Predicting Patient Flows and Hospital Case-Mix

Mayhew, Les; Gibberd, Robert; Hall, Hannah

doi:10.1068/a180619

Cited by 24 publications

(12 citation statements)

References 8 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The absolute difference between predicted and normative utilisation by small area is preferred, as an utilisation unit has the same value across geographic areas. This objective function 6,7,8 respects the principle of transfers (i.e. the measure of inequity decreases when there are transfers 6 Since equal opportunity of access for those in equal need across geographic areas is also a policy objective, it is desirable that redistribution of supply in the MPMs minimises differences between supply and need at the district level.…”

Section: Structure Of the Problemmentioning

confidence: 99%

“…The model minimises differences between predicted and normative utilisation numbers. Mayhew and Leonardi [22] have used a similar model with an Minimise average distance to be travelled by small area Minimise total average distance in the system Efficiency 7 Maximise total utilisation in the system Redistribution Minimise numbers of 'losers' and (eventually) 'winners' as a result of redistribution at the hospital level 8 objective function that uses the square of the difference between observed/predicted and expected/normative utilisation. The absolute difference between predicted and normative utilisation by small area is preferred, as an utilisation unit has the same value across geographic areas.…”

Section: Structure Of the Problemmentioning

confidence: 99%

“…Hence, the outputs from each model are juxtaposed with the estimated distribution of needs for the Portuguese district level, presented in [30]. 7 Efficiency is specifically defined here as the total level of (potential or predicted) production in the system, as measured by utilisation. Utilisation of hospital declines with the distance because patients have to travel -but this varies by hospital type, and flows depend on hospital size [28].…”

Section: Structure Of the Problemmentioning

confidence: 99%

“…We now briefly describe the strengths and weaknesses of each group of location-allocation models. 2 Spatial interaction models are a form of probability interaction modelling [6] that use information on population numbers, hospital size, distance, and a decay function to replicate the current pattern of patient flows between demand and treatment zones (for example [7][8][9]). Current spatial interaction models suffer from three methodological weaknesses.…”

Section: Literature Reviewmentioning

confidence: 99%

See 3 more Smart Citations

Modelling the redistribution of hospital supply to achieve equity taking account of patient's behaviour

Oliveira

Bevan

2006

Health Care Manage Sci

View full text Add to dashboard Cite

Policies that seek to achieve geographic equity in countries with a National Health Services (NHS) require information on how to change the distribution of supply to achieve greater equity in access and utilisation. Previous methods for analysing the impact of hospital changes have relied on crude assumptions on patients' behaviour in using hospitals. The approach developed in this study is a multi-modelling one based on two mathematical programming location-allocation models to redistribute hospital supply using different objective functions and assumptions about the utilisation behaviour of patients. These models show how different policy objectives seeking equity of geographic access or utilisation produce different results and imply trade-offs in terms of reduction in total utilisation.

show abstract

Section: Structure Of the Problemmentioning

confidence: 99%

Section: Structure Of the Problemmentioning

confidence: 99%

Section: Structure Of the Problemmentioning

confidence: 99%

Section: Literature Reviewmentioning

confidence: 99%

See 2 more Smart Citations

Modelling the redistribution of hospital supply to achieve equity taking account of patient's behaviour

Oliveira

Bevan

2006

Health Care Manage Sci

View full text Add to dashboard Cite

show abstract

“…In the last 20 years, several gravity models have been constructed to model hospital catchments because patients are increasingly choosing hospitals based on their accessibility [3,14,18,20,34]. The name 'gravity' model arises from their equivalence with Newton's law of gravity: force of attraction is proportional to the product of the masses (or size) of the two bodies involved and inversely proportional to a power of the distance between them [2,28].…”

mentioning

confidence: 99%

Modelling catchment areas for secondary care providers: a case study

Jones

Wardlaw

Crouch

et al. 2011

Health Care Manag Sci

View full text Add to dashboard Cite

Hospitals need to understand patient flows in an increasingly competitive health economy. New initiatives like Patient Choice and the Darzi Review further increase this demand. Essential to understanding patient flows are demographic and geographic profiles of health care service providers, known as 'catchment areas' and 'catchment populations'. This information helps Primary Care Trusts (PCTs) to review how their populations are accessing services, measure inequalities and commission services; likewise it assists Secondary Care Providers (SCPs) to measure and assess potential gains in market share, redesign services, evaluate admission thresholds and plan financial budgets. Unlike PCTs, SCPs do not operate within fixed geographic boundaries. Traditionally, SCPs have used administrative boundaries or arbitrary drive times to model catchment areas. Neither approach satisfactorily represents current patient flows. Furthermore, these techniques are time-consuming and can be challenging for healthcare managers to exploit. This paper presents three different approaches to define catchment areas, each more detailed than the previous method. The first approach 'First Past the Post' defines catchment areas by allocating a dominant SCP to each Census Output Area (OA). The SCP with the highest proportion of activity within each OA is considered the dominant SCP. The second approach 'Proportional Flow' allocates activity proportionally to each OA. This approach allows for cross-boundary flows to be captured in a catchment area. The third and final approach uses a gravity model to define a catchment area, which incorporates drive or travel time into the analysis. Comparing approaches helps healthcare providers to understand whether using more traditional and simplistic approaches to define catchment areas and populations achieves the same or similar results as complex mathematical modelling. This paper has demonstrated, using a case study of Manchester, that when estimating the catchment area of a planned new hospital, the extra level of detail provided by the gravity model may prove necessary. However, in virtually all other applications, the Proportional Flow method produced the optimal model for catchment populations in Manchester, based on several criteria: it produced the smallest RMS error; it addressed cross-boundary flows; the data used to create the catchment was readily available to SCPs; and it was simpler to reproduce than the gravity model method. Further work is needed to address how the Proportional Flow method can be used to reflect service redesign and handle OAs with zero or low activity. A next step should be the rolling out of the method across England and looking at further drill downs of data such as catchment by Healthcare Resource Group (HRG) rather than specialty level.

show abstract