http://orcid.org/0000-0001-7724-4706
Figures
Abstract
Background
The application of digital technology to improve health service delivery is increasing rapidly in Low- and Middle- Income Countries (LMICs). Digital tools such as electronic health (e-health) have been shown to improve healthcare quality, efficiency and patient satisfaction. However, evidence on health workers’ experiences using e-health services is limited in LMICs. This study examined the relationship between e-health usage and health workers’ motivation and job satisfaction.
Methods
This was a cross-sectional survey design involving health workers across public and private hospitals in the Accra Metropolitan Assembly (AMA). A structured questionnaire was designed and self-administered to 305 respondents. Partial Least Square-Structural Equation Modelling (PLS-SEM) was employed to analyse the data.
Results
Findings showed a significant positive association of job satisfaction with e-health (p < 0.01) and type of hospital (p < 0.01) but not motivation (p = 0.42). Although type of hospital significantly influenced job satisfaction (p < 0.01), it had no significant mediating effect on the relationship between e-health and job satisfaction. Finally, type of hospital interacted with e-health to moderate the association between e-health usage and job satisfaction.
Citation: Atinga RA, Abor PA, Suleman SJ, Anaba EA, Kipo B (2020) e-health usage and health workers’ motivation and job satisfaction in Ghana. PLoS ONE 15(9): e0239454. https://doi.org/10.1371/journal.pone.0239454
Editor: Barbara Schouten, Universiteit van Amsterdam, NETHERLANDS
Received: March 14, 2020; Accepted: September 7, 2020; Published: September 23, 2020
Copyright: © 2020 Atinga et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Data Availability: All relevant data are within the manuscript.
Funding: The authors received no specific funding for this work.
Competing interests: The authors have declared that no competing interests exist.
Introduction
The application of Information Communication Technology (ICT) in healthcare is increasing rapidly in most LMICs [1,2]. In some of these countries, policies and programmes have been adopted ostensibly to promote and sustain the adoption and usage of e-health to facilitate healthcare delivery. For instance, in the quest to improve healthcare delivery and generate reliable, accurate and complete information for health policy interventions, Ghana’s Ministry of Health and its agencies are promoting the standard implementation of e-health systems. Ghana has implemented a national e-health strategy to promote the delivery of responsive, affordable and optimal healthcare as well as enhance communication and the use of information for planning, management and delivery of health services [3].
A range of definitions of e-health have been proffered [4–6]. However, there is seeming convergence of the definitions that e-health is 'the use of ICT or other digital technology to capture medical records, administer treatment, manage health and improve healthcare delivery’ [5]. The application of e-health revolves around public health, business and medical informatics provided through the internet or other technologies. e-health forms part of the series of innovations being introduced in healthcare in response to the quest to reduce cost and improve efficiency of health service delivery [7,8]. e-health is shown to have significant benefits. Its adoption results in improved patient experience, quality diagnosis and treatment, reliable prescription and quality clinical decision making [7,8]. e-health usage leads to timely access to health information, such as administrative and patients’ records, diagnosis and treatment profile records [9,10]. In addition, the use of ICT enables health workers to easily capture, store, retrieve, analyse and transmit large volumes of health information within and across care delivery points [11]. e-health has been found to be more cost-effective in promoting health education, self-management support and tele-monitoring in several domains of healthcare [7,12].
While interest for e-health has been driven primarily by the desire to improve clinical and administrative efficiency, these interests may not reflect those of health workers. Sustained use of e-health is largely dependent on the experiences of end-users such as health workers using it for various facets of daily patient care and management [13]. Studies attest that health workers who perceive e-health systems as easy to use are more likely to be satisfied with their jobs [14–16]. It has been established that physicians with higher perceived efficiency in the usage of e-health reported higher job satisfaction and commitment to care [17]. In addition, health workers motivated by e-health adoption are more likely to show continuous commitment to its usage. On the other hand, health workers are more likely to resist or discontinue e-health, if their experiences about its usage is poor. There is evidence that physicians who reported difficulties in using e-health also reported difficulties with clinical performance [18].
