Examining the relationship between digital hospital level and patient safety culture: A study on healthcare workers

Abstract

Objective

This study aimed to examine the relationship between digital hospital level and patient safety culture among healthcare professionals and to determine whether digital hospital level is associated with patient safety culture in a hospital setting.

Methods

This cross-sectional descriptive and correlational study was conducted with healthcare professionals working in a university hospital in Turkey. Data were collected from 453 participants between September and November 2025 using a structured questionnaire. Digital hospital level was measured using the Digital Hospital Assessment Scale, which evaluates four dimensions: work and operations, management organization and cost, satisfaction, and patient safety. Patient safety culture was measured using the Turkish version of the Hospital Survey on Patient Safety Culture (TR–HSOPSC 2.0). Descriptive statistics, t-tests, ANOVA, Pearson correlation, and multiple regression analyses were performed.

Results

The findings showed that perceptions of digitalization and patient safety differed according to gender, education level, and professional role, with women reporting more positive views than men. A strong positive correlation was found between digital hospital level and patient safety culture (r = 0.812, p < 0.001), explaining 66% of the variance in digital hospital perceptions (Adjusted R² = 0.663).

Conclusions

The findings suggest that digitalization plays an important role in supporting safe healthcare services. Therefore, investments in digital technologies should be considered part of patient safety strategies. However, differences across professional groups indicate the need to improve the effective use of digital tools and supportive work environments. Future studies incorporating objective clinical data may further clarify the relationship between digitalization and patient safety. However, due to the cross-sectional design of the study, these findings should be interpreted as associations rather than causal relationships.

Keywords

digital health patient safety digital health applications digital hospital

Introduction

Digital health facilities can be defined as organizations that integrate electronic health records and related technologies to support clinical decision-making and administrative processes within a unified system.¹ Current research suggests that the adoption of digital systems is associated with reductions in errors, improved data accuracy, and more timely clinical decision-making, which may contribute to safer healthcare environments.^2,3 Compared to traditional paper-based systems, advanced digital tools may help reduce risks related to miscommunication and prescribing errors.^4,5

The implementation of digital technologies in healthcare settings has been associated with improvements in communication, data accessibility, and coordination across departments, as well as with potential reductions in medical errors.^6,7 Electronic health record (EHR) systems represent a key component of this transformation, as they may support more consistent clinical decision-making and facilitate information sharing among healthcare stakeholders.⁸ However, the effective use of these technologies does not depend solely on technical infrastructure but is also related to healthcare professionals’ attitudes and their willingness to integrate digital tools into routine practice.⁹ In this context, digital systems such as incident reporting tools and early warning mechanisms may assist in identifying potential risks and supporting preventive actions.¹⁰ Recent research has explored the potential of digital technologies in enhancing patient safety, including electronic medication management systems,⁴ clinical decision-support tools for early risk detection,⁵ and the use of electronic health records in relation to quality of care indicators.¹

Digital health technologies and artificial intelligence (AI) applications are increasingly integrated into healthcare systems and are associated with developments in clinical decision-making, data management, and healthcare delivery. Recent studies suggest that digital health systems should be understood not only as technical infrastructures but also as socio-technical systems that interact with organisational structures, healthcare professionals, and clinical workflows.¹¹

Transparency and explainability are important factors in the adoption of artificial intelligence (AI) in healthcare. Explainable AI (XAI) approaches may enhance trust by enabling healthcare professionals to understand how digital systems generate predictions and recommendations.¹² In addition, AI-supported decision-making tools have been associated with improved diagnostic accuracy and may assist healthcare professionals in managing complex clinical information, potentially contributing to safer healthcare practices.¹³

The integration of digital technologies into healthcare has been associated with improvements in communication, organisational learning, and data-driven decision-making. When designed to support human–technology collaboration, digital systems may enhance clinical performance and healthcare quality rather than replace human expertise.¹⁴ At the same time, the rapid expansion of digital health applications has raised important concerns regarding data security, ethical governance, and the responsible use of artificial intelligence. Addressing these issues is considered essential for the safe and sustainable implementation of digital technologies in healthcare settings.¹⁵

The studies conducted indicate that there is a gap in quantitative research examining the association of healthcare’s digital hospital level on patient safety culture, particularly on different aspects of this culture. To address this gap, this research aims to analyze in depth the relationship between the digital development levels of healthcare enterprises and the current state of patient safety culture using empirical information. In this study, the concept of digital hospital level refers to the degree to which hospitals integrate digital technologies into clinical, administrative, and communication processes. While previous studies have largely focused on specific digital tools or isolated health information technologies, there is limited evidence examining the overall level of digital hospital maturity as a comprehensive construct. This study addresses this gap by investigating the association between overall digital hospital level—rather than individual technologies—and patient safety culture. By adopting a more holistic perspective, the study aims to contribute to a better understanding of how digital transformation at the organisational level relates to safety culture in healthcare settings.

The relationship between digital health and patient safety

Digital transformation has become a major priority for healthcare institutions and policymakers, as digital health initiatives increasingly align with the core objectives of modern healthcare systems, including improving service quality, efficiency, and patient safety.¹⁶ Digital health technologies offer several advantages, such as reducing errors during medical treatments, improving the efficiency of healthcare services, accelerating decision-making processes through data analysis, and enabling patients to participate more actively in health management.^7,17 In particular, effective communication between healthcare professionals and patients plays a critical role in enhancing patient safety by reducing risks and optimizing healthcare delivery processes^10,18

Digital transformation in healthcare encompasses various technologies designed to support clinical decision-making and improve healthcare service delivery. These include electronic health records (EHR), clinical decision support systems, automated medication management systems, and mobile health (mHealth) applications.¹⁹ Research indicates that electronic systems used in medication management can significantly reduce prescription errors and improve workflow efficiency, although their direct effect on preventing patient harm may depend on implementation quality and system usability.⁴ With the widespread adoption of electronic health records, clinical data can now be collected, stored, and shared more efficiently across all phases of patient care within a unified digital environment. This shift has encouraged healthcare organizations to reconsider traditional approaches to quality and safety management and to increasingly rely on digital innovations to strengthen patient safety practices.² Integrated features such as automated alerts and safety notification mechanisms embedded in EHR platforms also contribute to safer clinical decision-making and reduce the likelihood of patient harm.^20,21

The digital maturity of healthcare institutions is frequently evaluated using the Electronic Medical Record Adoption Model (EMRAM), developed by the Healthcare Information and Management Systems Society (HIMSS). This model consists of eight stages that assess the degree to which hospitals adopt and effectively use digital technologies, including data sharing, clinical decision support tools, and information security mechanisms.²² Achieving higher EMRAM stages reflects greater digital maturity and indicates more advanced integration of electronic systems within clinical workflows. Hospitals that reach higher levels of digital maturity demonstrate improved performance in safety indicators, particularly in reducing medication errors and supporting data-driven clinical decision-making processes.⁶

