Clinical trial NCT02596087: Improving Quality by Maintaining Accurate Problems in the EHR
Official title: Improving Quality by Maintaining Accurate Problems in the Electronic Health Record
Study type: INTERVENTIONAL
Overall status: COMPLETED
Phase: NA
Conditions: Asthma
Atrial Fibrillation
Chronic Obstructive Pulmonary Disease
Coronary Artery Disease
Congestive Heart Failure
Hyperlipidemia
Hypertension
Myocardial Infarction
Sickle Cell Disease
Sleep Apnea
Smoking
Stroke
Tuberculosis
Lead sponsor: Brigham and Women's Hospital
Sponsor class: OTHER
Collaborators: name: Geisinger Clinic
name: Oregon Health and Science University
name: Vanderbilt University
First posted: 2015-11-04
Enrollment: 2386
Sex: ALL
Minimum age (years): 18
Standard ages: ADULT
OLDER_ADULT
Accepts healthy volunteers: yes
Allocation: RANDOMIZED
Intervention model: PARALLEL
Primary purpose: OTHER

Brief summary: The overall goal of the IQ-MAPLE project is to improve the quality of care provided to patients with several heart, lung and blood conditions by facilitating more accurate and complete problem list documentation. 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.

Detailed description: The clinical problem list is a cornerstone of the problem-oriented medical record. Problem lists are used in a variety of ways throughout the process of clinical care. In addition to its use by clinicians, the problem list is also critical for decision support and quality measurement.

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.

Eligibility criteria: Inclusion Criteria:

* All providers over the age of 18 that use the electronic health record at the specific site that the intervention is being observed.

Exclusion Criteria:

-

Primary outcomes:
measure: Measuring the rate of acceptance of alerts calculated by number of acceptances for each alert divided by the total number of unique presentations of the alert
timeFrame: Through study completion, or up to 1 year
measure: Determining the effect of problem list completion by comparing the number of study-related problems added to problem lists in the electronic health record
timeFrame: Through study completion, or up to 1 year
measure: Determining the quality of care impact of adding suggested problems to the problem list based on 4 outcome measures from NCQA's HEDIS 2013 measure set
timeFrame: Through study completion, or up to 1 year
Secondary outcomes:
measure: Evaluating process measures using key process measures for each study condition from CMS, NHLBI, and NQMC
timeFrame: Through study completion, or up to 1 year
Other outcomes:
Locations:
city: Boston
country: United States
facility: Brigham and Women's Hospital
state: Massachusetts
city: Portland
country: United States
facility: Oregon Health and Science University
state: Oregon
city: Camp Hill
country: United States
facility: Holy Spirit Hospital
state: Pennsylvania
city: Nashville
country: United States
facility: Vanderbilt University Medical Center
state: Tennessee
Study officials:
References:
citation: 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.
citation: 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.
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citation: 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.
citation: Carpenter JD, Gorman PN. Using medication list--problem list mismatches as markers of potential error. Proc AMIA Symp. 2002:106-10.
citation: 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.
citation: 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.
citation: 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.