Cuts in NHS waiting lists cannot be attributed to private sector
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PROVIDER: S-EPMC1309673 | biostudies-literature | 2005 Dec
REPOSITORIES: biostudies-literature
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| S-EPMC558279 | biostudies-literature
Project description:BackgroundThe National Health Service (NHS) outpatient waiting list is growing, affecting specialties like foot and ankle. Delays are due to increasing demand, limited resources, and administrative inefficiencies. Virtual clinics are being explored to reduce physical clinic burdens and provide timely care. This study investigates the effectiveness of virtual clinics in reducing prolonged waiting times in the foot and ankle specialty. Emissions from personal vehicles are a primary driver of climate change, which is a little recognized benefit of virtual clinics.MethodsWe analyzed outcomes from a virtual elective foot and ankle clinic, overseen by a specialist consultant, for new elective referrals over 4 months. Data for 175 patients were collected from Lorenzo, our electronic health records system. We also assessed the success rate of virtual consultations in terms of accurate diagnoses and effective treatment plans. Measured distance to the hospital based was on patients' residential address.ResultsThe virtual clinic effectively managed patients. Of the 175 patients, 48.6% completed treatment, and were discharged, and 53.7% were managed without face-to-face consultations. In addition, 66.3% did not need in-person visits; this includes patients treated and discharged and who were referred for investigations. In this clinic, avoiding 1 visit to the hospital by 116 patients saved travel of 1040 miles.ConclusionThe widespread adoption of virtual clinics can provide a convenient and cost-effective health care solution for patients and also potentially help reduce carbon emissions contributing to control global warming.Level of evidenceLevel IV, retrospective case series.
| S-EPMC11653461 | biostudies-literature
Project description:ObjectiveTo model the referral, diagnostic and treatment pathway for cardiovascular disease (CVD) in the English National Health Service (NHS) to provide commissioners and managers with a methodology to optimise patient flow and reduce waiting lists.Study designA systems dynamics approach modelling the CVD healthcare system in England. The model is designed to capture current and predict future states of waiting lists.SettingRoutinely collected, publicly available data streams of primary and secondary care, sourced from NHS Digital, NHS England, the Office of National Statistics and StatsWales.Data collection and extraction methodsThe data used to train and validate the model were routinely collected and publicly available data. It was extracted and implemented in the model using the PySD package in python.ResultsNHS cardiovascular waiting lists in England have increased by over 40% compared with pre- COVID-19 levels. The rise in waiting lists was primarily due to restrictions in referrals from primary care, creating a bottleneck postpandemic. Predictive models show increasing point capacities within the system may paradoxically worsen downstream flow. While there is no simple rate-limiting step, the intervention that would most improve patient flow would be to increase consultant outpatient appointments.ConclusionsThe increase in NHS CVD waiting lists in England can be captured using a systems dynamics approach, as can the future state of waiting lists in the presence of further shocks/interventions. It is important for those planning services to use such a systems-oriented approach because the feed-forward and feedback nature of patient flow through referral, diagnostics and treatment leads to counterintuitive effects of interventions designed to reduce waiting lists.
| S-EPMC10357301 | biostudies-literature
Project description:OBJECTIVES:To investigate the national distribution of prolonged waiting for elective day case and inpatient surgery, and to examine associations of prolonged waiting with markers of NHS capacity, activity in the independent sector, and need. SETTING:NHS hospital trusts in England. POPULATION:People waiting for elective treatment in the specialties of general surgery; ear, nose and throat surgery; ophthalmic surgery; and trauma and orthopaedic surgery. MAIN OUTCOME MEASURE:Numbers of people waiting six months or longer (prolonged waiting). Characteristics of trusts with large numbers waiting six months or longer were examined by using logistic regression. RESULTS:The distribution of numbers of people waiting for day case or elective surgery in all the specialties examined was highly positively skewed. Between 52% and 83% of patients waiting longer than six months in the specialties studied were found in one quarter of trusts, which in turn contributed 23-45% of the national throughput specific to the specialty. In general, there was little evidence to show that capacity (measured by numbers of operating theatres, dedicated day case theatres, available beds, and bed occupancy rate) or independent sector activity were associated with prolonged waiting, although exceptions were noted for individual specialties. There was consistent evidence showing an increase in prolonged waiting, with increased numbers of anaesthetists across all specialties and with increased bed occupancy rates for ear, nose and throat surgery. Markers of greater need for health care, such as deprivation score and rate of limiting long term illness, were inversely associated with prolonged waiting. CONCLUSION:In most instances, substantial numbers of patients waiting unacceptably long periods for elective surgery were limited to a small number of hospitals. Little and inconsistent support was found for associations of prolonged waiting with markers of capacity, independent sector activity, or need in the surgical specialties examined.
