Prioritizing people and rapid learning in times of crisis: A virtual learning initiative to support health workers during the COVID-19 pandemic.

The Western Cape province was the early epicentre of the coronavirus disease 2019 pandemic in South Africa and on the African continent. In this short article we report on an initiative set up within the provincial Department of Health early in the pandemic to facilitate collective learning and support for health workers and managers across the health system, emphasising the importance of leadership, systems resilience, nonhierarchical learning and connectedness. These strategies included regular and systematic engagement with organised labour, different ways of gauging and responding to staff morale, and daily 'huddles' for raid learning and responsive action. We propose three transformational actions that could deliver health systems that protect staff during good times and in times of system shocks. (a) Continuously invest in building the foundations of system resilience in good times, to draw on in an acute crisis situation. (b) Provide consistent leadership for an explicit commitment to supporting health workers through decisive action across the system. (c) Optimise available resources and partners, act on improvement ideas and obstacles. Build trusting relationships amongst and across actors.

The care of older cancer patients in the United Kingdom.

The ageing population poses new challenges globally. Cancer care for older patients is one of these challenges, and it has a significant impact on societies. In the United Kingdom (UK), as the number of older cancer patients increases, the management of this group has become part of daily practice for most oncology teams in every geographical area. Older cancer patients are at a higher risk of both under- and over-treatment. Therefore, the assessment of a patient's biological age and effective organ functional reserve becomes paramount. This may then guide treatment decisions by better estimating a prognosis and the risk-to-benefit ratio of a given therapy to anticipate and mitigate against potential toxicities/difficulties. Moreover, older cancer patients are often affected by geriatric syndromes and other issues that impact their overall health, function and quality of life. Comprehensive geriatric assessments offer an opportunity to identify and address health problems which may then optimise one's fitness and well-being. Whilst it is widely accepted that older cancer patients may benefit from such an approach, resources are often scarce, and access to dedicated services and research remains limited to specific centres across the UK. The aim of this project is to map the current services and projects in the UK to learn from each other and shape the future direction of care of older patients with cancer.

Deprescribing in older people.

Polypharmacy and multimorbidity are both currently rising. The number of medicines taken is the single biggest predictor of adverse drug events. Deprescribing is an approach to managing polypharmacy and reducing adverse outcomes. Multiple international evidence-based guidelines are emerging to promote discontinuation of high-risk medications, and use of alternative medical and non-pharmacological management. This review outlines the evidence base behind deprescribing, and suggests some pragmatic approaches to decision making around medication review.

Predictive model of length of stay in hospital among older patients.

BACKGROUND: Most National Health Service (NHS) hospital bed occupants are older patients because of their frequent admissions and prolonged length of stay (LOS). We evaluated demographic and clinical factors as predictors of LOS in a single NHS Trust and derived an equation to estimate LOS. METHODS: Stepwise logistic and linear regressions were used to predict prolonged LOS (upper-quintile LOS > 17 days) and LOS respectively, from demographic factors and acute and pre-existing conditions. RESULTS: Of 374 (men:women = 127:247) admitted patients (20% to orthogeriatric, 69% to general medical and 11% to surgical wards), median age of 85 years (IQR = 78-90), 77 had acute first hip fracture; 297 had previous hip fracture (median time since previous fracture = 2.4 years) and 21 (7.1%) had recurrent hip fracture, with median time since first fracture = 2.4 years. Median LOS was 6.5 days (IQR = 1.8-14.8), and 38 (10.2%) died after 4.8 days (IQR = 1.6-14.3). Prolonged LOS was associated with discharge to places other than usual residence: OR = 3.1 (95% CI 1.7-5.7), acute stroke: OR = 10.1 (3.7-26.7), acute first hip fractures: OR = 6.8 (3.1-14.8), recurrent hip fractures: OR = 9.5 (3.2-28.7), urinary tract infection/pneumonia: OR = 4.0 (2.1-8.0), other acute fractures: OR = 9.8 (3.0-32.3) and malignancy: OR = 15.0 (3.1-71.8). Predictive equation showed estimated LOS was 11.6 days for discharge to places other than usual residence, 15 days for pre-existing or acute stroke, 9-14 days for acute and recurrent hip fractures, infections, other acute fractures and malignancy; these factors together explained 32% of variability in LOS. CONCLUSIONS: A useful estimate of outcome and LOS can be made by constructing a predictive equation from information on hospital admission, to provide evidence-based guidance for resource requirements and discharge planning.

Traffic lights: putting a stop to unsafe patient transfers.

Poor handover between doctors is a recognised cause of error in hospitals.[1] Watford General Hospital is a busy acute trust in southern England, where high admission rates necessitate timely patient transfers from the acute admissions unit (AAU) to the medical wards. We found that doctors were infrequently informed of patient transfers, and they rarely handed over patient care when a patient was moved. Our aim was to minimise preventable harm to patients by prompting handover of clinically unstable patients, and patients with outstanding investigations or referrals, at the time of transfer. We introduced a traffic light tool to categorise patients on the medical take as red, amber, or green according to their clinical status at time of admission to AAU. The traffic light colour, which was assigned both on paper and electronically, was designed to prompt a verbal handover between doctors at the time of patient transfer from AAU.