===== Clinical Informatics =====

Clinical informatics is the application of computing methods including artificial intelligence in the delivery healthcare services. Predictive and causal models using big data and artificial intelligence will be increasingly used in clinical decision-making. These models will power a new generation of clinical decision support tools in the coming decade. The [[http://www.ccri.thevislab.com/|Center for Clinical Research Informatics (CCRI)]] in the [[http://www.dbmi.pitt.edu/|Department of Biomedical Informatics]] focuses on the development of clinical decision support tools that aid in very specific clinical tasks and are powered by predictive and causal models.

==== Pipeline ====
  * Step 1: develop models - Develop artificial intelligence models with excellent performance for a tightly specified clinical task 
  * Step 2: engineer decision support tool - Build decision support tool that applies models developed in step 1 to clinical data in real time
  * Step 3: evaluate prospectively - Evaluate decision support tool prospectively to establish efficacy and effect on clinical outcomes
  * Step 4: obtain certification and deploy clinically - Obtain FDA certification and license and deploy clinically

==== Example projects ====
  * The [[http://www.thevislab.com/lab/doku.php?id=lemr|Learning Electronic Medical Record (LEMR) system]] is an intelligent EMR system that highlights relevant patient data in the EMR in the Intensive Care Unit. The LEMR system uses predictive models to draw the physician’s attention to the right data of the right patient at the right time.	

  * Deciding whether to attempt salvage of an infected central venous catheter (CVC) can be challenging.  We are developing predictive models to predict retention of CVC lines. A clinical decision support tool that is powered by predictive models will help clinicians in making decisions about infected CVC lines.

  * Realtime intraoperative neurophysiological monitoring is used to identify adverse brain events during surgical procedures. We are developing predictive models to identify adverse events using intraoperative neurophysiological monitoring. A clinical decision support tool that is powered by predictive models will aid neurophysiologists in intraoperative monitoring.


==== Opportunities for research are available at various stages of training ====
  * For high school students through the summer [[https://upci.upmc.edu/UPCIAcademy/overview.cfm |Computer Science, Biology and Biomedical Informatics (CoSBBI)]] program
  * For undergraduate students through [[http://people.cs.pitt.edu/~ramirez/capstone/|Capstone projects]] in Computer Science and Bioinformatics majors
  * For medical students through [[http://www.omed.pitt.edu/curriculum/areas-of-concentration.php/|Areas of Concentration (AoC)]] program in [[http://www.omed.pitt.edu/curriculum/documents/BBIAOCDescriptionMarch2017.pdf|Bioengineering, Biotechnology, and Innovation]]
  * For MD/PhD trainees in the [[http://www.mdphd.pitt.edu/graduate-programs|biomedical informatics]] graduate program
  * For residents and fellows through research rotations
  * For graduate students in [[http://www.dbmi.pitt.edu/training-programs|biomedical informatics]], the [[http://www.isp.pitt.edu/|Intelligent System Program (ISP)]], and [[https://cs.pitt.edu/|computer science]]
  * For post-docs with graduate training in biomedical informatics, computer science, artificial intelligence, statistics, machine learning and related disciplines


==== Knowledge and skills in the following areas are desired ====
  * Probability and statistics
  * Computer science and programming
  * Artificial intelligence and machine learning
  * Experience with medical data


==== Contact ====
Shyam Visweswaran, MD, PhD\\
Director of Clinical Informatics, Department of Biomedical Informatics\\
Email: shv3@pitt.edu