Organizational Dynamics of Sustainable eHealth Implementation: A Case Study of eHMIS

Fanta, Getnet Bogale; Pretorius, Leon; Erasmus, Louwrence

doi:10.23919/picmet.2017.8125472

Cited by 9 publications

(17 citation statements)

References 23 publications

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“…[8][9][10][11] As the developed nations in the developing world even though the progress and the success rate are not satisfied there are a trial and deployment of eHealth technology in many sectors of the health arena. [12][13][14][15][16] Un despised changes in documentation practice, patient alerting, teleconsultation and evidence-based practice were showed and a big emphasis from the government side in deploying systems is also increased with a growing need of identifying crucial constructs for the sustainable adoption of eHealth technologies. 14,15 In Ethiopia, several eHealth technologies had been implemented like SmartCare, mobile ENAT messenger, maternal interactive voice record (IVR) and Health management information system (HMIS).…”

Section: Introductionmentioning

confidence: 99%

<p>The Applicability of the Modified Technology Acceptance Model (TAM) on the Sustainable Adoption of eHealth Systems in Resource-Limited Settings</p>

Kalayou¹,

Endehabtu²,

Tilahun³

2020

JMDH

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Background: The implementation of eHealth systems with a trial-and-error approach is very expensive and unsuccessful. So, this study aims to examine the constructs and relationships of the modified technology acceptance model (TAM) to determine whether it can be applied to assess health professional's behavioral intention to adopt eHealth systems in resource-limited settings or not. Methods: The institutional-based cross-sectional study design was conducted among a total of 384 healthcare professionals in referral hospitals of Amhara regional state, Ethiopia. Selfadministered questionnaire was used to collect the data, and the data were entered using Epiinfo version 7 and the descriptive data were analyzed using SPSS version 25. Structural equation modeling, using AMOS 22, was also applied to describe and validate the degree of relationships between variables. Results: The findings of the structural equation modeling (SEM) indicate that perceived usefulness has a significant influence on attitude (β =0.298, P<0.01) and intention to use eHealth (β =0.387, P<0.01). Perceived ease of use has significant influence on perceived usefulness (β=0.385, P<0.05) and attitude (β=0.347, P<0.05) and intention to use eHealth (β=0.339, P<0.01). Technical infrastructure has significant influence on attitude (β =0.412, P<0.01) and intention to use eHealth (β =0.355, P<0.01). The staffs IT experience has a significant influence on perceived usefulness (β =0.595, P<0.01) and attitude (β =0.267, P<0.05), but the effect of IT experience on the intention to use eHealth was not significant. Among all the constructs, healthcare professionals attitude towards eHealth showed the strongest effect on the intention to use eHealth systems (β = 0.52, P<0.01). Conclusion: Overall, this model describes 56.2% of the variance in behavioral intention to use eHealth systems. Therefore, the implementers should give priority in enhancing the organizations technical infrastructure, staff's IT skill, and their attitude towards eHealth by giving continuous support.

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Section: Introductionmentioning

confidence: 99%

<p>The Applicability of the Modified Technology Acceptance Model (TAM) on the Sustainable Adoption of eHealth Systems in Resource-Limited Settings</p>

Kalayou¹,

Endehabtu²,

Tilahun³

2020

JMDH

View full text Add to dashboard Cite

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“…The implementations of digital health systems are still in their nascent ages in developing countries and more needs to be done in understanding factors that impact on preimplementation to help get it right during the implementation and post-implementation. These are important components of organizational cultural readiness -the ability to adapt to changes brought about new systems (Fanta et al, 2017). On the one hand, while technical issues are no strange to systems implementation, low uptake and general digital health project failures could not only be attributed to technical issues, but also organizational and human-related issues have become key determinants of digital health successes on the other hand as non-technical factors.…”

Section: Resultsmentioning

confidence: 99%

PLS-SEM path analysis to determine the predictive relevance of e-Health readiness assessment model

et al. 2020

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Background and significance: There a sizable body of research addressing the evaluation of eHealth/health information technology (HIT) readiness using a standard readiness model in the domain of IS. However, there is a general lack of reliable indicators used in measuring readiness assessment factors, resulting in limited predictability. The availability of reliable measuring tools could help improve outcome of readiness assessments.Objective: Determining the predictive relevance of developed HIT model Materials and methods:We collected quantitative data from clinical and non-clinical (administrators) staff at Komfo Anokye Teaching Hospital (KATH), Kumasi Ghana using the traditional in-person distribution of paper-based survey, popularly known as drop and collect survey (DCS). We then used PLS-SEM path analysis to measure the predictive relevance of a block of manifest indicators of the readiness assessment factors.Results and discussion: Three important readiness assessment factors are thought to define and predict the structure of the KATH HIT/eHealth readiness survey data (Technology readiness (TR); Operational resource readiness (ORR); and Organizational cultural readiness (OCR). As many public healthcare organizations in Ghana have already gone paperless without any reliable HIT/eHealth guiding policy, there is a critical need for reliable HIT/eHealth regulatory policies readiness (RPR) and some improvement in HIT/eHealth strategic planning readiness (core readiness). Conclusion:The final model (R 2 = 0.558 and Q 2 = 0.378) suggest that TR, ORR, and OCR explained 55.8% of the total amount of variance in HIT/eHealth readiness in the case of KATH and the relevance of the overall paths of the model was predictive. Fit values (SRMR = 0.054; d_ULS = 6.717; d_G = 6.231; Chi 2 = 6,795.276; NFI = 0.739). Generally, the GoF for this SEM are encouraging and can substantially be improved.

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“…The included articles covered a range of eHealth applications. Some included a holistic cost-benefit analysis of various eHealth measures in care [2,20,21]. Other articles analyzed specific eHealth tools, such as patient web portals [22] or mobile-based interventions [23].…”

Section: Literature Analysismentioning

confidence: 99%

Visualizing Benefits of Case Management Software Using Utility Effect Chains

et al. 2023

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Labor shortages lead to crucial investment decisions, such as selecting software supporting work processes. The healthcare sector stands out because of additional restructuring due to demographic changes. This is particularly true for the care sector; hence, customized case management software (CMSW) solutions for healthcare professionals are being developed. In an increasingly profit-oriented healthcare system, sustainability, cost-effectiveness and quantification of benefits of investments play a major role. We analyzed research dealing with the benefits of case and care management software and, additionally, interviewed case managers who use recently developed CMSW within a case study. We used utility effect chains to visualize and quantify the gathered benefits of an information system (IS) investment along with the healthcare system in Germany. The findings show that benefits from care management software need to be seen more holistically. Utility effect chains can serve as a helpful instrument for the visualization of indirect benefits in healthcare. The most significant benefits of CMSW were found to be various cost savings for each of the participating stakeholders, a reduction in redundant entries of patient data and the prevention of cost-intensive revolving door cases. Additionally, the insight into patient records reduces time-consuming communication among health experts and family caregivers.

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Organizational Dynamics of Sustainable eHealth Implementation: A Case Study of eHMIS

Cited by 9 publications

References 23 publications

<p>The Applicability of the Modified Technology Acceptance Model (TAM) on the Sustainable Adoption of eHealth Systems in Resource-Limited Settings</p>

<p>The Applicability of the Modified Technology Acceptance Model (TAM) on the Sustainable Adoption of eHealth Systems in Resource-Limited Settings</p>

PLS-SEM path analysis to determine the predictive relevance of e-Health readiness assessment model

Visualizing Benefits of Case Management Software Using Utility Effect Chains

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