Safe working in a 7-day service. Experience of hip fracture care as documented by the UK National Hip Fracture Database.

Objective: to describe differences in care and 30-day mortality of patients admitted with hip fracture on weekends (Saturday-Sunday) compared to weekdays (Monday-Friday), and their relationship to the organisation of care. Methods: data came from the National Hip Fracture Database (NHFD) linked to ONS mortality data on 52,599 patients presenting to 162 units in England between 1 January and 31 December 2014. This was combined with information on geriatrician staffing and major trauma centre (MTC) status. 30-day mortality and care were compared for patients admitted at weekends and weekdays; separately for patients treated in units grouped by the mean level of input by geriatricians, weekend geriatrician clinical cover and MTC status. Differences were adjusted for variation in patients' characteristics. Results: there was no evidence of differences in 30-day mortality between patients admitted at weekends compared to weekdays (7.2 vs 7.5%, P = 0.3) before or after adjusting for patient characteristics in either MTCs or general hospitals. The proportion receiving a preoperative geriatrician assessment was lower at weekends (42.8 vs 60.7%, P < 0.001). 30-day mortality was lower in units with higher levels of geriatrician input, but there was no weekend mortality effect associated with lower levels of input or absence of weekend cover. Conclusion: there was no evidence of a weekend mortality effect among patients treated for hip fracture in the English NHS. It appears that clinical teams provide comparably safe and effective care throughout the week. However, greater geriatrician involvement in teams was associated with overall lower mortality.

A Cost Evaluation of COVID-19 Remote Home Monitoring Services in England.

BACKGROUND: Remote home monitoring services emerged as critical components of health care delivery from NHS England during the COVID-19 pandemic, aiming to provide timely interventions and reduce health care system burden. Two types of service were offered: referral by community health services to home-based care to ensure the right people were admitted to the hospital at the right time (called COVID Oximetry@home, CO@h); and referral by hospital to support patients' transition from hospital to home (called COVID-19 Virtual Ward, CVW). The information collected for the oxygen levels and other symptoms was provided via digital means (technology-enabled) or over the phone (analogue-only submission mode). This study aimed to evaluate the costs of implementing remote home monitoring for COVID-19 patients across 26 sites in England during wave 2 of the pandemic. Understanding the operational and financial implications of these services from the NHS perspective is essential for effective resource allocation and service planning. METHODS: We used a bottom-up costing approach at the intervention level to describe the costs of setting up and running the services. Twenty-six implementation sites reported the numbers of patients and staff involved in the service and other resources used. Descriptive statistics and multivariable regression analysis were used to assess cost variations and quantify the relationship between the number of users and costs while adjusting for other service characteristics. RESULTS: The mean cost per patient monitored was lower in the CO@h service compared with the CVW service (£527 vs £599). The mean cost per patient was lower for implementation sites using technology-enabled and analogue data submission modes compared with implementation sites using analogue-only modes for both CO@h (£515 vs £561) and CVW (£584 vs £612) services. The number of patients enrolled in the services and the service type significantly affected the mean cost per patient. CONCLUSIONS: Our analysis provides a framework for evaluating the costs of similar services in the future and shows that the implementation of these services benefit from the employment of tech-enabled data submission modes.

Youth violence intervention programme for vulnerable young people attending emergency departments in London: a rapid evaluation.

