Characteristics and effects of nurse dosing over-rides on computer-based intensive insulin therapy protocol performance

Journal of the American Medical Informatics Association

Volumn 18, Issue 3, 2011, Pages 251-258

  Campion, Thomas R ^a,b   May, Addison K ^a   Waitman, Lemuel R ^a,c   Ozdas, Asli ^a   Lorenzi, Nancy M ^a   Gadd, Cynthia S ^a  

^a VANDERBILT UNIVERSITY SCHOOL OF MEDICINE   (United States)

^b WEILL CORNELL MEDICAL COLLEGE   (United States)

^c UNIVERSITY OF KANSAS SCHOOL OF MEDICINE   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

GLUCOSE; INSULIN;

ADULT; AGED; ARTICLE; CLINICAL DECISION MAKING; CLINICAL DECISION SUPPORT SYSTEM; CLINICAL PROTOCOL; CONTROLLED STUDY; DATA BASE; DECISION SUPPORT SYSTEM; ETHNOGRAPHY; FEMALE; GLUCOSE BLOOD LEVEL; HUMAN; HYPERGLYCEMIA; HYPOGLYCEMIA; INSULIN RESISTANCE; INSULIN TREATMENT; INTENSIVE CARE NURSING; INTENSIVE CARE UNIT; JOB PERFORMANCE; MAJOR CLINICAL STUDY; MALE; MORTALITY; NORMAL HUMAN; NURSE DOSING OVER RIDES; PATIENT INFORMATION; RECOMMENDED DRUG DOSE; RETROSPECTIVE STUDY;

ADULT; ATTITUDE TO COMPUTERS; DECISION SUPPORT SYSTEMS, CLINICAL; DRUG DOSAGE CALCULATIONS; DRUG THERAPY, COMPUTER-ASSISTED; FEMALE; GUIDELINE ADHERENCE; HUMANS; HYPOGLYCEMIA; INSULIN; INTENSIVE CARE UNITS; MALE; MIDDLE AGED; NURSE'S PRACTICE PATTERNS; RETROSPECTIVE STUDIES; TENNESSEE;

EID: 79958293708     PISSN: 10675027     EISSN: 1527974X     Source Type: Journal    
DOI: 10.1136/amiajnl-2011-000129     Document Type: Article

References (52)

