HDR UK releases major upgrade to its Phenotype Library
4 November 2021
New features and functionality enhance quality and reproducibility of research using electronic health records
HDR UK today releases a major upgrade to its Phenotype Library, substantially improving the sharing of electronic phenotypes (definitions of how health data can be used to measure real-world concepts relevant to research and clinical care).
This upgrade transforms the Library from its prototype as a documentation resource to a powerful content creation and validation resource. The HDR UK Phenomics team have developed a modular, open-development, open-APIs, open standards infrastructure which can be enhanced and leveraged by the wider HDR UK research community.
The Phenotype Library is the largest national standards-driven library of citable phenotyping algorithms, metadata and tools for defining human disease, lifestyle risk factors and biomarkers in electronic health records (EHR) research. The Library now curates over 100,000 clinical ontology terms into 753 phenotypes from numerous contributing organisations across the UK, spanning critical disease areas including heart disease, cancer, COVID-19 and others.
Phenotypes are defined against 28 different research datasets and 14 coding systems, with more being added frequently. New contributions are welcome from anyone working in the field.
The Library is interoperable with other tools and resources, making it part of a broader ecosystem driving the next generation of health research methods. It is currently integrated with the metadata catalogue of the HDR UK Innovation Gateway, as well as Phenoflow, a tool enabling workflow-based computable phenotype definitions.
Further integrations with other tools and analysis workflows are possible using the REST API and client R package. We look forward to collaborating with others to use this resource on research and clinical settings with the potential to improve patient health and well-being.
Professor Harry Hemingway, Director of Health Data Research UK London:
“This substantial upgrade in the HDR UK Phenotype Library is a clear step forward in providing patients and clinicians with useful data-driven definitions of the diseases and conditions that matter. Now with more than 700 phenotypes, there is a much wider coverage of diseases, as well as interoperability with tools to generate further phenotypes using electronic health records for better healthcare and research.”
Colin Wilkinson, patient and member of the Phenomics for Patient Action Group
“Understanding disease and developing new treatments starts with a shared understanding of what each disease looks like. To unleash the power of health data we need that shared understanding to be in terms of things we find in health records. This catalogue is growing and this latest launch is a huge step forwards towards a full catalogue of human disease. Its potential is enormous.”
For more information, visit the Phenotype Library at https://phenotypes.healthdatagateway.org
For further enquiries, please contact: Natalie Fitzpatrick, HDR UK Phenomics Programme Manager firstname.lastname@example.org
A Phenotype is an observable and measurable piece of information that is relevant to health or healthcare. For example, it can be a disease (e.g. type 2 diabetes), a blood pressure measurement, a blood sugar value or a prescription of antibiotics.
The study of phenotypes is known as Phenomics, and multiple phenotypes constitute the Phenome.
Because Phenomics looks at the characteristics of multiple health conditions simultaneously, it provides a wider view, which is complementary to approaches that focus on one disease or one clinical speciality at a time. It allows researchers to develop consistent ways to define and understand the risk to an individual’s health across a wide range of diseases.
The HDR UK Phenotype Library
The enormous amount of health data is contained within Electronic Health Records (EHR) – information that is captured and recorded when a patient visits a health care setting e.g. symptoms, diagnoses, test results or prescriptions – is complex and often messy.
Our researchers have developed specialised algorithms that enable phenotypes to be extracted from this data. These algorithms identify and extract data from medical records using the clinical codes which are the building blocks of how information is recorded in healthcare (for example ICD-10).
The HDR UK Phenotype Library is a catalogue of these definitions which can then be used to support research. The information and tools contained in the Library support faster, higher quality, and more transparent research – using and maximising the value of the data contained in Electronic Health Records; thereby answering important questions that can improve health and healthcare.
The HDR UK Phenotype Library is a freely accessible platform that welcomes contributions from anyone in the field and makes content accessible to researchers, health care professionals, patients, and the public. Importantly, the Library can link to many other tools and resources which is important for driving the next generation of health research methods.
The Phenotype Library is also now integrated with the HDR UK Innovation Gateway, which holds extensive information about UK health data sources, and Phenoflow, a tool that enables the definitions to be automatically executed on health data.
Building a library for sharing tools to analyse health records
6 April 2023
HDR UK has created a library that is already improving the quality of research by enabling researchers to share and analyse different phenotypes.
New advances in understanding long COVID
20 February 2023
Researchers analysed the primary healthcare records of 7.4 million patients in England to develop a long COVID phenotype, strengthening further research to improve the understanding of the condition.
COVID-19 trajectories among 57 million adults in England: a cohort study using electronic health records
7 October 2022
In May, the HDR UK Impact Commitee selected Thygsen et al.'s paper as the Open Impact Publication of the Month for its impactful use of large datasets to track the impact of the COVID-19 pandemic