This study evaluates the effectiveness of an electronic health record based educational intervention (the EMC2 strategy) to improve patient understanding and use of higher-risk medications. Half of the participants will receive the intervention, while the other half will receive the usual amount of information (usual care).
Research has repeatedly demonstrated that individuals lack essential information on how to safely take prescribed (Rx) medications. A risk communication and surveillance strategy is needed in primary care to ensure that patients are adequately informed about medication risks and are taking prescribed regimens safely. The investigators devised an Electronic health record-based Medication Complete Communication (EMC2) Strategy that leverages electronic health record (EHR) and interactive voice response (IVR) technologies to: 1. prompt and guide provider counseling, 2. automate the delivery of Medication Guides at prescribing, 3. follow patients post-visit to confirm prescription understanding and use, and 4. deliver a care alert back to providers to inform them of any potential harms.
Study Type
INTERVENTIONAL
Allocation
RANDOMIZED
Purpose
HEALTH_SERVICES_RESEARCH
Masking
NONE
Enrollment
1,005
The intervention includes 1) distribution of simplified one-page medication guide summaries, 2) an automated follow-up call to assess medication safety and problematic side effects and 3) summary reports of call to providers with any concerns flagged for clinic follow-up.
Near North Health Services Corporation
Chicago, Illinois, United States
Medication Knowledge (0-100)
Adjusted Least-square means of Medication Knowledge are calculated based on patient's ability to identify each medication's purpose and side effects, risks, warnings and benefits using general linear mixed models, specifying the identity link (PROC GLIMMIX). Treatment assignment by time is the independent variable of interest and modeled as a fixed effect, and clinic as a random effect, with additional subject statement to model correlations with patient. Confounding variables, such as age, preferred language, race, education, health status, number of chronic diseases, drug class, and health literacy (Newest Vital Sign) are included as fixed effects in the model. Patients are asked 10 questions (a scale developed by our team), and each questions is scored as correct/incorrect, and percentage of correctly answered questions is calculated (0-100 with 100 as best). Results are presented as adjusted least square means with 95% Confidence Intervals
Time frame: Baseline to 3 Months post baseline
Probability of Prescription Medication Proper Use
Subjects will be asked to demonstrate proper use of the medication by indicating the correct dose (amount of medication taken each time), frequency (times per day), and total pills/units per day. For non-PRN medications, all must be answered correctly to be considered proper use (yes/no) , whereas for PRN medications, proper use is determined if the patient indicated the correct dose or less, the correct frequency or less, and the correct total pills/units or less. Proper use is modelled as a binary outcome, and General linear mixed models are used, specifying the logit link (PROC GLIMMIX). Treatment assignment by time is the independent variable of interest and modeled as a fixed effect, and clinic as a random effect, with additional subject statement to model correlations with patient. Confounding factors, such as drug class and health literacy (Newest Vital Sign) are also included in the model as fixed effects. Results are presented as adjusted least square means with 95% CI
Time frame: 1 Month post baseline to 3 Months post baseline
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