An Exploration of Student Perception Toward Interprofessional High-Fidelity Clinical Simulation.

OBJECTIVES: Interprofessional education is recognized for its potential for collaboration and teamwork, reflecting clinical practice; however, existing literature for simulation-based interprofessional education does not include Physician Associate (PA) students. This initiative aimed to explore the students' perception of interprofessional clinical simulation for PA students and allied health professional (AHP) students as part of our program development. METHODS: A high-fidelity simulation session was designed and conducted for volunteering students from the PA, paramedic science, and physiotherapy courses. We used a mixed-method electronic questionnaire consisting of 15 statements rated on a numerical rating scale (0-5) and four open-ended questions with unlimited free-text responses to explore student perceptions. Inductive thematic analysis was used for qualitative analysis. The session design was underpinned by Allport's (intergroup) contact hypothesis with an emphasis on mutual intergroup differentiation. RESULTS: Forty-six students participated in the simulation teaching, with 48% (n = 22) providing feedback. Overall student perception was mainly positive toward the interprofessional simulation; however, some barriers to learning were recognized. Based on the evaluation of our initiative and existing literature, we propose 5 top tips to promote an effective learning experience for students. (1) Understand the importance of interprofessional collaboration. (2) Establish clear roles. (3) Plan the scenarios in advance. (4) Maintain equal status between groups. (5) Provide clear instructions and expectations. CONCLUSION: To our knowledge, this is the first study of high-fidelity interprofessional simulation involving PA and AHP students. We successfully explored student perception which highlighted aspects that can impact learning. This pilot study demonstrated that interprofessional simulation is a feasible and acceptable form of learning for our students and highlighted how to improve future interprofessional simulation teaching sessions.

Evaluation of venous thromboembolism risk assessment models for hospital inpatients: the VTEAM evidence synthesis.

Background: Pharmacological prophylaxis during hospital admission can reduce the risk of acquired blood clots (venous thromboembolism) but may cause complications, such as bleeding. Using a risk assessment model to predict the risk of blood clots could facilitate selection of patients for prophylaxis and optimise the balance of benefits, risks and costs. Objectives: We aimed to identify validated risk assessment models and estimate their prognostic accuracy, evaluate the cost-effectiveness of different strategies for selecting hospitalised patients for prophylaxis, assess the feasibility of using efficient research methods and estimate key parameters for future research. Design: We undertook a systematic review, decision-analytic modelling and observational cohort study conducted in accordance with Enhancing the QUAlity and Transparency Of health Research (EQUATOR) guidelines. Setting: NHS hospitals, with primary data collection at four sites. Participants: Medical and surgical hospital inpatients, excluding paediatric, critical care and pregnancy-related admissions. Interventions: Prophylaxis for all patients, none and according to selected risk assessment models. Main outcome measures: Model accuracy for predicting blood clots, lifetime costs and quality-adjusted life-years associated with alternative strategies, accuracy of efficient methods for identifying key outcomes and proportion of inpatients recommended prophylaxis using different models. Results: We identified 24 validated risk assessment models, but low-quality heterogeneous data suggested weak accuracy for prediction of blood clots and generally high risk of bias in all studies. Decision-analytic modelling showed that pharmacological prophylaxis for all eligible is generally more cost-effective than model-based strategies for both medical and surgical inpatients, when valuing a quality-adjusted life-year at £20,000. The findings were more sensitive to uncertainties in the surgical population; strategies using risk assessment models were more cost-effective if the model was assumed to have a very high sensitivity, or the long-term risks of post-thrombotic complications were lower. Efficient methods using routine data did not accurately identify blood clots or bleeding events and several pre-specified feasibility criteria were not met. Theoretical prophylaxis rates across an inpatient cohort based on existing risk assessment models ranged from 13% to 91%. Limitations: Existing studies may underestimate the accuracy of risk assessment models, leading to underestimation of their cost-effectiveness. The cost-effectiveness findings do not apply to patients with an increased risk of bleeding. Mechanical thromboprophylaxis options were excluded from the modelling. Primary data collection was predominately retrospective, risking case ascertainment bias. Conclusions: Thromboprophylaxis for all patients appears to be generally more cost-effective than using a risk assessment model, in hospitalised patients at low risk of bleeding. To be cost-effective, any risk assessment model would need to be highly sensitive. Current evidence on risk assessment models is at high risk of bias and our findings should be interpreted in this context. We were unable to demonstrate the feasibility of using efficient methods to accurately detect relevant outcomes for future research. Future work: Further research should evaluate routine prophylaxis strategies for all eligible hospitalised patients. Models that could accurately identify individuals at very low risk of blood clots (who could discontinue prophylaxis) warrant further evaluation. Study registration: This study is registered as PROSPERO CRD42020165778 and Researchregistry5216. Funding: This award was funded by the National Institute for Health and Care Research (NIHR) Health Technology Assessment programme (NIHR award ref: NIHR127454) and will be published in full in Health Technology Assessment; Vol. 28, No. 20. See the NIHR Funding and Awards website for further award information.

