Public and Patient Research in chronosig

As someone working in public and patient involvement (PPI) for 20 years, I quake whenever IT (information technology), machine learning (ML), data science – and latterly – the apparently more general term AI (artificial intelligence) are mentioned.

In this inaugural blog post for the chronosig project, I will outline some core concepts and explain why PPI is required front-and-centre in the project.

Why did I become involved? Extracting information from large data sets, to analyse patterns and trends in disease and illness is the future for medical science. However reluctant I am to embrace this technology I have no option but to do so if I wish to be informed and importantly, influence adoption. So, four years ago I became a member of the CRIS-Oxford (Case Record Interactive Search) Oversight group. UK-CRIS is a national programme to develop the clinical record (often called the electronic health record, or EHR) into a data asset and research tool for NHS Mental Health Trusts. It was through this work I met one of the researchers who later became involved in the chronosig project.

The Problem

Firstly, what is chronosig? The acronym stands for CHRONOlogical SIGnatures. It is a project, funded by the National Institute of Health Research (NIHR), to develop a clinical decision support tool (CDST) that will deliver accurate, explainable and justified recommendations to assist clinicians expedite access to the right secondary mental health care.

To access specialist, secondary mental health care, a person must usually be referred to a community mental health team (CMHT), who review the referral alongside the person’s historical medical notes and then decide how to proceed. Referral and triage processes lack transparency (to patients and referrers), are capricious (with CMHTs appearing to use referral criteria and thresholds inconsistently) and result in frustration from ‘referral bouncing’ (Chew-Graham et al. 2007). Attempts at improving this interface – for example, with GPs and CMHTs using a standardised referral tool to ‘grade’ patient’s severity and unmet needs – have yielded poor results (Slade et al. 2008). Patient care is delayed because they are being triaged multiple times, by different teams, who may disagree on the most appropriate treatment team for the patient’s needs. It is hoped that chronosig will lead to improved and more accurate triage outcomes by automating some parts of the triage process.

chronosig uses Artificial Intelligence and Machine Learning

For a start, what is the difference, if any, between IT, data science, machine learning and artificial intelligence? Every time these terms arise I need to consult the internet to refresh my memory.

Given the fluidity of terms like artificial intelligence (AI) and machine learning (ML), here, I’ll mainly use the term ML to mean any system – implemented on a computer – where the system learns to associate some inputs to a desired (or target) output without being told explicitly what those associations are. In machine learning, this is called classification – learning what things are by seeing examples and being told the correct label or category. A simple example helps here: Imagine you’ve never seen any examples of fruits, so words like “apple,” “pineapple” and “banana” are meaningless to you. If I then train you to identify fruits, I might show you examples of red and green coloured round things and also, long, curved yellow things. Each time I show you an example, I ask you to tell me whether it’s an apple or a banana (or pineapple, grapefruit and so on). You are ‘rewarded’ if you are correct but ‘penalised’ if you’re incorrect and your goal is to adjust the responses you give so that you maximise your rewards. Over time, after seeing enough examples you will have learned that red/green round things are “apples” and that long, curved yellow things are “bananas.”

In some specific contexts, clinical decision making – such as “Should I offer a patient treatment X or Y?” – can be viewed in a similar way; which patients (represented as inputs to the system) should be given treatment X or treatment Y (represented as targets to the system). Again, as for our fruit example, the system isn’t explicitly programmed about which patients are suitable for each treatment but instead, it learns by being shown examples of patients who are known to be suited to treatment X and Y and the system gradually acquires implicit knowledge about which patients should be offered X or Y.

It is important to note that when training ML systems, the data curated for this purpose are historical patients where we already know the outcomes or decisions rather than being patients currently waiting for decisions to be made. In this clinical example, it is important that this historical data is representative so that the ML system learns “good practice” and doesn’t inherit flaws in the existing processes which produced the training data and which leads to harmful biases in machine learning applications.

Why PPI is Important to chronosig

From a PPI perspective, especially when ML is mentioned in the context of clinical decision support tools, I would like to emphasise the word support. The tool will not replace the clinician i.e. doctors, nurses, care-coordinators or any member of the team of clinicians making the decisions. chronosig is there to augment their usual, routine decision making processes and to help clinicians make better and quicker decisions with their patients, leading to more successful treatment and outcomes for the patient.

The proposed CDST uses natural language processing (NLP) to extract data from patients’ electronic health records (EHRs) and combines this with the referral information. NLP is a sub-field of artificial intelligence and machine learning that works to capture the complexity of written (and spoken) language in a way that computers can meaningfully process when automating tasks as diverse as translation between languages, answering questions or, in chronosig, extracting and representing important and relevant information that might be used to guide clinicians when making triage decisions.

