Abstract
Background
Japanese pharmacists aim to improve efficiency and communication by simplifying work processes and developing protocols. While assistants and robots have been shown to improve drug dispensing, reports on the efficiency of pharmacies with automated dispensing systems are limited. This study explores factors affecting pharmacist efficiency in dispensing.
Methods
77Daily reports from our hospital pharmacy (December 1, 2020–November 30, 2021) were retrospectively analyzed. The primary outcome was the mean duration of drug dispensing. Multiple regression analyses identified factors affecting dispensing time. Strategies to address these factors were implemented, and outcomes were evaluated using data from December 1, 2021–November 30, 2022.
Results
Univariate analysis identified that the prescription/pharmacist ratio, number of one-dose package (ODP) prescriptions, and powdered drugs significantly influenced dispensing time. Multivariate analysis confirmed that the prescription/pharmacist ratio (p < 0.001), ODP prescriptions (p < 0.001), and powdered drugs (p = 0.02) were key factors. A higher number of ODP prescriptions generally increased dispensing time. After implementing a new strategy for checking ODP, mean dispensing time decreased from 20.0 ± 4.0 to 18.5 ± 3.6 min (p < 0.001), and the percentage of tasks completed in under 20 min increased from 56.3 % to 73.6 % (p < 0.001). Dispensing times were reduced without changing staffing levels by reallocating tasks.
Conclusions
Optimizing the ODP verification workflow enhances dispensing efficiency without increasing pharmacist workload, highlighting the importance of prioritizing ODP prescriptions and implementing support tools for final checks, while further multicenter studies are needed to confirm these findings across diverse settings.
Keywords: Dispensing time, One-dose packaging, Work efficiency, Factors affecting dispensing time, Check dispensed drug, Pharmacist human resource, Dispensing operation
Background
Japan's population is rapidly aging, with individuals aged 65 years or older comprising 29.8 % in 2021, and this proportion is projected to reach 33.3 % by 2036.1 As the demand for medical services rises, the workforce available to provide medical care is expected to decline both absolutely and relatively. To ensure efficient healthcare delivery amidst these challenges, this study aimed to investigate factors influencing pharmacists' efficiency in dispensing tasks in an environment where dispensing support systems, such as Automated one-dose package (A-ODP) and computerized physician order entry systems (CPOE), are already implemented.
Hospital pharmacists' roles are becoming increasingly interpersonal, necessitating improved work efficiency to enhance their availability.2 To address this, drug dispensing assistants are permitted.3 Efforts to simplify pharmacists' tasks have been made to improve work efficiency.4,5 High workload and prolonged dispensing wait times negatively impact patient satisfaction and pharmacists' well-being, increasing the risk of dispensing errors.6,7 The 2022 revision of medical service fees in Japan expanded pharmacists' interpersonal services across various areas, requiring cross-team intervention.8 In Japan, pharmacists are responsible for most dispensing tasks, as dispensing assistants are only permitted to dispense tablets, which constitutes a small part of the entire process.3 Enhancing operational efficiency is essential to meet the increasing demand for pharmacists' services.
A-ODP9., 10., 11. and CPOE12 have been shown to improve dispensing efficiency and patient safety. While drug dispensing assistants and robots have been introduced, few studies have evaluated their additional impact on pharmacists' dispensing efficiency.
Methods
Study design
Our study was designed as a retrospective observational study with a before-and-after intervention design, in which factors associated with dispensing time were identified through retrospective data analysis, followed by an intervention targeting the identified factors to evaluate its impact on dispensing efficiency.
Study setting
The data used for the investigation were collected in the outpatient dispensing room of the Department of Pharmacy, Aichi Medical University Hospital. The hospital outpatient dispensing rate was 95 %. The dispensing room is the section responsible for dispensing drugs to outpatients and typically has an average of 18 pharmacists and 7 dispensing assistants on duty at any given time. Pharmacy students did not participate in dispensing activities throughout the entire study period.