Recent technological developments such as artificial intelligence, the Internet of Medical Things (IoMT), real-time patient monitoring systems, and advanced data analytics have further accelerated digital transformation in healthcare. Artificial intelligence–based decision support systems can assist clinicians in making more accurate diagnoses, predicting patient risks, and personalizing treatment decisions, thereby improving patient safety outcomes.^23,24 Similarly, IoMT technologies and integrated digital platforms allow continuous monitoring of patient conditions and enable early detection of potential complications, supporting preventive healthcare models focused on patient safety.²⁵

Digital technologies also play a significant role in improving incident reporting and learning processes within healthcare institutions. Electronic Incident Reporting Systems (EIRS) enable healthcare organizations to systematically identify, analyze, and prevent medical errors through structured reporting mechanisms.^26,27 Evidence suggests that digital reporting tools and mobile health applications can significantly increase the reporting of near-miss events and improve the timeliness of error reporting. For instance, the implementation of an ePRO-based mHealth application in an intensive care unit setting was found to increase near-miss reporting rates by more than two times and significantly improve timely reporting within one hour, contributing to improvements in key dimensions of patient safety culture such as communication openness and non-punitive response to errors.²⁸ Similarly, studies have shown that digital reporting systems strengthen organizational learning and support continuous improvement processes in healthcare institutions.^29,30

Beyond technological infrastructure, the successful implementation of digital systems also depends on organizational factors and the attitudes of healthcare professionals. From the perspective of healthcare workers, particularly nurses, the integration of digital health and telemedicine technologies has been associated with improvements in communication openness, teamwork, leadership support, and organizational learning, all of which are key components of a positive patient safety culture.³¹ Effective leadership and supportive organizational environments can further strengthen this process by creating a climate of trust and encouraging healthcare professionals to engage with safety protocols and digital systems without fear of blame.³²

Despite these benefits, several challenges remain in the effective implementation of digital health technologies in healthcare organizations. Resistance to technological change, insufficient training, technical difficulties, and financial constraints may limit the successful integration of digital tools into clinical workflows.³³ Therefore, continuous workforce education, organizational support, and user-centered system design are essential for maximizing the potential benefits of digital health technologies for patient safety. According to the World Health Organization, ensuring patient safety requires coordinated efforts to establish systems, processes, and organizational cultures that progressively eliminate risks in healthcare environments.³⁴ Digital health technologies play an important role in this process by improving diagnostic accuracy, supporting proactive risk management strategies, and enabling healthcare professionals to anticipate and prevent potential medical errors.^35,36

In addition, digital health systems should be viewed within broader socio-technical and organizational frameworks rather than solely as technological upgrades. For example, Matlin et al.³⁷ emphasize that digital infrastructures, governance mechanisms, and participatory design processes collectively influence the safety and effectiveness of healthcare delivery systems. This perspective highlights that digital transformation contributes not only to technological innovation but also to organizational learning and the development of safer healthcare environments.

Finally, patient safety culture encompasses multiple interrelated elements such as teamwork, open communication, non-punitive error reporting, supportive leadership, and continuous organizational learning.³⁸ Digital health technologies can strengthen these elements by facilitating communication among healthcare professionals, improving access to health information, and supporting incident reporting systems that encourage transparency and accountability.^39,40

Despite the growing body of literature highlighting the role of digital health technologies in improving patient safety and healthcare quality, empirical evidence examining how healthcare professionals perceive the relationship between digital hospital maturity and patient safety culture remains limited. Most previous studies have focused on specific digital tools such as electronic health records, incident reporting systems, or artificial intelligence applications, rather than examining the broader concept of digital hospital level as an organizational characteristic. In addition, relatively few studies have explored how healthcare workers’ perceptions of digital hospital infrastructure relate to the development of patient safety culture within healthcare institutions. Therefore, this study aims to investigate the relationship between digital hospital level and patient safety culture from the perspective of healthcare professionals.

Methods

Type of study

This study was designed as a cross-sectional descriptive and correlational survey conducted among healthcare workers in Turkey.

Population and sample selection

The target population of the study consisted of healthcare professionals working in Turkey. According to 2023 data from the Turkish Ministry of Health, approximately 1,420,000 healthcare workers are employed in the country. However, the study sample was drawn from healthcare workers employed at a university hospital in Konya. The survey used in the study was administered between September and November 2025 using online tools. According to the sample size table developed by Yazıcıoğlu and Erdoğan⁴¹ a minimum sample size of 384 participants is sufficient for a population larger than 1,000,000 at a 95% confidence level and a 5% margin of error.

Data collection tools

Data were collected using a three-part questionnaire.

The first part included questions regarding the participants’ socio-demographic characteristics, such as age, gender, marital status, education level, profession, years of professional experience, monthly income, weekly working hours, patient contact status, and digital hospital training status.

The second part of the questionnaire included the Digital Hospital Assessment Scale, developed by Gokkaya et al..⁴² The validity and reliability of the scale were previously tested, and the Cronbach’s alpha coefficient was reported as 0.92. The scale consists of 30 items and four sub-dimensions:

• Work and Operations (items 1–9)

• Management, Organization and Cost (items 10–18)

• Satisfaction (items 19–24)

• Patient Safety (items 25–30)

The third part of the questionnaire included the Patient safety culture was measured using the Turkish version of the Hospital Survey on Patient Safety Culture (TR–HSOPSC 2.0). The original scale was developed by Sorra et al.⁴³ and was later adapted into Turkish by Filiz and Yeşildağ.⁴⁴ The Turkish version demonstrated acceptable reliability, with Cronbach’s alpha coefficients ranging between 0.72 and 0.82. The scale consists of 32 items and five sub-dimensions:

• Work Area Assessment (A) (items 1–14)

• Manager Assessment (B) (items 15–17)

• Communication Process Assessment (C) (items 18–24)

• Reporting Patient Safety Incidents (D) (items 25–26)

• Overall Hospital Assessment (E) (items 27–32)

Statistical analysis

Statistical analyses were performed using IBM SPSS Statistics version 25.0. Descriptive statistics (frequency, percentage, mean and standard deviation) were used to summarize the data. Cronbach’s alpha coefficients were calculated to evaluate the internal consistency of the scales. The normality of the data distribution was assessed using skewness and kurtosis values.⁴⁵ Pearson correlation analysis was performed to examine the strength and direction of the relationship between digital hospital level and patient safety culture. Multiple linear regression analysis was conducted to examine the association between digital hospital level and patient safety culture. Digital hospital level was entered as the main independent variable, while patient safety culture was treated as the dependent variable. Multicollinearity was assessed using variance inflation factor (VIF) values. To examine the relationship of digital hospital level on patient safety culture, multiple linear regression analysis was conducted. In the regression model, patient safety culture was entered as the dependent variable, while digital hospital level was included as the main independent variable. Demographic variables such as age, gender, education level, profession, years of professional experience, and digital hospital training status were considered as potential control variables. Statistical significance was accepted at p < 0.05.

