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Clinical trials are prospective biomedical or behavioral research studies on human participants designed to answer specific questions about biomedical or behavioral interventions, including new treatments and known interventions that warrant further study and comparison. Clinical trials generate data on dosage, safety and efficacy. They are conducted only after they have received health authority/ethics committee approval in the country where approval of the therapy is sought. These authorities are responsible for vetting the risk/benefit ratio of the trial—their approval does not mean the therapy is 'safe' or effective, only that the trial may be conducted.
Health informatics is the study and implementation of computer structures and algorithms to improve communication, understanding, and management of medical information. It can be view as branch of engineering and applied science.
Pharmacogenomics, often abbreviated "PGx," is the study of the role of the genome in drug response. Its name reflects its combining of pharmacology and genomics. Pharmacogenomics analyzes how the genetic makeup of a patient affects their response to drugs. It deals with the influence of acquired and inherited genetic variation on drug response, by correlating DNA mutations with pharmacokinetic, pharmacodynamic, and/or immunogenic endpoints.
An electronic health record (EHR) is the systematized collection of patient and population electronically stored health information in a digital format. These records can be shared across different health care settings. Records are shared through network-connected, enterprise-wide information systems or other information networks and exchanges. EHRs may include a range of data, including demographics, medical history, medication and allergies, immunization status, laboratory test results, radiology images, vital signs, personal statistics like age and weight, and billing information.
A clinical decision support system (CDSS) is a health information technology that provides clinicians, staff, patients, and other individuals with knowledge and person-specific information to help health and health care. CDSS encompasses a variety of tools to enhance decision-making in the clinical workflow. These tools include computerized alerts and reminders to care providers and patients, clinical guidelines, condition-specific order sets, focused patient data reports and summaries, documentation templates, diagnostic support, and contextually relevant reference information, among other tools. CDSSs constitute a major topic in artificial intelligence in medicine.
In international trade, market access refers to a company's ability to enter a foreign market by selling its goods and services in another country. Market access is not the same as free trade, because market access is normally subject to conditions or requirements, whereas under ideal free trade conditions goods and services can circulate across borders without any barriers to trade. Expanding market access is therefore often a more achievable goal of trade negotiations than achieving free trade.
Disease or patient registries are collections of secondary data related to patients with a specific diagnosis, condition, or procedure, and they play an important role in post marketing surveillance of pharmaceuticals. Registries are different from indexes in that they contain more extensive data.
ClinicalTrials.gov is a registry of clinical trials. It is run by the United States National Library of Medicine (NLM) at the National Institutes of Health, and holds registrations from over 444,000 trials from 221 countries.
Patient participation is a trend that arose in answer to medical paternalism. Informed consent is a process where patients make decisions informed by the advice of medical professionals.
Marc Twagirumukiza is a Belgian senior physician and a senior clinical researcher in the fields of clinical pharmacology, cardiovascular and hypertension research. He is particularly oriented in data sciences and clinical drug development. He is also chair of the World Wide Web Consortium Healthcare Schema Vocabulary Community Group.
Digital health is a discipline that includes digital care programs, technologies with health, healthcare, living, and society to enhance the efficiency of healthcare delivery and to make medicine more personalized and precise. It uses information and communication technologies to facilitate understanding of health problems and challenges faced by people receiving medical treatment and social prescribing in more personalised and precise ways. The definitions of digital health and its remits overlap in many ways with those of health and medical informatics.
IDEAL is a framework for describing the stages of innovation in surgery and other interventional procedures. The purpose of IDEAL is to improve the quality of research in surgery by emphasizing appropriate methods, transparency of data and rigorous reporting of outcomes.
Artificial intelligence in healthcare is a term used to describe the use of machine-learning algorithms and software, or artificial intelligence (AI), to copy human cognition in the analysis, presentation, and understanding of complex medical and health care data, or to exceed human capabilities by providing new ways to diagnose, treat, or prevent disease. Specifically, AI is the ability of computer algorithms to approximate conclusions based solely on input data.
Health data is any data "related to health conditions, reproductive outcomes, causes of death, and quality of life" for an individual or population. Health data includes clinical metrics along with environmental, socioeconomic, and behavioral information pertinent to health and wellness. A plurality of health data are collected and used when individuals interact with health care systems. This data, collected by health care providers, typically includes a record of services received, conditions of those services, and clinical outcomes or information concerning those services. Historically, most health data has been sourced from this framework. The advent of eHealth and advances in health information technology, however, have expanded the collection and use of health data—but have also engendered new security, privacy, and ethical concerns. The increasing collection and use of health data by patients is a major component of digital health.
