Access to a tailored mobile application enhances medication adherence among young users of antidepressants.

Introduction: Patients' adherence to antidepressants is generally reported to be poor. This study examined whether users of selective serotonin reuptake inhibitors (SSRIs) and serotonin and norepinephrine reuptake inhibitors (SNRIs) enhance medication adherence following access to a mobile application (app) tailored for this patient group. The study addresses the implementation phase of medication adherence. Methods: The study was a single group pre-post intervention design. Data were collected using the validated OsloMet Adherence-to-medication Survey tool (OMAS-37) before and after app access. Pre-app access survey (Survey 1) was conducted via social media and online newspapers, encompassing 445 SSRI/SNRI users aged 18 years and above. Post-app access survey (Survey 2) was sent to 103 SSRI/SNRI users from Survey 1. Wilcoxon Signed Rank Test compared pre- and post-intervention adherence measurements. Pearson's chi-square tests and Fisher's exact tests compared study population categories. Results: Forty-two SSRI/SNRI users, median age 26 (IQR 17), 93% identifying as female, used the app while using the same antidepressant during the 2-month period between gaining access to the app and Survey 2. There was a statistically significant reduction in non-adherence score post-app access (z = 3.57, n = 42, p < 0.001) with medium effect size (r = 0.39), indicating enhanced adherence. Total non-adherence score decreased by 39% from pre-to post-access, and there was a 12% decrease in users scoring equivalent with poor adherence (score <2) post-access. Twenty-nine of 37 non-adherence causes improved, with three showing statistical significance. Of 42 responders, 50% (n = 21) indicated using the app one to two times, while 50% (n = 21) more than three times. Approximately 69% (n = 28) found it useful, and 43% (n = 18) felt safer in their use of antidepressants after access to the app. No significant preference was observed for the app over alternative sources of information. Discussion: Enhanced medication adherence was observed among antidepressant users following access to the tailored app. Further studies are warranted to evaluate the app applicability to a broader range of antidepressants users or other patient groups, encompassing those in the initiation phase of medication adherence. The app is intended as an easily accessible supplement to the information and advice provided by prescribing physicians and dispensing pharmacists.

Exploring patients' adherence to antibiotics by understanding their health knowledge and relational communication in encounters with pharmacists and physicians.

Background: Antibiotics are drugs essential for the treatment of bacterial infections. Widespread and often improper use of antibiotics are driving the emergence of antimicrobial resistance (AMR) globally. A better understanding of the communicated and understood use of antibiotics as well as improved adherence to treatments are needed to meet this public health threat. Objectives: The aim of the study is to explore how knowledge of antibiotic use is collected and communicated between patients, physicians, and pharmacists, and how patients seek, understand and use available information on antibiotics in adherence to prescribed treatment. Methods: Seven focus group interviews were conducted with community pharmacists (three groups, eleven participants), physicians/general practitioners (two groups, thirteen participants), and patients (two groups, eight participants) in Norway. Four focus group interviews were conducted offline and three online. The interview data were analyzed using systematic text condensation in a four-step, descriptive and explorative thematic analysis. Results: Three main themes were developed about patients' adherence to antibiotics: 1. patients' knowledge about antibiotics and AMR; 2. sources of information about antibiotics/AMR; and 3. relational communication. Patient knowledge about both antibiotics and AMR was somewhat limited, and showed considerable variation. Patients relied on the internet, chat sites, printed information, and face-to-face meetings with health professionals for information. Relational communication between patients, physicians, and pharmacists was found to be important in reducing misunderstandings.Vulnerability, limited time, and lack of communication were barriers to receiving and understanding information during patient-physician encounters. Increased knowledge about antibiotics and AMR may result in better adherence to prescribed medications. Conclusions: Patients seek information about antibiotics and AMR in three arenas; digital platforms, printed material and face to face encounters. However, patients often misunderstand important facts relating to this issue. Relational communication between patients, physicians, and pharmacists was important to ensure adherence to treatment regimens. Pharmacists are encouraged to use open-ended questions and build upon the information obtained from the physician to provide patients with tailored advice and ensure proper adherence. Pharmacists' contribution is crucial in optimizing antibiotic use and combating AMR, as they are the final healthcare point of contact before treatment initiation.

Positioning of community pharmacists in interactions with general practitioners and patients regarding prescribing and using antibiotics.

Interprofessional collaboration between general practitioners (GPs) and community pharmacists (CPs) is important for ensuring antibiotics are used correctly and combating antibiotic resistance. The study's main objective was to investigate how CPs, GPs and patients, respectively, position CPs in their interactions with patients on antibiotic-related matters in Norwegian pharmacies. Seven focus-group interviews were performed. Data were analyzed using systematic text condensation. Positioning theory was used to identify positions assigned to CPs by themselves, by GPs and by patients. CPs position themselves as helpful, accessible drug specialists responsible for advising on antibiotic use, but also consider themselves dependent on GP-supplied information to do so. GPs position CPs as helpful, responsible businesspeople who, however, lack clinical experience and are overzealous gatekeepers. Patients position CPs as helpful people who supply information in "everyday language" and as the GP's extended arm. Patients utter they are best served when GPs and CPs collaborate. This discrepancy is a barrier to optimal service to patients in general, and to proper antibiotic use in particular.

