InferMed

Objective: To examine patient-user symptom reporting to an AI-based online virtual triage (VT) and care-referral engine to assess patterns of mental health symptoms (MHS) reporting prior to and during the COVID-19 pandemic. Methods: The frequencies of 11 MHS reported through VT were analyzed over three time intervals: one year prior to the WHO declaring a global COVID-19 emergency; from pandemic declaration to a mid-point in US vaccine distribution/uptake; and one year thereafter. Results: A total of 4,346,987 VT encounters/interviews presenting somatic and MHS occurred, increasing over time and peaking in the COVID-19 post-vaccine interval with 2,257,553 encounters (51.9%). In 866,218 encounters (19.9%), at least one MHS was reported. MHS reporting declined across subsequent time intervals, was lowest in the COVID-19 post-vaccine period (19.1%), and slightly higher in the pre-pandemic and COVID-19 pre-vaccine intervals (p = 0.05). The most frequently reported symptoms were anxiety, sleep disorder, general anxiety, irritability, and nervousness. Women reported anxiety less often and nervousness and irritability more often. Individuals aged 60+ years reported anxiety and nervousness less frequently, insomnia and sleep disorder more often than individuals 18–39 and 40–59 years old, and sleep disorder more often than those aged 40–59 years in all periods (all p = 0.05). Conclusions: Overall VT usage for somatic and mental health symptom reporting and care referral increased dramatically during the pandemic. VT effectively screened and provided care referral for patient-users presenting with MHS. Virtual triage offers a valuable additional vehicle to detect mental health symptoms and potentially accelerate care referral for patients needing care.

Reason for withdrawal from publication This review is out of date, and the original authors are no longer available to update it. If you are interested in updating this review, please contact Managing Editor Anna Hobson at anna.hobson@ndcn.ox.ac.uk. At October 2015, a new author team is preparing a replacement review to focus on acute sickle cell crises in adults.

This paper presents an overview of computerised decision support for clinical practice. The concept of computer-interpretable guidelines is introduced in the context of the @neurIST project, which aims at supporting the research and treatment of asymptomatic unruptured cerebral aneurysms by bringing together heterogeneous data, computing and complex processing services. The architecture is generic enough to adapt it to the treatment of other diseases beyond cerebral aneurysms. The paper reviews the generic requirements of the @neurIST system and presents the innovative work in distributing executable clinical guidelines.

This chapter contains sections titled: Background Questions Implications of the available evidence References

In this presentation we shall review different ways of describing the processes of delivering patient care, and relate these to traditional workflow in business processes, and the concept of “careflow” proposed by Panzarasa and her colleagues in Pavia. We shall discuss the problem of designing a careflow application as a form of process modelling, to be contrasted with older paradigms ranging from rule-based alerts and reminders to Petri nets and Critical path analysis. We shall also consider the need for specialised formalisms for describing clinical processes, drawing on experience with workflow languages (e.g. BPEL4WS, BPMN), guideline modelling languages (e.g. GLIF, PROforma), AI planning languages (e.g. PDDL, OCL) and “agent” programming systems (e.g. LALO, 3APL). The adoption of clinical workflow technology will greatly benefit from the availability of appropriate languages for declaratively representing processes of care. To explore some of the requirements for a clinical workflow technology we will review and critique the PROforma process modelling language and the Arezzo® and Tallis toolsets which use it. The discussion will be illustrated with deployed applications and operational prototypes.