







































Y. Wang & H. Hamid/Future Technology                                                                                       May 2025| Volume 04 | Issue 02 | Pages 
61-75 

61 

 

 

 

Article 

Reconstructing pharmaceutical service 

competency framework: development of AI-

informed competency indicators and localized 

practices in China 
Yuqiu Wang, Hazrina Hamid* 

Lincoln University College, 47301 Petaling Jaya, Malaysia 

A R T I C L E   I N F O 
 

Article history: 
Received 20 March 2025  
Received in revised form 
25 April 2025 
Accepted 06 May 2025 
 
Keywords:  
Pharmaceutical service competency, Artificial 
intelligence, competency framework, Traditional 
Chinese medicine integration, Knowledge graph, 
Implementation science 
 
*Corresponding author 
Email address: 
hazrina@lincoln.edu.my 
 
DOI: 10.55670/fpll.futech.4.2.7 

A B S T R A C T 
 

This study introduces an innovative method for reconstructing pharmaceutical 
service competency frameworks. The approach integrates artificial intelligence 
technologies with localization practices specific to the Chinese context. 
Employing a mixed-methods sequential exploratory design, we analyzed six 
major international competency frameworks using natural language processing 
and machine learning techniques to extract 4,782 unique competency 
statements, which were subsequently classified with 91.4% accuracy into 
relevant domains. The resulting preliminary integrated framework—
comprising 5 domains, 24 competencies, and 103 behavioral indicators—
underwent localization through a modified Delphi process involving 32 
pharmaceutical stakeholders and verification via a national survey of 456 
pharmacists across 18 Chinese provinces. Implementation across diverse 
healthcare settings resulted in significant improvements in service quality 
metrics, including a 23.7% reduction in medication errors (p<0.01) and an 
18.6% increase in patient satisfaction. Cross-setting analysis revealed variable 
adaptability, with implementation feasibility scores ranging from 4.7/5 in 
tertiary hospitals to 3.2/5 in rural community pharmacies. Four critical success 
factors for effective framework adoption were identified: institutional 
leadership engagement, integration with existing quality systems, phased 
implementation, and dedicated training resources. The framework's distinctive 
features include competencies addressing the integration of traditional Chinese 
medicine with modern pharmacy practice and a modular structure enabling 
context-specific adaptation while maintaining core standards. This research 
contributes to bridging the gap between global standards and local realities in 
pharmaceutical competency development, demonstrating the potential of AI-
informed approaches to enhance framework relevance, efficiency, and 
effectiveness across diverse healthcare contexts. 

1. Introduction 

In the rapidly evolving healthcare landscape, 
pharmaceutical services have undergone significant 
transformation, moving beyond traditional dispensing roles 
to encompass comprehensive patient-centered care. This 
evolution necessitates a robust competency framework that 
can effectively guide pharmacists' professional development 
and ensure quality service delivery [1]. Despite the 
International Pharmaceutical Federation's (FIP) efforts to 
establish a Global Competency Framework (GCF), the 
applicability and effectiveness of such frameworks across 
diverse healthcare systems remain challenging due to 

variations in cultural, economic, and regulatory contexts [2]. 
While numerous studies have examined the adaptation of 
international pharmacy competency frameworks within 
specific national contexts [3], limited research has explored 
the integration of artificial intelligence (AI) methodologies in 
developing and localizing such frameworks. The 
pharmaceutical service landscape in China presents a unique 
case study for such integration, with its rapidly modernizing 
healthcare system, expanding pharmaceutical industry, and 
distinctive cultural and regulatory environment exemplifying 
the complexities of adapting international competency 
frameworks to local contexts [4]. 

 

 

Future Technology 

Open Access Journal 

https://doi.org/10.55670/fpll.futech.4.2.7 

 

May 2025| Volume 04 | Issue 02 | Pages 61-75  

Journal homepage: https://fupubco.com/futech 

 

ISSN 2832-0379 

mailto:hazrina@lincoln.edu.my
https://doi.org/10.55670/fpll.futech.4.2.7
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Y. Wang & H. Hamid/Future Technology                                                                                       May 2025| Volume 04 | Issue 02 | Pages 61-75 

62 

 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
The emergence of AI as a transformative force in 

healthcare offers unprecedented opportunities to enhance 
the development of competency frameworks through 
sophisticated data analysis, pattern recognition, and 
predictive modeling [5]. AI technologies can potentially 
identify competency gaps, predict future skill requirements, 
and customize frameworks to specific healthcare 
environments, thereby addressing the persistent challenge of 
framework relevance and adaptability [6]. The application of 
AI in this process can facilitate more nuanced analysis of these 
variations and more effective customization of competency 
indicators [7]. Recent advancements in AI applications for 
healthcare systems have demonstrated significant potential 
for improving service quality assessment and professional 
development frameworks [8]. Traditional frameworks often 
inadequately address China's specific challenges. These 
challenges include urban-rural disparities in pharmaceutical 
care, evolving regulatory requirements, and the integration of 
traditional Chinese medicine with modern pharmacy 
practice. Cross-national comparisons of pharmaceutical 
service quality indicators have highlighted significant 
variations in practice standards, service delivery models, and 
patient outcomes across different healthcare systems [9]. 
These variations underscore the importance of developing 
competency frameworks that are not only informed by 
international best practices but also responsive to local 
healthcare needs and priorities [10]. Studies on quality 
criteria in cross-country healthcare comparisons emphasize 
the need for contextually appropriate assessment 
frameworks that account for systemic differences while 
maintaining core quality standards [11]. The adaptation 
process requires careful consideration of local healthcare 
structures, cultural factors, and existing practice standards, as 
demonstrated by successful localization efforts in various 
countries [12]. Evidence from Thailand and other countries 
suggests that effective competency frameworks must balance 
international standards with local healthcare priorities and 
professional development pathways [13, 14]. 

This study aims to reconstruct the pharmaceutical 
service competency framework through an innovative 
approach that combines AI-informed analysis of international 
competency indicators with rigorous localization practices 
tailored to the Chinese healthcare context. The significance of 
this research lies in its potential to bridge the gap between 

global standards and local realities in pharmaceutical service 
delivery. By developing an AI-informed, culturally adaptive 
competency framework, this research contributes to the 
advancement of pharmaceutical care quality, the 
enhancement of pharmacist professional development, and 
ultimately, the improvement of patient outcomes in diverse 
healthcare settings [15]. Previous research has established 
strong connections between well-defined competency 
frameworks and improvements in service quality across 
pharmaceutical supply chains [16]. Integration of service 
quality assessment with competency frameworks has shown 
promising results in hospital pharmaceutical services [17]. 
Through a systematic approach to framework reconstruction 
that incorporates both international best practices and local 
contextual factors, this study addresses a critical need in 
pharmacy education and practice: the development of 
competency frameworks that are both globally informed and 
locally relevant, technologically innovative yet practically 
applicable in everyday pharmaceutical service delivery [18]. 

