Motives and Models
Our Values: AI needs to be trusted, so the motives behind its design, and its fitness for purpose should be made transaprent, and privacy protecting.
The motives behind AI models created, curated and assured using our design processes is described below
The motives behind AI models created, curated and assured using our design processes is described below
PRIDAR Implementation
PRIDAR is a method of prioritising the safe and effective deployment of AI in a healthcare setting. It considers audited best practice in terms of design, deployment, assurance and preparedness of all known AI systems, and enables the audit to be kept up to date via active Model Feedback from system users and clinicians
PRIDAR: AN AI RISK STRATIFICATION MODEL
Motivation
Health Boards are dependent upon a myriad of systems. Sometimes these systems have embodied in them mathematical models that are designed to predict outcomes e.g. diagnosis, or the need to apply resources to problems e.g. ambulances. Such systems are frequently described as having AI components. Some of these components comply with UK legislation, some do not. Some are fit to be released into healthcare, some are not. Healthcare Executive Boards that carry the risk of AI components failing, and seldom have the human resources to manage this risk. PRIDAR was created to automate AI risk management for Board Members.
Current ML/AI Model Label Reference Number: PRIDAR1
Version control: Version 1 - ABUHB (October 2020)
Release Terms: Version 1
Outcome: prioritisation of a healthcare systems risk occasioned by planned or implemented AI
Output: a risk ranking for an AI enabled system via a web user interface
Output to Trigger: risk mitigation at a Board Level
Target Population: UK NHS and Private Hospitals
Time of Prediction: immediate based on model feedback
Input data source: model feedback from the public, clinicians, and suppliers
Input data type: text and json from compliance reports
Training details: projects arising with a NHS health board
Model type: Hidden Markov to trigger more model feedback, and Bayesian Ranking to identify dependancies between AI systems
Motivation
Health Boards are dependent upon a myriad of systems. Sometimes these systems have embodied in them mathematical models that are designed to predict outcomes e.g. diagnosis, or the need to apply resources to problems e.g. ambulances. Such systems are frequently described as having AI components. Some of these components comply with UK legislation, some do not. Some are fit to be released into healthcare, some are not. Healthcare Executive Boards that carry the risk of AI components failing, and seldom have the human resources to manage this risk. PRIDAR was created to automate AI risk management for Board Members.
Current ML/AI Model Label Reference Number: PRIDAR1
Version control: Version 1 - ABUHB (October 2020)
Release Terms: Version 1
Outcome: prioritisation of a healthcare systems risk occasioned by planned or implemented AI
Output: a risk ranking for an AI enabled system via a web user interface
Output to Trigger: risk mitigation at a Board Level
Target Population: UK NHS and Private Hospitals
Time of Prediction: immediate based on model feedback
Input data source: model feedback from the public, clinicians, and suppliers
Input data type: text and json from compliance reports
Training details: projects arising with a NHS health board
Model type: Hidden Markov to trigger more model feedback, and Bayesian Ranking to identify dependancies between AI systems
WHATDAR Implementations
WHATDAR is an automated method of analysing term frequency in online text and call recordings. It creates lists and pictograms of common phrases which customers can use to better handle enquiries, or audit performance against service standards. These are then validated by subject matter experts, and documented in the governance procedures e.g. DPIA, DCB0129 et al
SISDA: THE SUICIDAL IDEATION MODEL
Motivation
People who reach out to mental health services can be suffering with suicidal thoughts and feelings. Once they express these in an online forms within the NHS there is a duty of care on the NHS to respond to the information provided quickly. A clinical model was needed that would enable the host of an online self referral form to trigger an email to clinicians alerting them to the existence of potential suicidal language in submissions. SISDA was developed to meet this need based on a research by Pete Burnap's Team at Cardiff University. This framework has subsequently been developed into a system audit and Service User & Clinically Validated Suicidal Ideation Signals Database and API (SISDA).
