Corresponding Author: Asep Irham
E-mail: [email protected]
INTRODUCTION
The current condition of global health security is
facing challenges in the Corona Virus Disease 2019 (COVID-19) pandemic (Rosyanti & Hadi,
2020). The pandemic forces us to quickly adapt to the
course of the COVID-19 disease, including providing the COVID-19 vaccine (E. E. Otenyo, 2023). In the exploding number of the vaccination program
injection, the vaccine industry also faces growth in adverse events following
immunization (AEFI) reports (Hadinegoro, 2016). The pharmaceutical companies that hold the marketing
authorization of the vaccine have the obligation of pharmacovigilance (PV)
activities. AEFI reports received by an Indonesian state-owned
biopharmaceutical company significantly increase from the regular vaccine
report. In 2021 the adverse events following immunization reports for the
COVID-19 vaccines reached approximately 15.088 case reports, including 316
serious cases of adverse events following immunization. This data is increasing
significantly from previous years due to many COVID-19 vaccinations that began
in early 2021.
The AEFI processing by the Indonesian
biopharmaceutical PV Departments is conducted manually. The case is processed
from the Individual Case Safety Report (ICSR) retrieval from the healthcare
workers, data confirmation, and data input to the internal PV application until
the case reports undergo causality assessment. The archiving process uses the
pharmacovigilance application developed by an Indonesian biopharmaceutical
company. However, the function is limited to only the data archival and
reporting without the ability to generate reports automatically and still needs
to input the case details manually.
This study utilizes Business Process Management to
resolve the problem in the organization. Business Process Management (BPM) is a
systematic approach to improving and optimizing the processes within an
organization (Nugraha et al., 2019). It involves process identification, process
discovery, as-is process modeling, process analysis, process redesign (or
process improvement), process implementation, and process monitoring and
controlling to increase efficiency, reduce costs, and improve overall
performance�(Marlon Dumas, 2013).
This study's purpose is to define the root cause of
the pharmacovigilance department in 2021 and explore the possibilities of
problem-solving using business process management (BPM), including the root
cause analysis of the problem and the study of the PV business process modeling.
The solution proposed in this study is according to the BPM methodology in
improving the business process, which includes process identification, process
modeling, process analysis, process redesign/improvement, process
implementation, and monitoring and controlling.
From the BPM process, we identify the benefit of
artificial intelligence as a novel solution for improving pharmacovigilance
case report processing. With the other solutions' synergy, the PV case
processing improvement can be increased to 219% case processing capabilities in
30 days from the previously 80.70% performance measurement.
METHOD
This study combines
qualitative and quantitative studies that propose implementing the new
artificial intelligence system for the PV Department. The methodology of this
research includes the internal and external situational analysis, started by
collecting the supporting data, analyzing the internal and external factors
that are influencing the business process of the PV Department by current SOP
and protocol analysis, analyzing the current status of the business process in
the PV department, analyze the preferred condition that aimed to be the goal of
the improvement, examine the gap between the current and preferred state,
analyze the root cause of the problem and propose the improvement using the
Business Process Management (BPM) with the phase of process identification,
process modeling, process analysis, process improvements, and also monitoring
and controlling.
To collect the
information, a semi-structured interview was conducted in this research with
selected personas who play a role in the pharmacovigilance case processing,
including the one who reviews and approves the case processing. The stakeholder
identification and stakeholder analysis are also conducted to narrow down the
people or organizations involved and have an influence on internal case
processing activities.
RESULTS AND DISCUSSION
Business
Process Management
Process
Identifications
In this phase, business problems are identified. The analyst is trying to
map the issue of the organization and taking measures to identify the root
cause analysis of the problem�the process includes
internal and external analysis (Stakeholder analysis, SWOT analysis, and POPIT
Model). SWOT analysis is often used to identify the elements that influence the
organization and the business process of the organization. The components in
the SWOT analysis are the Strengths, Weaknesses, Opportunities, and Threats (Cadle et al., 2010). POPIT Model defines process, organization, people, and
information/technology to analyze the internal factor affecting
pharmacovigilance case processing (Nuraeni Sayuti, 2013). The outcome of process identification is a process architecture that
provides a helicopter view of the organization's process and the relationship
between every factor that influences it.
Process Modeling
Process modeling in Business Process
Management (BPM) visually represents a process using flowcharts or process maps (Mendling
et al., 2013). This study utilizes Business Process
Model and Notation (BPMN).
