[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-122784-en":3,"doc-seo-122784-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":4,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},122784,1649267921044,"Ava Thompson","https://us-avatar.wpscdn.com/avatar/1800007509477c92dfb?_k=1782875107921204101",8,"Research & Report","Factors influencing machine learning application procurement in healthcare organisations - A case study","Research examines the factors shaping procurement decisions for machine learning applications within healthcare organisations. Drawing on interviews with five industry experts, the study applies the Technology-Organization-Environment (TOE) framework to analyse adoption and integration conditions. Key findings emphasise data security, stakeholder perspectives, rigorous regulatory compliance, and the effects of inter-vendor relationships. The work highlights procurement-process complexity in healthcare and supports vendor collaboration as a strategic enabler. Recommendations target both healthcare entities and ML vendors.","Sampo Uravirta  \nFactors influencing machine learning application procurement in healthcare organisations  \nA case study  \nMetropolia University of Applied Sciences Master's Degree  \nHealth Business Management Thesis  \nOctober 2023  \nAbstract  \nAuthor(s): Sampo Juhani Uravirta  \nTitle: Factors influencing machine learning application  \nprocurement in healthcare organisations  \nNumber of Pages: 43 pages + 1 appendices  \nDate: October 2023  \nDegree: Health Business Management  \nDegree Programme: Master's Degree Programme  \nInstructor(s): Miikka Putaala, Director of Business Development  \nTricia Cleland Silva, Senior Lecturer  \nIn the evolving landscape of healthcare, the advent of artificial intelligence (AI) has ushered in a transformative era, particularly with the prominence of machine learning (ML) applications. This research delves deep into the nuances influencing procurement decisions related to ML applications, drawing insights from interviews with five industry experts. Guided by the Technology-Organization-Environment (TOE) framework, this study clarifies the intricate factors for the successful adoption and seamless integration of ML applications in healthcare settings. The main findings highlight the paramount importance of data security, the weight of stakeholder perspectives, the rigours of regulatory compliance, and the dynamics of inter-vendor relationships. The study further sheds light on healthcare organisations' complexities when steering through the procurement process, underscoring the value of nurturing strategic alliances with vendors. Concluding, the research offers actionable recommendations for healthcare entities and ML vendors, advocating for a seamless convergence of technology and healthcare.  \nKeywords: machine learning (ML), artificial intelligence (AI), healthcare procurement, technology-organisation-environment, data security, regulatory compliance, technological adoption  \nContents  \n1 Introduction 5  \n1.1 Background and context 5  \n1.2 Research problem and questions 7  \n2 Literature review 9  \n2.1 Theoretical framework 9  \n2.1.1 Diffusion of Innovations (DOI) Theory 9  \n2.1.2 Technology-Organisation-Environment (TOE) framework 12  \n2.1.3 Use cases of DOI and TOE in healthcare 12  \n2.2 Factors influencing the procurement of ML applications 13  \n2.2.1 Technological limitations 15  \n2.2.2 Organisational limitations 16  \n2.2.3 Environmental limitations 16  \n3 Methodology 18  \n3.1 Method selection 18  \n3.1.1 Exploratory nature and sample size 19  \n3.1.2 Partner organisation 19  \n3.2 Data collection 20  \n3.3 Data analysis 22  \n3.3.1 Preparing the data 22  \n3.3.2 Coding methodology 22  \n3.3.3 Thematic analysis 24  \n3.4 Ethical considerations 25  \n4 Findings 26  \n4.1 IT infrastructure and requirements 26  \n4.2 Regulatory compliance 26  \n4.3 Procurement process 27  \n4.4 Factors influencing procurement strategies (TOE) 27  \n4.5 Summary of findings 30  \n5 Discussion 32  \n5.1 TOE framework: A comprehensive lens 32  \n5.2 Cloud integration in healthcare: data security and stakeholder trust 33  \n5.3 Regulatory compliance 34  \n5.4 The Procurement maze 35  \n5.5 Implications for successful integration and ethical limitations 35  \n6 Conclusions 37  \n6.1 Summary of findings 37  \n6.2 Recommendations for organisation and vendors 37  \n6.3 Limitations and future research 38  \n7 References 40  \nAppendices 44  \nAppendix 1: Interview Questions 44  \nIT Infrastructure 44  \nRequirements 44  \nPurchases 44  \nOther questions 44  \n1 Introduction  \nThe healthcare sector is probably