Literature DB >> 22019280

Barriers and facilitators to the use of computer-based intensive insulin therapy.

Thomas R Campion1, Lemuel R Waitman, Nancy M Lorenzi, Addison K May, Cynthia S Gadd.   

Abstract

PURPOSE: Computerized clinical decision support systems (CDSSs) for intensive insulin therapy (IIT) are increasingly common. However, recent studies question IIT's safety and mortality benefit. Researchers have identified factors influencing IIT performance, but little is known about how workflow affects computer-based IIT. We used ethnographic methods to evaluate IIT CDSS with respect to other clinical information systems and care processes.
METHODS: We conducted direct observation of and unstructured interviews with nurses using IIT CDSS in the surgical and trauma intensive care units at an academic medical center. We observed 49h of intensive care unit workflow including 49 instances of nurses using IIT CDSS embedded in a provider order entry system. Observations focused on the interaction of people, process, and technology. By analyzing qualitative field note data through an inductive approach, we identified barriers and facilitators to IIT CDSS use.
RESULTS: Barriers included (1) workload tradeoffs between computer system use and direct patient care, especially related to electronic nursing documentation, (2) lack of IIT CDSS protocol reminders, (3) inaccurate user interface design assumptions, and (4) potential for error in operating medical devices. Facilitators included (1) nurse trust in IIT CDSS combined with clinical judgment, (2) nurse resilience, and (3) paper serving as an intermediary between patient bedside and IIT CDSS.
CONCLUSION: This analysis revealed sociotechnical interactions affecting IIT CDSS that previous studies have not addressed. These issues may influence protocol performance at other institutions. Findings have implications for IIT CDSS user interface design and alerts, and may contribute to nascent general CDSS theory. 2011 Elsevier Ireland Ltd. All rights reserved.

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Year:  2011        PMID: 22019280      PMCID: PMC3226863          DOI: 10.1016/j.ijmedinf.2011.10.003

Source DB:  PubMed          Journal:  Int J Med Inform        ISSN: 1386-5056            Impact factor:   4.046


  40 in total

1.  Computer-based insulin infusion protocol improves glycemia control over manual protocol.

Authors:  Jeffrey B Boord; Mona Sharifi; Robert A Greevy; Marie R Griffin; Vivian K Lee; Ty A Webb; Michael E May; Lemuel R Waitman; Addison K May; Randolph A Miller
Journal:  J Am Med Inform Assoc       Date:  2007-02-28       Impact factor: 4.497

2.  A computerized insulin infusion titration protocol improves glucose control with less hypoglycemia compared to a manual titration protocol in a trauma intensive care unit.

Authors:  Marcus J Dortch; Nathan T Mowery; Asli Ozdas; Lesly Dossett; Hanqing Cao; Bryan Collier; Gwen Holder; Randolph A Miller; Addison K May
Journal:  JPEN J Parenter Enteral Nutr       Date:  2008 Jan-Feb       Impact factor: 4.016

3.  Reasons for declining computerized insulin protocol recommendations: application of a framework.

Authors:  K Sward; J Orme; D Sorenson; L Baumann; A H Morris
Journal:  J Biomed Inform       Date:  2008-04-11       Impact factor: 6.317

Review 4.  Medication-related clinical decision support in computerized provider order entry systems: a review.

Authors:  Gilad J Kuperman; Anne Bobb; Thomas H Payne; Anthony J Avery; Tejal K Gandhi; Gerard Burns; David C Classen; David W Bates
Journal:  J Am Med Inform Assoc       Date:  2006-10-26       Impact factor: 4.497

5.  Evaluation of nursing work effort and perceptions about blood glucose testing in tight glycemic control.

Authors:  Daleen Aragon
Journal:  Am J Crit Care       Date:  2006-07       Impact factor: 2.228

6.  Exploring barriers and facilitators to the use of computerized clinical reminders.

Authors:  Jason J Saleem; Emily S Patterson; Laura Militello; Marta L Render; Greg Orshansky; Steven M Asch
Journal:  J Am Med Inform Assoc       Date:  2005-03-31       Impact factor: 4.497

7.  An efficiency evaluation of protocols for tight glycemic control in intensive care units.

Authors:  Mark A Malesker; Pamela A Foral; Ann C McPhillips; Keith J Christensen; Julie A Chang; Daniel E Hilleman
Journal:  Am J Crit Care       Date:  2007-11       Impact factor: 2.228