Health workers are key stakeholders in the design, implementation and usage of e-health. Thus, continuous use of e-health is linked to how it motivates and contributes to job satisfaction of health workers [19]. However, little is known about the extent to which health workers who use e-health to deliver healthcare are motivated and satisfied with their jobs. Arguably, little has been documented about e-health experiences among various cadres of health workers, especially in LMICs context. In Ghana, existing studies have focused on only physicians’ experience using e-health systems [19,20]. Limited attention has been given to whether e-health motivates and promotes job satisfaction among other clinical cadres such as nurses and allied health professionals who represent a larger proportion of users of e-health systems in health facilities. To the best of our knowledge, this is the first study in Ghana to examine the effect of e-health on health worker job satisfaction and motivation to care. Findings from this study will significantly contribute to the burgeoning literature on e-health and inform practice, research and interventions to enhance e-health adoption and acceptance in health service organisations.
Methods
Study design and sampling
Cross-sectional survey design was employed to collect data in hospitals located within the Accra Metropolitan Assembly (AMA). The metropolis has about 12 public and 28 private hospitals. At the time of the study, 5 public and 10 private hospitals had implemented digital systems including the application of cloud computing for e-health in all facets of service delivery arrangements–medical records, physician order entry, prescribing, decision support systems, cash flow management among others. The rest of the hospitals were either paper-based systems or hybrid (combination of paper and e-health systems). Simple random sampling was used to select 3 hospitals each of the population of public and private facilities with e-health systems.
For each hospital, doctors, nurses, pharmacists and laboratory (lab) technicians who routinely use e-health to perform clinical functions were sampled. The nurses were randomly selected such that at least 3 nurses from each department or unit participated in the study. We initially applied random selection of doctors, pharmacists and lab technicians in the hospitals. However, routine heavy workload owing to their few numbers severely constrained efforts to contact and interview doctors and the lab technicians on our random selection lists. This prompted us to use opportunistic sampling selection of those available and willing to participate in the study.
Data collection methods
Data were collected using a questionnaire structured into 4 parts: background data, experience with e-health usage, and job satisfaction and motivation with the use of e-health. The background data included sex, age, profession, type of hospital and type of e-health system in use. Experience with the use of e-health, measured with 10 items asked respondents about their experiences using the e-health system for communicating diagnostic and treatment decisions, booking, prescribing, decision making support as well as the capture, storage and search of patient data. These questions were developed from a synthesis of the literature [21–23]. Based on the works of Boyer et al. [24] and Alharthi et al. [25], we measured job satisfaction using 6 items to solicit responses to whether the e-health system influenced respondents’ satisfaction with care delivery, commitment to work, clinical decisions and overall satisfaction with job. Motivation was measured with 4 items adopted from literature [26,27]. These questions asked respondents whether the e-health system motivates them to deliver quality services, work hard and achieve best therapeutic outcomes for patients. All the items were captured on a five-point Likert scale that ranged from 1 (strongly disagree) to 5 (strongly agree).
The questionnaire was pre-tested among 10 health workers in two hospitals within the AMA. Comments from the pre-test led to modification of the questionnaire by adding and dropping some items. Two of the authors SJS and BK led the data collection by visiting each hospital daily except weekends, to sample respondents and administer the questionnaire. Respondents completed the questionnaire at their own convenience during or after work except a few who completed and returned the tool at a different day. A total of 350 questionnaires were administered. However, 305 (a response rate of 81%) fully completed questionnaires were retrieved from the sampled nurses (140), doctors (58), pharmacists (51) and laboratory technicians (56). The study had the approval from Ghana Health Service.
Data analysis
Data were analysed using Partial Least Square- Structural Equation Modelling (PLS-SEM) which allowed for estimating a network of relationship between the latent and measured constructs as well as maximising statistical power of the non-parametric data collected [28,29]. The use of PLS-SEM requires using key indicator model assessments such as indicator reliability, internal consistency, composite reliability and convergency validity [29]. Indicator reliability was assessed using confirmatory factor analysis to determine the proportion of variance of each indicator explained by the constructs. We loaded all the 16 indicators in the factor analysis. However, as seen in Table 1, only 12 indicators were strongly loaded above the ≥ 0.7 threshold [30] as follows: e-health usage (4), job satisfaction (5) and motivation (3). Thus, the constructs explained more than 50% of the indicators variance. To determine the internal consistencies of the indicators in measuring each construct, Cronbach Alpha (CA) coefficients were computed. The results from Table 1 show that all the CA were well above the recommended ≥ 0.7 ceiling [31]. This is indicative of good internal consistency of the indicators of each construct.