Background: Youth violence intervention programmes involving the embedding of youth workers in NHS emergency departments to help young people (broadly aged between 11 and 24 years) improve the quality of their lives following their attendance at an emergency department as a result of violent assault or associated trauma are increasing across the NHS. This study evaluates one such initiative run by the charity Redthread in partnership with a NHS trust. Objectives: To evaluate the implementation and impact of a new youth violence intervention programme at University College London Hospital NHS Trust and delivered by the charity Redthread: (1) literature review of studies of hospital-based violent crime interventions; (2) evaluation of local implementation and of University College London Hospital staff and relevant local stakeholders concerning the intervention and its impact; (3) assessment of the feasibility of using routine secondary care data to evaluate the impact of the Redthread intervention; and (4) cost-effectiveness analysis of the Redthread intervention from the perspective of the NHS. Methods: The evaluation was designed as a mixed-methods multiphased study, including an in-depth process evaluation case study and quantitative and economic analyses. The project was undertaken in different stages over two years, starting with desk-based research and an exploratory phase suitable for remote working while COVID-19 was affecting NHS services. A total of 22 semistructured interviews were conducted with staff at Redthread and University College London Hospital and others (e.g. a senior stakeholder involved in NHS youth violence prevention policy). We analysed Redthread documents, engaged with experts and conducted observations of staff meetings to gather more in-depth insights about the effectiveness of the intervention, the processes of implementation, staff perceptions and cost. We also undertook quantitative analyses to ascertain suitable measures of impact to inform stakeholders and future evaluations. Results: Redthread's service was viewed as a necessary intervention, which complemented clinical and other statutory services. It was well embedded in the paediatric emergency department and adolescent services but less so in the adult emergency department. The diverse reasons for individual referrals, the various routes by which young people were identified, and the mix of specific support interventions provided, together emphasised the complexity of this intervention, with consequent challenges in implementation and evaluation. Given the relative unit costs of Redthread and University College London Hospital's inpatient services, it is estimated that the service would break even if around one-third of Redthread interventions resulted in at least one avoided emergency inpatient admission. This evaluation was unable to determine a feasible approach to measuring the quantitative impact of Redthread's youth violence intervention programme but has reflected on data describing the service, including costs, and make recommendations to support future evaluation. Limitations: The COVID-19 pandemic severely hampered the implementation of the Redthread service and the ability to evaluate it. The strongest options for analysis of effects and costs were not possible due to constraints of the consent process, problems in linking Redthread and University College London Hospital patient data and the relatively small numbers of young people having been engaged for longer-term support over the evaluation period. Conclusions: We have been able to contribute to the qualitative evidence on the implementation of the youth violence intervention programme at University College London Hospital, showing, for example, that NHS staff viewed the service as an important and needed intervention. In the light of problems with routine patient data systems and linkages, we have also been able to reflect on data describing the service, including costs, and made recommendations to support future evaluation. Future work: No future work is planned. Funding: National Institute for Health and Care Research Health Services and Delivery Research programme (RSET: 16/138/17).

Youth violence intervention programmes in the NHS embed specialist youth workers into a hospital's paediatric emergency departments. These staff can engage young people and encourage positive change in their lives. Youth violence intervention programmes are part of a broader national strategy to prevent violence among young people. To improve our knowledge of the impact on young people and the cost-effectiveness of youth violence intervention programmes, we carried out an evaluation of a youth violence intervention programme introduced in 2020 at University College London Hospital and run by the charity Redthread. We reviewed the international evidence on youth violence intervention programmes, and other studies of Redthread services but found few studies measuring impact within the NHS. We reviewed documents and conducted 22 interviews with University College London Hospital and Redthread staff among others. We found that the service is viewed positively by NHS staff. We also found that youth workers can help a young person to better engage in their medical care and treatment. Youth violence intervention programmes also provide a link with non­health-care services within the community. Overall, they help NHS staff to better support vulnerable young people following discharge from hospital. We also established the cost of delivering Redthread services per user was £1865. This compares with a cost per inpatient of £5789 for a group of patients similar to those helped by Redthread. The average cost of a Redthread-type patient attending the emergency department was £203. We looked at whether it was possible to measure whether Redthread reduced young people's re-admissions to the hospital's emergency departments. However, we concluded that fully answering this question was not possible over the timescale of the project. This was because of the impact of COVID-19 on Redthread and other paediatric services, the low numbers of young people engaged in a longer-term programme with Redthread (59) and difficulties with linking information from the hospital and Redthread. We have therefore made various recommendations in this report to improve the way that data are collected and linked to aid future evaluations.

Patient and staff experiences of using technology-enabled and analogue models of remote home monitoring for COVID-19 in England: A mixed-method evaluation.

OBJECTIVE: To evaluate patient and staff experiences of using technology-enabled ('tech-enabled') and analogue remote home monitoring models for COVID-19, implemented in England during the pandemic. METHODS: Twenty-eight sites were selected for diversity in a range of criteria (e.g. pre-hospital or early discharge service, mode of patient data submission). Between February and May 2021, we conducted quantitative surveys with patients, carers and staff delivering the service, and interviewed patients, carers, and staff from 17 of the 28 services. Quantitative data were analysed using descriptive statistics and both univariate and multivariate analyses. Qualitative data were interpreted using thematic analysis. RESULTS: Twenty-one sites adopted mixed models whereby patients could submit their symptoms using either tech-enabled (app, weblink, or automated phone calls) or analogue (phone calls with a health professional) options; seven sites offered analogue-only data submission (phone calls or face-to-face visits with a health professional). Sixty-two patients and carers were interviewed, and 1069 survey responses were received (18 % response rate). Fifty-eight staff were interviewed, and 292 survey responses were received (39 % response rate). Patients who used tech-enabled modes tended to be younger (p = 0.005), have a higher level of education (p = 0.011), and more likely to identify as White British (p = 0.043). Most patients found relaying symptoms easy, regardless of modality, though many received assistance from family or friends. Staff considered the adoption of mixed delivery models beneficial, enabling them to manage large patient numbers and contact patients for further assessment as needed; however, they suggested improvements to the functionality of systems to better fit clinical and operational needs. Human contact was important in all remote home monitoring options. CONCLUSIONS: Organisations implementing tech-enabled remote home monitoring at scale should consider adopting mixed models which can accommodate patients with different needs; focus on the usability and interoperability of tech-enabled platforms; and encourage digital inclusivity for patients.

