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Journal of Clinical Monitoring and Computing

, Volume 27, Issue 4, pp 443–448 | Cite as

Connecting the dots: rule-based decision support systems in the modern EMR era

  • Vitaly HerasevichEmail author
  • Daryl J. Kor
  • Arun Subramanian
  • Brian W. Pickering
Review Paper

Abstract

The intensive care unit (ICU) environment is rich in both medical device and electronic medical record (EMR) data. The ICU patient population is particularly vulnerable to medical error or delayed medical intervention both of which are associated with excess morbidity, mortality and cost. The development and deployment of smart alarms, computerized decision support systems (DSS) and “sniffers” within ICU clinical information systems has the potential to improve the safety and outcomes of critically ill hospitalized patients. However, the current generations of alerts, run largely through bedside monitors, are far from ideal and rarely support the clinician in the early recognition of complex physiologic syndromes or deviations from expected care pathways. False alerts and alert fatigue remain prevalent. In the coming era of widespread EMR implementation novel medical informatics methods may be adaptable to the development of next generation, rule-based DSS.

Keywords

Alert Decision support systems Sniffers Monitor EMR False-alert ICU 

Notes

Conflict of interest

The authors declare that they have no conflict of interest.

References

  1. 1.
    Power DJ. A brief history of decision support systems. Decis Support Syst. 2007;4(1969):1–18.Google Scholar
  2. 2.
    McDonald CJ. Protocol-based computer reminders, the quality of care and the non-perfectability of man. N Engl J Med. 1976;295(24):1351–5. doi: 10.1056/NEJM197612092952405.PubMedCrossRefGoogle Scholar
  3. 3.
    Manor-Shulman O, Beyene J, Frndova H, Parshuram CS. Quantifying the volume of documented clinical information in critical illness. J Crit Care. 2008;23(2):245–50. doi: 10.1016/j.jcrc.2007.06.003.PubMedCrossRefGoogle Scholar
  4. 4.
    Chambrin MC, Ravaux P, Calvelo-Aros D, Jaborska A, Chopin C, Boniface B. Multicentric study of monitoring alarms in the adult intensive care unit (ICU): a descriptive analysis. Intensive Care Med. 1999;25(12):1360–6.PubMedCrossRefGoogle Scholar
  5. 5.
    Byers JF, White SV. Patient safety: principles and practice. New York: Springer; 2004.Google Scholar
  6. 6.
    Tinker JH, Dull DL, Caplan RA, Ward RJ, Cheney FW. Role of monitoring devices in prevention of anesthetic mishaps: a closed claims analysis. Anesthesiology. 1989;71(4):541–6.PubMedCrossRefGoogle Scholar
  7. 7.
    Rothschild JM, Landrigan CP, Cronin JW, Kaushal R, Lockley SW, Burdick E, Stone PH, Lilly CM, Katz JT, Czeisler CA, Bates DW. The critical care safety study: the incidence and nature of adverse events and serious medical errors in intensive care. Crit Care Med. 2005;33(8):1694–700.PubMedCrossRefGoogle Scholar
  8. 8.
    Tsien CL, Fackler JC. Poor prognosis for existing monitors in the intensive care unit. Crit Care Med. 1997;25(4):614–9.PubMedCrossRefGoogle Scholar
  9. 9.
    O’Carroll TM. Survey of alarms in an intensive therapy unit. Anaesthesia. 1986;41(7):742–4.PubMedCrossRefGoogle Scholar
  10. 10.
    Lawless ST. Crying wolf: false alarms in a pediatric intensive care unit. Crit Care Med. 1994;22(6):981–5.PubMedCrossRefGoogle Scholar