Current ML/AI Model Reference Number: SISDA2
Version Control: Version 1 - Cardiff University Project (2017), Version 2 - CarefulAI Clinical Validation System (2019),
Release Terms: Version 2-3
Outcome: Identification of suicidal ideation in online text
Output: % likelihood of person needing to be quickly referred to a clinician for triage (Depending on pattern matching)
Output to Trigger: a person being engaged more quickly by a mental health clinician
Target Population: people that seek to triage people seeking mental health and wellbeing support using online data capture tools
Time of Prediction: Less than 1 second, (Depending on pattern matching)
Input data source: Online forms, Speech to Text translations
Input data type: Free text containing more that 4 words
Training details: Data in the form of Short form SMS and Blog Post messages Validated by Social Scientists and Mental Health Clinicians
Model type: Term Frequency/Inverse Document frequency
Motivation
People who reach out to mental health services can be suffering with suicidal thoughts and feelings. Once they express these in an online forms within the NHS there is a duty of care on the NHS to respond to the information provided quickly. A clinical model was needed that would enable the host of an online self referral form to trigger an email to clinicians alerting them to the existence of potential suicidal language in submissions. SISDA was developed to meet this need based on a research by Pete Burnap's Team at Cardiff University. This framework has subsequently been developed into a system audit and Service User & Clinically Validated Suicidal Ideation Signals Database and API (SISDA).
Current ML/AI Model Reference Number: SISDA2
Version Control: Version 1 - Cardiff University Project (2017), Version 2 - CarefulAI Clinical Validation System (2019),
Release Terms: Version 2-3
Outcome: Identification of suicidal ideation in online text
Output: % likelihood of person needing to be quickly referred to a clinician for triage (Depending on pattern matching)
Output to Trigger: a person being engaged more quickly by a mental health clinician
Target Population: people that seek to triage people seeking mental health and wellbeing support using online data capture tools
Time of Prediction: Less than 1 second, (Depending on pattern matching)
Input data source: Online forms, Speech to Text translations
Input data type: Free text containing more that 4 words
Training details: Data in the form of Short form SMS and Blog Post messages Validated by Social Scientists and Mental Health Clinicians
Model type: Term Frequency/Inverse Document frequency
SISDAR: THE SUICIDAL IDEATION MODEL REPORTING
Motivation
Organisations who manage mental health services, in some cases, have AI models to identify and triage people suffering with suicidal thoughts and feelings. Many (including SISDA users) use NLP to maintain databases of phrases to trigger the need for interventions by a human. SISDAR is a method of reporting on such activities in support of a zero suicide agenda
Current ML/AI Model Reference Number: SISDAR1
Version Control: Version 1 CarefulAI produced in collaboration with NHS and NHS Technology Suppliers
Release Terms: Version 1
Outcome: the number of true and false positive triggers arising from suicidal ideation phrase matching, the 20 anonymised words either side of a trigger is submitted via an API
Output to Trigger: a classification:
BLUE SISDA Logo: ability to trigger monthly reports on the number of times a clinician is alerted to suicidal ideation in text and/or speech
ORANGE SISDA Logo: evidence of clinically validated new phrases added / excluded each per quarter in their own system
GREEN SISDA Logo: evidence of new phrases added / excluded proposed each quarter for SISDA validation
Target Population: electronic triage users and designers
Time of Prediction: multi-class classifier
Input data source: electronic triage users and designers systems
Input data type: True positive trigger (TP): Integer, false Postive trigger (FP); 20 anonymised words either side of a trigger per (TP) and (FP)
Training details: SISDA validation
Model type: TF-IDF reporting on model use
VOICEDAR Implementations
VOICEDAR is a system used to classify and predict outcomes from human-to-human and human-to-computer voice interactions. It takes live and audio recorded dialogues and monologues, and enables classifiers to be created to identify a target signal. User cases include identifying when: to approve a person as having active listening or motivational interviewing skills; to suggest a call handler triages a caller for emergence services; a COPD patient presents themselves to primary care services .
