Right cot, right place, right time: improving the design and organisation of neonatal care networks

Abstract

Background: There is a tension in many health-care services between the expertise and efficiency that comes with centralising services and the ease of access for patients. Neonatal care is further complicated by the organisation of care into networks where different hospitals offer different levels of care and where capacity across, or between, networks may be used when local capacity is exhausted.

Objectives: To develop a computer model that could mimic the performance of a neonatal network and predict the effect of altering network configuration on neonatal unit workloads, ability to meet nurse staffing guidelines, and distance from the parents’ home location to the point of care. The aim is to provide a model to assist in planning of capacity, location and type of neonatal services.

Design: Descriptive analysis of a current network, economic analysis and discrete event simulation. During the course of the project, two meetings with parents were held to allow parent input.

Setting: The Peninsula neonatal network (Devon and Cornwall) with additional work extending to the Western network.

Main outcome measures: Ability to meet nurse staffing guidelines, cost of service provision, number and distance of transfers, average travel distances for parents, and numbers of parents with an infant over 50 km from home.

Data sources: Anonymised neonatal data for 7629 infants admitted into a neonatal unit between January 2011 and June 2013 were accessed from Badger patient care records. Nurse staffing data were obtained from a daily ring-around audit. Further background data were accessed from NHS England general practitioner (GP) Practice Profiles, Hospital Episode Statistics, Office for National Statistics and NHS Connecting for Health. Access to patient care records was approved by the Research Ethics Committee and the local Caldicott Guardian at the point of access to the data.

Results: When the model was tested against a period of data not used for building the model, the model was able to predict the occupancy of each hospital and care level with good precision (R2 > 0.85 for all comparisons). The average distance from the parents’ home location (GP location used as a surrogate) was predicted to within 2 km. The number of transfers was predicted to within 2%. The model was used to forecast the effect of centralisation. Centralisation led to reduced nurse requirements but was accompanied by a significant increase in parent travel distances. Costs of nursing depend on how much of the time nursing guidelines are to be met, rising from £4500 per infant to meet guidelines 80% of the time, to £5500 per infant to meet guidelines 95% of the time. Using network capacity, rather than local spare capacity, to meet local peaks in workloads can reduce the number of nurses required, but the number of transfers and the travel distance for parents start to rise significantly above ≈ 70% network capacity utilisation.

Conclusions: We have developed a model that predicts performance of a neonatal network from the perspectives of both the service provider and the parents of infants in care.

Future work: Application of the model at a national level.