High-risk Medications
Conditions
Brief summary
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).
Detailed description
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.
Interventions
BEHAVIORALEMC2 Strategy
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.
Sponsors
National Institute of Diabetes and Digestive and Kidney Diseases (NIDDK)
Boston Medical Center
University of North Carolina, Chapel Hill
University of Illinois at Chicago
Northwestern University
Study design
Allocation
RANDOMIZED
Intervention model
PARALLEL
Primary purpose
HEALTH_SERVICES_RESEARCH
Masking
NONE
Eligibility
Sex/Gender
ALL
Age
21 Years to No maximum
Healthy volunteers
No
Inclusion criteria
* 21 and older * English or spanish speaking * Primarily responsible for administering own medications * New prescription of one of 66 study medications on day of recruitment * Has a personal mobile or land line phone
Exclusion criteria
* Severe, uncorrectable vision * Hearing or cognitive impairments
Design outcomes
Primary
| Measure | Time frame | Description |
|---|---|---|
| Medication Knowledge (0-100) | Baseline to 3 Months post baseline | 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 |
Secondary
| Measure | Time frame | Description |
|---|---|---|
| Probability of Prescription Medication Proper Use | 1 Month post baseline to 3 Months post baseline | 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 |
Countries
United States
Participant flow
Participants by arm
| Arm | Count |
|---|---|
| Usual Care Subjects will receive the current standard of care. | 526 |
| EMC2 Strategy Subjects will receive the EMC2 Strategy. See description of strategy below.
EMC2 Strategy: 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. | 479 |
| Total | 1,005 |
Withdrawals & dropouts
| Period | Reason | FG000 | FG001 |
|---|---|---|---|
| 1 Month | Completed Evaluation by Baseline | 42 | 39 |
| 1 Month | Deemed Ineligible | 1 | 0 |
| 1 Month | Lost to Follow-up | 38 | 31 |
| 1 Month | Partial Interview | 0 | 4 |
| 1 Month | Withdrawal by Subject | 2 | 1 |
| 3 Months | Completed Evaluation at 1 Month | 100 | 89 |
| 3 Months | Lost to Follow-up | 25 | 21 |
| 3 Months | Withdrawal by Subject | 3 | 1 |
Baseline characteristics
| Characteristic | Usual Care | EMC2 Strategy | Total |
|---|---|---|---|
| Age, Continuous | 50.34 years STANDARD_DEVIATION 12.2 | 49.5 years STANDARD_DEVIATION 12.6 | 50.0 years STANDARD_DEVIATION 12.4 |
| Drug Class Anticonvulsants | 123 Participants | 95 Participants | 218 Participants |
| Drug Class Antidepressants | 145 Participants | 116 Participants | 261 Participants |
| Drug Class Diabetes | 24 Participants | 32 Participants | 56 Participants |
| Drug Class LABA | 38 Participants | 44 Participants | 82 Participants |
| Drug Class NSAID | 59 Participants | 61 Participants | 120 Participants |
| Drug Class Others | 112 Participants | 104 Participants | 216 Participants |
| Drug Class PPI | 25 Participants | 27 Participants | 52 Participants |
| Education College Graduate | 115 Participants | 83 Participants | 198 Participants |
| Education High School Graduate | 292 Participants | 318 Participants | 610 Participants |
| Education Less than High School | 116 Participants | 75 Participants | 191 Participants |
| Health Literacy (NVS) Adequate | 138 Participants | 100 Participants | 238 Participants |
| Health Literacy (NVS) Low | 172 Participants | 177 Participants | 349 Participants |
| Health Literacy (NVS) Marginal | 108 Participants | 111 Participants | 219 Participants |
| Preferred Language English | 479 Participants | 457 Participants | 936 Participants |
| Preferred Language Spanish | 47 Participants | 22 Participants | 69 Participants |
| Race/Ethnicity, Customized Race Black or African American | 304 Participants | 372 Participants | 676 Participants |
| Race/Ethnicity, Customized Race Hispanic or Latino | 96 Participants | 32 Participants | 128 Participants |
| Race/Ethnicity, Customized Race Others | 37 Participants | 24 Participants | 61 Participants |
| Race/Ethnicity, Customized Race White | 78 Participants | 35 Participants | 113 Participants |
| Self-reported Health Status Excellent/Very Good | 128 Participants | 113 Participants | 241 Participants |
| Self-reported Health Status Fair/Poor | 217 Participants | 187 Participants | 404 Participants |
| Self-reported Health Status Good | 179 Participants | 177 Participants | 356 Participants |
| Sex: Female, Male Female | 319 Participants | 299 Participants | 618 Participants |
| Sex: Female, Male Male | 207 Participants | 180 Participants | 387 Participants |
Adverse events
| Event type | EG000 affected / at risk | EG001 affected / at risk |
|---|---|---|
| deaths Total, all-cause mortality | 0 / 526 | 0 / 479 |
| other Total, other adverse events | 0 / 526 | 0 / 479 |
| serious Total, serious adverse events | 0 / 526 | 0 / 479 |
Outcome results
Primary
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
Population: 127 individuals were excluded from analysis, because their study med was either a short term medication or an antibiotics
| Arm | Measure | Group | Value (LEAST_SQUARES_MEAN) |
|---|---|---|---|
| Usual Care | Medication Knowledge (0-100) | Baseline | 71.2 score on a scale |
| Usual Care | Medication Knowledge (0-100) | 1 Month | 73.2 score on a scale |
| Usual Care | Medication Knowledge (0-100) | 3 Months | 73.2 score on a scale |
| EMC2 Strategy | Medication Knowledge (0-100) | Baseline | 71.9 score on a scale |
| EMC2 Strategy | Medication Knowledge (0-100) | 1 Month | 74.9 score on a scale |
| EMC2 Strategy | Medication Knowledge (0-100) | 3 Months | 75.0 score on a scale |
Secondary
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
Population: 248 participants were excluded, due to either completing their evaluation early, or not filling their medication
| Arm | Measure | Group | Value (LEAST_SQUARES_MEAN) |
|---|---|---|---|
| Usual Care | Probability of Prescription Medication Proper Use | 1 Month | 0.84 Probability |
| Usual Care | Probability of Prescription Medication Proper Use | 3 Month | 0.83 Probability |
| EMC2 Strategy | Probability of Prescription Medication Proper Use | 1 Month | 0.81 Probability |
| EMC2 Strategy | Probability of Prescription Medication Proper Use | 3 Month | 0.80 Probability |