A rapid mixed-methods evaluation of remote home monitoring models during the COVID-19 pandemic in England.

Background: Remote home monitoring services were developed and implemented for patients with COVID-19 during the pandemic. Patients monitored blood oxygen saturation and other readings (e.g. temperature) at home and were escalated as necessary. Objective: To evaluate effectiveness, costs, implementation, and staff and patient experiences (including disparities and mode) of COVID-19 remote home monitoring services in England during the COVID-19 pandemic (waves 1 and 2). Methods: A rapid mixed-methods evaluation, conducted in two phases. Phase 1 (July-August 2020) comprised a rapid systematic review, implementation and economic analysis study (in eight sites). Phase 2 (January-June 2021) comprised a large-scale, multisite, mixed-methods study of effectiveness, costs, implementation and patient/staff experience, using national data sets, surveys (28 sites) and interviews (17 sites). Results: Phase 1 Findings from the review and empirical study indicated that these services have been implemented worldwide and vary substantially. Empirical findings highlighted that communication, appropriate information and multiple modes of monitoring facilitated implementation; barriers included unclear referral processes, workforce availability and lack of administrative support. Phase 2 We received surveys from 292 staff (39% response rate) and 1069 patients/carers (18% response rate). We conducted interviews with 58 staff, 62 patients/carers and 5 national leads. Despite national roll-out, enrolment to services was lower than expected (average enrolment across 37 clinical commissioning groups judged to have completed data was 8.7%). There was large variability in implementation of services, influenced by patient (e.g. local population needs), workforce (e.g. workload), organisational (e.g. collaboration) and resource (e.g. software) factors. We found that for every 10% increase in enrolment to the programme, mortality was reduced by 2% (95% confidence interval: 4% reduction to 1% increase), admissions increased by 3% (-1% to 7%), in-hospital mortality fell by 3% (-8% to 3%) and lengths of stay increased by 1.8% (-1.2% to 4.9%). None of these results are statistically significant. We found slightly longer hospital lengths of stay associated with virtual ward services (adjusted incidence rate ratio 1.05, 95% confidence interval 1.01 to 1.09), and no statistically significant impact on subsequent COVID-19 readmissions (adjusted odds ratio 0.95, 95% confidence interval 0.89 to 1.02). Low patient enrolment rates and incomplete data may have affected chances of detecting possible impact. The mean running cost per patient varied for different types of service and mode; and was driven by the number and grade of staff. Staff, patients and carers generally reported positive experiences of services. Services were easy to deliver but staff needed additional training. Staff knowledge/confidence, NHS resources/workload, dynamics between multidisciplinary team members and patients' engagement with the service (e.g. using the oximeter to record and submit readings) influenced delivery. Patients and carers felt services and human contact received reassured them and were easy to engage with. Engagement was conditional on patient, support, resource and service factors. Many sites designed services to suit the needs of their local population. Despite adaptations, disparities were reported across some patient groups. For example, older adults and patients from ethnic minorities reported more difficulties engaging with the service. Tech-enabled models helped to manage large patient groups but did not completely replace phone calls. Limitations: Limitations included data completeness, inability to link data on service use to outcomes at a patient level, low survey response rates and under-representation of some patient groups. Future work: Further research should consider the long-term impact and cost-effectiveness of these services and the appropriateness of different models for different groups of patients. Conclusions: We were not able to find quantitative evidence that COVID-19 remote home monitoring services have been effective. However, low enrolment rates, incomplete data and varied implementation reduced our chances of detecting any impact that may have existed. While services were viewed positively by staff and patients, barriers to implementation, delivery and engagement should be considered. Study registration: This study is registered with the ISRCTN (14962466). Funding: This project was funded by the National Institute for Health and Care Research (NIHR) Health and Social Care Delivery Research programme (RSET: 16/138/17; BRACE: 16/138/31) and NHSEI and will be published in full in Health and Social Care Delivery Research; Vol. 11, No. 13. See the NIHR Journals Library website for further project information. The views expressed in this publication are those of the authors and not necessarily those of the National Institute for Health and Care Research or the Department of Health and Social Care.

