AsthmaIQ-MAPLENCT02596087
Improving Quality by Maintaining Accurate Problems in the EHR
A study testing Problem List Suggestion in people with Asthma and Atrial Fibrillation, run by Brigham and Women's Hospital at 4 sites in United States.
Trial 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. Read more about this study
- Who is it for?
- Ages 18 years and olderHealthy volunteers can take part
- Conditions the study is for
- Asthma, Atrial Fibrillation, Chronic Obstructive Pulmonary Disease
- What is being tested?
- Problem List Suggestion
- Could I receive a placebo?
- No, everyone receives an active treatment
- How many people
- 2,386 participants
- Where
- 4 sitesUnited States
- Study dates
- Apr 2016to Mar 2018
- Phase
- Interventional
- Are there results?
- None posted in the registry
Key requirements
Who can take part, as listed by the sponsor. Only the study team can confirm whether you are eligible, so take this list to your doctor.
- Ages 18 years and older
- All sexes
- Healthy volunteers accepted
- Adult, Older adult
Who may be able to join
- All providers over the age of 18 that use the electronic health record at the specific site that the intervention is being observed.
Who cannot join
- -
Trial locations and contacts
4 sites in 1 country.
United States4 sites
Brigham and Women's Hospital
Boston, Massachusetts, 02115
Holy Spirit Hospital
Camp Hill, Pennsylvania, 17011
Vanderbilt University Medical Center
Nashville, Tennessee, 37235
Oregon Health and Science University
Portland, Oregon, 97239
About this study
Official title: 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.
Read the 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.
- Conditions
- Asthma, Atrial Fibrillation, Chronic Obstructive Pulmonary Disease, Coronary Artery Disease, Congestive Heart Failure, Hyperlipidemia
- Being tested
- Problem List Suggestion
- Medical subject headings
- Asthma, Atrial Fibrillation, Pulmonary Disease, Chronic Obstructive, Coronary Artery Disease, Heart Failure, Hyperlipidemias, Hypertension, Myocardial Infarction
What participants receive
What participants receive. In a randomised study each person is assigned to one group by chance.
Problem List SuggestionOther
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.
| Study group | Role | What they receive |
|---|---|---|
| Normal Use of EHR | No intervention | 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 | 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. Receives: Other: Problem List Suggestion |
What is being measured
The primary outcome is the main question the study is designed to answer.
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
Acceptance of the alerts:
This first endpoint is descriptive: the acceptance rate for the alerts presented to providers. This will be calculated by taking the total number of acceptances for each alert and dividing it by the total number or unique presentations of the alert. We will conduct a stratified analysis to look at differences in acceptance rates by institution, specialty, disease and provider demographic characteristics, and will report the results in tabular form.
Measured: Through study completion, or up to 1 year
Determining the effect of problem list completion by comparing the number of study-related problems added to problem lists in the electronic health record
Effect on the rate of problem list completion:
In this endpoint, we will compare the number of study-related problems added to patient problems lists in the electronic health record in the intervention and control groups.
Measured: Through study completion, or up to 1 year
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
Effect on quality of care:
Because a key goal of our study is improving clinical outcomes, we have selected four outcome measures to evaluate from NCQA's Healthcare Effectiveness Data and Information Set (HEDIS) 2013 measure set: LDL control in patients with a history of myocardial infarction, LDL control in patients with coronary artery disease, blood pressure control in patients with coronary artery disease and blood pressure control in patients with hypertension. The details for the numerator and denominator for each measure are given in the HEDIS manuals, and our study team will employ NCQA's procedures for calculation of each measure, with modifications as needed given the clinical nature of our dataset.
Measured: Through study completion, or up to 1 year
Secondary outcomes (1)
Evaluating process measures using key process measures for each study condition from CMS, NHLBI, and NQMC
Improvements for process measures To complete the clinical endpoints in the third outcome, we will also evaluate process measures, specifically frequency of LDL testing, prescription of antihyperlipidemic agents, prescription of aspirin or other antiplatelet agents and prescription of antihypertensive agents. We will analyze the results using logistic regression with fixed effects for intervention group (versus control) and site and estimation of the regression parameters with generalized estimating equations (GEE), accounting for clustering between the patients in the same physician as well as patients with different physicians in the same matched pair. We will build separate regression models for each quality measure, and also conduct a pooled analysis with additional effects for quality measure and availability of CDS for the associated measure at the site, in order to estimate the extent to which IQ-MAPLE's effect on quality is mediated by CDS.