2. Data and methods 

2.1 Research design and data sources 
This study employed a mixed-methods sequential 

exploratory design combining systematic literature review, 
expert consultation, and computational analysis to develop a 
pharmaceutical service competency framework that is both 
internationally informed and locally adapted [19]. The 
research followed a four-phase protocol: 
Phase I: Systematic review of international competency 
frameworks 
Phase II: AI-assisted competency indicator extraction and 
analysis 
Phase III: Localization through expert consultation 
Phase IV: Verification and validation of the proposed 
framework 

The systematic review was conducted following PRISMA 
guidelines, with searches performed in five electronic 
databases: PubMed, Scopus, Web of Science, CNKI, and 
Wanfang [20]. The search strategy employed Boolean 
operators with key terms including "pharmacy competency 
framework," "pharmaceutical service quality," "AI in 
pharmacy practice," and "competency localization." The 
inclusion criteria specified publications from 2010 to 2024 in 
English and Chinese languages with full-text availability [21]. 
After duplicate removal and screening against 
inclusion/exclusion criteria, 78 documents were selected for 
the final analysis, including competency frameworks from 12 
countries and 6 international organizations. The sequential 
nature of this design allowed findings from each phase to 
inform subsequent phases, strengthening the methodological 
rigor and enabling triangulation of results across different 
data sources and analytical approaches [22]. This approach 
aligns with recommendations from Nasa et al. regarding 
methodological frameworks for healthcare competency 
studies [23]. 

2.2 Analysis of international pharmaceutical service 
competency frameworks 

2.2.1 Framework selection and evaluation criteria 
Six major pharmaceutical competency frameworks 

were selected for in-depth analysis based on their 
international recognition, comprehensiveness, and influence 
on global pharmacy practice standards [24]: 
• FIP Global Competency Framework (GCF) 
• Advanced Pharmacy Practice Framework for Australia 

Abbreviations 

AI Artificial Intelligence 

ACCP American College of Clinical Pharmacy 

APPF Advanced Pharmacy Practice Framework 

BERT Bidirectional Encoder Representations from Transformers 

FIP International Pharmaceutical Federation 

FLFP European Foundation Level Pharmacy Framework 

GCF Global Competency Framework 

GPhC General Pharmaceutical Council 

KMO Kaiser-Meyer-Olkin 

NAPRA National Association of Pharmacy Regulatory Authorities 

NLP Natural Language Processing 

NLTK Natural Language Toolkit 

PRISMA Preferred Reporting Items for Systematic Reviews and 

 Meta-Analyses 

RDF Resource Description Framework 

SPSS Statistical Package for the Social Sciences 

SVM Support Vector Machine 

TF-IDF Term Frequency-Inverse Document Frequency 

W Kendall's Coefficient of Concordance 

 

 

 

 

 

 

 



Y. Wang & H. Hamid/Future Technology                                                                                       May 2025| Volume 04 | Issue 02 | Pages 61-75 

63 

 

• American College of Clinical Pharmacy (ACCP) Clinical 
Pharmacist Competencies 

• General Pharmaceutical Council Framework (UK) 
• Canadian National Association of Pharmacy Regulatory 

Authorities' Professional Competencies 
• European Foundation Level Pharmacy Framework 
Each framework was evaluated using a 15-item assessment 
matrix adapted from Anderson et al.'s global pharmacy 
education perspective [25]. The evaluation criteria 
encompassed five domains: structural organization (3 items), 
content coverage (4 items), implementation guidance (3 
items), assessment methodologies (3 items), and cultural 
adaptability (2 items). Each criterion was scored on a scale of 
1-5, with higher scores indicating stronger alignment with 
international standards. 

2.2.2 Comparative analysis process 
The comparative analysis involved a three-stage 

process conducted by independent researchers with 
pharmaceutical backgrounds. First, content mapping 
identified common domains and competency clusters across 
frameworks. Second, gap analysis highlighted unique 
elements and potential areas for integration. Finally, 
consensus meetings resolved discrepancies in coding and 
interpretation [26]. The reliability of this analysis was 
ensured through the calculation of inter-rater agreement 
using Cohen's kappa coefficient: 

𝜅 =
𝑝0−𝑝𝑒

1−𝑝𝑒
             (1) 

Where 𝑝0 represents the observed agreement and 𝑝𝑒 
represents the expected agreement by chance [27-29]. A 
threshold of κ ≥ 0.80 was established to indicate substantial 
agreement between coders. 

2.3 AI-Assisted competency indicator system 
construction method 

2.3.1 Natural language processing and text mining 
The development of the competency indicator system 

was facilitated by AI technologies, specifically natural 
language processing (NLP) and machine learning algorithms 
[30]. Initially, textual data from the selected competency 
frameworks was preprocessed using NLP techniques, 
including tokenization, lemmatization, and stop-word 
removal to standardize terminology and reduce 
dimensionality [31]. Text mining procedures extracted key 
concepts and relationships using term frequency-inverse 
document frequency (TF-IDF) vectorization: 

𝑇𝐹 − 𝐼𝐷𝐹𝑖,𝑗 = 𝑇𝐹𝑖,𝑗 × log⁡(
𝑁

𝑑𝑓𝑖
)           (2) 

Where 𝑇𝐹𝑖,𝑗 is the frequency of term i in document j, N is the 

total number of documents, and 𝑑𝑓𝑖  is the number of 
documents containing term i [32]. 

2.3.2 Machine learning classification and knowledge 
graph construction 
A supervised machine learning approach was 

implemented to classify competency statements according to 
their conceptual similarity and hierarchical relationships. The 
classification model utilized an ensemble approach 
combining random forest and support vector machine 
algorithms, which demonstrated superior performance in 
preliminary testing [33]: 

𝐹1 = 2 ×
𝑝𝑟𝑒𝑐𝑖𝑠𝑖𝑜𝑛×𝑟𝑒𝑐𝑎𝑙𝑙

𝑝𝑟𝑒𝑐𝑖𝑠𝑖𝑜𝑛+𝑟𝑒𝑐𝑎𝑙𝑙
            (3) 

  

The F1-score, a harmonic mean of precision and recall, 
was used to evaluate model performance, with values 
exceeding 0.85 for all competency domains [34]. The 
relationships between competency domains, competencies, 
and behavioral indicators were mapped using knowledge 
graph construction techniques [35]. The knowledge graph G 
was defined as: 

𝐺 = (𝑉, 𝐸, 𝑅)                              (4) 

Where V represents the set of nodes (competency elements), 
E represents the edges (relationships), and R represents the 
types of relationships between nodes [36]. This visualization 
facilitated the identification of gaps in existing frameworks 
and informed the development of new competency 
indicators. Table 1 summarizes the AI techniques applied and 
their specific functions in the competency framework 
development process. 

Table 1. AI techniques applied in competency framework 
development 

 
 

2.4 Localization research and verification 
2.4.1 Modified delphi process 

The localization process employed a modified Delphi 
method to adapt the internationally derived competency 
framework to the Chinese healthcare context [37]. An expert 
panel comprising 32 stakeholders was purposively selected 
based on their expertise, professional background, and 
geographic distribution. The panel composition included 
hospital pharmacists (n=12), community pharmacists (n=6), 
pharmacy educators (n=8), healthcare administrators (n=4), 
and pharmaceutical policymakers (n=2) [38]. The Delphi 
process consisted of three sequential rounds: 
• Framework Review: Experts evaluated the relevance and 

appropriateness of each competency domain and indicator 
using a 5-point Likert scale and provided qualitative 
feedback. 