experiencing one of its most significant transformations in recent years. This is being driven by rapid technological advances and growing demand for more efficient and effective patient care. Among the many emerging technologies, machine learning (ML) has demonstrated colossal potential to transform the healthcare sector. These ML applications range from decease diagnostics to more personalised treatmentsand healthcare process optimisation. This th","cbCainVp0FsnhffR","https://ap.wps.com/l/cbCainVp0FsnhffR","pdf",352321,1,44,"English","en",105,"# Introduction\n## Background and context\n## Research problem and questions\n# Literature review\n## Theoretical framework\n## Factors influencing the procurement of ML applications\n# Methodology\n## Method selection\n## Data collection\n## Data analysis\n## Ethical considerations\n# Findings\n## IT infrastructure and requirements\n## Regulatory compliance\n## Procurement process\n## Factors influencing procurement strategies (TOE)\n## Summary of findings\n# Discussion\n## TOE framework: A comprehensive lens\n## Cloud integration in healthcare: data security and stakeholder trust\n## Regulatory compliance\n## The Procurement maze\n## Implications for successful integration and ethical limitations\n# Conclusions\n## Summary of findings\n## Recommendations for organisation and vendors\n## Limitations and future research\n# References\n## Appendices","[{\"question\":\"What framework does the study use to analyse procurement decisions for ML applications?\",\"answer\":\"The study uses the Technology-Organization-Environment (TOE) framework to clarify factors affecting adoption and integration of machine learning applications in healthcare settings.\"},{\"question\":\"Which factors are highlighted as most important for successful ML procurement?\",\"answer\":\"The main findings stress data security, stakeholder perspectives, regulatory compliance, and the dynamics of relationships between healthcare organisations and technology vendors.\"},{\"question\":\"Who provides the data for the research and how was it collected?\",\"answer\":\"Data comes from interviews with five industry experts. The methodology includes method selection, data collection, coding, thematic analysis, and ethical considerations.\"}]","Factors influencing machine learning application procurement in healthcare organisations - A case study | PDF",1785812879,111,{"code":4,"msg":31,"data":32},"ok",{"site_id":24,"language":23,"slug":33,"title":13,"keywords":34,"description":14,"schema_data":35,"social_meta":86,"head_meta":88,"extra_data":90,"updated_unix":28},"factors-influencing-machine-learning-application-procurement-in-healthcare-organisations-a-case-study","",{"@graph":36,"@context":85},[37,54,68],{"@type":38,"itemListElement":39},"BreadcrumbList",[40,44,48,51],{"item":41,"name":42,"@type":43,"position":20},"https://docshare.wps.com","Home","ListItem",{"item":45,"name":46,"@type":43,"position":47},"https://docshare.wps.com/document/","Document",2,{"item":49,"name":12,"@type":43,"position":50},"https://docshare.wps.com/document/research-report/",3,{"item":52,"name":13,"@type":43,"position":53},"https://docshare.wps.com/document/factors-influencing-machine-learning-application-procurement-in-healthcare-organisations-a-case-study/122784/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":23,"description":14,"dateModified":62,"datePublished":62,"encodingFormat":61,"isAccessibleForFree":63,"interactionStatistic":64},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":41,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-04",true,{"@type":65,"interactionType":66,"userInteractionCount":4},"InteractionCounter",{"@type":67},"ViewAction",{"@type":69,"mainEntity":70},"FAQPage",[71,77,81],{"name":72,"@type":73,"acceptedAnswer":74},"What framework does the study use to analyse procurement decisions for ML applications?","Question",{"text":75,"@type":76},"The study uses the Technology-Organization-Environment (TOE) framework to clarify factors affecting adoption and integration of machine learning applications in healthcare settings.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"Which factors are highlighted as most important for successful ML procurement?",{"text":80,"@type":76},"The main findings stress data security, stakeholder perspectives, regulatory compliance, and the dynamics of relationships between healthcare organisations and technology vendors.",{"name":82,"@type":73,"acceptedAnswer":83},"Who provides the data for the research and how was it collected?",{"text":84,"@type":76},"Data comes from interviews with five industry experts. 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