Understanding the enablers and barriers to implementing a patient-led escalation system: a qualitative study.

BACKGROUND: The management of acute deterioration following surgery remains highly variable. Patients and families can play an important role in identifying early signs of deterioration but effective contribution to escalation of care can be practically difficult to achieve. This paper reports the enablers and barriers to the implementation of patient-led escalation systems found during a process evaluation of a quality improvement programme Rescue for Emergency Surgery Patients Observed to uNdergo acute Deterioration (RESPOND). METHODS: The research used ethnographic methods, including over 100 hours of observations on surgical units in three English hospitals in order to understand the everyday context of care. Observations focused on the coordination of activities such as handovers and how rescue featured as part of this. We also conducted 27 interviews with a range of clinical and managerial staff and patients. We employed a thematic analysis approach, combined with a theoretically focused implementation coding framework, based on Normalisation Process Theory. RESULTS: We found that organisational infrastructural support in the form of a leadership support and clinical care outreach teams with capacity were enablers in implementing the patient-led escalation system. Barriers to implementation included making changes to professional practice without discussing the value and legitimacy of operationalising patient concerns, and ensuring equity of use. We found that organisational work is needed to overcome patient fears about disrupting social and cultural norms. CONCLUSIONS: This paper reveals the need for infrastructural support to facilitate the implementation of a patient-led escalation system, and leadership support to normalise the everyday process of involving patients and families in escalation. This type of system may not achieve its goals without properly understanding and addressing the concerns of both nurses and patients.

Contextual design requirements for decision-support tools involved in weaning patients from mechanical ventilation in intensive care units.

Weaning patients from ventilation in intensive care units (ICU) is a complex task. There is a growing desire to build decision-support tools to help clinicians during this process, especially those employing Artificial Intelligence (AI). However, tools built for this purpose should fit within and ideally improve the current work environment, to ensure they can successfully integrate into clinical practice. To do so, it is important to identify areas where decision-support tools may aid clinicians, and associated design requirements for such tools. This study analysed the work context surrounding the weaning process from mechanical ventilation in ICU environments, via cognitive task and work domain analyses. In doing so, both what cognitive processes clinicians perform during weaning, and the constraints and affordances of the work environment itself, were described. This study found a number of weaning process tasks where decision-support tools may prove beneficial, and from these a set of contextual design requirements were created. This work benefits researchers interested in creating human-centred decision-support tools for mechanical ventilation that are sensitive to the wider work system.

Digital health and care: emerging from pandemic times.

In 2020, we published an editorial about the massive disruption of health and care services caused by the COVID-19 pandemic and the rapid changes in digital service delivery, artificial intelligence and data sharing that were taking place at the time. Now, 3 years later, we describe how these developments have progressed since, reflect on lessons learnt and consider key challenges and opportunities ahead by reviewing significant developments reported in the literature. As before, the three key areas we consider are digital transformation of services, realising the potential of artificial intelligence and wise data sharing to facilitate learning health systems. We conclude that the field of digital health has rapidly matured during the pandemic, but there are still major sociotechnical, evaluation and trust challenges in the development and deployment of new digital services.

A comparison of machine learning algorithms in predicting COVID-19 prognostics.

ML algorithms are used to develop prognostic and diagnostic models and so to support clinical decision-making. This study uses eight supervised ML algorithms to predict the need for intensive care, intubation, and mortality risk for COVID-19 patients. The study uses two datasets: (1) patient demographics and clinical data (n = 11,712), and (2) patient demographics, clinical data, and blood test results (n = 602) for developing the prediction models, understanding the most significant features, and comparing the performances of eight different ML algorithms. Experimental findings showed that all prognostic prediction models reported an AUROC value of over 0.92, in which extra tree and CatBoost classifiers were often outperformed (AUROC over 0.94). The findings revealed that the features of C-reactive protein, the ratio of lymphocytes, lactic acid, and serum calcium have a substantial impact on COVID-19 prognostic predictions. This study provides evidence of the value of tree-based supervised ML algorithms for predicting prognosis in health care.

Assuring safe artificial intelligence in critical ambulance service response: study protocol.

