Improving Quality by Maintaining Accurate Problems in the EHR
NCT ID: NCT02596087
Last Updated: 2023-02-08
Study Results
The study team has not published outcome measurements, participant flow, or safety data for this trial yet. Check back later for updates.
Basic Information
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COMPLETED
NA
2386 participants
INTERVENTIONAL
2016-04-30
Brief Summary
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Detailed Description
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Patients with gaps in their problem list face significant risks. For example, if a hypothetical patient has diabetes properly documented, his clinician would receive appropriate alerts and reminders to guide care. Additionally, the patient might be included in special care management programs and the quality of care provided to him would be measured and tracked. Without diabetes on his problem list, he might receive none of these benefits.
In this study, the investigators developed an clinical decision support intervention that will identify patients with problem lists gaps. The investigators will alert providers of these likely gaps and offer providers the opportunity to correct them.
In the first aim, the investigators will design and validate a series of problem inference algorithms, using rule-based techniques on structured data in the electronic health record (EHR) and natural language processing on unstructured data. Both of these techniques will yield candidate problems that the patient is likely to have, and the results will be integrated. In Aim 2, the investigators will design clinical decision support interventions in the EHRs of the four study sites to alert physicians when a candidate problem is detected that is missing from the patient's problem list - the clinician will then be able to accept the alert and add the problem, override the alert, or ignore it entirely. In Aim 3, the investigators will conduct a randomized trial and evaluate the effect of the problem list alert on three endpoints: alert acceptance, problem list addition rate and clinical quality.
Conditions
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Study Design
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RANDOMIZED
PARALLEL
OTHER
SINGLE
Study Groups
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Normal Use of EHR
Sites will configure their EHR systems so that alerts will not be triggered for providers in the control arm if the patient does not have the condition on her/his problem list.
No interventions assigned to this group
Intervention Arm
Sites will configure their EHR systems so that alerts for these conditions will be triggered for providers in the intervention arm if the patient does not have the condition on her/his problem list. Each alert will be actionable and allow the provider to add the problem to her or his patient's problem list with a single click. The provider will also be able to override the rule of the patient does not have the condition (in which case the alert will not be displayed again unless new information that would trigger the alert is added to the patient's record), or defer the alert until later.
Problem List Suggestion
Sites will configure their EHR systems so that alerts for these conditions will be triggered for providers in the intervention arm if the patient does not have the condition on her/his problem list. each alert will be actionable and allow the provider to add the problem to her or his patient's problem list with a single click. The provider will also be able to override the rule of the patient does not have the condition (in which case the alert will not be displayed again unless new information that would trigger the alert is added to the patient's record), or defer the alert until later.
Interventions
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Problem List Suggestion
Sites will configure their EHR systems so that alerts for these conditions will be triggered for providers in the intervention arm if the patient does not have the condition on her/his problem list. each alert will be actionable and allow the provider to add the problem to her or his patient's problem list with a single click. The provider will also be able to override the rule of the patient does not have the condition (in which case the alert will not be displayed again unless new information that would trigger the alert is added to the patient's record), or defer the alert until later.
Eligibility Criteria
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Inclusion Criteria
Exclusion Criteria
18 Years
ALL
Yes
Sponsors
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Geisinger Clinic
OTHER
Oregon Health and Science University
OTHER
Vanderbilt University
OTHER
Brigham and Women's Hospital
OTHER
Responsible Party
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Adam Wright
Associate Professor of Medicine
Locations
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Brigham and Women's Hospital
Boston, Massachusetts, United States
Oregon Health and Science University
Portland, Oregon, United States
Holy Spirit Hospital
Camp Hill, Pennsylvania, United States
Vanderbilt University Medical Center
Nashville, Tennessee, United States
Countries
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References
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Wright A, Goldberg H, Hongsermeier T, Middleton B. A description and functional taxonomy of rule-based decision support content at a large integrated delivery network. J Am Med Inform Assoc. 2007 Jul-Aug;14(4):489-96. doi: 10.1197/jamia.M2364. Epub 2007 Apr 25.
Kaplan DM. Clear writing, clear thinking and the disappearing art of the problem list. J Hosp Med. 2007 Jul;2(4):199-202. doi: 10.1002/jhm.242. No abstract available.
Szeto HC, Coleman RK, Gholami P, Hoffman BB, Goldstein MK. Accuracy of computerized outpatient diagnoses in a Veterans Affairs general medicine clinic. Am J Manag Care. 2002 Jan;8(1):37-43.
Tang PC, LaRosa MP, Gorden SM. Use of computer-based records, completeness of documentation, and appropriateness of documented clinical decisions. J Am Med Inform Assoc. 1999 May-Jun;6(3):245-51. doi: 10.1136/jamia.1999.0060245.
Carpenter JD, Gorman PN. Using medication list--problem list mismatches as markers of potential error. Proc AMIA Symp. 2002:106-10.
Hartung DM, Hunt J, Siemienczuk J, Miller H, Touchette DR. Clinical implications of an accurate problem list on heart failure treatment. J Gen Intern Med. 2005 Feb;20(2):143-7. doi: 10.1111/j.1525-1497.2005.40206.x.
Wright A, Chen ES, Maloney FL. An automated technique for identifying associations between medications, laboratory results and problems. J Biomed Inform. 2010 Dec;43(6):891-901. doi: 10.1016/j.jbi.2010.09.009. Epub 2010 Sep 25.
Wright A, Pang J, Feblowitz JC, Maloney FL, Wilcox AR, Ramelson HZ, Schneider LI, Bates DW. A method and knowledge base for automated inference of patient problems from structured data in an electronic medical record. J Am Med Inform Assoc. 2011 Nov-Dec;18(6):859-67. doi: 10.1136/amiajnl-2011-000121. Epub 2011 May 25.
Other Identifiers
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2009P001846-14
Identifier Type: -
Identifier Source: org_study_id
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