Hospitals, Volume 2, Issue 4 (December 2025) – 5 articles

The increasing availability of free-text components in electronic medical records (EMRs) offers unprecedented opportunities for machine learning research, enabling improved disease phenotyping, risk prediction, and patient stratification. However, the use of narrative clinical data raises distinct ethical challenges that are not fully addressed by conventional frameworks for structured data. We conducted a narrative review synthesizing conceptual and empirical literature on ethical issues in free-text EMR research, focusing on privacy, fairness, autonomy, interpretability, and governance. We examined technical methods, including de-identification, differential privacy, bias mitigation, and explainable AI, alongside normative approaches, such as participatory design, dynamic consent models, and multi-stakeholder governance. Our analysis highlights persistent risks, including re-identification, algorithmic bias, and inequitable access, as well as limitations in current regulatory guidance across jurisdictions. We propose ethics-by-design principles that integrate ethical reflection into all stages of machine learning research, emphasize relational accountability to patients and stakeholders, and support global harmonization in governance and stewardship. Implementing these principles can enhance transparency, trust, and social value while maintaining scientific rigor. Ethical integration is therefore not optional but essential to ensure that machine learning research using free-text EMRs aligns with both clinical relevance and societal expectations. Full article

This Perspective presents a framework for US hospitals treating foreign patients to reconceptualize international healthcare trade by leveraging all four modes of trade in health services under the General Agreement on Trade in Services (GATS), which include information exchange (Mode 1), patient travel/medical tourism (Mode 2), commercial presence (Mode 3), and temporary movement of healthcare personnel (Mode 4). This framework illustrates how hospitals could adopt multi-modal approaches and describes the strategic implications for hospitals and their international patient programs. Historically, US hospitals have focused primarily on international patient travel (Mode 2), but this narrow approach creates vulnerability to disruption. Mode 2 exports by US hospitals have not recovered to pre-pandemic levels, making expansion into other modes essential for maintaining competitive advantages while mitigating systemic risks. Diversification into other modes, such as digital health and telemedicine (Mode 1), co-branding and managing facilities (Mode 3) and visiting professorships (Mode 4) are single-mode approaches for diversification. Multi-country clinical trials are an example of cross-border trade that addresses all four modes of GATS. Overall, this perspective provides a new framework for US providers engaged in or considering entry into international markets that does not solely rely on Mode 2 medical tourism but instead adopts a multi-modal, cross-border health service paradigm. Full article

Background: Artificial Intelligence (AI) holds significant potential to enhance operational efficiency and quality in healthcare. However, despite substantial investment, its widespread, sustained implementation is limited, necessitating a thorough risk assessment to overcome current adoption barriers. Methods: This scoping review, guided by the Arksey and Malley framework, systematically mapped 13 articles published between 2019 and 2024, sourced from five major databases (including CINAHL, Medline, and PubMed). A rigorous, systematic process involving independent data charting and critical appraisal, using the Critical Appraisal Skills Programme (CASP) tool, was implemented, followed by thematic synthesis to address the research questions. Results: AI demonstrates a significant positive impact on both operational efficiency (e.g., optimised resource allocation, reduced waiting times) and patient outcomes (e.g., improved patient-centred, proactive care, and identification of readmission risks). Major implementation hurdles identified include high costs, critical data security and privacy concerns, the risk of algorithmic bias, and significant staff resistance stemming from limited understanding. Conclusions: Healthcare managers must address key challenges related to cost, bias, and staff acceptance to leverage the potential of AI fully. Strategic investments, the implementation of robust data governance frameworks, and comprehensive staff training are crucial steps for mitigating risks and creating a more efficient, patient-centred, and effective healthcare system. Full article

Technological advancements driving smart healthcare transformation need new models and solutions for emerging technology challenges. The objective of this review paper is to introduce the concept of smart healthcare, identify its main characteristics, highlight the key drivers of its adoption (“Technological Advancements, Digital Citizen Societies, Shifting Models of Patient Care, Healthcare Workforce Shortages, Rising Costs of Healthcare Delivery, and Impacts of COVID-19”), and present the primary challenges associated with its implementation (“Reduced Human Interaction and Patient Monitoring, Data Accuracy and Reliability, Data Security and Privacy, Interoperability and System Performance, Ethical Concerns and Trust in AI, High Financial Costs”). The paper is written in simplified language to enable a wide range of healthcare stakeholders—particularly healthcare professionals with limited technical backgrounds—to develop a foundational understanding of smart healthcare. This knowledge can foster greater engagement in efforts to transform healthcare systems into smarter, more efficient models. Furthermore, the findings of this review may support future research efforts, especially those aimed at developing models or frameworks that facilitate the practical integration of smart healthcare beyond theoretical concepts, by offering a synthesized framework for SHC. Full article

Hospitals are complex systems that function most effectively when operations are coordinated and supported by real-time information and feedback loops. Sustained growth, quality improvement, and financial viability increasingly rely on data-based management (DBM), yet adoption and use vary widely across healthcare institutions. This study examined the enabling and hindering factors influencing DBM, with the aim of generating insights to strengthen data use and improve management of eye hospitals. A qualitative multiple case study design was employed in six purposefully selected eye hospitals in India, varying in size and baseline capacity for DBM. At each site, five to six key personnel were interviewed. Data collection involved audio-recorded interviews, transcripts, and field notes, and analysis followed a grounded theory approach using open and axial coding to identify themes, relationships, and develop a conceptual framework. Findings reaffirmed the core enablers—leadership commitment, data availability, and technology adoption. Additional drivers included operational adaptability, regulatory demands, systematic improvement practices, daily reporting, information policies, and the use of communication platforms such as WhatsApp. Key barriers were incomplete data entry, software limitations, inadequate analytical reporting, and inconsistent adherence to processes. Overall, effective DBM requires both foundational enablers and contextual drivers, while addressing barriers to institutionalizing data use and improving outcomes. Full article