OBJECTIVES

The objective of this study was to synthesize existing literature on nursing informatics (NI) and propose updates to the Philippine Nursing Informatics curriculum that embrace current trends and integrate a globally acknowledged framework.

A literature search was conducted on PubMed and ScienceDirect. This search identified 79 articles, of which only eight met the inclusion criteria. The Technology Informatics Guiding Education Reform (TIGER) initiative provided the framework for analyzing the literature review outcomes and for developing the revised course structure for the Nursing Informatics (NI) curriculum in the Philippines.

The revised course outline incorporated 31 topics across the six domains outlined by the TIGER framework. Upon comparison, it was found that numerous topics identified were absent from the existing NI curriculum in the Philippines. Key subjects identified for inclusion encompass research, examination of standards and terminologies, application in community health, cybersecurity, project management, and advocacy. These areas hold particular relevance for the Philippines, attributed to the limited recognition of NI and the ongoing advancements related to technological applications in healthcare.

The nursing informatics curriculum in the Philippines is not up to date, failing to align with global NI standards. It is recommended that a thorough revision and enhancement be undertaken to ensure alignment with international frameworks and current industry practices.

With the booming development of medical information technology and computer science, the medical services industry is gradually transiting from information technology to intelligence. The medical knowledge graph plays an important role in intelligent medical applications such as knowledge questions and answers and intelligent diagnosis, and is a key technology for promoting wise medical care and the basis for intelligent management of medical information. In order to fully exploit the great potential of knowledge graphs in the medical field, this paper focuses on five aspects: inter-drug relationship discovery, assisted diagnosis, personalized recommendation, decision support and intelligent prediction. The latest research progress on medical knowledge graphs is introduced, and relevant suggestions are made in light of the current challenges and problems faced by medical knowledge graphs to provide reference for promoting the wide application of medical knowledge graphs.
Pattern Recognition, Automated ; Medical Informatics

COVID-19 ; Humans ; Medical Informatics Applications ; Pandemics ; Pharmacists ; Pharmacy Service, Hospital/methods* ; Professional Role ; SARS-CoV-2 ; Safety Management/methods*

Cancers have been widely recognized as highly heterogeneous diseases, and early diagnosis and prognosis of cancer types have become the focus of cancer research. In the era of big data, efficient mining of massive biomedical data has become a grand challenge for bioinformatics research. As a typical neural network model, the autoencoder is able to efficiently learn the features of input data by unsupervised training method and further help integrate and mine the biological data. In this article, the primary structure and workflow of the autoencoder model are introduced, followed by summarizing the advances of the autoencoder model in cancer informatics using various types of biomedical data. Finally, the challenges and perspectives of the autoencoder model are discussed.
Algorithms ; Humans ; Informatics ; Neoplasms/diagnosis* ; Neural Networks, Computer

A clinical information navigation system based on 3D human body model is designed. The system extracts the key information of diagnosis and treatment of patients by searching the historical medical records, and stores the focus information in a predefined structured patient instance. In addition, the rule mapping is established between the patient instance and the three-dimensional human body model, the focus information is visualized on the three-dimensional human body model, and the trend curve can be drawn according to the change of the focus, meanwhile, the key diagnosis and treatment information and the original report reference function are provided. The system can support the analysis, storage and visualization of various types of reports, improve the efficiency of doctors' retrieval of patient information, and reduce the treatment time.
Diagnosis, Computer-Assisted ; Humans ; Medical Informatics Applications ; Models, Anatomic ; Software

BACKGROUND: It is not possible to measure how much activity is required to understand and code a medical data. We introduce an assessment method in clinical coding, and applied this method to neurosurgical terms.METHODS: Coding activity consists of two stages. At first, the coders need to understand a presented medical term (informational activity). The second coding stage is about a navigating terminology browser to find a code that matches the concept (code-matching activity). Systematized Nomenclature of Medicine – Clinical Terms (SNOMED CT) was used for the coding system. A new computer application to record the trajectory of the computer mouse and record the usage time was programmed. Using this application, we measured the time that was spent. A senior neurosurgeon who has studied SNOMED CT has analyzed the accuracy of the input coding. This method was tested by five neurosurgical residents (NSRs) and five medical record administrators (MRAs), and 20 neurosurgical terms were used.RESULTS: The mean accuracy of the NSR group was 89.33%, and the mean accuracy of the MRA group was 80% (p=0.024). The mean duration for total coding of the NSR group was 158.47 seconds, and the mean duration for total coding of the MRA group was 271.75 seconds (p=0.003).CONCLUSION: We proposed a method to analyze the clinical coding process. Through this method, it was possible to accurately calculate the time required for the coding. In neurosurgical terms, NSRs had shorter time to complete the coding and higher accuracy than MRAs.
Animals ; Clinical Coding ; Humans ; Medical Informatics ; Medical Record Administrators ; Methods ; Mice ; Neurosurgeons ; Systematized Nomenclature of Medicine

The analysis of big data in medical field cannot be isolated from the high quality clinical database, and the construction of first aid database in our country is still in the early stage of exploration. This paper introduces the idea and key technology of the construction of multi-parameter first aid database. By combining emergency business flow with information flow, an emergency data integration model was designed with reference to the architecture of the Medical Information Mart for Intensive Care III (MIMIC-III), created by Computational Physiology Laboratory of Massachusetts Institute of Technology (MIT), and a high-quality first-aid database was built. The database currently covers 22 941 medical records for 19 814 different patients from May 2015 to October 2017, including relatively complete information on physiology, biochemistry, treatment, examination, nursing, etc. And based on the database, the first First-Aid Big Data Datathon event, which 13 teams from all over the country participated in, was launched. The First-Aid database provides a reference for the construction and application of clinical database in China. And it could provide powerful data support for scientific research, clinical decision making and the improvement of medical quality, which will further promote secondary analysis of clinical data in our country.
Big Data ; Critical Care ; Databases, Factual ; Humans ; Medical Informatics

