These 3 presentations address operational issues of paramount imp
ortance within the healthcare industry with a view to using statistics for
the benefit of patients.
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Topic: Mo
del-informed decision support for planning and execution of clinical trial
s.
\nSuresh Ankolekar\, Cytel\nThe planning and e
xecution of clinical trials involve several decisions. What should be the
target duration for the trial? What should be the enrolment levels and lim
its per regions/countries? How many enrolment sites per regions/countries?
When do we open/close the sites? When do we plan the interim analyses? Ho
w do we optimise the enrolment plan? How do we optimise the drug supply? H
ow do we re-optimise at interim stages? These are hard questions that are
often addressed rather arbitrarily using ordinary spreadsheets. Decision-m
akers need to look beyond the averages and quantify uncertainty to be able
to manage it. In this talk we will focus on statistical models to support
enrolment planning. We will also talk about core simulation models used t
o optimise the drug supply. The emphasis will be on the simplicity of the
models that can be easily implemented\, even in spreadsheets.
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Topic: Recruitment modelling on in flight studies.
\nK
irstie McKay\, Covance\nHow modelling is used in Covan
ce to support study teams make patient and study delivery decision on in f
light studies with case study examples. \; How do we address recruitme
nt questions such as:
\n·\; \; \; \; Assessing an
d comparing site recruitment performance?
\n·\; \; \;
\; If recruitment is not on track\, what can we do?
\n·\;&
nbsp\; \; \; What is the best way to identify sites to focus our
attention on for recruitment? \;
\n·\; \; \; &nbs
p\; Once a study is underway and real recruitment data is available how do
we assess if timelines are on track or that \; \;specific study
requirements may be met (target patient subgroup requirement)? \;
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\nAlongside patient recruitment we need to monitor and assess when
an endpoint study may complete:
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nbsp\;What if a patient is only assessed for endpoint once every 6 months
&ndash\; how do we assess if we&rsquo\;re tracking as expected? \;
\n·\; \; \; \; \;Or what if the endpoints are n
ot tracking as expected how do we use the observed endpoint data to re-for
ecast the study end date?
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\nTopic: Outpatie
nt capacity planning tool for an NHS Hospital Trust.
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David Worthington\, Lancaster University\n
The NHS has a maximum waiting time target from referral to treatment (RTT)
of 18 weeks\, and hospital trusts that do not meet this target can face f
inancial penalties. Hospital trusts that fear that they are in danger of e
xceeding this target for some of their patients typically review their out
patient and inpatient capacities for the coming months in order to decide
whether extra capacity is needed\, and if so where it is needed.
\nIm
portant factors to consider in undertaking this exercise are the numbers a
nd types of appointments that patients need between referral and treatment
\, the numbers of patients currently partway through their pathway\, the n
umbers of new referrals expected to arrive and the capacities of the vario
us clinics to see patients. And if current capacities do not seem sufficie
nt to meet the target\, what revised capacities are required?
\nThis
talk will describe work undertaken to develop a capacity planning tool for
a Hospital Trust. It is a spreadsheet-based &lsquo\;what if &hellip\;. ?&
rsquo\; model which first applies simple stocks and flows principles to ca
lculate clinic throughputs\, from which future patient waiting times can t
hen be estimated. \;
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