Data Access in Connected Health Innovation: Managerial Orchestration Challenges and Solutions

Kemppainen, Laura; Pikkarainen, Minna; Hurmelinna‐Laukkanen, Pia; Reponen, Jarmo

doi:10.22215/timreview/1291

Cited by 14 publications

(6 citation statements)

References 18 publications

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“…Communication by stakeholders in an open innovative format in the health care ecosystem removes constraints and maximizes the concept of connected health (Kemppainen et al, 2019).…”

Section: Discussionmentioning

confidence: 99%

Open Innovation in Healthcare Ecosystem – a systematic review

Torab-Miandoab,

Samad-Soltani,

Rezaei-hachesu

2024

Preprint

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Background: Several countries are facing significant troubles of health services, particularly rising prices. Innovative technologies and services are expected to help boost medical quality and cut costs. In this sense, there is a lack of innovative work in spite of a growing interest in open innovation and approaches that advocate for expanded cooperation among various actors in healthcare. Objective: This paper describes the findings of a study concerning the commitment of the healthcare sector to open innovation. Materials and methods: The search for literature focused on English-language papers to 12 January 2020. Based on the indicated criteria for inclusion, 29 articles were included. Results: Results show that most experiments concentrate on the areas of pharmaceutical research (drug discovery) and health informatics (health information systems and infrastructures) that were brought out as concepts or applied as pilot and prototype. Conclusions: The participation of the healthcare sector limited in open innovation, and more work is required with an emphasis how to get open innovation.

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“…Communication by stakeholders in an open innovative format in the health care ecosystem removes constraints and maximizes the concept of connected health (Kemppainen et al, 2019).…”

Section: Discussionmentioning

confidence: 99%

Open Innovation in Healthcare Ecosystem – a systematic review

Torab-Miandoab,

Samad-Soltani,

Rezaei-hachesu

2024

Preprint

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“…For the continuous improvement of ML-based medical image detection, studies reported several institutions cooperating in a networked effort [50], building strategic partnerships [57], and developing trust, e.g. between hospitals [58]. This concerns image sharing networks, mutual access to verified-case datasets, and exchange of experience and criteria for optimization and standardization [53], as it takes place within the emerging field of radiomics [59].…”

Section: Coordination With the External World: Networking (9)mentioning

confidence: 99%

“…In addition to selecting appropriate ML algorithms, AI initiatives require a justification for the investment of significant resources such as hardware, software, and the human expertise (both computational and medical) [48] into which ML has to be efficiently integrated [58]. High-quality ground-truth data [39] comprising a substantial number of verified cases are a basic resource for ML training [53].…”

Section: Investing Additional Resources (5)mentioning

confidence: 99%

“…Further organizational challenges are the compensation for patients affected by low-quality outputs [52] in compliance with evolving laws [43]. Obviously, dealing with quality challenges involves non-technical, organizational measures [36] such as policies for accountability [63], governance frameworks [64], guidelines [58], or checklists [65].…”

Section: Ensuring Supervision and Quality (17)mentioning

confidence: 99%

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Keeping the Organization in the Loop as a General Concept for Human-Centered AI: The Example of Medical Imaging

Herrmann,

Pfeiffer

2023

Proceedings of the Annual Hawaii International Conference on System Sciences

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This study emanates from work on human-centered AI and the claim of "keeping the organization in the loop". A previous study suggests a systematic framework of organizational practices in the context of predictive maintenance, and identified four cycles: using AI, customizing AI, original task handling with support of AI, and dealing with contextual changes. Since we assume that these findings can be generalized for other kinds of applications of Machine Learning (ML), we contrast the management activities that support the four cycles and their interplay with a widely different domain: the usage of AI for radiology. Our literature analysis reveals a series of overlaps with the existing framework, but also results in the need for extensions, such as holistic consideration of workflows or supervision and quality assurance.

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“…(BUHMANN, 2021). Uma vez que a inovação na área é muitas vezes de natureza colaborativa e em rede (KEMPPAINEN, 2019) ), e disponibilizar novos produtos e serviços no mundo conectado é um fator de grande relevância para economia e crescimento de um país. (HEMPHILL, 2020).…”

Section: Introductionunclassified

inovação tecnológica e os desafios representados pela Inteligência Artificial

et al. 2023

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A Inteligência Artificial (IA) é definida como um amplo ramo da ciência da computação que lida com a construção de máquinas “inteligentes”, capazes de realizar tarefas que normalmente requerem inteligência humana e carrega um enorme potencial para novos serviços e produtos. O objetivo do presente estudo foi identificar os principais desafios para a inovação tecnológica apresentados pela utilização da Inteligência Artificial. Foi realizada uma revisão conhecida como Scoping Review, usando critérios do Cochrane Systematic Reviews associados ao PRISMA, na base Scopus e foram identificados 1.391 registros, usando os descritores artificial intelligence, innovation e challenges. Por meio dos métodos de seleção e classificação foram analisados 40 artigos, que sinalizam que as principais áreas da ciência que estão utilizando a Inteligência Artificial como inovação tecnológica são ciência da computação, engenharias e ciências sociais. Os principais desafios encontrados foram; permitir com IA ofertas de inovações autônomas, integrar a tecnologia de IA nas organizações para processos e resultados de inovação digital, falta de especialistas na área de IA e a necessidade de reformulação das leis e regulamentos referentes a IA.

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Data Access in Connected Health Innovation: Managerial Orchestration Challenges and Solutions

Cited by 14 publications

References 18 publications

Open Innovation in Healthcare Ecosystem – a systematic review

Open Innovation in Healthcare Ecosystem – a systematic review

Keeping the Organization in the Loop as a General Concept for Human-Centered AI: The Example of Medical Imaging

inovação tecnológica e os desafios representados pela Inteligência Artificial

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