  11. 11.
    Schoenberg R, Sands DZ, Safran C. Making ICU alarms meaningful: a comparison of traditional versus trend-based algorithms. In: proceedings/AMIA Annual Symposium. 1999:379–383.Google Scholar
  12. 12.
    Graham KC, Cvach M. Monitor alarm fatigue: standardizing use of physiological monitors and decreasing nuisance alarms. Am J Crit Care. 2010;19(1):28–34. doi: 10.4037/ajcc2010651. quiz 35.PubMedCrossRefGoogle Scholar
  13. 13.
    Meredith C, Edworthy J. Are there too many alarms in the intensive care unit? An overview of the problems. J Adv Nurs. 1995;21(1):15–20.PubMedCrossRefGoogle Scholar
  14. 14.
    Balogh D, Kittinger E, Benzer A, Hackl JM. Noise in the ICU. Intensive Care Med. 1993;19(6):343–6.PubMedCrossRefGoogle Scholar
  15. 15.
    Kahn DM, Cook TE, Carlisle CC, Nelson DL, Kramer NR, Millman RP. Identification and modification of environmental noise in an ICU setting. Chest. 1998;114(2):535–40.PubMedCrossRefGoogle Scholar
  16. 16.
    Little A, Ethier C, Ayas N, Thanachayanont T, Jiang D, Mehta S. A patient survey of sleep quality in the Intensive Care Unit. Minerva Anestesiol. 2012;78(4):406–14.PubMedGoogle Scholar
  17. 17.
    Institute of Medicine. To err is human. Building a safer health system. Washington: National Academy Press; 2000.Google Scholar
  18. 18.
    Norris PR, Dawant BM. Closing the loop in ICU decision support: physiologic event detection, alerts, and documentation. In: proceedings/AMIA Annual Symposium AMIA Symposium. 2001:498–502.Google Scholar
  19. 19.
    Shea S, Sideli RV, DuMouchel W, Pulver G, Arons RR, Clayton PD. Computer-generated informational messages directed to physicians: effect on length of hospital stay. J Am Med Inform Assoc. 1995;2(1):58–64.PubMedCrossRefGoogle Scholar
  20. 20.
    Gorges M, Markewitz BA, Westenskow DR. Improving alarm performance in the medical intensive care unit using delays and clinical context. Anesth Analg. 2009;108(5):1546–52.PubMedCrossRefGoogle Scholar
  21. 21.
    Arney D, Fischmeister S, Goldman JM, Lee I, Trausmuth R. Plug-and-play for medical devices: experiences from a case study. Biomed Instrum Technol. 2009;43(4):313–7. doi: 10.2345/0899-8205-43.4.313.PubMedCrossRefGoogle Scholar
  22. 22.
    Imhoff M, Kuhls S. Alarm algorithms in critical care monitoring. Anesth Analg. 2006;102(5):1525–37.PubMedCrossRefGoogle Scholar
  23. 23.
    Milholland K. Patient data management systems (PDMS). Computer technology for critical care nurses. Comput Nurs. 1988;6(6):237–43.PubMedGoogle Scholar
  24. 24.
    Clemmer TP, Gardner RM. Data gathering, analysis, and display in critical care medicine. Respir Care. 1985;30(7):586–601.PubMedGoogle Scholar
  25. 25.
    Adhikari N, Lapinsky SE. Medical informatics in the intensive care unit: overview of technology assessment. J Crit Care. 2003;18(1):41–7. doi: 10.1053/jcrc.2003.YJCRC9.PubMedCrossRefGoogle Scholar
  26. 26.
    Sittig DF, Ash JS, Zhang J, Osheroff JA, Shabot MM. Lessons from “Unexpected increased mortality after implementation of a commercially sold computerized physician order entry system”. Pediatrics. 2006;118(2):797–801. doi: 10.1542/peds.2005-3132.PubMedCrossRefGoogle Scholar
  27. 27.
    Patel VL, Zhang J, Yoskowitz NA, Green R, Sayan OR. Translational cognition for decision support in critical care environments: a review. J Biomed Inform. 2008;41(3):413–31. doi: 10.1016/j.jbi.2008.01.013.PubMedCrossRefGoogle Scholar
