Modelling the flow of congestive heart failure patients through a hospital system

The number of hospital admissions in England due to heart failure is projected to increase by over 50% during the next 25 years. This will incur greater pressures on hospital managers to allocate resources in an effective manner. A reliable indicator for measuring the quantity of resources consumed by hospital patients is their length of stay (LOS) in care. This paper proposes modelling the length of time heart failure patients spend in hospital using a special type of Markov model, where the flow of patients through hospital can be thought of as consisting of three stages of care—short-, medium- and longer-term care. If it is assumed that new admissions into the ward are replacements for discharges, such a model may be used to investigate the case-mix of patients in hospital and the expected patient turnover during some specified period of time. An example is illustrated by considering hospital admissions to a Belfast hospital in Northern Ireland, between 2000 and 2004.

HOME-BASED REHABILITATION AND ITS IMPACT ON HOSPITAL UTILIZATION

There is compelling evidence for the effectiveness of home-based occupational therapy and physiotherapy rehabilitation for community dwelling elderly who may struggle with basic activities and the functions of daily living and mobility. Nonetheless, an estimated 2% of home care’s elderly clients receive these therapies. Ontario’s home care data indicates that 78% of clients that could benefit from these specific therapies are not receiving them. The study examined a subset of elderly clients receiving home care following a hospital discharge during 2009-2010. The aim of this study was to: understand the difference between those home care clients who received occupational therapy or physiotherapy and those who did not; and determine if receiving these therapies impacted the utilization of hospital emergency departments and inpatient admissions. A retrospective cohort design and multivariate and survival analysis of hospital and home care administrative data structured the study. Results suggest that home-based rehabilitation is offered to a minority of the home care population. Distinct client characteristics and process variables significantly associated with the increased likelihood of receiving home-based occupational and physical therapies included: clients who were older, females, admitted to home care from hospital inpatient units, assessed as non-acute for clinical and service needs and required more home making support and assistance with activities of daily living. Almost one quarter of the total sample returned to hospital. Visits to emergency departments accounted for the greater part of hospital utilization and primarily for sub-acute general symptoms and signs, post-procedural complications, infections or acute episodes from chronic obstructive pulmonary disease and renal failure. Slightly over half of the clients returning to hospital did not receive home-based rehabilitation. Clients who received occupational therapy returned to the hospital sooner following their home care admission whereas clients receiving physiotherapy spent the longest time before rehospitalizing. The majority of the clients receiving occupational therapy were admitted to home care having just resolved sub-acute conditions or symptoms, many of which are known to influence functional and physical decline. Moreover, analysis of process variables indicated that the wait time for a referral to occupational therapy was two times longer compared to physiotherapy. These same clients also waited, on average, over one month before an occupational therapist’s first visit. The need to discriminate who receives home-based rehabilitation is essential to understanding how specific therapies contribute to improving systems outcomes. This study is the first examination that focuses specifically on home-based occupational therapy and physiotherapy rehabilitation and the client characteristics and process variables associated with receiving/not receiving these therapies and the impact these factors have on the time-to-rehospitalization.

Risk adjustment for hospital use using social security data: cross sectional small area analysis

Objectives: To identify demographic and socioeconomic determinants of need for acute hospital treatment at small area level. To establish whether there is a relation between poverty and use of inpatient services. To devise a risk adjustment formula for distributing public funds for hospital services using, as far as possible, variables that can be updated between censuses. Design: Cross sectional analysis. Spatial interactive modelling was used to quantify the proximity of the population to health service facilities. Two stage weighted least squares regression was used to model use against supply of hospital and community services and a wide range of potential needs drivers including health, socioeconomic census variables, uptake of income support and family credit, and religious denomination. Setting: Northern Ireland. Main outcome measure: Intensity of use of inpatient services. Results: After endogeneity of supply and use was taken into account, a statistical model was produced that predicted use based on five variables: income support, family credit, elderly people living alone, all ages standardised mortality ratio, and low birth weight. The main effect of the formula produced is to move resources from urban to rural areas. Conclusions: This work has produced a population risk adjustment formula for acute hospital treatment in which four of the five variables can be updated annually rather than relying on census derived data. Inclusion of the social security data makes a substantial difference to the model and to the results produced by the formula.