The advances of modern medicine have inadvertently led to a globally ageing population plagued primarily with non-communicable diseases. In addition to traditional medical approaches, nature-based therapy is becoming an increasingly attractive option, with its potential to holistically address physical and mental facets of health and well-being, and to complement 'preventive' and 'population health' strategies, both of which form the bedrock of sustainable healthcare. However, at present, there is vast heterogeneity in the implementation of nature-based therapy, limiting its widespread and sustainable use. The aim of this review is to practically examine and provide a focussed summary of the current evidence with a view to identifying existing gaps and limitations, and to propose directions for future research and implementation within the healthcare setting.
Humans ; Singapore ; Tertiary Care Centers ; Hospitals, Public ; Delivery of Health Care ; Nature ; Complementary Therapies/methods* ; Medicine, Traditional/methods*

Coalescence of traditional medicine Ayurveda and in silico technology is a rigor for supplementary development of future-ready effective traditional medicine. Ayurveda is a popular traditional medicine in South Asia, emanating worldwide for the treatment of metabolic disorders and chronic illness. Techniques of in silico biology are not much explored for the investigation of a variety of bioactive phytochemicals of Ayurvedic herbs. Drug repurposing, reverse pharmacology, and polypharmacology in Ayurveda are areas in silico explorations that are needed to understand the rich repertoire of herbs, minerals, herbo-minerals, and assorted Ayurvedic formulations. This review emphasizes exploring the concept of Ayurveda with in silico approaches and the need for Ayurinformatics studies. It also provides an overview of in silico studies done on phytoconstituents of some important Ayurvedic plants, the utility of in silico studies in Ayurvedic phytoconstituents/formulations, limitations/challenges, and prospects of in silico studies in Ayurveda. This article discusses the convergence of in silico work, especially in the least explored field of Ayurveda. The focused coalesce of these two domains could present a predictive combinatorial platform to enhance translational research magnitude. In nutshell, it could provide new insight into an Ayurvedic drug discovery involving an in silico approach that could not only alleviate the process of traditional medicine research but also enhance its effectiveness in addressing health care.
Network Pharmacology ; Medicine, Traditional ; Medicine, Ayurvedic ; Drug Discovery/methods* ; Delivery of Health Care

PURPOSE: The purpose of this study was to define and clarify the concept of political competence for nurses.METHODS: A hybrid model method was used to investigate the dimensions, attributes, and definitions of the concept. In the theoretical stage of the study, literature on nursing, politics, and other discipline were reviewed. In the fieldwork stage, individual in-depth interviews and focus groups interviews were conducted with politically seasoned experts or activists who had an understanding of the concept of political competence for extensive descriptions in nursing and field of health care.RESULTS: The concept of political competence was represented in four dimensions as political knowledge, political efficacy, political interaction, and political activity. In the political knowledge dimension, there were three attributes, namely, political knowledge, political information and systematic analysis ability. The political efficacy dimension had three attributes of internal political efficacy, external political efficacy, and self-pride of nursing profession. The political interaction dimension had three attributes of organizations and community service, networking, and persuasive power. The political activity dimension had six attributes of political leadership, political expression, assertive behavior, political advocacy, political participation, and policy intervention.CONCLUSION: This concept development might provide a basic understanding of developing a measurement tool and for constructing a theory promoting nurses' political competence.
Concept Formation ; Delivery of Health Care ; Focus Groups ; Leadership ; Mental Competency ; Methods ; Nursing ; Politics ; Professional Competence ; Seasons ; Social Welfare

PURPOSE: An injury surveillance information system (ISIS) collects, analyzes, and distributes data on injuries to promote health care delivery. The present study aimed to review the data elements and functional requirements of this system. METHOD: This study was conducted in 2019. Studies related to injury surveillance system were searched from January 2000 to September 2019 via the databases of PubMed, Web of Knowledge, ScienceDirect, and Scopus. Articles related to the epidemiology of injury, population survey, and letters to the editor were excluded, while the review and research articles related to ISISs were included in the study. Initially 324 articles were identified, and finally 22 studies were selected for review. Having reviewed the articles, the data needed were extracted and the results were synthesized narratively. RESULTS: The results showed that most of the systems reviewed in this study used the minimum data set suggested by the World Health Organization injury surveillance guidelines along with supplementary data. The main functions considered for the system were injury track, data analysis, report, data linkage, electronic monitoring and data dissemination. CONCLUSION ISISs can help to improve healthcare planning and injury prevention. Since different countries have various technical and organizational infrastructures, it is essential to identify system requirements in different settings.
Datasets as Topic ; Delivery of Health Care ; Health Information Systems ; Health Planning ; Humans ; Public Health Surveillance ; methods ; Wounds and Injuries ; prevention & control

The COVID-19 pandemic has had a major impact in healthcare systems across the world, with many hospitals having to come up with protocols and measures to contain the spread of the virus. This affects various specialties' clinical practices in many ways. Since early 2020 in Singapore, the Department of Otorhinolaryngology at Tan Tock Seng Hospital had to rapidly adapt to this pandemic as we provided services to the main healthcare facility combating the virus in our country. We had to design new workflows and also remain flexible in view of the ever-changing situation. There are 6 important domains for an otolaryngology department or any clinical department in general to consider when making adjustments to their practices in an outbreak: (1) clinical work, (2) education, (3) research, (4) safety of patients and staff, (5) morale of medical staff and (6) pandemic frontline work. We hope that the sharing of our experiences and the lessons learnt will be useful for both our local and international colleagues.
Ambulatory Care ; Biomedical Research ; COVID-19/prevention & control* ; Delivery of Health Care/methods* ; Education, Medical ; Elective Surgical Procedures ; Health Workforce ; Humans ; Morale ; Otolaryngology/methods* ; Otorhinolaryngologic Surgical Procedures ; Personal Protective Equipment ; Personnel Staffing and Scheduling ; SARS-CoV-2 ; Singapore/epidemiology* ; Workflow

