Jennifer Dixon

Effect of telehealth on use of secondary care and mortality: findings from the Whole System Demonstrator cluster randomised trial

Objective To assess the effect of home based telehealth interventions on the use of secondary healthcare and mortality. Design Pragmatic, multisite, cluster randomised trial comparing telehealth with usual care, using data from routine administrative datasets. General practice was the unit of randomisation. We allocated practices using a minimisation algorithm, and did analyses by intention to treat. Setting 179 general practices in three areas in England. Participants 3230 people with diabetes, chronic obstructive pulmonary disease, or heart failure recruited from practices between May 2008 and November 2009. Interventions Telehealth involved remote exchange of data between patients and healthcare professionals as part of patients’ diagnosis and management. Usual care reflected the range of services available in the trial sites, excluding telehealth. Main outcome measure Proportion of patients admitted to hospital during 12 month trial period. Results Patient characteristics were similar at baseline. Compared with controls, the intervention group had a lower admission proportion within 12 month follow-up (odds ratio 0.82, 95% confidence interval 0.70 to 0.97, P=0.017). Mortality at 12 months was also lower for intervention patients than for controls (4.6% v 8.3%; odds ratio 0.54, 0.39 to 0.75, P<0.001). These differences in admissions and mortality remained significant after adjustment. The mean number of emergency admissions per head also differed between groups (crude rates, intervention 0.54 v control 0.68); these changes were significant in unadjusted comparisons (incidence rate ratio 0.81, 0.65 to 1.00, P=0.046) and after adjusting for a predictive risk score, but not after adjusting for baseline characteristics. Length of hospital stay was shorter for intervention patients than for controls (mean bed days per head 4.87 v 5.68; geometric mean difference −0.64 days, −1.14 to −0.10, P=0.023, which remained significant after adjustment). Observed differences in other forms of hospital use, including notional costs, were not significant in general. Differences in emergency admissions were greatest at the beginning of the trial, during which we observed a particularly large increase for the control group. Conclusions Telehealth is associated with lower mortality and emergency admission rates. The reasons for the short term increases in admissions for the control group are not clear, but the trial recruitment processes could have had an effect. Trial registration number International Standard Randomised Controlled Trial Number Register ISRCTN43002091.

Case finding for patients at risk of readmission to hospital: development of algorithm to identify high risk patients

Abstract Objective To develop a method of identifying patients at high risk of readmission to hospital in the next 12 months for practical use by primary care trusts and general practices in the NHS in England. Data sources Data from hospital episode statistics showing all admissions in NHS trusts in England over five years, 1999-2000 to 2003-4; data from the 2001 census for England. Population All residents in England admitted to hospital in the previous four years with a subset of “reference” conditions for which improved management may help to prevent future admissions. Design Multivariate statistical analysis of routinely collected data to develop an algorithm to predict patients at highest risk of readmission in the next 12 months. The algorithm was developed by using a 10% sample of hospital episode statistics data for all of England for the period indicated. The coefficients for 21 most powerful (and statistically significant) variables were then applied against a second 10% test sample to validate the findings of the algorithm from the first sample. Results The key factors predicting subsequent admission included age, sex, ethnicity, number of previous admissions, and clinical condition. The algorithm produces a risk score (from 0 to 100) for each patient admitted with a reference condition. At a risk score threshold of 50, the algorithm identified 54.3% of patients admitted with a reference condition who would have an admission in the next 12 months; 34.7% of patients were “flagged” incorrectly (they would not have a subsequent admission). At risk score threshold levels of 70 and 80, the rate of incorrectly “flagged” patients dropped to 22.6% and 15.7%, but the algorithm found a lower percentage of patients who would be readmitted. The algorithm is made freely available to primary care trusts via a website. Conclusions A method of predicting individual patients at highest risk of readmission to hospital in the next 12 months has been developed, which has a reasonable level of sensitivity and specificity. Using various assumptions a “business case” has been modelled to demonstrate to primary care trusts and practices the potential costs and impact of an intervention using the algorithm to reduce hospital admissions.

Rethinking management of chronic diseases.

Recent organisational changes to the NHS are bound to affect the care of patients with chronic diseases. But will they help or hinder?

Mind the gap: the extent of the NHS nursing shortage

The NHS is struggling to recruit and retain nursing and midwifery staff in a time of high turnover rates and low morale. The problems are most acute in inner cities and teaching trusts. The government is tackling the crisis, but the reasons behind the staffing shortages are complex The government has a mission to “modernise” Britains NHS. Success will depend on NHS staff—in particular, whether their numbers can be boosted, whether staff can change how they work, and whether they can be motivated to “go the extra mile” for the NHS. Yet the service is struggling to attract and retain staff in crucial areas, particularly in nursing and midwifery. Here we assess the extent of recruitment and retention problems in nursing in England, comparing acute NHS trusts in London with those in other cities. In another article in this same issue we examine the governments initiatives for tackling these problems.1 #### Summary points The nursing and midwifery workforce comprises two broad groups of staff working in the NHS. The first group comprises registered nurses and registered midwives, who have a diploma or degree and who have registered with the Nursing and Midwifery Council (before 1 April 2002, the UK Central Council …

