EHR Nudges for Prescribing Errors

(SOMNUS Trial)

Overseen By Tara Knight, PhD

Age: 18+

Sex: Any

Trial Phase: Academic

Sponsor: University of Southern California

Disqualifiers: Pilot study, Investigator, Bipolar

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

Compensation: Varies

Number of Visits: 1

443 Participants Needed

Trial Summary

Will I have to stop taking my current medications?

The trial does not specify whether participants must stop taking their current medications. It focuses on how electronic health record changes can influence prescribing habits for Z-drugs, not on altering current medication regimens.

What data supports the effectiveness of the treatment Accountable Justification, Default Intervention?

Research shows that using nudges in electronic health records can help reduce errors and improve decision-making by guiding healthcare providers toward better practices. Although not all nudges are effective, some have successfully reduced waste and misuse in healthcare settings. ^{1 2 3 4 5}

Is the EHR nudge intervention safe for humans?

The studies on EHR nudges focus on changing healthcare provider behavior and do not report any direct safety concerns for patients. These interventions are designed to improve decision-making and reduce errors, suggesting they are generally safe for human use. ^{1 2 3 6 7}

How does the EHR nudge treatment differ from other treatments for prescribing errors?

The EHR nudge treatment is unique because it uses behavioral 'nudges' within electronic health records to subtly guide healthcare providers towards better prescribing practices without interruptive alerts, reducing errors and misuse. This approach leverages decision support systems to influence clinician behavior, which is different from traditional methods that often rely on direct interventions or alerts. ^{1 2 4 8 9}

What is the purpose of this trial?

The goal of this clinical trial is to learn if electronic health record (EHR) nudges (changes to the EHR that do not restrict freedom of choice or alter incentives) can reduce Z-drug prescribing in primary care clinics for patients with insomnia. The main questions it aims to answer are: 1. Can Z-drug prescribing be reduced by setting the dispense quantity default of new Z-drug orders in the EHR to 10 pills with 0 refills? 2. Can Z-drug prescribing be reduced by an EHR alert that suggests clinicians remove a Z-drug and/or add an evidence-based behavioral treatment for insomnia, followed by a request to justify their reasoning if the suggestion is not followed? 3. Does combining these two nudges reduce Z-drug prescribing? Researchers will compare each nudge individually and in combination to an guideline education control group to see if each nudge (separately and in combination) can reduce Z-drug prescribing. Clinician-participants will: 1. Complete an introductory educational module about treating insomnia and relevant EHR changes. 2. Complete their routine patient visits. 3. Either experience EHR changes when prescribing Z-drugs, including a Z-drug dispense quantity default of 10 pills for new orders, a prompt to remove or justify Z-drug orders, both, or neither.

Research Team

Jason Doctor, PhD

Principal Investigator

University of Southern California

Eligibility Criteria

This trial is for primary care clinicians. It aims to see if certain changes in the electronic health record system can help them prescribe fewer Z-drugs, which are medications used to treat insomnia.

Inclusion Criteria

Outpatient primary care clinician at Northwestern Medicine

Exclusion Criteria

Clinician participated in pilot study

Clinician-investigator for this trial

I have been diagnosed with bipolar disorder in the last year.

Timeline

Screening

Participants are screened for eligibility to participate in the trial

2-4 weeks

Educational Module

Clinician-participants complete an introductory educational module about treating insomnia and relevant EHR changes

1 week

1 visit (virtual)

Intervention

Clinicians experience EHR changes when prescribing Z-drugs, including a Z-drug dispense quantity default of 10 pills for new orders, a prompt to remove or justify Z-drug orders, both, or neither

18 months

Follow-up

Participants are monitored for safety and effectiveness after the intervention

12 months

Treatment Details

Interventions

• Accountable Justification
• Default Intervention

Trial Overview The study tests two 'nudges' within the EHR: setting a default dispense quantity of 10 pills with no refills for new Z-drug prescriptions, and an alert that prompts clinicians to consider removing a Z-drug or adding behavioral treatment for insomnia.

Participant Groups

Group I: Z-drug Default Quantity Experimental Treatment 1 Intervention

Clinicians randomized to this arm receive guideline education + 10 pill quantity defaults.

Group II: Redirection + Accountable Justification Experimental Treatment 1 Intervention

Clinicians randomized to this arm receive guideline education + alerts requiring justification of orders discordant with guidelines. Justifications are entered in the patient's medical record, and can be viewed by other clinicians.

Group III: Combined Experimental Treatment 2 Interventions

Clinicians randomized to this arm receive guideline education + 10 pill quantity defaults + alerts requiring justification of orders discordant with guidelines. Justifications are entered in the patient's medical record, and can be viewed by other clinicians

Group IV: Control Active Control 1 Intervention

Clinicians randomized to this arm receive guideline education prior to the trial.

Who Is Running the Clinical Trial?

University of Southern California

Lead Sponsor

Trials

956

Recruited

1,609,000+

Northwestern Medicine

Collaborator

Trials

14

Recruited

9,500+

National Heart, Lung, and Blood Institute (NHLBI)

Collaborator

Trials

3,987

Recruited

47,860,000+

Findings from Research

The use of nudge theory in clinical decision support systems (CDSS) could help bridge the gap between existing healthcare evidence and actual clinical practice, potentially improving patient outcomes.

Integrating nudges with electronic health records and artificial intelligence may enhance clinician adherence to guidelines, especially in areas lacking robust evidence, by standardizing behavior in uncertain clinical situations.

NCBI (Pubmed)

pubmed.ncbi.nlm.nih.gov

Nudging within learning health systems: next generation decision support to improve cardiovascular care. Chen, Y., Harris, S., Rogers, Y., et al.[2022]

References

1. England pubmed.ncbi.nlm.nih.gov

Behavioral "nudges" in the electronic health record to reduce waste and misuse: 3 interventions. [2023]

2. Netherlands pubmed.ncbi.nlm.nih.gov

User-Centered Development of a Behavioral Economics Inspired Electronic Health Record Clinical Decision Support Module. [2020]

3. Switzerland pubmed.ncbi.nlm.nih.gov

Implementation and Evaluation of Two Nudges in a Hospital's Electronic Prescribing System to Optimise Cost-Effective Prescribing. [2022]

4. England pubmed.ncbi.nlm.nih.gov

Nudging within learning health systems: next generation decision support to improve cardiovascular care. [2022]

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

Text Message Medication Adherence Reminders Automated and Delivered at Scale Across Two Institutions: Testing the Nudge System: Pilot Study. [2022]

6. Canada pubmed.ncbi.nlm.nih.gov

A Behavioral Economics-Electronic Health Record Module to Promote Appropriate Diabetes Management in Older Adults: Protocol for a Pragmatic Cluster Randomized Controlled Trial. [2022]

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

The impact of an electronic medical record nudge on reducing testing for hospital-onset Clostridioides difficile infection. [2021]

8. England pubmed.ncbi.nlm.nih.gov

Effectiveness of non-interruptive nudge interventions in electronic health records to improve the delivery of care in hospitals: a systematic review. [2023]

9. England pubmed.ncbi.nlm.nih.gov

Effect of two behavioural 'nudging' interventions on management decisions for low back pain: a randomised vignette-based study in general practitioners. [2022]