The ongoing pandemic in Singapore is part of a global pandemic caused by the novel severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). To control the spread of COVID-19 and prevent the healthcare system from being overwhelmed, 'circuit breaker' measures were introduced between 7 April and 1 June 2020 in Singapore. There is thus a crucial need for innovative approaches to the provision and delivery of healthcare in the context of safe-distancing by harnessing telemedicine, especially for patients with chronic diseases who have traditionally been managed in tertiary institutions. We present a summary of how the Virtual Monitoring Clinic has benefited the practice of our outpatient rheumatology service during the COVID-19 pandemic. The virtual consultations address the need for safe-distancing by limiting face-to-face appointments and unnecessary exposure of patients to the hospital where feasible. This approach ensures that the patients are monitored appropriately for drug toxicities and side-effects, maintained on good disease control, and provided with patient education.
Ambulatory Care/methods* ; Antirheumatic Agents/therapeutic use* ; COVID-19 ; Delivery of Health Care ; Humans ; Nurse Practitioners ; Pharmacists ; Rheumatic Diseases/drug therapy* ; Rheumatology/methods* ; SARS-CoV-2 ; Singapore ; Telemedicine/organization & administration* ; Tertiary Care Centers

Allergy and Immunology ; Ambulatory Care ; Autoimmune Diseases/therapy* ; COVID-19 ; Communicable Disease Control ; Connective Tissue Diseases/therapy* ; Delivery of Health Care/methods* ; Dermatology ; Humans ; Immunosuppressive Agents/therapeutic use* ; Patient Selection ; SARS-CoV-2 ; Singapore ; Skin Diseases, Vesiculobullous/therapy* ; Telemedicine/methods* ; Tertiary Care Centers ; Vasculitis/therapy*

OBJECTIVES: This study developed an information and communication technology (ICT)-based comprehensive health and social-needs assessment (CHSNA) system based on the International Classification of Functioning, Disability, and Health (ICF) with the aim of enhancing person-centered community care for community residents and supporting healthcare professionals and social workers who provide healthcare and social services in the community. METHODS: Items related to a CHSNA tool were developed and mapped with ICF codes. Experts validated the CHSNA system design and process using the Delphi method, and a pilot test of the initial version of the system was conducted. RESULTS: The following three steps of CHSNA were embedded in the system, which had a user-friendly screen and images: basic health assessment, life and activity assessment, and in-depth health assessment. The assessment results for the community residents were presented with visualized health profiles, including images, graphs, and an ICF model. CONCLUSIONS: The developed CHSNA system can be used by healthcare professionals, social workers, and community residents to evaluate the reasoning underlying health and social needs, to facilitate the identification of more appropriate healthcare plans, and to guide community residents to receive the best healthcare services. A CHSNA system can improve the implementation of standardized terminology utilizing the ICF and the accuracy of needs assessments of community residents.
Classification ; Community Health Services ; Decision Support Techniques ; Delivery of Health Care ; Methods ; Needs Assessment ; Patient-Centered Care ; Social Work ; Social Workers

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

OBJECTIVES: The incidence of type 2 diabetes mellitus has increased significantly in recent years. With the development of artificial intelligence applications in healthcare, they are used for diagnosis, therapeutic decision making, and outcome prediction, especially in type 2 diabetes mellitus. This study aimed to identify the artificial intelligence (AI) applications for type 2 diabetes mellitus care. METHODS: This is a review conducted in 2018. We searched the PubMed, Web of Science, and Embase scientific databases, based on a combination of related mesh terms. The article selection process was based on Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA). Finally, 31 articles were selected after inclusion and exclusion criteria were applied. Data gathering was done by using a data extraction form. Data were summarized and reported based on the study objectives. RESULTS: The main applications of AI for type 2 diabetes mellitus care were screening and diagnosis in different stages. Among all of the reviewed AI methods, machine learning methods with 71% (n = 22) were the most commonly applied techniques. Many applications were in multi method forms (23%). Among the machine learning algorithms applications, support vector machine (21%) and naive Bayesian (19%) were the most commonly used methods. The most important variables that were used in the selected studies were body mass index, fasting blood sugar, blood pressure, HbA1c, triglycerides, low-density lipoprotein, high-density lipoprotein, and demographic variables. CONCLUSIONS: It is recommended to select optimal algorithms by testing various techniques. Support vector machine and naive Bayesian might achieve better performance than other applications due to the type of variables and targets in diabetes-related outcomes classification.
Artificial Intelligence ; Blood Glucose ; Blood Pressure ; Body Mass Index ; Classification ; Decision Making ; Delivery of Health Care ; Diabetes Mellitus ; Diabetes Mellitus, Type 2 ; Diagnosis ; Fasting ; Incidence ; Lipoproteins ; Machine Learning ; Mass Screening ; Methods ; Support Vector Machine ; Triglycerides