A Focus Area Maturity Model for a Statewide Master Person Index

Duncan, Jeffrey; Xu, Wu; Narus, Scott P.; Nangle, Barry; Thornton, Sid; Facelli, Julio C.

doi:10.5210/ojphi.v5i2.4669

Cited by 4 publications

(2 citation statements)

References 13 publications

(11 reference statements)

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“…First, the focus area groups and the focus areas were defined. Each group was divided into several focus areas, defined as groups of related activities, events, deliverables, or products that support the domain [34]. Grouping the focus areas into labeled categories can increase model accessibility [31].…”

Section: Populatementioning

confidence: 99%

Development of a Maturity Model for Assessing Smart Cities: A Focus Area Maturity Model

Aljowder,

Ali,

Kurnia

2023

Smart Cities

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The concept of smart cities has gained significant attention due to the potential of smart cities to optimize city services and enhance citizens’ quality of life. Cities are investing in digital transformation to become smarter, sustainable, and resilient. Therefore, there is a need to build a comprehensive and holistic model to assess smart city initiatives. This paper aims to develop a model that can capture the maturity of smart city adoption across various functional domains. These domains are divided into focus areas that capture different dimensions of a smart city and grouped into seven groups: ICT, economy, environment, social, resources, services, and governance. Each focus area has a set of maturity levels that describe the capabilities and outcomes of the city at different stages of development. To develop the model, the focus areas were extracted from the literature based on 16 models that have been reviewed. Assessing these models helped in identifying gaps and building the foundation of the model. Using the information extracted from the literature, a focus area model was designed and developed. The model development included seven main phases, which were: scope, design, populate, test, deploy, and maintain. The current paper validates the proposed model using the Delphi method, which involves the participation of a panel of sixty field experts. The experts evaluated the model’s correctness and completeness based on their experience and provided feedback. This feedback was used to revise and finalize the model. The smart city maturity model provides a framework for benchmarking, planning, and improving smart city initiatives. Cities can use the model to measure their performance and evaluate their weaknesses and strengths. The model is also the most comprehensive in terms of the scope of the focus areas included, and the results show that the model has a high level of accuracy and consistency and can effectively assess smart city adoption.

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Section: Populatementioning

confidence: 99%

Development of a Maturity Model for Assessing Smart Cities: A Focus Area Maturity Model

Aljowder,

Ali,

Kurnia

2023

Smart Cities

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“…Integrated child health information systems [ 17 ], such as Utah’s Child Health Advanced Record Management (CHARM) [ 18 ], attempt to link these records using combinations of non-unique demographic identifiers such as name, date of birth, sex, address, and telephone number, and locally unique identifiers such as newborn screening kit numbers and birth certificate state file numbers. In addition, efforts such as Utah’s statewide master person index have attempted to link persons across public health and healthcare master person indices (MPIs) [ 19 ].…”

Section: Background and Significancementioning

confidence: 99%

Building an Ontology for Identity Resolution in Healthcare and Public Health

Duncan

Eilbeck

Narus

et al. 2015

OJPHI

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Integration of disparate information from electronic health records, clinical data warehouses, birth certificate registries and other public health information systems offers great potential for clinical care, public health practice, and research. Such integration, however, depends on correctly matching patient-specific records using demographic identifiers. Without standards for these identifiers, record linkage is complicated by issues of structural and semantic heterogeneity.Objectives: Our objectives were to develop and validate an ontology to: 1) identify components of identity and events subsequent to birth that result in creation, change, or sharing of identity information; 2) develop an ontology to facilitate data integration from multiple healthcare and public health sources; and 3) validate the ontology’s ability to model identity-changing events over time.Methods: We interviewed domain experts in area hospitals and public health programs and developed process models describing the creation and transmission of identity information among various organizations for activities subsequent to a birth event. We searched for existing relevant ontologies. We validated the content of our ontology with simulated identity information conforming to scenarios identified in our process models.Results: We chose the Simple Event Model (SEM) to describe events in early childhood and integrated the Clinical Element Model (CEM) for demographic information. We demonstrated the ability of the combined SEM-CEM ontology to model identity events over time.Conclusion: The use of an ontology can overcome issues of semantic and syntactic heterogeneity to facilitate record linkage.

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Focus Area Maturity Models: A Comparative Review

Sanchez-Puchol

Pastor-Collado

2017

Information Systems

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A Focus Area Maturity Model for a Statewide Master Person Index

Cited by 4 publications

References 13 publications

Development of a Maturity Model for Assessing Smart Cities: A Focus Area Maturity Model

Development of a Maturity Model for Assessing Smart Cities: A Focus Area Maturity Model

Building an Ontology for Identity Resolution in Healthcare and Public Health

Focus Area Maturity Models: A Comparative Review

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