A disadvantage of CA coefficients is that the indicators are unweighted with the assumption that they are all equally reliable. To give relative weight to each indicator’s reliability, Composite Reliability (CR) values were computed. According to Jöreskog [32], satisfactory CR values must range between 0.70 and 0.9. As shown in Table 1, all the CR values were within acceptable range [33–34]. The constructs’ convergent validity was assessed by computing Average Variance Extracted (AVE) values. Satisfactory convergent validity is achieved when the AVE values are 0.5+ [30]. As shown in Table 1, the AVE values ranged from 0.701 to 0.882. This demonstrates that each construct explained more than 50% of its indicators’ variance.
The associations between the dependent and independent variables were determine using path analysis. The path analysis was done by bootstrapping 5000 subsamples using two tailed tests (at α = 0.01). Positive and negative path coefficient demonstrates how variables influence each other in such directions. Additionally, the amount of variance in a path coefficient reflects the reliability of the influence [15]. The t-value associated with each of the standardised path coefficients is significant if p < 0.05 [13]. Arrows are used to illustrate the value-added paths that have significant influence (Fig 1).
Results
Descriptive statistics
The majority of the respondents were from public hospitals (59.7%), males (51.2%) and aged between 31–40 years (mean: 35; standard deviation: 29.6). The average job tenure was about 6 years (standard deviation: 9.1) (Table 2).
Path analysis
Results of the path coefficients are shown in Table 3. Findings showed a significant positive association of e-health with job satisfaction (p < 0.01) but not motivation (p = 0.42), demonstrating that health workers do not consider e-health system as an essential scheme of motivation at work. Further, the results revealed that type of hospital is significantly associated with job satisfaction (p < 0.001). Although e-health usage is positively associated with type of hospital, the type of hospital does not moderate the association between e-health usage and job satisfaction. Also, the type of hospital interacted with the type of e-health to moderate the relationship between e-health usage and job satisfaction. This suggests that the type of hospital more likely influences the type of e-health tools to be adopted, which in turn significantly influence job satisfaction of health workers. The path coefficients and t-values of the outer loadings are shown in Fig 1.
Discussion
Electronic health records or e-health is evolving rapidly in developing countries [6]. This demonstrates a shift from the traditional paper-based system to digital technology application in healthcare delivery. While studies have shown the impact of e-health systems on better patient records management, improved diagnostic and treatment decisions and overall improvement in quality of care delivery [35–36], the extent to which e-health systems influence health workers job satisfaction and motivation to care remains less explicit. Accordingly, this study examined the associations between e-health usage and health workers motivation and job satisfaction in public and private hospitals.
The findings revealed statistically significant positive relationship between e-health usage and job satisfaction. Thus, a unit increase in e-health usage will lead to 46% increase in job satisfaction, holding other factors constant. The strength of the relationship was moderate. This finding suggests that health workers who used digital systems to communicate, register and consult patients experienced satisfaction with their job. Also, respondents who felt e-health enhances healthcare delivery were more likely to show job satisfaction. These findings correspond to related studies such as Botella et al. [37] who found an increase in job satisfaction and positive emotions among health workers after the adoption of e-health [38]. Jones et al. [16] also found that physicians who perceived that e-health systems are easy to use were more likely to be satisfied with their job. Similarly, Williams et al. [17] revealed that physicians with higher perceived efficiency to use e-health reported higher job satisfaction. This implies that to improve job satisfaction among health workers, healthcare managers should endeavour to connect, or ensure that e-health services have an impact on performance outcomes of health workers. In addition, healthcare managers must ensure that health workers have the requisite ICT skills and competencies to use e-health during the delivery of healthcare. This may require capacity building through training. Capacity building is important because clinicians who participate in e-health proficiency training are more likely to report an increase in job satisfaction and commitment to care [16].