COVID-19 patients can experience very low oxygen levels, without feeling breathless. Patients may not realise there is a problem until they become extremely unwell, risking being admitted to hospital too late. To address this, COVID-19 remote home monitoring services were developed and later rolled out across England. Patients monitored oxygen levels at home using an 'oximeter' (a small device which clips on to your finger) and sent these readings to providers via phone or technology (e.g. an app). Patients could access further care if needed. We did not know whether these services worked, or what people felt about them. • How services were set up and used in England. • Whether services work (e.g. by reducing deaths and length of hospital stay). • How much they cost. • What patients, carers and staff think about these services (including differences between groups and telephone vs. technology). We looked at available existing evidence and collected data from eight services operating in the first wave of the pandemic. During the second wave of the pandemic, we used data available at a national level and conducted surveys (28 sites) and interviews (17 sites) with staff, patients and individuals involved in developing/leading services nationally. These services have been used worldwide, but they vary considerably. We found many things that help these services to be used (e.g. good communication) but also things that get in the way (e.g. unclear referrals). Our findings did not show that services reduce deaths or time in hospital. But these findings are limited by a lack of data. Staff and patients liked these services, but we found some barriers to delivering and using the service. Some groups found services harder to use (e.g. older patients, those with disabilities and ethnic minorities). Using technology helped with large patient groups, but it did not completely replace phone calls. Better information is needed to know whether these services work. Staff and patients liked these services. However, improvements may make them easier to deliver and use (e.g. further staff training and giving additional support to patients who need it).

Undertaking rapid evaluations during the COVID-19 pandemic: Lessons from evaluating COVID-19 remote home monitoring services in England.

Introduction: Rapid evaluations can offer evidence on innovations in health and social care that can be used to inform fast-moving policy and practise, and support their scale-up according to previous research. However, there are few comprehensive accounts of how to plan and conduct large-scale rapid evaluations, ensure scientific rigour, and achieve stakeholder engagement within compressed timeframes. Methods: Using a case study of a national mixed-methods rapid evaluation of COVID-19 remote home monitoring services in England, conducted during the COVID-19 pandemic, this manuscript examines the process of conducting a large-scale rapid evaluation from design to dissemination and impact, and reflects on the key lessons for conducting future large-scale rapid evaluations. In this manuscript, we describe each stage of the rapid evaluation: convening the team (study team and external collaborators), design and planning (scoping, designing protocols, study set up), data collection and analysis, and dissemination. Results: We reflect on why certain decisions were made and highlight facilitators and challenges. The manuscript concludes with 12 key lessons for conducting large-scale mixed-methods rapid evaluations of healthcare services. We propose that rapid study teams need to: (1) find ways of quickly building trust with external stakeholders, including evidence-users; (2) consider the needs of the rapid evaluation and resources needed; (3) use scoping to ensure the study is highly focused; (4) carefully consider what cannot be completed within a designated timeframe; (5) use structured processes to ensure consistency and rigour; (6) be flexible and responsive to changing needs and circumstances; (7) consider the risks associated with new data collection approaches of quantitative data (and their usability); (8) consider whether it is possible to use aggregated quantitative data, and what that would mean when presenting results, (9) consider using structured processes & layered analysis approaches to rapidly synthesise qualitative findings, (10) consider the balance between speed and the size and skills of the team, (11) ensure all team members know roles and responsibilities and can communicate quickly and clearly; and (12) consider how best to share findings, in discussion with evidence-users, for rapid understanding and use. Conclusion: These 12 lessons can be used to inform the development and conduct of future rapid evaluations in a range of contexts and settings.

The impact of post-hospital remote monitoring of COVID-19 patients using pulse oximetry: A national observational study using hospital activity data.

