Improving Quality by Maintaining Accurate Problems in the EHR
| Status: | Not yet recruiting |
|---|---|
| Conditions: | Asthma, Asthma, Atrial Fibrillation, Chronic Obstructive Pulmonary Disease, High Blood Pressure (Hypertension), High Cholesterol, Insomnia Sleep Studies, Peripheral Vascular Disease, Smoking Cessation, Cardiology, Cardiology, Cardiology, Infectious Disease, Neurology, Anemia |
| Therapuetic Areas: | Cardiology / Vascular Diseases, Hematology, Immunology / Infectious Diseases, Neurology, Psychiatry / Psychology, Pulmonary / Respiratory Diseases |
| Healthy: | No |
| Age Range: | 18 - Any |
| Updated: | 4/21/2016 |
| Start Date: | April 2016 |
Improving Quality by Maintaining Accurate Problems in the Electronic Health Record
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.
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.
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.
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.
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:
We found this trial at
4
sites
1211 Medical Center Dr
Nashville, Tennessee 37232
Nashville, Tennessee 37232
(615) 322-5000
Principal Investigator: Trent Rosenbloom, MD, MPH
Vanderbilt Univ Med Ctr Vanderbilt University Medical Center (VUMC) is a comprehensive healthcare facility dedicated...
Click here to add this to my saved trials
Brigham and Women's Hosp Boston’s Brigham and Women’s Hospital (BWH) is an international leader in...
Click here to add this to my saved trials
3181 Southwest Sam Jackson Park Road
Portland, Oregon 97239
Portland, Oregon 97239
503 494-8311
Principal Investigator: David Dorr, MD, MS
Oregon Health and Science University In 1887, the inaugural class of the University of Oregon...
Click here to add this to my saved trials
Click here to add this to my saved trials