EVALUATION OF PATIENTS' PERCEPTION OF
A BLOCKCHAIN SYSTEM FOR PRESCRIPTION
MANAGEMENT
EVALUACIÓN DE LA PERCEPCIÓN DE LOS PACIENTES
SOBRE UN SISTEMA DE BLOCKCHAIN PARA LA GESTIÓN
DE RECETAS MÉDICAS
Ricardo Burciaga Alarcón
Universidad Autónoma de Coahuila - México
Laura Cristina Vázquez de los Santos
Universidad Autónoma de Coahuila - México
pág. 9479
DOI: https://doi.org/10.37811/cl_rcm.v8i4.13105
Evaluation of patients' perception of a blockchain system for prescription
management
Ricardo Burciaga Alarcón
1
rburciaga@uadec.edu.mx
https://orcid.org/0000-0001-9711-6824
Universidad Autónoma de Coahuila
México
Laura Cristina Vázquez de los Santos
Laura_vazquez@uadec.edu.mx
https://orcid.org/0000-0002-0291-7774
Universidad Autónoma de Coahuila
México
ABSTRACT
The management of medical prescriptions without computational system can result in errors and security
issues. This study, conducted in Monclova, Coahuila, evaluates the perception and disposition of patients
from public and private health services towards implementing a secure computational system to improve
prescription and medical record management. A survey of 68 adults, using a mixed research
methodology. The results indicate that a secure and low-cost computational system would have positive
acceptance among the population, thereby validating the researcher's hypothesis. In conclusion, it was
determined that a secure computational system is a feasible solution to improve the management of
medical prescriptions, would be accepted by most of the patients, and could serve as a basis for future
developments in other areas of the healthcare sector. The literature on blockchain in prescription
management supports the system’s benefits. A 10% margin of error was used to maintain validity with
a smaller sample size.
Keywords: blockchain, prescription management, medical record management, patient disposition
1
Autor Principal
Correspondencia: rburciaga@uadec.edu.mx
pág. 9480
Evaluación de la percepción de los pacientes sobre un sistema de blockchain
para la gestión de recetas médicas
RESUMEN
La gestión de recetas médicas sin un sistema computacional puede dar lugar a errores y problemas de
seguridad. Este estudio, realizado en Monclova, Coahuila, evalúa la percepción y disposición de los
pacientes de servicios de salud públicos y privados hacia la implementación de un sistema
computacional seguro para mejorar la gestión de recetas y expedientes médicos. Se realizó una encuesta
a 68 adultos utilizando una metodología de investigación mixta. Los resultados indican que un sistema
computacional seguro y de bajo costo tendría una aceptación positiva entre la población, validando así
la hipótesis del investigador. En conclusión, se determinó que un sistema computacional seguro es una
solución factible para mejorar la gestión de recetas médicas, sería aceptado por la mayoría de los
pacientes, y podría servir como base para futuros desarrollos en otras áreas del sector salud. La literatura
sobre blockchain en la gestión de recetas respalda los beneficios del sistema. Se utilizó un margen de
error del 10% para mantener la validez con un tamaño de muestra reducido.
Palabras clave: blockchain, gestión de recetas, gestión de expedientes médicos, disposición del paciente
Artículo recibido 10 julio 2024
Aceptado para publicación: 15 agosto 2024
pág. 9481
INTRODUCTION
The management of medical prescriptions includes the issuance, control, and follow-up of treatments
prescribed to patients. In Monclova, Coahuila, the lack of an integrated system to manage these
prescriptions in both public and private services can cause prescription errors and security issues. This
study evaluates how patients perceive and accept a secure computational system that improves
efficiency and security in the management of medical records and prescriptions, considering a low or
zero cost.
Previous studies have shown that information technologies improve prescription management, but the
adoption of blockchain in this field is still limited (Goundrey-Smith, 2008; Joshi et al., 2016; Hölbl et
al., 2018). Research on blockchain in prescription management is limited in Mexico (Gupta, 2018;
Arroyo et al., 2019). This study contributes to the literature by evaluating the feasibility and
effectiveness of blockchain in healthcare systems in Mexico.