Background: There was a national roll out of 'COVID Virtual Wards' (CVW) during England's second COVID-19 wave (Autumn 2020 - Spring 2021). These services used remote pulse oximetry monitoring for COVID-19 patients following discharge from hospital. A key aim was to enable rapid detection of patient deterioration. It was anticipated that the services would support early discharge, reducing pressure on beds. This study is an evaluation of the impact of the CVW services on hospital activity. Methods: Using retrospective patient-level hospital admissions data, we built multivariate models to analyze the relationship between the implementation of CVW services and hospital activity outcomes: length of COVID-19 related stays and subsequent COVID-19 readmissions within 28 days. We used data from more than 98% of recorded COVID-19 hospital stays in England, where the patient was discharged alive between mid-August 2020 and late February 2021. Findings: We found a longer length of stay for COVID-19 patients discharged from hospitals where a CVW was available, when compared to patients discharged from hospitals where there was no CVW (adjusted IRR 1·05, 95% CI 1·01 to 1·09). We found no evidence of a relationship between the availability of CVW and subsequent rates of readmission for COVID-19 (adjusted OR 0.97, 95% CI 0.91 to 1·03). Interpretation: We found no evidence of early discharges or changes in readmissions associated with the roll out of COVID Virtual Wards across England. Our analysis made pragmatic use of national-scale hospital data, but it is possible that a lack of specific data (for example, on which patients were enrolled and on potentially important confounders) may have meant that true impacts, especially at a local level, were not ultimately discernible. It is important that future research is able to make use of better quality - preferably linked - data, from multiple sites. Funding: This is independent research funded by the National Institute for Health Research, Health Services & Delivery Research program (RSET Project no. 16/138/17; BRACE Project no. 16/138/31) and NHSE&I. NJF is an NIHR Senior Investigator.

The impact of remote home monitoring of people with COVID-19 using pulse oximetry: A national population and observational study.

BACKGROUND: Remote home monitoring of people testing positive for COVID-19 using pulse oximetry was implemented across England during the Winter of 2020/21 to identify falling blood oxygen saturation levels at an early stage. This was hypothesised to enable earlier hospital admission, reduce the need for intensive care and improve survival. This study is an evaluation of the clinical effectiveness of the pre-hospital monitoring programme, COVID oximetry @home (CO@h). METHODS: The setting was all Clinical Commissioning Group (CCG) areas in England where there were complete data on the number of people enrolled onto the programme between 2nd November 2020 and 21st February 2021. We analysed relationships at a geographical area level between the extent to which people aged 65 or over were enrolled onto the programme and outcomes over the period between November 2020 to February 2021. FINDINGS: For every 10% increase in coverage of the programme, mortality was reduced by 2% (95% confidence interval:4% reduction to 1% increase), admissions increased by 3% (-1% to 7%), in-hospital mortality fell by 3% (-8% to 3%) and lengths of stay increased by 1·8% (-1·2% to 4·9%). None of these results are statistically significant, although the confidence interval indicates that any adverse effect on mortality would be small, but a mortality reduction of up to 4% may have resulted from the programme. INTERPRETATION: There are several possible explanations for our findings. One is that CO@h did not have the hypothesised impact. Another is that the low rates of enrolment and incomplete data in many areas reduced the chances of detecting any impact that may have existed. Also, CO@h has been implemented in many different ways across the country and these may have had varying levels of effect. FUNDING: This is independent research funded by the National Institute for Health Research, Health Services & Delivery Research programme (RSET Project no. 16/138/17; BRACE Project no. 16/138/31) and NHSEI. NJF is an NIHR Senior Investigator.

Predicting who will use intensive social care: case finding tools based on linked health and social care data.

BACKGROUND: the costs of delivering health and social care services are rising as the population ages and more people live with chronic diseases. OBJECTIVES: to determine whether predictive risk models can be built that use routine health and social care data to predict which older people will begin receiving intensive social care. DESIGN: analysis of pseudonymous, person-level, data extracted from the administrative data systems of local health and social care organisations. SETTING: five primary care trust areas in England and their associated councils with social services responsibilities. SUBJECTS: people aged 75 or older registered continuously with a general practitioner in five selected areas of England (n = 155,905). METHODS: multivariate statistical analysis using a split sample of data. RESULTS: it was possible to construct models that predicted which people would begin receiving intensive social care in the coming 12 months. The performance of the models was improved by selecting a dependent variable based on a lower cost threshold as one of the definitions of commencing intensive social care. CONCLUSIONS: predictive models can be constructed that use linked, routine health and social care data for case finding in social care settings.

The implementation of remote home monitoring models during the COVID-19 pandemic in England.

BACKGROUND: There is a paucity of evidence for the implementation of remote home monitoring for COVID-19 infection. The aims of this study were to identify the key characteristics of remote home monitoring models for COVID-19 infection, explore the experiences of staff implementing these models, understand the use of data for monitoring progress against outcomes, and document variability in staffing and resource allocation. METHODS: This was a multi-site mixed methods study conducted between July and August 2020 that combined qualitative and quantitative approaches to analyse the implementation and impact of remote home monitoring models developed during the first wave of the COVID-19 pandemic in England. The study combined interviews (n = 22) with staff delivering these models across eight sites in England with the collection and analysis of data on staffing models and resource allocation. FINDINGS: The models varied in relation to the healthcare settings and mechanisms used for patient triage, monitoring and escalation. Implementation was embedded in existing staff workloads and budgets. Good communication within clinical teams, culturally-appropriate information for patients/carers and the combination of multiple approaches for patient monitoring (app and paper-based) were considered facilitators in implementation. The mean cost per monitored patient varied from £400 to £553, depending on the model. INTERPRETATION: It is necessary to provide the means for evaluating the effectiveness of these models, for example, by establishing comparator data. Future research should also focus on the sustainability of the models and patient experience (considering the extent to which some of the models exacerbate existing inequalities in access to care).

