Healthcare Informatics Research最新文献

Objectives: Since the easiest way to identify pills and obtain information about them is to distinguish them visually, many studies on image processing technology exist. However, no automatic system for generating pill image data has yet been developed. Therefore, we propose a system for automatically generating image data by taking pictures of pills from various angles. This system is referred to as the pill filming system in this paper.

Methods: We designed the pill filming system to have three components: structure, controller, and a graphical user interface (GUI). This system was manufactured with black polylactic acid using a 3D printer for lightweight and easy manufacturing. The mainboard controls data storage, and the entire process is managed through the GUI. After one reciprocating movement of the seesaw, the web camera at the top shoots the target pill on the stage. This image is then saved in a specific directory on the mainboard.

Results: The pill filming system completes its workflow after generating 300 pill images. The total time to collect data per pill takes 21 minutes and 25 seconds. The generated image size is 1280 × 960 pixels, the horizontal and vertical resolutions are both 96 DPI (dot per inch), and the file extension is .jpg.

Conclusions: This paper proposes a system that can automatically generate pill image data from various angles. The pill observation data from various angles include many cases. In addition, the data collected in the same controlled environment have a uniform background, making it easy to process the images. Large quantities of high-quality data from the pill filming system can contribute to various studies using pill images.

Objectives: The purpose of this study was to explore new ways of creating value in the medical field and to derive recommendations for the role of medical institutions and the government.

Methods: In this paper, based on expert discussion, we classified Internet of Things (IoT) technologies into four categories according to the type of information they collect (location, environmental parameters, energy consumption, and biometrics), and investigated examples of application.

Results: Biometric IoT diagnoses diseases accurately and offers appropriate and effective treatment. Environmental parameter measurement plays an important role in accurately identifying and controlling environmental factors that could be harmful to patients. The use of energy measurement and location tracking technology enabled optimal allocation of limited hospital resources and increased the efficiency of energy consumption. The resulting economic value has returned to patients, improving hospitals' cost-effectiveness.

Conclusions: Introducing IoT-based technology to clinical sites, including medical institutions, will enhance the quality of medical services, increase patient safety, improve management efficiency, and promote patient-centered medical services. Moreover, the IoT is expected to play an active role in the five major tasks of facility hygiene in medical fields, which are all required to deal with the COVID-19 pandemic: social distancing, contact tracking, bed occupancy control, and air quality management. Ultimately, the IoT is expected to serve as a key element for hospitals to perform their original functions more effectively. Continuing investments, deregulation policies, information protection, and IT standardization activities should be carried out more actively for the IoT to fulfill its expectations.

Objectives: Online misinformation has reached unprecedented levels during the coronavirus disease 2019 (COVID-19) pandemic. This study analyzed the magnitude and sentiment dynamics of misinformation and unverified information about public health interventions during a COVID-19 outbreak in Da Nang, Vietnam, between July and September 2020.

Methods: We analyzed user-generated online information about five public health interventions during the Da Nang outbreak. We compared the volume, source, sentiment polarity, and engagements of online posts before, during, and after the outbreak using negative binomial and logistic regression, and assessed the content validity of the 500 most influential posts.

Results: Most of the 54,528 online posts included were generated during the outbreak (n = 46,035; 84.42%) and by online newspapers (n = 32,034; 58.75%). Among the 500 most influential posts, 316 (63.20%) contained genuine information, 10 (2.00%) contained misinformation, 152 (30.40%) were non-factual opinions, and 22 (4.40%) contained unverifiable information. All misinformation posts were made during the outbreak, mostly on social media, and were predominantly negative. Higher levels of engagement were observed for information that was unverifiable (incidence relative risk [IRR] = 2.83; 95% confidence interval [CI], 1.33-0.62), posted during the outbreak (before: IRR = 0.15; 95% CI, 0.07-0.35; after: IRR = 0.46; 95% CI, 0.34-0.63), and with negative sentiment (IRR = 1.84; 95% CI, 1.23-2.75). Negatively toned posts were more likely to be misinformation (odds ratio [OR] = 9.59; 95% CI, 1.20-76.70) or unverified (OR = 5.03; 95% CI, 1.66-15.24).