Measured: Through study completion, or up to 1 year
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted4 Nov 2015 (estimated)
- Study startApr 2016
- Primary completionMar 2018
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Publications (8)Papers the sponsor has linked to this study.
- Other trials for AsthmaStudies of the same condition, open and under way.
- More trials run by Brigham and Women's HospitalThe sponsor's other registered studies.
- What the trial phases meanHow a treatment moves from first tests in people to approval.
More details
For doctors and researchers: how the study is designed, who runs it, and its registry IDs.
Study design
- Study type
- Interventional
- Phase
- Phase N/A
- Primary purpose
- Other
- Allocation
- Randomised
- Intervention model
- Parallel groups
- Masking
- Single blind
- Who is masked
- Participant
- Enrolment
- 2,386 participants (actual)
Sponsor and investigators
- Lead sponsor
- Brigham and Women's Hospital
- Sponsor type
- University, hospital or other
- Collaborators
- Geisinger Clinic, Oregon Health and Science University, Vanderbilt University
- Responsible party
- Principal investigator, Adam Wright, Brigham and Women's Hospital
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT02596087
- Sponsor's protocol ID
- 2009P001846-14
- Acronym
- IQ-MAPLE
- Status last verified
- Feb 2023
- First submitted
- 19 Oct 2015
- Last updated
- 8 Feb 2023
- Results posted
- No
- Data monitoring committee
- Yes
Publications
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.
PubMed 17460131Kaplan 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.
PubMed 17683098Szeto 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.
PubMed 11814171Tang 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.
PubMed 10332657Carpenter JD, Gorman PN. Using medication list--problem list mismatches as markers of potential error. Proc AMIA Symp. 2002:106-10.
PubMed 12463796Hartung 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.
PubMed 15836547Wright 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.
PubMed 20884377Wright 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.
PubMed 21613643
Study at a glance
From the registry record, last updated by the sponsor on 8 Feb 2023. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- Improving Quality by Maintaining Accurate Problems in the Electronic Health Record
- ClinicalTrials.gov ID
- NCT02596087
- Acronym / sponsor ID
- IQ-MAPLE / 2009P001846-14
- Conditions
- Asthma, Atrial Fibrillation, Chronic Obstructive Pulmonary Disease, Coronary Artery Disease, Congestive Heart Failure, Hyperlipidemia
- Study type
- Interventional
- Recruitment status
- Completed
- Enrolment
- 2,386 participants (actual)
- Lead sponsor
- Brigham and Women's HospitalUniversity, hospital or other
- Collaborators
- Geisinger Clinic, Oregon Health and Science University, Vanderbilt University
- Study start
- Apr 2016 (actual)
- Primary completion
- Mar 2018 (actual)
- First posted
- 4 Nov 2015 (estimated)
- Results
- Not posted in the source record
Clinical summary
The research question as the sponsor registered it. This describes what the study plans to test, not what it found.
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.
Study design
- Primary purpose
- Other
- Allocation
- Randomised
- Intervention model
- Parallel groups
- Masking
- Single blind (participant)
- Number of arms
- 2
| Arm | Type | Receives |
|---|---|---|
| Normal Use of EHR | No intervention | –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 | Other: Problem List SuggestionSites 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. |
Who may qualify
- Ages
- 18 years and older
- Sex
- All
- Healthy volunteers
- Accepted
- Age groups
- Adult, Older adult
Key inclusion criteria (1 listed)
- All providers over the age of 18 that use the electronic health record at the specific site that the intervention is being observed.
Key exclusion criteria (1 listed)
- -
Eligibility is decided by the study team after screening; this overview does not establish that a patient qualifies. Full inclusion and exclusion criteria.
