Electronic Medical Record Support for Diabetes Management

Recruiting at 2 trial locations

Overseen By Ariana Pichardo-Lowden, MD

Age: 18+

Sex: Any

Trial Phase: Academic

Sponsor: Milton S. Hershey Medical Center

Disqualifiers: Children

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

Approved in 2 Jurisdictions This treatment is already approved in other countries

Compensation: Varies

Number of Visits: 3

15732 Participants Needed

Trial Summary

What is the purpose of this trial?

The purpose of this study is to determine the impact of an electronic medical record clinical decision support tool on rates of dysglycemia in the hospital, and its clinical and economical outcomes. The study also evaluates the perspectives of providers regarding the tool's usefulness on disease management support, knowledge, and practice performance.

Will I have to stop taking my current medications?

The trial information does not specify whether you need to stop taking your current medications.

What data supports the effectiveness of the treatment Active Electronic Medical Record Inpatient Diabetes Clinical Decision Support?

Research shows that electronic medical record (EMR) systems with clinical decision support (CDS) tools can help improve diabetes management in hospitals by alerting healthcare providers to care gaps and offering management recommendations, which can lead to better glucose control and insulin use. ^{1 2 3 4 5}

Is the Electronic Medical Record Support for Diabetes Management safe for humans?

The use of electronic medical record systems with clinical decision support tools has been shown to reduce medication errors and improve patient care, suggesting they are generally safe for managing diabetes in hospital settings. ^{1 2 3 6 7}

How is the Active Electronic Medical Record Inpatient Diabetes Clinical Decision Support treatment different from other diabetes treatments?

This treatment is unique because it uses an electronic medical record system to provide real-time alerts and management recommendations to healthcare providers, helping them address gaps in inpatient diabetes care and optimize glucose control and insulin use. ^{1 2 4 8 9}

Research Team

Ariana Pichardo-Lowden, MD

Principal Investigator

Penn State College of Medicine

Eligibility Criteria

This trial is for adults over 18 years old with conditions like prediabetes, diabetes, and varying blood sugar levels who are hospitalized or visiting outpatient clinics at specific Penn State Health centers. Children under 18 cannot participate.

Inclusion Criteria

I am an adult patient hospitalized at a specified Penn State Health center.

I am over 18 and can walk on my own. I am a patient at Penn State Health, Hershey Medical Center.

You have received a warning or a message about managing a health condition.

Exclusion Criteria

I am under 18 years old.

Timeline

Screening

Participants are screened for eligibility to participate in the trial

2-4 weeks

Intervention

Participants are treated with the GlucAlert-CDS tool during the 'ON' phase, and without it during the 'OFF' phase, to assess its impact on clinical and economic outcomes.

36 months

Intermittent EMR access every 3 months

Follow-up

Participants are monitored for safety and effectiveness after treatment, including hospital readmission and post-discharge outcomes.

3 months

Treatment Details

Interventions

• Active Electronic Medical Record Inpatient Diabetes Clinical Decision Support

Trial Overview The study is testing an electronic tool that helps doctors make decisions about diabetes care in hospitals. It looks at how the tool affects blood sugar control and its impact on clinical outcomes and costs.

Participant Groups

Group I: Active Inpatient Diabetes Clinical Decision Support Experimental Treatment 1 Intervention

The Active arm consists of participants treated during the "ON" phase of the GlucAlert-CDS tool. The tool operates through an automated process of rules embedded in the EMR recognizing hypoglycemia (established or impending); recurrent hyperglycemia (in type 1 and 2 DM, or stress hyperglycemia-SH); and inappropriate insulin use (sliding scales monotherapy if recurrent hyperglycemia in type 2 DM or SH, or any time in type 1 DM). If the tool's criteria are met, an alert in the EMR will notify the provider with the clinically recommended treatment.