Process Analysis
Process analysis refers to examining and understanding a business
process's current state to identify improvement areas (Rashin
& Ghina, 2018). Process Analysis includes identifying
the problem�s root cause using the Current Reality Tree (CRT) and Performance
Measures of the process performance dimension to calculate the work cycle time
in pharmacovigilance case processing.
Process Improvement
Process improvement refers to changing a
business process to increase efficiency, reduce costs, and improve overall
performance. Process improvement aims to optimize the process to meet
organizations' and customers' needs (Pelanggan,
2013).
Process
Implementation
Process implementation covers organizational and process automation. In
implementing organizational change management, we conduct activities that are
required to change the way of working of all operators in the working process.
Process automation means developing improvement in the IT sector that supports
the current system to be more efficient and increase productivity (Salas et
al., 2022).
Monitoring
and Controlling
Monitoring and controlling a process
refers to measuring and monitoring the performance of a business process and
taking corrective action as needed to ensure that the process is operating
effectively and efficiently. The goal of monitoring and controlling is to
ensure that the process meets the organization's and its customer's
requirements and that any deviations from the expected performance are
identified and addressed on time (Sugiyanto, 2016).
Data Collection Method
The data gathered included primary and
secondary data. The primary data is collected directly by the researcher, and
secondary data is the data that can support the research developed by others in
the past. The primary data gathered in this research is
the aspiration gathered to the user of the process improvement, such as in the
Pharmacovigilance Department, Surveillance and Clinical Research Division, and
related stakeholders such as the Quality Assurance/Regulatory Affairs Division
and Human Capital Division. The data was gathered using the semi-structured
interview to collect the stakeholders' aspirations in improving the case
processing improvement. The secondary data is collected using existing
literature, books, journals, and articles to develop the analytical Framework
from the previous study and the conceptual framework defining the desired
process improvement based on the state-of-the-art implementation. The
conceptual Framework of the study is described in Figure 1.
Figure
1. Conceptual Framework of the improvement
in
the pharmacovigilance case processing.
From Figure 1 above, this
study uses the Input, Process, and Output (IPO) framework to design the
conceptual framework for this study. To identify the causality network of the
problem and the premises that can affect the output in the Pharmacovigilance
Department case processing, we need to review back to the case processing of
the Department.
Process Identification
The process
includes stakeholders� identification and analysis, SWOT analysis, and POPIT
model analysis to describe the input or as-is condition of the Indonesian
biopharmaceutical company pharmacovigilance department. The results of the
stakeholder analysis the plotted into the power and interest grid analysis, as
stated in figure 2. Diagram of Stakeholder Quadrant of Power and Interest. The
business situation analysis is also being conducted by SWOT analysis and POPIT
model analysis described in table 1 SWOT analysis and table 2 POPIT Model. The
process identification also locates the process problem and supports the root
cause analysis described in the Process Analysis phase. The identification of
the internal and external situational analysis using the SWOT was modified to
acquire the TOWS matrix to formulate the strategic problem solution.
Figure 1. Diagram of Stakeholder Quadrant of Power and Interest
������������� Figure 2 above describes
the stakeholder analysis result and divides the stakeholders based on their power
and interest in the project. From the analysis above, we conclude that the internal
stakeholders that have the high interest and high power to support the project
of the case processing improvements are the Director of Research and Business
Development, Director of Transformation and Digital, Head of Surveillance and
Clinical Research Division and Head of Pharmacovigilance Department.
Table
1.
SWOT Analysis
|
Strengths
|
Weaknesses
|
|
� Strong role in the vaccine development timeline after the
vaccine is marketed.
� An important role in the fulfillment of vaccine regulatory
requirements
� Strong partnership and collaboration in the post-marketing
surveillance and other pharmacovigilance activities both with domestic and
foreign partners
� Utilization of PV application used in the archiving of the AEFI
and ADR database to build the Pharmacovigilance System Master File
several
competent personnel (currently three staff, one head of department, and one
head of Division)
|
� High cost associated with internal PV case processing
� A limited number of personnel
� Inefficient and complex SOP and multiple internal stakeholders
� Inefficient and not digitally organized work documentation
(manual case input) and archiving in the in-house PV Application and their
limited features in streamlining the case processing.
� High waiting time for the case processing
� High workload as the staff does more than just the case
processing in their job.
