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Healthcare
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Drug Regulation, Drug Safety and Promoting Pharmacy Support for Patients
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Drug Regulation, Drug Safety and Promoting Pharmacy Support for Patients

A special issue of Healthcare (ISSN 2227-9032). This special issue belongs to the section "Medication Management".

Deadline for manuscript submissions: closed (30 April 2024) | Viewed by 7141

Special Issue Editor

Dr. Askal Ayalew Ali

E-Mail Website
Guest Editor
College of Pharmacy and Pharmaceutical Sciences, Institute of Public Health, Florida Agricultural and Mechanical University, Tallahassee, FL, USA
Interests: pharmacoepedimiology; health services research; oncology; health disparities; healthcare decision making; health economics/pharmacoeconomics

Special Issue Information

The scope of this Special Issue is to report on drug development process, and need of pharmacy support to improve drug outcomes.

Dear Colleagues,

The history of tragedies stemming from the drug development process led to strict drug regulation and the establishment of the Food and Drug Administration (FDA) to protect the public from harmful drugs. Even though the FDA reviews the drug approval application documents and process thoroughly, the evidence heavily depends on highly controlled randomized clinical trials that do not reflect real-world clinical practice. As a result, the need for pharmacovigilance studies to examine the long-term safety of approved drugs cannot be overstated. A cornerstone of clinical pharmacy is identifying, solving, and preventing drug-related problems. Therefore, the participation and interventions of pharmacists in improving medication safety need to be highlighted.

Healthcare, our open-access journal with an impact factor of 2.8, is currently soliciting submissions for a Special Issue entitled "Drug Regulation, Drug Safety and Promoting Pharmacy Support for Patients". In particular, we are inviting original articles and reviews on topics including, but not limited to, drug regulation, safety, and pharmacist support. We hope this Special Issue will inspire practitioners and researchers to share their practices and innovations to improve medication and patient safety. 

Thank you for your kind consideration. 

Best Regards,
Dr. Askal Ayalew Ali
Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Healthcare is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2700 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • drug safety
  • drug regulation
  • pharmacist role
  • medication management
  • patient safety
  • preventable medication harm/medication error
  • drug–/food–drug reactions
  • patient counseling services

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Further information on MDPI's Special Issue polices can be found here.

Published Papers (3 papers)

