Routine CTA screening identifies blunt cerebrovascular injuries missed by clinical risk factors.

Objectives: Current guidelines for screening for blunt cerebrovascular injury (BCVI) are commonly based on the expanded Denver criteria, a set of risk factors that identifies patients who require CT-angiographic (CTA) screening for these injuries. Based on previously published data from our center, we have adopted a more liberal screening guideline than those outlined in the expanded Denver criteria. This entails routine CTA of the neck for all blunt trauma patients already undergoing CT of the cervical spine and/or CTA of the chest. The aim of this study was to analyze the incidence of patients with BCVI who did not meet any of the risk factors included in the expanded Denver criteria. Methods: A retrospective review of all patients diagnosed with BCVI between June 2014 and December 2019 at a Level I Trauma Center were identified from the trauma registry. Medical records were reviewed for the presence or absence of risk factors as outlined in the expanded Denver criteria. Demographic data, time to CTA and treatment, BCVI grade, Glasgow Coma Scale and Injury Severity Score were collected. Results: During the study period, 17 054 blunt trauma patients were evaluated, and 29% (4923) underwent CTA of the neck to screen for BCVI. 191 BCVIs were identified in 160 patients (0.94% of all blunt trauma patients, 3.25% of patients screened with CTA). 16% (25 of 160) of patients with BCVI had none of the risk factors outlined in the Denver criteria. Conclusion: Our findings indicate that reliance on the expanded Denver criteria alone for BCVI screening will result in missed injuries. We recommend CTA screening in all patients with blunt trauma undergoing CT of the cervical spine and/or CTA of the chest to minimize this risk. Level of evidence: Level III, therapeutic/care management.

Modeling disease progression and treatment pathways for depression jointly using agent based modeling and system dynamics.

Introduction: Depression is a common mental health condition that affects millions of people worldwide. Care pathways for depression are complex and the demand across different parts of the healthcare system is often uncertain and not entirely understood. Clinical progression with depression can be equally complex and relates to whether or not a patient is seeking care, the care pathway they are on, and the ability for timely access to healthcare services. Considering both pathways and progression for depression are however rarely studied together in the literature. Methods: This paper presents a hybrid simulation modeling framework that is uniquely able to capture both disease progression, using Agent Based Modeling, and related care pathways, using a System Dynamics. The two simulation paradigms within the framework are connected to run synchronously to investigate the impact of depression progression on healthcare services and, conversely, how any limitations in access to services may impact clinical progression. The use of the developed framework is illustrated by parametrising it with published clinical data and local service level data from Wales, UK. Results and discussion: The framework is able to quantify demand, service capacities and costs across all care pathways for a range of different scenarios. These include those for varying service coverage and provision, such as the cost-effectiveness of treating patients more quickly in community settings to reduce patient progression to more severe states of depression, and thus reducing the costs and utilization of more expensive specialist settings.

Needs-led human resource planning for Sierra Leone in support of oral health.

BACKGROUND: In Sierra Leone (SL), a low-income country in West Africa, dental care is very limited, largely private, and with services focused in the capital Freetown. There is no formal dental education. Ten dentists supported by a similar number of dental care professionals (DCPs) serve a population of over 7.5 million people. The objective of this research was to estimate needs-led requirements for dental care and human resources for oral health to inform capacity building, based on a national survey of oral health in SL. METHODS: A dedicated operational research (OR) decision tool was constructed in Microsoft Excel to support this project. First, total treatment needs were estimated from our national epidemiological survey data for three key ages (6, 12 and 15 years), collected using the 'International Caries Classification and Management System (ICCMS)' tool. Second, oral health needs were extrapolated to whole population levels for each year-group, based on census demographic data. Third, full time equivalent (FTE) workforce capacity needs were estimated for mid-level providers in the form of Dental Therapists (DTs) and non-dental personnel based on current oral disease management approaches and clinical timings for treatment procedures. Fourth, informed by an expert panel, three oral disease management scenarios were explored for the national population: (1) Conventional care (CC): comprising oral health promotion (including prevention), restorations and tooth extraction; (2) Surgical and Preventive care (S5&6P and S6P): comprising oral health promotion (inc. prevention) and tooth extraction (D5 and D6 together, & at D6 level only); and (3) Prevention only (P): consisting of oral health promotion (inc. prevention). Fifth, the findings were extrapolated to the whole population based on demography, assuming similar levels of treatment need. RESULTS: To meet the needs of a single year-group of childrens' needs, an average of 163 DTs (range: 133-188) would be required to deliver Conventional care (CC); 39 DTs (range: 30-45) to deliver basic Surgical and Preventive care (S6P); 54 DTs for more extended Surgical and Preventive care (S5&6P) (range 38-68); and 27 DTs (range: 25-32) to deliver Prevention only (P). When scaled up to the total population, an estimated 6,147 DTs (range: 5,565-6,870) would be required to deliver Conventional care (CC); 1,413 DTs (range: 1255-1438 DTs) to deliver basic Surgical and Preventive care (S6P); 2,000 DTs (range 1590-2236) for more extended Surgical and Preventive care (S5&6P) (range 1590-2236); and 1,028 DTs to deliver Prevention only (P) (range: 1016-1046). Furthermore, if oral health promotion activities, including individualised prevention, could be delivered by non-dental personnel, then the remaining surgical care could be delivered by 385 DTs (range: 251-488) for the S6P scenario which was deemed as the minimum basic baseline service involving extracting all teeth with extensive caries into dentine. More realistically, 972 DTs (range: 586-1179) would be needed for the S5&6P scenario in which all teeth with distinctive and extensive caries into dentine are extracted. CONCLUSION: The study demonstrates the huge dental workforce needs required to deliver even minimal oral health care to the Sierra Leone population. The gap between the current workforce and the oral health needs of the population is stark and requires urgent action. The study also demonstrates the potential for contemporary epidemiological tools to predict dental treatment needs and inform workforce capacity building in a low-income country, exploring a range of solutions involving mid-level providers and non-dental personnel.