EMPDAR: MOTIVATIONAL INTERVIEWING COMPLIANCE MODEL
Motivation
In 2020/21 Clinicians and therapists were increasingly being asked to counsel people in difficult situations remotely because COVID restrictions negated the ability to have physical one-to-one meetings. Staff are being subsequently trained in motivational interviewing techniques. Much of this training is delivered remotely via 'watch and learn' video sessions, or via role-play between tutors and staff attending training. One aspect of motivational interviewing is to evidence that you have heard what matters to the subject of a counselling session by feeding back what you believe you have heard. The frequency with which this occurs is a signal of the degree of empathy a clinician or therapist has evidence, as is the matching of the speed with which two parties speak. To enable more people to receive interview training a model was to measure empathy engendered during training sessions.
Current ML/AI Model Label Reference Number: EMPDAR1, EMPDAR2
Version Control: Version 1 Meet Insight BBC Project (August 2015), Version 2 - IXN Motivational Interviewing Project
Release Terms: Version 1 , Version 2
Outcome: Identification of empathy in two-party dialogue
Output: the degree to which two parties mirror and match each other’s language and speaking rate
Output to trigger: a person evidencing compliance to a training standard
Target Population: system providers and trainers responsible for motivational interviewing
Time of Prediction: 33 seconds after a training session is uploaded for analysis
Input data source: call recordings
Input data type: wav, mp3 files
Training details: sound recordings of two parties in role play
Model type: LTSM on speech timings, and topic modelling
Motivation
In 2020/21 Clinicians and therapists were increasingly being asked to counsel people in difficult situations remotely because COVID restrictions negated the ability to have physical one-to-one meetings. Staff are being subsequently trained in motivational interviewing techniques. Much of this training is delivered remotely via 'watch and learn' video sessions, or via role-play between tutors and staff attending training. One aspect of motivational interviewing is to evidence that you have heard what matters to the subject of a counselling session by feeding back what you believe you have heard. The frequency with which this occurs is a signal of the degree of empathy a clinician or therapist has evidence, as is the matching of the speed with which two parties speak. To enable more people to receive interview training a model was to measure empathy engendered during training sessions.
Current ML/AI Model Label Reference Number: EMPDAR1, EMPDAR2
Version Control: Version 1 Meet Insight BBC Project (August 2015), Version 2 - IXN Motivational Interviewing Project
Release Terms: Version 1 , Version 2
Outcome: Identification of empathy in two-party dialogue
Output: the degree to which two parties mirror and match each other’s language and speaking rate
Output to trigger: a person evidencing compliance to a training standard
Target Population: system providers and trainers responsible for motivational interviewing
Time of Prediction: 33 seconds after a training session is uploaded for analysis
Input data source: call recordings
Input data type: wav, mp3 files
Training details: sound recordings of two parties in role play
Model type: LTSM on speech timings, and topic modelling
ARDAR: THE CALLER BREATHING RATE MODEL
Motivation
People who reached out to 111/999 services with breathing difficulties associated with COVID and Lung Disease during April 2019, in some cases, waited 1.5 hours to speak to a call handler. In cases where a person was in Acute Respiratory Distress, it was recognised there was a need to better triage callers. A model was needed that would enable the host of call handling services to recognise breathlessness in call recordings. ARDAR was first used to prove that one could differentiate between callers with normal breathing and those that did not. It was subsequently used to differentiate between phrases spoken by people with sputum colours, wellbeing rates, and MRC Dyspnoea scores that were normal, and those whose conditions had deteriorated by 99.7% for 5 days.