Ethical aspects of the study

Written informed consent was obtained from all participants prior to data collection. Participants were fully informed about the purpose of the study, the voluntary nature of participation, and their right to withdraw at any time without any consequences. This study, titled “Examining the Relationship Between Digital Hospital Level and Patient Safety Culture: A Study on Healthcare Workers” approved by the Istanbul Medipol University Non-Interventional Ethics Committee (decision dated 08/22/2025, numbered E E-10840098-202.3.02-556). All procedures performed in this study involving human participants were conducted in accordance with the ethical standards of the institutional and/or national research committee and with the 1964 Helsinki Declaration and its later amendments or comparable ethical standards. Participation in the study was voluntary and informed consent was obtained from all participants prior to data collection.

Results

To determine the reliability of the study, Cronbach’s Alpha analysis was performed for each scale. In the analyses, the Cronbach’s Alpha values for the Digital Hospital Assessment Scale and the Patient Safety Hospital Survey (TR–HSOPSC 2.0) were found to be 0.854 and 0.854, respectively. These ratios indicate that the study is reliable.

The descriptive statistics of the participants included in the study are shown in Table 1.

Table 1.

Descriptive findings regarding participants’ demographic characteristics.

Variables		Number	%
Age	18-25	103	22.7
	26-30	123	27.2
	31-40	144	31.8
	41 and over	83	18.3
Gender	Male	192	42.4
Gender	Female	261	57.6
Marital Status	Married	229	50.6
Marital Status	Single	224	49.4
Level of Education	High school	64	14.1
	Associate degree	86	19
	Degree	222	49
	Postgraduate	81	17.9
Occupation	Patient Services (Patient Advisor, Patient Registration and Admission, etc.)	93	20.5
	Physician	112	24.7
	Nurse	139	30.7
	Health Technician/Technologist (Radiology, Laboratory, Anesthesia, First Aid and Emergency Care, etc.)	109	24,1
Length of Service in the Profession	2 years and under	123	27.2
	3-5 years	115	25.4
	6-9 years	115	25.4
	10 years and over	100	22.1
Monthly Income (TL)	25.0001-35.000	104	23
	35.0001-45.000	102	22.5
	45.0001-65.000	132	29.1
	65.0001 ve over	115	25.4
Weekly Working Hours	45 hours and under	261	57.6
Weekly Working Hours	Over 45 hours	192	42.4
Contact with the Patient	Yes	317	70
Contact with the Patient	No	136	30
Status of Receiving Digital Hospital Training	Yes	257	56.7
Status of Receiving Digital Hospital Training	No	196	43.3

A total of healthcare workers participated in the study, of whom 22.7% were aged 18–25, 27.2% were aged 26–30, 31.8% were aged 31–40, and 18.3% were aged 41 and above. The sample consisted of 57.6% females and 42.4% males, while 49.4% were single and 50.6% were married. In terms of educational attainment, 14.1% had completed high school, 19% held an associate degree, 49% had a bachelor’s degree, and 17.9% had postgraduate education.

Regarding professional roles, 20.5% of participants were patient services staff, 24.7% were physicians, 30.7% were nurses, and 24.1% were healthcare technicians or technologists. In terms of professional experience, 27.2% had two years or less experience, 25.4% had 3–5 years, 25.4% had 6–9 years, and 22.1% had 10 years or more experience.

Monthly income distribution showed that 23% of participants earned between 25,001 and 35,000 TL, 22.5% earned between 35,001 and 45,000 TL, 29.1% earned between 45,001 and 65,000 TL, and 25.4% reported an income of 65,001 TL or above. In terms of working hours, 57.6% worked 45 hours or less per week, while 42.4% worked more than 45 hours.

Additionally, 70% of participants reported having direct contact with patients, whereas 30% worked in roles without patient contact. With regard to digital hospital training, 56.7% of participants reported having received training, while 43.3% had not.

Prior to the main analyses, the distribution of the data was assessed to determine the appropriate statistical tests. Normality was evaluated using skewness and kurtosis values for both the overall scales and their sub-dimensions. The results indicated that the data were normally distributed, and the relevant values are presented in Table 2.

Table 2.

Normal distribution values of scales and sub-factors.

	Skewness	Kurtosis
Digital Hospital Assessment Scale	-0.114	-0.873
Work and Operation Sub-factor	-0.057	-0.815
Management, Organization, and Cost Sub-Factor	-0.120	-0.795
Satisfaction Sub-factor	-0.185	-0.660
Patient Safety Sub-factor	-0.021	-0.726
Patient Safety Hospital Survey (TR–HSOPSC 2.0)	-0.198	-0.686
Work Area Assessment (A) Sub-factor	-0.172	-0.671
Manager Assessment (B) Sub-factor	-0.119	-0.858
Communication Process Assessment (C) Sub-factor	-0.197	-0.470
Reporting Patient Safety Incidents (D) Sub-factor	-0.099	-0.964
Overall Hospital Assessment (E) Sub-factor	-0.103	-0.580

As shown in Table 2, both the overall scales and all sub-dimensions demonstrated normal distribution. Accordingly, parametric tests were used in the analysis of the data. For comparisons involving more than two groups, appropriate post hoc tests (Tukey HSD, Scheffé, or Games–Howell) were applied following significant results. Within this framework, the findings of the difference analyses for the Digital Hospital Assessment Scale and its sub-dimensions are presented in Table 3, while the results for the Patient Safety Culture Survey (TR–HSOPSC 2.0) and its sub-dimensions are provided in Table 4.

Table 3.

Differences in variables according to the digital hospital assessment scale and sub-factors.

