A mixed methods study of the impact of consultant overnight working in an English Emergency Department.

BACKGROUND: There is a growing expectation that consultant-level doctors should be present within an ED overnight. However, there is a lack of robust evidence substantiating the impact on patient waiting times, safety or the workforce. OBJECTIVES: To evaluate the impact of consultant-level doctors overnight working in ED in a large university hospital. METHODS: We conducted a controlled interrupted time series analysis to study ED waiting times before and after the introduction of consultant night working. Adverse event reports (AER) were used as a surrogate for patient safety. We conducted interviews with medical and nursing staff to explore attitudes to night work. RESULTS: The reduction seen in average time in department relative to the day, following the introduction of consultant was non-significant (-12 min; 95% CI -28 to 4, p=0.148). Analysis of hourly arrivals and departures indicated that overnight work was inherited from the day. There were three (0.9%) moderate and 0 severe AERs in 1 year. The workforce reported that night working had a negative impact on sleep patterns, performance and well-being and there were mixed views about the benefits of consultant night presence. Additional time off during the day acted as compensation for night work but resulted in reduced contact with ED teams. CONCLUSIONS: Our single-site study was unable to demonstrate a clinically important impact of consultant night working on total time patients spend in the department. Our analysis suggests there may be more potential to reduce total time in department during the day, at our study site. Negative impacts on well-being, and likely resistance to consultant night working should not be ignored. Further studies of night working are recommended to substantiate our results.

Modelling self-management pathways for people with diabetes in primary care.

BACKGROUND: Self-management support to facilitate people with type 2 diabetes to effectively manage their condition is complex to implement. Organisational and system elements operating in relation to providing optimal self-management support in primary care are poorly understood. We have applied operational research techniques to model pathways in primary care to explore and illuminate the processes and points where people struggle to find self-management support. METHODS: Primary care clinicians and support staff in 21 NHS general practices created maps to represent their experience of patients' progress through the system following diagnosis. These were collated into a combined pathway. Following consideration of how patients reduce dependency on the system to become enhanced self-managers, a model was created to show the influences on patients' pathways to self-management. RESULTS: Following establishment of diagnosis and treatment, appointment frequency decreases and patient self-management is expected to increase. However, capacity to consistently assess self-management capabilities; provide self-management support; or enhance patient-led self-care activities is missing from the pathways. Appointment frequencies are orientated to bio-medical monitoring rather than increasing the ability to mobilise resources or undertake self-management activities. CONCLUSIONS: The model provides a clear visual picture of the complexities implicated in achieving optimal self-management support. Self-management is quickly hidden from view in a system orientated to treatment delivery rather than to enhancing patient self-management. The model created highlights the limited self-management support currently provided and illuminates points where service change might impact on providing support for self-management. Ensuring professionals are aware of locally available support and people's existing network support has potential to provide appropriate and timely direction to community facilities and the mobilisation of resources.

A facility location model for analysis of current and future demand for sexual health services.

In this paper we address the clinic location selection problem for a fully integrated Sexual Health Service across Hampshire. The service provides outpatient services for Genito-Urinary Medicine, contraceptive and reproductive health, sexual health promotion and a sexual assault referral centre. We aim to assist the planning of sexual health service provision in Hampshire by conducting a location analysis using both current and predicted patient need. We identify the number of clinic locations required and their optimal geographic location that minimise patient travel time. To maximise the chances of uptake of results we validate the developed simple algorithm with an exact method as well as three well-known, but complex meta-heuristics. The analysis was conducted using car travel and public transport times. Two scenarios were considered: current clinic locations only; and anywhere within Hampshire. The results show that the clinic locations could be reduced from 28 to 20 and still keep 90% of all patient journeys by public transport (e.g. by bus or train) to a clinic within 30 minutes. The number of clinics could be further reduced to 8 if the travel time is based on car travel times within 15 minutes. Results from our simple solution method compared favourably to the exact solution as well as the complex meta-heuristics.