Involving End Users to Mitigate Risk in IS Development Projects

Amrit, Chintan; Hillegersberg, Jos van; Diest, Bart van

doi:10.4018/joeuc.2013070105

Cited by 8 publications

(3 citation statements)

References 32 publications

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“…We cannot stress this enough, but the choice of the method used should always meet enduser expectations and expertise. [57][58][59][60] Models developed using machine learning approaches are usually seen as black boxes by clinicians and policy makers, and are either abandoned or lead to inappropriate use. [61][62][63] Sometimes simple models such as logistic regression provide greater interpretability inspiring more confidence in their use.…”

Section: Discussionmentioning

confidence: 99%

Developing and validating a predictive model for future emergency hospital admissions

Stylianou

Young²,

Peden

et al. 2022

Health Informatics J

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Although many emergency hospital admissions may be unavoidable, a proportion of these admissions represent a failure of the care system. The adverse consequences of avoidable emergency hospital admissions affect patients, carers, care systems and substantially increase care costs. The aim of this study was to develop and validate a risk prediction model to estimate the individual probability of emergency admission in the next 12 months within a regional population. We deterministically linked routinely collected data from secondary care with population level data, resulting in a comprehensive research dataset of 190,466 individuals. The resulting risk prediction tool is based on a logistic regression model with five independent variables. The model indicated a discrimination of area under the receiver operating characteristic curve of 0.9384 (95% CI 0.9325–0.9443). We also experimented with different probability cut-off points for identifying high risk patients and found the model’s overall prediction accuracy to be over 95% throughout. In summary, the internally validated model we developed can predict with high accuracy the individual risk of emergency admission to hospital within the next year. Its relative simplicity makes it easily implementable within a decision support tool to assist with the management of individual patients in the community.

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Section: Discussionmentioning

confidence: 99%

Developing and validating a predictive model for future emergency hospital admissions

Stylianou

Young²,

Peden

et al. 2022

Health Informatics J

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show abstract

“…The outputting system is made by watching results automatic report. Moreover, parts of the whole system are interconnected in order to facilitate transformation of data into information (Amrit, Hillegersberg & Diest, 2013). Once at field, the system itself acquires geographical data in order to locate the beneficiary, the field staff may download surveys from the system and set questions relying on project purpose, objectives and indicators.…”

Section: Management Information System At Survivors Fund Rwandamentioning

confidence: 99%

Influence of Utilization of Management Information System on Project Monitoring and Evaluation: A Case Study of Survivors Fund, Rwanda

Rafiki,

Nyabuga,

Alfred

2023

CJMS

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The study examined how the utilization of Management Information Systems influences monitoring and evaluation in an organization, by taking into account the Survivors Fund in Rwanda as a case study. The study analyzed the effectiveness and efficiency of the MIS, report, services, hardware, software, and process factors that influence the effectiveness and efficiency of Monitoring and Evaluation at SURF, Rwanda. The target population included 293 beneficiaries, 8 Field officers, 1 ICT Manager, and 1 Project Manager. Data was collected through questionnaires, interview guides, and secondary documented literature.The Multiple Regression Analysis showed a strong positive correlation between the independent variables (flexibility of the system, regular employee training on MIS infrastructure, adequate edit, balance, and internal control checks) and the dependent variable (timely results and feedback). The coefficient of determination (r2) was 1, indicating that 100% of the total variation in Y could be explained by the stochastic multiple regression equation model. The study also found a positive correlation between the independent variables (system use of hardware, compatibility with other standards appliances and software, completeness, clarity, organization of documentation, app providers' accessibility, quality product support) and the dependent variable (economic use of M&E resources). The coefficient of determination (r2) was 0.200, suggesting that 20% of the total variation in Y could be explained by the model, while the remaining 80% was due to unexplained factors. The study recommends Survivors Fund, Rwanda to check with the system provider and update the system with a consistent interface and compatibility with various devices. Additionally, the system should provide machine utilization reports.

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“…Gestão de portfólio de projetos, portanto, pode ser considerado como um processo de decisão dinâmico, em que uma lista de projetos ativos é constantemente atualizada e revisada. Pode utilizar quadros normativos e técnicas de avaliação, seleção e priorização, alocação de recursos, e compartilhamento de conhecimentos entre os projetos (MARTINSUO, 2013; AMRIT; VAN HILLEGERSBERG; VAN DIEST, 2013).…”

Section: Gerenciamento De Portfólios De Projetosunclassified

Sucesso no desenvolvimento de novos produtos na perspectiva do gerenciamento de portfólio.

Haddad¹

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Involving End Users to Mitigate Risk in IS Development Projects

Cited by 8 publications

References 32 publications

Developing and validating a predictive model for future emergency hospital admissions

Developing and validating a predictive model for future emergency hospital admissions

Influence of Utilization of Management Information System on Project Monitoring and Evaluation: A Case Study of Survivors Fund, Rwanda

Sucesso no desenvolvimento de novos produtos na perspectiva do gerenciamento de portfólio.

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