Abbreviation | OHDSI |
---|---|
Type | International collaborative |
Purpose | To improve health by empowering a community to collaboratively generate evidence that promotes better health decisions and better care. |
Headquarters | Columbia University |
Region served | International |
Website | www |
The Observational Health Data Sciences and Informatics, or OHDSI (pronounced "Odyssey") is an international collaborative effort aimed at improving health outcomes through large-scale analytics of health data. [1] The OHDSI effort includes diverse researchers and health databases worldwide, with its central coordinating center located at Columbia University. [2]
The group was derived from the Observational Medical Outcomes Partnership (OMOP), a public-private consortium based in the United States of America, created with the goal of improving the state of observational health data for better drug development, which started in response to the U.S. Food and Drug Administration (FDA) Amendments Act of 2007. [3] [4] OMOP developed a Common Data Model (CDM), standardizing the way observational data is represented. [3] After OMOP ended, this standard started being maintained and updated by OHDSI. [1]
As of February 2024, the most recent CDM is at version 6.0, while version 5.4 is the stable version used by most tools in the OMOP ecosystem. [5]
A randomized controlled trial is a form of scientific experiment used to control factors not under direct experimental control. Examples of RCTs are clinical trials that compare the effects of drugs, surgical techniques, medical devices, diagnostic procedures, diets or other medical treatments.
Health informatics is the study and implementation of computer structures and algorithms to improve communication, understanding, and management of medical information. It can be viewed as branch of engineering and applied science.
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.
eHealth describes healthcare services which are supported by digital processes, communication or technology such as electronic prescribing, Telehealth, or Electronic Health Records (EHRs). The use of electronic processes in healthcare dated back to at least the 1990s. Usage of the term varies as it covers not just "Internet medicine" as it was conceived during that time, but also "virtually everything related to computers and medicine". A study in 2005 found 51 unique definitions. Some argue that it is interchangeable with health informatics with a broad definition covering electronic/digital processes in health while others use it in the narrower sense of healthcare practice using the Internet. It can also include health applications and links on mobile phones, referred to as mHealth or m-Health. Key components of eHealth include electronic health records (EHRs), telemedicine, health information exchange, mobile health applications, wearable devices, and online health information. These technologies enable healthcare providers, patients, and other stakeholders to access, manage, and exchange health information more effectively, leading to improved communication, decision-making, and overall healthcare outcomes.
Public health surveillance is, according to the World Health Organization (WHO), "the continuous, systematic collection, analysis and interpretation of health-related data needed for the planning, implementation, and evaluation of public health practice." Public health surveillance may be used to track emerging health-related issues at an early stage and find active solutions in a timely manner. Surveillance systems are generally called upon to provide information regarding when and where health problems are occurring and who is affected.
Gunther Eysenbach is a German-Canadian researcher on healthcare, especially health policy, eHealth, and consumer health informatics.
The Journal of Medical Internet Research is a peer-reviewed open-access medical journal established in 1999 covering eHealth and "healthcare in the Internet age". The editors-in-chief are Gunther Eysenbach and Rita Kukafka. The publisher is JMIR Publications.
Philip Eric Bourne is an Australian bioinformatician, non-fiction writer, and businessman. He is currently Stephenson Chair of Data Science and Director of the School of Data Science and Professor of Biomedical Engineering and was the first associate director for Data Science at the National Institutes of Health, where his projects include managing the Big Data to Knowledge initiative, and formerly Associate Vice Chancellor at UCSD. He has contributed to textbooks and is a strong supporter of open-access literature and software. His diverse interests have spanned structural biology, medical informatics, information technology, structural bioinformatics, scholarly communication and pharmaceutical sciences. His papers are highly cited, and he has an h-index above 80.
Remote patient monitoring (RPM) is a technology to enable monitoring of patients outside of conventional clinical settings, such as in the home or in a remote area, which may increase access to care and decrease healthcare delivery costs. RPM involves the constant remote care of patients by their physicians, often to track physical symptoms, chronic conditions, or post-hospitalization rehab.
Translational bioinformatics (TBI) is a field that emerged in the 2010s to study health informatics, focused on the convergence of molecular bioinformatics, biostatistics, statistical genetics and clinical informatics. Its focus is on applying informatics methodology to the increasing amount of biomedical and genomic data to formulate knowledge and medical tools, which can be utilized by scientists, clinicians, and patients. Furthermore, it involves applying biomedical research to improve human health through the use of computer-based information system. TBI employs data mining and analyzing biomedical informatics in order to generate clinical knowledge for application. Clinical knowledge includes finding similarities in patient populations, interpreting biological information to suggest therapy treatments and predict health outcomes.
Infodemiology was defined by Gunther Eysenbach in the early 2000s as information epidemiology. It is an area of science research focused on scanning the internet for user-contributed health-related content, with the ultimate goal of improving public health. Later, it is also defined as the science of mitigating public health problems resulting from an infodemic.
David Bennett Madigan is an Irish-American statistician and academic. He is currently Provost and Senior Vice-President for Academic Affairs at Northeastern University. Previously he was Professor of Statistics at Columbia University. From 2013 to 2018 he was also the Executive Vice-President for Arts and Sciences and Dean of the Faculty of Arts and Sciences and from 2008 to 2013 he served as Chair of the Department of Statistics, both at Columbia University. He was Dean of Physical and Mathematical Sciences at Rutgers University (2005–2007), Director of the Institute of Biostatistics at Rutgers University (2003–2004), and Professor in the Department of Statistics at Rutgers University (2001–2007).
Genomic and medical data refers to an area within genetics that concerns the recording, sequencing and analysis of an organism's genome.
Artificial intelligence in healthcare is the application of 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 arrive at 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.
The COVID-19 Genomics UK (COG-UK) consortium was a group of academic institutions and public health agencies in the United Kingdom created in April 2020 to collect, sequence and analyse genomes of SARS-CoV-2 at scale, as part of COVID-19 pandemic response.
A common data model (CDM) can refer to any standardised data model which allows for data and information exchange between different applications and data sources. Common data models aim to standardise logical infrastructure so that related applications can "operate on and share the same data", and can be seen as a way to "organize data from many sources that are in different formats into a standard structure".
Noémie Elhadad is an American data scientist who is an associate professor of Biomedical Informatics at the Columbia University Vagelos College of Physicians and Surgeons. As of 2022, she serves as the Chair of the Department of Biomedical Informatics. Her research considers machine learning in bioinformatics, natural language processing and medicine.
Rebecca Grainger is a New Zealand academic rheumatologist, and is a full professor at the University of Otago, specialising in rheumatoid arthritis and osteoarthritis, and gout. She is also interested in the use of technology for medical education and digital health.