Contrary to an existing study [11], findings showed no statistically significant relationship between e-health usage and motivation. The disparities in findings may be due to variations in contextual factors. In addition, the differences in findings can be attributed to the fact that health workers are motivated by extrinsic and intrinsic motivators. Too often health workers are motivated by extrinsic factors like financial incentives, better conditions of service among others [39]. In this regard, e-health, although an extrinsic issue may not be a strong motivating factor, since it does not directly yield reward or utility. Moreover, it has been shown that health workers consider their profession as a call to serve. For this reason, they may be motivated by intrinsic rewards, such as self-esteem, and not necessarily by extrinsic elements such as e-health usage [40,41]. Nonetheless, making e-health systems attractive, user friendly and incentivising continuous use of e-health for improved healthcare delivery can greatly spur up motivation and greater performance of health workers.
It was found that the type of hospital significantly influenced job satisfaction. This suggests that health workers in public and private hospitals with e-health systems experienced differential levels of job satisfaction. Previous studies confirmed a positive relationship between hospital type and job satisfaction among health workers. For instance, a study by Gudeta [42] found that private health workers in Ethiopia were more likely to be satisfied with their jobs compared to those in the public hospitals. Rana [43] conducted a cross-sectional study among health workers and established that private hospitals’ employees were more satisfied with their jobs than those in the public hospitals. By implication, health workers in private hospitals were more likely to show greater satisfaction with e-health usage than their counterparts in public hospitals. Prior studies also found different factors influencing job satisfaction among public and private hospitals health workers. Waqar and Hamid [44], for example, established that while health professionals in public hospitals showed increased job satisfaction due to reasonable working hours, relatively better wages and other benefits, that of private hospital workers attributed job satisfaction to positive feedbacks on performance, professional growth and promotion system. These findings suggest that factors that influence job satisfaction differ with regards to the type of hospital. Therefore, healthcare managers in private and public hospitals should consider complementing financial and non-financial incentives that increases satisfaction with the use of e-health services.
We found significant negative association between hospital type interaction with e-health and job satisfaction. This imply that the combine effect of hospital type and e-health inversely influence health workers job satisfaction. Overall, the findings indicate that although e-health is an innovative healthcare delivery tool, it may not be functionally beneficial to health workers in the public and private sectors. Going forward, healthcare managers will have to conduct need assessment before implementing digital systems. In addition, healthcare managers should ensure that the necessary structures are put in place prior to adopting digital systems. This will greatly boost motivation and satisfaction with usage of digital technologies.
Limitations
Though this study provides valuable information for improving motivation and job satisfaction with e-health systems in Ghana, it is not without limitations. One limitation is our inability to recruit an equal number of participants from both private and public hospitals. Also, we conducted the study in an urban setting, which has different characteristics from rural settings. In this regard, interpretation of the findings must be done with caution. The cross-sectional nature and the small sample size of this study are also acknowledged as limitations.
Conclusion
The adoption of electronic technology in healthcare delivery is increasing rapidly across the globe. However, evidence on the impact of e-health on job satisfaction and motivation of health workers is limited in developing countries like Ghana. The objective of this study was to investigate the relationship between e-health usage association with job satisfaction and motivation among health workers in the Accra Metropolitan Assembly. We hypothesised that e-health usage will have a positive influence on job satisfaction and motivation. However, we found that e-health usage had positive statistically significant influence on job satisfaction, but not motivation. Our study provides empirical evidence for scaling up electronic health services to improve health workers utility and motivation to care for patients.
Acknowledgments
The authors would like to thank the management of the hospitals and all healthcare workers who participated in this study.
References
- 1. Yusif S, Jeffrey S. Preparedness for e-Health in developing countries: the case of Ghana. Journal of Health Informatics in Developing Countries. 2014;8(2):1–19.
- 2. Bedeley R, Palvia P. A study of the issues of e-health care in developing countries: the case of Ghana. 2014. Available form https://aisel.aisnet.org/cgi/viewcontent.cgi?article=1112&context=amcis2014
- 3.
Ministry of Health. Ghana E-health Strategy Ghana. Available from https://www.isfteh.org/files/media/ghana_national_ehealth_strategy.pdf.
- 4. Hallberg D, Salimi N. Qualitative and Quantitative Analysis of Definitions of e-Health and m-Health. Healthcare Informatics Research. 2020;26(2):119–128. pmid:32547809
- 5. Oh H, Rizo C, Enkin M, Jadad A. What is eHealth (3): a systematic review of published definitions. Journal of Medical Internet Research. 2005;7(1):1–12.
