EHR Alert System for Hemorrhagic Stroke

Overseen By Ernst Mayerhofer, MD

Age: 18+

Sex: Any

Trial Phase: Academic

Sponsor: Massachusetts General Hospital

Disqualifiers: Subdural, epidural hematoma, others

No Placebo Group All trial participants will receive the active study treatment (no placebo)

Compensation: Varies

Number of Visits: Unknown

What You Need to Know Before You Apply

What is the purpose of this trial?

This trial tests an alert system in electronic health records (EHR) to help doctors manage care for patients who have had a hemorrhagic stroke, a type of stroke caused by bleeding in the brain. The system aims to remind doctors to check important health markers, such as cholesterol and blood sugar levels, and to consider evaluations for sleep and hearing issues. This approach could lead to better treatment and outcomes for these patients. Individuals admitted to Massachusetts General Hospital with a primary diagnosis of hemorrhagic stroke might be suitable for this study. As an unphased trial, this study offers a unique opportunity to contribute to innovative healthcare solutions that could improve patient outcomes.

Do I need to stop my current medications for this trial?

The trial information does not specify whether you need to stop taking your current medications. It's best to discuss this with the trial coordinators or your doctor.

What prior data suggests that this EHR alert system is safe for patients with hemorrhagic stroke?

Research has shown that using electronic health records (EHR) in medical settings is generally safe for patients. In this trial, the treatment includes EHR alerts, which notify doctors to check important health factors like cholesterol and blood sugar levels.

Studies on EHR systems indicate they help doctors make better decisions without adding extra risk to patients. No reports have linked negative effects directly to the alerts themselves. Instead, they serve as reminders to check important health markers, potentially leading to better patient care.

In short, EHR alerts have not been associated with any safety concerns. This tool is designed to assist doctors, and no evidence suggests it harms patients. ^{1 2 3 4 5}

Why are researchers excited about this trial?

Researchers are excited about this trial because it introduces an innovative approach to managing hemorrhagic stroke care through an electronic health record (EHR) alert system. Unlike traditional methods that rely heavily on routine clinical assessments and follow-ups, this system proactively provides real-time, evidence-based recommendations directly to healthcare providers. By integrating alerts for important lab measurements like LDL and HbA1c, as well as suggesting follow-up referrals for sleep study and audiology, the system aims to enhance both in-patient and out-patient care. This immediate, data-driven decision support can potentially streamline the treatment process, improve patient outcomes, and ensure critical interventions are not overlooked.

What evidence suggests that this EHR alert system is effective for managing hemorrhagic stroke?

Research has shown that electronic health records (EHR) can significantly improve stroke care. In this trial, participants will use an EHR alert system designed to assist doctors with helpful alerts and reminders. Studies have found that these systems aid in making better treatment decisions by reminding healthcare providers to check important health measures like LDL (a type of cholesterol) and HbA1c (a marker for blood sugar control). This alert system can lead to more regular check-ups and better management of conditions often linked with strokes, such as sleep apnea and hearing problems. Overall, EHR alerts are believed to make stroke care more proactive and effective. ^{1 2 6 7 8}

Who Is on the Research Team?

Christopher D Anderson, MD MMSc

Principal Investigator

Brigham and Women's Hospital

Are You a Good Fit for This Trial?

This trial is for adults over 18 who have had a hemorrhagic stroke and are admitted to the Massachusetts General Hospital inpatient stroke service. It's not open to those with subdural or epidural hematoma, or anyone who doesn't fit the main inclusion criteria.

Inclusion Criteria

I am 18 or older and was admitted to Massachusetts General Hospital for a hemorrhagic stroke.

Exclusion Criteria

I have a blood clot between my skull and brain or in the outer covering of my brain.

