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: 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

Most people are now familiar with the concepts of big data, deep learning, machine learning, and artificial intelligence (AI) and have a vague expectation that AI using medical big data can be used to improve the quality of medical care. However, the expectation that big data could change the field of medicine is inconsistent with the current reality. The clinical meaningfulness of the results of research using medical big data needs to be examined. Medical staff needs to be clear about the purpose of AI that utilizes medical big data and to focus on the quality of this data, rather than the quantity. Further, medical professionals should understand the necessary precautions for using medical big data, as well as its advantages. No doubt that someday, medical big data will play an essential role in healthcare; however, at present, it seems too early to actively use it in clinical practice. The field continues to work toward developing medical big data and making it appropriate for healthcare. Researchers should continue to engage in empirical research to ensure that appropriate processes are in place to empirically evaluate the results of its use in healthcare.
Artificial Intelligence ; Delivery of Health Care ; Empirical Research ; Humans ; Learning ; Machine Learning ; Medical Informatics ; Medical Staff

The new 2019 Japanese Society of Hypertension (JSH) guidelines for the management of hypertension are now available; these update the previous guidelines published in 2014. The primary objective of the guideline is to provide all healthcare professionals with a standard management strategy and appropriate antihypertensive treatments to prevent hypertension-related target organ damage and cardiovascular events. The major changes in the new guideline relate to the definition of normal blood pressure (BP) and target BP. The terms ‘normal BP’ and ‘high normal BP’ used in the JSH 2014 guidelines are replaced with terms ‘high normal BP’ and ‘elevated BP,’ respectively. There was no change to the office BP diagnostic threshold for hypertension (140/90 mmHg). Recommended target office and home BP values for patients with hypertension aged <75 years and/or high-risk patients are <130/80 mmHg and <125/75 mmHg, respectively. Corresponding targets for elderly patients with hypertension (age≥75 years) are 140/90 and 135/85 mmHg, respectively. The goal is that these changes will contribute to reducing cardiovascular events, especially stroke and heart failure, in Japan. The dissemination of the JSH 2019 guidelines and implementation of a home BP-based approach by all general practitioners in Japan might be facilitated by digital hypertension management using health information technology.
Aged ; Antihypertensive Agents ; Asian Continental Ancestry Group ; Blood Pressure ; Delivery of Health Care ; Evidence-Based Practice ; General Practitioners ; Heart Failure ; Humans ; Hypertension ; Japan ; Medical Informatics ; Stroke

No abstract available.
Informatics