Every successful brand is defined by a set of core attributes that should be consistently communicated to audiences both inside and outside the organisation.
Our visual identity is one of the Sensyne Health brand attributes that help us to project a unique, consistent image. These guidelines provide instructions on how to correctly apply the elements of the company’s visual identity including logo, colours, typography and imagery.
The Sensyne Health logo is the cornerstone of our visual identity.
It should be instantly recognised by all relevant audiences across all communication materials and publications. This design element plays a defining role in creating a unique and memorable identity and must be included on all communication materials produced by Sensyne Health.
The landscape version below is the default choice for most uses.
Data is in our DNA. We process and analyse data and combine it with our Clinical AI technologies, to help people everywhere get better care.
At the core of our brand is a visual representation of data in the form of dots and dashes to represent binary data (ones and zeros). The dots & dashes are a recurring theme in all of the company’s logos, from our parent brand of Sensyne Health to the individual product logos, and often the icons and illustrations used throughout our product and marketing collateral.
To make sure the logo is always clear and legible, there is a minimum size requirement.
When reproducing our brand logo in print, the minimum width of the logo is 14mm/24mm.
For online or screen use, the minimum size is 74 pixels/108 pixels at 72 dpi.
Keeping our logo isolated from other elements is the key to preserving its presence and legibility.
A minimum amount of clear space should always surround the logo, separating it from headlines, text, imagery or outside edge of the document or application.
Typography and the corporate font play a significant role in creating a unique visual identity for a company.
The Source Sans Pro family of fonts was selected to complement the company’s contemporary style due to its clarity and legibility. It is a sans-serif typeface intended to work well in user interfaces.
Global greys are used throughout our creative work - apps, print and online. They should be the dominant colour, with our corporate or product colours used alongside as a highlight colour.
The Sensyne Health brand colours can be used in varying opacities, for use in secondary graphical elements.
These tint colours should be used sparingly and should not dominate the creative.
Product colours and other complimentary colours that may be used sparingly.
The examples below illustrate the clear and simple design of our icons.
Icons are informative tools. They must function in small sizes and they must be easy to understand and easily recognisable. Do not use drop-shadows, distortions or 3D effects.
You can see examples below of Sensyne Health’s style of illustration.
All Sensyne Health illustrations are designed by our in-house team.
The type of imagery we use should reflect our core attributes of energy, insight, compassion and collaboration.
These attributes are at the heart of our brand personality and the photography we choose should communicate one or more of these attributes, depending on the context.
A high level list of frequently used terms, acronyms and abbreviations used throughout the company.
Alert, Confusion, Voice, Pain, Unresponsive - A score within SEND, indicating a patient's level of consciousness. It is measured by what the patient responds to - e.g. patients who respond only to pain (but not, for example, to voice) score a P in the ACPVU scale.
Admit Discharge Transfer - A type of HL7 message that provides important information about trigger events, such as patient admit, discharge, transfer, registration, etc.
Application Programming Interface - The bit of software that is exposed to the outside world. For example, the API for the GDm-Health back-end system is a series of URLs that your browser can use to send and receive data.
A sequence of instructions or a set of rules that are followed to complete a task, investigate and solve a problem or perform a computation. They are unambiguous and in the case of Sensyne Health for [insert – e.g. calculations, data processing and automated reasoning].
Information which does not relate to an identified or identifiable natural person or to personal data rendered anonymous in such a manner that the data subject is not or no longer identifiable. Most medical studies use pseudo anonymised data, which refers to the processing of personal data in such a way that the data can no longer be attributed to a specific data subject without the use of additional information, as long as such additional information is kept separately and subject to technical and organisational measures to ensure non-attribution to an identified or identifiable individual.
The ability of a machine to demonstrate ‘intelligence’ – such as simulating human intelligence or exhibiting traits associated with a human mind such as learning and problem-solving - meaning that the device perceives its environment, rationalise and take actions that maximise its chance to successfully achieving its goals.
The average time a User spends on our site during a Session.
The British National Formulary (BNF) provides prescribers with information (including indications, dose and side effects) on medications which are prescribed in the UK and is published jointly by the British Medical Association and the Royal Pharmaceutical Society.
The % of Sessions on our website where the User only viewed one page.
Clinical Evaluation Report - The ‘Clinical Evaluation Report’ is a mandatory document required for CE marking. This is an output of the clinical evaluation which confirms conformity with the relevant safety and performance requirements associated with a medical device software product.