Examining the diagnostic pathway for lung cancer patients in Wales using discrete event simulation.

BACKGROUND: UK's National Health Service (NHS) has one of the poorest lung cancer survival rates in Europe. To improve patient outcomes, a single cancer pathway was introduced in the NHS. In this study, a Discrete Event Simulation was developed to understand bottlenecks during lung cancer treatment. METHODS: This study focused on the lung cancer diagnostic pathways at two Welsh hospitals. Discrete Event Simulation is a computer-based method that has been effectively used in demand and capacity planning. In this study, simulation models were developed for the current and proposed single cancer pathways. The validated models were used to provide Key Performance Indicators. Several "what-if" scenarios were considered for the current and proposed pathways. RESULTS: Under the current diagnostic pathway, the mean time to treatment for a surgery patient was 68 days at the Royal Glamorgan Hospital and 79 days at Prince Charles Hospital. For chemotherapy patients, the mean time to treatment was 52 days at the Royal Glamorgan Hospital and 57 days at Prince Charles Hospital. For radiotherapy patients, the mean time to treatment was 44 days at Royal Glamorgan Hospital and 54 days at Prince Charles Hospital. Ensuring that the patient attends their first outpatient appointment within 7 days and streamlining the diagnostic tests would have the potential to remove approximately 20 days from the current lung cancer pathway resulting in a 20-25% increase of patients receiving treatment within 62 days. Ensuring that patients begin their treatment within 21 days of diagnosis sees almost all patients comply with the 62-day target. CONCLUSIONS: Discrete Event Simulation coupled with a detailed statistical analysis provides a useful decision support tool which can be used to examine the current and proposed lung cancer pathways in terms of time spent on the pathway.

Covid-19 transmission modelling of students returning home from university.

We provide an open-source model to estimate the number of secondary Covid-19 infections caused by potentially infectious students returning from university to private homes with other occupants. Using a Monte-Carlo method and data derived from UK sources, we predict that an infectious student would, on average, infect 0.94 other household members. Or, as a rule of thumb, each infected student would generate (just less than) one secondary within-household infection. The total number of secondary cases for all returning students is dependent on the virus prevalence within each student population at the time of their departure from campus back home. Although the proposed estimation method is general and robust, the results are sensitive to the input data. We provide Matlab code and a helpful online app (http://bit.ly/Secondary_infections_app) that can be used to estimate numbers of secondary infections based on local parameter values. This can be used worldwide to support policy making.

Modified Needleman-Wunsch algorithm for clinical pathway clustering.

