Exploratory research in clinical and social pharmacy最新文献

Background

Artificial intelligence (AI) has the capability to analyze vast amounts of data and has been applied in various healthcare sectors. However, its effectiveness in aiding pharmacotherapy decision-making remains uncertain due to the intricate, patient-specific, and dynamic nature of this field.

Objective

This study sought to investigate the potential of AI in guiding pharmacotherapy decisions using clinical data such as diagnoses, laboratory results, and vital signs obtained from routine patient care.

Methods

Data of a previous study on medication therapy optimization was updated and adapted for the purpose of this study. Analysis was conducted using R software along with the tidymodels extension packages. The dataset was split into 74% for training and 26% for testing. Decision trees were selected as the primary model due to their simplicity, transparency, and interpretability. To prevent overfitting, bootstrapping techniques were employed, and hyperparameters were fine-tuned. Performance metrics such as areas under the curve and accuracies were computed.

Results

The study cohort comprised 101 elderly patients with multiple diagnoses and complex medication regimens. The AI model demonstrated prediction accuracies ranging from 38% to 100% for various cardiovascular drug classes. Laboratory data and vital signs could not be interpreted, as the effect and dependence were unclear for the model. The study revealed that the issue of AI lag time in responding to sudden changes could be addressed by manually adjusting decision trees, a task not feasible with neural networks.

Conclusion

In conclusion, the AI model exhibited promise in recommending appropriate medications for individual patients. While the study identified several obstacles during model development, most were successfully resolved. Future AI studies need to include the drug effect, not only the drug, if laboratory data is part of the decision. This could assist with interpreting their potential relationship. Human oversight and intervention remain essential for an AI-driven pharmacotherapy decision support system to ensure safe and effective patient care.

Background

Access to healthcare services is a major barrier to residents of the rural state of South Dakota. As a highly accessible member of the healthcare team, outpatient pharmacists can play a key role in a patient's healthcare journey. There is a need to identify the unique barriers and facilitators pharmacists in both rural and urban areas face to maximize the impact of their role.

Objective

The objective of this work was to compare perceptions of rural and urban pharmacists regarding the facilitators and barriers to providing patient care in South Dakota.

Methods

This qualitative project highlights results from interviews and focus group sessions with a convenience sample of South Dakota pharmacists. Participants were recruited using a referral word-of-mouth system, contracts with healthcare market research agencies, newspaper advertisements, and posters displayed in public locations in South Dakota. Practice location was characterized as rural or urban based on United States Department of Agriculture definitions. Findings from interviews and focus group sessions were coded and analyzed using content analysis by two student researchers.

Results

Participants included 12 rural-practicing and 21 urban-practicing pharmacists in South Dakota. In both rural and urban areas, key barriers included communication with providers (50% rural; 50% urban), lack of electronic health record access (25% rural; 14% urban), not enough staff (22% rural; 20% urban), and patient misunderstanding the scope of pharmacy (22% rural; 40% urban). Barriers specific to rural areas included time to provide services (22%), having smaller facilities (27%) and provider hesitation regarding collaborative practice agreements (29%). There were no urban-specific barriers. Facilitators specific to urban areas included frequent communication with patients (6.1%) and good quality support staff (9.1%). There were no rural-specific facilitators.

Conclusions

Next steps include increasing awareness of pharmacy-based patient care services, researching further to identify the extent to which facilitators and barriers influence the ability to initiate and sustain pharmacy services in rural and urban areas, and providing support to pharmacies to overcome barriers and leverage facilitators.

Objective: This study aims to evaluate the effectiveness of the Quality Risk Management (QRM) system in hospital pharmacy intravenous admixture services (PIVAS). Methods: Failure Modes and Effects Analysis (FMEA) and risk matrix methods were used to systematically assess the critical risk points in PIVAS. By collecting and comparing relevant data from 2019 to 2023, key performance indicators (KPIs) before and after the implementation of the QRM system were quantitatively evaluated. Results: The results showed that the safety and efficiency of pharmacy services significantly improved after the implementation of the QRM system. The medication error rate significantly decreased from 3.2% to 1.1%, the average medication preparation time reduced from 15.5 min to 8.2 min, and staff satisfaction increased from 6.0 to 8.5 points. Other indicators, such as cross-contamination rates and handling errors, also showed significant improvement (all outcomes p < 0.001). Discussion: Systematic risk management effectively enhanced the operational performance of PIVAS, reduced medication errors, and improved the quality of healthcare services. This study highlights the key role of QRM in enhancing medication safety and productivity, providing empirical support for the implementation of similar systems in other healthcare institutions.