1. Griesdale DE, de Souza RJ, van Dam RM, et al. Intensive insulin therapy and mortality among critically ill patients: a meta-analysis including NICE-SUGAR study data. CMAJ 2009;180:821-7.
2. Preiser JC, Devos P, Ruiz-Santana S, et al. A prospective randomised multi-centre controlled trial on tight glucose control by intensive insulin therapy in adult intensive care units: the Glucontrol study. Intensive Care Med 2009;35:1738-48.
3. Wiener RS, Wiener DC, Larson RJ. Benefits and risks of tight glucose control in critically ill adults: a meta-analysis. JAMA 2008;300:933-44.
4. Finfer S, Chittock DR, Su SY, et al. Intensive versus conventional glucose control in critically ill patients. N Engl J Med 2009;360:1283-97.
5. Fahy BG, Sheehy AM, Coursin DB. Glucose control in the intensive care unit. Crit Care Med 2009;37:1769-76.
6. Campion TR Jr, Waitman LR, May AK, et al. Social, organizational, and contextual characteristics of clinical decision support systems for intensive insulin therapy: a literature review and case study. Int J Med Inform 2010;79:31-43.
7. Eslami S, Abu-Hanna A, de Jonge E, et al. Tight glycemic control and computerized decision-support systems: a systematic review. Intensive Care Med 2009;35:1505-17.
8. Campion TR Jr, May AK, Waitman LR, et al. Effects of blood glucose transcription mismatches on a computer-based intensive insulin therapy protocol. Intensive Care Med 2010;36:1566-70.
9. Dossett LA, Collier B, Donahue R, et al. Intensive insulin therapy in practice: can we do it? J Parenter Enteral Nutr 2008;33:14-20.
10. Vogelzang M, Loef BG, Regtien JG, et al. Computer-assisted glucose control in critically ill patients. Intensive Care Med 2008;34:1421-7.
11. Mowery NT, Dortch MJ, Dossett LA, et al. Insulin resistance despite tight glucose control is associated with mortality in critically ill surgical patients. J Intensive Care Med 2009;24:242-51.
12. Dossett LA, Cao H, Mowery NT, et al. Blood glucose variability is associated with mortality in the surgical intensive care unit. Am Surg 2008;74:679-85; discussion 85.
13. Rood E, Bosman RJ, van der Spoel JI, et al. Use of a computerized guideline for glucose regulation in the intensive care unit improved both guideline adherence and glucose regulation. J Am Med Inform Assoc 2005;12:172-80.
14. Morris AH, Orme J Jr, Truwit JD, et al. A replicable method for blood glucose control in critically ill patients. Crit Care Med 2008;36:1787e95.
15. Sward K, Orme J Jr, Sorenson D, et al. Reasons for declining computerized insulin protocol recommendations: application of a framework. J Biomed Inform 2008;41:488-97.
16. Carr D, Benoit R. The role of interventional patient hygiene in improving clinical and economic outcomes. Adv Skin Wound Care 2009;22:74-8.
17. Miller RS, Norris PR, Jenkins JM, et al. Systems initiatives reduce healthcareassociated infections: a study of 22,928 device days in a single trauma unit. J Trauma 2010;68:23-31.
18. Zaydfudim V, Dossett LA, Starmer JM, et al. Implementation of a real-time compliance dashboard to help reduce SICU ventilator-associated pneumonia with the ventilator bundle. Arch Surg 2009;144:656-62.
19. Boord JB, Sharifi M, Greevy RA, et al. Computer-based insulin infusion protocol improves glycemia control over manual protocol. J Am Med Inform Assoc 2007;14:278-87.
20. Dortch MJ, Mowery NT, Ozdas A, et al. A computerized insulin infusion titration protocol improves glucose control with less hypoglycemia compared to a manual titration protocol in a trauma intensive care unit. JPEN J Parenter Enteral Nutr 2008;32:18-27.
21. Bode BW, Braithwaite SS, Steed RD, et al. Intravenous insulin infusion therapy: indications, methods, and transition to subcutaneous insulin therapy. Endocr Pract 2004;(10 Suppl 2):71-80.
22. White NH, Skor D, Santiago JV. Practical closed-loop insulin delivery. A system for the maintenance of overnight euglycemia and the calculation of basal insulin requirements in insulin-dependent diabetics. Ann Intern Med 1982;97:210-13.
23. Killelea BK, Kaushal R, Cooper M, et al. To what extent do pediatricians accept computer-based dosing suggestions? Pediatrics 2007;119:e69-75.
24. Egi M, Bellomo R, Reade MC. Is reducing variability of blood glucose the real but hidden target of intensive insulin therapy? Crit Care 2009;13:302.
25. Hermanides J, Vriesendorp TM, Bosman RJ, et al. Glucose variability is associated with intensive care unit mortality. Crit Care Med 2010;38:838-42.
26. Kizer KW. Serious reportable adverse events in healthcare: a consensus report. 2002. http://www.qualityforum.org/WorkArea/linkit.aspx?LinkIdentifier=id&ItemID=1221 (accessed 13 Feb 2010).