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Research

12 pages, 257 KiB  
Article
Knowledge, Attitude, and Perception of Health Care Providers Providing Medication Therapy Management (MTM) Services to Older Adults in Saudi Arabia
by Fawaz M. Alotaibi, Zainab M. Bukhamsin, Alanoud Nasser Alsharafaa, Ibrahim M. Asiri, Sawsan M. Kurdi, Dhafer M. Alshayban, Mohammed M. Alsultan, Bassem A. Almalki, Wafa Ali Alzlaiq and Mansour M. Alotaibi
Healthcare 2023, 11(22), 2936; https://doi.org/10.3390/healthcare11222936 - 10 Nov 2023
Cited by 1 | Viewed by 1208
Abstract
Introduction: Medication Therapy Management (MTM) is identified as a group of services provided to the patient in order to optimize the medication use in order to mitigate adverse drug reactions (ADRs), drug–drug interaction (DDI), and polypharmacy. Elderly populations above 60 years old are [...] Read more.
Introduction: Medication Therapy Management (MTM) is identified as a group of services provided to the patient in order to optimize the medication use in order to mitigate adverse drug reactions (ADRs), drug–drug interaction (DDI), and polypharmacy. Elderly populations above 60 years old are at high risk for Medication-related Problems (MRPs) due to several factors. Therefore, MTM programs showed good contributions globally regarding enhancing medication use in the elderly population. Thus, evident information regarding its implementation in Saudi Arabia is lacking in the literature. Objective: Our objective is to assess community pharmacists’ knowledge, attitude, and barriers to providing MTM services to the older adult population in Saudi Arabia. Methodology: A cross-sectional study has been conducted among community pharmacists across the Kingdom. It was survey-based research that was designed and conducted through (QuestionPro). The survey was distributed for the community pharmacists from Feb–May 2023 via (QuestionPro). Descriptive analysis was performed using SAS OnDemand to analyze the categorical variables and test it with the outcome of interest. Results: Out of the 528 participants who have viewed our questionnaire, 319 participants have completed the survey in 5 min average time. Most of our participants were male, holding a bachelor’s degree, and had an average working load of more than 40 h a week, respectively (84.95%, 92.48%, and 76.18%). In addition, the participants were from different regions of the Kingdom, which enhanced the generalizability of our findings. Moreover, 65.52% have reported a higher level of knowledge, while 34.48% have reported a moderate to low level of knowledge regarding MTM service. Most of those with a higher level of knowledge maintain a positive attitude regarding MTM service, its implementation, and dealing with older adult patients in the community pharmacy. In addition, lacking the time, training, and presence of a private consultation room were the top barriers to provide MTM services in the community pharmacy in Saudi Arabia. Conclusion: Educational sessions regarding MTM services among the older adult population are highly recommended for community pharmacists before its implementation. Full article
(This article belongs to the Special Issue Drug Regulation, Drug Safety and Promoting Pharmacy Support for Patients)
11 pages, 1723 KiB  
Article
Botulinum Toxin for Drooling in Adults with Diseases of the Central Nervous System: A Meta-Analysis
by Chih-Rung Chen, Yu-Chi Su, Hui-Chuan Chen and Yu-Ching Lin
Healthcare 2023, 11(13), 1956; https://doi.org/10.3390/healthcare11131956 - 6 Jul 2023
Viewed by 1726
Abstract
(1) Background: The purpose of this study was to determine whether the drooling of adult patients with diverse central nervous system diseases can be treated with botulinum toxin type A. (2) Methods: The Cochrane Library, MEDLINE, and Embase were all searched for studies [...] Read more.
(1) Background: The purpose of this study was to determine whether the drooling of adult patients with diverse central nervous system diseases can be treated with botulinum toxin type A. (2) Methods: The Cochrane Library, MEDLINE, and Embase were all searched for studies that fit the inclusion criteria. The patients in the studies had to be adults (>18 years old), and the studies had to be randomized placebo-controlled trials, controlled trials, or prospective studies. Each study had to have enough quantifiable data available for meta-analysis. The primary outcome measure was the Drooling Severity and Frequency Scale (DSFS). (3) Results: The meta-analysis comprised three studies. A statistically significant difference in DSFS score between the treatment and control groups was observed in the meta-analysis, with an overall standardized mean difference of −0.9377 (95% CI, −1.2919 to −0.5836; p < 0.0001). A total of seven studies were ineligible for inclusion in the meta-analysis and were only assessed as qualitative data. All qualitative studies showed a significant reduction in DSFS score a few weeks or months after the injection of botulinum toxin. (4) Conclusions: Botulinum toxin type A is safe and effective as a treatment for drooling in adult patients with central nervous system diseases. Full article
(This article belongs to the Special Issue Drug Regulation, Drug Safety and Promoting Pharmacy Support for Patients)
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Figure 1