Group II: Inactive Inpatient Diabetes Clinical Decision Support Active Control 1 Intervention

The Inactive arm consists of participants treated during the "OFF" phase of the GlucAlert-CDS Tool. Alerts will not be sent to provider's

Active Electronic Medical Record Inpatient Diabetes Clinical Decision Support is already approved in United States, European Union for the following indications:

Approved in United States as EMR CDS for:

• Hospital glycemic management
• Dysglycemia prevention
• Inpatient diabetes management

Approved in European Union as Inpatient Diabetes Clinical Decision Support Tool for:

• Inpatient diabetes care
• Glycemic control
• Hyperglycemia management

Who Is Running the Clinical Trial?

Milton S. Hershey Medical Center

Lead Sponsor

Trials

515

Recruited

2,873,000+

National Institutes of Health (NIH)

Collaborator

Trials

2,896

Recruited

8,053,000+

National Institute of Diabetes and Digestive and Kidney Diseases (NIDDK)

Collaborator

Trials

2,513

Recruited

4,366,000+

Findings from Research

Electronic health records (EHR) can significantly enhance the management of inpatient diabetes by providing clinical decision support tools, such as electronic order sets, which help reduce insulin errors and hypoglycemia rates.

Glycemic management dashboards allow healthcare providers to efficiently monitor blood glucose trends and adjust insulin regimens, improving overall patient care for those with diabetes and hyperglycemia in hospitals.

NCBI (Pubmed)

pubmed.ncbi.nlm.nih.gov

Electronic Health Record-Based Decision-Making Support in Inpatient Diabetes Management. Gerwer, JE., Bacani, G., Juang, PS., et al.[2022]

The implementation of an electronic medical record (EMR) clinical decision support (CDS) tool significantly reduced recurrent hyperglycemia events in hospitalized patients with diabetes, showing a decrease from 3701 to 3342 events during the active period.

The CDS tool also effectively decreased inappropriate sliding scale insulin (SSI) use in type 1 diabetes patients, with a reduction from 22 to 10 instances, indicating improved insulin management in this group.

NCBI (Pubmed)

pubmed.ncbi.nlm.nih.gov

Clinical decision support to improve management of diabetes and dysglycemia in the hospital: a path to optimizing practice and outcomes. Pichardo-Lowden, A., Umpierrez, G., Lehman, EB., et al.[2021]

Clinical decision support systems (CDSS) have shown promise in improving the care of hospitalized patients with diabetes, with 9 out of 10 studies reporting a reduction in mean blood glucose levels during inpatient stays.

The use of CDSS led to a decrease in sliding scale insulin use and an increase in basal-bolus insulin regimens, although only one study reported a significant increase in hypoglycemic events, indicating a need for further research to confirm these benefits.

NCBI (Pubmed)

pubmed.ncbi.nlm.nih.gov

Clinical decision support systems in the care of inpatients with diabetes in non-critical care setting: systematic review. Nirantharakumar, K., Chen, YF., Marshall, T., et al.[2022]

A new computer-aided decision support system (CDSS) for diabetes care can be easily implemented in primary care settings using simple fax technology, making it accessible without needing complex electronic medical records.

The CDSS quickly generates patient-specific recommendations by processing information from faxed forms, demonstrating that effective decision support can be achieved in various healthcare environments.

NCBI (Pubmed)

pubmed.ncbi.nlm.nih.gov

Using old technology to implement modern computer-aided decision support for primary diabetes care. Hunt, DL., Haynes, RB., Morgan, D.[2018]

The CAD/DM Smart Form is a Clinical Decision Support System designed to enhance the management of chronic diseases like coronary artery disease and diabetes mellitus.

A pilot study indicates that this Smart Form could significantly improve patient care, suggesting its effectiveness in clinical settings.

NCBI (Pubmed)

pubmed.ncbi.nlm.nih.gov

Improving management of chronic diseases with documentation-based clinical decision support: results of a pilot study. Schnipper, JL., McColgan, KE., Linder, JA., et al.[2019]

In a study of 79 hospitalized patients with type 2 diabetes, using a computerized system (GlucoTab®) for blood glucose documentation and insulin dosing significantly reduced manual calculation errors compared to a paper-based protocol, which had a higher rate of errors leading to hypoglycemia.

