Pre-Implementation evaluation of a nationwide information system for university hospitals: lessons learned from a study in Brazil

Mussi, Clarissa Carneiro; Pereira, Christine; Lacerda, Rogério Tadeu de Oliveira; Santos, Ernani Marques dos

doi:10.1080/0144929x.2018.1430850

Cited by 12 publications

(12 citation statements)

References 33 publications

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“…Therefore, the item was removed from the measurement instrument. The nature of health care settings, wherein diverse perspectives, power asymmetry, and politically led changes co-exist, supports this approach [ 26 ]. The use of an AI-enabled clinical decision support system tends to be mandatory, thus it is difficult to interpret users’ evaluations with respect to system usage.…”

Section: Discussionmentioning

confidence: 99%

“…Second, attention to evaluation of clinical decision support systems, in general, and AI-enabled clinical decision support systems, in particular, remains weak [ 23 ], which has resulted in a paucity of data on safety, effectiveness, cost benefits, and impacts of AI-enabled clinical decision support systems on patients and health systems [ 24 , 25 ]. Finally, the evaluation of AI-enabled clinical decision support systems is a learning and knowledge-gaining process, and it also helps to identify the gaps to be filled [ 26 ]. Findings of comprehensive evaluations could be used to help improve implementations [ 27 ].…”

Section: Introductionmentioning

confidence: 99%

“…It has increasingly been acknowledged that evaluations of such systems are based on a sociological understanding of the complex practices in which the information technologies are to function [ 28 ]. A careful balance between social and technical value is required in order to ensure that unwanted consequences do not pose a threat to patients [ 26 ] and clinical practices.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Evaluation Framework for Successful Artificial Intelligence–Enabled Clinical Decision Support Systems: Mixed Methods Study

Ji¹,

Genchev²,

Huang³

et al. 2021

J Med Internet Res

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Background Clinical decision support systems are designed to utilize medical data, knowledge, and analysis engines and to generate patient-specific assessments or recommendations to health professionals in order to assist decision making. Artificial intelligence–enabled clinical decision support systems aid the decision-making process through an intelligent component. Well-defined evaluation methods are essential to ensure the seamless integration and contribution of these systems to clinical practice. Objective The purpose of this study was to develop and validate a measurement instrument and test the interrelationships of evaluation variables for an artificial intelligence–enabled clinical decision support system evaluation framework. Methods An artificial intelligence–enabled clinical decision support system evaluation framework consisting of 6 variables was developed. A Delphi process was conducted to develop the measurement instrument items. Cognitive interviews and pretesting were performed to refine the questions. Web-based survey response data were analyzed to remove irrelevant questions from the measurement instrument, to test dimensional structure, and to assess reliability and validity. The interrelationships of relevant variables were tested and verified using path analysis, and a 28-item measurement instrument was developed. Measurement instrument survey responses were collected from 156 respondents. Results The Cronbach α of the measurement instrument was 0.963, and its content validity was 0.943. Values of average variance extracted ranged from 0.582 to 0.756, and values of the heterotrait-monotrait ratio ranged from 0.376 to 0.896. The final model had a good fit (χ262=36.984; P=.08; comparative fit index 0.991; goodness-of-fit index 0.957; root mean square error of approximation 0.052; standardized root mean square residual 0.028). Variables in the final model accounted for 89% of the variance in the user acceptance dimension. Conclusions User acceptance is the central dimension of artificial intelligence–enabled clinical decision support system success. Acceptance was directly influenced by perceived ease of use, information quality, service quality, and perceived benefit. Acceptance was also indirectly influenced by system quality and information quality through perceived ease of use. User acceptance and perceived benefit were interrelated.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Evaluation Framework for Successful Artificial Intelligence–Enabled Clinical Decision Support Systems: Mixed Methods Study

Ji¹,

Genchev²,

Huang³

et al. 2021

J Med Internet Res

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“…Dari rangkuman faktor kegagalan, sebanyak 12 literatur merupakan studi mengenai faktor kegagalan pada pemerintah [8], [9], [18], [19], [10]- [17] dan 6 literatur merupakan studi faktor kegagalan pada lembaga dibawah pemerintah [20]- [25]. Secara umum, faktor-faktor kegagalan pada dua studi ini sama dan mirip.…”

Section: Hasil Dan Pembahasanunclassified

Evaluasi Faktor Kegagalan Sistem Informasi Pada Kesiapan Penerapan E-Government: Studi Literatur

Pamungkas

Nugroho

Sulistyo³

2020

JIKO

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“…Their research demonstrated that "problems that usually arise from the implementation and use of nationwide HIS can be anticipated and dealt with beforehand when preimplementation qualitative evaluations are performed". [4] Kowalski et al (2018) illustrated in their article entitled "Formative evaluation and adaptation of pre-and early implementation of diabetes shared medical appointments to maximize sustainability and adoption" the critical role and great importance that the preimplementation assessments have during the implementation stage and on the future success or failure of the project. [5] The table below illustrates an example of sustainability evaluation tool for a road project: Quantifying sustainable practices associated with the construction and maintenance process.…”

Section: Literature Reviewmentioning

confidence: 99%

A New Cost in Measuring Investment Performance - Cost of Non – Intervention

Manta¹,

Dima²,

Andreica³

2019

Risk in Contemporary Economy

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The purpose of this paper is to present a new type of cost that should be taken into consideration in the process of evaluation. The cost proposed improves the accuracy of the final results of this process, concerning the opportunity for implementation of the future investment project. The proposal of the cost was made based on the literature review carried out on the topic of investment project evaluation and on the authors' practical experience as a project evaluator and project manager. The paper's originality is supported by the fact that it brings in a new type of cost in measuring investment projects' performance.

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Pre-Implementation evaluation of a nationwide information system for university hospitals: lessons learned from a study in Brazil

Cited by 12 publications

References 33 publications

Evaluation Framework for Successful Artificial Intelligence–Enabled Clinical Decision Support Systems: Mixed Methods Study

Evaluation Framework for Successful Artificial Intelligence–Enabled Clinical Decision Support Systems: Mixed Methods Study

Evaluasi Faktor Kegagalan Sistem Informasi Pada Kesiapan Penerapan E-Government: Studi Literatur

A New Cost in Measuring Investment Performance - Cost of Non – Intervention

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