Synthetic cannabinoid use in a case series of patients with psychosis presenting to acute psychiatric settings : Clinical presentation and management issues

Background: Novel Psychoactive Substances (NPS) are a heterogeneous class of synthetic molecules including synthetic cannabinoid receptor agonists (SCRAs). Psychosis is associated with SCRAs use. There is limited knowledge regarding the structured assessment and psychometric evaluation of clinical presentations, analytical toxicology and clinical management plans of patients presenting with psychosis and SCRAs misuse. Methods: We gathered information regarding the clinical presentations, toxicology and care plans of patients with psychosis and SCRAs misuse admitted to inpatients services. Clinical presentations were assessed using the PANSS scale. Vital signs data were collected using the National Early Warning Signs tool. Analytic chemistry data were collected using urine drug screening tests for traditional psychoactive substances and NPS. Results: We described the clinical presentation and management plan of four patients with psychosis and misuse of SCRAs. Conclusion: The formulation of an informed clinical management plan requires a structured assessment, identification of the index NPS, pharmacological interventions, increases in nursing observations, changes to leave status and monitoring of the vital signs. The objective from using these interventions is to maintain stable physical health whilst rapidly improving the altered mental state.Peer reviewedFinal Published versio

Modelling physiological deterioration in post-operative patient vital-sign data

Patients who undergo upper-gastrointestinal surgery have a high incidence of post-operative complications, often requiring admission to the intensive care unit several days after surgery. A dataset comprising observational vital-sign data from 171 post-operative patients taking part in a two-phase clinical trial at the Oxford Cancer Centre, was used to explore the trajectory of patients’ vital-sign changes during their stay in the post-operative ward using both univariate and multivariate analyses. A model of normality based vital-sign data from patients who had a “normal” recovery was constructed using a kernel density estimate, and tested with “abnormal” data from patients who deteriorated sufficiently to be re-admitted to the intensive care unit. The vital-sign distributions from “normal” patients were found to vary over time from admission to the post-operative ward to their discharge home, but no significant changes in their distributions were observed from halfway through their stay on the ward to the time of discharge. The model of normality identified patient deterioration when tested with unseen “abnormal” data, suggesting that such techniques may be used to provide early warning of adverse physiological events

The social practice of rescue: the safety implications of acute illness trajectories and patient categorisation in medical and maternity settings.

The normative position in acute hospital care when a patient is seriously ill is to resuscitate and rescue. However, a number of UK and international reports have highlighted problems with the lack of timely recognition, treatment and referral of patients whose condition is deteriorating while being cared for on hospital wards. This article explores the social practice of rescue, and the structural and cultural influences that guide the categorisation and ordering of acutely ill patients in different hospital settings. We draw on Strauss et al.'s notion of the patient trajectory and link this with the impact of categorisation practices, thus extending insights beyond those gained from emergency department triage to care management processes further downstream on the hospital ward. Using ethnographic data collected from medical wards and maternity care settings in two UK inner city hospitals, we explore how differences in population, cultural norms, categorisation work and trajectories of clinical deterioration interlink and influence patient safety. An analysis of the variation in findings between care settings and patient groups enables us to consider socio-political influences and the specifics of how staff manage trade-offs linked to the enactment of core values such as safety and equity in practice

Vital signs prediction and early warning score calculation based on continuous monitoring of hospitalised patients using wearable technology

In this prospective, interventional, international study, we investigate continuous monitoring of hospitalised patients’ vital signs using wearable technology as a basis for real-time early warning scores (EWS) estimation and vital signs time-series prediction. The collected continuous monitored vital signs are heart rate, blood pressure, respiration rate, and oxygen saturation of a heterogeneous patient population hospitalised in cardiology, postsurgical, and dialysis wards. Two aspects are elaborated in this study. The first is the high-rate (every minute) estimation of the statistical values (e.g., minimum and mean) of the vital signs components of the EWS for one-minute segments in contrast with the conventional routine of 2 to 3 times per day. The second aspect explores the use of a hybrid machine learning algorithm of kNN-LS-SVM for predicting future values of monitored vital signs. It is demonstrated that a real-time implementation of EWS in clinical practice is possible. Furthermore, we showed a promising prediction performance of vital signs compared to the most recent state of the art of a boosted approach of LSTM. The reported mean absolute percentage errors of predicting one-hour averaged heart rate are 4.1, 4.5, and 5% for the upcoming one, two, and three hours respectively for cardiology patients. The obtained results in this study show the potential of using wearable technology to continuously monitor the vital signs of hospitalised patients as the real-time estimation of EWS in addition to a reliable prediction of the future values of these vital signs is presented. Ultimately, both approaches of high-rate EWS computation and vital signs time-series prediction is promising to provide efficient cost-utility, ease of mobility and portability, streaming analytics, and early warning for vital signs deterioration