How Patient Data is Used in chronosig

In EHRs, there is a lot of ‘free text’ ie the notes made by various clinicians relating to the patient’s diagnosis, symptoms of illness, previous treatments, and observations from clinicians. Using NLP, this free text is ‘translated’ into format that facilitates a computer making decisions or inferences. For example, the computer is able to see how different or similar two patients are on the basis of what is written in their medical notes – much like the fruit example where “round” fruits are more likely to be apples than bananas. Similar patients would be expected to receive similar treatments in the same way that both red and green ‘versions’ of apples are indeed both examples of apples despite differing in one property (i.e. their colour).

Using confidential medical data requires robust safeguards – some of which are described on the Frequently Asked Questions page.

Concerns about Machine Learning

Recently, there has been scrutiny around the ways ML can fail different groups of people in society especially those from under-represented, minority groups. Problems in ML systems have exposed that people can be denied employment opportunity, timely and appropriate healthcare as well as making inappropriate assumptions about a person’s sexual orientation. Rightly, we should be sceptical about the scope, impacts and injustice that technology can make real without the appropriate oversight and stakeholder involvement – even when the proposed application of ML appears benevolent.

Given that just and equitable access to healthcare is a clear risk in chronosig, patients are involved throughout the lifespan of this project. The first PPI deliverable is a stakeholder impact assessment using the FAST (Leslie 2019) principles (fairness, accountability, sustainability and transparency) in parallel to dataset curation. Dataset curation is the pooling and cleaning of vast amounts of data – sometimes with humans adding information to identify categories – so that it may be preserved and maintained for use over time. Any data used in ML projects should be carefully inspected to ensure that it is representative and appropriately anonymised. Where representativeness cannot be guaranteed, steps to mitigate this must be implemented at the outset. Our stakeholder impact assessment will include, for example, PPI definitions of an acceptable referral i.e. what characteristics stakeholders expect should be included or excluded from referrals. For example, features of a patient’s presentation which are thought to negatively bias clinical teams during existing, routine practice might be flagged for removal from the data set used in chronosig. A concrete example of this would be diagnoses such as personality disorders that have historically been shown to be stigmatising and can result in clinical services denying access to care. There may be other information which patients and the public feel that should not be included in referrals to triage teams even if this is current practice in NHS services. In this way, the PPI process will inform us not just about how technology needs safeguards but also, about how people experience their initial interactions with the triage process more generally.


What is both novel and exciting about chronosig is that it potentially offers the opportunity for a transparent and equitable triage clinical decision support tool informed by patient and public input and this is especially important to patients, their families as well as other clinicians who refer people to secondary mental health services.

From a PPI perspective, there are other interesting facets; given the obscurity in the triage process in secondary mental healthcare, it will be interesting to see how technology can mitigate (rather than entrench) existing practices that patients find unhelpful or frustrating. The use of a CDST as something that augments and supports rather than replaces clinicians is also interesting and should be welcomed particularly given the pace at which AI and ML has been promoted in healthcare (often with questionable results). The difficult history the NHS has had with information technology has not always been recognised (BMA 2019), and this challenge must be kept in mind for future AI applications (Joshi and Morley 2019).


BMA. 2019. “Technology, Infrastructure and Data Supporting NHS Staff.” British Medical Association.

Chew-Graham, Carolyn, Mike Slade, Carolyn Montana, Mairi Stewart, and Linda Gask. 2007. “A Qualitative Study of Referral to Community Mental Health Teams in the UK: Exploring the Rhetoric and the Reality.” BMC Health Services Research 7 (1): 117.

Joshi, I, and J Morley. 2019. “Artificial Intelligence: How to Get It Right.” NHSx.

Leslie, David. 2019. “Understanding Artificial Intelligence Ethics and Safety: A Guide for the Responsible Design and Implementation of AI Systems in the Public Sector.” The Alan Turing Institute.

Slade, M., L. Gask, M. Leese, P. McCrone, C. Montana, R. Powell, M. Stewart, and C. Chew-Graham. 2008. “Failure to Improve Appropriateness of Referrals to Adult Community Mental Health Services–Lessons from a Multi-Site Cluster Randomized Controlled Trial.” Family Practice 25 (3): 181–90.

Julia Hamer-Hunt
Julia Hamer-Hunt
Patient and Public Involvement Lead

I work within the Department of Psychiatry and the Oxford Health Biomedical Research Centre to promote patient and public involvement and engagement (PPI/E) in research.