Primary care trusts

Premature reorganisation, with mergers, may be harmful

Conditions for Which Onset or Hospital Admission is Potentially Preventable by Timely and Effective Ambulatory Care

Objectives: To identify, using a consensus development process, a list of common conditions likely to be ambulatory care sensitive (ACS); i.e. conditions for which practicable improvements in access to timely and effective ambulatory care in the English National Health Service would either reduce the incidence of the condition or avoid substantial proportions of current hospital admissions. Methods: Three panels of clinicians each reviewed about a third of an initial list of 174 conditions commonly recorded as hospital discharge diagnoses for residents of the North West Thames Region. For each condition, panellists estimated the proportion of cases currently admitted to hospital for which, given timely and effective ambulatory care: onset of disease could have been prevented; admission for existing disease could have been prevented; admission, once indicated, should take place within 48 hours. After an introductory meeting to discuss and clarify the task, panel members completed three rounds of a questionnaire, with postal feedback between each round, and a second meeting to discuss interim results before the final round. Seventeen general practitioners (GPs) and 17 hospital specialists working in the region comprised the panels. Results: The panels considered that for 30 of the 174 conditions at least 70% of admissions to hospital could be avoided, either by prevention of disease onset or timely and effective ambulatory care, though predominantly through the latter. For each of a further 66 conditions, 50–69% of admissions could be prevented. Within-panel agreement between hospital specialists and GPs was generally good, although the GPs tended to give slightly higher scores for avoidability of admissions than the specialists. There was marked convergence of scores in succeeding rounds. Conclusions: Although a consensus-based list of ACS conditions cannot be definitive, the clear view of the panels was that the scope for avoiding admission through better ambulatory care is very substantial.

What do we know about fundholding in general practice

The general practice fundholding scheme was introduced four years ago. So far its impact has not been formally evaluated nationally, but review of published research shows some trends. Fundholding has curbed prescribing costs and given general practitioners greater power to lever improvements in hospital services--for example, reducing waiting times for hospital treatment--but fundholding practices may have received more money than non-fundholding practices. The impact of fundholding on transactions costs, equity, and quality of care (particularly for patients of non-fundholding general practitioners) is unknown. Research into costly reforms such as fundholding needs to be coordinated.

Effect of telecare on use of health and social care services: findings from the Whole Systems Demonstrator cluster randomised trial

Objective: to assess the impact of telecare on the use of social and health care. Part of the evaluation of the Whole Systems Demonstrator trial. Participants and setting: a total of 2,600 people with social care needs were recruited from 217 general practices in three areas in England. Design: a cluster randomised trial comparing telecare with usual care, general practice being the unit of randomisation. Participants were followed up for 12 months and analyses were conducted as intention-to-treat. Data sources: trial data were linked at the person level to administrative data sets on care funded at least in part by local authorities or the National Health Service. Main outcome measures: the proportion of people admitted to hospital within 12 months. Secondary endpoints included mortality, rates of secondary care use (seven different metrics), contacts with general practitioners and practice nurses, proportion of people admitted to permanent residential or nursing care, weeks in domiciliary social care and notional costs. Results: 46.8% of intervention participants were admitted to hospital, compared with 49.2% of controls. Unadjusted differences were not statistically significant (odds ratio: 0.90, 95% CI: 0.75–1.07, P = 0.211). They reached statistical significance after adjusting for baseline covariates, but this was not replicated when adjusting for the predictive risk score. Secondary metrics including impacts on social care use were not statistically significant. Conclusions: telecare as implemented in the Whole Systems Demonstrator trial did not lead to significant reductions in service use, at least in terms of results assessed over 12 months. International Standard Randomised Controlled Trial Number Register ISRCTN43002091.

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 (R2 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.

Is secondary preventive care improving? Observational study of 10-year trends in emergency admissions for conditions amenable to ambulatory care

Objective To identify trends in emergency admissions for patients with clinical conditions classed as ‘ambulatory care sensitive’ (ACS) and assess if reductions might be due to improvements in preventive care. Design Observational study of routinely collected hospital admission data from March 2001 to April 2011. Admission rates were calculated at the population level using national population estimates for area of residence. Participants All emergency admissions to National Health Service (NHS) hospitals in England from April 2001 to March 2011 for people residents in England. Main outcome measures Age-standardised emergency admissions rates for each of 27 specific ACS conditions (ICD-10 codes recorded as primary or secondary diagnoses). Results Between April 2001 and March 2011 the number of admissions for ACS conditions increased by 40%. When ACS conditions were defined solely on primary diagnosis, the increase was less at 35% and similar to the increase in emergency admissions for non-ACS conditions. Age-standardised rates of emergency admission for ACS conditions had increased by 25%, and there were notable variations by age group and by individual condition. Overall, the greatest increases were for urinary tract infection, pyelonephritis, pneumonia, gastroenteritis and chronic obstructive pulmonary disease. There were significant reductions in emergency admission rates for angina, perforated ulcers and pelvic inflammatory diseases but the scale of these successes was relatively small. Conclusions Increases in rates of emergency admissions suggest that efforts to improve the preventive management of certain clinical conditions have failed to reduce the demand for emergency care. Tackling the demand for hospital care needs more radical approaches than those adopted hitherto if reductions in emergency admission rates for ACS conditions overall are to be seen as a positive outcome of for NHS.