VA Integrated Medication Manager

Electronic Health Records Clinical Trial

— IMM  

Official title:

Veterans Affairs Integrated Medication Manager

Clinical Trial Details — Status: Completed

Administrative data

• NCT number: NCT01787175
• Other study ID #: 5R18HS017186-03
• Status: Completed
• Phase: N/A
• First received: January 22, 2013
• Last updated: February 5, 2013
• Start date: October 2007
• Est. completion date: March 2011

Study information

• Verified date: February 2013
• Source: University of Utah
• Contact: n/a
• Is FDA regulated: No
• Health authority: United States: Institutional Review Board
• Study type: Observational

Clinical Trial Summary

The purpose of this study is to advance the science of healthcare informatics and to improve medication management through the development of a new approach to the electronic medical record called the Integrated Medication Manager (IMM).

Description:

In an attempt to address problems patient non-compliance with quality goals barriers to access and integration of health information that impede achievement of treatment goals, the VA is developing a new approach to the electronic medical record. The VA is moving away from the paper-chart metaphor and towards an integrated representation of the patient's status and care process across time. One of the first steps in the development phase has been to explicitly relate patient conditions, therapies, and goals in the domain of pharmacotherapy. This is called Integrated Medication Management and draws on Hollnagel's Contextual Control Model. Providers will be able to plan care and create orders directly in the context of these explicit relationships. This application will be implemented nationwide through a web interface embedded within the existing Computerized Patient Record System (CPRS), the graphical user interface to VA Information Systems (VistA).

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.

Recruitment information / eligibility

• Status: Completed
• Enrollment: 166
• Est. completion date: March 2011
• Est. primary completion date: March 2011
• Accepts healthy volunteers: Accepts Healthy Volunteers
• Gender: Both
• Age group: 18 Years and older

Eligibility

Aim 1:

Inclusion Criteria:

• Currently practicing at one of the five selected VA medical center sites (Salt Lake City, UT; Asheville, NC; West Haven, CT; Seattle, WA; American Lakes, WA)

• Practicing in primary care outpatient clinics

• Prescribing provider

• Practiced for one year at the VA

Exclusion Criteria:

• None

Aim 2:

Inclusion Criteria:

• Practiced in primary care for at least two years

• Third year residents with two years of residency in internal medicine or family practice

• Do not have to be currently practicing

Exclusion Criteria:

Study Design

N/A

Related Conditions & MeSH terms

• Electronic Health Records

Intervention

Other:
• 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.

Locations

Country	Name	City	State
United States	VA SLC Health Care System	Salt Lake City	Utah

Sponsors (2)

Lead Sponsor	Collaborator
University of Utah	Agency for Healthcare Research and Quality (AHRQ)

Country where clinical trial is conducted

United States, 

References & Publications (19)

Asch SM, McGlynn EA, Hogan MM, Hayward RA, Shekelle P, Rubenstein L, Keesey J, Adams J, Kerr EA. Comparison of quality of care for patients in the Veterans Health Administration and patients in a national sample. Ann Intern Med. 2004 Dec 21;141(12):938-45. — View Citation

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. — View Citation

Bradley EH, Bogardus ST Jr, Tinetti ME, Inouye SK. Goal-setting in clinical medicine. Soc Sci Med. 1999 Jul;49(2):267-78. — View Citation

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. — View Citation

Crosson JC, Stroebel C, Scott JG, Stello B, Crabtree BF. Implementing an electronic medical record in a family medicine practice: communication, decision making, and conflict. Ann Fam Med. 2005 Jul-Aug;3(4):307-11. — View Citation

Fox J, Alabassi A, Black E, Hurt C, Rose T. Modelling clinical goals: a corpus of examples and a tentative ontology. Stud Health Technol Inform. 2004;101:31-45. — View Citation

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. Review. — View Citation

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. — View Citation

Miller RH, Sim I. Physicians' use of electronic medical records: barriers and solutions. Health Aff (Millwood). 2004 Mar-Apr;23(2):116-26. — View Citation

Morris AH. Developing and implementing computerized protocols for standardization of clinical decisions. Ann Intern Med. 2000 Mar 7;132(5):373-83. — View Citation

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. Review. — View Citation

Perlin JB, Pogach LM. Improving the outcomes of metabolic conditions: managing momentum to overcome clinical inertia. Ann Intern Med. 2006 Apr 4;144(7):525-7. — View Citation

Phillips LS, Branch WT, Cook CB, Doyle JP, El-Kebbi IM, Gallina DL, Miller CD, Ziemer DC, Barnes CS. Clinical inertia. Ann Intern Med. 2001 Nov 6;135(9):825-34. Review. — View Citation

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. — View Citation

Taatz H. [The problem of the time factor in orthodontic treatment]. Stomatol DDR. 1976 Feb;26(2):102-5. German. — View Citation

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. — View Citation

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. Epub 2006 Oct 26. — View Citation

Weir CR. Linking information needs with evaluation: the role of task identification. Proc AMIA Symp. 1998:310-4. — View Citation

Xiao Y, Hunter WA, Mackenzie CF, Jefferies NJ, Horst RL. Task complexity in emergency medical care and its implications for team coordination. LOTAS Group. Level One Trauma Anesthesia Simulation. Hum Factors. 1996 Dec;38(4):636-45. — View Citation

* Note: There are 19 references in all — Click here to view all references

Outcome

Type	Measure	Description	Time frame	Safety issue
Primary	Amount of time to complete Assessment and Plan	Each participant had 10 minutes maximum to review the patient case and write an Assessment and Plan.	10 minutes	No
Primary	Accuracy of written Assessment and Plan in terms of control and status	Each participant had 10 minutes maximum to review the patient case and write an Assessment and Plan.	10 minutes	No
Secondary	Identification of planned monitoring and follow up encounters in Assessment and Plan	Each participant had 10 minutes maximum to review the patient case and write an Assessment and Plan.	10 minutes	No