Mobile Networks and Applications

, Volume 12, Issue 2–3, pp 215–228 | Cite as

A Pervasive Computing System for the Operating Room of the Future

  • Sheetal Agarwal
  • Anupam Joshi
  • Tim Finin
  • Yelena Yesha
  • Tim Ganous
Article

Abstract

We describe a prototype context aware perioperative information system to capture and interpret data in an operating room of the future. The captured data is used to construct the context of the surgical procedure and detect medically significant events. Such events, and other state information, are used to automatically construct an electronic medical encounter record (EMR). The EMR records and correlates significant medical data and video streams with an inferred higher-level event model of the surgery. Information from sensors such as Radio Frequency Identification (RFID) tags provides basic context information including the presence of medical staff, devices, instruments and medication in the operating room (OR). Patient monitoring systems and sensors such as pulse oximeters and anesthesia machines provide continuous streams of physiological data. These low level data streams are processed to generate higher-level primitive events, such as a nurse entering the OR. A hierarchical knowledge-based event detection system correlates primitive events, patient data and workflow data to infer high-level events, such as the onset of anesthesia. The resulting EMR provides medical staff with a permanent record of the surgery that can be used for subsequent evaluation and training. The system can also be used to detect potentially significant errors. It seeks to automate some of the tasks done by nursing staff today that detracts from their ability to attend to the patient.

Keywords

pervasive computing system operating room electronic medical encounter record medical informatics 

