Towards an Encompassing Maturity Model for the Management of Hospital Information Systems

Carvalho, João Vidal; Rocha, Álvaro; Vasconcelos, José Braga de

doi:10.1007/s10916-015-0288-1

Cited by 28 publications

(12 citation statements)

References 26 publications

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“…There are many algorithms based on clonal selection in the literature, most of which have been applied to optimization problems (e.g., CLONALG [10] and opt-IA is used in [11], and the algorithm to B cells in [12]), and multi-objective optimization (see [13]). From a computational point of view, the idea of clonal selection leads to algorithms that iteratively improve the possible solutions to a given problem through is almost the same as clonal selection, except that there exists a mechanism of deletion that destroys the cells having a certain inception of affinity amidst.…”

Section: Clonal Selectionmentioning

confidence: 99%

An immune memory and negative selection to visualizing clinical pathways from electronic health record data

Berquedich

Kamach

Masmoudi

et al. 2020

IJEECS

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Clinical pathways indicate the applicable treatment order of interventions. In this paper we propose a data-driven methodology to extract common clinical pathways from patient-centric Electronic Health Record data (EHR). The analysis of patient's, can lead to better regarding pathologies. The proposed algorithmic methodology consist to designing a system of control and analysis of patient records based on an analogy between the elements of the new EHRs and the biological immune systems. The detection of patient profiles ensured by biclustering Matrix. We rely on biological immunity to develop a set of models for structuring knowledge extracted from EHR and to make pathway analysis decisions. A specific analysis of the functional data leds to the detection of several types of patients who share the same EHR information. This methodology demonstrates its ability to simultaneously processing data, and is able to providing information for understanding and identifying the path of patients as well as predicting the path of future patients.

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Section: Clonal Selectionmentioning

confidence: 99%

An immune memory and negative selection to visualizing clinical pathways from electronic health record data

Berquedich

Kamach

Masmoudi

et al. 2020

IJEECS

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“…This is intended to identify the 'anticipated, typical, logical and desired evolution path' to readiness for an organisation. 13 The implementation of these digital technologies is to enable transfers of patient care between different agencies at admission, referral or discharge. This includes the management of medicines, and the establishment and monitoring of clinical standards.…”

Section: What Is Digital Maturity?mentioning

confidence: 99%

“…The NHS has committed to generating its first digital maturity index, which will be annually updated. 13 This index has informed pre-requisites to readiness for each of the 73 local digital roadmaps that are now championed by appointed chief clinical information officers across 211 clinical commissioning groups.…”

Section: Twittermentioning

confidence: 99%

Digital maturity: are we ready to use technology in the NHS?

Johnston¹

2017

Future Healthcare Journal

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Digital maturity assessments (DMAs) are a self-assessment mechanism for organisations. They can be effectively utilised to generate local digital roadmaps. In their simplest form, these allow organisations to understand their state of readiness to integrate digital technologies. This is achieved by assessing the capability and compatibility of their information systems to communicate or interface both within and across organisations. Through utilising and responding to the fi ndings of DMAs, it is thought that the NHS will be better able to provide a patient-centred service to meet local needs within a national framework. It is this exchange and integration of information across health and social care systems that will drive innovation and transformation in the NHS.

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“…1,2 Hospital information systems (HIS) managers often look at the mistakes experienced in these organizations and wonder what could have been done to avoid them. 3 The fact is that these errors are usually natural symptoms of the growth and maturation of organizations, which often derive from the development that brought the organization to its current maturity stage. 4 This phenomenon of change, experienced by an organization from inception to maturity, reflects the principles underlying stages of growth theory and the current context of information systems (IS) in health organizations.…”

Section: Introductionmentioning

confidence: 99%

Maturity of hospital information systems: Most important influencing factors

Carvalho

Rocha

Abreu

2017

Health Informatics J

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Maturity models facilitate organizational management, including information systems management, with hospital organizations no exception. This article puts forth a study carried out with a group of experts in the field of hospital information systems management with a view to identifying the main influencing factors to be included in an encompassing maturity model for hospital information systems management. This study is based on the results of a literature review, which identified maturity models in the health field and relevant influencing factors. The development of this model is justified to the extent that the available maturity models for the hospital information systems management field reveal multiple limitations, including lack of detail, absence of tools to determine their maturity and lack of characterization for stages of maturity structured by different influencing factors.

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Towards an Encompassing Maturity Model for the Management of Hospital Information Systems

Cited by 28 publications

References 26 publications

An immune memory and negative selection to visualizing clinical pathways from electronic health record data

An immune memory and negative selection to visualizing clinical pathways from electronic health record data

Digital maturity: are we ready to use technology in the NHS?

Maturity of hospital information systems: Most important influencing factors

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