Remote home monitoring (virtual wards) for confirmed or suspected COVID-19 patients: a rapid systematic review.

BACKGROUND: the aim of this review was to analyze the implementation and impact of remote home monitoring models (virtual wards) for confirmed or suspected COVID-19 patients, identifying their main components, processes of implementation, target patient populations, impact on outcomes, costs and lessons learnt. METHODS: we carried out a rapid systematic review on models led by primary and secondary care across seven countries (US, Australia, Canada, The Netherlands, Ireland, China, UK). The main outcomes included in the review were: impact of remote home monitoring on virtual length of stay, escalation, emergency department attendance/reattendance, admission/readmission and mortality. The search was updated on February 2021. We used the PRISMA statement and the review was registered on PROSPERO (CRD: 42020202888). FINDINGS: the review included 27 articles. The aim of the models was to maintain patients safe in the appropriate setting. Most models were led by secondary care and confirmation of COVID-19 was not required (in most cases). Monitoring was carried via online platforms, paper-based systems with telephone calls or (less frequently) through wearable sensors. Models based on phone calls were considered more inclusive. Patient/career training was identified as a determining factor of success. We could not reach substantive conclusions regarding patient safety and the identification of early deterioration due to lack of standardized reporting and missing data. Economic analysis was not reported for most of the models and did not go beyond reporting resources used and the amount spent per patient monitored. INTERPRETATION: future research should focus on staff and patient experiences of care and inequalities in patients' access to care. Attention needs to be paid to the cost-effectiveness of the models and their sustainability, evaluation of their impact on patient outcomes by using comparators, and the use of risk-stratification tools.

Factors associated with variation in hospital use at the end of life in England.

OBJECTIVE: To identify the relative importance of factors influencing hospital use at the end of life. DESIGN: Retrospective cohort study of person and health system effects on hospital use in the past 12 months modelling differences in admissions, bed days and whether a person died in hospital. SETTING: Residents in England for the period 2009/2010 to 2011/2012 using Hospital Episodes Statistics (HES) data from all acute care hospitals in England funded by the National Health Service (NHS). PARTICIPANTS: 1 223 859 people registered with a GP in England who died (decedents) in England (April 2009-March 2012) with a record of NHS hospital care. MAIN OUTCOME MEASURES: Hospital admissions, and hospital bed days and place of death (in or out of hospital) in the past 12 months of life. RESULTS: The mean number of admissions in the past 12 months of life averaged 2.28 occupying 30.05 bed days-excluding 9.8% of patients with no hospital history. A total of 50.8% of people died in hospital. Difference in hospital use was associated with a range of patient descriptors (age, gender and ethnicity). The variables with the greatest 'explanatory power' were those that described the diagnoses and causes of death. So, for example, 65% of the variability in the model of hospital admissions was explained by diagnoses. Only moderate levels of variation were explained by the hospital provider variables for admissions and deaths in hospital, though the impacts on total bed days was large. CONCLUSIONS: Comparative analyses of hospital utilisation should standardise for a range of patient specific variables. Though the models indicated some degree of variability associated with individual providers, the scale of this was not great for admissions and death in hospital but the variability associated with length of stay differences suggests that attempts to optimise hospital use should look at differences in lengths of stay and bed use. This study adds important new information about variability in admissions by diagnostic group, and variability in bed days by diagnostic group and eventual cause of death.

Stable sonoluminescence within a water hammer tube.

The sonoluminescence (SL) from the collapse of a single gas bubble within a liquid can be produced repetitively using an acoustic resonator. An alternative technique using a water hammer tube, producing SL from bubbles of greater size, is described here. A sealed vertical tube partly filled with a liquid and a gas at low pressure is subjected to vertical vibrations. The oscillation of the pressure within the liquid column, due to inertial forces, excites cavitation bubbles to grow and collapse. Rotation is used to confine the bubbles to the axis of the tube. Bright SL emissions were observed in a number of liquids. Repetitive emission was produced from bubbles in condensed phosphoric acid. Bubbles of 0.4 mm ambient radius (containing 2x 10(14) xenon atoms) were excited by vibration at 35 Hz. Approximately 10(12) photons were emitted per collapse in the range 400-700 nm (over four orders of magnitude greater than the brightest SL reported previously), corresponding to a 1% efficiency of the conversion of mechanical energy into light.