Introduction: Community pharmacies, as unique and accessible healthcare venues, are ideal locations to implement interventions aiming to improve patient care. However, these interventions may increase workload or disrupt workflow for community pharmacists, technicians, and other staff members, threatening long-term sustainment. There are growing calls from the field of implementation science to design for intervention sustainment and maintenance by maximizing innovation fit. Senior Safe™, an intervention to facilitate safer over-the-counter (OTC) product selection by older adults, serves as a case study to examine the congruence between Innovation Factors and community pharmacy Inner Context constructs and their implications for workload and sustainment.

Methods: Using the Exploration, Preparation, Implementation, Sustainment (EPIS) framework, this qualitative study identified factors surrounding Senior Safe implementation. Semi-structured interviews were conducted with staff from pharmacies where Senior Safe was implemented. Two coders independently analyzed interview transcripts using deductive analysis based on EPIS constructs. Thematic analysis was used to generate three themes that encapsulated innovation fit.

Results: Nineteen pharmacy staff members participated, with the majority reporting no significant change in their workload or workflow due to Senior Safe. Interview feedback supported a pre-existing culture of the healthcare system to engage patients, of leadership commitment to patient safety initiatives, and of an amplified role of pharmacy technicians.

Discussion and Conclusion: Pharmacy staff interviews revealed congruence between Innovation Factors and Inner Context that likely yielded intervention workload neutrality. This study highlighted the importance for researchers to consider maintenance and sustainability when designing and implementing an intervention and the critical influence of culture and leadership support during this process.

Background

Self-medication among university students is becoming a health concern, especially during examinations and stress. This pattern of medication use among students could lead to adverse health consequences if it is not addressed and tackled. Thus, this study investigated the most associated factors with this practice among students.

Methods

An observational cross-sectional study was conducted among university students in Northern Borders Province, Saudi Arabia. A total of 220 students were selected and took part in the survey. Data was collected through a self-administered questionnaire. The survey was distributed among participants to answer five sections: demographic and socioeconomic characteristics, health status problems, satisfaction with academic performance, and self-medication questions. The content validity was tested using a pilot sample of 30 students. The descriptive, univariate, and multivariate analyses were conducted using the Statistical Package for Social Science program.

Results

In the present sample of university students, the response rate was 100%. Approximately one-third reported using medications without prescriptions, and 83% of them have used medications three times at most during the past 12 months. Headache was the most common reason for use (59%), followed by fever (20%). Adjusted multivariate analysis showed that students who were 21–24 years of age (OR = 3.79, 95% CI = 1.21–11.82), female (OR = 2.43, 95% CI = 1.03–5.72), and living alone in private housing (OR = 3.62, 95% CI = 1.32–9.90) were at high risk of self-medication as compared to their counterparts. However, students in the last years of college (fourth (OR = 0.14, 95% CI = 0.03–0.62), fifth (OR = 0.21, 95% CI = 0.05–0.95), sixth (OR = 0.05, 95% CI = 0.01–0.35)) were at lower risk as compared to their counterparts.

Conclusion

The study found that self-medication was common among university students, with higher rates among those who were 21–24 years old, female, and living alone in private housing, but lower rates among students in the later years of college. Educational programs and awareness campaigns should target students who are at higher risk of practicing self-medication to avoid misuse of over-the-counter medications.

Background

In the U.S. alone, medication non-adherence is estimated to cause 1 in 10 hospitalizations, approximately 125,000 deaths annually, and cost the U.S. healthcare system just under $300 billion each year. Patients in medically underserved areas (MUAs) are particularly vulnerable to all forms of non-adherence and downstream morbidity and mortality; however, the extent to which primary medication non-adherence (i.e., prescription abandonment) affects the underserved is still largely unknown.

Objectives

To assess the difference in rates of abandonment of quality measured prescriptions in areas that are medically underserved compared to areas that are not. The secondary objective is to assess the impact that the COVID-19 pandemic had on rates of prescription abandonment in both MUAs and those that are not.

Methods

In this retrospective study, data on abandoned, quality measured prescriptions were collected and analyzed using Chi-Square analyses from one regional division of a large grocery-chain pharmacy containing ninety-one pharmacies located in Tennessee, Mississippi, Arkansas, Kentucky, and Missouri. The primary objective used 2019 data while the secondary objective used data from April – November of both 2019 and 2020.

Results

Patients from MUAs abandoned quality measured prescriptions at a higher rate of 5.44% compared to 4.77% of those not living in these areas (P < 0.01). This study also discovered that during the COVID-19 pandemic, MUAs had a decrease in abandonment from 6.14% in 2019 to 6.02% in 2020 (P < 0.01). Those from non-MUAs had non-significant change in abandonment (P = 0.87).

