Medical cyber crises and biotechnological syndromes: a multisite clinical simulation study focused on digital health complaints.

BACKGROUND: Biotechnological syndromes refer to the illnesses that arise at the intersection of human physiology and digital technology. Implanted technologies can malfunction (eg, runaway pacemakers, hacked insulin pumps), and consumer technologies can be exploited to impose adverse health effects (eg, technology-facilitated abuse, hacks on epilepsy websites inducing seizures). Through a series of clinical simulation events, our study aimed to (1) evaluate the ability of physicians to respond to biotechnological syndromes, (2) explore gaps in training impeding effective patient care in digital cases, and (3) identify clinical cases due to digital technology arising in the population. METHODS: This was a multisite clinical simulation study. Between Jan 1 and July 1, 2023, four half-day clinical simulation events focused on digital pathologies were delivered across three NHS sites in London and the East Midlands. Participants (n=14) ranged in seniority from clinical medical students through to hospital consultants. Ethics approval was attained from University College London. Participant performance was scored by one researcher, using mark schemes built from the Objective Structured Clinical Examinations (OSCEs) format of UK Medical Schools. Qualitative and quantitative feedback was collected from participants following each of the four scenarios. Participants were asked to identify clinical challenges present in each simulation, discuss cases within their own practice, and evaluate the usefulness of the educational material. FINDINGS: Participants reported a wide range of examples within their own practice (eg, insulin pumps malfunctioning due to Apple watches, cardiac arrests due to faults in ventilators). Participants described barriers to treatment in simulations, including low diagnostic suspicion of technological failures, little education on biotechnological mechanisms, a lack of available expertise, and uncertainty regarding effective therapeutics. In the subjective feedback, participants reported the lowest levels of confidence when managing cases relating to software issues in medical devices, both in terms of confidence in their own ability to deliver care (mean scores: 3·6/10 junior staff, 5·8/10 senior staff) and in their teams (3·8/10 juniors, 6·8/10 seniors). INTERPRETATION: In our digital society, clinical cases related to technology are likely to increase in the population. At present, a lack of clinical awareness, education, training material, and appropriate guidelines are some of the barriers that health-care professionals face when treating these patients. FUNDING: None.

Receiving threatening or obscene messages from a partner and mental health, self-harm and suicidality: results from the Adult Psychiatric Morbidity Survey.

PURPOSE: Threatening or obscene messaging is repeated, unwanted texts, emails, letters or cards experienced by the recipient as threatening or obscene, and causing fear, alarm or distress. It is rarely examined as an aspect of intimate partner violence. We describe the prevalence of exposure to threatening/obscene messaging from a current or ex-partner; characteristics of victims; and associations with other forms of violence and abuse, mental disorder, self-harm, and suicidality. METHODS: Cross-sectional probability-sample survey of the general population in England aged 16 + . Multivariable regression modelling tested associations between receipt of threatening/obscene messaging and current common mental disorder, past-year self-harm and suicidality. RESULTS: Threatening/obscene messages were received from a current/ex-partner by 6.6% (95%CI: 5.9-7.3) of adults who had been in a relationship; 1.7% received these in the past year. Victims were more likely to be female, under 35, single or divorced, socioeconomically disadvantaged, and to have experienced other forms of sexual and partner violence and abuse. Those who received threatening/obscene messages in the past year were more likely to experience common mental disorder (adjusted odds ratio 1.89; 1.01-3.55), self-harm (2.31; 1.00-5.33), and suicidal thoughts (2.00; 1.06-3.78). CONCLUSION: Threatening/obscene messaging commonly occurs in the context of intimate partner violence. While often occurring alongside sexual and physical violence, messaging has an additional association with mental disorder and suicidality. Routine enquiry in service settings concerning safety, including those working with people who have escaped domestic violence, should ask about ongoing contact from previous as well as current partners. This should include asking about messaging, as well as other forms of potentially technology-enabled abuse which may become increasingly common.

Safeguarding the "Internet of Things" for Victim-Survivors of Domestic and Family Violence: Anticipating Exploitative Use and Encouraging Safety-by-Design.

Smart, Internet-connected devices-the so-called "Internet of Things" (IoT)-pose significant threats to victim-survivors of domestic and family violence (DFV). IoT systems have been used to abuse, harass, monitor, intimidate, and gaslight victim-survivors. We present findings from an abusability analysis that examined 13 IoT devices and allowed us to make several observations about common vulnerabilities to victim-survivors of DFV. We argue that IoT manufacturers must be encouraged to factor in the implications of DFV in the design of their products. Additionally, technology-facilitated abuse in DFV contexts must feature in industry and government safety-by-design approaches. Our results suggest ways IoT devices can be modified at low cost to alleviate opportunities for misuse, and we endorse IoT manufacturers to consider those risks early in the design stage.