Choosing a model to predict hospital admission: an observational study of new variants of predictive models for case finding.

OBJECTIVES: To test the performance of new variants of models to identify people at risk of an emergency hospital admission. We compared (1) the impact of using alternative data sources (hospital inpatient, A&E, outpatient and general practitioner (GP) electronic medical records) (2) the effects of local calibration on the performance of the models and (3) the choice of population denominators. DESIGN: Multivariate logistic regressions using person-level data adding each data set sequentially to test value of additional variables and denominators. SETTING: 5 Primary Care Trusts within England. PARTICIPANTS: 1 836 099 people aged 18-95 registered with GPs on 31 July 2009. MAIN OUTCOME MEASURES: Models to predict hospital admission and readmission were compared in terms of the positive predictive value and sensitivity for various risk strata and with the receiver operating curve C statistic. RESULTS: The addition of each data set showed moderate improvement in the number of patients identified with little or no loss of positive predictive value. However, even with inclusion of GP electronic medical record information, the algorithms identified only a small number of patients with no emergency hospital admissions in the previous 2 years. The model pooled across all sites performed almost as well as the models calibrated to local data from just one site. Using population denominators from GP registers led to better case finding. CONCLUSIONS: These models provide a basis for wider application in the National Health Service. Each of the models examined produces reasonably robust performance and offers some predictive value. The addition of more complex data adds some value, but we were unable to conclude that pooled models performed less well than those in individual sites. Choices about model should be linked to the intervention design. Characteristics of patients identified by the algorithms provide useful information in the design/costing of intervention strategies to improve care coordination/outcomes for these patients.

Overlap of hospital use and social care in older people in England.

OBJECTIVES: To link pseudonymous health and social care use data in order to determine what proportion of older people access hospital and social care services. METHODS: Retrospective analysis of linked, pseudonymous, routine service use data of people aged 75 and over (n = 133,055) drawn from the operational systems of four primary care trusts and their corresponding local authorities in England. RESULTS: Fourteen percent of older people received local authority-funded social care in one year, 59% accessed NHS hospital care and 10% accessed both types of service. Most people using social care also used a hospital service (71%). This was a higher proportion than for people who did not use social care services (57%, P < 0.001). However, the use of hospitals varied by type of social care such that the residents of care homes had fewer admissions to hospital, fewer Accident and Emergency attendances and fewer outpatient visits than people receiving high intensity home care. CONCLUSIONS: Using routine data from large populations, we have demonstrated interactions in the use of hospital care and social care for older people. Residents of care homes tend to use hospitals less frequently than people receiving home care. More detailed work is required to explain this phenomenon.

The role of matched controls in building an evidence base for hospital-avoidance schemes: a retrospective evaluation.

OBJECTIVE: To test whether two hospital-avoidance interventions altered rates of hospital use: "intermediate care" and "integrated care teams." DATA SOURCES/STUDY SETTING: Linked administrative data for England covering the period 2004 to 2009. STUDY DESIGN: This study was commissioned after the interventions had been in place for several years. We developed a method based on retrospective analysis of person-level data comparing health care use of participants with that of prognostically matched controls. DATA COLLECTION/EXTRACTION METHODS: Individuals were linked to administrative datasets through a trusted intermediary and a unique patient identifier. PRINCIPAL FINDINGS: Participants who received the intermediate care intervention showed higher rates of unscheduled hospital admission than matched controls, whereas recipients of the integrated care team intervention showed no difference. Both intervention groups showed higher rates of mortality than did their matched controls. CONCLUSIONS: These are potentially powerful techniques for assessing impacts on hospital activity. Neither intervention reduced admission rates. Although our analysis of hospital utilization controlled for a wide range of observable characteristics, the difference in mortality rates suggests that some residual confounding is likely. Evaluation is constrained when performed retrospectively, and careful interpretation is needed.

Development of a predictive model to identify inpatients at risk of re-admission within 30 days of discharge (PARR-30).