Figure 1
<p>Flow diagram showing the number of studies selected for this study.</p> Full article ">Figure 2
<p>(<b>a</b>) Forest plot of the four randomized, placebo-controlled trials included for this meta-analysis [<a href="#B15-healthcare-11-01956" class="html-bibr">15</a>,<a href="#B16-healthcare-11-01956" class="html-bibr">16</a>,<a href="#B17-healthcare-11-01956" class="html-bibr">17</a>]. The random effects model is used. Each treatment group with its respective dosage is studied separately. The time points of each study are combined as a mean and are not interpreted as independent groups. A statistically significant improvement in DSFS score was observed, with an overall standardized difference in means of −0.938 (95% CI, −1.292 to −0.584; <span class="html-italic">p</span> &lt; 0.0001). (<b>b</b>) Heterogeneity is assessed with the <span class="html-italic">I</span><sup>2</sup> statistic (0%). Moderate or high heterogeneity in this meta-analysis is unlikely. The forest plot and the effect size and measure are created and calculated using Comprehensive Meta-Analysis software.</p> Full article ">Figure 3
<p>Risk of bias graph and summary [<a href="#B15-healthcare-11-01956" class="html-bibr">15</a>,<a href="#B16-healthcare-11-01956" class="html-bibr">16</a>,<a href="#B17-healthcare-11-01956" class="html-bibr">17</a>].</p> Full article ">
14 pages, 1748 KiB  
Article
Application of Machine Learning Algorithms to Predict Uncontrolled Diabetes Using the All of Us Research Program Data
by Tadesse M. Abegaz, Muktar Ahmed, Fatimah Sherbeny, Vakaramoko Diaby, Hongmei Chi and Askal Ayalew Ali
Healthcare 2023, 11(8), 1138; https://doi.org/10.3390/healthcare11081138 - 15 Apr 2023
Cited by 8 | Viewed by 3683
Abstract
There is a paucity of predictive models for uncontrolled diabetes mellitus. The present study applied different machine learning algorithms on multiple patient characteristics to predict uncontrolled diabetes. Patients with diabetes above the age of 18 from the All of Us Research Program were [...] Read more.
There is a paucity of predictive models for uncontrolled diabetes mellitus. The present study applied different machine learning algorithms on multiple patient characteristics to predict uncontrolled diabetes. Patients with diabetes above the age of 18 from the All of Us Research Program were included. Random forest, extreme gradient boost, logistic regression, and weighted ensemble model algorithms were employed. Patients who had a record of uncontrolled diabetes based on the international classification of diseases code were identified as cases. A set of features including basic demographic, biomarkers and hematological indices were included in the model. The random forest model demonstrated high performance in predicting uncontrolled diabetes, yielding an accuracy of 0.80 (95% CI: 0.79–0.81) as compared to the extreme gradient boost 0.74 (95% CI: 0.73–0.75), the logistic regression 0.64 (95% CI: 0.63–0.65) and the weighted ensemble model 0.77 (95% CI: 0.76–0.79). The maximum area under the receiver characteristics curve value was 0.77 (random forest model), while the minimum value was 0.7 (logistic regression model). Potassium levels, body weight, aspartate aminotransferase, height, and heart rate were important predictors of uncontrolled diabetes. The random forest model demonstrated a high performance in predicting uncontrolled diabetes. Serum electrolytes and physical measurements were important features in predicting uncontrolled diabetes. Machine learning techniques may be used to predict uncontrolled diabetes by incorporating these clinical characteristics. Full article
(This article belongs to the Special Issue Drug Regulation, Drug Safety and Promoting Pharmacy Support for Patients)
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Figure 1

Figure 1
<p>Flow diagram for data processing and machine learning-based model development steps in UDM using AoU Research Program data.</p> Full article ">Figure 2
<p>Uncontrolled DM prediction accuracy across models of machine learning in the AoU Research Program. The panel in the left top corner (<bold>A</bold>) shows the prediction accuracy of ML models for both genders. The right top corner (<bold>B</bold>) is the prediction accuracy for females, and the right lower panel (<bold>C</bold>) is the prediction accuracy for males.</p> Full article ">Figure 3
<p>Comparison of receiver operating characteristic (ROC) curves of the ML models implemented to predict UDM in AoU, 2023. AUC: area under the curve. The diagonal line dotted in orange indicates that true positive rate is equal to false positive rate.</p> Full article ">Figure 4
<p>Feature importance for the prediction of UDM in AoU Research Program 2023.</p> Full article ">
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