Dispensing process at the study setting
A flowchart of the drug dispensing process is shown in Fig. 1. Prescriptions were automatically received and printed from the electronic terminals. In our hospital's pharmacy, the dispensing room is divided into several sections: tablet picking, powdered drug dispensing, solution drug dispensing, and one-dose packages (ODPs). Drug dispensing assistants were primarily responsible for drug selection. Pharmacists prepared ODPs, dispensed tablets, powdered drugs, solutions, and topical drugs, evaluated prescriptions, and checked the dispensed drugs. A fully A-ODP system was used to prepare the ODPs (Fig. 2). The study site has installed dispensing automation systems such as LW-K (TOSHO, Japan) for powdered drugs, LW-KU (TOSHO, Japan) for solutions, and Xana-4001 U2 Advance (TOSHO, Japan) for A-ODP system, but have not installed one for tablet selection, which is still handled by drug dispensing assistants. The A-ODP system does not require pharmacists to input the prescription details; data are automatically entered into the automatic packaging machine, and the medication is automatically packaged. However, pharmacists manually dispense powdered drugs, solutions, and topical drugs. Given the high volume of prescriptions received in the dispensing room, averaging more than 100 per hour, some of these prescriptions are initially selected by drug dispensing assistants and later reviewed by pharmacists. As a result, the bottleneck in the dispensing process determining the overall dispensing time lies in pharmacists verifying dispensed medications. As we aimed to improve the workflow and use real-world daily work data, ethics approval or informed consent was not required, as decided by the ethics board of Aichi Medical University.
Fig. 1.
Flow of the drug dispensing process in the pharmacy.
Fig. 2.
One-dose package handling steps.
A: Drugs that the patient must take in one package. B: Pharmacists must check each tablet in all the packages. C: Fully automated one-dose package system operated by a pharmacist. If pharmacists dispense drugs using ODP, patients can take just one package at the indicated time. The patients were not confused about taking the medicines. However, pharmacists spent more time checking the packaged drugs than the sheeted ones.
Data collection
Data were retrospectively collected from daily reports from December 1, 2020 to November 30, 2021. We collected data on the maximum number of pharmacists who worked on a given day, the number of prescriptions, ODP prescriptions, medicines prescribed as powdered or solution drugs, the number of drugs prescribed, and the number of prescription days. Only data recorded between 8:30 and 17:00 on weekdays were included, while data from 17:00 to 8:30, weekends (Saturdays and Sundays), and Japanese public holidays—when outpatient care was closed at our hospital—were excluded.
A “Daily report” is a written record that summarizes the activities, tasks, and accomplishments of a pharmacy department over a specific workday. The daily report consisted of the number of pharmacists on duty, number of prescriptions, number of solutions, number of powdered drugs, and number of ODP, which were entered into Microsoft Excel at the end of each day's work (Fig. 3). The number of pharmacists was entered from the work shift list, and the number of prescriptions, powdered drugs, solutions, and ODP were recorded automatically by the respective dispensing support systems. The number of prescriptions requiring multiple dispensing methods (e.g., solution, powdered drug, and ODP) was counted as one prescription each. This data was then collected and stored on the hospital's electronic medical record system daily.
Fig. 3.
Daily Report Format for Pharmacy Operations.
This table format is used to record daily pharmacy operations. The columns include various metrics such as the number of pharmacists on duty, the number of different types of prescriptions processed, and overtime details. Notably, while this example is presented in English, the actual daily report form is documented in Japanese.
Furthermore, we investigated the dispensing time, which was defined as the time from receiving a prescription to finished for dispensing the drug and was collected from the system of pharmacy department. The system records both the time the prescription is received and the time the dispensing is completed. When physicians order medications for a patient, the prescription data is automatically transmitted to the pharmacy department's system. Upon completing the dispensing process, the pharmacist clicks the unique prescription number assigned to the prescription in the system to notify the patient that their medication is ready. The system records the timestamps for all prescriptions. The dispensing time was calculated as the difference between the received time and the time when dispensing was completed. Data on the same items were collected from December 1, 2021 to November 30, 2022, for the intervention group to evaluate factors affecting outcomes.
Outcomes
The primary outcome of this study was the identification of factors associated with longer dispensing time through retrospective analysis. To validate the robustness of the identified factors, we performed an intervention targeting these factors, with the secondary outcome of evaluating the change in mean dispensing time and the percentage of the average daily dispensing time of less than 20 min13 before and after the intervention.
Analysis of factors affecting the dispensing time
Univariate analysis was used to explore the factors affecting the dispensing time. Factors with a correlation coefficient greater than 0.2 (r > 0.2) were included as covariates in the multiple regression analysis to assess their impact on dispensing time. Furthermore, in cases where factors were considered to exhibit multicollinearity, the factor with the highest correlation coefficient was selected for inclusion in the multiple regression analysis. Additionally, the variance inflation factor (VIF) values were calculated to confirm the possibility of multicollinearity. In addition, standardized regression coefficients were calculated to compare the effects of each factor on the dispensing time. Efforts were made to reduce dispensing time with respect to the identified factors. The evaluation of the regression model was conducted using the Adjusted R-squared and F-statistic to assess the model fit.