Access control, interoperability, provenance, and data integrity are issues that can be improved with
blockchain technology in the healthcare sector (Hasselgren et al., 2020). Vera (2020), defines a medical
prescription as a written order to dispense medication. Goundrey-Smith (2008), asserts that electronic
prescriptions improve communication in medication orders. Reddy and Aggarwal (2015) point out that
an electronic prescription facilitates data management in clinical settings. Joshi et al. (2016) found that
handwritten prescriptions have more errors, while computerized ones have fewer.
Wager et al. (2017) indicate that healthcare providers face issues with interoperability, usability, and
security in information technologies. Gupta (2018) highlights the need for efficient and secure systems
to manage medical records. Fernández and Quesada (2017) emphasize that ICTs facilitate the provision
of information and communication in healthcare.
Information technology has transformed healthcare services globally, promoting connectivity and rapid
information exchange among experts (Kumari, 2022). Blockchain allows for secure and transparent
information recording (Álvarez, 2018; Holbrook, 2020). Gupta (2018) mentions that blockchain
emerged to create a secure financial system. Arroyo et al. (2019) highlight that blockchain was
developed alongside bitcoin to ensure transaction trust.
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Blockchain ensures information integrity with cryptographic protocols, using tools such as hashing
(Arroyo et al., 2019). A hash is an algorithm that generates a fixed-length output from an input;
additionally, blockchain uses asymmetric cryptography with public and private keys (Rojo, 2018).
According to Kaur et al. (2023, pp. 216-218), the nature of blockchain, allows for greater security,
transparency, and decentralization in transactions.
Blockchain provides a secure and immutable record of medical prescriptions, enhancing information
efficiency and security in the healthcare system. Its implementation in prescription management requires
detailed planning and analysis. In Monclova, Coahuila, the healthcare system faces challenges in
managing medical prescriptions. Blockchain technology is promising but rarely used in this field in
Mexico, creating a gap in prescription management.
According to Martos et al. (2006, p. 423), experts from the World Health Organization state that medical
records should clearly identify the person, be reliable, concise, logically organized, consistent, resistant
to deterioration, uniform in forms, identify those who make entries, and be easily accessible when
needed.
Medical history is a data record that facilitates diagnosis and planning of recovery actions (Ornelas,
2013). Currently, in Monclova, there is no computerized system to access prescription histories.
How does the implementation of a blockchain system influence patient willingness to use it and access
medical information in Monclova, considering a low or zero cost? Blockchain decentralizes information
and ensures transparent record management (Holbrook, 2020). Although its adoption in the healthcare
sector in Mexico is in its early stages, this research is relevant for advancing the implementation of
emerging technologies and addressing similar issues in the country.
This research aims to generate knowledge about the feasibility and applicability of a blockchain-based
computational system for the management of medical prescriptions in Monclova, Coahuila, from the
patients' perspective.
METHODS
The research design was a mixed qualitative-quantitative approach, descriptive in nature, with the
intention of deeply understanding the problem and its practical solutions. The following shows the
formula for calculating sample size for finite populations includes n as the sample size, N as the total
pág. 9483
population size, Z as the confidence level, p as the expected proportion, q as its complement, an e as the
margin of error.
(1)
In this research, the population to which the survey was applied consisted of 116,060 adults living in the
city of Monclova, Coahuila. The sample consisted of 68 individuals. A margin of error of 10% instead
of 5% reduced the sample size to 68 subjects. This decision was justified for several reasons: time and
budget constraints prevented a larger sample, the study sought preliminary results to guide future
research, and in exploratory studies, a higher margin of error was acceptable. Despite the higher margin
of error, the results still provided valuable information to assess the feasibility of implementing
blockchain in prescription management in Monclova, Coahuila, and could serve as a basis for more
detailed studies.
The sampling was quantitative and simple random. The methodological design was pre-experimental
with only a post-test and one group. The technique used was the survey, and the instrument was a
questionnaire with a priori categories and a 5-point Likert scale. The results were entered into SPSS
software.
The questionnaire was validated through a content validation process, where experts reviewed the
questions to ensure their relevance and clarity. Additionally, a pilot test was conducted with 10
participants to identify problems and adjust the questionnaire. The pilot test revealed that some questions
needed restructuring to improve comprehension. After the adjustments, the final questionnaire was
considered valid and reliable for the main research.
The alternative and null hypotheses were:
H1: If the importance of the medical history was recognized and a system with blockchain technology
was implemented in the management of medical prescriptions in the city of Monclova, Coahuila, then
patient willingness to use the system would increase and access to medical information would improve,
as long as the application cost was low or zero.