People who are admitted to hospital are at risk of blood clots that can cause serious illness or death. Patients are often given low doses of blood-thinning drugs to reduce this risk. However, these drugs can cause side effects, such as bleeding. Hospitals currently use complex risk assessment models (risk scores, which usually include patient, disease, mobility and intervention factors) to determine the individual risk of blood clots and identify people most likely to benefit from blood-thinning drugs. There are a lot of different risk scores and we do not know which one is best. We also do not know how these scores compare to each other or whether using scores to decide who should get blood-thinning drugs provides good value for money to the NHS. We reviewed all previous studies of risk scores. We found that they did not predict blood clots very well and we could not recommend one score over another. We then created a mathematical model to simulate the use of blood-thinning drugs in people admitted to hospital. The model suggested that giving blood-thinning drugs to everyone who could have them would probably provide the best value for money, in medical patients. Our findings were the same, but less certain, for surgical patients. We also collected information from four NHS hospitals to explore possibilities for future research. Our work showed that routinely collected electronic data on blood clots and bleeding events is not very accurate and that using different scores could result in variable use of blood-thinning medications. Our findings suggest that it may be better value to the NHS and better for patients if we were to offer blood-thinning medications to everyone on admission to hospital, without using any risk score. However, this approach needs further research to ensure it is safe and effective. Such research would not be able to rely on routine electronic data to identify blood clots or bleeding events, in isolation.

The 'bIUreactor': An Open-Source 3D Tissue Research Platform.

We developed the open-source bIUreactor research platform for studying 3D structured tissues. The versatile and modular platform allows a researcher to generate 3D tissues, culture them with oxygenated perfusion, and provide cyclic loading, all in their own lab (in laboratorium) for an all in cost of $8,000 including 3D printer, printing resin, and electronics. We achieved this by applying a design philosophy that leverages 3D printing, open-source software and hardware, and practical techniques to produce the following: 1. perfusible 3D tissues, 2. a bioreactor chamber for tissue culture, 3. a module for applying cyclic compression, 4. a peristaltic pump for providing oxygenated perfusion to 3D tissues, 5. motor control units, and 6. open-source code for running the control units. By making it widely available for researchers to investigate 3D tissue models and easy for them to use, we intend for the bIUreactor to democratize 3D tissue research, therefore increasing the pace and scale of biomedical research discoveries using 3D tissue models.

Effectiveness and cost effectiveness of pharmacological thromboprophylaxis for medical inpatients: decision analysis modelling study.

Objective: To determine the balance of costs, risks, and benefits for different thromboprophylaxis strategies for medical patients during hospital admission. Design: Decision analysis modelling study. Setting: NHS hospitals in England. Population: Eligible adult medical inpatients, excluding patients in critical care and pregnant women. Interventions: Pharmacological thromboprophylaxis (low molecular weight heparin) for all medical inpatients, thromboprophylaxis for none, and thromboprophylaxis given to higher risk inpatients according to risk assessment models (Padua, Caprini, IMPROVE, Intermountain, Kucher, Geneva, and Rothberg) previously validated in medical cohorts. Main outcome measures: Lifetime costs and quality adjusted life years (QALYs). Costs were assessed from the perspective of the NHS and Personal Social Services in England. Other outcomes assessed were incidence and treatment of venous thromboembolism, major bleeds including intracranial haemorrhage, chronic thromboembolic complications, and overall survival. Results: Offering thromboprophylaxis to all medical inpatients had a high probability (>99%) of being the most cost effective strategy (at a threshold of £20 000 (€23 440; $25 270) per QALY) in the probabilistic sensitivity analysis, when applying performance data from the Padua risk assessment model, which was typical of that observed across several risk assessment models in a medical inpatient cohort. Thromboprophylaxis for all medical inpatients was estimated to result in 0.0552 additional QALYs (95% credible interval 0.0209 to 0.1111) while generating cost savings of £28.44 (-£47 to £105) compared with thromboprophylaxis for none. No other risk assessment model was more cost effective than thromboprophylaxis for all medical inpatients when assessed in deterministic analysis. Risk based thromboprophylaxis was found to have a high (76.6%) probability of being the most cost effective strategy only when assuming a risk assessment model with very high sensitivity is available (sensitivity 99.9% and specificity 23.7% v base case sensitivity 49.3% and specificity 73.0%). Conclusions: Offering pharmacological thromboprophylaxis to all eligible medical inpatients appears to be the most cost effective strategy. To be cost effective, any risk assessment model would need to have a very high sensitivity resulting in widespread thromboprophylaxis in all patients except those at the very lowest risk, who could potentially avoid prophylactic anticoagulation during their hospital stay.