BACKGROUND: Lung cancer is the cancer with the highest morbidity and mortality at home and abroad at present. Using computed tomography (CT) to screen lung cancer nodules is a huge workload. To test the effect of artificial intelligence in automatic identification of lung cancer by using artificial intelligence to find the lung cancer nodules automatically in the chest CT of 1 mm and 5 mm thick. METHODS: 5,000 cases of T1 stage lung cancer patients with 1 mm and 5 mm layer thickness were respectively labeled and learned by computer neural network, the algorithm of forming pulmonary nodules was carried out. 500 cases of chest CT in T1 stage lung cancer patients with 1 mm and 5 mm thickness were tested by artificial intelligence formation, and the sensitivity and specificity were compared with artificial reading. RESULTS: Using artificial intelligence to read chest CT 500 in 5 mm, the sensitivity was 95.20%, the specificity was 93.20%, and the Kappa value of two times repeated read was 0.926,1. For 1 mm chest CT 500 cases, the sensitivity is 96.40%, the specificity is 95.60%, and the Kappa reads two times is 0.938,6. Compared with 5 doctors, the same CT sets with 1 mm thickness were read. The detection rates of artificial intelligence and artificial reading were similar to those of lung cancer nodules and negative control read films, and there was no significant difference between them. In the comparison of the same CT slices with 5 mm thickness, the number of detection of lung cancer nodules by artificial intelligence is better than that of artificial reading, and the sensitivity is higher, but the number of false messages is increased and the specificity is slightly worse. CONCLUSIONS The automatic learning of early lung cancer chest CT images by artificial intelligence can achieve high sensitivity and specificity of early lung cancer identification, and assist doctors in the diagnosis of lung cancer.
Artificial Intelligence ; Humans ; Lung Neoplasms ; diagnosis ; pathology ; Medical Informatics ; methods ; Neoplasm Staging

OBJECTIVES: The objective of this study was to investigate the clinical decision support (CDS) functions and digitalization of clinical documents of Electronic Medical Record (EMR) systems in Korea. This exploratory study was conducted focusing on current status of EMR systems. METHODS: This study used a nationwide survey on EMR systems conducted from July 25, 2018 to September 30, 2018 in Korea. The unit of analysis was hospitals. Respondents of the survey were mainly medical recorders or staff members in departments of health insurance claims or information technology. This study analyzed data acquired from 132 hospitals that participated in the survey. RESULTS: This study found that approximately 80% of clinical documents were digitalized in both general and small hospitals. The percentages of general and small hospitals with 100% paperless medical charts were 33.7% and 38.2%, respectively. The EMR systems of general hospitals are more likely to have CDS functions of warnings regarding drug dosage, reminders of clinical schedules, and clinical guidelines compared to those of small hospitals; this difference was statistically significant. For the lists of digitalized clinical documents, almost 93% of EMR systems in general hospitals have the inpatient progress note, operation records, and discharge summary notes digitalized. CONCLUSIONS: EMRs are becoming increasingly important. This study found that the functions and digital documentation of EMR systems still have a large gap, which should be improved and made more sophisticated. We hope that the results of this study will contribute to the development of more sophisticated EMR systems.
Appointments and Schedules ; Decision Support Systems, Clinical ; Electronic Health Records ; Health Information Exchange ; Hope ; Hospitals, General ; Humans ; Inpatients ; Insurance, Health ; Korea ; Medical Informatics ; Medical Records ; Medical Records Systems, Computerized ; Surveys and Questionnaires

OBJECTIVES: This study analyzed the health technology trends and sentiments of users using Twitter data in an attempt to examine the public's opinions and identify their needs. METHODS: Twitter data related to health technology, from January 2010 to October 2016, were collected. An ontology related to health technology was developed. Frequently occurring keywords were analyzed and visualized with the word cloud technique. The keywords were then reclassified and analyzed using the developed ontology and sentiment dictionary. Python and the R program were used for crawling, natural language processing, and sentiment analysis. RESULTS: In the developed ontology, the keywords are divided into ‘health technology‘ and ‘health information‘. Under health technology, there are are six subcategories, namely, health technology, wearable technology, biotechnology, mobile health, medical technology, and telemedicine. Under health information, there are four subcategories, namely, health information, privacy, clinical informatics, and consumer health informatics. The number of tweets about health technology has consistently increased since 2010; the number of posts in 2014 was double that in 2010, which was about 150 thousand posts. Posts about mHealth accounted for the majority, and the dominant words were ‘care‘, ‘new‘, ‘mental‘, and ‘fitness‘. Sentiment analysis by subcategory showed that most of the posts in nearly all subcategories had a positive tone with a positive score. CONCLUSIONS: Interests in mHealth have risen recently, and consequently, posts about mHealth were the most frequent. Examining social media users' responses to new health technology can be a useful method to understand the trends in rapidly evolving fields.
Biomedical Technology ; Biotechnology ; Boidae ; Data Mining ; Informatics ; Medical Informatics ; Methods ; Natural Language Processing ; Privacy ; Public Opinion ; Social Media ; Telemedicine