An analysis of NHS 111 demand for primary care services: A retrospective cohort study.

The NHS 111 service triages over 16,650,745 calls per year and approximately 48% of callers are triaged to a primary care disposition, such as a telephone appointment with a general practitioner (GP). However, there has been little assessment of the ability of primary care services to meet this demand. If a timely service cannot be provided to patients, it could result in patients calling 999 or attending emergency departments (ED) instead. This study aimed to explore the patient journey for callers who were triaged to a primary care disposition, and the ability of primary care services to meet this demand. We obtained routine, retrospective data from the Connected Yorkshire research database, and identified all 111 calls between the 1st January 2021 and 31st December 2021 for callers registered with a GP in the Bradford or Airedale region of West Yorkshire, who were triaged to a primary care disposition. Subsequent healthcare system access (111, 999, primary and secondary care) in the 72 hours following the index 111 call was identified, and a descriptive analysis of the healthcare trajectory of patients was undertaken. There were 56,102 index 111 calls, and a primary care service was the first interaction in 26,690/56,102 (47.6%) of cases, with 15,470/26,690 (58%) commenced within the specified triage time frame. Calls to 999 were higher in the cohort who had no prior contact with primary care (58% vs 42%) as were ED attendances (58.2% vs 41.8), although the proportion of avoidable ED attendances was similar (10.5% vs 11.8%). Less than half of 111 callers triaged to a primary care disposition make contact with a primary care service, and even when they do, call triage time frames are frequently not met, suggesting that current primary care provision cannot meet the demand from 111.

Modelling NHS England 111 demand for primary care services: a discrete event simulation.

OBJECTIVES: This feasibility study aimed to model in silico the current healthcare system for patients triaged to a primary care disposition following a call to National Health Service (NHS) 111 and determine the effect of reconfiguring the healthcare system to ensure a timely primary care service contact. DESIGN: Discrete event simulation. SETTING: Single English NHS 111 call centre in Yorkshire. PARTICIPANTS: Callers registered with a Bradford general practitioner who contacted the NHS 111 service in 2021 and were triaged to a primary care disposition. PRIMARY AND SECONDARY OUTCOME MEASURES: Face validity of conceptual model. Comparison between real and simulated data for quarterly counts (and 95% CIs) for patient contact with emergency ambulance (999), 111, and primary and secondary care services. Mean difference and 95% CIs in healthcare system usage between simulations and difference in mean proportion of avoidable admissions for callers who presented to an emergency department (ED). RESULTS: The simulation of the current system estimated that there would be 39 283 (95% CI 39 237 to 39 328) primary care contacts, 2042 (95% CI 2032 to 2051) 999 calls and 1120 (95% CI 1114 to 1127) avoidable ED attendances. Modifying the model to ensure a timely primary care response resulted in a mean percentage increase of 196.1% (95% CI 192.2% to 199.9%) in primary care contacts, and a mean percentage decrease of 78.0% (95% CI 69.8% to 86.2%) in 999 calls and 88.1% (95% CI 81.7% to 94.5%) in ED attendances. Avoidable ED attendances reduced by a mean of -26 (95% CI -35 to -17). CONCLUSION: In this simulated study, ensuring timely contact with a primary care service would lead to a significant reduction in 999 and 111 calls, and ED attendances (although not avoidable ED attendance). However, this is likely to be impractical given the need to almost double current primary care service provision. Further economic and qualitative research is needed to determine whether this intervention would be cost-effective and acceptable to both patients and primary care clinicians.

Modelling palliative and end-of-life resource requirements during COVID-19: implications for quality care.

OBJECTIVES: The WHO estimates that the COVID-19 pandemic has led to more than 1.3 million deaths (1 377 395) globally (as of November 2020). This surge in death necessitates identification of resource needs and relies on modelling resource and understanding anticipated surges in demand. Our aim was to develop a generic computer model that could estimate resources required for end-of-life (EoL) care delivery during the pandemic. SETTING: A discrete event simulation model was developed and used to estimate resourcing needs for a geographical area in the South West of England. While our analysis focused on the UK setting, the model is flexible to changes in demand and setting. PARTICIPANTS: We used the model to estimate resourcing needs for a population of around 1 million people. PRIMARY AND SECONDARY OUTCOME MEASURES: The model predicts the per-day 'staff' and 'stuff' resourcing required to meet a given level of incoming EoL care activity. RESULTS: A mean of 11.97 hours of additional community nurse time, up to 33 hours of care assistant time and up to 30 hours additional care from care assistant night sits will be required per day as a result of out of hospital COVID-19 deaths based on the model prediction. Specialist palliative care demand is predicted to increase up to 19 hours per day. An additional 286 anticipatory medicine bundles per month will be necessary to alleviate physical symptoms at the EoL care for patients with COVID-19: an average additional 10.21 bundles of anticipatory medication per day. An average additional 9.35 syringe pumps could be needed to be in use per day. CONCLUSIONS: The analysis for a large region in the South West of England shows the significant additional physical and human resource required to relieve suffering at the EoL as part of a pandemic response.

