=Paper=
{{Paper
|id=Vol-1520/paper21
|storemode=property
|title=Why Hybrid Case-Based Reasoning Will Change the Future of Health Science and Healthcare
|pdfUrl=https://ceur-ws.org/Vol-1520/paper21.pdf
|volume=Vol-1520
|dblpUrl=https://dblp.org/rec/conf/iccbr/Funk15
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==Why Hybrid Case-Based Reasoning Will Change the Future of Health Science and Healthcare==
https://ceur-ws.org/Vol-1520/paper21.pdf
199
Why hybrid Case-Based Reasoning will Change
the Future of Health Science and Healthcare
Peter Funk
School of Innovation, Design and Engineering,
Mälardalen University, PO Box 883 SE-721 23, Västerås, Sweden
{firstname.lastname}@mdh.se
Abstract, The rapid development of the medical field makes it impossible even for
experts in the field to keep up with new treatments and experience. Already in 2010
all medical knowledge doubled in 3,5 years, to keep up to date with all development
even in a narrow field is today far beyond human capacity. The need for decision
support is increasingly important to ensure optimal treatment of patients, especially
if patients are not “standard patients” matching a gold standard treatment. By
ensuring confidentiality and collecting structured cases on a large scale will enable
clinical decision support far beyond what is possible today and will be a major leap
in healthcare.
Already in 2010 all medical knowledge doubled every 3.5 years and is expected to double
every 7 months in 2020 [1]. 20 years ago physicians met and discussed medical cases over
a cup of coffee, an efficient way of sharing experience and disseminating knowledge.
Times are changing; physicians say they don’t have time for this any more. In a modern
and efficient healthcare organisation there is no longer room for experience sharing and
patients are treated according to guidelines. Many physicians I have discussed with admit
that the consequence is that as much as 30% of patients don’t receive optimal treatment.
The amount of medical knowledge is already huge, so it often takes years for new results
to spread and even specialists are not able to keep up to date with all developments in
their own area. Also some physicians mentioned the use of “golden standard” having the
consequence that not all patients get an optimal treatment on an individual level [2]. To
illustrate this situation Fig. 1 shows what some physicians see as a problem.
The need for more individualized treatment is recognized today, but to make this come
true is not easy for a number of reasons, one suggested reason given by a physician is the
lack of support in hospitals for individualized treatments “No one questions your actions
if you follow a gold standard and something goes wrong, but if you divert from it and
something goes wrong, you are in a difficult situation”. Sharing experience on patients
Copyright © 2015 for this paper by its authors. Copying permitted for private and
academic purposes. In Proceedings of the ICCBR 2015 Workshops. Frankfurt, Germany.
200
that do not fit the standard treatment is essential in order to reach a higher degree of
individualization. And it is not always possible to wait for evidence. A valuable ability in
humans is that we are able to learn from anecdotal cases and improve performance.
Fig 1. If treatment Y is better than treatment X, then it may be tempting to make treatment Y to a
recommended gold standard. But what about the 26% which don’t get the best treatment? If we can
identify which individuals respond best on X and which respond best to Y, we are able to give every
patient their optimal treatment.
Key problems to improve with high relevance in the medical area:
• Limited time to share experience among clinicians/physicians.
• Limited time to acquire relevant knowledge/experience related to patients
• Keeping up with all new medical knowledge
• Dissemination of new knowledge and experience at the point of need
• How to individualise treatment of patients so all get an optimal treatment
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1. What can Case-based reasoning offer?
Imagine you have a patient in front of you, the CBR system immediately says “your
patient’s symptoms are very similar to 47 other patients in Europe, where there are 4
different treatments, patients with treatment C recovered within 2 weeks, twice as fast as
with treatment A and B, there is no difference in treatment cost. Based on my experience
(all my cases) a modification of treatment C is recommended (due to your patient having
diabetes). In France there is an alternative treatment D (8 patients) with recovery time of
less than 10 days, the cost for this treatment is 5 times higher”. The system offers
• Advice at the point of care tailored for the patient and physician
• Dissemination of experience from new treatments/procedures
• Second opinion for an experienced clinician, transfer experience to a less
experienced clinicians
• It can explain and justify all its conclusions and findings
We can provide all this with CBR and I cannot see how this can be solved without
case-based clinical decision support systems. All the different foundational methods and
techniques are already available in research, to mention some [3,4,5], but to achieve a
transformation of the healthcare system we need a large scale approach since it requires a
change in how patient cases are recorded and stored in order to preserve privacy and
enable experience reuse.
