Press

1. Virginia Commonwealth University Health System presents Smart Anesthesia Manager in Cerner Users North Atlantic RUG conference
    

2. Perimatics Optimizer highlighted in University of Washington study published in Journal of American College of Surgeons.
    

3. GeekWire: Univ. of Washington spinout takes on $5.2B hospital scheduling problem with AI analytics
    

4. Perimatics spins off from UW COMOTION® and licenses SMART ANESTHESIA MANAGER 
    

5. GeekWire: Health-tech-startup aims to make surgery more efficient through data science
    

6. Global University Venturing: Perimatics wakes up to $1M

Publications

1. Nair BG, Newman S, Peterson GN, Wu W, Schwid HA; Feedback Mechanisms Including Real-Time Electronic Alerts to Achieve Near 100% Timely Prophylactic Antibiotic Administration in Surgical Cases.  Anesthesia & Analgesia; 111(5):1293-1300; 2010. (https://pubmed.ncbi.nlm.nih.gov/20841414/)
    

2. Nair BG, Peterson GN, Schwid HA; Electronic Reminders to Improve Timely Antibiotic Doses. [Letter to the editor] Anesthesia & Analgesia; 113(5):1284; 2011. (https://pubmed.ncbi.nlm.nih.gov/22021803/)
    

3. Nair BG, Newman SF, Peterson GN, Schwid HA; Automated electronic reminders to improve redosing of antibiotics during surgical cases: comparison of two approaches. Surg Infect; 12(1):57-63; 2011. (https://pubmed.ncbi.nlm.nih.gov/21166624/)
    

4. Nair BG, Peterson GN, Newman S, Wu W, Schwid HA; Improved Documentation of β-blocker Quality Measure through Anesthesia Information Management System and Real-time Notification of Documentation Errors. Jt Comm J Qual Patient Saf; 38(6):283-8; 2012. (https://pubmed.ncbi.nlm.nih.gov/22737780/)
    

5. Nair BG, Newman SF, Peterson GN, Schwid HA; Smart Anesthesia Manager™ - A Real-time Decision Support System for Anesthesia Care during Surgery. IEEE Trans Biomed Eng; 60(1):207-10; 2013. (https://pubmed.ncbi.nlm.nih.gov/22736635/)
    

6. Nair BG, Peterson GN, Neradilek MB, Newman SF, Huang EY, Schwid HA. Reducing Wastage of Inhalation Anesthetics using Real-time Decision Support to Notify of Excessive Fresh Gas Flow. Anesthesiology; 118(4):874-84; 2013. (https://pubmed.ncbi.nlm.nih.gov/23442753/)
    

7. Nair BG, Peterson GN, Schwid HA. In reply. Anesthesiology;119(5):1228; 2013. (https://pubmed.ncbi.nlm.nih.gov/24195951/)
    

8. Nair BG, Horibe M, Newman S, Wu W, Schwid HA; Near Real-time Notification of Gaps in Cuff Blood Pressure Recordings for Improved Patient Monitoring. J Clin Monit Comput; 27(3):265-71; 2013. (https://pubmed.ncbi.nlm.nih.gov/23283561/)
    

9. Nair BG, Horibe M, Newman S, Wu W, Peterson GN, Schwid HA. AIMS-based Near Real-time Decision Support to Manage Intraoperative Hypotension and Hypertension. Anesth Analg; 118(1):206-14; 2014. (https://pubmed.ncbi.nlm.nih.gov/24247227/)
    

10. Jelacic S, Bowdle A, Nair BG, Kusulos D, Bower L, Togashi K. A System for Anesthesia Drug Administration Using Barcode Technology: The Codonics Safe Label System and Smart Anesthesia Manager.Anesth Analg;121(2):410-21; 2015. (https://pubmed.ncbi.nlm.nih.gov/24859078/)
     

11. Nair BG, Grunzweig K, Peterson GN, Horibe M, Neradilek MB, Newman S, Van Norman G, Schwid HA, Hao W, Hirsch IB, Dellinger P; Intraoperative Blood Glucose Management: Impact of a Real-Time Decision Support System on Adherence to Institutional Protocol. J Clin Monit Comput; 30(3):301-12; 2016. (https://pubmed.ncbi.nlm.nih.gov/26067402/)
     

12. Nair BG, Gabel E, Hofer I, Schwid H, Cannesson M. Intraoperative Clinical Decision Support for Anesthesia — a Narrative Review of Available Systems. Anesth Analg; 124(2):603-617; 2017. (https://pubmed.ncbi.nlm.nih.gov/28099325/)
     

13. Kiatchai T, Colletti AA, Lyons L, Grant RS, Vavilala MS, Nair BG. Development and Feasibility Evaluation of a Real-Time Clinical Decision Support for Trauma Anesthesia Care. Applied Clinical Informatics; 8(1):80-96; 2017. (https://pubmed.ncbi.nlm.nih.gov/28119992/)
     

14. Shah AC, Nair BG, Spiekerman CF, Bollag LA.  Process Optimization and Digital Quality Improvement to Enhance Timely Initiation of Epidural Infusions and Postoperative Pain Control.  Anesth Analg; 128(5):953-961; 2019. (https://pubmed.ncbi.nlm.nih.gov/30138173/)
     

15. Colletti AA, Kiatchai T, Lyons VH, Nair BG, Grant RM, Vavilala MS. Feasibility and Indicator Outcomes Using Computerized Clinical Decision Support in Pediatric Traumatic Brain Injury Anesthesia Care. Paediatr Anaesth; 29(3):271-279; 2019. (https://pubmed.ncbi.nlm.nih.gov/ 30609176/)
     

16. Shah AC, Oh D, Xue AH, Lang JD, Nair BG. An Electronic Handoff Tool to Facilitate Transfer of Care from Anesthesia to Nursing in Intensive Care Units. Health Informatics Journal; 25(1):3-16; 2019. (https://pubmed.ncbi.nlm.nih.gov/29231091/)
     

17. Bartek MA, Saxena RC, Solomon S, Fong CT, Behara LD, Venigandla R, Velagapudi K, Lang JD, Nair BG. Improving Operating Room Efficiency: Machine Learning Approach to Predict Case-Time Duration. J Am Coll Surg; 229(4):346-354; 2019. (https://pubmed.ncbi.nlm.nih.gov/31310851/)
     

18. Bartek MA, Saxena RC, Solomon S, Fong CT, Lang JD, Nair BG, Behara LD, Venigandla R, Velagapudi K. Accurately Predicting Case-Time Duration: In reply to Dexter and Epstein. J Am Coll Surg; 229(6):634-635; 2019. (https://pubmed.ncbi.nlm.nih.gov/31767058/)
     

19. Nair AA, Velagapudi M, Behara L, Venigandla R, Fong CT, Horibe M, Nair BG. Hyper-G: An Artificial Intelligence Tool for Optimal Decision-Making and Management of Blood Glucose Levels in Surgery Patients. Methods Inf Med; 58(2-03):79-85; 2019. (https://pubmed.ncbi.nlm.nih.gov/31398727/)
     

20. Nair AA, Velagapudi MA, Lang JA, Behara L, Venigandla R, Velagapudi N, Fong CT, Horibe M, Lang JD, Nair BG. Machine learning approach to predict postoperative opioid requirements in ambulatory surgery patients. PLoS One; 15(7):e0236833; 2020. (https://pubmed.ncbi.nlm.nih.gov/32735604/)