Development and validation of a new non-disease-specific survey tool to assess self-reported adherence to medication.

Background: Patients' non-adherence to medication affects both patients themselves and healthcare systems. Consequences include higher mortality, worsening of disease, patient injuries, and increased healthcare costs. Many existing survey tools for assessing adherence are linked to specific diseases and assessing medication-taking behavior or identifying barriers or beliefs. This study aimed to develop and validate a new non-disease-specific survey tool to assess self-reported medication-taking behavior, barriers, and beliefs in order to quantify the causes of non-adherence and measure adherence. Methods: The survey tool was developed after literature searches and pilot testing. Validation was conducted by assessing the psychometric properties of content, construct, reliability, and feasibility. Content validity was assessed by subject matter experts and construct validity by performing exploratory factor analysis. Reliability assessment was performed by calculating internal consistency, Cronbach's alpha and test/retest reliability, intraclass correlation coefficient (ICC), and standard error of measurement (SEm). A receiver operating characteristic (ROC) curve and the Lui method were used to calculate the statistical cut-off score for good versus poor adherence. Survey responses from Norwegian medication users over 18 years recruited via social media were used for the factor analysis and Cronbach's alpha. Results: The final survey tool contains 37 causes of non-adherence connected to medication-taking behavior and barriers to adherence and beliefs associated with adherence. The overall result for all 37 items demonstrated reliable internal consistency, Cronbach's alpha = 0.91. The factor analysis identified ten latent variables for 29 items, explaining 61.7% of the variance. Seven of the latent variables showed reliable internal consistency: medication fear and lack of effect, conditional practical issues, pregnancy/breastfeeding, information issues, needlessness, lifestyle, and avoiding stigmatization (Cronbach's alpha = 0.72-0.86). Shortage showed low internal consistency (Cronbach's alpha = 0.59). Impact issues and personal practical issues showed poor internal consistency (Cronbach's alpha = 0.51 and 0.48, respectively). The test/retest reliability ICC = 0.89 and SEm = 1.11, indicating good reliability. The statistical cut-off score for good versus poor adherence was 10, but the clinical cut-off score was found to be 2. Conclusion: This survey tool, OMAS-37 (OsloMet Adherence to medication Survey tool, 37 items), demonstrated to be a valid and reliable instrument for assessing adherence. Further studies will examine the ability of the tool for measuring adherence enhancing effect following interventions.

Improving hospital bed utilisation through simulation and optimisation: with application to a 40% increase in patient volume in a Norwegian General Hospital.

PURPOSE: This paper analyses the problem of allocating beds among hospital wards in order to minimise crowding. METHOD: We present a generic discrete event simulation model of patient flow through the wards of a hospital. In the generic model, each ward can have separate probability distributions for arrival times and length of stay, which may be time dependent. Output of the model is a matrix, with statistics on the utilisation of different hypothetical numbers of beds for each ward. This matrix is fed into an allocation algorithm, which distributes the available beds among the wards in an optimal way. We define bed utilisation either in terms of how often it is in use (prevalence), or in terms of how often a newly arriving patient is placed in it (incidence). For these classes of utilisation measures we develop efficient allocation algorithms, which we prove to be optimal. APPLICATION: The model was applied to Akershus University Hospital in Norway. In 2011, some of the wards of this hospital experienced a high occupancy rate, while others had a lower utilisation. Our model was applied in order to reallocate the hospital beds among the wards. For each ward, acute arrivals were modelled with Poisson-distributions with time-varying intensity, while elective arrivals were programmed to arrive in specific numbers at specific times. The arrival rates were based on empirical data for 2010, scaled up by an expected increase of 40% due to a restructuring of the hospital districts in Oslo and the greater metropolitan area in 2011. Length of stay was modelled as beta-distributions, using a combination of subject matter experts' evaluations and empirical data from 2010. The model has been verified and validated. RESULTS: Intuitively, both prevalence (average number of crowding beds in use) and incidence (number of patients placed in crowding beds) might seem like relevant optimisation criteria. However, our experiments show that prevalence optimisation gives more sensible solutions than incidence optimisation, as the latter tends to sacrifice entire wards where length of stay is long and patient turnover is slow. Prevalence optimisation was therefore used. The main results show that when the bed distribution is optimised, the share of crowding patient nights is reduced from 6.5% to 4.2%. CONCLUSION: This model provides a powerful tool for optimising hospital bed utilisation, and the application showed an important reduction in crowding bed usage. The generic model is flexible, as the level of detail in the modelling of arrivals and length of stay can vary according to the data available and accuracy required.