The computerized system not only eliminated user calculation errors but also improved workflow efficiency, although it still faced some workflow deviations that could lead to hyperglycemia, highlighting the importance of integrating decision support in diabetes management.

NCBI (Pubmed)

pubmed.ncbi.nlm.nih.gov

Impact of errors in paper-based and computerized diabetes management with decision support for hospitalized patients with type 2 diabetes. A post-hoc analysis of a before and after study. Donsa, K., Beck, P., Höll, B., et al.[2017]

In a study of 2448 overridden medication alerts in six ICUs, 81.6% of these overrides were deemed appropriate, indicating that most clinical decision support (CDS) alerts were correctly disregarded by healthcare providers.

However, inappropriate overrides were significantly linked to a higher risk of adverse drug events (ADEs), with a sixfold increase in the likelihood of ADEs occurring after such overrides, highlighting the need for improved CDS systems to reduce unnecessary alerts.

NCBI (Pubmed)

pubmed.ncbi.nlm.nih.gov

Prospective evaluation of medication-related clinical decision support over-rides in the intensive care unit. Wong, A., Amato, MG., Seger, DL., et al.[2019]

The study found that EMR-based clinical decision support (CDS) significantly reduced A1c levels in adults with diabetes by 0.26 percent, indicating improved diabetes management.

The cost-effectiveness analysis showed that EMR-based CDS increased quality-adjusted life years (QALYs) by 0.04 at an incremental cost of $3,017 per QALY, suggesting that it can enhance patient care without significantly raising healthcare costs.

NCBI (Pubmed)

pubmed.ncbi.nlm.nih.gov

Cost-effectiveness of an electronic medical record based clinical decision support system. Gilmer, TP., O'Connor, PJ., Sperl-Hillen, JM., et al.[2022]

A systematic review of 33 studies found that electronic medical records (EMR) and clinical decision-support systems (CDSS) can improve diabetes care processes, but most evidence comes from high-income countries, limiting applicability to low- and middle-income countries (LMICs).

While patients reported feeling empowered and receiving better attention from providers due to EMR-CDSS tools, challenges such as technical difficulties and the 'mechanization' of care were also noted, indicating a need for tailored solutions in LMICs.

NCBI (Pubmed)

pubmed.ncbi.nlm.nih.gov

Review of electronic decision-support tools for diabetes care: a viable option for low- and middle-income countries? Ali, MK., Shah, S., Tandon, N.[2022]

References

1. United States pubmed.ncbi.nlm.nih.gov

Electronic Health Record-Based Decision-Making Support in Inpatient Diabetes Management. [2022]

2. England pubmed.ncbi.nlm.nih.gov

Clinical decision support to improve management of diabetes and dysglycemia in the hospital: a path to optimizing practice and outcomes. [2021]

3. England pubmed.ncbi.nlm.nih.gov

Clinical decision support systems in the care of inpatients with diabetes in non-critical care setting: systematic review. [2022]

4. United States pubmed.ncbi.nlm.nih.gov

Using old technology to implement modern computer-aided decision support for primary diabetes care. [2018]

5. United States pubmed.ncbi.nlm.nih.gov

Improving management of chronic diseases with documentation-based clinical decision support: results of a pilot study. [2019]

6. Ireland pubmed.ncbi.nlm.nih.gov

Impact of errors in paper-based and computerized diabetes management with decision support for hospitalized patients with type 2 diabetes. A post-hoc analysis of a before and after study. [2017]

7. England pubmed.ncbi.nlm.nih.gov

Prospective evaluation of medication-related clinical decision support over-rides in the intensive care unit. [2019]

8. United States pubmed.ncbi.nlm.nih.gov

Cost-effectiveness of an electronic medical record based clinical decision support system. [2022]

9. United States pubmed.ncbi.nlm.nih.gov

Review of electronic decision-support tools for diabetes care: a viable option for low- and middle-income countries? [2022]