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Learning from marketing: Rapid development of medication messages that engage patients.

ABSTRACT: To adapt marketing approaches in a health services environment.Researchers and advertising professionals partnered in developing advertising-style messages designed to activate patients pre-identified as having chronic kidney disease to ask providers about recommended medications. We assessed feasibility of the development process by evaluating partnership structure, costs, and timeframe. We tested messages with patients and providers using preliminary surveys to refine initial messages and subsequent focus groups to identify the most persuasive ones.The partnership achieved an efficient structure, $14,550 total costs, and 4-month timeframe. The advertising team developed 11 initial messages. The research team conducted surveys and focus groups with a total of 13 patients and 8 providers to identify three messages as most activating. Focus group themes suggested the general approach of using advertising-style messages was acceptable if it supported patient-provider relationships and had a credible evidence base. Individual messages were more motivating if they elicited personal identification with imagery, particular emotions, active patient role, and message clarity.We demonstrated feasibility of a research-advertising partnership and acceptability and likely impact of advertising-style messages on patient medication-seeking behavior.Healthcare systems may want to replicate our adaptation of marketing approaches to patients with chronic conditions.

SUBMITTER: Yank V

PROVIDER: S-EPMC4684954 | biostudies-literature | 2015 Aug

REPOSITORIES: biostudies-literature

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Learning from marketing: Rapid development of medication messages that engage patients.

Yank Veronica V Tribett Erika E Green Lydia L Pettis Jasmine J

Patient education and counseling 20150314 8

<h4>Objective</h4>To adapt marketing approaches in a health services environment.<h4>Methods</h4>Researchers and advertising professionals partnered in developing advertising-style messages designed to activate patients pre-identified as having chronic kidney disease to ask providers about recommended medications. We assessed feasibility of the development process by evaluating partnership structure, costs, and timeframe. We tested messages with patients and providers using preliminary surveys t ...[more]

PMID: 25913245

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Project description:BackgroundStroke is a major cause of morbidity and mortality, especially in low and middle income countries. Medical management is the mainstay of therapy to prevent recurrence of stroke. Current estimates are that only 1 in 6 patients have perfect adherence to medication schedules. Using SMS (Short Messaging Service) as reminders to take medicines have been used previously for diseases such as diabetes and HIV with moderate success. We aim to explore the effectiveness and acceptability of SMS in increasing adherence to medications in patients with stroke.MethodsThis will be a randomized, controlled, assessor blinded single center superiority trial. Adult participants with access to a cell phone and a history of stroke longer than 1 month on multiple risk modifying medications will be selected from Neurology and Stroke Clinic. They will be randomized into two parallel groups in a 1:1 ratio via block technique with one group receiving the standard of care as per institutional guidelines while the parallel group receiving SMS reminders for each dose of medicine in addition to the standard of care. In addition intervention group will receive messages for lifestyle changes, medication information, risk factors and motivation for medication adherence. These will bemodeled on Social Cognitive Theory and Health Belief Model and will be categorized by Michies Taxonomy of Behavioral Change Communication. Patient compliance to medicines will be measured at baseline and then after 2 months in each group by using the Morisky Medication Adherence Scale. The change in compliance to medication regimen after the intervention and the difference between the two groups will be used to determine the effectiveness of SMS reminders as a tool to increase medication compliance. The acceptability of the SMS will be determined by a tool designed for this study whose attributes are based Rogers Diffusion of innovation theory. A sample size of 86 participants in each arm will be sufficient to detect a difference of 1 point on the MMAS with a power of 90 % and significance level of 5 % between the two groups; using an attrition rate of 15 %, 200 participants in all will be randomized.DiscussionThe SMS for Stroke Study will provide evidence for feasibility and effectiveness of SMS in improving post stroke medication adherence in an LMIC setting.Trial registrationhttps://clinicaltrials.gov/ct2/show/NCT01986023 11 /11/2013.

Project description:BackgroundThis study assesses the feasibility of using machine learning methods such as Random Forests (RF), Artificial Neural Networks (ANN), Support Vector Regression (SVR) and Self-Organizing Feature Maps (SOM) to identify and determine factors associated with hypertensive patients' adherence levels. Hypertension is the medical term for systolic and diastolic blood pressure higher than 140/90 mmHg. A conventional medication adherence scale was used to identify patients' adherence to their prescribed medication. Using machine learning applications to predict precise numeric adherence scores in hypertensive patients has not yet been reported in the literature.MethodsData from 160 hypertensive patients from a tertiary hospital in Kuala Lumpur, Malaysia, were used in this study. Variables were ranked based on their significance to adherence levels using the RF variable importance method. The backward elimination method was then performed using RF to obtain the variables significantly associated with the patients' adherence levels. RF, SVR and ANN models were developed to predict adherence using the identified significant variables. Visualizations of the relationships between hypertensive patients' adherence levels and variables were generated using SOM.ResultMachine learning models constructed using the selected variables reported RMSE values of 1.42 for ANN, 1.53 for RF, and 1.55 for SVR. The accuracy of the dichotomised scores, calculated based on a percentage of correctly identified adherence values, was used as an additional model performance measure, resulting in accuracies of 65% (ANN), 78% (RF) and 79% (SVR), respectively. The Wilcoxon signed ranked test reported that there was no significant difference between the predictions of the machine learning models and the actual scores. The significant variables identified from the RF variable importance method were educational level, marital status, General Overuse, monthly income, and Specific Concern.ConclusionThis study suggests an effective alternative to conventional methods in identifying the key variables to understand hypertensive patients' adherence levels. This can be used as a tool to educate patients on the importance of medication in managing hypertension.

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Learning from marketing: Rapid development of medication messages that engage patients.

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Learning from marketing: Rapid development of medication messages that engage patients.

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