Conclusion

Patients in MUAs abandon quality measured prescriptions at a statistically significant higher rate when compared to patients who live in areas that are not considered to be medically underserved. Moreover, during the COVID-19 pandemic patients living in MUAs had a statistically significant decrease in prescription abandonment while those in non-MUAs did not statistically change.

Introduction

Students in pharmacy are positive towards integrating artificial intelligence and ChatGPT into their practice. The aim of this study was to investigate the direct short-term learning effect of using Chat GPT by pharmacy students.

Methods

This was an experimental randomized study. Students were allocated into two groups; the intervention group (n = 15) used all study tools and ChatGPT, while the control group (n = 16) used all study tools, except ChatGPT. Differences between groups was measured by how well they performed on a knowledge test before and after a short study period.

Results

No significant difference was found between the intervention and control groups in level of competence in the pretest score (p = 0.28). There was also no significant effect of using ChatGPT, with a mean adjusted difference of 0.5 points on a 12-point scale. However there was a trend towards a higher proportion of ChatGPT participants having a large (at least four point) increase in score (4 out of 15) vs control group (1 out of 16).

Conclusion

There is a potential for positive effects of ChatGPT on learning outcomes in pharmacy students, however the current study was underpowered to measure a statistically significant effect of ChatGPT on short term learning.

Background

While suboptimal medication adherence remains an obstacle to the management of hypertension and diabetes in China, few studies have investigated associated factors with medication adherence on different dimensions simultaneously.

Objective

To systematically examine associated patient, family, and community factors with suboptimal medication adherence among people with hypertension and/or type 2 diabetes in China.

Methods

The study stratified a random sample of 622 adults aged 45 years or older with hypertension and/or type 2 diabetes from three southeast cities in China in 2019. Trained interviewers used the Morisky Green Levine Medication Adherence Scale, Self-Efficacy to Manage Chronic Disease (SEMCD) Scale, and the Family Adaptability, Partnership, Growth, Affection, and Resolve (APGAR) Scale to assess medication adherence, self-efficacy, and family function, respectively. Participants also reported their perceived satisfaction with community health services (quantity, quality, affordability, and overall acceptance). The study used the multivariable logistic regression to assess the association of patient, family, and community factors with suboptimal medication adherence.

Results

Among the participants, 42.9% reported suboptimal medication adherence. In the multivariable logistic regression model, male participants (odds ratio [OR] = 0.55, p = 0.001) had higher medication adherence compared to females. Having a self-efficacy score that was lower than or equal to the sample mean was significantly associated with lower adherence (OR = 1.44, p = 0.039). Participants unsatisfied with the affordability of community health services and medicine had lower adherence (OR = 2.18, p = 0.028) than those neutral or satisfied. There were no significant associations between family function and medication adherence.

Conclusions

Sex, self-efficacy, and perceived affordability of community health services were important factors associated with medication adherence. Healthcare professionals are recommended to consider multiple factors and leverage services and resources in community health centers when promoting medication adherence.

Background

Older patients experience challenges when taking polypharmacy. Studies have applied different interventions to improve adherence to polypharmacy. However, inconsistencies in outcomes have impeded the synthesis of evidence. To generate high-quality studies and selectively report outcomes, a Core Outcome Set (COS) is advocated.

Objective(s)

This study explored stakeholders' perspectives about the challenges older patients face when taking polypharmacy, strategies to overcome each challenge, and outcomes of importance that may contribute to COS development.

Methods

Semi-structured interviews were undertaken with academics, healthcare professionals, and public participants. A series of open-ended questions investigated challenges with adherence to polypharmacy in older patients and strategies to overcome these challenges. A list of outcomes (n = 7) compiled from previous studies associated with adherence to polypharmacy was presented to participants for their views. Content analysis was conducted to identify key themes and outcomes proposed by participants.

Results

Participants suggested 11 multidimensional healthcare system-related, medication-related, patient-related, and socioeconomic-related challenges and 16 educational and behavioural strategies associated with adherence to polypharmacy in older patients. Participants agreed with the importance of the seven outcomes presented and suggested a further six outcomes they deemed to be important for use in trials aimed at improving adherence to appropriate polypharmacy in older patients.

Conclusions

Adherence to polypharmacy was deemed challenging, requiring supportive interventions. A list of 13 outcomes in the context of adherence to appropriate polypharmacy in older patients was identified to inform a future study that will develop a COS for clinical trials targeting interventions to improve adherence to appropriate polypharmacy in older patients.