The Patient Typology about deprescribing and medication-related decisions: A quantitative exploration

An Educational Intervention to Promote Central Nervous System-Active Deprescribing in Dementia: A Pilot Study

BACKGROUND

Central nervous system (CNS)-active polypharmacy (defined as concurrent exposure to three or more antidepressant, antipsychotic, antiseizure, benzodiazepine, opioid, or nonbenzodiazepine benzodiazepine receptor agonists) is associated with significant potential harms in persons living with dementia (PLWD).We conducted a pilot trial to assess a patient nudge intervention's implementation feasibility and preliminary effectiveness to prompt deprescribing conversations between PLWD experiencing CNS-active polypharmacy and their primary care clinicians ("clinicians").

METHODS

We used the electronic health record to identify PLWD prescribed CNS-active polypharmacy in primary care clinics from two health systems. Clinics were assigned to intervention (n = 10) or control (n = 12), with PLWD in intervention clinics mailed an educational brochure to prompt discussion with clinicians about the appropriateness of their CNS-active regimen. We conducted chart reviews for evidence of documentation related to these medications and used the electronic health record (EHR) to assess preliminary effectiveness 120 days after sending the brochure (e.g., number of CNS-active medications prescribed, change in total standardized daily dose [TSDD] of CNS-active medications, and change in prevalence of CNS-active polypharmacy). We interviewed 10 clinicians from intervention clinics to assess their perceptions about the acceptability of the intervention.

RESULTS

PLWD in the intervention group (n = 61) and control group (n = 68) had an average age of 72.4 years (standard deviation [SD] 9.7), 62.8% were female, and 84.5% were white. We did not find any documented evidence of conversations related to CNS-active medications between PLWD who received the brochure and their primary care clinicians. After 120 days, there was no significant between-group difference in the mean number of CNS-active medications prescribed (- 1.0 [SD 1.3] versus - 1.0 [SD 1.3]), mean TSDD (- 1.6 [SD 6.0] versus - 1.3 [SD 5.8]), or the percentage of patients with CNS-active polypharmacy (52.6% versus 50.4%). Interviews with clinicians suggested they were aware that combinations of CNS-active medications were not ideal; however, they reported inheriting patients who were already on these medications, and they did not have sufficient clinic time or access to safer alternatives to overcome patient hesitation to deprescribe.

CONCLUSIONS

A direct-to-patient mailed educational brochure did not demonstrate feasibility in provoking deprescribing conversations between PLWD and clinicians or preliminary effectiveness in decreasing CNS-active polypharmacy.

Latent Class Analysis Identifies Four Distinct Patient Deprescribing Typologies Among Older Adults in Four Countries

Background and Objectives

Polypharmacy, the concurrent use of multiple medicines, is a growing concern among older adults and those with chronic conditions. Deprescribing through dose reduction or discontinuing selected medicines is a strategy for reducing medicine-related harm. The Patient Deprescribing Typology was developed using qualitative methods to describe the varying factors that are important to older adults when they consider deprescribing. The objective of this study was to use quantitative methods to define distinct classes of older adults via the Patient Deprescribing Typology.

Research Design and Methods

This study used a cross-sectional experimental design in which data was collected via an online survey from participants 65 years and older in Australia, the Netherlands, the United Kingdom, and the United States. A latent class analysis was performed using the 4-item Patient Deprescribing Typology that collected data about the beliefs about the importance of medicines, how older adults learn about medicines, medicine decision-making preferences, and attitudes towards stopping medicines.

Results

Older adults (n = 2,250) were a median of 70 years and 2-thirds reported that their highest level of education was an associate's degree or trade school or less. We identified 4 distinct Patient Deprescribing Typology classes: Class 1 "Trusts their doctor" (41.6%), Class 2 "Makes own decisions" (30.2%), Class 3 "Avoids deprescribing" (15.5%), and Class 4 'Medicines not important' (12.7%).

Discussion and Implications

Older adults report diverse perspectives about deprescribing, emphasizing the need for tailored communication strategies in clinical settings. Additional research is needed to examine older adults' preferences in real-world contexts to refine and improve deprescribing interventions.

Clinical Trial Registration

NCT04676282.

Investigating Older Adults' Perceptions of AI Tools for Medication Decisions: Vignette-Based Experimental Survey

BACKGROUND

Given the public release of large language models, research is needed to explore whether older adults would be receptive to personalized medication advice given by artificial intelligence (AI) tools.

OBJECTIVE

This study aims to identify predictors of the likelihood of older adults stopping a medication and the influence of the source of the information.

METHODS

We conducted a web-based experimental survey in which US participants aged ≥65 years were asked to report their likelihood of stopping a medication based on the source of information using a 6-point Likert scale (scale anchors: 1=not at all likely; 6=extremely likely). In total, 3 medications were presented in a randomized order: aspirin (risk of bleeding), ranitidine (cancer-causing chemical), or simvastatin (lack of benefit with age). In total, 5 sources of information were presented: primary care provider (PCP), pharmacist, AI that connects with the electronic health record (EHR) and provides advice to the PCP ("EHR-PCP"), AI with EHR access that directly provides advice ("EHR-Direct"), and AI that asks questions to provide advice ("Questions-Direct") directly. We calculated descriptive statistics to identify participants who were extremely likely (score 6) to stop the medication and used logistic regression to identify demographic predictors of being likely (scores 4-6) as opposed to unlikely (scores 1-3) to stop a medication.

RESULTS

Older adults (n=1245) reported being extremely likely to stop a medication based on a PCP's recommendation (n=748, 60.1% [aspirin] to n=858, 68.9% [ranitidine]) compared to a pharmacist (n=227, 18.2% [simvastatin] to n=361, 29% [ranitidine]). They were infrequently extremely likely to stop a medication when recommended by AI (EHR-PCP: n=182, 14.6% [aspirin] to n=289, 23.2% [ranitidine]; EHR-Direct: n=118, 9.5% [simvastatin] to n=212, 17% [ranitidine]; Questions-Direct: n=121, 9.7% [aspirin] to n=204, 16.4% [ranitidine]). In adjusted analyses, characteristics that increased the likelihood of following an AI recommendation included being Black or African American as compared to White (Questions-Direct: odds ratio [OR] 1.28, 95% CI 1.06-1.54 to EHR-PCP: OR 1.42, 95% CI 1.17-1.73), having higher self-reported health (EHR-PCP: OR 1.09, 95% CI 1.01-1.18 to EHR-Direct: OR 1.13 95%, CI 1.05-1.23), having higher confidence in using an EHR (Questions-Direct: OR 1.36, 95% CI 1.16-1.58 to EHR-PCP: OR 1.55, 95% CI 1.33-1.80), and having higher confidence using apps (EHR-Direct: OR 1.38, 95% CI 1.18-1.62 to EHR-PCP: OR 1.49, 95% CI 1.27-1.74). Older adults with higher health literacy were less likely to stop a medication when recommended by AI (EHR-PCP: OR 0.81, 95% CI 0.75-0.88 to EHR-Direct: OR 0.85, 95% CI 0.78-0.92).

CONCLUSIONS

Older adults have reservations about following an AI recommendation to stop a medication. However, individuals who are Black or African American, have higher self-reported health, or have higher confidence in using an EHR or apps may be receptive to AI-based medication recommendations.