Can acutely ill patients predict their outcomes? A scoping review.

INTRODUCTION: The full impact of an acute illness on subsequent health is seldom explicitly discussed with patients. Patients' estimates of their likely prognosis have been explored in chronic care settings and can contribute to the improvement of clinical outcomes and patient satisfaction. This scoping review aimed to identify studies of acutely ill patients' estimates of their outcomes and potential benefits for their care. METHODS: A search was conducted in PubMed, Embase, Web of Science and Google Scholar, using terms related to prognostication and acute care. After removal of duplicates, all articles were assessed for relevance by six investigator pairs; disagreements were resolved by a third investigator. Risk of bias was assessed according to the Cochrane Handbook for Systematic Reviews of Interventions. RESULTS: Our search identified 3265 articles, of which 10 were included. The methods of assessing self-prognostication were very heterogeneous. Patients seem to be able to predict their need for hospital admission in certain settings, but not their length of stay. The severity of their symptoms and the burden of their disease are often overestimated or underestimated by patients. Patients with severe health conditions and their relatives tend to be overoptimistic about the likely outcome. CONCLUSION: The understanding of acutely ill patients of their likely outcomes and benefits of treatment has not been adequately studied and is a major knowledge gap. Limited published literature suggests patients may be able to predict their need for hospital admission. Illness perception may influence help-seeking behaviour, speed of recovery and subsequent quality of life. Knowledge of patients' self-prognosis may enhance communication between patients and their physicians, which improves patient-centred care.

Predicting acute clinical deterioration with interpretable machine learning to support emergency care decision making.

The emergency department (ED) is a fast-paced environment responsible for large volumes of patients with varied disease acuity. Operational pressures on EDs are increasing, which creates the imperative to efficiently identify patients at imminent risk of acute deterioration. The aim of this study is to systematically compare the performance of machine learning algorithms based on logistic regression, gradient boosted decision trees, and support vector machines for predicting imminent clinical deterioration for patients based on cross-sectional patient data extracted from electronic patient records (EPR) at the point of entry to the hospital. We apply state-of-the-art machine learning methods to predict early patient deterioration, based on their first recorded vital signs, observations, laboratory results, and other predictors documented in the EPR. Clinical deterioration in this study is measured by in-hospital mortality and/or admission to critical care. We build on prior work by incorporating interpretable machine learning and fairness-aware modelling, and use a dataset comprising 118, 886 unplanned admissions to Salford Royal Hospital, UK, to systematically compare model variations for predicting mortality and critical care utilisation within 24 hours of admission. We compare model performance to the National Early Warning Score 2 (NEWS2) and yield up to a 0.366 increase in average precision, up to a [Formula: see text] reduction in daily alert rate, and a median 0.599 reduction in differential bias amplification across the protected demographics of age and sex. We use Shapely Additive exPlanations to justify the models' outputs, verify that the captured data associations align with domain knowledge, and pair predictions with the causal context of each patient's most influential characteristics. Introducing our modelling to clinical practice has the potential to reduce alert fatigue and identify high-risk patients with a lower NEWS2 that might be missed currently, but further work is needed to trial the models in clinical practice. We encourage future research to follow a systematised approach to data-driven risk modelling to obtain clinically applicable support tools.