H0: If the importance of the medical history was not recognized and a system with blockchain
technology was not implemented in the management of medical prescriptions in the city of Monclova,
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Coahuila, then patient willingness to use the system would not increase nor would access to medical
information improve, regardless of the application cost.
The methodological process used in the research included the hypothesis validation method to analyze
the empirical data generated during the implementation of blockchain technology in the healthcare
system. A mixed approach was adopted, combining qualitative and quantitative methods. In the
qualitative part, patients' opinions were analyzed through surveys, while in the quantitative part,
variables related to operational efficiency and information security were measured. The surveys,
consisting of 10 questions, were designed to capture the perception of patients from health centres about
the implementation of a secure computational system for managing medical prescriptions.
The statistical analysis included techniques such as normality analysis, variable correlation, and factor
analysis. The researcher's hypothesis was evaluated using a Student's t-test with a 5% significance level.
Additionally, frequency and percentage analyses were conducted to interpret the opinions of respondents
on the studied variables, along with a descriptive analysis to understand data behavior through measures
of central tendency. Data normality was verified using the central limit theorem and graphs to visualize
data distribution. The Pearson correlation coefficient was used to examine the relationship between pairs
of significant variables, and a multidimensional factor analysis was conducted to explore the underlying
structure of the relationships among the analyzed variables.
RESULTS
The variables used in this research are shown in Table 1.
Table 1 Study and measurement variables
No.
ABBREVIATED
VARIABLE
IMPORTANCE
OF THE
RESEARCHER
VARIABLES
1 AMI 1
Access to Medical
Information
2 CA 3
Cost of the
Application
3 WUS 2
Willingness to Use the
System
4 IAPH
Immediate Access to
Prescription History
5 IS 5
Information Security
6 PDA
Preference for Digital
Access
7 UMI
Update of Medical
Information
8 ICPC 4
Importance of Cross-
Platform Compatibility
9 IP
Information Privacy
10 SAM
Security Against
Modifications
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Statistical data processing
Significance level. Denoted as (α) and set at 0.5% equal to 0.05.
Instrument reliability and reliability analysis. The formula to calculate Cronbach's alpha is as follows:
(2)
The value obtained with Cronbach's alpha is 0.705 (acceptable). The set of variables measured a latent
and unidimensional aspect of the individuals surveyed using the measurement instrument applied to ten
variables and 68 subjects, based on the application of the questionnaire and the Likert scale (5). These
values were determined using SPSS.V25. Statistical tests on the data. Samples from 68 subjects were
analysed based on the research instrument.
Hypothesis testing. A parametric test called Student's t-test was performed with a significance level of
5% = 0.05, where all p-values were less than 0.05, thus rejecting the null hypothesis H0 and accepting
the research hypothesis H1.
The one-sample Student's t-test was conducted to determine if the means of the evaluated variables are
significantly different from the reference value, which in this case is 3. This reference value was chosen
because it represents a neutral point on the Likert scale used in the questionnaire.
Table 2 One-Sample Student’s T-test
Lower Upper
AMI 26.816 67 0 1.60294 1.4836 1.7223
CA 17.263 67 0 1.41176 1.2485 1.575
WUS 15.736 67 0 1.25 1.0914 1.4086
IAPH 9.881 67 0 1.04412 0.8332 1.255
IS 13.014 67 0 1.20588 1.0209 1.3908
PDA 6.726 67 0 0.86765 0.6102 1.1251
UMI 6.669 67 0 0.85294 0.5977 1.1082
ICPC 12.033 67 0 1.10294 0.92 1.2859
IP 5.951 67 0 0.79412 0.5278 1.0605
SAM 20.346 67 0 1.60294 1.4457 1.7602
Test Value = 3
t
Df
Sig. (2-
tailed)
Mean
Difference
95% Confidence
Interval of the
Difference
According to Table 2, all t values are positive and high, indicating that the means of all variables are
significantly greater than 3. The p values (Sig. (2-tailed)) are all 0.000, confirming that the observed
differences are statistically significant. The confidence intervals do not include the reference value (3)
in any case, reinforcing the significance of the differences. Since all p values are less than 0.05, the null
pág. 9486
hypothesis (H0) is rejected, and the research hypothesis (H1) is accepted for all variables. This suggests
that the perceptions and attitudes of the respondents towards the evaluated variables are significantly
positive, as the means of all variables are higher than the neutral point of 3 on the Likert scale used.