Conclusions: Misinformation and unverified information during the outbreak showed clustering, with social media being particularly affected. This indepth assessment demonstrates the value of analyzing online "infodemics" to inform public health responses.

Objectives: Symptomatic treatment is an essential component in the overall treatment of multiple sclerosis (MS). However, knowledge in this regard is confusing and scattered. Physicians also have challenges in choosing symptomatic treatment based on the patient's condition. To share, update, and reuse this knowledge, the aim of this study was to provide an ontology for MS symptomatic treatment.

Methods: The Symptomatic Treatment of Multiple Sclerosis Ontology (STMSO) was developed according to Ontology Development 101 and a guideline for developing good ontologies in the biomedical domain. We obtained knowledge and rules through a systematic review and entered this knowledge in the form of classes and subclasses in the ontology. We then mapped the ontology using the Basic Formal Ontology (BFO) and Ontology for General Medical Sciences (OGMS) as reference ontologies. The ontology was built using Protégé Editor in the Web Ontology Language format. Finally, an evaluation was done by experts using criterion-based approaches in terms of accuracy, clarity, consistency, and completeness.

Results: The knowledge extraction phase identified 110 articles related to the ontology in the form of 626 classes, 40 object properties, and 139 rules. Five general classes included "patient," "symptoms," "pharmacological treatment," "treatment plan," and "measurement index." The evaluation in terms of standards for biomedical ontology showed that STMSO was accurate, clear, consistent, and complete.

Conclusions: STMSO is the first comprehensive semantic representation of the symptomatic treatment of MS and provides a major step toward the development of intelligent clinical decision support systems for symptomatic MS treatment.

Objectives: Academic electronic medical records (AEMRs) can be utilized for a variety of educational programs that can enhance nursing students' nursing informatics and clinical reasoning competencies. This study aimed to identify the applicability and effectiveness of simulation education incorporating AEMRs.

Methods: We developed simulation education scenarios incorporating AEMRs and evaluated them with 76 third- and fourth-year nursing students from five nursing schools using a mixed-methods design. We incorporated three simulation case scenarios involving preeclampsia, diabetes mellitus, and myocardial infarction into the AEMRs. After the simulation education, participants' feedback on the usability of the AEMR system and their self-efficacy for AEMR utilization were collected via self-reported surveys. Subsequently, the simulation education incorporating AEMRs was evaluated through a focus group interview. The survey data were examined using descriptive statistics, and thematic analysis was done for the focus group interview data.

Results: The average mean scores for the AEMR system's usability and participants' self-efficacy for AEMR utilization were 5.36 of 7 and 3.96 of 5, respectively. According to the focus group interviews, the participants were satisfied with the simulation education incorporating AEMRs and recognized their confidence in AEMR utilization. In addition, participants addressed challenges to simulation education incorporating AEMRs, including the need for pre-education and AEMR utilization difficulties.

Conclusions: Nursing students were satisfied with and recognized the value of simulation education incorporating AEMRs. Although the actual application of simulation education incorporating AEMRs remains challenging, further research can help develop and implement this approach for nursing students.

Objectives: The purpose of this study was to identify patterns of self-harm risk factors from suicide and self-harm surveillance reports in Thailand.

Methods: This study analyzed data from suicide and self-harm surveillance reports submitted to Khon Kaen Rajanagarindra Psychiatric Hospital, Thailand. The process of identifying patterns of self-harm risk factors involved: data preprocessing (namely, data preparation and cleaning, missing data management using listwise deletion and expectation-maximization techniques, subgrouping factors, determining the target factors, and data correlation for learning); classifying the risk of self-harm (severe or mild) using 10-fold cross-validation with the support vector machine, random forest, multilayer perceptron, decision tree, k-nearest neighbors, and ensemble techniques; data filtering; identifying patterns of self-harm risk factors using 10-fold cross-validation with the classification and regression trees (CART) technique; and evaluating patterns of self-harm risk factors.