Variables	Digital hospital assessment scale X̄ (Ss)	Work and operation sub-factorX̄ (Ss)	Management organization and cost sub-factor X̄ (Ss)	Satisfaction sub-factorX̄ (Ss)	Patient safety sub-factor X̄ (Ss)
Age
18-25	3.04 (0.68)	3.00 (0.75)	3.10 (0.80)	3.04 (0.84)	3.03 (0.91)
26-30	3.03 (0.72)	2.95 (0.78)	3.04 (0.82)	3.20 (0.85)	3.00 (0.81)
31-40	3.17 (0.68)	3.15 (0.82)	3.25 (0.78)	3.23 (0.88)	3.18 (0.86)
41 and over	3.21 (0.66)	3.07 (0.79)	3.24 (0.73)	3.16 (0.94)	3.16 (0.86)
Test Statistic	1.76^b	1.61^b	2.00^b	1.01^b	1.33^b
p-Value	0.154	0.185	0.112	0.387	0.263
Note: Age showed a small effect size (η² = 0.012).
Gender
Male	2.86 (0.67)	2.80 (0.78)	2.91 (0.78)	2.89 (0.87)	2.84 (0.83)
Female	3.29 (0.64)	3.23 (0.74)	3.33 (0.75)	3.36 (0.83)	3.27 (0.84)
Test Statistic	-6.947^a	-5.922^a	-5.820^a	-5.810^a	-5.32^a
p-Value	<0.001	<0.001	<0.001	<0.001	<0.001
Note: Gender demonstrated a medium effect size (d = 0.65).
Marital Status
Married	3.15 (0.67)	3.09 (0.78)	3.18 (0.75)	3.16 (0.87)	3.16 (0.82)
Single	3.07 (0.71)	2.99 (0.79)	3.12 (0.83)	3.16 (0.88)	3.02 (0.90)
Test Statistic	1.191^a	1.351^a	0.838^a	-0.019^a	1.754^a
p-Value	0.234	0.177	0.403	0.985	0.080
Note: Marital status showed a small effect size (d = 0.11).
Level of Education
High school	2.71 (0.68)	2.60 (0.77)	2.71 (0.76)	2.78 (0.96)	2.80 (0.85)
Associate degree	3,12 (0,68)	3,06 (0.78)	3,23 (0.79)	3,18 (0.71)	2,99 (0.93)
Degree	3,11 (0,68)	3,063 (0.78)	3,15 (0.77)	3,16 (0.94)	3,09 (0.82)
Postgraduate	3,42 (0,56)	3,35 (0.68)	3,45 (0.73)	3,45 (0.66)	3,46 (0.81)
Test Statistic	13.717^b	11.410^b	11.332^b	7.383^b	8.202^b
p-Value	<0.001	<0.001	<0.001	<0.001	<0.001
Cases of Difference	1 ≠ 2, 3, 4	1 ≠ 2, 3, 4	1 ≠ 2, 3, 4	1 ≠ 2, 3, 4	1 ≠ 4
	2 ≠ 1, 4	2 ≠ 1	2 ≠ 1	2 ≠ 4	2 ≠ 4
	3 ≠ 1, 4	3 ≠ 1, 4	3 ≠ 1	3 ≠ 1, 4	3 ≠ 4
	4 ≠ 1, 2, 3	4 ≠ 1, 3	4 ≠ 1, 3	4 ≠ 1, 2, 3	4 ≠ 1, 2, 3
Note: Education level demonstrated a medium effect size (η² = 0.084).
Occupation
Patient Services (Patient Advisor, Patient Registration and Admission, etc.)	2.76 (0.54)	2.72 (0.68)	2.83 (0.67)	2.79 (0.74)	2.70 (0.75)
Physician	3.45 (0.50)	3.35 (0.61)	3.47 (0.63)	3.566 (0.74)	3.46 (0.80)
Nurse	3.50 (0.56)	3.45 (0.69)	3.55 (0.70)	3.568 (0.75)	3.44 (0.74)
Health Technician/Technologist (Radiology, Laboratory, Anesthesia, First Aid and Emergency Care, etc.)	2.57 (0.60)	2.50 (0.72)	2.61 (0.71)	2.55 (0.77)	2.61 (0.78)
Test Statistic	83.554^b	54.268^b	53.133^b	55.311^b	40.320^b
p-Value	<0.001	<0.001	<0.001	<0.001	<0.001
Cases of Difference	1 ≠ 2, 3	1 ≠ 2, 3	1 ≠ 2, 3	1 ≠ 2, 3	1 ≠ 2, 3
	2 ≠ 1, 4	2 ≠ 1, 4	2 ≠ 1, 4	2 ≠ 1, 4	2 ≠ 1, 4
	3 ≠ 1, 4	3 ≠ 1, 4	3 ≠ 1, 4	3 ≠ 1, 4	3 ≠ 1, 4
	4 ≠ 2, 3	4 ≠ 2, 3	4 ≠ 2, 3	4 ≠ 2, 3	4 ≠ 2, 3
Note: Occupation showed a very large effect size (η² = 0.358).
Length of Service in the Profession (Year)
2 years and under	3.11 (0.72)	3.012 (0.81)	3.141 (0.84)	3.22 (0.91)	3.08 (0.88)
3-5 years	3.09 (0.65)	3.007 (0.72)	3.16 (0.78)	3.11 (0.84)	3.09 (0.83)
6-9 years	3.12 (0.68)	3.05 (0.79)	3.18 (0.80)	3.20 (0.82)	3.07 (0.84)
10 years and over	3.14 (0.70)	3.13 (0.84)	3.140 (0.75)	3.12 (0.94)	3.15 (0.90)
Test Statistic	0.091^b	0.586^b	0.077^b	0.432^b	0.219^b
p-Value	0.965	0.625	0.972	0.730	0.884
Note: Length of service in the profession showed a negligible effect size (η² = 0.001).
Monthly Income (TL)
25.0001-35.000	2.97 (0.68)	2.92 (0.74)	2.99 (0.78)	2.99 (0.90)	3.00 (0.91)
35.0001-45.000	3.07 (0.72)	3.03 (0.84)	3.13 (0.82)	3.10 (0.89)	2.99 (.85)
45.0001-65.000	3.18 (0.68)	3.08 (0.74)	3.21 (0.80)	3.264 (0.92)	3.176 (0.84)
65.0001 ve over	3.21 (0.66)	3.15 (0.83)	3.27 (0.75)	3.261 (0.75)	3.177 (0.83)
Test Statistic	2.770^b	1.650^b	2.510^b	2.516^b	1.620^b
p-Value	0.041	0.177	0.058	0.058	0.184
Cases of Difference	1 ≠ 4	-	-	-	-
	2 ≠ 1, 3, 4
	3 ≠ 1, 3, 4
	4 ≠ 1
Note: Monthly income showed a small effect size (η² = 0.018).
Weekly Working Hours
45 hours and under	3.09 (0.71)	3.02 (0.82)	3. 16 (0.81)	3.14 (0.87)	3. 05 (0.87)
Over 45 hours	3.02 (0.66)	3.07 (0.75)	3. 14 (0.77)	3.18 (0.88)	3.15 (0.85)
Test Statistic	-0.567^a	-0.707^a	0.245^a	-0.438^a	-1.172^a
p-Value	0.571	0.480	0.807	0.662	0.242
Note: Weekly working hours showed a negligible effect size (Cohen’s d = 0.05).
Contact with the Patient
Yes	3.12 (0.68)	3.06 (0.79)	3.16 (0.80)	3.16 (0.85)	3.10 (0.85)
No	3.09 (0.70)	3.01 (0.78)	3.14 (0.77)	3.15 (0.93)	3.08 (0.88)
Test Statistic	0.406^a	0.660^a	0.278^a	0.102^a	0.214^a
p-Value	0.685	0.509	0.781	0.919	0.831
Note: Contact with the patient showed a negligible effect size (d = 0.04).
Status of Receiving Digital Hospital Training
Yes	3.11 (0,69)	3.06 (0.77)	3.14 (0.80)	3.16 (0.89)	3.09 (0.84)
No	3.11 (0,69)	3.02 (0.81)	3.17 (0.78)	3.16 (0.86)	3.09 (0.89)
Test Statistic	-0.029^a	0.443^a	-0.513^a	0.086^a	-0.088^a
p-Value	0.977	0.658	0.608	0.932	0.930
Note: Status of receiving digital hospital training showed a negligible effect size (d = 0.00).