Current ML /AI Model Label Reference Number: ARDAR2
Version Control: Version 1 - Welsh Ambulance Service Project (June 2020), Version 2 - CarefulAI Team Wellbeing Project (October 2021)
Release Terms: Version 2 for research purposes (MHRA/FDA/Patent Approvals Pending for public Use))
Outcome: Identification of breathlessness that would trigger a request to attend an Emergency Department
Output: % likelihood of a person needing to be quickly referred to a clinician for triage (Depending on telephony system)
Output to trigger: a person being engaged more quickly by a call handler
Target Population: systems that seek to triage people with an existing or potential lung disease
Time of Prediction: Less than 1 second, (Depending on the deployment of a containerised model within a call handling system or phone)
Input data source: Pre-processed speech in the form of call recordings or live feeds
Input data type: de-identified sound data
Training details: Call recordings validated by 111 call managers relating to people that required an ambulance because of breathing difficulties and those that required an ambulance for other reasons, 120 days of call recordings/self-reported breathlessness data provided by 35 people with positive PCR tests
Model type: a Logistic Regression and Mel Frequency Cepstral Coefficients model
Motivation
People who reached out to 111/999 services with breathing difficulties associated with COVID and Lung Disease during April 2019, in some cases, waited 1.5 hours to speak to a call handler. In cases where a person was in Acute Respiratory Distress, it was recognised there was a need to better triage callers. A model was needed that would enable the host of call handling services to recognise breathlessness in call recordings. ARDAR was first used to prove that one could differentiate between callers with normal breathing and those that did not. It was subsequently used to differentiate between phrases spoken by people with sputum colours, wellbeing rates, and MRC Dyspnoea scores that were normal, and those whose conditions had deteriorated by 99.7% for 5 days.
Current ML /AI Model Label Reference Number: ARDAR2
Version Control: Version 1 - Welsh Ambulance Service Project (June 2020), Version 2 - CarefulAI Team Wellbeing Project (October 2021)
Release Terms: Version 2 for research purposes (MHRA/FDA/Patent Approvals Pending for public Use))
Outcome: Identification of breathlessness that would trigger a request to attend an Emergency Department
Output: % likelihood of a person needing to be quickly referred to a clinician for triage (Depending on telephony system)
Output to trigger: a person being engaged more quickly by a call handler
Target Population: systems that seek to triage people with an existing or potential lung disease
Time of Prediction: Less than 1 second, (Depending on the deployment of a containerised model within a call handling system or phone)
Input data source: Pre-processed speech in the form of call recordings or live feeds
Input data type: de-identified sound data
Training details: Call recordings validated by 111 call managers relating to people that required an ambulance because of breathing difficulties and those that required an ambulance for other reasons, 120 days of call recordings/self-reported breathlessness data provided by 35 people with positive PCR tests
Model type: a Logistic Regression and Mel Frequency Cepstral Coefficients model
NUDGESHARE: implementations
NudgeShare is the design approach we take for data gathering using devices that are in the control of people who are prepared to share data with our customer. It is our method of creating ethical models with self reported data.
Nudgeshare implementations make use of:
Nudgeshare implementations make use of:
- Informed consent to plain language data sharing agreements, and customer's needs in relation to;
- Transmission of data in line with pre-defined Information Governance and Data Security & Management procedures
- Data de-identification e.g. not creating systems that a ‘motivated intruder’ could use to re-identify a person providing data.