A tool used in clinical practice to predict the one-yearrisk of ischaemic stroke in patients with atrial fibrillation. It is commonlyused in conjunction with the HAS-BLED score to inform the decision to prescribeanticoagulation to patients with atrial fibrillation.
Content Management System - This is the system we use to update/edit/ add content to the website. Using the CMS we can add new content such as news articles, team members and press releases.
Chronic Obstructive Pulmonary Disease - an umbrella term used to describe progressive lung diseases including emphysema, chronic bronchitis, and refractory (non-reversible) asthma. This disease is characterised by increasing breathlessness.
Computer On Wheels - These are used around the hospital, a computer on a trolley basically.
(DEFN) Classification is the general process of grouping items into categories. Within the field of machine learning, classification algorithms describe the subset of machine learning algorithms which automatically assign items into categories based on a learned mathematical representation using ‘training data’. One example is a decision tree algorithm which classifies if a patient has heart failure or not.
The part of a clinical trial where a group of participants receives an intervention / treatment considered to be effective (or active) by health care providers – as opposed to a placebo.
The part of a clinical trial where people who are not receiving the substance with a proposed effect (i.e. they are receiving a placebo) and are not actively enrolled into a trial.
Instead of collecting data from patients recruited for a trial who have been assigned to the control arm, synthetic control arms model those comparators using real-world data that has previously been collected from sources such as health data generated during routine care, including electronic health records; administrative claims data; patient-generated data from fitness trackers or home medical equipment; disease registries; and historical clinical trial data.
(DEFN) A care/clinical pathway is a complex intervention for the mutual decision-making and organisation of care processes for a well-defined group of patients during a well-defined period. The aim of a care pathway is to enhance the quality of care across the continuum by improving risk-adjusted patient outcomes, promoting patient safety, increasing patient satisfaction, and optimising the use of resources.
Grouping patients, admissions or other entities into groups based on similar characteristics. These characteristics also differ between groups.
A group that doesn't receive a treatment or other intervention in a study. In machine learning this could be used to define a population that doesn't have an outcome that is being predicted.
Data Engineering & Analytics - We have an engineering team who Analyse Data.
This is the part of the company that works on ML/AI. Check out the DS section of our website for more info!
A representation of the data that is suitable for machine learning algorithms. This is commonly a vector of features / characteristics per entity but can also be a sequence of features.
A type of machine learning that trains multiple layers of a network to learn characteristics from low level features to high-level features from raw input.
A structured and standardised approach of recording diseases, disorders and symptoms. Currently, the International Statistical Classification of Diseases and Related Health Problems (ICD) revision 10 is the standard for medical classification.
The process of reducing the number of variables that are considered. This is often performed to identify and visualise structure within a high dimensional space. An embedding is this low dimensional space that the entity is now represented in.
Definition of done - a list of criteria which must be met before a product increment "often a user story" is considered completed or "done".
Definition of Ready - Used to identify whether an issue (currently of any type: bug, story, task) is ready for the dev team to start working on it. This optimises development time by front-loading the discussion of anything unclear before the technical implementation begins.
Score out of 100 measuring how well a website ranks on a search engine. (50+ is considered good).
Data that is generated during a patient's journey in the medical system and is captured in an electronic format.
A subtype of a disease / condition, which is defined by a common underlying cause / mechanism usually referred to as the pathophysiology.
(Pronounced FIRE) - Fast Healthcare Interoperability Resources - Standard for the implementation of HL7 messaging in a simplified and more atomic version than traditional heavyweight HL7 messaging.
Failure Mode & Effects Analysis - The process of reviewing as many components, assemblies, and subsystems as possible to identify potential failure modes in a system and their causes and effects.
The process of selecting a subset of relevant features (variables, predictors) for use in model construction.
An individual measurable property or characteristic of a phenomenon being observed.
A process to take a network model that has already been trained for a given task, and make it perform a second similar task.
Followers are people who receive our posts in Twitter or LinkedIn.
Glasgow Coma Score - The GCS is a score which indicates a patient's level of consciousness. It includes scores for eye, verbal, and motor responses. The minimum score is a 3 which indicates deep coma or a brain-dead state. The maximum is 15 which indicates a fully awake patient (the original maximum was 14, but the score has since been modified).