Real-world evidence (RWE) in medicine is the clinical evidence regarding the usage and potential benefits or risks of a medical product derived from analysis of real-world data (RWD). RWE can be generated by different study designs or analyses, including but not limited to, randomized trials, including large simple trials, pragmatic trials, and retrospective or prospective observational studies. In the USA the 21st Century Cures Act required the FDA to expand the role of real world evidence.
Learning health systems (LHS) are health and healthcare systems in which knowledge generation processes are embedded in daily practice to improve individual and population health. At its most fundamental level, a learning health system applies a conceptual approach wherein science, informatics, incentives, and culture are aligned to support continuous improvement, innovation, and equity, and seamlessly embed knowledge and best practices into care delivery
Drug repositioning is the repurposing of an approved drug for the treatment of a different disease or medical condition than that for which it was originally developed. This is one line of scientific research which is being pursued to develop safe and effective COVID-19 treatments. Other research directions include the development of a COVID-19 vaccine and convalescent plasma transfusion.
The treatment and management of COVID-19 combines both supportive care, which includes treatment to relieve symptoms, fluid therapy, oxygen support as needed, and a growing list of approved medications. Highly effective vaccines have reduced mortality related to SARS-CoV-2; however, for those awaiting vaccination, as well as for the estimated millions of immunocompromised persons who are unlikely to respond robustly to vaccination, treatment remains important. Some people may experience persistent symptoms or disability after recovery from the infection, known as long COVID, but there is still limited information on the best management and rehabilitation for this condition.
In an adaptive design of a clinical trial, the parameters and conduct of the trial for a candidate drug or vaccine may be changed based on an interim analysis. Adaptive design typically involves advanced statistics to interpret a clinical trial endpoint. This is in contrast to traditional single-arm clinical trials or randomized clinical trials (RCTs) that are static in their protocol and do not modify any parameters until the trial is completed. The adaptation process takes place at certain points in the trial, prescribed in the trial protocol. Importantly, this trial protocol is set before the trial begins with the adaptation schedule and processes specified. Adaptions may include modifications to: dosage, sample size, drug undergoing trial, patient selection criteria and/or "cocktail" mix. The PANDA provides not only a summary of different adaptive designs, but also comprehensive information on adaptive design planning, conduct, analysis and reporting.
Chloroquine and hydroxychloroquine are anti-malarial medications also used against some auto-immune diseases. Chloroquine, along with hydroxychloroquine, was an early experimental treatment for COVID-19. Neither drug prevents SARS-CoV-2 infection.
References
- ↑ Cowley, Andrea. "What is real world data?". CRC Australia. Clinical Research Corporation. Retrieved 8 May 2018.
- ↑ Bian, Jiang; Lyu, Tianchen; Loiacono, Alexander; Viramontes, Tonatiuh Mendoza; Lipori, Gloria; Guo, Yi; Wu, Yonghui; Prosperi, Mattia; George, Thomas J; Harle, Christopher A; Shenkman, Elizabeth A (2020-12-09). "Assessing the practice of data quality evaluation in a national clinical data research network through a systematic scoping review in the era of real-world data". Journal of the American Medical Informatics Association. 27 (12): 1999–2010. doi:10.1093/jamia/ocaa245. ISSN 1527-974X. PMC 7727392 . PMID 33166397.
- ↑ "TriNetX and Takeda Enter Into Agreement to Drive Access to Real-World Data and Analytics". TriNetX. 2021-04-26. Retrieved 2022-02-04.
- 1 2 "Framework for FDA's Real-World Evidence Program". FDA.
- ↑ Yendewa, George A.; Perez, Jamie Abraham; Schlick, Kayla; Tribout, Heather; McComsey, Grace A. (2021-07-01). "Clinical Features and Outcomes of Coronavirus Disease 2019 Among People Who Have Human Immunodeficiency Virus in the United States: A Multi-center Study From a Large Global Health Research Network (TriNetX)". Open Forum Infectious Diseases. 8 (7): ofab272. doi:10.1093/ofid/ofab272. PMC 8244788 . PMID 34435074 – via Oxford Academic.
- ↑ "Use of patient disease registries for regulatory purposes – methodological and operational considerations".
- ↑ "Overview | NICE real-world evidence framework | Guidance | NICE". www.nice.org.uk. 23 June 2022. Retrieved 2022-09-06.
Sources
- Real-World Evidence—What Is It and What Can It Tell Us? The New England Journal of Medicine, Dec. 6, 2016
- Mahajan, Rajiv. “Real World Data: Additional Source for Making Clinical Decisions.” International Journal of Applied and Basic Medical Research 5.2 (2015): 82. PMC. Web. 5 May 2018.