� Dependencies to the strict qualification of the pharmacovigilance
personnel
� Budget constraint in human capital development, staffing, and
application development (Human Capital Division is reluctant to increase PV
Department staffing)
|
|
Opportunities
|
Threats
|
|
� Support from other Division within the company, especially QA/RA
� Advancement in PV application technology using AI and NLP.
� The potential of the PV application to be used in pharmaceutical
SOE holdings subsidiaries and further developed as Software as a Service
(SaaS) to other pharmaceutical companies.
� Improvement of the case processing SOP with internal and
external stakeholders, including harmonization of the case processing report
format
� Competency improvement opportunities by training the PV
personnel to increase work efficiencies.
Increasing
the number of qualified PV personnel
|
� Vulnerability to changes in government policies and regulations.
� Multiple external stakeholders that cause inefficiencies in case
of processing (increase in waiting time)
� Nature of the AEFI/ADR reports that is usually unstructured and
in a different format
� Vastly changing global health conditions such as pandemics
� Complex nature of case processing causing application
development harder.
|
In table 1 above, The SWOT analysis describes
the internal and external condition of the organization that can be discovered
from the internal and external analysis with the data gathered from several
sources, such as the semi-structured interview, analyzed to know the actual condition
of the working environment and internal business process analysis by collecting
SOP data.
Table 2. POPIT Model
|
Factors
|
Problem Observed
|
|
Process
|
� Inefficient manual input of case processing
� High waiting time for case processing
� The nature of the reports received could be
more structured.
� The high number of case reports in the
pandemic situation
|
|
Organization
|
� Inefficient and complex SOP
� Multiple internal stakeholders
� Budget constraints in human capital and
improvement development
� The Human Capital division is still
reluctant to increase the staffing in the PV Dept
|
|
People
|
� High requirements of pharmacovigilance
staff
� High salary and investment for training
� The low number of available qualified
personnel for the PV staff
|
|
Information/Technology (IT)
|
� No automated features of the Bio Farma current
PV Application
� High budget for application improvement and
development
� Complex requirements and compliance in the
business process make application development is harder
|
Table 2 describes the framework used in this
study to analyze the input of the organization based on the process,
organization, people, and information/technology or POPIT modeling.
Table 3. TOWS Matrix Formulate the Strategic Problem Solution
|
TOWS
Matrix
|
Strategy
|
|
Strength/Opportunity
(SO): utilize internal organization strengths to exploit external
opportunities:
|
� Strengthening the synergy between internal units in the company
to support the post-marketing vaccine development role and regulatory
requirements (especially with QA/RA)
� Strengthening the support from partners and regulatory
institutions to build the harmonized AEFI/ADR report to be more structured
and easily inputted.
� Utilization of advanced technology in the pharmacovigilance
system application to improve the current PV application
� Human resources recruitment and training of the personnel to be
fully competent and efficient
|
|
Weakness/Opportunity
(WO): overcome the weakness and seize the opportunity available
|
� Support from other Divisions to prioritize the budget for
improving the PV unit case processing.
� Synergy to develop a more streamlined SOP of PV case reporting,
including direct utilization of the PV app by the QA/RA Division
� Integrating advanced case processing technology to add the
limited features of the PV application to be more efficient and organized in
the case intake.
� Improve the case reporting by external stakeholders (Komnas
KIPI) to be more structured and complete and reduce the waiting time.
� Additional staff to reduce the work burden
|
|
Strength/Threat
(ST): exploit internal strength to overcome the threat from external
|
� PV Department has a significant role in the national healthcare
resilience in the vaccine post-marketing safety data and activities. Synergy
with the regulatory institution can make pharmacovigilance activities easier.
� Strengthening collaboration with contractual manufacturer
partners reduces the risk of competitors taking up the company's market
share.
� Efficiencies in the case processing activities can make the data
and safety information easier to study and communicate with external
stakeholders.
� Utilization of advanced technologies can flexibly adapt to the
ever-changing pharmaceutical industry regulatory requirements, locally and
globally.
� Opportunity to develop and improve the PV application as a SaaS
to be implemented in the pharmaceutical holding company subsidiaries and
other pharmaceutical company
� API Integration of the current BioFarma PV application can make
case processing with new technology more accessible to develop
|
|
Weakness/Threat
(WT): overcome the weakness and seize the opportunity available
|
� Budget increase improving the PV unit capabilities to overcome
the uncertainty of the regulatory and government policies.
