Stressors, Resources, and Strain Associated with Digitization Processes of Medical Staff Working in Neurosurgical and Vascular Surgical Hospital Wards: A Multimethod Study
Abstract
:1. Introduction
1.1. Theoretical Framework
1.1.1. Technostress
1.1.2. Job Demands–Resources Model
1.1.3. Technology Acceptance Model
1.2. The Digitization-Associated Stressor and Strain Situation of Medical Staff
Current State of Research
1.3. Objectives
2. Materials and Methods
2.1. Study Design and Study Population
2.2. Data Collection
2.3. Measures
2.3.1. Sociodemographic and Work-Related Variables
2.3.2. Techno-Stressors and Technology-Associated Resources
2.3.3. Preventive Measures
2.3.4. Work- and Health-Related Outcomes
2.3.5. Usage Frequency and Attitudes Regarding Digital Technologies
2.4. Statistical Data Analysis
3. Results
3.1. Sample Description
3.2. Descriptive Statistical Analysis
3.2.1. Usage Frequency and Usage Duration of Digital Documentation Technologies
3.2.2. Techno-Stressors and Stress-Inducing Aspects of Documentation Technologies
3.2.3. Technostress Inhibitors/Resources
3.2.4. Status Quo and Benefits of Implemented Preventive Measures
3.2.5. Technology Acceptance
3.2.6. Work- and Health-Related Outcomes
3.3. Qualitative Data Analysis
3.3.1. Stress-Inducing Aspects of Digital Documentation Technologies
“Here, systems are installed without prior consultation and we are then supposed to test them. This is thought to save money. However, the time required is significantly higher and such behavior is not worthwhile in the long term”. (P81; Engl. translation of original citation.)
“Problem in my eyes: The development/acquisition of new IT systems always has the focus on controlling/administration, the daily MEDICAL aspect is mapped far too little. The result is absolutely unintuitive systems with multiple analysis functions that hinder rather than promote the daily application process”. (P44; Engl. translation of original citation.)
3.3.2. Benefits of Implemented Preventive Measures
“As a head physician, you have a very good influence on developments, this does not apply to the same extent for subordinate levels”. (P65; Engl. translation of original citation.)
“Training courses are offered but often cannot be attended in everyday life due to lack of time”. (P104; Engl. Translation of original citation.)
“Preventive measures, as mentioned above, do not take place to any significant extent”. (P82; Engl. translation of original citation.)
3.3.3. Criticism Regarding Implemented Preventive Measures
“There is no involvement of end users in decision-making processes, and digitization is years behind competitors or the requirements of the KhZG in terms of implementation. Familiarization with existing software is largely left to the users themselves”. (P82; Engl. Translation of original citation.)
3.3.4. Wish for Additional Preventive Measures
“Involvement in decision-making processes, familiarization with new software and technology, availability and competent support from the IT department”. (P82; Engl. translation of original citation.)
“One could also buy systems/software whose functional benefit has been proven elsewhere”. (P11; Engl. translation of original citation.)
