The analyses show that costs and outcomes of intermediate
care are partly explained by differences in
patient and service characteristics, however, the impact
of service skill mix is limited (Table 2). There is weak
evidence (p = 0.090) that the ratio of support staff to
qualified staff impact on health gains (measured by the
change in EQ-5D) seen during care, with higher proportions
of support staff being associated with greater
improvement. There is stronger evidence (p = 0.011)
that higher numbers of different types of staff are associated
with lower costs.
There are several possible explanations for the greater
improvements in EQ-5D in patients when who utilise
more support staff (SS) relative to qualified staff (QS).
Qualitative feedback from the same study suggests that
support staff spend more time with patients than qualified
staff, and perform more of the ‘hands on’ work,
which may lead to better improvements in outcome.Alternatively, it could mean that additional SS allow a
better service to be delivered, for example, increasing
the number of SS staff may allow for service development.
This second interpretation is in line with findings
seen in general practice [33].
This second interpretation is less plausible as some
aspects of service expansion will be controlled for by the
‘total number of staff’ variable within the regression. In
other words, increasing SS staff without reducing QS
staff is not responsible for the better outcomes associated
with the higher support staff to qualified staff ratios.
Other possible explanations are that intermediate care
patients may not require the intensive or specialised
treatment of support staff, thus a higher ratio of SS to
QS may be the optimum combination that will lead to
better outcomes. Similarly, it may be that those patients
who do require more specialised input are directed to
services that provide that input.
The impact of greater numbers of different types of
staff on costs could reflect economies produced by specialisation.
Understanding how costs were calculated
within the National Evaluation is important before considering
this issue further. Cost per patient was calculated
based on a cost per day for the entire service
based on budgets and an individual patient’s length of
care. So, cost per patient is driven either by the service
budget or length of stay. As the relationship between
number of different types of staff and length of care is
small and statistically insignificant, it appears that the
effect is through the size of the service budget. The
mechanism by which service budgets are reduced is
open to speculation. Two possible processes are reduced
number of visits and/or the use of smaller numbers of
staff
The analyses show that costs and outcomes of intermediate
care are partly explained by differences in
patient and service characteristics, however, the impact
of service skill mix is limited (Table 2). There is weak
evidence (p = 0.090) that the ratio of support staff to
qualified staff impact on health gains (measured by the
change in EQ-5D) seen during care, with higher proportions
of support staff being associated with greater
improvement. There is stronger evidence (p = 0.011)
that higher numbers of different types of staff are associated
with lower costs.
There are several possible explanations for the greater
improvements in EQ-5D in patients when who utilise
more support staff (SS) relative to qualified staff (QS).
Qualitative feedback from the same study suggests that
support staff spend more time with patients than qualified
staff, and perform more of the ‘hands on’ work,
which may lead to better improvements in outcome.Alternatively, it could mean that additional SS allow a
better service to be delivered, for example, increasing
the number of SS staff may allow for service development.
This second interpretation is in line with findings
seen in general practice [33].
This second interpretation is less plausible as some
aspects of service expansion will be controlled for by the
‘total number of staff’ variable within the regression. In
other words, increasing SS staff without reducing QS
staff is not responsible for the better outcomes associated
with the higher support staff to qualified staff ratios.
Other possible explanations are that intermediate care
patients may not require the intensive or specialised
treatment of support staff, thus a higher ratio of SS to
QS may be the optimum combination that will lead to
better outcomes. Similarly, it may be that those patients
who do require more specialised input are directed to
services that provide that input.
The impact of greater numbers of different types of
staff on costs could reflect economies produced by specialisation.
Understanding how costs were calculated
within the National Evaluation is important before considering
this issue further. Cost per patient was calculated
based on a cost per day for the entire service
based on budgets and an individual patient’s length of
care. So, cost per patient is driven either by the service
budget or length of stay. As the relationship between
number of different types of staff and length of care is
small and statistically insignificant, it appears that the
effect is through the size of the service budget. The
mechanism by which service budgets are reduced is
open to speculation. Two possible processes are reduced
number of visits and/or the use of smaller numbers of
staff
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