Patients not meeting above inclusion criteria

Timeline for a Trial Participant

Screening

Participants are screened for eligibility to participate in the trial

2-4 weeks

Index Hospitalization

Implementation of EHR-based notification module to increase awareness of risk factors and comorbidities during acute hemorrhagic stroke hospitalization

During hospitalization

In-patient visit

Follow-up

EHR-based notifications for follow-up care, including referrals for sleep study and audiology, and management of LDL and HbA1c levels

6 months

Out-patient visits

Extended Follow-up

Monitoring of LDL and HbA1c measurements over a 12-month period surrounding the stroke event

12 months

What Are the Treatments Tested in This Trial?

Interventions

• In-patient and out-patient electronic health record alert

Trial Overview The study aims to see if alerting healthcare providers through electronic health records about risk factors and comorbidities can improve management of patients after a hemorrhagic stroke. The focus is on increasing checks for cholesterol (LDL) levels, blood sugar control (HbA1c), sleep apnea, and hearing issues.

How Is the Trial Designed?

Group I: Electronic Health Record Notification Experimental Treatment 1 Intervention

For eligible patients presenting with an acute hemorrhagic stroke, a recommendation to measure low-density lipoprotein (LDL) and glycated hemoglobin A1c (HbA1c) together with their last measurement dates will be displayed in the patient's electronic health record through a best practice alert (BPA). The alert will display for the patient's provider when they first open the patient's chart. The provider may accept the automatically generated orders for both measurements displayed in the BPA, may modify one or both of the orders, or choose to dismiss the BPA. For patients that follow-up with the out-patient stroke clinic and received the in-patient intervention, a second BPA will suggest referrals to sleep study and audiology. The alert will display for the patient's provider when they first open the patient's chart. The provider may accept one or both of the referrals suggested by the BPA, or may choose to dismiss the BPA.

Who Is Running the Clinical Trial?

Massachusetts General Hospital

Lead Sponsor

Trials

3,066

Recruited

13,430,000+

Tufts University

Collaborator

Trials

271

Recruited

595,000+

Boston University

Collaborator

Trials

494

Recruited

9,998,000+

Citations

1. pmc.ncbi.nlm.nih.gov pmc.ncbi.nlm.nih.gov/articles/PMC10980121/

Automated Extraction of Stroke Severity from Unstructured ... Multi-center electronic health records (EHR) can support quality improvement initiatives and comparative effectiveness research in stroke care.

2. pmc.ncbi.nlm.nih.gov pmc.ncbi.nlm.nih.gov/articles/PMC8593798/

Stroke Outcome Measurements From Electronic Medical ... This study aims to compare the effectiveness of state-of-the-art automatic text classification methods in classifying data to support the prediction of ...

3. bmcmedinformdecismak.biomedcentral.com bmcmedinformdecismak.biomedcentral.com/articles/10.1186/s12911-019-1010-x

Assessing stroke severity using electronic health record data Machine learning models built on EHR data can be used to determine proxies for stroke severity. This enables severity to be incorporated in ...

4. ahajournals.org ahajournals.org/doi/10.1161/JAHA.124.036386

Automated Extraction of Stroke Severity From Unstructured ... Multicenter electronic health records can support quality improvement and comparative effectiveness research in stroke.

5. nature.com nature.com/articles/s41598-024-73570-x

An intelligent learning system based on electronic health ... This study used data from electronic health records (EHR) to develop an intelligent learning system for stroke prediction. Three autoencoder ...

6. medinform.jmir.org medinform.jmir.org/2021/11/e29120/

Stroke Outcome Measurements From Electronic Medical ... This study aims to compare the effectiveness of state-of-the-art automatic text classification methods in classifying data to support the prediction of ...

7. sciencedirect.com sciencedirect.com/science/article/pii/S1532046423002435

Clinical outcome prediction using observational ... Observational supervision with audit logs improved the performance of ML models trained to predict important clinical outcomes in patients with AKI and acute ...

8. pmc.ncbi.nlm.nih.gov pmc.ncbi.nlm.nih.gov/articles/PMC10989051/

Machine Learning-Based Prediction of Stroke in Emergency ... The results indicate that available clinical information can be used for building EHR-based stroke prediction models and ED stroke alert systems ...