Qualitative review of usability problems in health information systems for radiology

Dias, Camila Rodrigues; Pereira, Marluce Rodrigues; Freire, André Pimenta

doi:10.1016/j.jbi.2017.10.004

Cited by 17 publications

(17 citation statements)

References 31 publications

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“…In addition to the algorithmic details of an AI application, the way the application can be integrated into the work environment has an impact on the job of the radiologist. Usability is essential to ensure that radiologists use the application in their daily work [ 25 , 26 ]. The seamless PACS integration of many of the investigated AI applications facilitates radiologists’ efforts in using these applications.…”

Section: Discussionmentioning

confidence: 99%

Promises of artificial intelligence in neuroradiology: a systematic technographic review

2020

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Purpose To conduct a systematic review of the possibilities of artificial intelligence (AI) in neuroradiology by performing an objective, systematic assessment of available applications. To analyse the potential impacts of AI applications on the work of neuroradiologists. Methods We identified AI applications offered on the market during the period 2017–2019. We systematically collected and structured information in a relational database and coded for the characteristics of the applications, their functionalities for the radiology workflow and their potential impacts in terms of ‘supporting’, ‘extending’ and ‘replacing’ radiology tasks. Results We identified 37 AI applications in the domain of neuroradiology from 27 vendors, together offering 111 functionalities. The majority of functionalities ‘support’ radiologists, especially for the detection and interpretation of image findings. The second-largest group of functionalities ‘extends’ the possibilities of radiologists by providing quantitative information about pathological findings. A small but noticeable portion of functionalities seek to ‘replace’ certain radiology tasks. Conclusion Artificial intelligence in neuroradiology is not only in the stage of development and testing but also available for clinical practice. The majority of functionalities support radiologists or extend their tasks. None of the applications can replace the entire radiology profession, but a few applications can do so for a limited set of tasks. Scientific validation of the AI products is more limited than the regulatory approval.

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Section: Discussionmentioning

confidence: 99%

Promises of artificial intelligence in neuroradiology: a systematic technographic review

2020

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“…The prototype, shown in Figure 1, exposes the clarity and simplicity necessary for the user to start and complete a task, highlighting the influence of usability and the importance of adapting to the context of using the product; it offers the user the ability to perform his/her tasks in a way pleasant, within effective and efficient parameters (Nielsen & Loranger, 2007;Pádua, 2012). The approach with the user prevents his/her fatigue and confusion that could later result in his/her performance (example: loss of time, rework to insert information, insertion of incomplete information) or, still, in the rejection of the information system (Dias et al, 2017;Khajouei et al, 2017).…”

Section: Item Arrangementmentioning

confidence: 99%

“…Usability problems must be identified and addressed both during the development of the system and during and after implementation, with a prominent role in improving patient care and the quality of information (Dias et al, 2017).…”

Section: Item Arrangementmentioning

confidence: 99%

“…Furthermore, problems related to usability are alluded to as significant barriers to the adoption of health information systems that result in losses for decision making, time management and productivity of professionals and can induce professionals to enter wrong information (Dias, Pereira, & Freire, 2017;Khajouei, Esfahani, & Jahani, 2017).…”

Section: Introductionmentioning

confidence: 99%

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Electronic Unified Health System: opportunities for improvement

Sampaio

Évora

2020

Acta Sci. Health Sci.

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This study aimed to develop and evaluate a prototype with opportunities for improvement for the e-SUS Primary Care module, integrated in the proprietary system used at the research site, with a view to improving the health information system. It is an applied research, with a qualitative approach, in the action-research modality. The population consisted of all (30) Community Health Agents assigned to six Family Health Centers in the city of Ribeirão Preto, State of São Paulo, Brazil and six computer experts, who agreed to participate in the research by signing the Informed Consent Form. The study was developed in three stages. Stage 1 - Knowledge brought by the Community Health Agent (CHA): observation, by the researcher, of the execution of professional activities in the Electronic Unified Health System of Primary Health Care and unstructured interview, between May and July 2016; Stage 2 - Construction of the prototype with opportunities for improvement for the e-SUS AB module. We used the AXURE RP Pro 7.0 software. Stage 3- Evaluation of the prototype by the CHA and computer experts. An evaluation instrument was developed considering the specifications described by ISO/IEC 25010, 9241 and 14598 with the parameters: poor, fair, good and excellent. The results show the importance of knowing the aspects related to the way of working, or behavior, that can influence the final quality of the data inserted in the Health Information Systems. In the same way, such systems aim to obtain inputs for the definition of the requirements and goals of usability, in order to meet the different user roles identified, and also reinforces the relevance of looking at own systems, acquired in the private market or developed in the municipality. The rapprochement between the researcher and the people involved in the investigated situation allowed for the detection of gaps between the indispensable components to carry out the actions that aim to consolidate computerization in health and, above all, to outline potential solutions to the problems identified.

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“…In addition, users should have the option to easily collaborate on information, also in special interest groups. Thus, a special focus must be on visualization of data, in such a manner that the user can intuitively understand the information (Marcial, 2014;Dias et al, 2017).…”

Section: Introduction: the Opportunities And Challengesmentioning

confidence: 99%

Better Patient Outcomes Through Mining of Biomedical Big Data

Suter‐Crazzolara

2018

Front. ICT

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Digitalization is changing healthcare today. Big data analytics of medical information allows diagnostics, therapy and development of personalized medicines, to provide unprecedented treatment. This leads to better patient outcomes, while containing costs. In this review, opportunities, challenges and solutions for this health-data revolution are discussed. Integration and near-instant-response analytics across large datasets can support care-givers and researchers to test and discard hypotheses more quickly. Physicians want to compare a patient to other similar patients, to learn and communicate about treatment best-practices with peers, across large cohorts and sets of parameters. Real-time interactions between physician and patient are becoming more important, allowing "live" support of patients instead of single interactions once every few weeks. Researchers from many disciplines (biomedical, payers, governments) want to interpret large anonymized datasets, to uncover trends in drug-candidate behavior, treatment regimens, clinical trials or reimbursements, and to act on those insights. These opportunities are however met by daunting challenges. Biomedical information is available in data silos of structured and unstructured formats (doctor letters, patient records, omics data, device data). Efficient usage of biomedical information is also hampered by data privacy concerns. This has led to a highly-regulated industry, as a result of which digitalization in healthcare has progressed slower than in other industries. This review concludes with examples of how integration and interpretation of big data can be used to break down data silos and pave the way to better patient outcomes, value-based care, and the creation of an intelligent enterprise for healthcare.

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Qualitative review of usability problems in health information systems for radiology

Cited by 17 publications

References 31 publications

Promises of artificial intelligence in neuroradiology: a systematic technographic review

Promises of artificial intelligence in neuroradiology: a systematic technographic review

Electronic Unified Health System: opportunities for improvement

Better Patient Outcomes Through Mining of Biomedical Big Data

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