Evaluation of the efforts made to reduce the dispensing time
To evaluate the effectiveness of our efforts, we compared the dispensing time data between one year before (from December 1, 2020, to November 30, 2021) and after (from December 1, 2021, to November 30, 2022) implementing the changes. To minimize bias, we matched data based on factors affecting dispensing time. Propensity scores were calculated for these factors, which were selected through a multiple regression analysis focusing on their impact on dispensing time.
Sensitivity analysis
We conducted additional analyses to assess the robustness of the findings under alternative assumptions and to assess the risk of bias. Briefly, we calculated E-values to estimate the strength of the association between unmeasured confounders and the secondary outcome.14,15 In addition, a multiple regression analysis was performed without including the prescription/pharmacist (P/P) ratio. In addition, the Mann–Whitney U test and chi-square test were used to analyze the primary and secondary outcomes, respectively, in evaluating the intervention, and the E-value was calculated for the secondary outcome. The calculation of the E-value began with determining the odds ratio (OR) for the percentage of waiting times less than 20 min before and after the intervention. The OR was calculated as OR = (post-intervention odds)/(pre-intervention odds). Finally, the E-value was computed using the formula E-value = OR + √(OR × (OR - 1)) to assess the robustness of the observed effect to potential unmeasured confounders. A difference-in-differences analysis and an interrupted time series analysis were contemplated as sensitivity analyses; however, they were not conducted due to the unavailability of a control group, and the intervention commenced at the end of the year.
Statistical analysis
The sample size was not calculated as this was an exploratory study. Data are expressed as the mean ± standard deviation. The least-squares method was used to analyze the relationships between the primary outcome and continuous variables. Pearson's correlation analysis was performed to analyze the correlations between the primary outcomes.
Multiple regression analysis was performed to determine covariates significantly influencing the primary outcome. In addition, Welch's t-test was used to compare the dispensing time before and after making efforts as an evaluation of the efforts. Independent predictive variables were considered statistically significant at P < 0.05. The data were analyzed using EZR16 version 1.55, a customized R software package tailored to include commonly employed statistical functions in biostatistics.
Results
Baseline characteristics
A total of 250 dispensing data points were collected before the effort. The study data are presented in Table 1. The average dispensing time was 20.1 ± 4.1 min. The average number of pharmacists was 18.5 ± 2.3 pharmacists/day. The total number of prescriptions, ODP prescriptions, powdered drugs, and solution drug prescriptions was 999.4 ± 112.7, 30.0 ± 4.1, 63.9 ± 17.0, and 10.0 ± 4.1 prescriptions/day, respectively. The P/P ratio was 54.6 ± 7.5.
Table 1.
Baseline characteristics of pharmacies.
| Value (n = 250) | |
|---|---|
| Mean time for dispensing the drugs (min) | 20.1 ± 4.1 |
| Pharmacists (/day) | 18.5 ± 2.3 |
| Total number of prescriptions (/day) | 999.4 ± 112.7 |
| One-dose packages (/day) | 30.0 ± 4.1 |
| Powdered drugs (/day) | 63.9 ± 17.0 |
| Solution drugs (/day) | 10.0 ± 4.1 |
| number of medications (/prescription) | 3.69 ± 0.17 |
| Nnumber of prescription days (day) | 37.8 ± 1.9 |
| Prescription/pharmacist ratio | 54.6 ± 7.5 |
Analysis of the factors affecting the dispensing time
Univariate analysis revealed that the P/P ratio (r = 0.532) and number of ODP prescriptions (r = 0.441), number of medications per prescription (r = 0.304) and powdered drugs (r = 0.293) significantly affected the dispensing time. The total number of prescriptions was excluded from the multivariate analysis because the P/P ratio, which had a higher correlation coefficient, was included instead. A bivariate correlation matrix for the factors included in the multiple regression analysis is presented in Table 2. No high correlations between factor pairs were observed.
Table 2.