- 6. Li J, Land LPW, Ray P, Chattopadhyaya S. E-Health readiness framework from Electronic Health Records perspective. International Journal of Internet and Enterprise Management. 2010;6(4):326–348.
- 7. Ossebaard HC, Van Gemert-Pijnen L. eHealth and quality in health care: implementation time. International journal for quality in health care. 2016;28(3):415–419. pmid:27029590
- 8. Minichiello V, Rahman S, Dune T, Scott J, Dowsett G. E-health: potential benefits and challenges in providing and accessing sexual health services. BMC Public Health. 2013;13(1):790–797.
- 9.
Botha M, Botha A, Herselman M, editors. The Benefits and Challenges of e-Health Applications: A Content Analysis of the South African context. International Conference on Computer Science, Computer Engineering, and Social Media; 2014: Citeseer.
- 10. Teviu E, Aikins M, Abdulai T, Sackey S, Boni P, Afari E, et al. Improving medical records filing in a municipal hospital in Ghana. Ghana medical journal. 2012;46(3):136–141. pmid:23661826
- 11. Norman ID, Aikins M, Binka F. Ethics and electronic health information technology: challenges for evidence-based medicine and the physician-patient relationship. Ghana medical journal. 2011;45(3):115–124. pmid:22282579
- 12. Pomerleau M. Electronic health record: are you ready for the next step? Nursing for women's health. 2008;12(2):151–156. pmid:18377577
- 13. Miao R, Wu Q, Wang Z, Zhang X, Song Y, Zhang H, et al. Factors that influence users’ adoption intention of mobile health: a structural equation modeling approach. International Journal of Production Research. 2017;55(19):5801–5815.
- 14. Chen L-H. Job satisfaction among information system (IS) personnel. Computers in human behavior. 2008;24(1):105–118.
- 15. Bahadori M, Alimohammadzadeh K, Abdolkarimi K, Ravangard R. Factors Affecting Physicians’ Attitudes Towards the Implementation of Electronic Health Records Using Structural Equation Modeling Modeling (SEM). Shiraz E-Medical Journal. 2017;18(11). http://dx.doi.org/10.5812/semj.13729
- 16. Jones CD, Holmes GM, Lewis SE, Thompson KW, Cykert S, DeWalt DA. Satisfaction with electronic health records is associated with job satisfaction among primary care physicians. Journal of Innovation in Health Informatics. 2013;21(1):18–20.
- 17. Williams DC, Warren RW, Ebeling M, Andrews AL, Teufel RJ II. Physician Use of Electronic Health Records: Survey Study Assessing Factors Associated With Provider Reported Satisfaction and Perceived Patient Impact. JMIR medical informatics. 2019;7(2):e10949. pmid:30946023
- 18. Al-Mujaini A, Al-Farsi Y, Al-Maniri A, Ganesh A. Satisfaction and perceived quality of an electronic medical record system in a tertiary hospital in oman. Oman medical journal. 2011;26(5):324–238. pmid:22125726
- 19. Menachemi N, Powers T, Au DW, Brooks RG. Predictors of physician satisfaction among electronic health record system users. Journal for Healthcare Quality. 2010;32(1):35–41. pmid:20151590
- 20.
Dastagir MT, Chin HL, McNamara M, Poteraj K, Battaglini S, Alstot L, editors. Advanced proficiency EHR training: effect on physicians’ EHR efficiency, EHR satisfaction and job satisfaction. AMIA Annual Symposium Proceedings; 2012: American Medical Informatics Association.
- 21. Neijenhuijs KI, van der Hout A, Veldhuijzen E, Scholten-Peeters GG, van Uden-Kraan CF, Cuijpers P, et al. Translation of the eHealth Impact Questionnaire for a population of Dutch electronic health users: validation study. Journal of Medical Internet Research. 2019;21(8):e13408. pmid:31452516
- 22. Kelly L, Jenkinson C, Ziebland S. Measuring the effects of online health information for patients: item generation for an e-health impact questionnaire. Patient Education and Counseling. 2013;93(3):433–438. pmid:23598293
- 23. Kelly L, Ziebland S, Jenkinson C. Measuring the effects of online health information: Scale validation for the e-Health Impact Questionnaire. Patient education and counseling. 2015;98(11):1418–1424. pmid:26162953
- 24. Boyer L, Baumstarck-Barrau K, Belzeaux R, Azorin J, Chabannes J, Dassa D, et al. Validation of a professionals’ satisfaction questionnaire with electronic medical records (PSQ-EMR) in psychiatry. European Psychiatry. 2011;26(2):78–84. pmid:20418071
- 25. Alharthi H, Youssef A, Radwan S, Al-Muallim S, Zainab A-T. Physician satisfaction with electronic medical records in a major Saudi Government hospital. Journal of Taibah University Medical Sciences. 2014;9(3):213–218.