• Indicator Refinement: Focused on modifying indicators 
that achieved less than 75% consensus, with experts 
suggesting adjustments to improve cultural and contextual 
fit. 

Technique Algorithm/Method Function in 

Framework 

Development 

Performance 

Metric 

Text 

Preprocessing 

NLTK, SpaCy Standardization 

of competency 

descriptors 

Vocabulary 

reduction: 

68% 

Term 

Extraction 

TF-IDF, n-grams Identification of 

key 

competency 

concepts 

Precision: 

0.87 

Text 

Classification 

Random Forest, 

SVM 

Categorization 

of competency 

statements 

F1-score: 

0.89 

Semantic 

Analysis 

Word2Vec, BERT Similarity 

assessment 

between 

competencies 

Cosine 

similarity 

>0.75 

Knowledge 

Graph 

Neo4j, RDF Relationship 

mapping 

between 

competencies 

Node 

connectivity: 

0.82 



Y. Wang & H. Hamid/Future Technology                                                                                       May 2025| Volume 04 | Issue 02 | Pages 61-75 

64 

 

• Framework Validation: Assessment of the revised 
framework's practical applicability across different 
pharmaceutical service settings in China. 

Consensus was defined using the following criterion: 

𝐶𝑜𝑛𝑠𝑒𝑛𝑠𝑢𝑠⁡𝑅𝑎𝑡𝑒 =
𝑁𝑢𝑚𝑏𝑒𝑟⁡𝑜𝑓⁡𝑒𝑥𝑝𝑒𝑟𝑡𝑠⁡𝑟𝑎𝑖𝑡𝑖𝑛𝑔⁡𝑖𝑡𝑒𝑚⁡𝑎𝑠⁡4⁡𝑜𝑟⁡5

𝑇𝑜𝑡𝑎𝑙⁡𝑛𝑢𝑚𝑏𝑒𝑟⁡𝑜𝑓⁡𝑒𝑥𝑝𝑒𝑟𝑡𝑠
× 100%        (5) 

Items achieving ≥75% consensus were retained, while those 
below this threshold were either modified or eliminated 
based on expert feedback [39]. 

2.4.2 National survey 
To supplement the expert panel insights, a national 

survey was conducted with 456 practicing pharmacists 
across 18 Chinese provinces. Stratified random sampling 
ensured representation across hospital settings (tertiary, 
secondary, primary), community pharmacies, and specialized 
pharmaceutical services [40]. The survey instrument 
contained 42 items addressing perceived competency needs, 
practice challenges, and contextual factors influencing 
pharmaceutical service delivery in China [41]. Response data 
was analyzed using descriptive statistics and factor analysis 
to identify latent constructs underlying competency 
requirements in the Chinese context [42]. The integration of 
survey findings with Delphi results enhanced the ecological 
validity of the framework localization process. 

2.5 Data analysis methods 
2.5.1 Quantitative analysis 

Statistical analysis was conducted using SPSS version 
26.0 and R version 4.1.2. For the framework comparison, 
descriptive statistics characterized the distribution of 
competency domains and indicators across frameworks. The 
Delphi study results were analyzed using non-parametric 
statistics, including Kendall's coefficient of concordance (W) 
to assess agreement among experts [43]: 

𝑊 =
12∑(𝑅𝑗−�̅�)

2

𝑚2(𝑛3−𝑛)
             (6) 

Where 𝑅𝑗 is the sum of ranks for the jth item, �̅� is the mean of 

the rank sums, m is the number of experts, and n is the 
number of items being ranked [44]. 
Factor analysis using principal component extraction with 
varimax rotation was applied to survey data to identify 
underlying competency dimensions. The Kaiser-Meyer-Olkin 
(KMO) measure verified sampling adequacy (KMO = 0.87), 
and Bartlett's test of sphericity confirmed appropriateness 
for factor analysis (p < 0.001) [45]. 

2.5.2 Qualitative Analysis 
Qualitative data from expert feedback underwent 

thematic analysis involving open coding, category 
development, and theme identification [46]. A constant 
comparative method facilitated the refinement of themes and 
identification of relationships between concepts [47]. NVivo 
12 software supported the organization and visualization of 
qualitative findings. The integration of quantitative and 
qualitative analyses enabled methodological triangulation, 
enhancing the robustness and validity of the resultant 
pharmaceutical service competency framework [48]. The 
final framework was further validated through comparative 
analysis with existing Chinese pharmaceutical practice 
standards to identify areas of alignment and divergence [49, 
50]. 

 

 

3. Results 

3.1 Comparative analysis of international 
pharmaceutical service competency frameworks 

3.1.1 Structural and content comparison 
The comparative analysis of six international 

pharmaceutical service competency frameworks revealed 
both common elements and distinctive characteristics across 
different jurisdictions. Table 2 presents the structural 
comparison of these frameworks, highlighting variations in 
organizational approach, granularity, and scope. 

Table 2. Structural comparison of international pharmaceutical 
service competency frameworks 

 

Content analysis identified six common competency domains 
across frameworks: (1) pharmaceutical care and patient-
centered services, (2) professional and ethical practice, (3) 
communication and collaboration, (4) leadership and 
management, (5) education and research, and (6) quality 
assurance and improvement. However, significant variations 
were observed in the emphasis placed on different domains. 
North American frameworks (ACCP, NAPRA) placed greater 
emphasis on clinical interventions and specialized 
pharmaceutical care (28-32% of competency indicators), 
while European frameworks prioritized communication and 
interprofessional collaboration (24-27% of indicators) [29]. 
Figure 1 presents a comparative analysis of competency 
domain distribution across six international pharmaceutical 
frameworks, highlighting the variations in emphasis placed 
on different domains. 

3.1.2 Framework adaptation and implementation 
strategies 
The analysis revealed three predominant approaches 

to framework adaptation: (1) direct adoption with minimal 
modification, (2) selective adaptation of specific domains, and 
(3) complete restructuring with incorporation of selected 
elements. Countries with established pharmaceutical 
education systems typically employed selective adaptation 
(e.g., Australia, Canada), while developing nations more 
commonly utilized direct adoption approaches [43]. 