Frequency and percentage calculation. The conclusions of the analysis of the ten analysed variables were
compared with the relevance that each subject assigned to the questionnaire responses to be objectified
and measured according to the research purposes, as can be seen in Tables 3, 4 and 5.
Table 3 Frequency analysis
Access to Medical
Information
Cost of the Application
Willingness to Use the
System
Immediate Access to
Prescription History
Information Security
Preference for Digital
Access
Update of Medical
Information
Importance of Cross-
Platform Compatibility
Information Privacy
Security Against
Modifications
Total
1 Irrelevant 0 0 0 0 1 4 2 1 3 0 11
2 Slightly relevant 0 0 0 0 1 2 7 1 7 0 18
3 Moderately relevant 0 7 8 24 5 13 10 7 10 6 90
4 Very relevant 27 26 35 17 37 29 29 40 29 15 284
5 Totally relevant 41 35 25 27 24 20 20 19 19 47 277
Total 68 68 68 68 68 68 68 68 68 68 680
Table 4 Percentage analysis
Access to Medical
Information
Cost of the Application
Willingness to Use the
System
Immediate Access to
Prescription History
Information Security
Preference for Digital Access
Update of Medical
Information
Importance of Cross-Platform
Compatibility
Information Privacy
Security Against
Modifications
1 Irrelevant 0% 0% 0% 0% 1% 4% 2% 1% 3% 0%
2 Slightly relevant 0% 0% 0% 0% 1% 2% 7% 1% 7% 0%
3 Moderately relevant 0% 7% 8% 25% 5% 14% 10% 7% 10% 6%
4 Very relevant 28% 27% 36% 18% 39% 30% 30% 42% 30% 16%
5 Totally relevant 43% 36% 26% 28% 25% 21% 21% 20% 20% 49%
Total 71% 71% 71% 71% 71% 71% 71% 71% 71% 71%
Table 5 General frequency and percentage
Number on
the scale
Description of the
scale
Frequency
of counted
opinions
Total
percentage
68
opinions
given
10
questions
to 68
subjects
1 Irrelevant 11 2%
2 Slightly relevant 18 3%
3 Moderately relevant 90 13%
4 Very relevant 284 42%
5 Totally relevant 277 41%
Total 680 100%
The frequency and percentage analysis showed how the surveyed subjects expressed their opinions
about the analysed variables. The values in the cells indicate how many subjects selected each level of
relevance for each variable. The General Frequency and Percentage Table provides an overall summary
pág. 9487
of how many times each level of relevance was selected across the entire survey. The "Very relevant"
level was selected 284 times, representing 42% of the total responses.
Calculation of descriptive statistics
Table 6 Descriptive statistic table and measures of central tendency
AMI CA WUS IAPH IS PDA UMI ICPC IP SAM
Mean 4.6 4.41 4.25 4.04 4.21 3.87 3.85 4.1 3.79 4.6
Median 5 5 4 4 4 4 4 4 4 5
Mode 5 5 4 5 4 4 4 4 4 5
Std.
Deviation
0.49 0.67 0.65 0.87 0.76 1.06 1.05 0.76 1.1 0.65
Range 1 2 2 2 4 4 4 4 4 2
Minimum 4 3 3 3 1 1 1 1 1 3
Maximum 5 5 5 5 5 5 5 5 5 5
Statistics
The analysis of the ten variables indicated that Access to Medical Information (V1), Cost of the
Application (V2), Willingness to Use the System (V3), Immediate Access to Prescription History (V4),
Information Security (V5), Importance of Cross-Platform Compatibility (V8), and Security Against
Modifications (V10) obtained high average values on the Likert scale (4.60, 4.41, 4.25, 4.04, 4.21, 4.10,
and 4.60 respectively), indicating their high relevance. The remaining variables showed average values,
at a moderately relevant level. The median also reflected the importance of these variables, with high
values indicative of their relevance. The standard deviations suggest moderate dispersion of the data
around the mean.
Most opinions were in a mid to high range on the measurement scale, confirming the Access to Medical
Information (AMI) in relation to the patients' willingness to use the system as long as the application
cost is low or zero. In calculating these data, values ranged from Minimum (Irrelevant) to Maximum
(Very relevant).
