Trends in hospital inpatient episodes for signs, symptoms and ill-defined conditions: Observational study of older people's hospital episodes in England, 1995-2003

School of Nursing and Midwifery, University of Southampton, Highfield, Southampton, SO17 1BJ, UK.
Age and Ageing (Impact Factor: 3.64). 08/2008; 37(4):455-8. DOI: 10.1093/ageing/afn099
Source: PubMed
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    • "Nearly 22% of the sample received R-codes, suggesting that the incidence rate in acute medical settings is higher than would be predicted from international data on all hospital admissions amongst older people. This finding contrasts with estimates of up to 10% of all hospital admissions internationally [21] and 7% in England [11], but is consistent with higher rates in studies focused on urgent care settings [9,10,14]. If these are indeed avoidable admissions, the scale of the problem presented by ill-defined conditions in acute medical settings may have been underestimated. "
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    ABSTRACT: Rising rates of unplanned admissions among older people are placing unprecedented demand on health services internationally. Unplanned hospital admissions for ill-defined conditions (coded with an R prefix within Chapter XVIII of the International Classification of Diseases-10) have been targeted for admission avoidance strategies, but little is known about these admissions. The aim of this study was to determine the incidence and factors predicting ill-defined (R-coded) hospital admissions of older people and their association with health outcomes. Retrospective analysis of unplanned hospital admissions to general internal and geriatric medicine wards in one hospital over 12 months (2002) with follow-up for 36 months. The study was carried out in an acute teaching hospital in England. The participants were all people aged 65 and over with unplanned hospital admissions to general internal and geriatric medicine. Independent variables included time of admission, residence at admission, route of admission to hospital, age, gender, comorbidity measured by count of diagnoses. Main outcome measures were primary diagnosis (ill-defined versus other diagnostic code), death during the hospital stay, deaths to 36 months, readmissions within 36 months, discharge destination and length of hospital stay. Incidence of R-codes at discharge was 21.6%, but was higher in general internal than geriatric medicine (25.6% v 14.1% respectively). Age, gender and co-morbidity were not significant predictors of R-code diagnoses. Admission via the emergency department (ED), out of normal general practitioner (GP) hours, under the care of general medicine and from non-residential care settings increased the risk of receiving R-codes. R-coded patients had a significantly shorter length of stay (5.91 days difference, 95% CI 4.47, 7.35), were less likely to die (hazard ratio 0.71, 95%CI 0.59, 0.85) at any point, but were as likely to be readmitted as other patients (hazard ratio 0.96 (95% CI 0.88, 1.05). R-coded diagnoses accounted for 1/5 of emergency admission episodes, higher than anticipated from total English hospital admissions, but comparable with rates reported in similar settings in other countries. Unexpectedly, age did not predict R-coded diagnosis at discharge. Lower mortality and length of stay support the view that these are avoidable admissions, but readmission rates particularly for further R-coded admissions indicate on-going health care needs. Patient characteristics did not predict R-coding, but organisational features, particularly admission via the ED, out of normal GP hours and via general internal medicine, were important and may offer opportunity for admission reduction strategies.
    BMC Geriatrics 10/2011; 11(1):62. DOI:10.1186/1471-2318-11-62 · 1.68 Impact Factor
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    ABSTRACT: Key Words: Rise in emergency admissions, increasing medical admissions, short stay emergency admission, mechanisms for growth, vague diagnoses, signs & symptoms, demography, same (zero) day stay admissions, new infectious immune disease Key Points • Seven mechanisms for growth appear to regulate the trends in emergency admissions. • Demographic change, re-admissions and rising expectations account for less than a 2% per annum increase and fail to explain the more dynamic changes. • A major part of the gap between the demographic and actual increase is due to short stay (mainly zero day) emergency admissions and it is perhaps better to treat these as an extension of ambulatory care (which includes A&E) rather than emergency admissions per se. • Length of stay (LOS) efficiency for emergency admission should be re-calibrated by excluding zero day stay emergency admissions as should apparent rates of 'admission' for particular diagnoses. • The seasonal variation in admission to the medical group of specialties is shown to be very high and this refutes claims that emergency admissions are 'predictable'. • If hospitals and primary care are to allocate scarce resources to minimise costs then supporting tools which forecast health based on the weather and environment are an absolute necessity. Series Summary Part one of this series discusses how the more widely recognised factors such as an aging population, re-admissions and increasing expectations of medicine only contribute to a baseline increase in emergency admissions. Seasonal variation is discussed in the context of the predictability or otherwise of emergency admissions in response to changes in the weather and the wider environment. However these are unable to explain the observed cyclical events occurring every four to six years in medicine and even longer cycles in surgical and trauma admissions. These cycles are discussed in part two while part three investigates the implications to bed planning. The current methods for estimating the size of hospitals and bed pools within hospitals are shown to be inappropriate to the real needs of emergency care. Part four investigates financial risk associated with emergency admissions.
    British Journal of Health Care Management 04/2009; 15(4). DOI:10.12968/bjhc.2009.15.4.41722
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    ABSTRACT: Explores demographic and socio-economic factors behind patient non-attendance at outpatient clinics.
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