Gestational diabetes mellitus - defined as any degree of glucose intolerance with onset or first recognition during pregnancy (1). The definition applies whether insulin or only diet modification is used for treatment and whether or not the condition persists after pregnancy.
A risk prediction tool used in clinical practice which provides an estimate of the risk of major bleeding over one year in patients taking anticoagulants for a trial fibrillation.
Health Level-7, where the "7" refers to the layer in the OSI Model - A set of international standards for transfer of clinical and administrative data between software applications used by various healthcare providers.
The task of choosing a set of optimal hyperparameters for a learning algorithm. A hyperparameter is a parameter whose value is used to control the learning process.
The International Statistical Classification of Diseases and Related Health Problems is a classification system produced by the World Health Organisation and contains a coding system for diseases as well as other medical information such as symptoms and causes of disease or injury.
Independent Negotiable Valuable Estimable Small and Testable - a description of a good user story that is ready of the team to work on, it should have all of these properties.
Used to refer to immunotherapy drugs used to treat cancer, trials of such drugs, etc.
The total number of people that see the content.
JSON Web Token - A code that allows someone to make requests of the GDm-Health API, issued by whatever is in charge of authentication (e.g. Auth0). Think reception (Auth0) lending you a temporary swipecard (the JWT) to access rooms in a building (the API endpoints).
Keyword rankings refer to your web page’s position within search results for a particular keyword search query. When a user searches for the particular keyword, your ranking URL would be the web page that is listed for that keyword search. One web page can rank for several relevant search keywords and phrases.
Laboratory Information Management System
Medical Device Regulation - The EU Medical Device Regulation came into force in 2017, and overhauls the existing Medical Device Directive (MDD). The three-year transition period original end date was 26th May 2020. But this has been extended a year due to COVID.
Modified Early Obstetric Warning Score - An early warning score used to assess patients in maternity wards.
A subset of artificial intelligence which relies on algorithms and statistical models that allow a computer system to perform a specific task such as identifying subpopulations within a patient cohort. In contrast to explicitly programmed algorithms, ML algorithms are data driven and learn a mathematical representation based on training data.
Meltwater uses Natural Language Processing model supported by AI and machine learning algorithms to judge which group (positive, negative or neutral) the content belongs to. Each document, i.e. a tweet, is analysed based on its words and tonality. To be reliable, the text typically needs to contain easily identifiable positive/negative words for the content to be labelled as such. Where the label is neutral, the model has not identified positive/negative words or tonality in the content. It’s worth remembering that explicit endorsements are rare in healthcare, however, sharing activity along with neutral commentary from partners/influencers etc should generally be viewed in a positive context.
Positive – positive/endorsing language and tone Neutral - positive context, instructive/factual language and tone Negative – negative/critical language and tone
National Early Warning Score - An early warning score used to assess adult patients in hospital. Used nationally across many trusts.
National Early Warning Score (2) - An updated version of NEWS, with updated thresholds and the addition of COPD-specific scoring
The National Institute for Health and Care Excellence is a UK body which produces national evidence-based guidelines in several areas including clinical practice.
Classification of Interventions and Procedures, Version 4. It is the procedural classification used by clinical coders within National Health Service (NHS) hospitals of NHS England, NHS Scotland, NHS Wales and Health and Social Care in Northern Ireland.
Observation Result - In the HL7 Standard, an Observation Result (ORU) is usually in response to an order and provides clinical observations.
Oxford University Hospitals
Paediatric Early Warning Score - An early warning score used to assess children in paediatric wards. Different charts/thresholds are used for different age groups.
Post Market Surveillance
Performance Test - a way of testing software by exposing it to very heavy use - e.g. bombarding a website with page visits.
A group of patients that display a particular set of common characteristics. This could be defined by disease, geography, or outcomes, for example. Within Sensyne, patient cohorts are requested from NHS trusts to answer a particular clinical question.
Clinically-relevant endpoints, such as occurrence or elapse of the disease, as well as death or any other important events.
Division of a larger patient group into subgroups with particular phenotypes or endotypes. Identification of these subgroups can improve patient selection for clinical trials or treatment.
Patient or disease subtypes are subtypes of a wider disease definition that display different phenotypes, endotypes or respond differently to a treatment, for examples.
A set of observable physical and clinical characteristics.
Tailoring of medical treatment to the individual characteristics of each patient to classify individuals into subpopulations that differ in their susceptibility to a particular disease or their response to a specific treatment.