Bivariate Correlation Matrix of Factors Included in the Multiple Regression Analysis.
| Powdered drugs | ODP | Number of medications | P/P ratio | |
|---|---|---|---|---|
| powdered drugs | 1 | 0.368 | 0.270 | 0.160 |
| ODP | 0.368 | 1 | 0.353 | 0.309 |
| Number of medications | 0.270 | 0.353 | 1 | 0.232 |
| P/P ratio | 0.160 | 0.309 | 0.232 | 1 |
P/P ratio: Prescription/pharmacist ratio, ODP: One-dose packages.
Multivariate analysis revealed that the P/P ratio (p < 0.001), number of ODP prescriptions (p < 0.001), and powdered drugs (p = 0.02) influenced the dispensing time. In contrast, the number of medications per prescription was not significant (p = 0.095). The VIF value was 1.128 for the P/P ratio, 1.326 for ODP prescriptions, and 1.188 for powdered drugs. The adjusted R-squared was 0.3748, and the F-statistic was 49.75 with a p-value of <0.001.
The standardized regression coefficients were 1.717, 0.969, and 0.465, respectively, and the contribution of the number of ODP prescriptions to dispensing time was second only to that of the P/P ratio (Table 3). Sensitivity analyses without adjustment for the p/p ratio yielded similar findings.
Table 3.
Univariate and multivariate analyses of the factors affecting the dispensing time.
| Univariate |
Multivariate |
||||||
|---|---|---|---|---|---|---|---|
| R | 95 % CI | P value | Coefficient | 95 % CI | β | P value | |
| Pharmacists (/day) | −0.174 | −0.292 to −0.0512 | <0.01 | NA | |||
| Total number of prescriptions (/day) | 0.476 | 0.373–0.568 | <0.001 | NA | |||
| One-dose packages (/day) | 0.441 | 0.335–0.536 | <0.001 | 0.137 | 0.072–0.203 | 0.969 | <0.001 |
| Powdered drugs (/day) | 0.293 | 0.175–0.402 | <0.001 | 0.027 | 0.001–0.054 | 0.465 | <0.05 |
| Solution drugs (/day) | 0.091 | −0.0339 to 0.213 | 0.153 | NA | |||
| Number of medications (/prescription) | 0.304 | 0.186–0.414 | <0.001 | 2.274 | −0.397 to 4.946 | 0.378 | 0.095 |
| Prescription days (day) | 0.160 | 0.0365–0.279 | <0.05 | ||||
| Prescription/pharmacist ratio | 0.532 | 0.436–0.617 | <0.001 | 0.230 | 0.172–0.288 | 1.718 | <0.001 |
| Intercept | −6.728 | −16.01 to 2.55 | 0.154 | ||||
R: correlation coefficient, CI: confidence interval, P/P ratio: Prescription/pharmacist ratio, β: Standardized regression coefficient.
Evaluation of the efforts made to reduce the dispensing time
To reduce dispensing time, one pharmacist was assigned to perform final checks of ODP prescriptions only, while other pharmacists only performed final checks of non-ODP prescriptions from December 1, 2021 (Fig. 4). Under this new strategy, when no ODP prescriptions are pending, the pharmacist assigned to ODP checks assists with non-ODP prescriptions. However, upon receiving any ODP prescriptions, their priority immediately shifts back to ODP verification. As our pharmacy consistently receives ODP prescriptions, this pharmacist primarily focuses on ODP checks. In cases where multiple ODP prescriptions were concurrently available, and the final verification process was identified as time-consuming, a pharmacist, typically responsible for the final check of non-ODP prescriptions, would also address ODP prescriptions as required. The years of experience for pharmacists (Years after graduation) did not differ significantly before and after the intervention (Before: 16.4 ± 11.5 years, After: 19.50 ± 12.16 years, p = 0.334). Therefore, this variable was excluded from subsequent analyses.
Fig. 4.
Flow of the Drug Dispensing Process in the Pharmacy Post-Improvement (new ODP-checking strategy).
* If no ODP prescriptions are pending, this pharmacist assists with “Prescription rechecking / Rechecking dispensed drug.”
A total of 250 dispensing data points were collected before the effort, and 245 data points were collected after the effort. Matched data sets were generated, resulting in 159 data points for each group. The matched data are presented in Table 4. The matched data were created based on propensity scores calculated for the P/P ratio, ODP prescriptions, and powdered drugs. A significant reduction in dispensing time was observed before and after making an effort (p < 0.001). The percentage of dispensing times below 20 min also decreased considerably (p = 0.001). The sensitivity analysis using the Mann–Whitney U test revealed a similar finding. The effort was associated with under 20-min dispensing time with an odds ratio of 2.96 (95 % CI, 1.67–5.22; p < 0.001; E-value, 3.86).