� Efficient case processing by synergy with the external
stakeholders to develop a more streamlined process and reduce waiting time.
|
����������� From table 3 above, we can conclude that
the strategy can be taken in problem-solving by analyzing the elements in the
SWOT strategy. The TOWS matrix strategy above formulated the solution strategy
based on the organization's strength/opportunity (SO strategy),
weakness/opportunity (WO strategy), strength/threat (SO strategy), and
weakness/threat (WT strategy).
Process
Modeling
Process modeling in the individual case processing of the
pharmacovigilance in an Indonesian biopharmaceutical company using BPMN is
described in Figure 3, Business Process Modelling and Notation of Case
Processing in the Pharmacovigilance Department.
Figure 2. Business Process Modelling and Notation of
Case
Processing in the Pharmacovigilance Department
Figure 3
above describes the process of as-is case processing, which starts with case
retrieval until the case is finished to be analyzed and stored in the
pharmacovigilance department�s PV application. The case processing takes a
manual input method, and we can analyze in the picture above that the process
is inefficient.
Process Analysis
Process
analysis can be divided into qualitative and quantitative process analysis. The
qualitative process analysis is conducted by the root cause analysis of the
case processing performance impairment using CRT. This analysis is formulated
based on process identification and modeling. The CRT diagram is described in
Figure 4. Current Reality Tree analysis in the Pharmacovigilance Department.
Figure 3. Current Reality Tree analysis in the
Pharmacovigilance Department
Based on
Figure 4 above, we can conclude that the root cause of the underperformance of
the PV Department is based on several factors. The factors include external and
internal factors. The quantitative analysis is the performance analysis of the
pharmacovigilance department using the Key Performance Indicator (KPI)
calculation based on the number of cases successfully processed within 30 days
after the PV Department retrieves the case minimum preferred achievement is
90%. In 2021, the KPI achievement was only 80.70% due to the sudden increase in
case reports received during a single day. Currently, the case report
processing is limited to only the serious AEFI cases due to the insignificance
of the non-serious AEFI cases to be followed up. In 2021, from 316 serious AEFI
reports accepted, 61 reports (19.30%) were completed in more than 30 days. The
average number of case processing days is 37.19, with a standard deviation is
37.74 days. The processing time of the case ranged from 1 day to 193 days. The
variability of the case processing cycle time is primarily due to the
variability of the case report completeness. The report data is usually
unstructured and only contains a scanned document. The staff must manually
retype and input the case into the pharmacovigilance database application. The
detail of the case processing time data and performance is presented in Table
4. Case Processing Time of Pharmacovigilance Department
in 2021.
Table 4. Case Processing Time of Pharmacovigilance Department in 2021
|
Number of cases
|
processing days (shortest)
|
total processing days by three staff (min)
|
|
114
|
2
|
76 days
|
|
processing hour (longest)
|
total processing days by three staff (max)
|
|
4
|
152 days
|
From Table 4 above, the processing
time of completing individual case reports usually takes 2-4 days, with 50% of
regular working hours allocated for the ICSR case reporting. If the working
hour every day is 8 hours, the processing time of each ICSR is about 8-16
hours. These are the cycle time of standard case processing in the condition
that data sent from the external stakeholders are complete. For the incomplete
data, the processing time of each ICSR is varied. In 2021, the PV department
received 316 cases of serious AEFI that need to be processed. We can calculate
the approximate working days needed to process the cases in 2021. We assume
that all data required for analysis in the case reports are complete and that
all the staff receives the same number of cases to process. The case reports PV
staff process in one year is 105,3 cases, or we round it up to 106 cases
yearly. The estimated fastest accumulated working days for the case processing
for each staff is 212. If we take the number of working days in one year as 260
days, the PV team can still finish the report 100% with slack days of 260 days-212
= 48 days for each staff to do additional waiting time.
At the time 2021, there was 114 serious ICSR case received in one
day on 19 March 2021. This means that for all the 114 reports received on the
same day, the staff should still finish the case processing in 30 days. The
available staff for the case processing in the PV department is only three
staff to do all the data collection of the ICSR and coordinate with the
reporter about the validity and completeness of data, manual case input to the
PV application, completing the data for the follow-up reports, and making
causality analysis in the PV application. Below is the calculation of the ideal
working time of processing 114 cases by three staff for 8 hours working time
and 50% allocation of workload is going to the case processing, and the report
condition is complete:
The duration (days) of the case
processing is allocated at 50% of the total working time in one day for case
processing, and the number of staff is 3. From the formula above, we can
calculate the entire processing days described in Table 4:
This
concludes that processing 114 cases received in one day is impossible to have
30 days performance target. The above calculation is applied in the case of
ICSR, assuming all the reports are complete. If the ICSR data is incomplete,
there will be an additional waiting time to coordinate and complete the data.