Output of a machine learning algorithm to unseen data.
An algorithm used in clinical practice which predicts a patients risk of developing a heart attack or stroke in the next 10 years. It is commonly used inform the decision to prescribe statins to at risk patients.
Royal Berkshire Hospital
Data collected and stored without a preconditioned cause, and reflects the natural volume and content of patient medical history.
Real-world studies provide a line of complementary evidence to the other studies of observational or experimental design (randomised controlled trials (RCTs)). RCTs are still held as the gold standard for investigating causality, but real-world studies produce essential evidence of therapeutic effectiveness in real-world setting. Evidence from Real-world studies is very important to understand the utility of medical approaches in a broader and more representative patient population.
Statistical method used in a variety of disciplines that attempts to determine the strength and character of the relationship between one dependent variable (usually denoted by Y) and a series of other variables (known as independent variables).
A set of practices used in agile project management that emphasise daily communication and the flexible reassessment of plans that are carried out in short, iterative phases of work.
Statement of Intended Use - What you say on the label that the device is to be used for.
Standard Operating Procedure - a set of step-by-step instructions to walk the reader through a task.
Single Sign On - Clinicians log into their computers with a swipe of their ID card, this is single sign-on.
(pronounced Swift) South Warwickshire Foundation Trust - One of our customers, often referred to as "Trustomers"..
Subtype of machine learning which contains un- and supervised learning aspects. In this scenario, labelled data is only available for a fraction of the complete dataset. One example is the prediction of patients with a normal or preserved ejection fraction based on their electronical healthcare records while only a small portion of the patients were being tested.
The percentage of coverage compared to a defined peer group.
Spam Score represents the percentage of sites with similar features we've found to be penalized or banned by Google. Spam Score is based on our machine learning model which identified 27 common features among the millions of banned or penalized sites in the data we fed it. A score of 1%-30% is considered a Low Spam Score. A score of 31%-60% is considered a Medium Spam Score. A score of 61%-100% is considered a High Spam Score.
A time-bound stream of activity that represents the engineering team delivering one or more incremental shippable improvements to the product.
Subtype of machine learning which aims to discover patterns within data by making explicit use of (often human annotated) labels. An example is an algorithm which predicts the likelihood of patients having a particular disease by learning the underlying patterns within the patient history. In contrast to unsupervised learning, supervised learning algorithms aim to minimise the difference between the prediction and the actual label.
Trust Integration Engine - the "Interface Engine" that acts as a broker of HL7 Messages in any hospital network around the world - not just the NHS Trusts.
Tables, lists and figures. Usually a term used to describe a document containing summary statistics.
A commonly used technique within machine learning to develop robust algorithms. Thereby the available data is split into a training, validation and test set. An algorithm is trained on the validated on the training and validation set, respectively, to find the optimal set of algorithm parameters. The optimal model is further evaluated on a completely independent test set.
A machine learning technique which aims to transfer knowledge which was gained by training an ML algorithm on one problem and applying it to another different problem. Transfer learning approaches are often used when only a small dataset is available for the target problem.
Usability Engineering Report / Usability Engineering File - This report makes up part of the Validation and Verification documentation for regulatory. This report is needed prior to each product launch or major release.
User Interface - UI covers the way in which you interact with our apps/website etc via buttons, text. How it looks, typography, colours, stylings - how it ties in with our design system and branding.
The User Requirements Specification is a required regulatory document that specifies what the user expects the software to be able to do.
User Experience - When you do something like drive a car, use an app or visit a retail outlet - you are having an experience, you are the user. UX aims to improve this for you by putting themselves in your shoes, understanding your requirements and providing solutions to the problems using creative thinking.
Unique Users represent a particular individual who has visited our site.
Subtype of machine learning which aims to discover undetected patterns within data without any pre-existing labels. An example is the discovery of patient subpopulations in a larger cohort. Unsupervised learning algorithms try to optimise different optimisation functions such as finding optimal clusters or lower dimensional representations of the input data.
A unit of work for a SCRUM team to complete.
Validation and Verification - Part of the regulatory documentation, of which the UER makes up part of it.
Subtype of machine learning, which is also referred to as weak supervision and is related to supervised learning. In weak learning scenarios, very noisy and imprecise sources are used to generate labels. Weak learning approaches are often considered, when the acquisition of precise labels is either not possible, time-consuming, or expensive.