  28. 28.
    Herasevich V, Pickering BW, Dong Y, Peters SG, Gajic O. Informatics infrastructure for syndrome surveillance, decision support, reporting, and modeling of critical illness. Mayo Clin Proc. 2010;85(3):247–54.PubMedCrossRefGoogle Scholar
  29. 29.
    Chaudhry B, Wang J, Wu S, Maglione M, Mojica W, Roth E, Morton SC, Shekelle PG. Systematic review: impact of health information technology on quality, efficiency, and costs of medical care. Ann Intern Med. 2006;144(10):742–52.PubMedCrossRefGoogle Scholar
  30. 30.
    Blumenthal D. Launching HITECH. N Eng J Med. 2010;362(5):382–5. doi: 10.1056/NEJMp0912825.CrossRefGoogle Scholar
  31. 31.
    Blumenthal D. Stimulating the adoption of health information technology. N Engl J Med. 2009;360(15):1477–9. doi: 10.1056/NEJMp0901592.PubMedCrossRefGoogle Scholar
  32. 32.
    Raschke RA, Gollihare B, Wunderlich TA, Guidry JR, Leibowitz AI, Peirce JC, Lemelson L, Heisler MA, Susong C. A computer alert system to prevent injury from adverse drug events: development and evaluation in a community teaching hospital. JAMA. 1998;280(15):1317–20.PubMedCrossRefGoogle Scholar
  33. 33.
    Haug PJ, Gardner RM, Tate KE, Evans RS, East TD, Kuperman G, Pryor TA, Huff SM, Warner HR. Decision support in medicine: examples from the HELP system. Comput Biomed Res Int J. 1994;27(5):396–418.CrossRefGoogle Scholar
  34. 34.
    Evans RS, Larsen RA, Burke JP, Gardner RM, Meier FA, Jacobson JA, Conti MT, Jacobson JT, Hulse RK. Computer surveillance of hospital-acquired infections and antibiotic use. JAMA. 1986;256(8):1007–11.PubMedCrossRefGoogle Scholar
  35. 35.
    Mc Donald CJ. Use of a computer to detect and respond to clinical events: its effect on clinician behavior. Ann Intern Med. 1976;84(2):162–7.PubMedCrossRefGoogle Scholar
  36. 36.
    Rivers E, Nguyen B, Havstad S, Ressler J, Muzzin A, Knoblich B, Peterson E, Tomlanovich M. Early goal-directed therapy in the treatment of severe sepsis and septic shock. N Engl J Med. 2001;345(19):1368–77.PubMedCrossRefGoogle Scholar
  37. 37.
    Moorman JR, Lake DE, Griffin MP. Heart rate characteristics monitoring for neonatal sepsis. IEEE Trans Biomed Eng. 2006;53(1):126–32.PubMedCrossRefGoogle Scholar
  38. 38.
    Mandl KD, Overhage JM, Wagner MM, Lober WB, Sebastiani P, Mostashari F, Pavlin JA, Gesteland PH, Treadwell T, Koski E, Hutwagner L, Buckeridge DL, Aller RD, Grannis S. Implementing syndromic surveillance: a practical guide informed by the early experience. J Am Med Inform Assoc. 2004;11(2):141–50.PubMedCrossRefGoogle Scholar
  39. 39.
    Centers for Disease Control Prevention. Biological and chemical terrorism: strategic plan for preparedness and response. Recommendations of the CDC strategic planning workgroup. MMWR. 2000;49 (RR-4).Google Scholar
  40. 40.
    Herasevich V, Yilmaz M, Khan H, Hubmayr RD, Gajic O. Validation of an electronic surveillance system for acute lung injury. Intensive Care Med. 2009;35(6):1018–23.PubMedCrossRefGoogle Scholar
  41. 41.
    Azzam HC, Khalsa SS, Urbani R, Shah CV, Christie JD, Lanken PN, Fuchs BD. Validation study of an automated electronic acute lung injury screening tool. JAMIA. 2009;16(4):503–8. doi: 10.1197/jamia.M3120.PubMedGoogle Scholar
  42. 42.
    Koenig HC, Finkel BB, Khalsa SS, Lanken PN, Prasad M, Urbani R, Fuchs BD. Performance of an automated electronic acute lung injury screening system in intensive care unit patients. Crit Care Med. 2011;39(1):98–104. doi: 10.1097/CCM.0b013e3181feb4a0.PubMedCrossRefGoogle Scholar