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References

  1. 1.
    Bardram JE (2003) Hospitals of the future ubiquitous computing support for medical work in hospitals. In: Proceedings of UbiHealth 2003 the 2nd international workshop on ubiquitous computing for pervasive healthcare applicationsGoogle Scholar
  2. 2.
    Bardram JE (2004) Applications of context-aware computing in hospital work examples and design principles. In: Proceedings of the 2004 ACM symposium on applied computingGoogle Scholar
  3. 3.
    Bar-Or A, Goddeau D, Healey J, Kontothanassis L, Logan B, Nelson A, Thong J (2004) BioStream: a system architecture for real-time processing of physiological signals. Technical report, Hewlett-Packard LabsGoogle Scholar
  4. 4.
    Bloom M (1994) Techniques to identify clinical contexts during automated data analysis. J Clin Monit Comput 10(1): 17–22CrossRefGoogle Scholar
  5. 5.
    Bodenreider O (2004) The unified medical language system (UMLS): integrating biomedical terminology. Nucleic Acids Res 32:267–270CrossRefGoogle Scholar
  6. 6.
    Briggs B (2003) Electronic records. protection on the road to patient safety. Health Data Manag 11(5):34–8, 40Google Scholar
  7. 7.
    Chandrasekaran S (2003) TelegraphCQ: continuous dataflow processing for an uncertain world. CIDR 2003Google Scholar
  8. 8.
    CIMIT (2006) For integration of medicine and innovative technology. http://www.cimit.org/, accessed April 2006
  9. 9.
    Coleman WP, Seigel JH, Giovanni I, DeGaetano A, Goordarzi S, Tacchino RM (1990) Probability and the patient state space. J Clin Monit Comput 7(4):201–215CrossRefGoogle Scholar
  10. 10.
    Corazzon R (2006) Ontology, a resource guide for philosophers. http://www.formalontology.it, accessed March 2006
  11. 11.
    Edwards M, Moczygemba J (2004) Reducing medical errors through better documentation. Health Care Manag 23(4):329–333Google Scholar
  12. 12.
    Electronic Medical Records (2006) http://www.expert-system.com/medical_errors.htm, accessed April 2006
  13. 13.
    Friedman-Hill E (2003) Jess in action: Java rule-based systems. Manning Publications Co., Greenwich, CT, USA. http://herzberg.ca.sandia.gov/jess/
  14. 14.
    Harrop P, Das R (2007) RFID for Healthcare and Pharmaceuticals 2007–2017. Technical report, IDTechEx. http://www.idtechex.com/products/en/view.asp?productcategoryid=101
  15. 15.
    Hunter J, McIntosh N (1999) Knowledge-based event detection in complex time series data. In: AIMDM ’99: Proceedings of the joint European conference on artificial intelligence in medicine and medical decision making. Springer, London, UK, pp 271–280Google Scholar
  16. 16.
    Krol M, Reich DL (1999) The algorithm for detecting critical conditions during anesthesia. In: CBMS ’99: Proceedings of the 12th IEEE symposium on computer-based medical systems. IEEE Computer Society, Washington, DC, USA, p 208Google Scholar
  17. 17.
    Levine W, Meyer M, Brzezinski P, Robbins J, Sandberg W (2005) Computer automated total perioperative situational awareness and safety systems. Comput Assist Radiol Surg 1281:856–861Google Scholar
  18. 18.
    Liu X, Corner MD, Shenoy P (2006) Ferret: RFID locationing for pervasive multimedia. In: Proceedings of Ubicomp 2006, pp 422–440Google Scholar
  19. 19.
    Agency for HealthCare Research and Quality (2000) Medical errors: the scope of the problem. Fact sheet, Publication No. AHRQ 00-P037. Rockville, MD. http://www.ahrq.gov/qual/errback.htm
  20. 20.
    Mkivirta A, Sukuvaara T, Koski E, Kari A (1993) A knowledge-based alarm system for monitoring cardiac operated patients: technical construction and evaluation. J Clin Monit Comput 10(2):117–126CrossRefGoogle Scholar
  21. 21.
    Nagargadde A, Srihar V, Ramamritham K (2007) Representation and processing of information related to real world events. Knowl-based Syst 20(1):1–16CrossRefGoogle Scholar
  22. 22.
    Navabi M, Mylrea K, Watt R (1989) Detection of false alarms using an integrated anesthesia monitor. In: IEEE engineering in medicine and biology society 11th annual conferenceGoogle Scholar
  23. 23.
    Orchard B (2006) Fuzzy sets. http://www.iit.nrc.ca/IR_public/fuzzy/fuzzyJDocs/FuzzySet.html, accessed April 2006
  24. 24.
    Rector A, Rogers J (1996) The GALEN ontology. In: Medical Informatics Europe (MIE 96)Google Scholar
  25. 25.
    Roberts J, Stalow C, Hedges J (2005) Clinical procedures in emergency medicine. ElsevierGoogle Scholar
  26. 26.
    Roediger J, Salmon P (2006) Making changes in charts: do’s and don’ts. http://www.muschealth.com/professionals/ppd/chartshanges.htm, accessed November 2006
  27. 27.
    Satava R (2005) Telesurgery, robotics, and the future of telemedicine. Eur Surg 37(5):304–307CrossRefGoogle Scholar
  28. 28.
    Schecke T, Langen T, Popp H, Rau G, KŁsmacher H, Kalff G (1993) Knowledge-based decision support for patient monitoring in cardioanesthesia. J Clin Monit Comput 10(1): 1–11CrossRefGoogle Scholar
  29. 29.
    Sukuvaara T, Koski E, MŁkivirta A, Kari A (1996) Clinicians’ opinions on alarm limits and urgency of therapeutic responses. J Clin Monit Comput 12(2):85–88Google Scholar
  30. 30.
    Sutherland JV (2004) RECIPE for real time process improvement in healthcare. In: 13th Annual PHYSICIAN-COMPUTER CONNECTION symposiumGoogle Scholar
  31. 31.
    Tsien CL (2000) Event discovery in medical time series data. In: American Medical Informatics Association (AMIA) symposiumGoogle Scholar
  32. 32.
    Tsien CL (2000) Trend finder: automated detection of alarmable trends. Technical report, Massachusetts Institute of TechnologyGoogle Scholar
  33. 33.
    Thompson TG, Brailer DJ (2004) The decade of health information technology: delivering consumer-centric and information-rich health care—framework for strategic action. Technical report, US Department of Health and Human ServicesGoogle Scholar

Copyright information

© Springer Science+Business Media, LLC 2007

Authors and Affiliations

  • Sheetal Agarwal
    • 1
  • Anupam Joshi
    • 2
  • Tim Finin
    • 2
  • Yelena Yesha
    • 2
  • Tim Ganous
    • 3
  1. 1.IBM India Research LabNew DelhiIndia
  2. 2.Department of Computer Science and Electrical EngineeringUniversity of Maryland, Baltimore CountyBaltimoreUSA
  3. 3.University of Maryland Medical SchoolBaltimoreUSA

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