Using computer simulation to model the expansion needs of the ambulatory emergency care unit at Derriford Hospital.

Ambulatory emergency care (AEC) units can treat a wide variety of patients referred from the emergency department (ED) and acute general practitioners, helping to ease ED pressures and reduce unnecessary hospital admissions. We developed a simulation model to assess the potential benefits of expanding the size and opening times of the AEC unit at Derriford Hospital in Plymouth, both separately and in combination. The model predicted that an expanded size of AEC unit could help to improve the effectiveness of the unit, but not as much as extending the opening hours of the unit, which was predicted to result in a significant impact on the number of patients that can be seen and treated in the AEC unit. The hospital used the evidence generated by the model to support a business to case to extend opening hours of the AEC unit, and open the AEC unit at the weekend.

Can agent-based simulation be used as a tool to support polypharmacy prescribing practice?

Objective: We sought to develop a simulation modelling method to help better understand the complex interplay of factors that lead to people with type 2 diabetes and asthma not taking all of their medication as prescribed when faced with multiple medications (polypharmacy). Research design and methods: In collaboration with polypharmacy patients, general practitioners, pharmacists and polypharmacy researchers, we developed a map of factors that directly and indirectly affect somebody’s decision to take their medication as prescribed when faced with multiple type 2 diabetes and asthma medications. We then translated these behavioural influences into logical rules using data from the literature and developed a proof-of-concept agent-based simulation model that captures the medicine-taking behaviours of those with type 2 diabetes and asthma taking multiple medications and which predicts both the clinical effectiveness and rates of adherence for different combinations of medications. Conclusions: The model we have developed could be used as a prescription support tool or a way of estimating medicine-taking behaviour in cost-effectiveness analyses.

What factors are driving increasing demand for community nursing?

Demand for district nursing services is increasing significantly. With increasing economic pressures, services are struggling to meet increases in demand, and are looking to become more proactive in planning for future demand. Traditional quantitative forecasting methods have limited use, because of the complexity of inter-linking factors that potentially drive demand for community services. Qualitative system dynamics approaches can be useful to model the complex interplay of causal factors leading to an effect, such as increased demand for services, and identify particular areas of concern for future focus. We ran a facilitated qualitative system dynamics workshop with representatives working across community nursing services in Cornwall. The generated models identified 7 key areas of concern that could be significantly contributing to demand for district nursing services. We outline the identified problem areas in this paper, and discuss potential recommendations to reduce their effects based on causal links identified in the models.

Can the retinal screening interval be safely increased to 2 years for type 2 diabetic patients without retinopathy?

OBJECTIVE: In the U.K., people with diabetes are typically screened for retinopathy annually. However, diabetic retinopathy sometimes has a slow progression rate. We developed a simulation model to predict the likely impact of screening patients with type 2 diabetes, who have not been diagnosed with diabetic retinopathy, every 2 years rather than annually. We aimed to assess whether or not such a policy would increase the proportion of patients who developed retinopathy-mediated vision loss compared with the current policy, along with the potential cost savings that could be achieved. RESEARCH DESIGN AND METHODS: We developed a model that simulates the progression of retinopathy in type 2 diabetic patients, and the screening of these patients, to predict rates of retinopathy-mediated vision loss. We populated the model with data obtained from a National Health Service Foundation Trust. We generated comparative 15-year forecasts to assess the differences between the current and proposed screening policies. RESULTS The simulation model predicts that implementing a 2-year screening interval for type 2 diabetic patients without evidence of diabetic retinopathy does not increase their risk of vision loss. Furthermore, we predict that this policy could reduce screening costs by ~25%. CONCLUSIONS: Screening people with type 2 diabetes, who have not yet developed retinopathy, every 2 years, rather than annually, is a safe and cost-effective strategy. Our findings support those of other studies, and we therefore recommend a review of the current National Institute for Health and Clinical Excellence (NICE) guidelines for diabetic retinopathy screening implemented in the U.K.