 

 

Framework Number 

of 

Domains 

Number  

of 

Competencies 

Number of 

Behavioral 

Indicators 

Development 

Approach 

Update 

Frequency 

FIP GCF 4 20 100 Consensus-

based 

5 years 

Australia 

APPF 

5 30 114 Evidence-

based 

3-5 years 

ACCP 

Framewo

rk 

6 27 92 Expert 

panel 

10 years 

UK GPhC 4 18 76 Regulatory-

driven 

5 years 

Canadian 

NAPRA 

5 24 88 Collaborativ

e 

7 years 

European 

FLFP 

4 19 81 Consensus-

based 

Not 

specified 



Y. Wang & H. Hamid/Future Technology                                                                                       May 2025| Volume 04 | Issue 02 | Pages 61-75 

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Implementation strategies varied considerably, with 

educational integration being the most common pathway 
(identified in 78% of reviewed literature), followed by 
regulatory enforcement (57%), and professional 
development programs (49%). The analysis indicated a 
significant correlation between implementation approach 
and framework sustainability (r = 0.74, p < 0.01), with 
integrated educational-regulatory approaches demonstrating 
higher sustainability metrics [44]. 

3.2 AI-based competency indicator system construction 
3.2.1 Text mining and semantic analysis results 

Applying natural language processing and text mining 
techniques to competency framework documents generated 
a corpus of 4,782 unique competency statements after 
preprocessing. Vector space modeling using TF-IDF identified 
214 high-frequency competency-related terms across the six 
frameworks. Figure 2 illustrates the distribution of these 
terms across the main competency domains, revealing 
terminology clusters specific to each domain. 
Semantic similarity analysis using word embeddings revealed 
significant overlap in conceptual content across frameworks 
despite terminological variations. The cosine similarity 
matrix demonstrated high similarity between the FIP GCF and 
UK GPhC frameworks (0.87), while the ACCP framework 
showed greater distinctiveness (average similarity of 0.62 

 
 
 
 
 
 
with other frameworks) [45]. This distinctiveness was 
primarily attributed to its greater emphasis on clinical 
specialization and advanced practice roles. 

3.2.2 Knowledge graph and competency classification 
The knowledge graph construction resulted in a 

network comprising 653 nodes (competency elements) and 
1,892 edges (relationships), revealing the complex 
interconnections between competency domains and 
indicators. Network analysis identified six central 
competency clusters with high betweenness centrality, 
indicating their role as bridge concepts across different 
domains. These bridge competencies included "medication 
review" (centrality = 0.78), "interprofessional collaboration" 
(centrality = 0.71), and "evidence-based practice" (centrality 
= 0.68) [46]. Figure 3 presents a knowledge graph 
visualization of the competency relationships, demonstrating 
the complex interconnections between domains, 
competencies, and behavioral indicators. Network 
visualization of the competency framework structure 
showing domains (large circles), competencies (small 
circles), and relationships between elements. Red-outlined 
nodes represent bridge competencies with high betweenness 
centrality. 
 

Figure 1. Comparative analysis of competency domain distribution across international frameworks. (a) Radar chart representation 

showing relative emphasis of competency domains across six pharmaceutical frameworks. (b) Grouped bar chart representation of the 

percentage distribution of competency indicators by domain and framework. 



Y. Wang & H. Hamid/Future Technology                                                                                       May 2025| Volume 04 | Issue 02 | Pages 61-75 

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Figure 3. Knowledge graph visualization of competency 
relationships 

The supervised machine learning classification model 
achieved an overall accuracy of 91.4% in categorizing 
competency statements into appropriate domains and 
hierarchical levels. Performance metrics varied across 
domains, with the highest precision observed for 
"pharmaceutical care" (0.94) and lowest for "leadership and 
management" (0.83). The classification revealed that 68% of 
competency indicators could be mapped across multiple 
frameworks, while 32% were unique to specific national or 
regional contexts [47]. Based on the AI-assisted analysis, a 
preliminary integrated competency framework was 
constructed comprising 5 domains, 24 competencies, and 103 
behavioral indicators. This preliminary framework 
incorporated elements from all analyzed international 
frameworks while eliminating redundancies and resolving 
terminology inconsistencies. 

 

 

 

 
3.3 Analysis of Chinese pharmaceutical service 

environment characteristics 
3.3.1 Healthcare system context and regulatory 

environment 
The analysis of the Chinese pharmaceutical service 

environment identified several distinctive characteristics that 
significantly influence competency requirements. China's 
healthcare system features a three-tiered hospital 
classification system with varying pharmaceutical service 
scope and complexity. The regulatory environment is 
characterized by rapid evolution, with 14 major 
pharmaceutical-related policy changes implemented 
between 2018-2023 [48]. Survey data from 456 practicing 
pharmacists revealed that regulatory compliance was ranked 
as the highest priority competency area (mean importance 
score = 4.67 ± 0.42 on a 5-point scale), followed by medication 
safety (4.53 ± 0.38) and therapeutic knowledge (4.49 ± 0.45). 
This emphasis on regulatory aspects contrasts with 
international frameworks, where clinical decision-making 
and patient-centered care typically receive the highest 
priority ratings [49]. 

3.3.2 Practice settings and service delivery models 
Analysis of practice settings identified three 

predominant pharmaceutical service delivery models in 
China: hospital-based clinical pharmacy services (42%), 
community pharmacy dispensing and counseling (37%), and 
specialized services including traditional Chinese medicine 
integration (21%). Each setting demonstrated distinct 
competency priorities and challenges, as illustrated in Figure 
4. Hospital pharmacists reported increasing clinical 
responsibilities but identified significant competency gaps in 
specialized therapeutic areas (gap score = 1.87 on a 3-point 
scale) and research methodology (gap score = 2.13).  

 
 

Figure 2. Term frequency distribution across competency domains. (a) Bubble chart visualization of high-frequency terms by TF-IDF score 

across six competency domains. (b) Heatmap representation of the top five terms from each competency domain, color-coded by TF-IDF 

relevance score. 



Y. Wang & H. Hamid/Future Technology                                                                                       May 2025| Volume 04 | Issue 02 | Pages 61-75 

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Community pharmacists highlighted challenges in 
balancing commercial pressures with professional service 
delivery (identified by 76% of respondents) and maintaining 
contemporary therapeutic knowledge (gap score = 1.92) [50]. 
Figure 5 provides a comparative analysis of competency gaps 
across different practice settings, highlighting the distinct 
challenges faced by pharmacists in hospital and community 
environments. The integration of traditional Chinese 
medicine with modern pharmaceutical practice emerged as a 
unique characteristic, with 68% of respondents indicating the 
need for competencies specific to this integration. This 
finding highlighted a significant gap in international 
frameworks, which typically do not address traditional 
medicine integration within pharmacy practice 
competencies. 

3.4 Implementation and effectiveness evaluation of 
localization practices 

3.4.1 Delphi process outcomes 
The modified Delphi process resulted in significant 

refinement of the preliminary competency framework to 
enhance its relevance and applicability to the Chinese context. 
Table 3 summarizes the changes made through the three-
round consultation process. Expert consensus (≥75% 
agreement) was achieved for all framework elements by the 
conclusion of Round 3. The final consensus rates ranged from 
78.1% to 100%, with the highest agreement for domains 
related to pharmaceutical care (96.9%) and professional 
ethics (100%).  

 

 

 

 

 

The Kendall's coefficient of concordance showed 
increasing agreement across rounds, from W = 0.68 in Round 
1 to W = 0.87 in Round 3, indicating strong final consensus 
among experts [37]. Figure 6 illustrates the evolution of 
consensus throughout the Delphi process, showing the 
progressive refinement of the framework and increasing 
agreement among experts across the three rounds. 

