Right cot, right place, right time: improving the design and organisation of neonatal care networks – a computer simulation study

Abstract

BackgroundThere 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.ObjectivesTo 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.DesignDescriptive 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.SettingThe Peninsula neonatal network (Devon and Cornwall) with additional work extending to the Western network.Main outcome measuresAbility 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 sourcesAnonymised 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.ResultsWhen 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.ConclusionsWe 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 workApplication of the model at a national level.FundingThe National Institute for Health Research Health Services and Delivery Research programme.

Simulation of a Novel Schedule for Intensivist Staffing to Improve Continuity of Patient Care and Reduce Physician Burnout

“…Many industries rely on simulation to inform decision-making regarding resource scheduling, as such methods allow optimization of interventions and testing of new concepts in compressed time (30). Simulation has been increasingly applied to the study of health technologies and systems (31-34). Our modeling approach is flexible and rapidly adaptable for considering various scheduling alternatives as further recommendations and regulations are promulgated.…”

Patient acuity and nurse staffing challenges in Norwegian neonatal intensive care units

“…The finding of larger fluctuations in smaller units is similar to findings Allen et al . () described. They proposed the difference between the 10th and 90th percentile as a marker of fluctuations (Allen et al .…”

“…The finding of larger fluctuations in smaller units is similar to findings Allen et al (2015) described. They proposed the difference between the 10th and 90th percentile as a marker of fluctuations (Allen et al 2015). When administering a plan for staffing in a unit, the difference between the median and 90th percentile might be a more practical measure.…”

“…Small NICUs are needed due to the long geographical distances in the country. A computer simulation study performed in a UK neonatal network demonstrated that small neonatal units might be more difficult to staff appropriately due to larger fluctuations in patient occupancy (Allen et al 2015).…”

“…Rapid changes in patient number and acuity represent rapid and unplanned changes in the need for nurses. Units with great fluctuations in patient occupancy, particularly fluctuations in patients in need of intensive care, will be faced with the need for a flexible staff to accommodate the changes (Allen et al 2015). Research has addressed challenges related to nurse staffing and the need for nurses at all times during the day and week (De Cordova et al 2012).…”