Download CSE5810: Intro to Biomedical Informatics

CSE5810: Intro to Biomedical Informatics
CSE
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The Role of AI in
Clinical Decision
Support
Saahil Moledina
University of
Connecticut
saahil.moledina@ucon
n.edu
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Clinical Decision Support in
Biomedical Informatics:
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Introduction:
 The Clinical Decision support in biomedical
informatics is the knowledge that is provided to
assist the clinician and/or patients for assisting
them in making decisions regarding choice of
treatment.
 These decisions are tried to be made easy by
giving the knowledge of the outcomes and
complications of the treatment chosen.
 Now, Clinical Decision Support systems are
systems are systems designed to do the clinical
decision support and process them using AI and
machine learning.
 For these systems to work efficiently it needs to
combine the efforts of the patients, clinicians,
nurses and decision aids.
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Research Areas:
 Artificial Intelligence
 Machine Learning
 User Interfaces
 Data Mining
 Data warehousing
 Medicine
 Algorithms
Benefits of CDS:
 Increased quality of care and enhanced health
outcomes
 Avoidance of errors and adverse events
 Improved efficiency, cost-benefit, and provider
and patient satisfaction
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Stakeholders:
 Patients
 Clinicians, nurses, Physicians.
 Vendors.
 Hospitals/clinics.
Standards:
 HL7 version 3( representation of patient data for
Clinical decision support).
 Infobutton ( Context-Aware Retrieval Application)
 GLIF (knowledge representation)
 Arden Syntax (knowledge representation)
 GELLO (Common Expression Language)
 Unified Medical Language System and component
terminologies (e.g., SNOMED, LOINC, RxNorm)
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An important aspect for clinical decision making is the
patients perspective of their health problems and
preferences for the treatment.
One of the biggest problem in clinical decision support
systems is integrating the patient perspective in
decision making.
For this we need to make the use of Shared Decision
making (SDM).
This results into a new problem on how to develop a
clinical decision support system for SDM.
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Features of a Clinical Decision Support with SDM are:
 Provide Clinicians with the health problems
associated with a patient’s illness.
 Treatment Options
 Benefits and Risks of the treatment.
 Patient Preferences.
 Acceptable to the clinicians.
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To make such a system knowledge about the
following things are vital:
 Clinical Domain
 for understanding the decision problem. E.g. coronary
artery.

Decision Science and research of SDM
 to draw out the patient’s preferences.

Biomedical informatics
 Algorithms and technologies.

Organizational knowledge
 To adapt the system to the practices and workflows of
the clinicians .
 To adapt to the settings where these systems are used.
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There are two major types of system used for drawing
out the patient’s preferences in clinical decision
making:
 DA(Decision Aids)
 Assists patients in difficult decision making.
 It does that by giving the patients information about the
various choices about the various treatments available
and its outcome.
 DA’s need to be working in supplementary of the
clinician counseling.
 Its seen that the results of the patients working with
clinicians and DA’s have been really good.
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 DA’s are useful only when a decision is difficult. E.g.
more than one treatment recommended, outcomes of the
treatment uncertain, complications, tradeoff between
outcomes or small chance of a grave outcome.
 The drawback with DA’s is that these systems have
narrow segment of decisions of choice of treatments
hence other systems preferred.
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CHOICE(Creating better Health Outcomes by
Improving Communication about patients
Expectations):
 This system is for the clinicians to teach them how to
draw out the preferences from a patient.
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 It is a system that is mostly used by nurses which
collect the preferences from the patient bedside and
integrate it in the model.
 It has been seen that the congruence of the actual
problem patient is having and patient’s self assessment
is very high.
 It is also easy to use.
 Hence, CHOICE is used to develop, implement and
evaluate CDSS for SDM.
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Model:
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Artificial Intelligence is used to process and analyze
the data for Clinical Decision support since the
framework of system in Biology and Medicine are
very complex.
There are a lot of challenges that one has to face to
implement these techniques.
These challenges are:
 feature selection
 Visualization
 classification
 data warehousing
 data mining
 analysis of the biological networks
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Key Technical Problems:
 Challenges in implementing AI.
 Training the Dataset.
 Developing generic Algorithm to handle data.
 Integrating it with EHR’s
 Out of Control Alerts.
Key People Problems:
 Using the CDSS.
 Poor UI.
 Training the people to use the system.
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Conclusion:
 Hence, this shows that the use of Clinical decision
support system is necessary since it gives an
improvement in the patient satisfaction because of
his involvement in the decision of the treatment
used to cure him/her. It also shows that there are
many challenges that we have to face to make it as
accurate and efficient as possible. But also one
thing is clear that it is still not possible to replace
the physician, clinician or nurse it can only assist
them and it can never be fully trusted since it can
never be 100% accurate.
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Thank You
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Questions
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