| S-EPMC140273 | biostudies-literature
Project description:BackgroundThe intention to more effectively mobilise and integrate the capabilities of the community pharmacy workforce within primary care is clearly stated within National Health Service (NHS) England policy. The Pharmacy Integration Fund (PhIF) was established in 2016 to support the development of clinical pharmacy practice in a range of primary care settings, including community pharmacy.ObjectiveThis study sought to determine how PhIF funded learning pathways for post-registration pharmacists and accuracy checking pharmacy technicians enabled community pharmacy workforce transformation, in what circumstances, and why.MethodsRealist evaluation. We identified two main programme theories underpinning the PhIF programme and tested these theories against data collected through 41 semi-structured qualitative interviews with community pharmacist and pharmacy technician learners, educational supervisors, and community pharmacy employers.ResultsThe data supported the initial programme theories and indicated that the learning pathway for post-registration pharmacists had also provided opportunity for pharmacists to develop and consolidate their clinical skills before pursuing an independent prescribing qualification. Employer support was a key factor influencing learner participation, whilst employer engagement was mediated by perceptions of value expectancy and clarity of purpose. The study also highlights the influence of contextual factors within the community pharmacy setting on opportunities for the application of learning in practice.ConclusionsWhen designing and implementing workforce transformation plans and funded service opportunities that require the engagement of a diverse range of private, for-profit businesses within a mixed economy setting, policymakers should consider the contextual factors and mechanisms influencing participation of all stakeholder groups.
| S-EPMC11542810 | biostudies-literature
Project description:Introduction. Reducing hospital waiting lists for elective procedures is a policy concern in the National Health Service (NHS) in England. Following growth in waiting lists after COVID-19, the NHS published an elective recovery plan that includes an aim to prioritize patients from deprived areas. We use a previously developed model to estimate the health and health inequality impact under hypothetical targeted versus universal policies to reduce waiting time. Methods. We use a Markov model to estimate the health impact of waiting, by index of multiple deprivation quintile group, for 8 elective procedures. We estimate patients' remaining quality-adjusted life-years (QALYs) with baseline waiting times and under 2 hypothetical policy scenarios: 1) a universal policy in which all patients receive an equal reduction in wait and 2) a targeted policy in which patients living in the most deprived quintile are prioritized. We estimate individual and population level health under each of the 2 policies and compare it with baseline. We also estimate how health inequality changes from baseline using the slope index of inequality, reflecting the difference in health between the least and most deprived quintile based on QALYs. Results. A universal reduction in waiting time is estimated to improve overall population health but increase health inequality. A targeted reduction would achieve nearly the same overall health gain and would also increase population-level health inequalities but to a lesser extent than the universal policy would. Discussion. If the NHS is successful in prioritizing patients on waiting lists from the most deprived areas, this may result in smaller increases in health inequalities while maintaining a similar level of overall health gain compared with a universal policy.HighlightsThe NHS elective recovery plans include prioritizing patients who live in the most deprived areas of England.Evaluating a hypothetical targeted wait time reduction policy against a universal wait time reduction policy suggests almost the same level of population health gain could be achieved while lessening the negative impact on health inequality.Expected outcomes of government health policies should be quantified to explore the impact on both health maximization and health inequality minimization, as both represent legitimate policy concerns.
| S-EPMC11748141 | biostudies-literature
Project description: Not available
| S-EPMC10932752 | biostudies-literature
Project description:During the past decades, the traditional state monopoly in urban water management has been debated heavily, resulting in different forms and degrees of private sector involvement across the globe. Since the 1990s, China has also started experiments with new modes of urban water service management and governance in which the private sector is involved. It is premature to conclude whether the various forms of private sector involvement will successfully overcome the major problems (capital shortage, inefficient operation, and service quality) in China's water sector. But at the same time, private sector involvement in water provisioning and waste water treatments seems to have become mainstream in transitional China.
| S-EPMC2359833 | biostudies-other
Project description:ObjectivesTo provide estimates for how different treatment pathways for the management of severe aortic stenosis (AS) may affect National Health Service (NHS) England waiting list duration and associated mortality.DesignWe constructed a mathematical model of the excess waiting list and found the closed-form analytic solution to that model. From published data, we calculated estimates for how the strategies listed under Interventions may affect the time to clear the backlog of patients waiting for treatment and the associated waiting list mortality.SettingThe NHS in England.ParticipantsEstimated patients with AS in England.Interventions(1) Increasing the capacity for the treatment of severe AS, (2) converting proportions of cases from surgery to transcatheter aortic valve implantation and (3) a combination of these two.ResultsIn a capacitated system, clearing the backlog by returning to pre-COVID-19 capacity is not possible. A conversion rate of 50% would clear the backlog within 666 (533-848) days with 1419 (597-2189) deaths while waiting during this time. A 20% capacity increase would require 535 (434-666) days, with an associated mortality of 1172 (466-1859). A combination of converting 40% cases and increasing capacity by 20% would clear the backlog within a year (343 (281-410) days) with 784 (292-1324) deaths while awaiting treatment.ConclusionA strategy change to the management of severe AS is required to reduce the NHS backlog and waiting list deaths during the post-COVID-19 'recovery' period. However, plausible adaptations will still incur a substantial wait to treatment and many hundreds dying while waiting.
| S-EPMC9207579 | biostudies-literature
Project description: Not available
| S-EPMC1116147 | biostudies-literature