Presentation on theme: "MOLEDINA-1 CSE 5810 CSE5810: Intro to Biomedical Informatics The Role of AI in Clinical Decision Support Saahil Moledina University of Connecticut"— Presentation transcript:
MOLEDINA-1 CSE 5810 CSE5810: Intro to Biomedical Informatics The Role of AI in Clinical Decision Support Saahil Moledina University of Connecticut saahil.moledina@ucon n.edu
MOLEDINA-2 CSE 5810 Clinical Decision Support in Biomedical Informatics:
MOLEDINA-3 CSE 5810 CDS in Biomedical Informatics 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.
MOLEDINA-4 CSE 5810 CDS in Biomedical Informatics 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
MOLEDINA-5 CSE 5810 CDS in Biomedical Informatics 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)
MOLEDINA-6 CSE 5810 CDS in Biomedical Informatics 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.
MOLEDINA-7 CSE 5810 CDS in Biomedical Informatics
MOLEDINA-8 CSE 5810 CDS in Biomedical Informatics 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.
MOLEDINA-9 CSE 5810 CDS in Biomedical Informatics 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.
MOLEDINA-10 CSE 5810 CDS in Biomedical Informatics 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.
MOLEDINA-11 CSE 5810 CDS in Biomedical Informatics 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. 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.
MOLEDINA-12 CSE 5810 CDS in Biomedical Informatics 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.
MOLEDINA-13 CSE 5810 CDS in Biomedical Informatics Model:
MOLEDINA-14 CSE 5810 CDS in Biomedical Informatics
MOLEDINA-15 CSE 5810 CDS in Biomedical Informatics 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
MOLEDINA-16 CSE 5810 CDS in Biomedical Informatics 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.
MOLEDINA-17 CSE 5810 CDS in Biomedical Informatics 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.