Process
Improvement
Process improvement includes the TOWS matrix formulated using the
SWOT and POPIT model analysis in the Process Identification and BPMN in the
Process Modelling. The process redesign or improvement utilizes the heuristic
redesign method applied to the business process in the BPMN by contact
reduction, integration, activity elimination, adding extra resources, using
specialists, and activity automation. The proposed process improvement in the
pharmacovigilance case processing is described in Figure 5. BPMN Diagram of the
Improved Process.
Figure 4. BPMN Diagram of the Improved Process.
Figure 5 above describes the proposed solution formulated in this study.
The solution includes adding the staff and utilizing advanced case processing
technology, including artificial intelligence.
Process
Implementation
The process
implementation is the transition phase of the as-is process to the to-be
process and taking the preparation for the changes required (Aradea & Himawan, 2013). The aspects covering process implementation include
organizational changes and process automation (Oracle, 2022). Corporate change management refers to the activities required to
change the way of working for all stakeholders directly involving the process (Wibowo, 2021). These activities include:
1.
Describe the
changes to the internal stakeholders with the aim that the participants
understand what changes are being implemented and why these changes are
beneficial in the process improvement and improving the organization's key
performance indicator.
2.
Taking change
management measures. The company was implementing change control in the
operation of the company. This is the part of Good Manufacturing Practice
commonly practiced in pharmaceutical companies. To maintain the quality, any
changes in the SOP, including the new process and automation in ICSR
processing, must be reviewed, and approved by Quality Assurance and Regulatory
Affairs (QA/RA) Division.
3.
Deployment
and training to the user of the improved process to ensure a smooth transition
to the new process
Process
automation is another part of process implementation. Process automation utilizes
information technology to develop and deploy the IT system that supports the
to-be process. In this proposed study, the performance improved by using
automation in the case intake to reduce staff workload by eliminating manual
case intake and processing.
The early
implementation phase is making boundaries or the requirements to define the
improvement project�s scope. From the interview with the Head of Surveillance
and Clinical research, the user requirement for the early adaptation of the implementation
of AI and case processing technologies is in the automation of the case intake.
By using AI, the unstructured data can be recognized and identified and input
the case report form automatically to the as is the company PV application.
Case intake is the process in pharmacovigilance that takes the most workload
(50% of daily work hours) and most budget (40 % to 80% of the departmental
budget) (Salas et al., 2022).
With the
process automation implementation in case processing, the expected efficiencies
are cutting the case processing cycle time. Oracle, an IT company, reports that
the artificial intelligence pharmacovigilance platform can cut down individual
case inputting to the application from 20 minutes to only 1 minute with 90%
accuracy. The average processing time reduction in the case intake is up to 50%
(Oracle, 2022).
Monitoring
and Controlling
The
monitoring and controlling phase of the BPM in improving the PV department can
be conducted by measuring processing time and process quality. The measurement
can of the time is focused on the time measurement of the case intake. The
measurement must be displayed in the control dashboard to be quickly evaluated
by the managers.
For the
proposed improvement system, it is recommended that the evaluation of the total
cycle time of case processing is divided into processing time and waiting time.
Processing time refers to the time the system works to process the ICSR.
Waiting time is measured by the system's idle time between the initial ICSR
input and the follow-up report input until the ICSR is declared complete by the
PV staff. The analysis of waiting time can be conducted as the justification if
the new system's performance still needs to reach the preferred performance
indicator.
Another
process dimension is the number of days of individual ICSR case processing. The
KPI of the PV department is stated as the percentage of cases processed within
30 days with a minimum of 90% of achievement of total ICSR finished processed.
The system is expected to monitor the KPI calculation in real time.
The
application's accuracy value measurement must also be documented in the
application. The monitoring method of the application accuracy is by
verification of the data correctness of the imputed report. The metric used is
the number of correct prediction values of data inputted divided by the total
data inputted.
The AI's
accuracy in the benchmarks by Schmider is expected to reach 79% of the correct
prediction value (Schmider et al., 2019). This benchmark will be used as the basis of the quality
performance indicator of the proposed application.