3.4.2 Framework validation and performance metrics 
The localized framework was validated through 

implementation in six pilot sites representing different 
healthcare settings: two tertiary hospitals, one secondary 
hospital, two community pharmacy chains, and one 
specialized oncology center. Performance metrics were 
established for each competency domain and measured at 
baseline and after a six-month implementation period. 
Implementation resulted in statistically significant 
improvements across all competency domains, with the 
largest improvements observed in "pharmaceutical care 
delivery" (mean score increase from 3.21 to 4.12 on a 5-point 
scale, p < 0.001) and "interprofessional collaboration" 
(increase from 2.98 to 3.87, p < 0.001). Pharmacist self-
efficacy scores increased by an average of 27.4% across all 
domains [39]. Figure 7 presents the implementation 
outcomes across pilot sites, demonstrating improvements in 
competency scores, medication safety indicators, and patient 
satisfaction metrics after framework implementation. 

 

 

Figure 4. Pharmaceutical Service Delivery Models in China. (a) Distribution of three dominant service delivery models in China's 

pharmaceutical sector. (b) Radar chart comparing key characteristics of each service model on a 5-point scale. (c) Primary challenges 

associated with each pharmaceutical service delivery model. 



Y. Wang & H. Hamid/Future Technology                                                                                       May 2025| Volume 04 | Issue 02 | Pages 61-75 

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Figure 5. Competency Gaps Analysis in Different Practice Settings. (a) Radar chart comparing competency gaps between hospital and 

community pharmacists on a 3-point scale. (b) Percentage of pharmacists identifying specific competency challenges in different practice 

settings. (c) Heat map visualization of competency gap severity across different practice environments. 

Figure 6. Delphi process consensus evolution. (a) Line graph showing consensus percentage evolution across three Delphi rounds for six 

competency domains, with Kendall's coefficient of concordance plotted on the secondary axis. (b) Stacked bar chart illustrating the number 

and types of framework modifications in each Delphi round. (c) Heat map visualization of consensus achievement by domain and round. 



Y. Wang & H. Hamid/Future Technology                                                                                       May 2025| Volume 04 | Issue 02 | Pages 61-75 

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Table 3. Framework modifications through the Delphi process 

Modification 

Type 

Round 

1 

Round 

2 

Round 

3 

Examples 

Domain 

restructuring 

2 1 0 Separation of 

"Professional 

Ethics" from 

"Professional 

Practice" 

Addition of 

competencies 

5 2 0 "Integration of 

traditional Chinese 

medicine 

knowledge" 

Removal of 

competencies 

3 1 0 "Independent 

prescribing" (not 

applicable in the 

Chinese context) 

Modification of 

indicators 

27 14 5 Adaptation of 

"medication 

reconciliation" to 

reflect the Chinese 

hospital workflow 

Terminology 

adjustment 

31 18 7 Alignment with 

Chinese 

Pharmacopoeia 

terminology 

 

Organizational impact assessment revealed 
improvements in medication safety indicators, including a 
23.7% reduction in medication errors (p < 0.01) and a 17.3% 
increase in appropriate interventions for high-risk 
medications (p < 0.05). Patient satisfaction with 
pharmaceutical services increased by 18.6% across pilot 
sites, with the largest improvements in information provision 
(31.2%) and consultation quality (24.7%) [40]. 

3.5 Applicability analysis of the new framework in 
different healthcare institutions 

3.5.1 Cross-setting adaptability assessment 
The applicability of the localized competency 

framework was assessed across different healthcare 
institutions through comparative analysis of implementation 
outcomes and stakeholder feedback. The framework 
demonstrated variable adaptability across settings, as shown 
in Table 4. 

The analysis revealed that tertiary hospitals and 
specialized centers demonstrated the highest 
implementation feasibility (scores of 4.7 and 4.6, 
respectively), while rural community pharmacies faced 
significant implementation challenges (score of 3.2). 
Implementation barriers were predominantly resource-
related in primary care and rural settings, while tertiary 
hospitals reported challenges related to complexity and 
specialization requirements [42]. Figure 8 visualizes the 
framework adaptability across different healthcare setting 
types, comparing implementation feasibility, competency 
coverage adequacy, and implementation challenges across 
various practice environments. 

3.5.2 Institutional implementation strategies and 
outcomes 
Comparative analysis of implementation strategies 

across settings identified four critical success factors for 
effective framework adoption: (1) institutional leadership 
engagement, (2) integration with existing quality systems, (3) 
phased implementation approach, and (4) dedicated training 
resources.  

Table 4. Framework adaptability across healthcare settings 

Setting Implementati

on Feasibility 

(1-5) 

Competency 

Coverage 

Adequacy (1-

5) 

Major 

Implementation 

Challenges 

Setting-

Specific 

Adaptation

s Required 

Tertiary 

hospitals 

4.7 4.8 Resource 

allocation, 

Specialist 

knowledge 

requirements 

Advanced 

clinical 

domain 

expansion 

Secondary 

hospitals 

4.2 4.5 Staff capacity, 

Workload 

balance 

Simplified 

assessment 

tools 

Primary 

healthcare 

3.8 4.1 Infrastructure 

limitations, 

Training needs 

Focus on 

essential 

services 

Urban 

community 

pharmacies 

4.1 4.3 Commercial 

pressures, Staff 

turnover 

Business 

integration 

components 

Rural 

community 

pharmacies 

3.2 3.9 Resource 

constraints, 

Geographic 

isolation 

Telemedicin

e 

components 

Specialized 

centers 

4.6 4.2 Highly 

specialized 

knowledge 

requirements 

Disease-

specific 

modules 

 

Settings that incorporated all four factors achieved 
significantly higher implementation scores (mean = 4.5) 
compared to those addressing only one or two factors (mean 
= 3.2, p < 0.001) [41]. The framework demonstrated 
significant versatility through modular implementation, with 
institutions prioritizing different competency domains based 
on their service focus. Tertiary hospitals emphasized 
advanced clinical and research competencies, while 
community pharmacies prioritized patient education and 
basic pharmaceutical care domains. This modularity enabled 
institutions to tailor the framework to their specific service 
priorities while maintaining core competency standards [48]. 
Long-term sustainability assessment conducted at the 12-
month point in pilot sites indicated that framework 
integration into institutional quality systems (observed in 4 
of 6 sites) and alignment with professional advancement 
pathways (observed in 5 of 6 sites) were significantly 
associated with sustained implementation (χ² = 7.83, p < 
0.01). These findings suggest that institutional 
embeddedness is a critical factor for framework sustainability 
beyond the initial implementation phase [41].  

 

 



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70 

 

 

 

 

 

 

 
 
 
 
 
 
 
 
 
 
 
 
 

 
 
 

 

 
 
 
 
 
 

 

 
 
 

Figure 7. Implementation Outcomes Across Pilot Sites. (a) Pre- and post-implementation competency scores showing average 

improvement across domains. (b) Heatmap of implementation outcomes showing percentage improvement by site and domain. (c) 

Medication safety indicators showing percentage change after framework implementation. (d) Patient satisfaction improvements across 

five assessment categories. 

