Factors influencing machine learning application procurement in healthcare organisations - A case study

Aug 4, 2026

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.

LanguageEnglish

CategoryResearch & Report

FormatPDF

Pages44

Reading Time1 h 51 min

AccessFree to download

Introduction

Background and context

Research problem and questions

Literature review

Theoretical framework

Factors influencing the procurement of ML applications

Methodology

Method selection

Data collection

Data analysis

Ethical considerations

Findings

IT infrastructure and requirements

Regulatory compliance

Procurement process

Factors influencing procurement strategies (TOE)

Summary of findings

Discussion

TOE framework: A comprehensive lens

Cloud integration in healthcare: data security and stakeholder trust

Regulatory compliance

The Procurement maze

Implications for successful integration and ethical limitations

Conclusions

Summary of findings

Recommendations for organisation and vendors

Limitations and future research

References

Appendices

Sampo Uravirta Factors influencing machine learning application procurement in healthcare organisations A case study Metropolia University of Applied Sciences Master's Degree Health Business Management Thesis October 2023 Abstract Author(s): Sampo Juhani Uravirta Title: Factors influencing machine learning application procurement in healthcare organisations Number of Pages: 43 pages + 1 appendices Date: October 2023 Degree: Health Business Management Degree Programme: Master's Degree Programme Instructor(s): Miikka Putaala, Director of Business Development Tricia Cleland Silva, Senior Lecturer In 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. Keywords: machine learning (ML), artificial intelligence (AI), healthcare procurement, technology-organisation-environment, data security, regulatory compliance, technological adoption Contents 1 Introduction 5 1.1 Background and context 5 1.2 Research problem and questions 7 2 Literature review 9 2.1 Theoretical framework 9 2.1.1 Diffusion of Innovations (DOI) Theory 9 2.1.2 Technology-Organisation-Environment (TOE) framework 12 2.1.3 Use cases of DOI and TOE in healthcare 12 2.2 Factors influencing the procurement of ML applications 13 2.2.1 Technological limitations 15 2.2.2 Organisational limitations 16 2.2.3 Environmental limitations 16 3 Methodology 18 3.1 Method selection 18 3.1.1 Exploratory nature and sample size 19 3.1.2 Partner organisation 19 3.2 Data collection 20 3.3 Data analysis 22 3.3.1 Preparing the data 22 3.3.2 Coding methodology 22 3.3.3 Thematic analysis 24 3.4 Ethical considerations 25 4 Findings 26 4.1 IT infrastructure and requirements 26 4.2 Regulatory compliance 26 4.3 Procurement process 27 4.4 Factors influencing procurement strategies (TOE) 27 4.5 Summary of findings 30 5 Discussion 32 5.1 TOE framework: A comprehensive lens 32 5.2 Cloud integration in healthcare: data security and stakeholder trust 33 5.3 Regulatory compliance 34 5.4 The Procurement maze 35 5.5 Implications for successful integration and ethical limitations 35 6 Conclusions 37 6.1 Summary of findings 37 6.2 Recommendations for organisation and vendors 37 6.3 Limitations and future research 38 7 References 40 Appendices 44 Appendix 1: Interview Questions 44 IT Infrastructure 44 Requirements 44 Purchases 44 Other questions 44 1 Introduction The 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

FAQs

What framework does the study use to analyse procurement decisions for ML applications?

The study uses the Technology-Organization-Environment (TOE) framework to clarify factors affecting adoption and integration of machine learning applications in healthcare settings.

Which factors are highlighted as most important for successful ML procurement?

The main findings stress data security, stakeholder perspectives, regulatory compliance, and the dynamics of relationships between healthcare organisations and technology vendors.

Who provides the data for the research and how was it collected?

Data comes from interviews with five industry experts. The methodology includes method selection, data collection, coding, thematic analysis, and ethical considerations.

Aug 4, 2026

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