Cases of DifferenceX̄: Mean. Ss: Standard Deviation.

^aT-test.

^bOne-Way Analysis of Variance (ANOVA).

Note. Effect size statistics were calculated to determine the magnitude of the differences. Cohen’s d was used for independent samples t-tests, while eta squared (η²) was used for ANOVA analyses.

Table 4.

Differences in variables according to the patient safety hospital survey (tr–HSOPSC 2.0) and subfactors.

Variables	Patient safety hospital survey (TR–HSOPSC 2.0) X̄ (Ss)	Work area assessment (A) sub-factor X̄ (Ss)	Manager evaluation (B) sub-factorX̄ (Ss)	Evaluation of the communication process (C) sub-factor X̄ (Ss)	Patient safety event reporting (D) sub-factorX̄ (Ss)	Overall assessment of the hospital (E) sub-factor X̄ (Ss)
Age
18-25	3.12 (0.68)	3.14 (0.75)	3.18 (1.10)	3.06 (0.80)	3.39 (1.14)	3.01 (0.83)
26-30	3.19 (0.68)	3.16 (0.73)	3.16 (1.05)	3.29 (0.81)	3.25 (1.22)	3.16 (0.85)
31-40	3.22 (0.65)	3.28 (0.68)	3.14 (1.04)	3.25 (0.83)	3.07 (1.26)	3.13 (0.89)
41 and over	3.17 (0.62)	3.12 (0.65)	3.11 (0.99)	3.33 (0.77)	3.09 (1.22)	3.15 (0.81)
Test Statistic	0.509b	1.290b	0.080b	2.196b	1.700b	.649b
p-Value	0.676	0.277	0.971	0.088	0.166	0.584
Note: Age showed a small effect size (η² = 0.009).
Gender
Male	2.93 (0.65)	2.93 (0.71)	2.86 (1.05)	2.96 (0.80)	2.97 (1.18)	2.91 (0.85)
Female	3.36 (0.61)	3.37 (0.65	3.35 (0.99)	3.42 (0.76)	3.35 (1.21)	3.25 (0.82)
Test Statistic	-7.091a	-6.770a	-5.047a	-6.174a	-3.292a	-4.309a
p-Value	<0.001	<0.001	<0.001	<0.001	0.001	<0.001
Note: Gender demonstrated a medium effect size (d = 0.50).
Marital Status
Married	3.19 (0.63)	3.21 (0.69)	3.09 (1.01)	3.22 (0.79)	3.18 (1. 17)	3.14 (0.83)
Single	3.16 (0.69)	3.15 (0.72)	3.20 (1.08)	3.23 (0.83)	3.20 (1.26)	3.07 (0.88)
Test Statistic	0.420a	0.898a	-1.091a	-0.163a	-0.153a	0.885a
p-Value	0.674	0.369	0.276	0.870	0.879	0.377
Note: Marital status showed a small effect size (d = 0.16).
Level of Education
High school	2.85 (0.67)	2.86 (0.76)	3.03 (1.08)	2.77 (0.79)	2.84 (1.17)	2.85 (0.88)
Associate degree	3.23 (0.70)	3.21 (0.78)	3.23 (1.06)	3.34 (0.83)	3.24 (1.25)	3.11 (0.85)
Degree	3.17 (0.65)	3.17 (0.68)	3.08 (1.03)	3.25 (0.81)	3.19 (1.23)	3.08 (0.87)
Postgraduate	3.43 (0.50)	3.45 (0.55)	3.35 (1.03)	3.44 (0.65)	3.44 (1.11)	3.40 (0.71)
Test Statistic	9.635b	8.695b	1.741b	9.673b	2.928b	5.372b
p-Value	<0.001	<0.001	0.158	<0.001	0.033	0.001
Cases of Difference	1 ≠ 2. 3, 4	1 ≠ 2, 3, 4	-	1 ≠ 2, 3, 4	1 ≠ 4	1 ≠ 4
	2 ≠ 1	2 ≠ 1		2 ≠ 1
	3 ≠ 1, 4	3 ≠ 1, 4		3 ≠ 1
	4 ≠ 1, 3	4 ≠ 1, 3		4 ≠ 1
Note: Education level demonstrated a medium effect size (η² = 0.052).
Occupation
Patient Services (Patient Advisor, Patient Registration and Admission, etc.)	2,93 (0.53)	2.90 (0.62)	3.06 (0.89)	3.01 (0.69)	3.02 (1.14)	2.82 (0.76)
Physician	3.51 (0.49)	3.48 (0.52)	3.43 (1.05)	3.63 (0.68)	3.46 (1.17)	3.47 (0.72)
Nurse	3.52 (0.53)	3.54 (0.61)	3.49 (0.94)	3.52 (0.67)	3.52 (1.20)	3.48 (0. 70)
Health Technician/Technologist (Radiology, Laboratory, Anesthesia, First Aid and Emergency Care, etc.)	2.62 (0.60)	2.67 (0.66)	2.50 (0.99)	2.64 (0.79)	2.66 (1.14)	2.53 (0.81)
Test Statistic	75.886b	56.767b	25.222b	47.148b	13.709b	46.332b
p-Value	<0.001	<0.001	<0.001	<0.001	<0.001	<0.001
Cases of Difference	1 ≠ 2, 3, 4	1 ≠ 2, 3,	1 ≠ 3, 4	1 ≠ 2, 3, 4	1 ≠ 3	1 ≠ 2, 3
	2 ≠ 1, 4	2 ≠ 1, 4	2 ≠ 4	2 ≠ 1, 4	2 ≠ 4	2 ≠ 1, 4
	3 ≠ 1, 4	3 ≠ 1, 4	3 ≠ 4	3 ≠ 1, 4	3 ≠ 4	3 ≠ 1, 4
	4 ≠ 1, 2, 3	4 ≠ 2, 3	4 ≠ 1, 2, 3	4 ≠ 1, 2, 3	4 ≠ 2, 3	4 ≠ 2, 3
Note: Occupation showed a very large effect size (η² = 0.210).
Length of Service in the Profession (Year)
2 years and under	3.19 (0.65)	3.22 (0.66)	3.22 (1.08)	3.19 (0.84)	3.13 (1.21)	3.11 (0.85)
3-5 years	3.12 (0.65)	3.14 (0.71)	3.05 (1.08)	3.22 (0.83)	3.08 (1.23)	3.01 (0.78)
6-9 years	3.22 (0.63)	3.22 (0.68)	3.16 (0.99)	3.27 (0.75)	3.35 (1.26)	3.16 (0.83)
10 years and over	3.19 (0.73)	3.16 (0.80)	3.17 (1.06)	3.26 (0.83)	3.22 (1.14)	3.18 (0.95)
Test Statistic	0.490b	0.410b	0.592b	0.271b	1.104b	0.891b
p-Value	0.690	0.746	0.621	0.846	0.347	0.446
Note: Length of service in the profession showed a negligible effect size (η² = 0.002).
Monthly Income (TL)
25.0001-35.000	3.06 (0.66)	3.05 (0.72)	3.00 (1.09)	3.16 (0.78)	3.03 (1.27)	3.00 (0.83)
35.0001-45.000	3.10 (0.73)	3.13 (0.76)	2.93 (1.05)	3.21 (0.91)	3.10 (1.23)	3.01 (0.91)
45.0001-65.000	3.24 (0.65)	3.24 (0.70)	3.26 (1.05)	3.30 (0.82)	3.26 (1.19)	3.15 (0.87)
65.0001 ve over	3.29 (0.58)	3.30 (0.64)	3.35 (0.95)	3.25 (0.73)	3.35 (1.17)	3.27 (0.79)
Test Statistic	3.100b	2.879b	4.175b	0.621b	1.558b	2.414b
p-Value	0.027	0.036	0.006	0.602	0.199	0.066
Cases of Difference	1 ≠ 4	1 ≠ 4	2 ≠ 4	-	-	-
Note: Monthly income showed a small effect size (η² = 0.011).
Weekly Working Hours
45 hours and under	3.17 (0.70)	3.17 (0.73)	3.15 (1.07)	3.22 (0.85)	3.20 (1.24)	3.10 (0.89)
Over 45 hours	3.18 (0.61)	3.19 (0.67)	3.14 (1.01)	3.24 (0.75)	3.18 (1.18)	3.12 (0.81)
Test Statistic	-0.253a	-0.321a	0.179a	-0.362a	0.140a	-0.186a
p-Value	0.800	0.748	0.858	0.717	0.889	0.853
Note: Weekly working hours showed a negligible effect size (d = 0.11).
Contact with the Patient
Yes	3.19 (0.66)	3.21 (0.71)	3.13 (1.06)	3.24 (0.8)	3.21 (1.20)	3.12 (0.85)
No	3.14 (0.66)	3.11 (0.70)	3.19 (1.02)	3.21 (0.81)	3.13 (1.26)	3.07 (0.85)
Test Statistic	0.830a	1.302a	-0.565a	0.290a	0.665a	0.632a
p-Value	0.407	0.194	0.572	0.772	0.506	0.528
Note: Weekly working hours showed a negligible effect size (d = 0.11).
Status of Receiving Digital Hospital Training
Yes	3.15 (0.66)	3.18 (0.71)	3.13 (1.04)	3.21 (0.81)	3.17 (1.21)	3.03 (0.83)
No	3.20 (0.66)	3.18 (0.70)	3.17 (1.05)	3.26 (0.81)	3.22 (1.22)	3.20 (0.88)
Test Statistic	-0.798a	-0.012a	-0.404a	-0.649a	-0.460a	-2.087a
p-Value	0.425	0.991	0.687	0.517	0.646	0.037
Note: Status of receiving digital hospital training showed a negligible effect size (d = 0.00).