Figure 8. Framework Adaptability by Healthcare Setting Type. (a) Bubble chart comparing implementation feasibility and competency 

coverage adequacy across healthcare settings, with bubble size indicating implementation challenge complexity. (b) Detailed overview of 

implementation challenges and setting-specific adaptations required for each healthcare environment. 



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71 

 

The cross-setting analysis ultimately informed the 
development of a tiered implementation model that stratifies 
competency requirements according to practice setting, 
professional role, and career stage. This tiered approach 
enhances the framework's flexibility while maintaining 
coherence across diverse pharmaceutical service contexts 
within the Chinese healthcare system [50]. Figure 9 presents 
the tiered implementation model developed for diverse 
healthcare settings. The three-tier hierarchical model 
illustrates the stratification of pharmaceutical competency 
requirements according to practice setting, professional role, 
and career stage. 

 
Figure 9. Tiered implementation model for diverse healthcare 
settings 

4. Discussion 

4.1 Advantages and limitations of AI in competency 
framework development 
The integration of artificial intelligence methodologies in 

the development of pharmaceutical service competency 
frameworks represents a significant advancement over 
traditional manual approaches. The text mining and 
knowledge graph techniques employed in this study 
demonstrated superior efficiency in processing large volumes 
of competency data, analyzing 78 documents and extracting 
4,782 unique competency statements—a scale that would be 
impractical through conventional methods [30]. The machine 
learning classification achieved 91.4% accuracy in 
categorizing competency indicators, compared to the 76-82% 
accuracy reported in previous studies using manual 
classification [32]. This enhanced precision facilitated more 
comprehensive identification of competency gaps and 
relationships between domains. However, several limitations 
of AI application were identified. The semantic analysis was 
constrained by language-specific nuances, particularly when 
translating competency statements between English and 
Chinese. This challenge echoes findings from Asada et al., who 
noted that pharmaceutical knowledge representation across 
languages requires specialized domain adaptation of NLP 
models [34]. Additionally, the knowledge graph construction 
was limited by the quality and comprehensiveness of 

available framework documentation, with older frameworks 
often lacking the detailed behavioral indicators necessary for 
granular analysis. These limitations underscore the 
importance of combining AI-driven approaches with expert 
validation to ensure contextual appropriateness of the 
resulting framework [6]. 

4.2 Cultural adaptability challenges and localization 
process 
The cross-national transferability of competency 

frameworks presents significant challenges, as evidenced by 
the substantial modifications required during the localization 
process. Our findings revealed that 46% of competency 
indicators required contextual adaptation to align with 
Chinese healthcare practices and cultural values, a proportion 
higher than the 28-35% reported in similar studies conducted 
in other Asian countries [16]. Figure 10 presents a 
comparative analysis of competency adaptation 
requirements across countries, highlighting the substantial 
differences in cultural adaptation needs between China and 
other Asian healthcare contexts. The Delphi process identified 
three critical dimensions of cultural adaptation: regulatory 
alignment, practice setting relevance, and terminology 
harmonization. The key finding is that China's 46% 
adaptation requirement is significantly higher than that of 
other Asian countries (28-35%). The Chinese pharmaceutical 
environment's distinctive characteristics, particularly the 
integration of traditional Chinese medicine with modern 
pharmacy practice, necessitated novel competency indicators 
not present in international frameworks. This finding aligns 
with Suwannaprom et al.'s work in Thailand, where cultural 
health beliefs similarly required specific competency 
adaptations [12]. However, our approach of using AI to 
systematically identify adaptable core competencies differs 
from previous studies that relied primarily on manual 
consensus methods. This methodological innovation 
facilitated more objective identification of universal versus 
culture-specific competencies [31]. The modularity of the 
developed framework represents a significant advancement 
in addressing the challenges of cross-national adaptation. By 
structuring the framework with a common core of universal 
competencies supplemented by context-specific modules, we 
created a more flexible system for international adaptation 
than the rigid frameworks previously described in the 
literature [27]. 

4.3 Implementation strategies and quality improvement 
impact 
The varied implementation outcomes across different 

healthcare settings highlight the importance of contextual 
factors in framework adoption. Tertiary hospitals achieved 
significantly higher implementation scores (4.7/5) compared 
to rural community pharmacies (3.2/5), suggesting that 
resource availability and organizational complexity 
significantly influence implementation success [49]. Our 
findings on the four critical success factors for effective 
framework adoption (institutional leadership engagement, 
quality system integration, phased implementation, and 
dedicated training) expand on the three-factor model 
proposed by Jackson et al., adding quality system integration 
as a crucial element [44]. The documented improvements in 
medication safety indicators following framework 
implementation (23.7% reduction in medication errors, 
p<0.01) demonstrate the framework's potential to drive 
tangible quality improvements.  

 



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72 

 

 

 

 

 

 

These results surpass the 15-18% reductions reported in 
previous studies of competency-based interventions [27], 
suggesting that the AI-informed, culturally adapted approach 
may yield superior outcomes. The correlation between 
implementation approach and framework sustainability 
(r=0.74, p<0.01) underscores the importance of integrated 
educational-regulatory strategies for long-term impact. 

4.4 Research limitations and future directions 
This study has several limitations that should be 

acknowledged. First, the six-month implementation period 
provides only preliminary evidence of framework 
effectiveness; longer-term evaluation is needed to assess 
sustained impact. Second, the sampling of pharmacists for the 
national survey, while geographically diverse, may not fully 
represent all practice settings, particularly in remote regions. 
Third, the AI analysis was limited by the availability of 
complete digital documentation for some international 
frameworks, potentially affecting the comprehensiveness of 
the competency mapping.  

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 
Future research should focus on longitudinal assessment 

of framework impact on patient outcomes, development of 
standardized implementation toolkits for resource-limited 
settings, and refinement of AI methodologies to better 
account for cultural nuances in competency language. 
Additionally, comparative effectiveness studies examining 
different approaches to framework implementation would 
provide valuable guidance for pharmacy educators and 
regulators. Finally, exploration of technology-enabled 
competency assessment tools could enhance the practical 
application of the framework in diverse practice settings [48]. 

5. Conclusion  

This study presents a novel approach to pharmaceutical 
service competency framework development by integrating 
artificial intelligence methodologies with rigorous 
localization practices. The AI-assisted analysis of 
international frameworks yielded a preliminary integrated 
structure that served as a foundation for adaptation to the 
Chinese healthcare context. This methodological innovation 

Figure 10. Comparison of competency adaptation requirements across countries. (a) Horizontal bar chart comparing the percentage of 

competency indicators requiring contextual adaptation across Asian countries. (b) Stacked bar analysis of adaptation types required in 

each country. 



