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

What You Need to Know Before You Apply

What is the purpose of this trial?

This trial explores whether changes to electronic health records (EHR) can reduce the prescription of Z-drugs, commonly used for insomnia. It examines two main strategies: setting a default limit on the number of pills prescribed (Default Intervention) and providing alerts that suggest alternative treatments (Accountable Justification). The trial aims to determine if these nudges, either alone or together, can encourage doctors to prescribe fewer Z-drugs. It is suitable for outpatient primary care clinicians at Northwestern Medicine who haven't participated in related studies and don’t have patients with bipolar disorder in their care list. As an unphased trial, this study offers a unique opportunity to contribute to innovative research that could improve prescribing practices and patient care.

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 prior data suggests that these EHR nudges are safe for reducing Z-drug prescribing?

Studies have shown that electronic health records (EHRs) can help reduce medication mistakes and make prescribing safer. For example, research suggests that setting default options in EHRs, such as a specific number of pills, can guide doctors in prescribing medications. This approach helps ensure patients receive safer and more suitable prescriptions.

However, EHR systems can still contain errors. Some studies indicate that if not managed carefully, EHRs can sometimes lead to more medication mistakes. Overall, well-designed EHR systems tend to support better prescribing practices.

This trial focuses on using EHR prompts, such as setting default pill amounts and requesting reasons for certain prescriptions. These methods aim to reduce inappropriate prescribing. The trial is labeled "Not Applicable," indicating it does not involve testing a new drug or device. The safety of these EHR methods generally depends on careful use and monitoring. ^{1 2 3 4 5}

Why are researchers excited about this trial?

Researchers are excited about this trial because it explores innovative ways to reduce prescribing errors through electronic health record (EHR) nudges. Unlike traditional methods that rely solely on clinician education, this approach introduces accountability by requiring clinicians to justify guideline-discordant orders, which are then visible to their peers. Additionally, using defaults like a 10-pill quantity for prescriptions aims to subtly guide clinicians towards safer prescribing practices. These strategies hope to enhance patient safety and improve adherence to guidelines, offering a proactive solution to reduce errors that current educational interventions alone might not address.

What evidence suggests that these EHR nudges are effective for reducing Z-drug prescribing in insomnia?

Research has shown that changes to electronic health records (EHRs), such as requiring doctors to explain their prescribing decisions and setting a default number of pills, can reduce prescribing mistakes. In this trial, some clinicians will participate in the "Redirection + Accountable Justification" arm, where they must justify orders that do not align with guidelines, thereby increasing awareness and reducing errors. Others will join the "Z-drug Default Quantity" arm, where a default number of pills is set, providing a clear starting point to minimize mistakes. These methods aim to enhance medication safety without limiting doctors' options. They have succeeded in other areas, suggesting they could also help reduce the prescribing of Z-drugs for insomnia. ^{5 6 7 8 9}

Who Is on the Research Team?

Jason Doctor, PhD

Principal Investigator

University of Southern California

Are You a Good Fit for This Trial?

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 for a Trial Participant

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

What Are the Treatments Tested in This Trial?

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.

How Is the Trial Designed?

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+

Published Research Related to This Trial

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]

Citations

1. ncbi.nlm.nih.gov ncbi.nlm.nih.gov/books/NBK519065/

Medication Dispensing Errors and Prevention - NCBI - NIH The incidence of medication errors is 30% higher in patients who are prescribed five or more drugs and 38% higher in those 75 years or older.

2. ejhp.bmj.com ejhp.bmj.com/content/early/2025/04/30/ejhpharm-2025-004533

Tackling medication errors: how a systems approach ... Data utilisation from electronic prescribing and electronic hospital pharmacy systems has generally improved medication safety, although ...

3. pmc.ncbi.nlm.nih.gov pmc.ncbi.nlm.nih.gov/articles/PMC5424837/

A Just Culture Approach to Managing Medication Errors Health care organizations must be accountable for both system failures and human errors and engineer effective controls such as barriers, redundancies, and ...

4. amcp.org amcp.org/concepts-managed-care-pharmacy/medication-errors

Medication Errors Common causes of medication error include incorrect diagnosis, prescribing errors, dose miscalculations, poor drug distribution practices.

5. academic.oup.com academic.oup.com/jamia/article/28/1/167/5961439

How effective are electronic medication systems in reducing ... In that study, although the authors reported a significant reduction in prescribing errors that had targeted CDS, no linear association was ...

6. pmc.ncbi.nlm.nih.gov pmc.ncbi.nlm.nih.gov/articles/PMC9524548/

Impact of Implementing Electronic Health Records on ... This study showed that an HIMSS stage 6 hospital could experience an increase in medication errors following implementation of EHRs.

7. bmcnurs.biomedcentral.com bmcnurs.biomedcentral.com/articles/10.1186/s12912-024-01936-7

The effect of electronic medical records on medication errors ... This study examines the position of the qualified nursing staff regarding the impact of electronic medical records systems on factors related to patient safety.

8. healthaffairs.org healthaffairs.org/doi/10.1377/hlthaff.2018.0725

A Prescription For Enhancing Electronic Prescribing Safety While electronic prescribing has been shown to reduce medication errors and improve prescribing safety, it is vulnerable to error-prone ...

9. sciencedirect.com sciencedirect.com/science/article/pii/S2667276623001464

E-prescribing and medication safety in community settings Electronic prescribing (e-prescribing) is one intervention used to enhance the safety and quality of prescribing by decreasing medication errors and reducing ...