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.

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: 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 aim of this study is to develop an intelligent diagnostic system utilizing machine learning for data cleansing, then build an intelligent model and obtain new cutoff values for APRI (aspartate aminotransferase-to-platelet ratio) and FIB-4 (fibrosis score) for the prediction and staging of fibrosis in children with chronic hepatitis C (CHC). METHODS: Random forest (RF) was utilized in this study for data cleansing; then, prediction and staging of fibrosis, APRI and FIB-4 scores and their areas under the ROC curve (AUC) have been obtained on the cleaned dataset. A cohort of 166 Egyptian children with CHC was studied. RESULTS: RF, APRI, and FIB-4 achieved high AUCs; where APRI had AUCs of 0.78, 0.816, and 0.77; FIB-4 had AUCs of 0.74, 0.828, and 0.78; and RF had AUCs of 0.903, 0.894, and 0.822, for the prediction of any type of fibrosis, advanced fibrosis, and differentiating between mild and advanced fibrosis, respectively. CONCLUSIONS: Machine learning is a valuable addition to non-invasive methods of liver fibrosis prediction and staging in pediatrics. Furthermore, the obtained cutoff values for APRI and FIB-4 showed good performance and are consistent with some previously obtained cutoff values. There was some agreement between the predictions of RF, APRI and FIB-4 for the prediction and staging of fibrosis.
Area Under Curve ; Child ; Cohort Studies ; Dataset ; Fibrosis ; Forests ; Hepacivirus ; Hepatitis C ; Hepatitis C, Chronic ; Hepatitis ; Humans ; Liver Cirrhosis ; Machine Learning ; Medical Informatics ; Pediatrics ; ROC Curve

OBJECTIVES: Conventional radiological processes have been replaced by digital images and information technology systems within South Africa and other developing countries. Picture Archiving and Communication Systems (PACS) technology offers many benefits to institutions, medical personnel and patients; however, the implementation of such systems can be a challenging task. It has been documented that South Africa has been using PACS for more than a decade in public hospitals with moderate success. The aim of this study was to identify and describe the PACS challenges endured by PACS vendors during implementation in the South African public healthcare sector. METHODS: This was achieved by engaging in a methodological approach that was qualitative in nature collecting data through semi structured interviews from 10 PACS experts/participants which were later analysed qualitatively. RESULTS: The findings show that PACS vendors have countless challenges, some of which include space, insufficient infrastructure, image storage capacity, system maturity and vendor related concerns. It was clear that the PACS experts readily offered contextually appropriate descriptions of their encounters during PACS implementations in South African public healthcare institutions. CONCLUSIONS: PACS vendors anticipate these challenges when facing a public healthcare institution and it is recommended that the hospital management and potential PACS stakeholders be made aware of these challenges to mitigate their effects and aid in a successful implementation.
Commerce ; Delivery of Health Care ; Developing Countries ; Health Care Sector ; Hospitals, Public ; Humans ; Information Storage and Retrieval ; Medical Informatics ; Medical Informatics Computing ; Radiography ; Radiology Information Systems ; South Africa

OBJECTIVES: Genetic disorders are the main causes of many other diseases. Integrating genetic data into Electronic Health Records (EHRs) can facilitate the management of genetic information and care of patients in clinical practices. The aim of this study was to identify the main requirements for integrating genetic data into the EHR system from the medical geneticists' perspectives. METHODS: The research was completed in 2018 and consisted of two phases. In the first phase, the main requirements for integrating genetic data into the EHR system were identified by reviewing the literature. In the second phase, a 5-point Likert scale questionnaire was developed based on the literature review and the results derived from the first phase. Then, the Delphi method was applied to reach a consensus about the integration requirements. RESULTS: The findings of the first phase showed that data elements, including patients' and healthcare providers' personal data, clinical and genetic data, technical infrastructure, security issues and functional requirements, should be taken into account before data integration. In the second phase, a consensus was reached for most of the items (mean ≥3.75). The items with a mean value of less than 2.5 did not achieve a consensus and were removed from the final list. CONCLUSIONS: The integration of genetic data into the EHRs can provide a ground for increasing accuracy and precision in the diagnosis and treatment of genetic disorders. Such integration requires adequate investments to identify users' requirements as well as technical and non-technical issues.
Consensus ; Delivery of Health Care ; Diagnosis ; Electronic Health Records ; Genetics ; Humans ; Investments ; Medical Informatics ; Methods