consensus on requirements for trustworthy AI.

Methods: This review divided the requirements for trustworthy medical AI into explainability, fairness, privacy protection, and robustness, investigated research trends in the literature on AI in healthcare, and explored the criteria for trustworthy AI in the medical field.

Results: Explainability provides a basis for determining whether healthcare providers would refer to the output of an AI model, which requires the further development of explainable AI technology, evaluation methods, and user interfaces. For AI fairness, the primary task is to identify evaluation metrics optimized for the medical field. As for privacy and robustness, further development of technologies is needed, especially in defending training data or AI algorithms against adversarial attacks.

Conclusions: In the future, detailed standards need to be established according to the issues that medical AI would solve or the clinical field where medical AI would be used. Furthermore, these criteria should be reflected in AI-related regulations, such as AI development guidelines and approval processes for medical devices.

Objectives: Distributed research networks (DRNs) facilitate multicenter research by enabling the use of multicenter data; therefore, they are increasingly utilized in healthcare fields. Despite the numerous advantages of DRNs, it is crucial to understand researchers' acceptance of these networks to ensure their effective application in multicenter research. In this study, we sought to identify the factors influencing the adoption of DRNs among researchers in Korea.

Methods: We used snowball sampling to collect data from 149 researchers between July 7 and August 28, 2020. Five factors were used to formulate the hypotheses and research model: data accessibility, usefulness, ease of use, data security risk, and intention to use DRNs. We applied a structural equation model to identify relationships within the research model.

Results: Data accessibility and data security were critical to the acceptance and use of DRNs. The usefulness of DRNs partially mediated the relationship between data accessibility and the intention to use DRNs. Interestingly, ease of use did not influence the intention to use DRNs, but it was affected by data accessibility. Furthermore, ease of use impacted the perceived usefulness of DRNs.

Conclusions: This study highlighted major factors that can promote the broader adoption and utilization of DRNs. Consequently, these findings can contribute to the expansion of active multicenter research using DRNs in the field of healthcare research.

Objectives: Enhancing critical care efficacy involves evaluating and improving system functioning. Benchmarking, a retrospective comparison of results against standards, aids risk-adjusted assessment and helps healthcare providers identify areas for improvement based on observed and predicted outcomes. The last two decades have seen the development of several models using machine learning (ML) for clinical outcome prediction. ML is a field of artificial intelligence focused on creating algorithms that enable computers to learn from and make predictions or decisions based on data. This narrative review centers on key discoveries and outcomes to aid clinicians and researchers in selecting the optimal methodology for critical care benchmarking using ML.

Methods: We used PubMed to search the literature from 2003 to 2023 regarding predictive models utilizing ML for mortality (592 articles), length of stay (143 articles), or mechanical ventilation (195 articles). We supplemented the PubMed search with Google Scholar, making sure relevant articles were included. Given the narrative style, papers in the cohort were manually curated for a comprehensive reader perspective.

Results: Our report presents comparative results for benchmarked outcomes and emphasizes advancements in feature types, preprocessing, model selection, and validation. It showcases instances where ML effectively tackled critical care outcome-prediction challenges, including nonlinear relationships, class imbalances, missing data, and documentation variability, leading to enhanced results.

Conclusions: Although ML has provided novel tools to improve the benchmarking of critical care outcomes, areas that require further research include class imbalance, fairness, improved calibration, generalizability, and long-term validation of published models.

Objectives: This article explores the secondary use provisions of the European Health Data Space (EHDS), proposed by the European Commission in May 2022, and offers policy recommendations for South Korea.

Methods: The authors analyzed the texts of the EHDS proposal and other documents published by the European Union, as well as surveyed the relevant literature.

Results: The EHDS proposal seeks to create new patient rights over electronic health data collected and used for primary care; and establish a data sharing system for the re-use of electronic health data for secondary purposes, including research, the provision of personalized healthcare, and developing healthcare artificial intelligence (AI) applications. These provisions envisage requiring both private and public data holders to share certain types of electronic health data on a mandatory basis with third parties. New government bodies, called health data access bodies, would review data access applications and issue data permits.

Conclusions: The overarching aim of the EHDS proposal is to make electronic health data, which are currently held in the hands of a small number of organizations, available for re-use by third parties to stimulate innovation and research. While it will be very challenging for South Korea to adopt a similar scheme and require private entities to share their proprietary data with third parties, the South Korean government should consider making at least health data collected through publicly funded research more readily available for secondary use.

Objectives: The objective of this study was to develop and validate a multicenter-based, multi-model, time-series deep learning model for predicting drug-induced liver injury (DILI) in patients taking angiotensin receptor blockers (ARBs). The study leveraged a national-level multicenter approach, utilizing electronic health records (EHRs) from six hospitals in Korea.

Methods: A retrospective cohort analysis was conducted using EHRs from six hospitals in Korea, comprising a total of 10,852 patients whose data were converted to the Common Data Model. The study assessed the incidence rate of DILI among patients taking ARBs and compared it to a control group. Temporal patterns of important variables were analyzed using an interpretable timeseries model.

Results: The overall incidence rate of DILI among patients taking ARBs was found to be 1.09%. The incidence rates varied for each specific ARB drug and institution, with valsartan having the highest rate (1.24%) and olmesartan having the lowest rate (0.83%). The DILI prediction models showed varying performance, measured by the average area under the receiver operating characteristic curve, with telmisartan (0.93), losartan (0.92), and irbesartan (0.90) exhibiting higher classification performance. The aggregated attention scores from the models highlighted the importance of variables such as hematocrit, albumin, prothrombin time, and lymphocytes in predicting DILI.

Conclusions: Implementing a multicenter-based timeseries classification model provided evidence that could be valuable to clinicians regarding temporal patterns associated with DILI in ARB users. This information supports informed decisions regarding appropriate drug use and treatment strategies.