- Local differential privacy e.g. actively making 30% of data inaccurate, and ensuring that customers never know which 30% of self reported data is incorrect
- On-device machine learning to classify data, and predict outcomes
- Federated learning e.g. not gathering data into 'data lakes', rather, making predictions and classifications on the device a user retains data on e.g. gathering model error rates for and not data
WELLDAR: NUDGESHARE MY DATA MODEL
Motivation
People that are expected to experience poor wellbeing i.e. COPD patients suffer greatly during exacerbations. Such exacerbations are often associated with emergency admissions. If a person were able to predict when an exacerbation is likely to occur then they, and primary care services, could better manage exacerbations outside of an emergency setting. To understand if this was possible a tool had to be designed. NudgeShare in WELLDAR enables people to visualise their wellbeing data with clinical scores, and share this data with a Senior Responsible Officers (SROs). The aim being that SROs, in line with ICO regulations and the necessary consents, aggregate on device findings to build classifiers (e.g ARDAR, and share it without prejudicing the identity of individuals e.g. Wellbeing Maps)
Current Data Model Reference Number: WELLDAR2 and WELLDAR3
Version Control: Version 1 - GDS Project (November 2019), Version2 - Carers Trust (April 2020), Version 3 - IXN Project (August 2021)
Release Terms: Version 1 and 2 , Version 3
Outcome: de-identified data, protected by local differential privacy, on device testing of models, model error reporting
Output: ethically collected data that could be used to predict the need for human intervention
Output to Trigger: a researcher / data scientist being able to use ethical data to build care models
Target Population: mobile phone users with COPD
Time of Prediction: Less than 30 seconds on device
Input data source: on device sensors and forms
Input data type: steps, free text, speech
Training details: on device classifiers for speech rate trained with RASA
Model type: privacy protecting federated machine learning
Motivation
People that are expected to experience poor wellbeing i.e. COPD patients suffer greatly during exacerbations. Such exacerbations are often associated with emergency admissions. If a person were able to predict when an exacerbation is likely to occur then they, and primary care services, could better manage exacerbations outside of an emergency setting. To understand if this was possible a tool had to be designed. NudgeShare in WELLDAR enables people to visualise their wellbeing data with clinical scores, and share this data with a Senior Responsible Officers (SROs). The aim being that SROs, in line with ICO regulations and the necessary consents, aggregate on device findings to build classifiers (e.g ARDAR, and share it without prejudicing the identity of individuals e.g. Wellbeing Maps)
Current Data Model Reference Number: WELLDAR2 and WELLDAR3
Version Control: Version 1 - GDS Project (November 2019), Version2 - Carers Trust (April 2020), Version 3 - IXN Project (August 2021)
Release Terms: Version 1 and 2 , Version 3
Outcome: de-identified data, protected by local differential privacy, on device testing of models, model error reporting
Output: ethically collected data that could be used to predict the need for human intervention
Output to Trigger: a researcher / data scientist being able to use ethical data to build care models
Target Population: mobile phone users with COPD
Time of Prediction: Less than 30 seconds on device
Input data source: on device sensors and forms
Input data type: steps, free text, speech
Training details: on device classifiers for speech rate trained with RASA
Model type: privacy protecting federated machine learning
CAREDAR: NUDGESHARE MY DATA MODEL
Motivation
Family members and extended family members bear 90% of the social care burden in the UK. When such people become full-time unpaid carers their stress levels rise, and wellbeing can be negatively effected. This situation was exacerbated during COVID lockdowns in 2020, new full time carers frequently presented to their GP within 6 months of taking their new role with mental health and physical health issues relating to stress and loneliness. A carers group recognised that the most effective way of reducing stress and improving wellbeing was to network carers together and to nudge them to walk and socialise more. A model was needed that could be deployed on carers mobile phones that would protect their privacy, learn about what activity and movement levels generated good self-reported well-being scores and identify the most effective time to nudge carers to walk and socialise.
Current ML / AI Model Label Reference Number: CAREDAR2
Versions: Version 1 - Cabinet Office Project (November 2019), Version 2 IXN Generalised Model (February 2021)
Release Terms: Version1 , Version 2
Outcome: identify when a carer’s wellbeing could be improved by activity and socialisation, and enable carers to publish their wellbeing
Output: an in-app message suggesting carers share their status with fellow carers
Output to Trigger: a person engaging another person on issues that matter to them
Target Population: full-time carers with a mobile phone
Time of Prediction: it takes 3 weeks to establish a ground truth for optimal well being related to movement and social contact
Input data source: self-reported network contact, and a phone pedometer, and locally differential private wellbeing reports
Input data type: messages sent to network contacts, self reported wellbeing scores, and steps
Training details: 93 Carers Trust members with CareCare (the model manifested in an app design) reporting on the fitness for purpose of in-app nudges
Model type: an on device Logistic Regression model that updates global models using local differential privacy data and federated machine learning.