Clinical pathways are used to guide clinicians to provide a standardised delivery of care. Because of their standardisation, the aim of clinical pathways is to reduce variation in both care process and patient outcomes. When learning clinical pathways from data through data mining, it is common practice to represent each patient pathway as a string corresponding to their movements through activities. Clustering techniques are popular methods for pathway mining, and therefore this paper focuses on distance metrics applied to string data for k-medoids clustering. The two main aims are to firstly, develop a technique that seamlessly integrates expert information with data and secondly, to develop a string distance metric for the purpose of process data. The overall goal was to allow for more meaningful clustering results to be found by adding context into the string similarity calculation. Eight common distance metrics and their applicability are discussed. These distance metrics prove to give an arbitrary distance, without consideration for context, and each produce different results. As a result, this paper describes the development of a new distance metric, the modified Needleman-Wunsch algorithm, that allows for expert interaction with the calculation by assigning groupings and rankings to activities, which provide context to the strings. This algorithm has been developed in partnership with UK's National Health Service (NHS) with the focus on a lung cancer pathway, however the handling of the data and algorithm allows for application to any disease type. This method is contained within Sim.Pro.Flow, a publicly available decision support tool.

Alternative scenarios: harnessing mid-level providers and evidence-based practice in primary dental care in England through operational research.

BACKGROUND: In primary care dentistry, strategies to reconfigure the traditional boundaries of various dental professional groups by task sharing and role substitution have been encouraged in order to meet changing oral health needs. AIM: The aim of this research was to investigate the potential for skill mix use in primary dental care in England based on the undergraduate training experience in a primary care team training centre for dentists and mid-level dental providers. METHODS: An operational research model and four alternative scenarios to test the potential for skill mix use in primary care in England were developed, informed by the model of care at a primary dental care training centre in the south of England, professional policy including scope of practice and contemporary evidence-based preventative practice. The model was developed in Excel and drew on published national timings and salary costs. The scenarios included the following: "No Skill Mix", "Minimal Direct Access", "More Prevention" and "Maximum Delegation". The scenario outputs comprised clinical time, workforce numbers and salary costs required for state-funded primary dental care in England. RESULTS: The operational research model suggested that 73% of clinical time in England's state-funded primary dental care in 2011/12 was spent on tasks that may be delegated to dental care professionals (DCPs), and 45- to 54-year-old patients received the most clinical time overall. Using estimated National Health Service (NHS) clinical working patterns, the model suggested alternative NHS workforce numbers and salary costs to meet the dental demand based on each developed scenario. For scenario 1:"No Skill Mix", the dentist-only scenario, 81% of the dentists currently registered in England would be required to participate. In scenario 2: "Minimal Direct Access", where 70% of examinations were delegated and the primary care training centre delegation patterns for other treatments were practised, 40% of registered dentists and eight times the number of dental therapists currently registered would be required; this would save 38% of current salary costs cf. "No Skill Mix". Scenario 3: "More Prevention", that is, the current model with no direct access and increasing fluoride varnish from 13.1% to 50% and maintaining the same model of delegation as scenario 2 for other care, would require 57% of registered dentists and 4.7 times the number of dental therapists. It would achieve a 1% salary cost saving cf. "No Skill Mix". Scenario 4 "Maximum Delegation" where all care within dental therapists' jurisdiction is delegated at 100%, together with 50% of restorations and radiographs, suggested that only 30% of registered dentists would be required and 10 times the number of dental therapists registered; this scenario would achieve a 52% salary cost saving cf. "No Skill Mix". CONCLUSION: Alternative scenarios based on wider expressed treatment need in national primary dental care in England, changing regulations on the scope of practice and increased evidence-based preventive practice suggest that the majority of care in primary dental practice may be delegated to dental therapists, and there is potential time and salary cost saving if the majority of diagnostic tasks and prevention are delegated. However, this would require an increase in trained DCPs, including role enhancement, as part of rebalancing the dental workforce.

Workforce skill mix: modelling the potential for dental therapists in state-funded primary dental care.

BACKGROUND: South Central Strategic Health Authority [SHA], with a population of four million, is one of 10 regions of England with responsibility for workforce planning. AIM: To explore future scenarios for the use of the skill mix within the dental team to inform the commissioning of dental therapy training. METHOD: Data on population demography, oral health needs and demands, dental workforce, activity and dental utilisation were used to create demand (needs-informed) and supply models. Population trends and changing oral health needs and dental service uptake were included in the demand model. Linear programming was used to obtain the optimal make-up of the dental team. Based on the optimal scenario, workforce volumes and costs were examined across a range of scenarios up to 2013. RESULTS: Baseline levels of dental therapists were low and estimated as only achieving 10-20% of the current potential job competency. The optimal exploratory scenario in terms of costs and volume of staff was based on dental therapists working full time and providing 70% of routine care that is within their current job competency; this scenario required 483 therapists by 2013, a figure that appeared achievable. Increasing the level of job competency provided by therapists revealed potentially higher benefits in terms of reduced cost and requiring fewer dentists. CONCLUSION: The findings suggest that dental therapists can play a more significant role in the provision of primary dental care, both currently and in future; they also highlight the need for health services to routinely collect data that can inform workforce analysis and planning.