Table 4.
Matched data before and after making the effort.
| Before (n = 159) | After (n = 159) | P value | |
|---|---|---|---|
| Mean time for dispensing the drugs (min) | 20.0 ± 4.0 | 18.5 ± 3.6 | <0.001 |
| Mean time for dispensing the ODP drugs (min) | 32.3 ± 5.7 | 32.7 ± 6.4 | 0.568 |
| Mean time for dispensing the non-ODP drugs (min) | 20.1 ± 4.1 | 18.4 ± 3.6 | <0.001 |
| Mean time for dispensing the drugs less than 20 min (%) | 88 (55.7) | 117 (73.6) | <0.001 |
| Pharmacists (/day) | 18.9 ± 2.2 | 19.4 ± 2.6 | 0.076 |
| Total number of prescriptions (/day) | 1001 ± 111 | 1025 ± 120 | 0.07 |
| One-dose packages (/day) | 31.3 ± 17.4 | 31.6 ± 16.6 | 0.655 |
| Powdered drugs (/day) | 62.8 ± 117.2 | 64.3 ± 116.0 | 0.438 |
| Solution drugs (/day) | 10.2 ± 14.3 | 10.2 ± 13.9 | 0.936 |
| number of medications per prescription | 3.69 ± 0.17 | 3.51 ± 0.22 | <0.001 |
| Nnumber of prescription days (day) | 37.76 ± 1.82 | 37.32 ± 2.15 | <0.05 |
| Prescription/pharmacist ratio | 53.4 ± 16.9 | 53.6 ± 18.7 | 0.864 |
Discussion
This study revealed that ODP prescriptions were an independent risk factor for prolonged dispensing times, contributing more significantly than the number of solutions and powdered drug prescriptions. Appropriate intervention in ODP processes reduced the dispensing time not only for ODP prescriptions but also for non-ODP prescriptions, indicating that ODP prescriptions can affect overall dispensing efficiency.
Although the dispensing time for powdered and solution drugs is generally longer due to manual preparation, their effect on final checking time was smaller than that of ODP prescriptions. This is likely because ODP verification requires pharmacists to check individual tablet imprints, making the process time-consuming. This is attributed to the necessity of confirming the imprint on every single tablet, across all packages, for each day of administration. Consequently, the verification process requires checking the number of drugs multiplied by the number of packages and the number of administration days, which significantly exceeds the time required for non-ODP prescriptions, typically dispensed in PTP sheets (often 10 or 14 tablets/sheet). It was thus considered that having multiple pharmacists simultaneously perform ODP prescription verification would create a bottleneck for the entire dispensing room. The simultaneous verification of multiple ODP prescriptions by several pharmacists reduced the availability of pharmacists for checking non-ODP prescriptions, thereby lowering overall efficiency.
Previous studies have reported that automated dispensing systems, including A-ODP and CPOE, can improve dispensing efficiency and patient safety.9., 10., 11., 12.,17 Furushima et al. predicted that A-ODP would reduce the ODP waiting time from 23.1 to 11.5 min.18 However, our study suggested that ODP prescriptions may not only prolong their own dispensing time but also, indicating that the implementation of A-ODP alone might not be sufficient to fully mitigate the impact of ODP-related delays. In Japanese pharmacies, checking time has been shown to have a greater impact on dispensing time than preparing time. Moreover, congestion affects waiting time primarily by prolonging checking time rather than preparing time.19 Our findings align with this, as interventions in ODP verification led to a reduction in overall waiting time.
The study was conducted at a large hospital pharmacy with a relatively low out-of-hospital prescription rate. The P/P ratio was approximately 55, meeting the standard placement criteria for hospital pharmacists in Japan.20 Since many hospitals operate under similar staffing conditions, our findings are likely applicable to other hospital pharmacies facing increased demands for interpersonal services and operational efficiency.