^aT-test.

^bOne-Way Analysis of Variance (ANOVA).

The analyses indicated that there were no statistically significant differences in digital hospital assessment scale scores and their sub-dimensions with respect to age, marital status, professional experience, weekly working hours, patient contact, and digital hospital training.

A statistically significant difference was observed in the overall digital hospital assessment scale scores across income levels; however, no significant differences were identified in its sub-dimensions.

In terms of gender, statistically significant differences were found in both the overall scale and its four sub-dimensions (work and operation, management organization and cost, satisfaction, and patient safety), with female participants reporting higher mean scores than male participants.

Furthermore, statistically significant differences were observed across groups based on education level and occupation.

The analyses indicated that there were no statistically significant differences in patient safety culture scores (TR–HSOPSC 2.0) and its sub-dimensions with respect to age, marital status, professional experience, weekly working hours, and patient contact.

Significant differences were observed across several variables and sub-dimensions. In terms of education level, statistically significant differences were identified in the work area (A), communication process (C), reporting of patient safety incidents (D), and overall hospital assessment (E) sub-dimensions, as well as in the managers’ assessment (B) sub-dimension.

For monthly income, significant differences were found in the overall patient safety culture score (TR–HSOPSC 2.0), as well as in the work area (A) and management (B) sub-dimensions. Regarding digital hospital training, statistically significant differences were observed in the overall patient safety culture score and in several sub-dimensions, including work area (A), management (B), communication process (C), and reporting of patient safety incidents (D), whereas the overall hospital assessment (E) sub-dimension showed a more limited variation.

In addition, gender and occupation were associated with statistically significant differences in both the overall patient safety culture score and all five sub-dimensions.

Finally, the relationships between participants’ digital hospital assessment levels and patient safety culture (TR–HSOPSC 2.0), including its sub-dimensions (A–E), were examined, and the results are presented in Tables 5 and 6.

Table 5.

Correlation analysis according to the variables of the study.

	Digital hospital assessment scale	Patient safety hospital survey (TR–HSOPSC 2.0)	Work area assessment (A) sub-factor	Managerial assessment (B) sub-factor	Evaluation of the communication process (C) sub-factor	Reporting patient safety incidents (D) sub-factor	Overall assessment of the hospital (E) sub-factor
Digital Hospital Assessment Scale	1
Patient Safety Hospital Survey (TR–HSOPSC 2.0)	0.812**	1
Work Area Assessment (A) Sub-factor	0.761**	0.917**	1
Managerial Assessment (B) Sub-factor	0.503**	0.646**	0.510**	1
Evaluation of the Communication Process (C) Sub-factor	0.670**	0.815**	0.640**	0.434**	1
Reporting Patient Safety Incidents (D) Sub-factor	0.438**	0.563**	0.431**	0.309**	0.421**	1
Overall Assessment of the Hospital (E) Sub-factor	0.628**	0.796**	0.449**	0.449**	0.558**	0.366**	1

The digital hospital assessment levels of the participants were positively correlated with overall patient safety culture (r = 0.812, p < .001), as well as with its sub-dimensions, including work area (A) (r = 0.761, p < .001), management (B) (r = 0.503, p < .001), communication process (C) (r = 0.670, p < .001), reporting of patient safety incidents (D) (r = 0.438, p < .01), and overall hospital assessment (E) (r = 0.628, p < .001).

Table 6.

Multicollinearity statistics.

	Condition index	Tolerance	VIF
Work Area Assessment (A) Sub-factor	8.242	0.440	2.273
Managerial Assessment (B) Sub-factor	10.075	0.700	1.429
Evaluation of the Communication Process (C) Sub-factor	12.710	0.527	1.898
Reporting Patient Safety Incidents (D) Sub-factor	14.588	0.769	1.300
Overall Assessment of the Hospital (E) Sub-factor	18.631	0.544	1.839

Multicollinearity diagnostics indicated that tolerance values ranged between 0.440 and 0.769, while VIF values ranged between 1.300 and 2.273. In addition, condition index values were below the critical threshold of 30, suggesting that multicollinearity was not a concern in the regression model. Multicollinearity diagnostics showed that VIF values were below the acceptable threshold.