OBJECTIVES: To develop an algorithm for identifying inpatients at high risk of re-admission to a National Health Service (NHS) hospital in England within 30 days of discharge using information that can either be obtained from hospital information systems or from the patient and their notes. DESIGN: Multivariate statistical analysis of routinely collected hospital episode statistics (HES) data using logistic regression to build the predictive model. The model's performance was calculated using bootstrapping. SETTING: HES data covering all NHS hospital admissions in England. PARTICIPANTS: The NHS patients were admitted to hospital between April 2008 and March 2009 (10% sample of all admissions, n=576 868). MAIN OUTCOME MEASURES: Area under the receiver operating characteristic curve for the algorithm, together with its positive predictive value and sensitivity for a range of risk score thresholds. RESULTS: The algorithm produces a 'risk score' ranging (0-1) for each admitted patient, and the percentage of patients with a re-admission within 30 days and the mean re-admission costs of all patients are provided for 20 risk bands. At a risk score threshold of 0.5, the positive predictive value (ie, percentage of inpatients identified as high risk who were subsequently re-admitted within 30 days) was 59.2% (95% CI 58.0% to 60.5%); representing 5.4% (95% CI 5.2% to 5.6%) of all inpatients who would be re-admitted within 30 days (sensitivity). The area under the receiver operating characteristic curve was 0.70 (95% CI 0.69 to 0.70). CONCLUSIONS: We have developed a method of identifying inpatients at high risk of unplanned re-admission to NHS hospitals within 30 days of discharge. Though the models had a low sensitivity, we show how to identify subgroups of patients that contain a high proportion of patients who will be re-admitted within 30 days. Additional work is necessary to validate the model in practice.

Do 'virtual wards' reduce rates of unplanned hospital admissions, and at what cost? A research protocol using propensity matched controls.

BACKGROUND: This retrospective study will assess the extent to which multidisciplinary case management in the form of virtual wards (VWs) leads to changes in the use of health care and social care by patients at high risk of future unplanned hospital admission. VWs use the staffing, systems and daily routines of a hospital ward to deliver coordinated care to patients in their own homes. Admission to a VW is offered to patients identified by a predictive risk model as being at high risk of unplanned hospital admission in the coming 12 months. STUDY DESIGN AND DATA COLLECTION METHODS: We will compare the health care and social care use of VW patients to that of matched controls. Controls will be drawn from (a) national, and (b) local, individual-level pseudonymous routine data. The costs of setting up and running a VW will be determined from the perspectives of both health and social care organizations using a combination of administrative data, interviews and diaries. METHODS OF ANALYSIS: Using propensity score matching and prognostic matching, we will create matched comparator groups to estimate the effect size of virtual wards in reducing unplanned hospital admissions. CONCLUSIONS: THIS STUDY WILL ALLOW US TO DETERMINE RELATIVE TO MATCHED COMPARATOR GROUPS: whether VWs reduce the use of emergency hospital care; the impact, if any, of VWs on the uptake of primary care, community health services and council-funded social care; and the potential costs and savings of VWs from the perspectives of the national health service (NHS) and local authorities.

A person based formula for allocating commissioning funds to general practices in England: development of a statistical model.

OBJECTIVES: To develop a formula for allocating resources for commissioning hospital care to all general practices in England based on the health needs of the people registered in each practice DESIGN: Multivariate prospective statistical models were developed in which routinely collected electronic information from 2005-6 and 2006-7 on individuals and the areas in which they lived was used to predict their costs of hospital care in the next year, 2007-8. Data on individuals included all diagnoses recorded at any inpatient admission. Models were developed on a random sample of 5 million people and validated on a second random sample of 5 million people and a third sample of 5 million people drawn from a random sample of practices. SETTING: All general practices in England as of 1 April 2007. All NHS inpatient admissions and outpatient attendances for individuals registered with a general practice on that date. SUBJECTS: All individuals registered with a general practice in England at 1 April 2007. MAIN OUTCOME MEASURES: Power of the statistical models to predict the costs of the individual patient or each practice's registered population for 2007-8 tested with a range of metrics (R(2) reported here). Comparisons of predicted costs in 2007-8 with actual costs incurred in the same year were calculated by individual and by practice. RESULTS: Models including person level information (age, sex, and ICD-10 codes diagnostic recorded) and a range of area level information (such as socioeconomic deprivation and supply of health facilities) were most predictive of costs. After accounting for person level variables, area level variables added little explanatory power. The best models for resource allocation could predict upwards of 77% of the variation in costs at practice level, and about 12% at the person level. With these models, the predicted costs of about a third of practices would exceed or undershoot the actual costs by 10% or more. Smaller practices were more likely to be in these groups. CONCLUSIONS: A model was developed that performed well by international standards, and could be used for allocations to practices for commissioning. The best formulas, however, could predict only about 12% of the variation in next year's costs of most inpatient and outpatient NHS care for each individual. Person-based diagnostic data significantly added to the predictive power of the models.