Healthcare 4.0: trends, challenges and research directions

Tortorella, Guilherme Luz; Fogliatto, Flávio Sanson; Vergara, Alejandro; Vassolo, Roberto; Sawhney, Rapinder

doi:10.1080/09537287.2019.1702226

Cited by 159 publications

(87 citation statements)

References 118 publications

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“…Four databases were searched to identify works available on ST management: Scopus, PubMed, Web of Science (WOS), and ScienceDirect (SD). They are identified by Augusto and Tortorella [ 29 ] and Tortorella et al [ 30 ] as predominant in review studies in the healthcare area. Keywords were selected from ten articles reported in Fogliatto et al [ 6 ], whose study is aligned with our research questions.…”

Section: Methodsmentioning

confidence: 99%

Approaches to the rationalization of surgical instrument trays: scoping review and research agenda

Santos

Fogliatto

Zani

et al. 2021

BMC Health Serv Res

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Background Surgical Tray Rationalization (STR) consists of a systematic reduction in the number of surgical instruments to perform specific procedures without compromising patient safety while reducing losses in the sterilization and assembly of trays. STR is one example of initiatives to improve process performance that have been widely reported in industrial settings but only recently have gained popularity in healthcare organizations. Methods We conduct a scoping review of the literature to identify and map available evidence on surgical tray management. Five methodological stages are implemented and reported; they are: identifying research questions, identifying relevant studies, study selection, charting the data, and collating, summarizing and reporting the results. Results We reviewed forty-eight articles on STR, which were grouped according to their main proposed approaches: expert analysis, lean practices, and mathematical programming. We identify the most frequently used techniques within each approach and point to their potential contributions to operational and economic dimensions of STR. We also consolidate our findings, proposing a roadmap to STR with four generic steps (prepare, rationalize, implement, and consolidate) and recommended associated techniques. Conclusions To the best of our knowledge, ours is the first study that reviews and systematizes the existing literature on the subject of STR. Our study closes with the proposition of future research directions, which are presented as nine research questions associated with the four generic steps proposed in the STR roadmap.

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Section: Methodsmentioning

confidence: 99%

Approaches to the rationalization of surgical instrument trays: scoping review and research agenda

Santos

Fogliatto

Zani

et al. 2021

BMC Health Serv Res

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“…For the hospital facilities, AI is one of the most powerful technologies from the perspectives of data, computing power and algorithms. Research in Health 4.0 has been conducted in an interdisciplinary way with a diversified set of applications and functionalities and in terms of its implementation, it has been more commonly found in hospitals' information flows, especially the ones related to healthcare treatments [20]. In this context, it is also necessary to consider and to assess the prevailing opinions and expectations among stakeholders regarding ICT health solutions, such as the improvement of factors that affect quality of life, quality of health care, patient's knowledge, monetary aspects, or data security and privacy [21].…”

Section: La Fe University Hospitalmentioning

confidence: 99%

Artificial Intelligence for Hospital Health Care: Application Cases and Answers to Challenges in European Hospitals

et al. 2021

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The development and implementation of artificial intelligence (AI) applications in health care contexts is a concurrent research and management question. Especially for hospitals, the expectations regarding improved efficiency and effectiveness by the introduction of novel AI applications are huge. However, experiences with real-life AI use cases are still scarce. As a first step towards structuring and comparing such experiences, this paper is presenting a comparative approach from nine European hospitals and eleven different use cases with possible application areas and benefits of hospital AI technologies. This is structured as a current review and opinion article from a diverse range of researchers and health care professionals. This contributes to important improvement options also for pandemic crises challenges, e.g., the current COVID-19 situation. The expected advantages as well as challenges regarding data protection, privacy, or human acceptance are reported. Altogether, the diversity of application cases is a core characteristic of AI applications in hospitals, and this requires a specific approach for successful implementation in the health care sector. This can include specialized solutions for hospitals regarding human–computer interaction, data management, and communication in AI implementation projects.

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“…Five databases were consulted: Pubmed, Science Direct, Scopus, Web of Science, and Medline. The rst four bases were chosen following the recommendation of Tortorella et al [16], who conducted a scoping study on a related topic. The inclusion of Medline was due to its recurrent use in systematic review articles on hospital LOS forecasting, e.g., [11], [14], [13].…”

Section: Identi Cation Of Relevant Studiesmentioning

confidence: 99%

Forecasting the Length-of-Stay of Pediatric Patients in Hospitals: A Scoping Review

Medeiros

Fogliatto

ROCHA

et al. 2021

Preprint

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Background: Healthcare management faces complex challenges in allocating hospital resources and predicting patients' length-of-stay (LOS) is critical in effectively managing those resources. This work aims to identify approaches used to forecast the LOS of Pediatric Patients in Hospitals (LOS–P), and patients' populations and environments used to develop the models. Methods: Based on a scoping review, 28 studies were identified and analyzed. Methods reported in the literature were classified according to the stage in which they are used in the modeling process: (i) pre-processing of data, (ii) variable selection, and (iii) cross-validation. Results: Forecasting models are most often applied to newborn patients and, consequently, in neonatal intensive care units. Regression analysis is the most widely used modeling approach; techniques associated with Machine Learning are still incipient and mostly used in emergency departments to model patients in specific situations.Conclusions: The studies' main benefits include informing family members about the patient's expected discharge date and enabling hospital resources' allocation and planning. Main research gaps are associated with the lack of generalization of forecasting models and limited reported applicability in hospital management. This study also provides a practical guide to methods applied for LOS–P forecasting and a future research agenda.

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Healthcare 4.0: trends, challenges and research directions

Cited by 159 publications

References 118 publications

Approaches to the rationalization of surgical instrument trays: scoping review and research agenda

Approaches to the rationalization of surgical instrument trays: scoping review and research agenda

Artificial Intelligence for Hospital Health Care: Application Cases and Answers to Challenges in European Hospitals

Forecasting the Length-of-Stay of Pediatric Patients in Hospitals: A Scoping Review

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