Introduction: Early recognition of out-of-hospital cardiac arrest (OHCA) by ambulance service call centre operators is important so that cardiopulmonary resuscitation can be delivered immediately, but around 25% of OHCAs are not picked up by call centre operators. An artificial intelligence (AI) system has been developed to support call centre operators in the detection of OHCA. The study aims to (1) explore ambulance service stakeholder perceptions on the safety of OHCA AI decision support in call centres, and (2) develop a clinical safety case for the OHCA AI decision-support system. Methods and analysis: The study will be undertaken within the Welsh Ambulance Service. The study is part research and part service evaluation. The research utilises a qualitative study design based on thematic analysis of interview data. The service evaluation consists of the development of a clinical safety case based on document analysis, analysis of the AI model and its development process and informal interviews with the technology developer. Conclusions: AI presents many opportunities for ambulance services, but safety assurance requirements need to be understood. The ASSIST project will continue to explore and build the body of knowledge in this area.

Am I safe? An Interpretative Phenomenological Analysis of Vulnerability as Experienced by Patients With Complications Following Surgery.

Abdominal surgery carries with it risks of complications. Little is known about patients' experiences of post-surgical deterioration. There is a real need to understand the psychosocial as well as the biological aspects of deterioration in order to improve care and outcomes for patients. Drawing on in-depth interviews with seven abdominal surgery survivors, we present an idiographic account of participants' experiences, situating their contribution to safety within their personal lived experiences and meaning-making of these episodes of deterioration. Our analysis reveals an overarching group experiential theme of vulnerability in relation to participants' experiences of complications after abdominal surgery. This encapsulates the uncertainty of the situation all the participants found themselves in, and the nature and seriousness of their health conditions. The extent of participants' vulnerability is revealed by detailing how they made sense of their experience, how they negotiated feelings of (un)safety drawing on their relationships with family and staff and the legacy of feelings they were left with when their expectations of care (care as imagined) did not meet the reality of their experiences (care as received). The participants' experiences highlight the power imbalance between patients and professionals in terms of whose knowledge counts within the hospital context. The study reveals the potential for epistemic injustice to arise when patients' concerns are ignored or dismissed. Our data has implications for designing strategies to enable escalation of care, both in terms of supporting staff to deliver compassionate care, and in strengthening patient and family involvement in rescue processes.

A systems analysis of the COVID-19 pandemic response in the United Kingdom - Part 1 - The overall context.

The most common reaction to suggesting that we could learn valuable lessons from the way the current pandemic has been/ is being handled, is to discourage the attempt; as it is suggested that it can all be done more accurately and authoritatively after the inevitable Public Inquiry (Slater, 2019). On the other hand, a more constructive approach, is to capture and understand the work that was actually done.This would include normal activities, as well as positive adaptations to challenges and failures that may have occurred. Such an approach aimed at improving what worked, rather than blaming people for what went wrong, has the potential to contribute more successfully to controlling the consequences of the current crisis. Such an approach should thus be aimed at detecting and feeding back lessons from emerging and probably unexpected behaviours and helping to design the system to adapt better to counter the effects. The science and discipline of Human Factors (HF) promotes system resilience. This can be defined as an organisation's ability to adjust its functioning before, during or after significant disturbances (such as a pandemic), enabling adaptation and operation under both anticipated and unanticipated circumstances. A "functional" approach methodology enables the identification of where the system and its various interdependent functions (an activity or set of activities that are required to give a certain output), could be improved and strengthened; if not immediately, at least for the future. Along these lines, suggestions for adding key resilience functions are additionally identified and outlined. The application and insights gained from this functional approach to the 2015 MERS-Cov pandemic in South Korea has been seen as contributing substantially to the effective response to the current crisis in that country (Min, submitted for publication). In this paper, we present an overarching framework for a series of projects that are planned to carry out focussed systems-based analysis to generate learning from key aspects of the COVID-19 pandemic response in the United Kingdom.

The contribution of human factors and ergonomics to the design and delivery of safe future healthcare.

Human factors and ergonomics (HF/E) is concerned with the design of work and work systems. There is an increasing appreciation of the value that HF/E can bring to enhancing the quality and safety of care, but the professionalisation of HF/E in healthcare is still in its infancy. In this paper, we set out a vision for HF/E in healthcare based on the work of the Chartered Institute of Ergonomics and Human Factors (CIEHF), which is the professional body for HF/E in the UK. We consider the contribution of HF/E in design, in digital transformation, in organisational learning and during COVID-19.

Will the COVID-19 pandemic transform infection prevention and control in surgery? Seeking leverage points for organizational learning.