Multiple regression analysis was conducted to examine the relationship between digital hospital level and patient safety culture. The results of the analysis are presented in Table 7.

Table 7.

Regression analysis according to the variables of the study.

Guides	B	SE	ß	t	95% CI	P	Regression result
Constant	0.409	0.093		4.389	0.227 – 0.591	p < 0.001	Adjusted R² = 0.663 F (5,447) = 175.962 p < 0.001
Work Area Assessment (A) Sub-factor	0.427	0.040	0.438	10.592	0.349 – 0.505	p < 0.001
Managerial Assessment (B) Sub-factor	0.057	0.022	0.086	2.634	0.014 – 0.100	0.009
Evaluation of the Communication Process (C) Sub-factor	0.203	0.032	0.238	6.296	0.140 – 0.266	p < 0.001
Reporting Patient Safety Incidents (D) Sub-factor	0.037	0.018	0.065	2.088	0.002 – 0.072	0.037
Overall Assessment of the Hospital (E) Sub-factor	0.126	0.030	0.155	4.174	0.067 – 0.185	p < 0.001

Note. Regression coefficients together with 95% confidence intervals were calculated to improve the interpretability of the regression estimates.

SE: Standard Error.

Multiple regression analysis was performed to examine the associations between the sub-dimensions of patient safety culture and digital hospital level. The results indicated that the overall model was statistically significant (F (5,447) = 175.96, p < .001) and accounted for approximately 66.3% of the variance in digital hospital level (Adjusted R² = .663).

Among the variables included in the model, the work area (A) sub-dimension showed the strongest association with digital hospital level (β = .438, B = .427, p < .001). The communication process (C) sub-dimension was also significantly associated with digital hospital level (β = .238, B = .203, p < .001), followed by the overall hospital assessment (E) sub-dimension (β = .155, B = .126, p < .001).

In addition, the management (B) sub-dimension (β = .086, B = .057, p = .009) and reporting of patient safety incidents (D) sub-dimension (β = .065, B = .037, p = .037) were also significantly associated with digital hospital level, although with comparatively smaller effect sizes.

Overall, all sub-dimensions included in the model were positively associated with digital hospital level within this sample. The work area sub-dimension demonstrated the highest standardized regression coefficient, indicating the strongest relative association among the variables examined.

Based on the regression model, digital hospital level can be expressed as follows:

Digital Hospital Level = 0.409 + 0.427(A) + 0.057(B) + 0.203(C) + 0.037(D) + 0.126(E).

where A represents work area, B represents management, C represents communication process, D represents reporting of patient safety incidents, and E represents overall hospital assessment.

Discussion

The primary aim of this study was to explore healthcare workers’ perceptions of the relationship between hospital digitalization and patient safety culture. The findings suggest that perceptions of patient safety culture are strongly associated with the level of digitalization within hospitals. In particular, the regression analyses indicated that digital hospital level was significantly associated with patient safety culture and its sub-dimensions, accounting for approximately 66% of the variance (R² = .66).

While this relatively high proportion of explained variance highlights the potential relevance of digital transformation in relation to patient safety culture, these findings should be interpreted as indicative of associations rather than causal relationships due to the cross-sectional and self-reported nature of the data.

The findings related to digital hospital assessment further indicated that perceptions of hospital digitalization varied significantly according to monthly income, education level, and gender. Higher levels of education may be associated with greater digital literacy and familiarity with technological systems. Similarly, individuals in higher income groups may be more likely to occupy roles such as physicians or administrators, which may involve greater exposure to digital systems and organizational decision-making processes.

However, no statistically significant differences were observed in digital hospital perceptions with respect to age, marital status, professional experience, or weekly working hours. These findings are partially consistent with previous studies. For example, Lin et al.⁴⁶ suggested that lower educational levels and older age may be associated with challenges in adopting digital tools in healthcare settings. Similarly, Sakrak and Doğan⁴⁷ reported that both the frequency of digital system use and satisfaction with such systems tend to increase with higher educational attainment. Ross et al.⁴⁸ also highlighted that patterns of technology use may differ by gender, particularly in the context of healthcare and crisis situations.

The findings related to patient safety culture indicated statistically significant differences across education level, monthly income, gender, and professional role. Higher levels of education may be associated with greater knowledge and awareness of patient safety protocols and risk management practices. Similarly, differences observed across income levels and professional roles may reflect variations in responsibilities, access to resources, and levels of engagement in safety-related processes. In contrast, no statistically significant differences were identified in patient safety perceptions with respect to age, marital status, professional experience, or weekly working hours. These findings are broadly consistent with previous literature. Monaca et al.⁴⁹ suggested that the effectiveness of patient safety initiatives may vary according to demographic and socioeconomic characteristics. Similarly, Kim and Nam⁵⁰ reported that patient safety outcomes are associated with factors such as healthcare professionals’ knowledge levels, the quality of nurse–patient relationships, and patient engagement in safety practices. Xiao et al.⁵¹ also identified an association between nurses’ patient safety competencies and their income levels. In addition, Bagnasco et al.⁵² reported significant differences in patient safety culture across gender, professional roles, and clinical contexts.

A strong positive association was observed between the level of hospital digitalization and patient safety culture (r = .812). Regression analysis further indicated that digital hospital level was associated with approximately 66% of the variance in patient safety culture (Adjusted R² = .663). While the strength of this association suggests that healthcare professionals tend to perceive digital hospital infrastructure as closely linked to patient safety practices, these findings should be interpreted with caution. Given the cross-sectional and self-reported nature of the data, the results reflect associations rather than causal relationships. From a practical perspective, these findings may indicate that improvements in digital infrastructure are related to aspects of safer clinical processes, such as communication systems, incident reporting, and coordination among healthcare teams. However, such interpretations should be considered as indicative rather than definitive. Previous studies have emphasized that digital health infrastructures should be evaluated within broader socio-technical and organizational contexts. Matlin et al.³⁷ argued that digital solutions, governance structures, and participatory design collectively shape the safety and effectiveness of healthcare delivery, particularly for vulnerable populations. This perspective suggests that digitalization extends beyond technological implementation and may be part of a broader strategy to support safe and equitable healthcare systems. At the same time, the literature also highlights potential challenges. Abdelaziz et al.⁵³ noted that health technologies may be associated with patient safety through multiple mechanisms, including unequal access to digital systems, increased workload for healthcare professionals, overreliance on technological tools, ambiguity in accountability, and reduced human interaction in care delivery. Similarly, Kopanz et al.⁵⁴ suggested that digital tools such as electronic medication records, alert systems, and decision-support technologies may be associated with reductions in medication errors and improvements in patient safety outcomes.