Data normality (Central Limit Theorem). For this statistical test, the averages obtained from the ten
variables used in the study were considered.
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Table 7 Table of variable averages
VARIABLE AVERAGE
AMI 4.6
CA 4.41
WUS 4.25
IAPH 4.04
IS 4.21
PDA 3.87
UMI 3.85
ICPC 4.1
IP 3.79
SCS 4.6
Average of averages 4.17
From the above data, the normality graph was obtained, as shown in Fig. 1.
Fig. 1. Normality graph of the variable averages at plus/minus one sigma
According to the distribution of points, all variables were considered "normal." Additionally, variables
V1, V2, V3, V5, V8, and V10 were identified as significant in relation to the proposed hypothesis.
Pearson correlation analysis
Table 8 Pearson correlation table
AMI CA WUS IAPH IS PDA UMI ICPC IP SAM
AMI 1
CA -0.219 1
WUS 0.081 0.169 1
IAPH 0.007 0.07 -0.15 1
IS -0.057 0.268 0.015 0.053 1
PDA -0.187 0.452 0.22 0.135 0.273 1
UMI -0.171 0.38 0.227 0.202 0.168 0.475 1
ICPC 0.071 -0.055 0.219 0.152 0.092 0.296 0.15 1
IP -0.07 0.518 0.114 0.337 0.175 0.729 0.437 0.116 1
SAM -0.127 0.242 0.026 0.401 0.107 0.204 0.306 0.054 0.322 1
Correlations
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Interpretation of values from the correlation table
The Pearson analysis revealed positive correlations between the Cost of the Application (CA) and
Patient Preference for Digital Access (PDA) with a correlation of (r = 0.452), as well as between the
Cost of the Application (CA) and Concern for Information Privacy (IP) with a correlation of (r = 0.518).
These results show that as the cost of the application decreases, patients are more inclined to adopt
digital systems for accessing their prescriptions and medical records. Additionally, a lower cost is
associated with a higher value placed on information privacy, indicating that patients seek not only
economically accessible solutions but also secure and reliable ones.
The Pearson correlation of (r = 0.401) between Immediate Access to Prescription Histories (IAPH) and
Security Against Modifications (SAM) indicates a moderate positive relationship. This suggests that
improving immediate access to prescription histories also tends to enhance security against
modifications.
The Pearson analysis revealed positive correlations between Preference for Digital Access (PDA) and
two additional variables. First, a moderate positive correlation (r = 0.475) was found between Preference
for Digital Access and Update of Medical Information (UMI). This suggests that patients who prefer
digital access also value the timely updating of their medical information, indicating an expectation that
the digital system will be efficient and keep the information current. Additionally, a strong positive
correlation (r = 0.729) was observed between Preference for Digital Access (PDA) and Information
Privacy (IP). This implies that patients who show a high preference for digital access also have a high
concern for the privacy of their medical information. The strong correlation indicates that privacy is a
critical factor for patients when considering the use of digital systems to manage their medical data.
The Pearson analysis showed a moderate positive correlation (r = 0.437) between Update of Medical
Information (UMI) and Information Privacy (IP). This indicates that patients who value the constant
updating of their medical information also tend to be concerned about the privacy of their data.
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Factor analysis
Table 9 Principal component analysis table
Total
% of
Variance
Cumulativ
e %
Total
% of
Variance
1 3.064 30.641 30.641 3.064 30.641
2 1.344 13.439 44.081 1.344 13.439
3 1.247 12.465 56.546 1.247 12.465
4 0.946 9.46 66.006
5 0.883 8.832 74.838
6 0.794 7.941 82.778
7 0.577 5.772 88.55
8 0.498 4.979 93.529
9 0.457 4.566 98.095
10 0.19 1.905 100
Total Variance Explained
30.641
44.081
56.546
Component
Initial Eigenvalues
Cumulative %
Extraction Sums of Squared Loadings
The table obtained from the Factor Analysis indicates that three factors with eigenvalues greater than 1
have been generated, according to Kaiser's criterion. These factors explain 56.546% of the total variance
of the phenomenon indicated in this study. Despite the identification of three factors, the review suggests
that only two can be considered as valid constructs for the final analysis.