BACKGROUND: In response to the coronavirus disease of 2019 (COVID-19) pandemic, healthcare systems worldwide have stepped up their infection prevention and control efforts in order to reduce the spread of the infection. Behaviours, such as hand hygiene, screening and cohorting of patients, and the appropriate use of antibiotics have long been recommended in surgery, but their implementation has often been patchy. METHODS: The current crisis presents an opportunity to learn about how to improve infection prevention and control and surveillance (IPCS) behaviours. The improvements made were mainly informal, quick and stemming from the frontline rather than originating from formal organizational structures. The adaptations made and the expertise acquired have the potential for triggering deeper learning and to create enduring improvements in the routine identification and management of infections relating to surgery. RESULTS: This paper aims to illustrate how adopting a human factors and ergonomics perspective can provide insights into how clinical work systems have been adapted and reconfigured in order to keep patients and staff safe. CONCLUSION: For achieving sustainable change in IPCS practices in surgery during COVID-19 and beyond we need to enhance organizational learning potentials.

Human factors/ergonomics to support the design and testing of rapidly manufactured ventilators in the UK during the COVID-19 pandemic.

BACKGROUND: This paper describes a rapid response project from the Chartered Institute of Ergonomics & Human Factors (CIEHF) to support the design, development, usability testing and operation of new ventilators as part of the UK response during the COVID-19 pandemic. METHOD: A five-step approach was taken to (1) assess the COVID-19 situation and decide to formulate a response; (2) mobilise and coordinate Human Factors/Ergonomics (HFE) specialists; (3) ideate, with HFE specialists collaborating to identify, analyse the issues and opportunities, and develop strategies, plans and processes; (4) generate outputs and solutions; and (5) respond to the COVID-19 situation via targeted support and guidance. RESULTS: The response for the rapidly manufactured ventilator systems (RMVS) has been used to influence both strategy and practice to address concerns about changing safety standards and the detailed design procedure with RMVS manufacturers. CONCLUSION: The documents are part of a wider collection of HFE advice which is available on the CIEHF COVID-19 website (https://covid19.ergonomics.org.uk/).

Outcome Measures Reported in Published Clinical Research Studies in Craniosynostosis: A Systematic Review.

CONTEXT: The fair comparison of treatment interventions for craniosynostosis across different studies is expected to be impaired by incomplete reporting and the use of inconsistent outcomes. OBJECTIVE: This review assessed the outcomes currently reported in studies of craniosynostosis, and whether these outcomes are formally defined and prespecified in the study methods. DATA SOURCES, SEARCH TERMS, AND STUDY SELECTION: Studies were sourced via an electronic, multi-database literature search for "craniosynostosis." All primary, interventional research studies published from 2011 to 2015 were reviewed. DATA EXTRACTION: Two independent researchers assessed each study for inclusion and performed the data extraction. For each study, data were extracted on the individual outcomes reported, and whether these outcomes were defined and prespecified in the methods. DATA SYNTHESIS AND RESULTS: Of 1027 studies screened, 240 were included and proceeded to data extraction. These studies included 18,365 patients.2192 separate outcomes were reported. Of these, 851 outcomes (38.8%) were clearly defined, 1394 (63.6%) were prespecified in the study methods."Clinical and functional" was the most commonly reported outcome theme (900 outcomes, 41.1%), and "patient-reported" outcomes the least (7 outcomes, 0.3%)."Duration of surgery" was the most commonly reported single outcome (reported 80 times). "Cranial index" was the most variably defined outcome (18 different definitions used). CONCLUSION: The outcomes reported following treatment interventions for craniosynostosis are incompletely and variably defined. Improving definitions for these outcomes may aid comparison of different management strategies and improve craniosynostosis care. Suboptimal prespecification of these outcomes in the study methods implied that outcome reporting bias cannot be excluded.

Using FRAM to explore sources of performance variability in intravenous infusion administration in ICU: A non-normative approach to systems contradictions.

Systems contradictions present challenges that need to be effectively managed, e.g. due to conflicting rules and advice, goal conflicts, and mismatches between demand and capacity. We apply FRAM (Functional Resonance Analysis Method) to intravenous infusion practices in an intensive care unit (ICU) to explore how tensions and contradictions are managed by people. A multi-disciplinary team including individuals from nursing, medical, pharmacy, safety, IT and human factors backgrounds contributed to this analysis. A FRAM model investigation resulting in seven functional areas are described. A tabular analysis highlights significant areas of performance variability, e.g. administering medication before a prescription, prioritising drugs, different degrees of double checking and using sites showing early signs of infection for intravenous access. Our FRAM analysis has been non-normative: performance variability is not necessarily wanted or unwanted, it is merely necessary where system contradictions cannot be easily resolved and so adaptive capacity is required to cope.