3.4. Analytical Statistical Analysis
4. Discussion
4.1. Perceived Usability, Perceived Ease of Use, and Technostress
4.2. Specification of Digital Documentation Technology-Associated Stressors and Resources
4.3. Status of Implementation and Perception of Preventive Measures
4.4. Relationships among Techno-Stressors and Technostress inhibitors and Burnout Symptoms, General Health Status, and Job Satisfaction
4.5. Age and Gender-Related Differences in Technostress Levels and Burnout Symptoms
4.6. Influence of Preventive Measures on Technostress
4.7. Strengths and Limitations
4.8. Implications for Further Research and Practice
4.8.1. Recommendations for Future Research
4.8.2. Practical Implications
5. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Acknowledgments
Conflicts of Interest
Abbreviations
References
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Characteristic | Frequency (n) | Percentage (%) |
---|---|---|
Gender | ||
Male | 78 | 68.4 |
Female | 36 | 31.6 |
Age | ||
20–29 years | 3 | 2.6 |
30–39 years | 27 | 23.7 |
40–49 years | 27 | 23.7 |
50–59 years | 37 | 32.5 |
60 years and older | 20 | 17.5 |
Surgical department | ||
Neurosurgery | 52 | 45.6 |
Vascular surgery | 62 | 54.4 |
Job position | ||
Assistant physician | 15 | 13.2 |
Specialist physician | 8 | 7 |
Senior physician | 53 | 46.5 |
Head physician | 38 | 33.3 |
Extent of current employment | ||
Working full time (≥35 h/week) | 104 | 91.2 |
Working part time (15–34 h/week) | 10 | 8.8 |
Duration of employment with employer | ||
<1 years | 9 | 7.9 |
1–<2 years | 7 | 6.1 |
2–<3 years | 9 | 7.9 |
3–<4 years | 2 | 1.8 |
≥4 years | 87 | 76.3 |
Overall clinical experience | ||
<5 years | 4 | 3.5 |
5–<10 years | 17 | 14.9 |
10–<15 years | 16 | 14 |
15–<20 years | 15 | 13.2 |
20–<25 years | 22 | 19.3 |
≥25 years | 40 | 35.1 |
Hospital sponsorship | ||
Commercial sponsor (profit-oriented) | 20 | 17.5 |
Public sponsor | 65 | 57 |
Independent sponsor (non-profit, charity, church) | 29 | 25.4 |
Regional structure of employer | ||
Municipal | 71 | 62.3 |
Provincial | 41 | 36 |
Rural | 2 | 1.8 |
Characteristic | Frequency (n) | Percentage (%) |
---|---|---|
Usage frequency | ||
Daily usage | 113 | 99.1 |
Usage several times per week | 1 | 0.9 |
Usage duration (estimated per day) | ||
<1 h | 2 | 1.8 |
1–<2 h | 28 | 24.6 |
2–<3 h | 44 | 38.6 |
3–<4 h | 22 | 19.3 |
4–<5 h | 11 | 9.6 |
5 h or more | 7 | 6.1 |
Techno-Stressors | Mean (M) | Standard Deviation (SD) |
---|---|---|
Techno-overload | 3.09 | 0.86 |
Techno-complexity | 2.25 | 0.87 |
Techno-uncertainty | 3.03 | 0.88 |
Overall expression of techno-stressors | 2.79 | 0.65 |
Technostress inhibitors/Resources | Mean (M) | Standard Deviation (SD) |
---|---|---|
Literacy facilitation | 3.10 | 1.05 |
Involvement facilitation | 1.91 | 0.90 |
Overall expression of resources | 2.51 | 0.85 |
Variable Expression | Mean (M) | Standard Deviation (SD) |
---|---|---|
Burnout symptoms | 2.92 | 0.97 |
General health status | 7.62 | 1.84 |
Job satisfaction | 4.05 | 0.63 |
Items/Main Categories | Subcategories |
---|---|
Stress-inducing aspects |
|
Benefits of implemented preventive measures |
|
Criticism regarding implemented preventive measures |
|
Wish for additional preventive measures |
|
Overall Expression of Techno-Stressors | Perceived Usefulness (PU) | Perceived Ease of Use (PEOU) | ||||
