SweetP - answers and questions from the front line
Ian Wells1
1
Clinical Computing Section, Department of Medical Physics, Royal Surrey County Hospital,
Egerton Road, Guildford, Surrey GU2 7XX UK, +44 1483 464 039,
ian.wells@royalsurrey.nhs.uk
Keywords: Diabetes, clinical database, medical reasoning
Abstract
SweetP, a specialized database for Diabetes and Endocrinology clinics, is one of a number of
systems developed by the Clinical Computing section in the Medical Physics Department at the
Royal Surrey County Hospital. This work is an integral part of the Trust’s EPR strategy, and
aims to fill the gaps between the basic clinical functions in the Trust’s central EPR system
(OASIS from Capula Elan) and the expectations of the more demanding clinicians.
SweetP has been in routine use since 1999, and has recorded over 4900 patient episodes. It has
been well received by both the clinicians and their medical secretary, and attracted positive
comments in the fieldwork report for the "Powerful yet fragile instruments of change" project.
SweetP has been built using the 4D range of software tools [1], which have been the first choice
for in-house development in the Trust since 1987, and is tightly integrated with OASIS.
Support and on-going enhancement is through a ‘cooperative’ of internal and external 4D
developers, and SweetP is freely available under an open source code licence.
The SweetP project has supplied answers to a number of questions, not the least of which being
how to convert busy and demanding clinicians from paper records to an EPR solution. It has
also provided an invaluable platform for clinical computing research projects and a testing
ground for new ideas. The experience gained from both the first version of SweetP and other
clinical database developments during the last three years has led to the construction of an
advanced clinical database shell which is now being used for most new projects. SweetP itself
is currently being re-implemented using this shell and will incorporate a number of new
features including support for the full NSF Diabetes User Dataset.
One of the major requirements for the new version is a mechanism to control the behaviour of
data items as they are entered and displayed, and so the shell incorporates an EAV (entity
attribute value) manager inspired by the classic medical expert system MYCIN [2]. This
should meet the current requirements and provide a platform for further investigations into this
important area.
The cognitive aspects of the real-time use of computers by clinicians rarely feature in the
specifications for clinical computer systems, but are of considerable local interest and concern.
Although the range and definition of the actual data items recorded can be the subject of
lengthy debate, the impact that entering the data may have on the clinical decisions being made
seems to attract little attention.
If one is to accept the empirical evidence that clinicians generally reason backwards, and are
restricted by the capacity of short term memory to a maximum of around five simultaneous
hypotheses [3], then the order and emphasis of the information displayed can potentially
influence the outcome. If an additional hypothesis is suggested then there is a danger that the
correct hypothesis may be prematurely discarded or merged with another leading to a less
acceptable clinical decision.
Acknowledgement
I would like to thank Dr Alex Taylor of the Digital World Research Centre, University of
Surrey, for encouraging me to submit this abstract and for making available his fieldwork
report.
References
1.
www.4duk.com
2.
Buchanan BG, Shortliffe EH eds, Rule-Based Expert Systems: the MYCIN Experiments
of the Stanford Heuristic Programming Project, Addison-Wesley 1985.
3.
Elstein AS, Shulman LS, Sprafka SA, Medical Problem Solving: an Analysis of Clinical
Reasoning, Harvard University Press, 1978