From the
analysis above, the solution to the underperformance of the Indonesian
biopharmaceutical company pharmacovigilance unit can be concluded into three
main actions: the addition of human resources, the implementation of artificial
intelligence in the case processing, and the approach to the related external
stakeholders to make the case reporting more efficient and the data is more
structured and complete.
Adding human resources to the PV department is the quick-win
solution to problem-solving. Adding one personnel of PV staff can add the case
processed approximately 65 to 130 cases per year. The below formula can
calculate the whole processing days:
Total processing days = 
Suppose we refer to the calculation of total case processing days
in one year (260 working days per year) and assume that the average duration of
case processing is three days. In that case, we can calculate the staff needed
to achieve the performance index.
This calculation shows that the
number of additional staff for the PV department is approximately one person.
The limitation of this calculation is that we assume that the data of the case
received by the PV department is complete and that there is no waiting time for
the case completion verification by the staff. The Head of Clinical Research
approves the addition of human resources in the PV department. It is currently
undergoing recruitment in the Human Capital Division.
The
implementation of artificial intelligence can improve the processing time of
case processing in the PV department. The expected performance of the
application implementation is that it can reduce the processing time to up to
50% of the case processing cycle time (Oracle, 2022). The expected accuracy of the application to gather data from the
unstructured report is 79% based on the literature benchmark. The performance
is even better in some prominent vendors that reached 90% accuracy.
Implementing artificial intelligence in the ICSR processing can also be
realized soon as the cost for the PV application improvement is already
budgeted.
As of 2021,
serious case processing is reached 80.70% of the performance indicator, below
the preferred indicator of 90%. In the proposed use of artificial intelligence
utilization in the company's PV application, it is assumed that manual input by
PV staff is eliminated so that processing time is reduced and the performance
indicator is targeted to 100%. As from the literature review from Oracle
reports, if the increased performance of the case processing cycle time is
reduced to 50%, we can calculate the total case processing capabilities of the
PV department (Oracle, 2022). The average processing day for individual cases by manual input
is assumed to be three days. In the utilization of AI, it is becoming 1,5 days.
Using the formula of total processing days, and if we assume the total
processing days within one year is 260 days, the calculated number of cases
that can be processed within one year are as follow:
Total processing days =
This means
the performance of the case processing without the addition of the staff is
increasing by 165%. Furthermore, the number of cases processed in the addition
of staff is increasing by 694 cases per year (219% of case performance).
This
performance can be further increased if the Komnas KIPI is willing to increase
the efficiency of the case reporting to the pharmaceutical company and make the
report complete and more structured. Integrating their AEFI report website with
the manufacturer�s PV system to send the AEFI data directly to the
manufacturer�s PV system will eliminate the manual case reporting by their
staff. The real-life example of the PV application integration with the government
institution is already running with the integration of PV application to the
Indonesian BPOM eMESO (electronic Monitoring Efek Samping Obat) website. This
combination of the proposed solution will further increase the capability of
case processing performance in the pharmacovigilance practice.
CONCLUSION
This study concludes that the impaired
performance of the pharmacovigilance department in Indonesian biopharmaceutical
companies can have several root causes, as we analyzed in the root cause analysis:
The pandemic is disrupting the way AEFI case is reported. In the 2021 case, the
PV department received 114 case reports in one day. This makes the target of
the performance indicator impossible to reach by the PV department in its
current state. A low number of staff in the PV department. The pandemic is also
making medical professionals more focused on healthcare services than
pharmaceutical companies to be pharmacovigilance professionals; for the staff
of PV, a medical doctor is required as PV activities need a medical decision in
the causality assessment. The nature of case reports from the reporters is
usually manual and unstructured, making the case processing manually
inefficient. Case processing in pharmacovigilance activities is a complex process
for documenting pharmaceutical product safety. This makes the process's
learning curve high and requires high-quality human resources, making
recruitment harder. The SOP is becoming more complex as it must meet the
regulatory requirements of local and global regulatory institutions. Budget
constraint limits the working process adaptation in the pandemic situation. By
identifying the root cause of the underperformance of the organization, we can
analyze the solution. Then we can propose the best practices that can be
applied to the organization. Human resources addition, change in process and
SOP, and approach to the external stakeholders to make the case processing
efficient is the solution that we can do based on the analysis of this study.
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