Tumour doubling times and the length bias in breast cancer screening programmes.

Screening for early detection of breast cancer is considered to be an important element of preventive medicine. In this paper, we use numerical simulations to examine the length bias in regular interval screening programmes, by computing the doubling times of breast cancer tumours detected through regular mammographies compared to self-detection. Our analysis shows that doubling times of tumours detected by a regular screening programme are longer than doubling times in the original whole population and considerably longer than those self-detected. Hence regular interval mammographies may be missing a high proportion of fast growing tumours and therefore the benefits of current screening programmes may need to be re-evaluated. We examine the likely size of the length bias for the present UK breast cancer screening programme and perform a sensitivity analysis by varying the screen detection probabilities to reflect future advances in mammographic detection.

Trends in NHS primary dental care for older people in England: implications for the future.

OBJECTIVE: To examine trends over time in the National Health Service (NHS) dental service provision for older people in England and consider the implications for future care. BACKGROUND: The number and proportion of older people in the population nationally are increasing and their oral health needs are changing as more people retain teeth into older age. MATERIALS AND METHODS: Descriptive analysis of routinely collected activity data for primary dental care in England was undertaken over the 6-year period, 1999/2000-2004/05. Data were cleaned, grouped into age bands for analysis. Activity rates per head of population were calculated for key aspects of care. Population forecasts and national survey data were used to inform future predictions of care required. RESULTS: There was an increase in absolute terms of total items (14.4%) and items per capita (10.7%) between 1999/2000 and 2004/05, with a peak of 15.7 million items claimed in 2003/04. Over the 6-year period, increases per capita were seen for dental examinations (11.3%), periodontal treatments (8.9%), fillings (1.9%), crowns (14.3%) and extractions (4.9%) amongst older people as a group. In contrast, there was a marked decrease in the episodes of care involving domiciliary care (-46.4%) and upper complete dentures (-32.9%) per capita over this period, items that were most commonly provided for the oldest age-band (85 years and over). Apart from complete dentures, the demand for care is predicted to increase. CONCLUSION: The volume of primary dental care provided for older people has been increasing at a rate which exceeds population growth, but remains low. Planners and commissioners should urgently review the provision of dental care of older people to ensure that their oral health needs are being met particularly in relation to dentures and domiciliary care.

A review and comparison of classification algorithms for medical decision making.

Within a health care setting, it is often desirable from both clinical and operational perspective to capture the uncertainty and variability amongst a patient population, for example to predict individual patient outcomes, risks or resource needs. Homogeneity brings the benefits of increased certainty in individual patient needs and resource utilisation, thus providing an opportunity for both improved clinical diagnosis and more efficient planning and management of health care resources. A number of classification algorithms are considered and evaluated for their relative performances and practical usefulness on different types of health care datasets. The algorithms are evaluated using four criteria: accuracy, computational time, comprehensibility of the results and ease of use of the algorithm to relatively statistically naive medical users. The research has shown that there is not necessarily a single best classification tool, but instead the best performing algorithm will depend on the features of the dataset to be analysed, with particular emphasis on health care data, which are discussed in the paper.

A framework for operational modelling of hospital resources.

The provision of hospital resources, such as beds, operating theatres and nurses, is a matter of considerable public and political concern and has been the subject of widespread debate [1,2,31. The political element of healthcare emphasises the need for objective methods and tools to inform the debate and provide a better foundation for decision-making. There is considerable scope for operational models to be widely used for this purpose. An appreciation of the dynamics governing a hospital system, and the flow of patients through it, point towards the need for sophisticated capacity models reflecting the complexity, uncertainty, variability and limited resources. Working alongside managers and clinicians from participating hospitals, this paper proposes a generic framework for modelling of hospital resources in the light of perceived user-needs and real-life hospital processes. The proposed framework incorporates the need for patient classification techniques to be adopted, which forms a key differentiator between this approach and other attempts to produce practical capacity planning and management tools. Statistically and clinically meaningful patient groupings may then be fed into developed simulation models and individual patients from each group passed through the particular hospital system of concern. The effectiveness of the framework is demonstrated through the development and use of an integrated hospital capacity tool.