Study Results
Outcome measurements, participant flow, baseline characteristics, and adverse events have been published for this study.
View full resultsBasic Information
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COMPLETED
NA
58 participants
INTERVENTIONAL
2007-10-31
2011-03-31
Brief Summary
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Detailed Description
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Aim 1: Identify cognitive components of providers' therapeutic decision making in the field.
Aim 2. Refine and evaluate the Integrated Medication Manager using simulation studies.
* Aim 2.a. Refine interfaces and logic of the Integrated Medication Manager.
* Aim 2.b. Compare the performance of the Integrated Medication Manager and usual CPRS.
All hypotheses (below) test the use of IMM versus usual electronic medical record (EMR).
* Speed of decision-making will be faster.
* Accuracy of data interpretation (clinical assessment) will be higher.
* Appropriateness of therapeutic plans will be higher.
* Efficiency of gathering information will be higher.
* Common ground measures will be higher.
* Appropriateness of therapeutic plans will be higher when relevant data is outside the usual time horizon.
* Appropriateness of therapeutic plans will be higher when complex associations among patient therapies and goals exist.
* Appropriateness of therapeutic plans will be no lower when relevant data is not captured by the displays of the IMM.
* Appropriateness of therapeutic plans will be higher when highly salient data is not germane to the most important problem.
* Appropriateness of therapeutic plans will be higher when cognitive load is high due to interruptions.
Conditions
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Study Design
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RANDOMIZED
PARALLEL
HEALTH_SERVICES_RESEARCH
NONE
Study Groups
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Integrated Medication Manager
Experienced providers that participated in the EHR simulations. Half of the providers were assigned to use the new Integrated Medication Manager (intervention) during the simulation. The other half were assigned the VA's CPRS to use (standard EHR). Providers were randomly assigned which system to use.
Integrated Medication Manager
A theory based electronic health record. Half of the provider participants were assigned the IMM to use. The other half were assigned the VA's CPRS EHR to use for the simulation. Providers were randomly assigned to a EHR to use.
Standard EHR
Experienced providers that participated in the EHR simulations. Half of the providers were assigned to use the new Integrated Medication Manager (intervention) during the simulation. The other half were assigned the VA's CPRS to use (standard EHR). Providers were randomly assigned which system to use.
No interventions assigned to this group
Interventions
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Integrated Medication Manager
A theory based electronic health record. Half of the provider participants were assigned the IMM to use. The other half were assigned the VA's CPRS EHR to use for the simulation. Providers were randomly assigned to a EHR to use.
Other Intervention Names
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Eligibility Criteria
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Inclusion Criteria
* Third year residents with two years of residency in internal medicine or family practice
* Do not have to be currently practicing
Exclusion Criteria
18 Years
ALL
Yes
Sponsors
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Agency for Healthcare Research and Quality (AHRQ)
FED
University of Utah
OTHER
Responsible Party
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Principal Investigators
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Jonathan Nebeker, MD, MS
Role: PRINCIPAL_INVESTIGATOR
University of Utah
Locations
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VA SLC Health Care System
Salt Lake City, Utah, United States
Countries
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References
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Nebeker JR, Hurdle JF, Bair BD. Future history: medical informatics in geriatrics. J Gerontol A Biol Sci Med Sci. 2003 Sep;58(9):M820-5. doi: 10.1093/gerona/58.9.m820.
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Hayward RA, Asch SM, Hogan MM, Hofer TP, Kerr EA. Sins of omission: getting too little medical care may be the greatest threat to patient safety. J Gen Intern Med. 2005 Aug;20(8):686-91. doi: 10.1111/j.1525-1497.2005.0152.x.
Tinetti ME, Bogardus ST Jr, Agostini JV. Potential pitfalls of disease-specific guidelines for patients with multiple conditions. N Engl J Med. 2004 Dec 30;351(27):2870-4. doi: 10.1056/NEJMsb042458. No abstract available.
Garg AX, Adhikari NK, McDonald H, Rosas-Arellano MP, Devereaux PJ, Beyene J, Sam J, Haynes RB. Effects of computerized clinical decision support systems on practitioner performance and patient outcomes: a systematic review. JAMA. 2005 Mar 9;293(10):1223-38. doi: 10.1001/jama.293.10.1223.
Shekelle PG. Invited commentary: Implementation of health information technology: an important but challenging field of inquiry. Proc (Bayl Univ Med Cent). 2006 Oct;19(4):313. doi: 10.1080/08998280.2006.11928190. No abstract available.
Weir CR, Nebeker JJ, Hicken BL, Campo R, Drews F, Lebar B. A cognitive task analysis of information management strategies in a computerized provider order entry environment. J Am Med Inform Assoc. 2007 Jan-Feb;14(1):65-75. doi: 10.1197/jamia.M2231. Epub 2006 Oct 26.
Berg CA, Strough JN, Calderone KS, Sansone C, Weir C. The role of problem definitions in understanding age and context effects on strategies for solving everyday problems. Psychol Aging. 1998 Mar;13(1):29-44. doi: 10.1037//0882-7974.13.1.29.
Weir CR. Linking information needs with evaluation: the role of task identification. Proc AMIA Symp. 1998:310-4.
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Campbell M, Grimshaw J, Steen N. Sample size calculations for cluster randomised trials. Changing Professional Practice in Europe Group (EU BIOMED II Concerted Action). J Health Serv Res Policy. 2000 Jan;5(1):12-6. doi: 10.1177/135581960000500105.
Miller RH, Sim I. Physicians' use of electronic medical records: barriers and solutions. Health Aff (Millwood). 2004 Mar-Apr;23(2):116-26. doi: 10.1377/hlthaff.23.2.116.
Bradley EH, Bogardus ST Jr, Tinetti ME, Inouye SK. Goal-setting in clinical medicine. Soc Sci Med. 1999 Jul;49(2):267-78. doi: 10.1016/s0277-9536(99)00107-0.
Other Identifiers
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