- 26.
Govindaraju R, Hadining AF, Chandra DR, editors. Physicians’ Adoption of Electronic Medical Records: Model Development Using Ability–Motivation—Opportunity Framework2013; Berlin, Heidelberg: Springer Berlin Heidelberg.
- 27. Duhm J, Fleischmann R, Schmidt S, Hupperts H, Brandt SA. Mobile electronic medical records promote workflow: Physicians’ perspective from a survey. JMIR mHealth and uHealth. 2016;4(2):1–9.
- 28. Ringle CM, Wende S, Becker J-M. SmartPLS 3. Boenningstedt: SmartPLS GmbH2015.
- 29. Hair JF, Ringle CM, Sarstedt M. Partial least squares structural equation modeling: Rigorous applications, better results and higher acceptance. Long Range Planning. 2013;46(1–2):1–12.
- 30. Hair JF, Risher JJ, Sarstedt M, Ringle CM. When to use and how to report the results of PLS-SEM. European Business Review. 2019;31(1):2–24.
- 31.
Field A. Discovering statistics using IBM SPSS statistics. London: Sage; 2013.
- 32. Jöreskog KG. Simultaneous factor analysis in several populations. Psychometrika. 1971;36(4):409–426.
- 33.
Pallant J. SPSS survival manual: McGraw-Hill Education (UK); 2013.
- 34. Chen R-S, Tsai C-C. Gender differences in Taiwan university students' attitudes toward web-based learning. Cyberpsychology & behavior. 2007;10(5):645–654.
- 35. Wilson EV, Lankton NK. Modeling patients' acceptance of provider-delivered e-health. Journal of the American Medical Informatics Association. 2004;11(4):241–248. pmid:15064290
- 36. Yellowlees P, Marks S, Hilty D, Shore JH. Using e-health to enable culturally appropriate mental healthcare in rural areas. Telemedicine and e-Health. 2008;14(5):486–492. pmid:18578685
- 37. Botella C, Etchemendy E, Castilla D, Baños RM, García-Palacios A, Quero S, et al. An e-health system for the elderly (Butler Project): A pilot study on acceptance and satisfaction. CyberPsychology & Behavior. 2009;12(3):255–262.
- 38. Maillet É, Mathieu L, Sicotte C. Modeling factors explaining the acceptance, actual use and satisfaction of nurses using an Electronic Patient Record in acute care settings: An extension of the UTAUT. International journal of medical informatics. 2015;84(1):36–47. pmid:25288192
- 39. Adzei FA, Atinga RA. Motivation and retention of health workers in Ghana's district hospitals. Journal of Health Organization and Management. 2012;26(4):467–485.
- 40. Goetz K, Campbell S, Broge B, Dörfer C, Brodowski M, Szecsenyi J. The impact of intrinsic and extrinsic factors on the job satisfaction of dentists. Community dentistry and oral epidemiology. 2012;40(5):474–480. pmid:22519887
- 41. Galletta M, Portoghese I, Battistelli A. Intrinsic motivation, job autonomy and turnover intention in the Italian healthcare: The mediating role of affective commitment. Journal of Management research. 2011;3(2):1–19.
- 42. Gudeta NS. Comparative Study on Job Satisfaction among Health Workers in Public and Private Sector Hospitals at South-West Shoa Zone, Oromia Regional State, Ethiopia. 2017.
- 43. Rana A. Job Satisfaction among Healthcare Employees: An Empiricial Analysis. International Journal of Commerce, Business and Management. 2014;3(4):526–532.
- 44. Waqar S, Hamid S. Job satisfaction among nurses in public and private hospitals of rawalpindi and islamabad. Pakistan Journal of Public Health. 2016;6(4):44–48.