Performance of admission pathways within acute medicine services: Analysis from the Society for Acute Medicine Benchmarking Audit 2022 and comparison with performance 2019 - 2021.

Urgent and emergency care services face increasing pressure, impacting patient care. We evaluated the performance of acute medicine services, assessing clinical quality indicators for unplanned medical admissions to acute hospital services. 152 acute UK hospital services accepting unplanned admissions to acute and general internal medicine completed a day-of-care survey incorporating organisational structure questionnaire and patient-level data over a pre-defined 24-hour period in June 2022. Clinical quality indicators were: Early Warning Score (EWS) measurement within 30 min of hospital arrival; clinician assessment within 4 h; assessment by consultant physician within 6 h (daytime) or 14 h (night-time). Results were compared with 2019, 2020, 2021. 7293 sequential patients were included (and compared with 19,817 patients across 2019-2021). In 2022, 69% of patients (95%CI 67.7-69.9%) had an EWS documented within 30 min. 79% of patients (95%CI 77.8-79.7%) were reviewed by a clinical decision maker within 4 h of hospital arrival. Patients assessed in Same Day Emergency Care services were more likely to meet this target than those assessed in Acute Medical Units or Emergency Departments (OR 2.4, 95%CI 2.02-2.87, p<0.001). Overall, 50% of patients received consultant physician review within the target time (3065/6161, 95%CI 48.5-51.0%); performance varied with time of arrival and location of initial assessment. Performance against all three clinical quality indicators was lower than 2019, 2020 and 2021 (p<0.001 for all). Performance against all quality indicators within acute medicine services is deteriorating. However, performance in Same Day Emergency Care Units is greater than in Acute Medical Units or Emergency Departments.

Decision-analysis modeling of effectiveness and cost-effectiveness of pharmacologic thromboprophylaxis for surgical inpatients using variable risk assessment models or other strategies.

BACKGROUND: Surgical inpatients are at a risk of venous thromboembolism (VTE), which can be life-threatening or result in chronic complications. Thromboprophylaxis reduces the VTE risk but incurs costs and may increase bleeding risk. Risk assessment models (RAMs) are currently used to target thromboprophylaxis at high-risk patients. OBJECTIVES: To determine the balance of cost, risk, and benefit for different thromboprophylaxis strategies in adult surgical inpatients, excluding patients who underwent major orthopedic surgery or were under critical care and pregnant women. METHODS: Decision analytic modeling was performed to estimate the following outcomes for alternative thromboprophylaxis strategies: thromboprophylaxis usage; VTE incidence and treatment; major bleeding; chronic thromboembolic complications; and overall survival. Strategies compared were as follows: no thromboprophylaxis; thromboprophylaxis for all; and thromboprophylaxis given according to RAMs (Caprini and Pannucci). Thromboprophylaxis is assumed to be given for the duration of hospitalization. The model evaluates lifetime costs and quality-adjusted life-years (QALYs) within England's health and social care services. RESULTS: Thromboprophylaxis for all surgical inpatients had a 70% probability of being the most cost-effective strategy (at a £20 000 per QALY threshold). RAM-based prophylaxis would be the most cost-effective strategy if a RAM with a higher sensitivity (99.9%) were available for surgical inpatients. QALY gains were mainly due to reduced postthrombotic complications. The optimal strategy was sensitive to several other factors such as the risk of VTE, bleeding and postthrombotic syndrome, duration of prophylaxis, and patient age. CONCLUSION: Thromboprophylaxis for all eligible surgical inpatients seemed to be the most cost-effective strategy. Default recommendations for pharmacologic thromboprophylaxis, with the potential to opt-out, may be superior to a complex risk-based opt-in approach.

National Early Warning Scores Following Emergency Hospital Transfer: Implications for Care Home Residents.