Simulation-based research for digital health pathologies: A multi-site mixed-methods study.

Background: The advance of digital health technologies has created new forms of potential pathology which are not captured in current clinical guidelines. Through simulation-based research, we have identified the challenges to clinical care that emerge when patients suffer from illnesses stemming from failures in digital health technologies. Methods: Clinical simulation sessions were designed based on patient case reports relating to (a) medical device hardware errors, (b) medical device software errors, (c) complications of consumer technology and (d) technology-facilitated abuse. Clinicians were recruited to participate in simulations at three UK hospitals; audiovisual suites were used to facilitate group observation of simulation experience and focused debrief discussions. Invigilators scored clinicians on performance, clinicians provided individual qualitative and quantitative feedback, and extensive notes were taken throughout. Findings: Paired t-tests of pre and post-simulation feedback demonstrated significant improvements in clinician's diagnostic awareness, technical knowledge and confidence in clinical management following simulation exposure (p < 0.01). Barriers to care included: (a) low suspicion of digital agents, (b) attribution to psychopathology, (c) lack of education in technical mechanisms and (d) little utility of available tests. Suggested interventions for improving future practice included: (a) education initiatives, (b) technical support platforms, (c) digitally oriented assessments in hospital workflows, (d) cross-disciplinary staff and (e) protocols for digital cases. Conclusion: We provide an effective framework for simulation training focused on digital health pathologies and uncover barriers that impede effective care for patients dependent on technology. Our recommendations are relevant to educators, practising clinicians and professionals working in regulation, policy and industry.

Safeguarding patients from technology-facilitated abuse in clinical settings: A narrative review.

Safeguarding vulnerable patients is a key responsibility of healthcare professionals. Yet, existing clinical and patient management protocols are outdated as they do not address the emerging threats of technology-facilitated abuse. The latter describes the misuse of digital systems such as smartphones or other Internet-connected devices to monitor, control and intimidate individuals. The lack of attention given to how technology-facilitated abuse may affect patients in their lives, can result in clinicians failing to protect vulnerable patients and may affect their care in several unexpected ways. We attempt to address this gap by evaluating the literature that is available to healthcare practitioners working with patients impacted by digitally enabled forms of harm. A literature search was carried out between September 2021 and January 2022, in which three academic databases were probed using strings of relevant search terms, returning a total of 59 articles for full text review. The articles were appraised according to three criteria: (a) the focus on technology-facilitated abuse; (b) the relevance to clinical settings; and (c) the role of healthcare practitioners in safeguarding. Of the 59 articles, 17 articles met at least one criterion and only one article met all three criteria. We drew additional information from the grey literature to identify areas for improvement in medical settings and at-risk patient groups. Technology-facilitated abuse concerns healthcare professionals from the point of consultation to the point of discharge, as a result clinicians need to be equipped with the tools to identify and address these harms at any stage of the patient's journey. In this article, we offer recommendations for further research within different medical subspecialities and highlight areas requiring policy development in clinical environments.

A Systematic Literature Review of the Use of Computational Text Analysis Methods in Intimate Partner Violence Research.

Purpose: Computational text mining methods are proposed as a useful methodological innovation in Intimate Partner Violence (IPV) research. Text mining can offer researchers access to existing or new datasets, sourced from social media or from IPV-related organisations, that would be too large to analyse manually. This article aims to give an overview of current work applying text mining methodologies in the study of IPV, as a starting point for researchers wanting to use such methods in their own work. Methods: This article reports the results of a systematic review of academic research using computational text mining to research IPV. A review protocol was developed according to PRISMA guidelines, and a literature search of 8 databases was conducted, identifying 22 unique studies that were included in the review. Results: The included studies cover a wide range of methodologies and outcomes. Supervised and unsupervised approaches are represented, including rule-based classification (n = 3), traditional Machine Learning (n = 8), Deep Learning (n = 6) and topic modelling (n = 4) methods. Datasets are mostly sourced from social media (n = 15), with other data being sourced from police forces (n = 3), health or social care providers (n = 3), or litigation texts (n = 1). Evaluation methods mostly used a held-out, labelled test set, or k-fold Cross Validation, with Accuracy and F1 metrics reported. Only a few studies commented on the ethics of computational IPV research. Conclusions: Text mining methodologies offer promising data collection and analysis techniques for IPV research. Future work in this space must consider ethical implications of computational approaches.