To achieve this we need more elaborate case structures enabling hybrid case-based
reasoning including experience sharing, knowledge discovery, data mining. Many
approaches also address distributed knowledge sources [8] and under uncertainty [9] and
case-based reasoning theory is today increasingly diverse and advanced able to address
challenges preciously difficult to solve [10] and there is progress in integrating electronic
patient record system with CBR [11]. One approach developed for medical application
used in the Pain-Out project [5,6] is a two-layered case structure, see Fig. 2.
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Fig 2. Extended case structure used in clinical application [3]
When explaining the concept of case-based reasoning for clinicians, the response is
often “such a tool would dramatically change and improve my work and healthcare”:
• Patient records become sources of experience and knowledge and provide
supplementary information not currently accessible for diagnosis and treatment by
clinicians at the point of care
• Clinicians will be able to easily and instantly share experience around specific case
issues
• Dissemination of new clinical experience will be efficient and at the point of need.
• Patients will receive personalised and more informed diagnosis and care.
2. Example case
One project where we explored some of the issues is in the PAIN-OUT decision
support tool. With over 40,000 cases as our “experience base“ we developed a tool, giving
clinicians relevant information specifically compiled for the patient at hand (comorbidity,
age, weight and other factors taken into account). Similar patients are identified amongst
the cases and the treatment and outcome is analyzed and presented.
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Fig 3. Example of a clinical decision support tool that provides physician with personalised
information tailored for the patient at hand.
Fig 4. Example of how medical cases can be used to support clinicians.
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3. Conclusions
We have summarized some important issues where case-based decision support can
help.
Clinical case based reasoning enables:
• second opinion for an experienced clinician
• dissemination of experience from new treatments/procedures
• transfer experience to a less experienced clinician
• link to relevant research and clinical studies
• other clinicians experience (annotated cases)
The requirements are that cases are collected where symptoms, diagnosis and outcome of
treatment is recorded. In many medical registries this is unfortunately not available,
especially the outcome is rarely recorded and it is often difficult or impossible to
reconstruct the cases.
Reference
[1] Peter Densen, MD. Challenges and Opportunities Facing Medical Education. Trans Am Clin Climatol
Assoc. 2011; 122: 48–58.PMCID: PMC3116346
[2] Timmermans, Stefan, and Marc Berg. The gold standard: The challenge of evidence-based medicine and
standardization in health care. Temple University Press, 2010.
[3] Begum, Shahina, et al. "Case-based reasoning systems in the health sciences: a survey of recent trends and
developments." Systems, Man, and Cybernetics, Part C: Applications and Reviews, IEEE Transactions on
41.4 (2011): 421-434.
[4] C Marling, S Montani, I Bichindaritz, P Funk, Synergistic case-based reasoning in medical domains
Expert systems with applications, 41. 2 (2014): 249-259.
[5] Ahmed M.U., Funk P., A Computer Aided System for Post-operative Pain Treatment Combining
Knowledge Discovery and Case-Based Reasoning, In Case-Based Reasoning Research and Development,
pp. 3-16. Springer Berlin Heidelberg, 2012.
[6] Rothaug et. al, Patients' perception of postoperative pain management: Validation of the International Pain
Outcomes (IPO) Questionnaire, The Journal of Pain 14.11 (2013): 1361-1370, Churchill Livingstone.
[7] Ahmed, Mobyen Uddin, and Peter Funk. "Mining rare cases in post-operative pain by means of outlier
detection." Signal Processing and Information Technology (ISSPIT), 2011 IEEE International Symposium
on. IEEE, 2011.
[8] Reichle, Meike, Kerstin Bach, and Klaus-Dieter Althoff. "Knowledge engineering within the application-
independent architecture SEASALT." International Journal of Knowledge Engineering and Data Mining
1.3 (2010): 202-215.
[9] Bruland, Tore, Agnar Aamodt, and Helge Langseth. "Architectures integrating case-based reasoning and
bayesian networks for clinical decision support." Intelligent Information Processing V. Springer Berlin
Heidelberg, 2010. 82-91.
[10] Richter, Michael M., and Rosina O. Weber. "Case-Based Reasoning." A Textbook (2013). ISBN 978-3-
642-40166-4, Springer Verlag.
[11] van den Branden, M., Wiratunga, N., Burton, D., & Craw, S. (2011). Integrating case-based reasoning
with an electronic patient record system. Artificial Intelligence in Medicine, 51(2), 117-123.