According to a systematic review, automation systems in pharmacies reduce preparing time by an average of 19 to 49 s.21 In our study, despite the implementation of A-ODP, the dispensing time for all prescriptions, including non-ODP prescriptions, was reduced by approximately 90 s. This suggests that simply introducing automation is not sufficient for efficiency gains; rather, optimizing the workflow to align with automation systems is essential. Identifying factors that influence dispensing time and optimizing pharmacist allocation remains meaningful even in an era of increased pharmacy automation. Specifically, our findings underscore the critical need for pharmacies to develop tailored strategies for ODP verification, potentially by assigning dedicated pharmacists to ODP checks or implementing specialized software/hardware for rapid imprint verification, to minimize the bottleneck effect. This targeted approach not only streamlines ODP processing but also frees up other pharmacists to focus on non-ODP prescriptions, thereby enhancing overall workflow efficiency and reducing patient wait times. Enhancing operational efficiency can improve patient satisfaction by reducing dispensing time and may also allow pharmacists to devote more time to patient care and other clinical responsibilities beyond dispensing. Since pharmacist involvement in clinical practice contributes to reducing medication errors and improving patient outcomes, improving dispensing efficiency could support the expansion of pharmacists' clinical roles.
This study has several limitations. First, the study employed an uncontrolled before-and-after design, which inherently introduces the potential for confounding factors and various biases, notably from staff. Specifically, we acknowledge that the impact of differences in pharmacists' experience levels could not be entirely ruled out, given staff turnover within the pharmacy and the accumulation of one year's experience by existing pharmacists. Furthermore, the influence of the Hawthorne Effect also could not be eliminated due to this design. Second, the single-center setting significantly limits the generalizability of the findings, as the specific operational environment and patient population may not be representative of other pharmacies. Therefore, to confirm and generalize these results, future multicenter studies with more diverse settings and robust designs are strongly recommended. Third, the outcome variable was based on mean dispensing time per day, which does not account for variations in the shortest and longest dispensing times. Additionally, while propensity score matching was used to minimize bias, external factors such as pharmacy workload and patient volume might not have been fully adjusted for.
This study clarified that ODP prescriptions significantly impact dispensing time and overall operational efficiency. Optimizing the ODP verification workflow improved dispensing efficiency without increasing the number of pharmacists. These findings suggest that prioritizing ODP prescriptions and implementing support tools for final checks are crucial for improving operational efficiency in the future, even in pharmacies with advanced automation systems. These findings, while promising, necessitate further investigation in multicenter studies with more rigorous designs to generalize these results across different settings.
Conclusions
Optimizing the ODP verification workflow enhances dispensing efficiency without increasing pharmacist workload, highlighting the importance of prioritizing ODP prescriptions and implementing support tools for final checks, while further multicenter studies are needed to confirm these findings across diverse settings.
Ethics approval and consent to participate
Not applicable. As we aimed to improve the workflow and use real-world daily work data, ethics approval or informed consent was not required, as decided by the ethics board of Aichi Medical University.
Consent for publication
Not applicable.
Availability of data and materials
All data generated or analyzed during this study are included in this published article.
A statement to confirm that all methods were carried out in accordance with relevant guidelines and regulations
All methods were carried out in accordance with relevant guidelines and regulations.
Funding
The authors declare that they have no conflicts of interest.
CRediT authorship contribution statement
Takahiro Kato: Writing – review & editing, Methodology, Data curation, Writing – original draft, Investigation, Validation, Formal analysis. Miki Kato: Data curation. Kazuyo Nagashiba: Data curation. Masayuki Takeuchi: Data curation, Formal analysis. Masafumi Onishi: Supervision, Funding acquisition.
Declaration of generative AI and AI-assisted technologies in the writing process
During the preparation of this work the authors used Chat GPT in order to improve language and readability. After using this service, the authors reviewed and edited the content as needed and take full responsibility for the content of the publication.
Declaration of competing interest
The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.\
Acknowledgments
We would like to thank Editage (www.editage.com) for the English language editing.
Contributor Information
Takahiro Kato, Email: takahirokato207@gmail.com.
Miki Kato, Email: katou.miki.860@mail.aichi-med-u.ac.jp.
Kazuyo Nagashiba, Email: nagashiba.kazuyo.571@mail.aichi-med-u.ac.jp.
Masayuki Takeuchi, Email: takeuchi.masayuki.530@mail.aichi-med-u.ac.jp.
Masafumi Onishi, Email: oonishi.masafumi.565@mail.aichi-med-u.ac.jp.
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Associated Data
This section collects any data citations, data availability statements, or supplementary materials included in this article.
Data Availability Statement
All data generated or analyzed during this study are included in this published article.