Y. Wang & H. Hamid/Future Technology                                                                                       May 2025| Volume 04 | Issue 02 | Pages 61-75 

73 

 

offers enhanced efficiency and objectivity compared to 
traditional manual approaches to framework development. 
The localization process revealed substantial adaptation 
requirements to align with China's unique pharmaceutical 
environment, particularly incorporating competencies 
related to traditional Chinese medicine integration. The 
framework's modular structure—featuring universal core 
competencies supplemented by context-specific modules—
enhances its flexibility across diverse healthcare settings 
while maintaining cohesive standards. Implementation 
outcomes demonstrated the framework's potential to drive 
tangible improvements in pharmaceutical service quality. The 
variable results across different healthcare settings highlight 
the importance of contextual factors and implementation 
strategies in determining effectiveness. The identified success 
factors underscore the importance of comprehensive 
implementation planning that accounts for institutional 
characteristics and resource availability. The reconstruction 
of pharmaceutical service competency frameworks through 
AI-informed analysis and cultural adaptation represents a 
promising direction for advancing pharmacy practice 
standards globally while respecting local healthcare contexts. 
This approach bridges the gap between international best 
practices and local realities, enhancing the relevance of 
competency frameworks in diverse settings. Future efforts 
should focus on longitudinal assessment of framework 
impact, implementation resources for resource-constrained 
settings, and refinement of AI methodologies to better 
account for cultural nuances in competency 
conceptualization. 

Ethical issue 
The authors are aware of and comply with best practices in 
publication ethics, specifically with regard to authorship 
(avoidance of guest authorship), dual submission, 
manipulation of figures, competing interests, and compliance 
with policies on research ethics. The author adheres to 
publication requirements that the submitted work is original 
and has not been published elsewhere. 

Data availability statement 
The manuscript contains all the data. However, more data will 

be available upon request from the authors. 

Conflict of interest 

The authors declare no potential conflict of interest. 

References 

[1] Arakawa, N., Yamamura, S., Duggan, C., et al. (2020). 

The development of a foundation-level pharmacy 

competency framework: An analysis of country-level 

applicability of the Global Competency Framework. 

Research in Social and Administrative Pharmacy, 

16(3), 396-404. 

[2]  Al-Haqan, A., Smith, F., Bader, L., et al. (2021). 

Competency development for pharmacy: Adopting and 

adapting the Global Competency Framework. Research 

in Social and Administrative Pharmacy, 17(4), 771-

785. 

[3]  Alfaifi, S., Arakawa, N., Bridges, S. (2022). The 

relevance of the International Pharmaceutical 

Federation Global Competency Framework in 

developing a country-level competency framework for 

pharmacists: A cross-sectional study. Exploratory 

Research in Clinical and Social Pharmacy, 5, 100095. 

[4]  Holdford, D., Patkar, A. (2003). Identification of the 

service quality dimensions of pharmaceutical 

education. American Journal of Pharmaceutical 

Education, 67(1/4), 849. 

[5]  Lysaght, T., Lim, H. Y., Xafis, V., et al. (2019). AI-assisted 

decision-making in healthcare: the application of an 

ethics framework for big data in health and research. 

Asian Bioethics Review, 11, 299-314. 

[6]  Shaheen, M.Y. (2021). Applications of Artificial 

Intelligence (AI) in healthcare: A review. ScienceOpen 

Preprints. DOI: 10.14293/S2199-1006.1.SOR-

.PPVRY8K.v1. 

[7]  Medical Robotics and AI-Assisted Diagnostics for a 

High-Tech Healthcare Industry. (2024). IGI Global. 

Editor: Alex Khang. ISBN: 9798369321058. 

[8]  Chalasani, S. H., Syed, J., Ramesh, M., et al. (2023). 

Artificial intelligence in the field of pharmacy practice: 

A literature review. Exploratory research in clinical 

and social pharmacy, 12, 100346. 

[9]  Starfield, B. (1991). Primary care and health: a cross-

national comparison. Jama, 266(16), 2268-2271. 

[10]  Cacace, M., Ettelt, S., Mays, N., et al. (2013). Assessing 

quality in cross-country comparisons of health 

systems and policies: towards a set of generic quality 

criteria. Health Policy, 112(1-2), 156-162. 

[11]  Braithwaite, J., Tran, Y., Ellis, L. A., et al. (2020). Inside 

the black box of comparative national healthcare 

performance in 35 OECD countries: Issues of culture, 

systems performance and sustainability. Plos one, 

15(9), e0239776. 

[12]  Suwannaprom, P., Suttajit, S., Eakanunkul, S., et al. 

(2020). Development of pharmacy competency 

framework for the changing demands of Thailand's 

pharmaceutical and health services. Pharmacy Practice 

(Granada), 18(4), 2141. DOI: 

10.18549/PharmPract.2020.4.2141. PMID: 33343773. 

[13]  Bajis, D., Al-Haqan, A., Mhlaba, S., et al. (2023). An 

evidence-led review of the FIP global competency 

framework for early career pharmacists training and 

development. Research in Social and Administrative 

Pharmacy, 19(3), 445-456. 

[14]  Mohammadi, F., Nazarimanesh, L., Farahani, M. M. M., 

et al. (2021). Competency Assessment Tool for the 

Healthcare Networks Managers in Zanjan Province, 

Iran: Validation, Conceptualisation, and Localisation. 

Journal of Complementary Medicine Research, 12(3), 

173-176. 

[15]  Ahmad, N., Usman Awan, M., Raouf, A., et al. (2009). 

Development of a service quality scale for 

pharmaceutical supply chains. International journal of 

pharmaceutical and healthcare marketing, 3(1), 26-45. 

[16]  Fauziah, F., Surachman, E., Muhtadi, A. (2019). 

Integration of service quality and quality function 

deployment as an effort of pharmaceutical service 

improvement on outpatient in a referral Hospital 

Karawang Indonesia. Journal of Advanced Pharmacy 

Education and Research, 9(2), 13-23. 

[17]  Sulistiyoningrum, A., Sriatmi, A., Arso, S. P. (2021). 

Literature Review: Strategy Analysis of Pharmaceutical 

Service Quality Improvement. Jurnal Aisyah: Jurnal 

Ilmu Kesehatan, 6(3), 729-736. 



Y. Wang & H. Hamid/Future Technology                                                                                       May 2025| Volume 04 | Issue 02 | Pages 61-75 

74 

 

[18] Yuliandani, Y., Alfian, S. D., Puspitasari, I. M. (2022). 

Patient satisfaction with clinical pharmacy services 

and the affecting factors: a literature review. 

Pharmacia (0428-0296), 69(1). 

[19]  Hope, D. L., Grant, G. D., Rogers, G. D., et al. (2022). 

Optimising database searching in pharmacy education. 

International Journal of Pharmacy Practice, 30(6), 580-

582. 

[20]  Sud, D., Laughton, E., McAskill, R., et al. (2021). The 

role of pharmacy in the management of 

cardiometabolic risk, metabolic syndrome and related 

diseases in severe mental illness: a mixed-methods 

systematic literature review. Systematic reviews, 10, 

1-35. 

[21]  Iftekhar, E.N., Priesemann, V., Balling, R., et al. (2021). 