OBJECTIVE: Care home residents have high rates of hospital admission. The UK National Early Warning Score (NEWS2) standardizes the secondary care response to acute illness. However, the ability of NEWS2 to predict adverse health outcomes specifically for care home residents is unknown. This study explored the relationship between NEWS2 on admission to hospital and resident outcome 7 days later. DESIGN: Repeated cross-sectional study. SETTING AND PARTICIPANTS: Data on UK care home residents admitted to 160 hospitals in two 24-hour periods (2019 and 2020). METHOD: Chi-squared and Kruskal-Wallis tests, and multinomial regression were used to explore the association between low (score ≤2), intermediate (3-4), high (5-6), and critically high (≥7) NEWS2 on admission and each of the following: discharge on day of admission, admission and discharge within 7 days, prolonged hospital admission (>7 days), and death. RESULTS: From 665 resident admissions across 160 hospital sites, NEWS2 was low for 54%, intermediate for 18%, high for 13%, and critically high for 16%. The 7-day outcome was 10% same-day discharge, 47% admitted and subsequently discharged, 34% remained inpatients, and 8% died. There is a significant association between NEWS2 and these outcomes (P < .001). Compared with those with low NEWS2, residents with high and critically high NEWS2 had 3.6 and 9.5 times increased risk of prolonged hospitalization [relative risk ratio (RRR) 3.56; 95% CI 1.02-12.37; RRR 9.47; CI 2.20-40.67], respectively. The risk of death was approximately 14 times higher for residents with high NEWS2 (RRR 13.62; CI 3.17-58.49) and 54 times higher (RRR 53.50; CI 11.03-259.54) for critically high NEWS2. CONCLUSION AND IMPLICATIONS: Higher NEWS2 measurements on admission are associated with an increased risk of hospitalization up to 7 days duration, prolonged admission, and mortality for care home residents. NEWS2 may have a role as an adjunct to acute care decision making for hospitalized residents.

Using Vital Signs to Place Acutely Ill Patients Quickly and Easily into Clinically Helpful Pathophysiologic Categories: Derivation and Validation of Eight Pathophysiologic Categories in Two Distinct Patient Populations of Acutely Ill Patients.

BACKGROUND: Early warning scores reliably identify patients at risk of imminent death, but do not provide insight into what may be wrong with the patient or what to do about it. OBJECTIVE: Our aim was to explore whether the Shock Index (SI), pulse pressure (PP), and ROX Index can place acutely ill medical patients in pathophysiologic categories that could indicate the interventions required. METHODS: A retrospective post-hoc analysis of previously obtained and reported clinical data for 45,784 acutely ill medical patients admitted to a major regional referral Canadian hospital between 2005 and 2010 and validated on 107,546 emergency admissions to four Dutch hospitals between 2017 and 2022. RESULTS: SI, PP, and ROX values divided patients into eight mutually exclusive physiologic categories. Mortality was highest in patient categories that included ROX Index value < 22, and a ROX Index value < 22 multiplied the risk of any other abnormality. Patients with a ROX Index value < 22, PP < 42 mm Hg, and SI > 0.7 had the highest mortality and accounted for 40% of deaths within 24 h of admission, whereas patients with a PP ≥ 42 mm Hg, SI ≤ 0.7, and ROX Index value ≥ 22 had the lowest risk of death. These results were the same in both the Canadian and Dutch patient cohorts. CONCLUSIONS: SI, PP, and ROX Index values can place acutely ill medical patients into eight mutually exclusive pathophysiologic categories with different mortality rates. Future studies will assess the interventions needed by these categories and their value in guiding treatment and disposition decisions.

The United Kingdom's National Early Warning Score: should everyone use it? A narrative review.

This review critiques the benefits and drawbacks of the United Kingdom's National Early Warning Score (NEWS). Potential developments for the future are considered, as well as the role for NEWS in an emergency department (ED). The ability of NEWS to predict death within 24 h has been well validated in multiple clinical settings. It provides a common language for the assessment of clinical severity and can be used to trigger clinical interventions. However, it should not be used as the only metric for risk stratification as its ability to predict mortality beyond 24 h is not reliable and greatly influenced by other factors. The main drawbacks of NEWS are that measuring it requires trained professionals, it is time consuming and prone to calculation error. NEWS is recommended for use in acute UK hospitals, where it is linked to an escalation policy that reflects postgraduate experience; patients with lower NEWS are first assessed by a junior clinician and those with higher scores by more senior staff. This policy was based on expert opinion that did not consider workload implications. Nevertheless, its implementation has been shown to improve the efficient recording of vital signs. How and who should respond to different NEWS levels is uncertain and may vary according to the clinical setting and resources available. In the ED, simple triage scores which are quicker and easier to use may be more appropriate determinants of acuity. However, any alternative to NEWS should be easier and cheaper to use and provide evidence of outcome improvement.

