Improving Quality by Maintaining Accurate Problems in the EHR (IQ-MAPLE)

Mass General Brigham

Status: Completed

Conditions: Coronary Artery Disease; Stroke; Sleep Apnea; Myocardial Infarction; Sickle Cell Disease; Smoking; Congestive Heart Failure; Hypertension; Atrial Fibrillation; Asthma; Tuberculosis; Hyperlipidemia; Chronic Obstructive Pulmonary Disease

Treatments: Other: Problem List Suggestion

Study type: Interventional

Funder types: Other

Identifiers

NCT02596087

2009P001846-14

Details and patient eligibility

About

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.

Full 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.

Enrollment: 2,386 patients

Sex: All

Ages: 18+ years old

Volunteers: Accepts Healthy Volunteers

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

Trial design

Primary purpose: Other

Allocation: Randomized

Interventional model: Parallel Assignment

Masking: Single Blind

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2,386 participants in 2 patient groups

Normal Use of EHR

No Intervention group

Description:

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.

Intervention Arm

Experimental group

Description:

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.

Treatment:

Other: Problem List Suggestion