Recent studies have also highlighted the potential contributions of emerging digital technologies to patient safety. Mayer et al.⁵⁵ reported that sensor technologies combined with machine learning algorithms may support patient safety by assisting in fall prevention and enabling early detection of clinical deterioration. Similarly, Arjmandnia and Alimohammadi⁵⁶ demonstrated that the integration of artificial intelligence into healthcare systems may be associated with reduced clinical risks and improved safety outcomes in surgical settings. Luo et al.⁵⁷ further emphasized that electronic prescription systems and virtual healthcare platforms may contribute to maintaining patient safety, particularly during public health emergencies such as the COVID-19 pandemic. In addition, Volkan et al.⁵⁸ reported that digital transformation initiatives in healthcare institutions are associated with improvements in patient safety culture. These findings suggest that digital health systems should not be considered solely as technological innovations but rather as socio-technical infrastructures that integrate governance, user participation, and institutional capacity. As highlighted by Matlin et al.,³⁷ the effectiveness and safety of digital health interventions appear to depend not only on technological infrastructure but also on organisational readiness and the ability of healthcare professionals to effectively use digital tools.

In this context, digital transformation in healthcare may be interpreted as part of a broader system-level approach aimed at supporting both the safety and equity of healthcare delivery. Previous research has suggested that well-designed digital health infrastructures may contribute to safer healthcare systems by improving information accessibility, strengthening coordination among healthcare providers, and supporting evidence-based decision-making.³⁷

Although hospital digitalization and patient safety culture are conceptually distinct constructs, the findings of this study indicate that healthcare professionals tend to perceive these two dimensions as closely interconnected in their daily clinical practice. This perception may reflect the increasing relevance of digital technologies in relation to safe healthcare delivery and organizational learning processes. Given the cross-sectional design of this study, causal relationships between hospital digitalization and patient safety culture cannot be established. Therefore, the findings should be interpreted as indicative of associations within the study context. Future longitudinal or experimental studies are needed to better understand the directionality and underlying mechanisms of this relationship. In addition, variables such as professional role, years of experience, and prior exposure to digital health systems may be associated with variations in these perceptions and could be further explored in future research. Overall, these findings suggest that hospital digitalization may be associated with aspects of patient safety culture at the organizational level, rather than indicating a direct causal effect.

Limitations of the study

This study has several limitations that should be considered when interpreting the findings. First, the study was conducted in a single hospital setting, which may limit the generalisability of the results to other healthcare institutions with different organisational structures, resources, and levels of digital maturity.

Second, the data were based on self-reported perceptions of healthcare professionals. While such data provide valuable insights into subjective evaluations, they may be subject to response bias and may not fully reflect objective patient safety outcomes or actual digital system performance.

Third, the cross-sectional design of the study precludes any inference of causal relationships between digital hospital level and patient safety culture. Therefore, the findings should be interpreted as indicative of associations within the specific study context.

Finally, although several demographic and professional variables were examined, other potential influencing factors—such as organisational culture, leadership style, and prior experience with digital health systems—were not included and may have affected the observed relationships.

Conclusion

The outcomes of research investigating the related to digital technology adoption in hospitals on patient safety reveal that such digital advancements are essential for providing safe healthcare services. The results show that digitalization accounts for two-thirds of the differences seen in patient safety. Thus, it highlights that digitalization is more than just a technological upgrade; it is vital for minimizing medical mistakes, including those related to medication, and promoting a safety-oriented environment for patients. In summary, raising the level of digital adoption in healthcare facilities greatly aids in enhancing patient safety.

The findings of this research have significant consequences for those managing healthcare systems and making policy choices. Investments in technology aimed at enhancing patient safety in digitized healthcare settings ought to be classified as direct investments in patient safety. Nevertheless, disparities linked to particular demographic features indicate that relying solely on technology will not guarantee patient safety. Different levels of understanding based on educational background clearly highlight the necessity for tailored training initiatives. Although the digital transformation of hospitals is often perceived as purely a technical matter, achieving success demands that digitalization and patient safety be ingrained in the organizational culture. In this context, healthcare leaders need to exhibit the essential leadership skills to avoid these differences in perception from creating a gap in digital access. Variations in demographics reveal that the key to ensuring patient safety lies not in technological solutions but in enhancing the overall experience for users. This represents a crucial managerial duty in safeguarding patient well-being.

This research adds valuable insights to the existing body of work, though it has some drawbacks. These drawbacks open doors for further investigation. The objective of this research is to assess how healthcare professionals view various issues. Upcoming studies can offer stronger proof by combining digitalization metrics with measurable clinical quality factors like infection and medication error rates. Moreover, enhancing the model’s explanatory strength by incorporating factors like digital literacy and organizational culture would greatly benefit the field. Also, performing comparisons between various types of hospitals and different geographic areas would greatly enrich the literature.

Supplemental material

Supplemental material - Examining the relationship between digital hospital level and patient safety culture: A study on healthcare workers

Supplmental material for Examining the relationship between digital hospital level and patient safety culture: A study on healthcare workers by Mehmet Avdan, Sefer Aygün, Yunus Emre Tanış, Yeter Demir Uslu and Yaşar Gökalp in Digital Health.

Supplemental material

Supplemental material - Examining the relationship between digital hospital level and patient safety culture: A study on healthcare workers

Supplemental material

Supplemental material - Examining the relationship between digital hospital level and patient safety culture: A study on healthcare workers

Footnotes

ORCID iDs

Mehmet Avdan

Yaşar Gökalp

Ethical considerations

Written informed consent was obtained from all participants prior to data collection. Participants were fully informed about the purpose of the study, the voluntary nature of participation, and their right to withdraw at any time without any consequences. This study, titled “ Examining the Relationship Between Digital Hospital Level and Patient Safety Culture: A Study on Healthcare Workers” approved by the Istanbul Medipol University Non-Interventional Ethics Committee (decision dated 08/22/2025, numbered E E-10840098-202.3.02-556). All procedures performed in this study involving human participants were conducted in accordance with the ethical standards of the institutional and/or national research committee and with the 1964 Helsinki Declaration and its later amendments or comparable ethical standards. Participation in the study was voluntary and informed consent was obtained from all participants prior to data collection.

Consent to participate

Dear participants, this survey has been created to conduct a study titled “Examining the Relationship Between Digital Hospital Level and Patient Safety Culture: A Study on Healthcare Workers.” The information you provide will not be shared with third parties in any way, your identity will not be disclosed, and it will only be used for scientific purposes. Please answer the following questions with the response that best suits you. Provide only one answer for each item. Answering the questions sincerely is important for the scientific value of the study.

Author contributions

M.A contributed to the preparation of the ıntroduction, literature review, and abstract sections. S.A conducted the data analysis and contributed to the methods section. Y.E.T was responsible for data collection. Y.G contributed to the discussion and conclusion sections. Y.D.U contributed to the evaluation of the study and provided additional input to the ıntroduction section.

Funding

The authors received no financial support for the research, author ship and/or publication of this article.

Declaration of conflicting interests

The authors declared no potential conflicts of interest with respect to the research, authorship and/or publication of this article.

Data Availability Statement

Data belonging to participants can be made available upon request.*

Guarantor

MA, SA and YG.

Supplemental material

Supplemental material for this article is available online.

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