The impact of frailty and geriatric syndromes on metrics of acute care performance: results of a national day of care survey.

Background: Frailty is associated with a range of adverse clinical outcomes in the acute hospital setting. We sought to determine whether frailty and related factors affected clinical processes such as time to assessment during emergency hospital admission within the National Health Service (NHS) in the UK. Methods: The Society for Acute Medicine Benchmarking Audit (SAMBA) is an annual cross-sectional day of care survey. SAMBA 2022 was conducted on Thursday 23rd June 2022. We assessed whether the Clinical Frailty Scale (CFS) and presence of a geriatric syndrome affected performance against nationally recognised clinical quality indicators based on time to initial assessment and time to consultant review. CFS was graded into robust (CFS1-3), mild (CFS 4-5), moderate (CFS 6), severe (CFS7-8) and terminal illness (CFS 9). Plausible values were created for missing variables using multi-level multiple imputation. The association was described using mixed effect generalised linear models adjusting for initial National Early Warning Score 2 (NEWS2) and time of arrival. Findings: A total of 152 hospitals provided patient level data relating to 7248 emergency medical admissions. Patients with mild, moderate and severe frailty were less likely to be assessed within 4 h of arrival (adjusted OR, mild 0.79, 95% CI 0.68-0.96, moderate 0.67 95% CI 0.53-0.84, severe, 0.75 95% CI 0.58-0.96, terminally ill 0.59 95% CI 0.23-1.43) and less likely to be achieve the clinical quality indicator for consultant review (adjusted OR, mild 0.69 95% CI 0.58-0.83, moderate 0.55 95% CI 0.44-0.70, severe 0.54 95% CI 0.41-0.69, terminally ill 0.76 95% CI 0.42-1.5). Patients with geriatric syndromes were also less likely to be assessed within 4 h of arrival (adjusted OR 0.66 95% CI 0.56-0.76) or by a consultant within the recommended time frame (adjusted OR 0.45 95% CI 0.39-0.51). The difference was partially explained by differential use of SDEC pathways. Sub-group analysis of 5148 patients assessed outside of SDEC areas demonstrated patients with geriatric syndromes (adjusted OR 0.71, 95% CI 0.60-0.83), but not frailty defined by CFS were less likely to be assessed within 4 h of arrival. Moderate and severe frailty and the presence of a geriatric syndrome were associated with a decreased likelihood of achieving the consultant review standard (moderate, adjusted OR 0.75, 95% CI 0.59-0.94, severe adjusted OR 0.75 95% CI 0.58-0.96, geriatric syndrome adjusted OR 0.59, 95% CI 0.50-0.69). Interpretation: Frailty is associated with delayed clinical assessment. This association may suggest a systemic issue with clinical prioritisation, with important implications for acute care policy. Funding: The database for SAMBA is funded by the Society for Acute Medicine.

Guest Editorial - Curry clubs, uncertain times, and social media.

COVID-19, the eternal hospital winter, heatwaves, global warming, energy costs, inflation, and an unnecessary war. We truly do live in uncertain times. That said, we would wager our grandparents said the same thing. What gets us through is family, friends and out shared communities, including acute medicine. Which brings us to this edition of the journal, where many excellent articles will hopefully distract our reader from all the doom and gloom, and instead light up your grey cells.

Are changes in vital signs, mobility, and mental status while in hospital measures of the quality of care?

INTRODUCTION: Little is known of the changes in patients' health condition while in hospital in low-resource settings. The aim of this exploratory study is to examine dependency of patients on hospital admission and discharge in a low-resource sub-Saharan hospital. METHODS: We carried out a retrospective observational study of changes in the health condition, as reflected by their mental status, mobility and vital signs, of 5,888 consecutive patients between hospital admission and discharge. RESULTS: Mental status, mobility and vital signs were normal in 25% of patients on hospital admission and 30% of patients at discharge. Although very few patients with normal mental status, mobility and vital signs on admission died in hospital, the condition of 40% of them deteriorated. CONCLUSION: No comparative data on changes in health condition between hospital admission and discharge have been published. Our proposed health condition categories identify changes that may matter most to patients and should be considered as a standard metric of hospital care.

Nurse staffing levels within acute care: results of a national day of care survey.

