Ward-Based Care of Patients Following Discharge from Critical Care: a Mixed Methods Study

Background Historically, critical care research and policy focused on survival to intensive care unit (ICU) discharge. However, as critical care innovation has progressed, emphasis has shifted to the quality of survival beyond ICU discharge. There is significant focus on the long-term rehabilitation needs of patients who have required critical care, but very little evidence exists about the period between ICU and hospital discharge. Care during this time should be focused on recovery and rehabilitation, crucial in limiting the long-term morbidity associated with critical illness. Every year in the UK approximately 163,000 patients are admitted to an ICU. Despite patients being assessed as ready for discharge from ICU, having either recovered from the acute phase of critical illness or transitioned to end-of-life care, over 8,000 of the 139,000 discharged to a ward die before hospital discharge. Design This study aimed to explore the post-ICU in-hospital care period, answering the research question: What challenges and problems in care exist in the management of post-ICU ward patients? A convergent parallel exploratory mixed methods design was selected, integrating two methods: retrospective case record review (RCRR), including initial overview reviews and further in-depth analysis of the records of patients who death was judged probably avoidable, and survivors; and semi-structured interviews. The paper and electronic medical records of 300 patients discharged across three UK ICUs and who subsequently died before hospital discharge were reviewed using an established RCRR methodology. For twenty patients who died their death was judged as probably avoidable and subject to further in-depth review, together with the records of twenty survivors, for comparison. The 40 in-depth reviews examined problems in care delivery and underlying contributory human factors. In parallel, patients (n= 18), family members (n= 8) and staff (n= 30) (total n=56) were interviewed about their experiences of post-ICU ward care, with the aim of identifying challenges in care delivery and potential improvements. Results Primary data were integrated to develop an interdependent multi-layered description of post-ICU ward care, identifying challenges to care delivery at the patient, ward and organisational level. At the patient level, data were combined which revealed a clear picture of post-ICU patients as dependent, vulnerable and complex, contributing to the concept of post-ICU patients as other than general ward patients – having different care needs. These differences posed challenges to care delivery due to the constraints of workload, skill mix and leadership which were identified at the ward level and emphasised the otherness of post-ICU ward patients. Overarching characteristics at the organisational level, such as limitations in out-of-hours care provision, training and resources constrained the ability of the ward to meet the high demands of this complex group of patients. The characteristics identified at each level had the potential to impede continuity of care between ICU and the ward which had a profound impact on both patients and staff resulting in fear and anxiety. Critical Care Outreach Teams were identified as having a key role in supporting wards to manage patients transferred from ICU, although competing priorities can lead to limited capacity to offer comprehensive follow-up of post-ICU patients. Conclusion This study has critically examined the challenges faced by patients and staff following transfer from ICU to the ward. Post-ICU patients were demonstrated to be perceived as other than, or somehow different from, general ward patients, with the current system of care struggling to meet their needs. The findings of this study will inform the development of a complex intervention to improve care delivery for this complex, vulnerable patient cohort. This study was conducted prior to the 2020 COVID-19 pandemic, but offers insight into the current challenges in managing the significant increase in patients being discharged from ICU

Reporting guideline for the early-stage clinical evaluation of decision support systems driven by artificial intelligence: DECIDE-AI

A growing number of artificial intelligence (AI)-based clinical decision support systems are showing promising performance in preclinical, in silico evaluation, but few have yet demonstrated real benefit to patient care. Early-stage clinical evaluation is important to assess an AI system's actual clinical performance at small scale, ensure its safety, evaluate the human factors surrounding its use and pave the way to further large-scale trials. However, the reporting of these early studies remains inadequate. The present statement provides a multi-stakeholder, consensus-based reporting guideline for the Developmental and Exploratory Clinical Investigations of DEcision support systems driven by Artificial Intelligence (DECIDE-AI). We conducted a two-round, modified Delphi process to collect and analyze expert opinion on the reporting of early clinical evaluation of AI systems. Experts were recruited from 20 pre-defined stakeholder categories. The final composition and wording of the guideline was determined at a virtual consensus meeting. The checklist and the Explanation & Elaboration (E&E) sections were refined based on feedback from a qualitative evaluation process. In total, 123 experts participated in the first round of Delphi, 138 in the second round, 16 in the consensus meeting and 16 in the qualitative evaluation. The DECIDE-AI reporting guideline comprises 17 AI-specific reporting items (made of 28 subitems) and ten generic reporting items, with an E&E paragraph provided for each. Through consultation and consensus with a range of stakeholders, we developed a guideline comprising key items that should be reported in early-stage clinical studies of AI-based decision support systems in healthcare. By providing an actionable checklist of minimal reporting items, the DECIDE-AI guideline will facilitate the appraisal of these studies and replicability of their findings