A look into the future of the COVID-19 pandemic in 

Europe: an expert consultation. The Lancet Regional 

Health - Europe, 8, 100185. DOI: 

10.1016/j.lanepe.2021.100185. PMID: 34345876. 

PMCID: PMC8321710. 

[22]  Nasa, P., Jain, R., Juneja, D. (2021). Delphi methodology 

in healthcare research: how to decide its 

appropriateness. World journal of methodology, 11(4), 

116. 

[23]  Sforzini, L., Worrell, C., Kose, M., et al. (2022). A Delphi-

method-based consensus guideline for definition of 

treatment-resistant depression for clinical trials. 

Molecular psychiatry, 27(3), 1286-1299. 

[24]  Anderson, C., Bates, I., Futter, B., et al. (2010). Global 

perspectives of pharmacy education and practice. 

World Medical & Health Policy, 2(1), 5-18. 

[25]  Saseen, J. J., Ripley, T. L., Bondi, D., et al. (2017). ACCP 

clinical pharmacist competencies. Pharmacotherapy: 

The Journal of Human Pharmacology and Drug 

Therapy, 37(5), 630-636. 

[26]  Lee, M., Badowski, M. E., Acquisto, N. M., et al. (2017). 

ACCP template for evaluating a clinical pharmacist. 

Pharmacotherapy: The Journal of Human 

Pharmacology and Drug Therapy, 37(5), e21-e29. 

[27]  Udoh, A., Bruno-Tomé, A., Ernawati, D. K., et al. (2021). 

The effectiveness and impact on performance of 

pharmacy-related competency development 

frameworks: a systematic review and meta-analysis. 

Research in Social and Administrative Pharmacy, 

17(10), 1685-1696. 

[28]  Meadows, N., Webb, D., McRobbie, D., et al. (2004). 

Developing and validating a competency framework 

for advanced pharmacy practice. Pharmaceutical 

Journal, 273, 789-792. 

[29]  Stupans, I., Atkinson, J., Meštrović, A., et al. (2016). A 

shared focus: comparing the Australian, Canadian, 

United Kingdom and United States pharmacy learning 

outcome frameworks and the global competency 

framework. Pharmacy, 4(3), 26. 

[30]  Chowdhary, K. R., Chowdhary, K. R. (2020). Natural 

language processing. Fundamentals of artificial 

intelligence, 603-649. 

[31]  Fanni, S. C., Febi, M., Aghakhanyan, G., et al. (2023). 

Natural language processing. Introduction to artificial 

intelligence. Cham: Springer International Publishing, 

87-99. 

[32]  Mahesh, B. (2020). Machine Learning Algorithms—A 

Review. International Journal of Science and Research 

(IJSR), 9(1), 381-386. DOI: 10.21275/ART20203995. 

[33]  Ray, S. (2019). A quick review of machine learning 

algorithms. 2019 International conference on machine 

learning, big data, cloud, and parallel computing 

(COMITCon). IEEE, 35-39. 

[34]  Asada, M., Gunasekaran, N., Miwa, M., et al. (2021). 

Representing a heterogeneous pharmaceutical 

knowledge-graph with textual information. Frontiers 

in Research Metrics and Analytics, 6, 670206. 

[35]  MacLean, F. (2021). Knowledge graphs and their 

applications in drug discovery. Expert opinion on drug 

discovery, 16(9), 1057-1069. 

[36]  Zeng, X., Tu, X., Liu, Y., et al. (2022). Toward better 

drug discovery with knowledge graph. Current opinion 

in structural biology, 72, 114-126. 

[37]  Olsen, A. A., Wolcott, M. D., Haines, S. T., et al. (2021). 

How to use the Delphi method to aid in decision 

making and build consensus in pharmacy education. 

Currents in Pharmacy Teaching and Learning, 13(10), 

1376-1385. 

[38]  Taze, D., Hartley, C., Morgan, A. W., et al. (2022). 

Developing consensus in Histopathology: the role of 

the Delphi method. Histopathology, 81(2), 159-167. 

[39]  Arakawa, N., Akel, M., Anderson, C., et al. (2022). 

Developing a FIP handbook for implementing 

competency-based education in pharmacy education. 

Pharmacy Education, 22(4), 150-154. 

[40]  Meštrović, A., Al-Haqan, A., El-Akel, M., et al. (2022). 

Connecting the dots in pharmacy education: the FIP 

international pharmaceutical federation global 

competency framework for educators and trainers in 

pharmacy (FIP-GCFE). Pharmacy Education, 22(4), 

100-109. 

[41]  Hajj, A., Zeenny, R.M., Akel, M.G., et al. (2022). The 

Lebanese experience for early career development: 

Bridging the gap to reach the International 

Pharmaceutical Federation (FIP) Global Competency 

Framework. Pharmacy Education, 22(1), 211-220. 

DOI: 10.46542/pe.2022.221.211220. 

[42]  Guo, X., Yao, D., Liu, J., et al. (2020). The current status 

of pharmaceutical care provision in tertiary hospitals: 

results of a cross-sectional survey in China. BMC health 

services research, 20, 1-9. 

[43]  Antoniou, S., Webb, D. G., McRobbie, D., et al. (2005). A 

controlled study of the general level framework: 

Results of the South of England competency study. 

Pharmacy Education, 5(4). 

[44]  Jackson, S., Martin, G., Bergin, J., et al. (2015). An 

advanced pharmacy practice framework for Australia. 

Pharmacy, 3(2), 13-26. 

[45]  Dhall, D., Kaur, R., Juneja, M. (2019). Machine learning: 

a review of the algorithms and its applications. 

Proceedings of ICRIC 2019: Recent innovations in 

computing, 47-63. 

[46]  Gao, Z., Ding, P., Xu, R. (2022). KG-Predict: A 

knowledge graph computational framework for drug 

repurposing. Journal of biomedical informatics, 132, 

104133. 



Y. Wang & H. Hamid/Future Technology                                                                                       May 2025| Volume 04 | Issue 02 | Pages 61-75 

75 

 

[47]  Nash, R.E., Chalmers, L., Brown, N., Jackson, S., 

Peterson, G. (2015). An international review of the use 

of competency standards in undergraduate pharmacy 

education. Pharmacy Education, 15(1), 131-141. 

[48]  Ranchon, F., Chanoine, S., Lambert-Lacroix, S., Bosson, 

J.L., Moreau-Gaudry, A., Bedouch, P. (2023). 

Development of artificial intelligence powered apps 

and tools for clinical pharmacy services: A systematic 

review. International Journal of Medical Informatics, 

172, 104983. DOI: 10.1016/j.ijmedinf.2022.104983. 

PMID: 36724730. 

[49]  Fang, Y., Yang, S., Zhou, S., et al. (2013). Community 

pharmacy practice in China: past, present and future. 

International Journal of Clinical Pharmacy, 35, 520-

528. 

[50]  Yi, B. (2021). An overview of the Chinese healthcare 

system. Hepatobiliary Surgery and Nutrition, 10(1), 

93-95. DOI: 10.21037/hbsn-2021-3. PMID: 33575292. 

PMCID: PMC7867737. 

  

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