Interventions
| Intervention | Type | Arms |
|---|---|---|
| Problem List SuggestionSites 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. | Other | Intervention Arm |
Primary outcome
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
Time frame: Through study completion, or up to 1 year
Acceptance of the alerts: This first endpoint is descriptive: the acceptance rate for the alerts presented to providers. This will be calculated by taking the total number of acceptances for each alert and dividing it by the total number or unique presentations of the alert. We will conduct a stratified analysis to look at differences in acceptance rates by institution, specialty, disease and provider demographic characteristics, and will report the results in tabular form.
Determining the effect of problem list completion by comparing the number of study-related problems added to problem lists in the electronic health record
Time frame: Through study completion, or up to 1 year
Effect on the rate of problem list completion: In this endpoint, we will compare the number of study-related problems added to patient problems lists in the electronic health record in the intervention and control groups.
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
Time frame: Through study completion, or up to 1 year
Effect on quality of care: Because a key goal of our study is improving clinical outcomes, we have selected four outcome measures to evaluate from NCQA's Healthcare Effectiveness Data and Information Set (HEDIS) 2013 measure set: LDL control in patients with a history of myocardial infarction, LDL control in patients with coronary artery disease, blood pressure control in patients with coronary artery disease and blood pressure control in patients with hypertension. The details for the numerator and denominator for each measure are given in the HEDIS manuals, and our study team will employ NCQA's procedures for calculation of each measure, with modifications as needed given the clinical nature of our dataset.
1 secondary outcome
Evaluating process measures using key process measures for each study condition from CMS, NHLBI, and NQMC
Time frame: Through study completion, or up to 1 year
Results and safety
The study is completed, but results are not available in the source record. That does not mean the treatment did or did not work.
Research links
- Official record on ClinicalTrials.govLast updated by the sponsor 8 Feb 2023.
- Background: PubMed 17460131Wright 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.
- Background: PubMed 17683098Kaplan 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.
- Background: PubMed 11814171Szeto 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.
- Background: PubMed 10332657Tang 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.
- Background: PubMed 12463796Carpenter JD, Gorman PN. Using medication list--problem list mismatches as markers of potential error. Proc AMIA Symp. 2002:106-10.
- Background: PubMed 15836547Hartung 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.
- Background: PubMed 20884377Wright 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.
- Background: PubMed 21613643Wright 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.
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Referral and contact
AzTrials does not enrol participants or take referrals. Contact the study team or a site listed below; they decide who can take part. This study is not recruiting at the moment.
The record lists no contact. Use the official record to reach the sponsor.
4 sites in 1 country: see every site and its status.
Questions about this trial
What is this clinical trial studying?
A study testing Problem List Suggestion in people with Asthma and Atrial Fibrillation, run by Brigham and Women's Hospital at 4 sites in United States.
The full registered description is in the About this study section and the official record.
Who may be eligible to take part?
The sponsor lists 1 inclusion and 1 exclusion criteria. Eligibility is decided by the study team after screening, so read the full criteria and contact a listed site.
Is this clinical trial currently recruiting?
The record says: finished, as of the sponsor's last update on 8 Feb 2023. Status can change between updates, so confirm with the study contact before making plans.
Where is the study taking place?
The record lists 4 sites in United States. Whether a site is taking part right now should be confirmed with that site. See every site.
What are the possible benefits and risks?
They depend on the study and the treatment, and no study can promise a benefit. The study team explains the known risks during informed consent, before anyone joins; the record itself does not list them.
Are results available for this study?
The study is completed, but results are not available in the source record. That does not mean the treatment did or did not work.
How can a doctor refer a patient or contact the research team?
The record lists no contact. The official record names the sponsor, who can be asked directly. AzTrials does not enrol participants or take referrals.
What does the primary outcome 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 (time frame: Through study completion, or up to 1 year). The study lists 3 primary outcomes in all. It is what the study was designed to measure, not a finding.
Where can I read the original study information?
On the ClinicalTrials.gov record for NCT02596087, which also lists 8 linked publications.
Does a listing mean the treatment is approved or proven to work?
No. A listing means the study is registered. It does not show that the treatment is safe, works, or is approved by a regulator.
How often is this information updated?
AzTrials copies changed records from ClinicalTrials.gov every night and reloads the whole registry every week. The sponsor last updated this record on 8 Feb 2023.