Table 10 Factor component matrix of the study phenomenon indicating 3 factors
1 2
AMI -0.253 0.231
CA 0.678 0.032
WUS 0.265 0.712
IAPH 0.389 -0.592
IS 0.381 0.067
PDA 0.817 0.226
UMI 0.702 0.069
ICPC 0.274 0.384
IP 0.822 -0.054
SAM 0.514 -0.471
3
0.565
-0.423
0.029
0.21
Component
0.107
0.525
-0.174
-0.035
-0.013
0.62
After discrimination in the previous table, considering only factor loadings greater than 0.5, a table is
presented that shows the arrangement of the three obtained factors, indicating the variables involved in
each factor.
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Table 11 Factor component table of the phenomenon using fact loading greater than 0.5.
Variable Factor Factor Loading
CA 0.678
PDA 0.817
UMI 0.702
IP 0.822
SAM 0.514
WUS 2 0.712
AMI 0.565
IS 0.525
ICPC 0.62
1
3
The constructs (hypotheses) generated are as follows
Construct 1: Factor 1 reflects the acceptance and positive perception of a digital system for managing
medical information, based on five key variables: Cost of the Application (CA), Preference for Digital
Access (PDA), Update of Medical Information (UMI), Information Privacy (IP), and Security Against
Modifications (SAM). A low or zero cost increases patients' willingness to use the system. The
preference for digital access is based on ease of use and convenience. Constant updating of medical
information is crucial for effectiveness. Privacy and security against modifications are essential for
patient trust. These variables reinforce each other, driving the acceptance of the digital system.
Construct 2: Factor 3 reflects the acceptance of a digital system for managing medical information,
based on three key variables: Access to Medical Information (AMI), Information Security (IS), and
Cross-Platform Compatibility (ICPC). Access to medical information ensures that patients and
professionals obtain relevant data in a timely manner. Information security protects the privacy and
integrity of data, preventing unauthorized access and modifications. Cross-platform compatibility
ensures that the system works on various devices and operating systems, increasing its accessibility.
These variables are interrelated, as a secure and compatible system facilitates access to medical
information, reinforcing trust in its use and creating a robust and reliable digital system.
Although the factor analysis suggested a second factor with an eigenvalue greater than 1, only one
variable (WUS: Willingness to Use the System) has a significant factor loading (0.712). The second
variable (Importance of Cross-Platform Compatibility) has a factor loading (0.384), which does not meet
the minimum threshold to be considered in a robust construct. As observed, the underlying constructs
obtained (factors) have a consistent relationship and confirm the initial hypothesis of this study.
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DISCUSSION
The results of this study provide valuable evidence on the feasibility of implementing a computational
system based on blockchain technology in the management of medical prescriptions. The positive
perception of patients towards security and access to information reinforces the need to consider this
solution in the healthcare sector. The findings are consistent with previous studies that have highlighted
the benefits of secure and accessible technologies in terms of efficiency and data protection (Gupta,
2018; Holbrook, 2020).
Factor analysis identified two main constructs, suggesting that patients' perceptions are grouped around
technological integration and information security. This indicates that any future implementation of a
computational system based on blockchain technology in the healthcare sector should focus on these
aspects to maximize acceptance and effectiveness.
The main limitation of the study is the sample size and the 10% margin of error, which, although
justified, could have influenced the precision of the results. However, the findings provide a solid basis
for future studies that can use larger samples and lower margins of error to validate and expand these
results. It is recommended that future research continues to explore this field, considering the limitations
and strengths identified in this study.
CONCLUSIONS
The study on the integration of a computational system based on blockchain technology for the
management of medical prescriptions in Monclova, Coahuila, from the patients' perspective, generated
important information. The implementation of this system is perceived as a viable solution that improves
information security and access to prescriptions and medical records, as long as the application cost is
low or zero. The empirical data obtained through surveys and analysed with various statistical
techniques support the hypothesis that a secure computational system can effectively address the current
problems in the management of medical prescriptions. Variables related to security, information
updating, and cross-platform compatibility stood out as key factors.
Statistical tests, such as the Student's t-test and Pearson correlation analysis, confirmed that patients'
perceptions of the evaluated variables are significantly positive. Additionally, factor analysis identified
two main constructs underlying these perceptions: one related to technological integration in medical
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information management and another with data security and accessibility. These constructs are
consistent and confirm the initial hypothesis of the study.
Acknowledgments
Appreciation is extended to the Autonomous University of Coahuila for the facilities granted to carry
out this research.
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