INTRODUCTION: The relationship between nurse staffing levels and patient safety is well recognised. Inadequate provision of nursing staff is associated with increased medical error, as well as higher morbidity and mortality. Defining what constitutes safe nurse staffing levels is complex. A range of guidance and planning tools are available to inform staffing decisions. The Society for Acute Medicine (SAM) recommend a 'nurse-to-bed'ratio of greater than 1:6. Whether this standard accurately reflects the pattern and intensity of work on the Acute Medical Unit (AMU) is unclear. METHODS: Nurse staffing levels in AMUs were explored using the Society for Acute Medicine Benchmarking Audit 2019 (SAMBA19). Data from 122 acute hospitals were analysed. Nurse-to-bed ratios were calculated and compared. Estimates of the total nursing time available within the acute care system were compared to estimates of the time required to perform nursing activities. RESULTS: The total number of AMU beds across all 122 units was 4997. The mean daytime nurse-to-bed ratio was 1:4.3 and the mean night time nurse-to-bed ratio was 1:5.2. The SAM standard of a nurse to bed ratio of greater than 1:6 was achieved in 99 units (81.9%) during daytime hours and achieved by 74 units (60.6%) at night. The estimated time required to deliver direct clinical care was 35,698 h. A deficit of 4128 h (11.5% of time required) was estimated, representing the time difference between the total number of nursing hours available with current staffing and the estimated time needed for direct clinical care across all participating units. CONCLUSION: This UK-wide study suggests a significant proportion of AMUs do not meet the recommenced SAM staffing levels, particularly at night. A difference was observed between the total number of nursing hours within the acute care system and the estimated time required to perform direct nursing activities. This suggests a workforce shortage of nurses within acute care at the system level.

An atypical presentation of orthostatic hypotension and falls in an older adult.

Introduction: Falls are a significant cause of morbidity and mortality in older adults. Orthostatic hypotension (OH) is very common in this cohort of patients and is a significant risk for falls and associated injuries. We present the case of an 89-year-old female who fell at home, witnessed by her husband. OH was identified during the clinical assessment and considered to be the predominant contributing factor, although the clinical presentation was not associated with classical symptoms. Case presentation: The patient lost balance while turning away from the kitchen sink; she noted some instability due to a complaint of generalised weakness in both of her legs. No acute medical illness or traumatic injury was identified. A comprehensive history was obtained that identified multiple intrinsic and extrinsic risk factors for falling. The cardiovascular examination was unremarkable except for OH, with a pronounced reduction in systolic blood pressure of 34 mmHg at the three-minute interval and which reproduced some generalised weaknesses in the patient's legs and slight instability. Although classical OH symptoms were not identified, this was considered to be the predominant factor contributing to the fall. A series of recommendations was made to primary and community-based care teams based upon a rapid holistic review; this included a recommendation to review the patient's dual antihypertensive therapy. Conclusion: It is widely known that OH is a significant risk factor for falls, but asymptomatic or atypical presentations can make diagnosis challenging. Using the correct technique to measure a lying and standing blood pressure, as defined by the Royal College of Physicians, is crucial for accurate diagnosis and subsequent management. Ambulance clinicians are ideally placed to undertake this quick and non-invasive assessment to identify OH in patients that have fallen.

A Feasibility Study of the My Strengths Training for Life™ (MST4Life™) Program for Young People Experiencing Homelessness.

My Strengths Training for Life™ (MST4Life™) is a positive youth development program for improving wellbeing and social inclusion in young people experiencing homelessness. MST4Life™ addresses a gap in strengths-based programs aimed at promoting healthy and optimal development in vulnerable older adolescents/emerging adults. The program was co-developed with a UK housing service as part of a long-term (>8 years) community−academic partnership. This mixed-methods study describes a key step in developing and evaluating the program: exploring its feasibility and acceptability with 15 homeless young people (Mean age = 19.99 years, SD = 2.42; 60% male, 40% female). Participants experienced 8 weekly sessions within their local community, followed by a 4-day/3-night residential outdoor adventure trip. In addition to their attendance records, the viewpoints of the participants and their support workers were obtained using diary rooms and focus groups. Feasibility was indicated via the themes of attendance, engagement, and reaction. The findings suggested that young people enjoyed and perceived a need for the program, that they considered the program and its evaluation methods to be acceptable, and that both the community-based and outdoor adventure residential phases could be implemented as planned. Minor modifications are needed to recruitment strategies before it is more widely rolled out and evaluated.