---|---|---|---|---|---|---|
Overall expression of techno-stressors | Pearson correlation | 1 | −0.372 ** | −0.452 ** | ||
Sig. (2-tailed) | 0.000 | 0.000 | ||||
N | 114 | 114 | 114 | |||
Bootstrap 1 | Bias | 0 | −0.005 | 0.000 | ||
Std. Error | 0 | 0.082 | 0.085 | |||
95% Confidence Interval | Lower | −0.529 | −0.610 | |||
Upper | −0.225 | −0.275 |
Predictors | b a | SEa | t | p |
---|---|---|---|---|
Perceived usefulness (PU) | −0.183 (−0.318, −0.048) | 0.068 | −2.684 | <0.05 |
Perceived ease of use (PEOU) | 0.311 (−0.478, −0.144) | 0.084 | −3.689 | <0.05 |
Overall Expression of Techno-Stressors | Burnout Symptoms | General Health Status | Job Satisfaction | ||||
---|---|---|---|---|---|---|---|
Overall expression of techno-stressors | Pearson correlation | 1 | 0.214 * | −0.194 * | −1.82 | ||
Sig. (2-tailed) | 0.022 | 0.038 | 0.053 | ||||
N | 114 | 114 | 114 | 114 | |||
Bootstrap 1 | Bias | 0 | −0.003 | −0.005 | −0.004 | ||
Std. Error | 0 | 0.102 | 0.120 | 0.115 | |||
95% Confidence Interval | Lower | 0.008 | −0.411 | −0.400 | |||
Upper | 0.395 | 0.027 | 0.041 |
Predictors | b a | SE a | t | p |
---|---|---|---|---|
Outcome of Burnout Symptoms | ||||
Techno-overload | 0.442 (0.242, 0.641) | 0.101 | 4.382 | <0.001 |
Techno-complexity | −0.590 | 0.096 | −0.613 | >0.05 |
Techno-uncertainty | −0.110 | 0.083 | −1.316 | >0.05 |
Notation. R2 = 0.125 (n = 114, p < 0.001). | ||||
Outcome of Job Satisfaction | ||||
Techno-overload | −0.163 (-0.297, −0.029) | 0.068 | −2.402 | <0.05 |
Techno-complexity | −0.068 | 0.064 | −1.052 | >0.05 |
Techno-uncertainty | 0.088 | 0.056 | 1.567 | >0.05 |
Notation. R2 = 0.068 (n = 114, p < 0.05). |
Overall Expression of Technostress Inhibitors | Burnout Symptoms | General Health Status | Job Satisfaction | ||||
---|---|---|---|---|---|---|---|
Overall expression of technostres inhibitors | Pearson correlation | 1 | −0.009 | 0.118 | 0.190 | ||
Sig. (2-tailed) * | 0.924 | 0.211 | 0.043 | ||||
N | 114 | 114 | 114 | 114 | |||
Bootstrap 1 | Bias | 0 | −0.004 | −0.005 | 0.001 | ||
Std. Error | 0 | 0.091 | 0.097 | 0.088 | |||
95% Confidence Interval | Lower | −0.196 | −0.069 | 0.009 | |||
Upper | 0.167 | 0.298 | 0.355 |
Level of Measure Implementation | Reducing Stressors | Strengthening Resources |
---|---|---|
Technological |
|
|
Organizational |
|
|
Individual |
|
|
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Tell, A.; Westenhöfer, J.; Harth, V.; Mache, S. Stressors, Resources, and Strain Associated with Digitization Processes of Medical Staff Working in Neurosurgical and Vascular Surgical Hospital Wards: A Multimethod Study. Healthcare 2023, 11, 1988. https://doi.org/10.3390/healthcare11141988
Tell A, Westenhöfer J, Harth V, Mache S. Stressors, Resources, and Strain Associated with Digitization Processes of Medical Staff Working in Neurosurgical and Vascular Surgical Hospital Wards: A Multimethod Study. Healthcare. 2023; 11(14):1988. https://doi.org/10.3390/healthcare11141988
Chicago/Turabian StyleTell, Anika, Joachim Westenhöfer, Volker Harth, and Stefanie Mache. 2023. "Stressors, Resources, and Strain Associated with Digitization Processes of Medical Staff Working in Neurosurgical and Vascular Surgical Hospital Wards: A Multimethod Study" Healthcare 11, no. 14: 1988. https://doi.org/10.3390/healthcare11141988