Reducing avoidable hospital admission in older people: Health status, frailty and predicting risk of ill-defined conditions diagnoses in older people admitted with collapse
Introduction
Recent UK health policy has focused on reducing unplanned hospital admissions, instead emphasizing care in the community and chronic disease management (DH, 1992, DH, 2000b, DH, 2001, DH, 2004, King's Fund, 2010). However, admission reduction targets have not been met and unplanned admissions continue to rise at an unsustainable rate (Purdy et al., 2009, Robinson, 2010), placing pressure on emergency care systems and hampering planned care. Attention has therefore shifted to targeting specific groups at high risk of avoidable admission (Wanless Report, 2006), for example, patients with ill-defined conditions. Internationally, after hospital discharge, diagnoses are recorded using codes from the World Health Organisation (WHO) ICD version 9 or 10. ‘Ill-defined conditions’ is the term that was used in ICD-9 to describe episodes with no definitive diagnosis (‘symptoms, signs and ill-defined conditions’) and although the newer ICD-10 refers to these as R-codes (symptoms, signs and abnormal laboratory findings’), the term ‘ill-defined conditions’ has remained in clinical use.
These codes are associated with increasing age and account for almost 552,730 hospital episodes per year in England for the over 75 s, more than 13.9% of all inpatient episodes for that age group (Hospital Episodes and Statistics, 2011, Walsh, 2007, Walsh et al., 2008). Ill-defined conditions are an international problem (Walsh, 2007, Walsh et al., 2011), with estimates of incidence ranging from 5% to 8% (Anderson et al., 1999, Condelius et al., 2008, Elmstahl and Wahlfrid, 1999, Halfon et al., 2002, Ingold et al., 2000, Kardas and Ratajczyk-Pakalska, 2003, Meerding et al., 1998). Recent reports from the US indicate an incidence of 7% (Agency for Healthcare Research and Quality (AHRQ), 2009) and this is higher in Australia (9% – Australian Institute of Health and Welfare (AIHW), 2011). In England, incidence is notably higher in emergency care settings where these discharge codes are assigned to approximately 20% of medical discharges among older people (Walsh and Roberts, 2005, Walsh et al., 2011).
Analysis of outcomes for patients assigned these codes suggests that their health problems may be less acute (Walsh et al., 2011) and indicative of frailty and functional problems (Walsh et al., 2012, Wanless, 2006). Organisational factors, such as out-of-hours admission, are important predictors of this type of diagnosis (Walsh et al., 2011) and policy reports have suggested that improved community management may be effective in reducing the number of patients assigned R-codes at discharge (DH, 2000a, Wanless, 2006). An alternative approach to the management of these patients would be to divert them from hospital when they present to unscheduled care services. However, there is no clinical evidence to support the view that these patients could avoid hospital admission yet this information is needed if we are to develop alternative management strategies.
This study set out to provide the preliminary clinical data to inform future management and care planning for this patient group. We aimed to describe the medical and functional status of older patients assigned codes for ill-defined conditions (R-codes), including the prevalence of frailty, and to determine whether routinely available clinical and demographic information could predict assignment these codes at discharge. We posed the following research questions:
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What is the medical and functional status of older people discharged from hospital with an ICD-10 R-code?
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Can routinely collected clinical and demographic data predict assignment of R-codes at discharge?
Section snippets
Design
A pilot, prospective, observational study to describe the physical and functional status of older people discharged from hospital with ICD-10 R-codes and identify predictors of those codes.
Study setting and population
The study was based in a large secondary and tertiary care hospital in England. The recruitment strategy aimed to identify patients who presented at hospital with symptoms that would be most likely to result in ill-defined condition/R-codes. The hospital coding team confirmed that the presenting complaints of
Results
Recruitment and data collection took place between November 2006 and August 2008. 342 patients were screened for study participation. Of these, 35 refused and 227 were excluded due to cognitive impairment (n = 98), participation in another study (n = 4), or they were too unwell to consent (n = 125). Eighty patients were recruited into the study.
The majority were admitted with ‘collapse’ as their ‘presenting complaint’, followed by ‘fall’, and only 1 patient presented with ‘syncope’. At discharge,
Discussion
Despite current policy attention on admission avoidance for patients assigned ill-defined conditions codes, this is the first study to describe the health status of this patient group. The primary aim was to recruit a sample with a high likelihood of ill-defined conditions diagnosis at discharge in order to describe the health and functional status of this group. The sampling strategy yielded a high proportion of ill-defined codes, as anticipated, reflecting the utility of patients admitted
Conclusions
Identification and diversion of older people at risk of hospital admission for ill-defined conditions is problematic. Differentiating patients at risk of ill-defined conditions using routine clinical data is unlikely to be a fruitful strategy because no clinical features were significant predictors of this diagnostic coding. However, this study highlighted the importance of social and organisational factors in increasing risk of IDC diagnosis for older people. This, combined with the high rates
Funding
This study was funded by the Faculty of Medicine, Health and Life Sciences at the University of Southampton.
Conflict of interest statement
None.
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