Automation of Patient Trajectory Management: A deep-learning system for critical care outreach

The application of machine learning models to big data has become ubiquitous, however their successful translation into clinical practice is currently mostly limited to the field of imaging. Despite much interest and promise, there are many complex and interrelated barriers that exist in clinical settings, which must be addressed systematically in advance of wide-spread adoption of these technologies. There is limited evidence of comprehensive efforts to consider not only their raw performance metrics, but also their effective deployment, particularly in terms of the ways in which they are perceived, used and accepted by clinicians. The critical care outreach team at St Vincent’s Public Hospital want to automatically prioritise their workload by predicting in-patient deterioration risk, presented as a watch-list application. This work proposes that the proactive management of in-patients at risk of serious deterioration provides a comprehensive case-study in which to understand clinician readiness to adopt deep-learning technology due to the significant known limitations of existing manual processes. Herein is described the development of a proof of concept application uses as its input the subset of real-time clinical data available in the EMR. This data set has the noteworthy challenge of not including any electronically recorded vital signs data. Despite this, the system meets or exceeds similar benchmark models for predicting in-patient death and unplanned ICU admission, using a recurrent neural network architecture, extended with a novel data-augmentation strategy. This augmentation method has been re-implemented in the public MIMIC-III data set to confirm its generalisability. The method is notable for its applicability to discrete time-series data. Furthermore, it is rooted in knowledge of how data entry is performed within the clinical record and is therefore not restricted in applicability to a single clinical domain, instead having the potential for wide-ranging impact. The system was presented to likely end-users to understand their readiness to adopt it into their workflow, using the Technology Adoption Model. In addition to confirming feasibility of predicting risk from this limited data set, this study investigates clinician readiness to adopt artificial intelligence in the critical care setting. This is done with a two-pronged strategy, addressing technical and clinically-focused research questions in parallel

Reliable Patient Monitoring: A Clinical Study in a Step-down Hospital Unit

This paper presents the design, deployment, and empirical study of a wireless clinical monitoring system that collects pulse and oxygen saturation readings from patients. The primary contribution of this paper is an in-depth clinical trial that assesses the feasibility of wireless sensor networks for patient monitoring in general (non-ICU) hospital units. The trial involved 32 patients monitored in a step-down cardiology unit at Barnes-Jewish Hospital, St. Louis. During a total of 31 days of monitoring, the network achieved high reliability (median 99.92%, range 95.21% - 100%). The overall reliability of the system was dominated by sensing reliability (median 80.55%, range 0.38% - 97.69%) of the pulse oximeters. Sensing failures usually occurred in short bursts, although long bursts were also present and were caused by the sensor disconnections. We show that the sensing reliability could be significantly improved through oversampling and by implementing a disconnection alarm system that incurs minimal intervention cost. Our results also indicate that the system provided sufficient resolution to support the detection of clinical deterioration in two patients who were transferred to the ICU. The results show the feasibility of using wireless sensor networks for patient monitoring and may guide future research. We also report lessons learned from the deployment in the clinical environments with patient users