Motivation
Family members and extended family members bear 90% of the social care burden in the UK. When such people become full-time unpaid carers their stress levels rise, and wellbeing can be negatively effected. This situation was exacerbated during COVID lockdowns in 2020, new full time carers frequently presented to their GP within 6 months of taking their new role with mental health and physical health issues relating to stress and loneliness. A carers group recognised that the most effective way of reducing stress and improving wellbeing was to network carers together and to nudge them to walk and socialise more. A model was needed that could be deployed on carers mobile phones that would protect their privacy, learn about what activity and movement levels generated good self-reported well-being scores and identify the most effective time to nudge carers to walk and socialise.
Current ML / AI Model Label Reference Number: CAREDAR2
Versions: Version 1 - Cabinet Office Project (November 2019), Version 2 IXN Generalised Model (February 2021)
Release Terms: Version1 , Version 2
Outcome: identify when a carer’s wellbeing could be improved by activity and socialisation, and enable carers to publish their wellbeing
Output: an in-app message suggesting carers share their status with fellow carers
Output to Trigger: a person engaging another person on issues that matter to them
Target Population: full-time carers with a mobile phone
Time of Prediction: it takes 3 weeks to establish a ground truth for optimal well being related to movement and social contact
Input data source: self-reported network contact, and a phone pedometer, and locally differential private wellbeing reports
Input data type: messages sent to network contacts, self reported wellbeing scores, and steps
Training details: 93 Carers Trust members with CareCare (the model manifested in an app design) reporting on the fitness for purpose of in-app nudges
Model type: an on device Logistic Regression model that updates global models using local differential privacy data and federated machine learning.
SKINDAR: THE DATA MODEL
Motivation
There is a national shortage of Dermatologists. The end result is that when GPs take digital photographs of suspected tumours so they can email them to a dermatologist for assessment they wait for a very long time to get feedback. To reduce the time to get feedback many organisations are attempting to build classifiers that can automatically identify cancer type. The problem that exists is that there is not a national annotated database of cancer images-gathered using mobile phone devices. So CarefulAI and students from UCL IXN developed a method of gathering such data into a pipeline that could feed classifier development in the NHS.
Current ML/AI Model Label Reference Number: SKINDAR2
Version control: Version 1 - NHS GOSH (April 2018). Version 2 - IXN Generalised model (August 2019)
Release Terms: Version 1 - 2
Outcome: human annotated digital images for use in classifier development
Output: an android app that enables images to be gathered annotated an shared with an online repository
Output to Trigger: the development of image classifiers by academic researchers
Target Population: UK
Time of Prediction: once an image is captured clinicians can annotate an image in 15 seconds
Input data source: mobile phone photographs
Input data type: png, jpeg, 4-12MP, with annotation in the file name
Training details: human annotated images
Model type: annotated images suitable for Canny-Deriche type algorithm models
Motivation
There is a national shortage of Dermatologists. The end result is that when GPs take digital photographs of suspected tumours so they can email them to a dermatologist for assessment they wait for a very long time to get feedback. To reduce the time to get feedback many organisations are attempting to build classifiers that can automatically identify cancer type. The problem that exists is that there is not a national annotated database of cancer images-gathered using mobile phone devices. So CarefulAI and students from UCL IXN developed a method of gathering such data into a pipeline that could feed classifier development in the NHS.
Current ML/AI Model Label Reference Number: SKINDAR2
Version control: Version 1 - NHS GOSH (April 2018). Version 2 - IXN Generalised model (August 2019)
Release Terms: Version 1 - 2
Outcome: human annotated digital images for use in classifier development
Output: an android app that enables images to be gathered annotated an shared with an online repository
Output to Trigger: the development of image classifiers by academic researchers
Target Population: UK
Time of Prediction: once an image is captured clinicians can annotate an image in 15 seconds
Input data source: mobile phone photographs
Input data type: png, jpeg, 4-12MP, with annotation in the file name
Training details: human annotated images
Model type: annotated images suitable for Canny-Deriche type algorithm models
