1. Firmado digitalmente por NOMBRE FERNANDEZ ENGO JOSE - NIF
NOMBRE FERNANDEZ 31254607E
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NIF 31254607E, c=es, o=FNMT, ou=fnmt clase 2 ca
ENGO JOSE - NIF 31254607E Motivo: Autor
Fecha: 2012.04.24 12:17:40 +02'00'
The use of colective intelligence algorithms in clinical data mining. A way to a non-intrusive
clinical support decision system. The idea.
Abstract: Nowadays, modern Healthcare IT Systems try to have access to timely and accurate
patient medical information. However, in our Clinical System based in Primary Attention
Pratitioners and Hospitals Specialists, every clininician has his own diagnosis and treatment
from the same symptoms, that is to say the same original data. That’s no scientific foundation
to goal a qualified medicine. A collective intelligence system aplied on clinical decisions can
face clinicians againts their usual endogamic way of see their profession.
Keywords: collective intelligence, decision support systems, medicine based on evidence
No sustainable Healthcare System
Different diagnosis from the same symptoms, different treatments for the same diagnosis over
the same patient profile, a system where the third leading cause of death are medical errors
(surgical errors, mistaken diagnostics, incorrect prescribing, inadequate cares, etc.) reveals a
clearly unsustainable healthcare system. Not only because its economic perspective, indeed in
a public supported healthcare system these numbers are unassumable, but (and mainly) it´s a
science.
The Doctor’s Syndrom: scientific egocentrism
“My patient’s medical record is mine, at least the part I’ve written.” “Yah, well, you’ve already
a radiology report from your shoulder but I need another one I trust”… these are very typical
sentences we can hear when we’re patients as well as we’re IT consultants. And the famous
“It’s my instinct”. There is no space for methodologies, learning process… With this attitude is
very difficult to achieve an efficient way to apply all the power in information management
that IT systems can offer. I would like to say that there are evidences of a slow change in these
positions; we’ve clinical therapies development by interdisciplinary groups leading by phisics,
engineers, and so on. For example researches of cancer therapies based on the physic of
interphases model, researches of new ways of drug administration throught nanotechnologies
dispositives, etc. Of course, very effective interdisciplinary groups leading by clinicians. But the
daily job of a Doctor remains a very local decision, if not individual, system.
Of course, clinicians need to see an advantage in the use of IT systems, in a correct way of
recording, etc. and one of the missions of IT professional is to make it easy but neither doctors
nor the systems have the necessary level of mature. Perhaps we must make a critical change.
An on evidence-based model based on extended clinical decisions… and collective
intelligence treatment
We introduce here our differential point of view. Usually, the collective intelligence has a social
and unexpertise focus. These powerfull algorithms are only applied to social networks and
from a patient point of view.
And, in the other way, we have a collective that has no regular systems to give an answer to
the same problems with the same boundary variables. So, I think we can apply the same tools

2. than in a normal social network with a little change in the focus. We’re going to inspect a
professional knowledge background. And we’ve a very interesting collective from the CI point
of view because this is a very large collective and they register many times every day with the
necessary variability.
Although some of these algorithms are likely to apply in every decision support system
(recommendations, grouping, etc.) but in this kind of environment we can apply the wide
range of utilities: filtering documents to detect the most influential opinions (diagnosis from a
concrete group of symptoms , create tree decision models based in the evaluation of the
clinician and his tendency to a concrete group of decisions, create a “priced” model to
diagnosis, find independent characteristics to detect tendencies or external influences in
prescriptions (for example), find experts with an optimal ratio in the liability of their diagnosis,
etc.
Electronical Health Record: the ground level
Obviously, we need a complete and solid EHR system to achieve a solid and general clinical
support decision system.
- First we need a solid relationship between symptoms and diagnosis so we need an EHR
with a good level of codification in both sides. In this way we can use ontological
systems to treat the clinician’s natural language.
- Second we need to contrast the statistical validation of every decision against the
results of analize these relationships with collective intelligence algorithms. I think
that’s going to be a surprise on the revelation of so many disfunctions.
And, of course, we need to obtain a solid and interoperable EHR model to apply our tools. In
this way we need a Reference Model implemented as well as a Clinical Information Model to
evolve our system without ballast. This system must be:
- federated to integrate all the views of the bussines inside the organisation
- with a solid Governance Model to keep a sustainable grown in horizontal way as well
as in a vertical way and
- with a solid trusteeing system to guarantee the contribution from systems outside the
organisation. That’s a normal way in anglosaxon countries but in our Public Model is
not so usual.
In this point we can already apply every Collective Intelligence tool, especially in a wide
Healthcare Public System (like the Andalusian, Swedish or the Obama’s Project). We need a
little amount of use to a correct implementation of these tools but this is only a question of
time and education. Time is a very relative variable in a wide system. For example, the
Andalusian appointment system has 400M appoinments recorded. But the education has more
difficult solution because nowadays it‘s depending from the Doctor’s mind. And the quality of
registered data in an EHR system is a sine qua non condition to a statistical treatment.
Toward an integrated system

3. Usually, to engage the level for CI application we must walk through two previous levels:
Expert Systems and Group Decision Support Systems. This is a normal evolution in this kind of
systems. However I think that in a Clinical Wide Environment we can try to apply the three
levels in only one step growing over business domains and not over the scope of the system.
This point of view has a very strong relationship with my vision to apply a SOA model to a
Public Healthcare System (the cornerstone of my speech in the Master).
Conclusions
Altought the motivations reflected in “Doctor’s Simdrom…” are obviously an overstatement,
the ground of the reflection is in order. Today, medicine is not a logical science in the wide
sense of the term. A Medicine based on evidence has yet many enemies, especially among
Primary Care Doctors. So the medical knowledge system is yet like an opinion based system
with a short susbtrate of logical thought but not a solid data based system. And first records in
wide EHR systems have not the necessary quality to apply statistical tools with a good level of
reliability. Perhaps we can plain to change the entire framework with a correct orientation of
the feedback to the clinicians.
References
- Toby Segaran “Collective Intelligence” (2008) – O’Really
- “Integrating Decision Support Systems: Expert, Group and Collective Inteligence”
Stephen Diasio, Nuria Agell – ESADE Bussines School