27. Eslami S, de Keizer NF, de Jonge E, et al. A systematic review on quality indicators for tight glycaemic control in critically ill patients: need for an unambiguous indicator reference subset. Crit Care 2008;12:R139.
28. Corbin JM, Strauss AL. Basics of Qualitative Research: Techniques and Procedures for Developing grounded Theory. Los Angeles: SAGE Publications, 2008.
29. Friedman CP, Wyatt J. Evaluation Methods in Biomedical Informatics. New York: Springer, 2006.
30. Callen JL, Westbrook JI, Braithwaite J. The effect of physicians' long-term use of CPOE on their test management work practices. J Am Med Inform Assoc 2006;13:643-52.
31. Unertl KM, Weinger MB, Johnson KB, et al. Describing and modeling workflow and information flow in chronic disease care. J Am Med Inform Assoc 2009;16:826-36.
32. Lincoln YS, Guba EG. Naturalistic Inquiry. Beverly Hills, CA: Sage Publications, 1985.
33. Anger KE, Szumita PM. Barriers to glucose control in the intensive care unit. Pharmacotherapy 2006;26:214e28.
34. Davidson PC, Steed RD, Bode BW. Glucommander: a computer-directed intravenous insulin system shown to be safe, simple, and effective in 120,618 h of operation. Diabetes Care 2005;28:2418-23.
35. Juneja R, Roudebush C, Kumar N, et al. Utilization of a computerized intravenous insulin infusion program to control blood glucose in the intensive care unit. Diabetes Technol Ther 2007;9:232-40.
36. Hermayer KL, Neal DE, Hushion TV, et al. Outcomes of a cardiothoracic intensive care web-based online intravenous insulin infusion calculator study at a Medical University Hospital. Diabetes Technol Ther 2007;9:523e34.
37. Toschlog EA, Newton C, Allen N, et al. Morbidity reduction in critically ill trauma patients through use of a computerized insulin infusion protocol: a preliminary study. J Trauma 2007;62:1370e5; discussion 5-6.
38. Geissbuhler A, Miller RA. A new approach to the implementation of direct care-provider order entry. Proc AMIA Annu Fall Symp 1996:689-93.
39. McCoy AB, Waitman LR, Gadd CS, et al. A computerized provider order entry intervention for medication safety during acute kidney injury: a quality improvement report. Am J Kidney Dis 2010;56:832-41.
40. Miller RA, Waitman LR, Chen S, et al. The anatomy of decision support during inpatient care provider order entry (CPOE): empirical observations from a decade of CPOE experience at Vanderbilt. J Biomed Inform 2005;38:469-85.
41. Stead WW, Baker W, Harris TR, et al. A fast track to IAIMS: the Vanderbilt University strategy. Proc Annu Symp Comput Appl Med Care 1992:527-31.
42. Stead WW, Borden R, Bourne J, et al. The Vanderbilt University fast track to IAIMS: transition from planning to implementation. J Am Med Inform Assoc 1996;3:308-17.
43. Kaplan B. Evaluating informatics applicationsdclinical decision support systems literature review. Int J Med Inform 2001;64:15-37.
44. Kaplan B. Evaluating informatics applicationsdsome alternative approaches: theory, social interactionism, and call for methodological pluralism. Int J Med Inform 2001;64:39-56.
45. Lin CP, Payne TH, Nichol WP, et al. Evaluating clinical decision support systems: monitoring CPOE order check override rates in the Department of Veterans Affairs' Computerized Patient Record System. J Am Med Inform Assoc 2008;15:620-6.
46. Patterson ES, Cook RI, Render ML. Improving patient safety by identifying side effects from introducing bar coding in medication administration. J Am Med Inform Assoc 2002;9:540-53.
47. Unertl KM, Weinger M, Johnson K. Variation in use of informatics tools among providers in a diabetes clinic. AMIA Annu Symp Proc 2007:756-60.
48. Reddy MC, Pratt W, Dourish P, et al. Asking questions: information needs in a surgical intensive care unit. Proc AMIA Symp 2002:647-51.
49. Richardson JE, Ash JS. The effects of hands-free communication device systems: communication changes in hospital organizations. J Am Med Inform Assoc 2010;17:91-8.
50. Richardson JE, Shah-Hosseini S, Fiadjoe JE, et al. The effects of a hands-free communication device system in a surgical suite. J Am Med Inform Assoc 2011;18:70-2.
51. Pachler C, Plank J, Weinhandl H, et al. Tight glycaemic control by an automated algorithm with time-variant sampling in medical ICU patients. Intensive Care Med 2008;34:1224-30.
52. Saager L, Collins GL, Burnside B, et al. A randomized study in diabetic patients undergoing cardiac surgery comparing computer-guided glucose management with a standard sliding scale protocol. J Cardiothorac Vasc Anesth 2008;22:377-82.