  43. 43.
    Colpaert K, Hoste EA, Steurbaut K, Benoit D, Van Hoecke S, De Turck F, Decruyenaere J. Impact of real-time electronic alerting of acute kidney injury on therapeutic intervention and progression of RIFLE class. Crit Care Med. 2012;40(4):1164–70. doi: 10.1097/CCM.0b013e3182387a6b.PubMedCrossRefGoogle Scholar
  44. 44.
    Nelson JL, Smith BL, Jared JD, Younger JG. Prospective trial of real-time electronic surveillance to expedite early care of severe sepsis. Ann Emerg Med. 2011;57(5):500–4. doi: 10.1016/j.annemergmed.2010.12.008.PubMedCrossRefGoogle Scholar
  45. 45.
    Herasevich V, Afessa B, Chute CG, Gajic O. Designing and testing computer based screening engine for severe sepsis/septic shock. AMIA Annual Symposium proceedings/AMIA Symposium. 2008;966.Google Scholar
  46. 46.
    Herasevich V, Tsapenko M, Kojicic M, Ahmed A, Kashyap R, Venkata C, Shahjehan K, Thakur SJ, Pickering BW, Zhang J, Hubmayr RD, Gajic O. Limiting ventilator-induced lung injury through individual electronic medical record surveillance. Crit Care Med. 2011;39(1):34–9.PubMedCrossRefGoogle Scholar
  47. 47.
    Mandl KD, Overhage JM, Wagner MM, Lober WB, Sebastiani P, Mostashari F, Pavlin JA, Gesteland PH, Treadwell T, Koski E, Hutwagner L, Buckeridge DL, Aller RD, Grannis S. Implementing syndromic surveillance: a practical guide informed by the early experience. JAMIA. 2004;11(2):141–50. doi: 10.1197/jamia.M1356.PubMedGoogle Scholar
  48. 48.
    Reddy MC, Pratt W, McDonald DW, Shabot MM. Challenges to physicians’ use of a wireless alert pager. AMIA Annu Symp Proc. 2003:544–548.Google Scholar
  49. 49.
    Duncan RG, Shabot MM. Secure remote access to a clinical data repository using a wireless personal digital assistant (PDA). Proceedings/AMIA Annual Symposium. 2000:210–214.Google Scholar
  50. 50.
    Major K, Shabot MM, Cunneen S. Wireless clinical alerts and patient outcomes in the surgical intensive care unit. Am Surg. 2002;68(12):1057–60.PubMedGoogle Scholar
  51. 51.
    Zhu X, Lord W. Using a context-aware medical application to address information needs for extubation decisions. AMIA Annual Symposium proceedings/AMIA Symposium AMIA Symposium. 2005;1169.Google Scholar
  52. 52.
    Ahmed A, Chandra S, Herasevich V, Gajic O, Pickering BW. The effect of two different electronic health record user interfaces on intensive care provider task load, errors of cognition, and performance. Crit Care Med. 2011;39(7):1626–34. doi: 10.1097/CCM.0b013e31821858a0.PubMedCrossRefGoogle Scholar
  53. 53.
    Pickering BW, Litell JM, Herasevich V, Gajic O. Clinical review: the hospital of the future—building intelligent environments to facilitate safe and effective acute care delivery. Crit Care. 2012;16(2):220. doi: 10.1186/cc11142.PubMedCrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media New York 2013

Authors and Affiliations

  • Vitaly Herasevich
    • 1
    • 2
    Email author
  • Daryl J. Kor
    • 1
    • 2
  • Arun Subramanian
    • 1
    • 2
  • Brian W. Pickering
    • 1
    • 2
  1. 1.Division of Critical Care Medicine, Department of AnesthesiologyMayo ClinicRochesterUSA
  2. 2.Multidisciplinary Epidemiology and Translational Research in Intensive Care (METRIC)Mayo ClinicRochesterUSA

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