8.  A replicable method for blood glucose control in critically Ill patients.

Authors:  Alan H Morris; James Orme; Jonathon D Truwit; Jay Steingrub; Colin Grissom; Kang H Lee; Guoliang L Li; B Taylor Thompson; Roy Brower; Mark Tidswell; Gordon R Bernard; Dean Sorenson; Katherine Sward; Hui Zheng; David Schoenfeld; Homer Warner
Journal:  Crit Care Med       Date:  2008-06       Impact factor: 7.598

9.  Utilization of a computerized intravenous insulin infusion program to control blood glucose in the intensive care unit.

Authors:  Rattan Juneja; Corbin Roudebush; Nilay Kumar; Angela Macy; Adam Golas; Donna Wall; Cheryl Wolverton; Deborah Nelson; Joni Carroll; Samuel J Flanders
Journal:  Diabetes Technol Ther       Date:  2007-06       Impact factor: 6.118

10.  Design and implementation of GRIP: a computerized glucose control system at a surgical intensive care unit.

Authors:  Mathijs Vogelzang; Felix Zijlstra; Maarten W N Nijsten
Journal:  BMC Med Inform Decis Mak       Date:  2005-12-19       Impact factor: 2.796

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  10 in total

Review 1.  The future is now: software-guided intensive insulin therapy in the critically ill.

Authors:  Rishi Rattan; Stanley A Nasraway
Journal:  J Diabetes Sci Technol       Date:  2013-03-01

2.  Acceptability of Clinical Decision Support Interface Prototypes for a Nursing Electronic Health Record to Facilitate Supportive Care Outcomes.

Authors:  Janet Stifter; Vanessa E C Sousa; Alessandro Febretti; Karen Dunn Lopez; Andrew Johnson; Yingwei Yao; Gail M Keenan; Diana J Wilkie
Journal:  Int J Nurs Knowl       Date:  2017-09-19       Impact factor: 1.222

Review 3.  Timing of Insulin with Meals in the Hospital: a Systems Improvement Approach.

Authors:  Kathleen Dungan
Journal:  Curr Diab Rep       Date:  2019-11-04       Impact factor: 4.810

Review 4.  Artificial Intelligence Applications in Health Care Practice: Scoping Review.

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Review 5.  Informatics Solutions for Application of Decision-Making Skills.

Authors:  Christine W Nibbelink; Janay R Young; Jane M Carrington; Barbara B Brewer
Journal:  Crit Care Nurs Clin North Am       Date:  2018-04-04       Impact factor: 1.326

6.  Toward Meaningful Care Plan Clinical Decision Support: Feasibility and Effects of a Simulated Pilot Study.

Authors:  Gail M Keenan; Karen Dunn Lopez; Yingwei Yao; Vanessa E C Sousa; Janet Stifter; Alessandro Febretti; Andrew Johnson; Diana J Wilkie
Journal:  Nurs Res       Date:  2017 Sep/Oct       Impact factor: 2.381

7.  Use of Simulation to Study Nurses' Acceptance and Nonacceptance of Clinical Decision Support Suggestions.

Authors:  Vanessa E C Sousa; Karen Dunn Lopez; Alessandro Febretti; Janet Stifter; Yingwei Yao; Andrew Johnson; Diana J Wilkie; Gail M Keenan
Journal:  Comput Inform Nurs       Date:  2015-10       Impact factor: 1.985

8.  Using a sociotechnical framework to understand adaptations in health IT implementation.

Authors:  Laurie Lovett Novak; Richard J Holden; Shilo H Anders; Jennifer Y Hong; Ben-Tzion Karsh
Journal:  Int J Med Inform       Date:  2013-04-03       Impact factor: 4.046

Review 9.  Review of Social and Organizational Issues in Health Information Technology.

Authors:  Craig E Kuziemsky
Journal:  Healthc Inform Res       Date:  2015-07-31

10.  Multiple perspectives on clinical decision support: a qualitative study of fifteen clinical and vendor organizations.

Authors:  Joan S Ash; Dean F Sittig; Carmit K McMullen; Adam Wright; Arwen Bunce; Vishnu Mohan; Deborah J Cohen; Blackford Middleton
Journal:  BMC Med Inform Decis Mak       Date:  2015-04-24       Impact factor: 2.796

  10 in total

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