Results: The random forest technique was most accurate for classifying the risk of self-harm, with specificity, sensitivity, and F-score of 92.84%, 93.12%, and 91.46%, respectively. The CART technique was able to identify 53 patterns of self-harm risk, consisting of 16 severe self-harm risk patterns and 37 mild self-harm risk patterns, with an accuracy of 92.85%. In addition, we discovered that the type of hospital was a new risk factor for severe selfharm.

Conclusions: The procedure presented herein could identify patterns of risk factors from self-harm and assist psychiatrists in making decisions related to self-harm among patients visiting hospitals in Thailand.

Objectives: The objective of this study was to investigate the factors associated with website operation among medical facilities.

Methods: A cross-sectional study design was employed to investigate 1,519 hospitals, 33,043 medical clinics (MCs), and 18,240 dental clinics (DCs) as of 2020. The main outcome variable was analyzed according to technological, organizational, and environmental factors.

Results: The percentages of small hospitals, MCs, and DCs with websites were 26.4%, 9.0%, and 6.6%, respectively. For small hospitals, the nearby presence of a subway station (odds ratio [OR] = 2.772; 95% confidence interval [CI], 1.973-3.892; p < 0.0001) was the only factor significantly associated with website operation status. Among medical and dental clinics, the percentage of specialists-MCs (OR = 1.002; 95% CI, 1.000-1.004; p = 0.0175) and DCs (OR = 1.002; 95% CI, 1.001-1.004; p = 0.0061), the nearby presence of a subway station-MCs (OR = 2.954; 95% CI, 2.613-3.339; p < 0.0001) and DCs (OR = 3.444; 95% CI, 2.945-4.028; p < 0.0001), and the number of clinics in the local area-MCs (OR = 1.029; 95% CI, 1.026-1.031; p < 0.0001) and DCs (OR = 1.080; 95% CI, 1.066-1.093; p < 0.0001)-were significantly associated with website operation.

Conclusions: Clinics are critically affected by internal and external factors regarding website operation relative to small hospitals. Healthcare policymakers involved with information technologies may need to pay attention to those factors associated with website dispersion among small clinics.

Objectives: Early hemorrhage detection in intensive care units (ICUs) enables timely intervention and reduces the risk of irreversible outcomes. In this study, we aimed to develop a machine learning model to predict hemorrhage by learning the patterns of continuously changing, real-world clinical data.

Methods: We used the Medical Information Mart for Intensive Care databases (MIMIC-III and MIMIC-IV). A recurrent neural network was used to predict severe hemorrhage in the ICU. We developed three machine learning models with an increasing number of input features and levels of complexity: model 1 (11 features), model 2 (18 features), and model 3 (27 features). MIMIC-III was used for model training, and MIMIC-IV was split for internal validation. Using the model with the highest performance, external verification was performed using data from a subgroup extracted from the eICU Collaborative Research Database.

Results: We included 5,670 ICU admissions, with 3,150 in the training set and 2,520 in the internal test set. A positive correlation was found between model complexity and performance. As a measure of performance, three models developed with an increasing number of features showed area under the receiver operating characteristic (AUROC) curve values of 0.61-0.94 according to the range of input data. In the subgroup extracted from the eICU database for external validation, an AUROC value of 0.74 was observed.

Conclusions: Machine learning models that rely on real clinical data can be used to predict patients at high risk of bleeding in the ICU.

Objectives: This study was conducted to document the development and usability testing of SIKRIBO, a tuberculosis screening application.

Methods: The SIKRIBO application was developed using design science research methodology, which has six steps: problem identification and motivation, definition of objectives for a solution, product design and development, demonstration, evaluation, and communication. A system usability scale (SUS) questionnaire was used to assess application usability. A total of 20 health cadres (trained community members) and health workers participated in the usability tests.

Results: Two versions of the application were developed: Android-based for users and web-based for administrators. The Android-based version has four main menus: Find Tuberculosis, Tuberculosis Education, Latest Info, and Profile. The web version is accessible to health workers, as well as the research team and application developers who monitor and manage the user-conducted screenings. The average SUS score was 76 (standard deviation, 8.00).

Conclusions: This application was developed to help detect active tuberculosis cases in the community. The SUS results indicate that the application is highly usable. Thus, SIKRIBO is expected to be broadly implemented to increase tuberculosis case detection through active community participation.