The purpose of this study test the effectiveness of the Universal Medication Schedule (UMS), which was designed as a strategy to standardize and simplify medication instructions to support safe and effective prescription drug use among diabetic.
Research has shown the UMS (1) improves patients' understanding of how much to take of a medicine and when, and (2) reduces the number of times per day patients would take a multi-drug regimen. In this study, UMS tools will be exported into a second electronic health record platform to demonstrate ease of dissemination. Also, as patients may require assistance outside of clinic visits to adapt their prescription regimen to the UMS, this study will test the potential benefit of daily short message service (SMS) text reminders via cell phone. We will conduct a three-arm, provider-randomized controlled trial among English and Spanish-speaking adults taking three or more prescription drugs to evaluate the effectiveness of the UMS strategy, with and without SMS text reminders, to improve patient understanding, consolidation, and adherence compared to usual care.We will conduct a three-arm, provider-randomized trial at two community health centers in Chicago, IL to evaluate the UMS and UMS+SMS text reminder strategies compared to usual care. English and Spanish-speaking patients who are prescribed three or more medications will be recruited and assessed by phone at baseline, three months, and six months.
Study Type
INTERVENTIONAL
Allocation
RANDOMIZED
Purpose
HEALTH_SERVICES_RESEARCH
Masking
NONE
Enrollment
452
Patients of providers randomized to the UMS arm will receive study-related educational tools at their primary care visit to support the understanding, regimen consolidation, and use of prescriptions.
In addition to the components from the UMS strategy arm, patients will receive daily text reminders for 7 days, with the option of extending reminders, following a study medication prescription.
Northwestern University
Chicago, Illinois, United States
Prescription Understanding
Predictive probabilities of prescription understanding will be calculated based on patients' ability to correctly dose their prescription medications using unadjusted Generalized Estimating Equation models, specifying the binomial family and logit link, with medication as the unit of analysis. Correct dosing per medication will be scored as yes or no, reflecting having demonstrated all of the following: proper dose (# of pills), spacing (hours between doses), frequency (# of times per day), and total pills per day. Results are presented as predicted probabilities with 95% Confidence Intervals.
Time frame: 6 months after baseline
Medication Knowledge
Predictive probabilities of medication knowledge will be calculated based on patients' ability to identify each medication's purpose and side effects, risks, warnings, and benefits using unadjusted Generalized Estimating Equation models, specifying the binomial family and logit link, with medication as the unit of analysis. Patients will be asked through a structured questionnaire about each of the above via structured, open-ended items. Each medication will be scored as correct/incorrect if the participant knew the purpose and could name at least 1 side effect, risk, or warning of the medication. Results are presented as predicted probabilities with 95% Confidence Intervals.
Time frame: 6 months after baseline
Medication Adherence: Pill Count
Predictive probabilities of medication adherence will be calculated based on telephone pill counts using unadjusted Generalized Estimating Equation models, specifying the binomial family and logit link, with medication as the unit of analysis. Pills taken/pills prescribed will be calculated for each medication and scored as adherent for that medication if that score is between 80% and 120%. Results are presented as predicted probabilities with 95% Confidence Intervals.
Time frame: 6 months after baseline
Medication Adherence: PMAQ
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Predictive probabilities of medication adherence will be calculated based on a 4-item validated Patient Medication Adherence Questionnaire (PMAQ) using unadjusted Generalized Estimating Equation models, specifying the binomial family and logit link, with medication as the unit of analysis. The PMAQ assesses adherence behaviors by asking patients to self-report missed/wrong doses in past 4 days, scoring each medication as adherent for that medication if no missed doses were reported. Results are presented as predicted probabilities with 95% Confidence Intervals.
Time frame: 6 months after baseline
Medication Adherence: Pharmacy Records
Predictive probabilities of primary medication adherence will be calculated based on the proportion of days covered (PDC) with medication obtained from pharmacy records using unadjusted Generalized Estimating Equation models, specifying the binomial family and logit link, with medication as the unit of analysis. PDC is calculated by summing the number of days' supply obtained by a patient during a given time period and dividing by the number of days for which the patient was prescribed the medication. Each medication is scored as adherent if the patient was covered for that medication more than 80% of the time. Results are presented as predicted probabilities with 95% Confidence Intervals.
Time frame: 6 months after baseline