Recent Directions in Telemedicine: Review of Trends in Research and Practice

This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/4.0/) which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.Objectives: Healthcare is now routinely delivered by telecommunications-based services in all developed countries and an increasing number of developing countries. Telemedicine is used in many clinical specialities and across numerous healthcare settings, which range from mobile patient-centric applications to complex interactions amongst clinicians in tertiary referral hospital settings. This paper discusses some recent areas of significant development and progress in the field with the purpose of identifying strong trends in both research and practice activities. Methods: To establish the breadth of new ideas and directions in the field, a review of literature was made by searching PubMed for recent publications including terms (telemedicine OR telehealth) AND (challenge OR direction OR innovation OR new OR novel OR trend), for all searchable categories. 3,433 publications were identified that have appeared since January 1, 2005 (2,172 of these since January 1, 2010), based on a search conducted on June 1, 2015. Results: The current interest areas in these papers span both synchronous telemedicine, including intensive care, emergency medicine, and mental health, and asynchronous telemedicine, including wound and burns care, dermatology and ophthalmology. Conclusions: It is concluded that two major drivers of contemporary tele medicine development are a high volume demand for a particular clinical service, and/or a high criticality of need for clinical exper tise to deliver the service. These areas offer promise for further study and enhancement of applicable telemedicine methods and have the potential for large-scale deployments internationally, which would contribute significantly to the advancement of healthcare

Wearable continuous vital sign monitoring for deterioration detection and clinical outcomes in hospitalised patients

 Current practice uses physiological early warning scoring (EWS) systems to monitor “standard” vital signs, including heart rate (HR), respiratory rate (RR), blood pressure (BP), oxygen saturations (SpO2) and temperature, coupled with a graded response such as referral for a senior review or increasing monitoring frequency. Early detection of the deteriorating patient is a known challenge within hospital environments, as EWS is dependent on correct frequency of physiological observations tailored to specific patient needs, that can be time consuming for healthcare professionals, resulting in missed or incomplete observations. Wearable monitoring systems (WMS) may bring the potential to fill the gap in vital sign monitoring between traditional intermittent manual measurements and continuous automatic monitoring. However, evidence on the feasibility and impact of WMS implementation remains scarce. The virtual High Dependency Unit (vHDU) project was designed to develop and test the feasibility of deploying a WMS system in the hospital ward environment. This doctoral work aims to critically analyse the roadmap work of the vHDU project, containing ten publications distributed throughout 7 chapters. Chapter 1 (with 3 publications) includes a systematic review and meta-analysis identifying the lack of statistical evidence of the impact of WMS in early deterioration detection and associated clinical outcomes, highlighting the need for high-quality randomised controlled trials (RCTs). It also supports the use of WMS as a complement, and not a substitute, for standard and direct care. Chapter 2 explores clinical staff and patient perceptions of current vital sign monitoring practices, as well as their early thoughts on the use of WMS in the hospital environment through a qualitative interview study. WMS were seen positively by both clinical and patient groups as a potential tool to bridge the gap between manual observations and the traditional wired continuous automatic systems, as long as it does not add more noise to the wards nor replaces direct contact from the clinical staff. In chapter 3, the wearability of 7 commercially available wearables (monitoring HR, RR and SpO2) was assessed, advocating for the use of pulse oximeters without a fingertip probe and a small chest patch to improve worn times from the patients. Out of these, five devices were submitted to measurement accuracy testing (chapter 4, with 3 publications) under movement and controlled hypoxaemia, resulting in the validation of a chest patch (monitoring HR and RR) and proving the diagnostic accuracy of 3 pulse oximeters (monitoring pulse rate, PR and SpO2) under test. These results were timely for the final selection of the devices to be integrated in our WMS, namely vHDU system, explored in chapter 5, outlining the process for its development and rapid deployment in COVID-19 isolation wards in our local hospital during the pandemic. This work is now converging in the design of a feasibility RCT to test the impact of the vHDU system (now augmented with blood pressure and temperature monitoring, completing all 5 vital signs) versus standard care in an unbiased environment (chapter 6). This will also ascertain the feasibility for a multicentre RCT, that may in the future, contribute with the much-needed statistical evidence to my systematic review and meta-analysis research question, highlighted in chapter 1. Finally, chapter 7 includes a critical reflection of the vHDU project and overall doctoral work, as well as its contributions to the field of wearable monitoring.<p class="MsoNormal"/