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The purpose of this study is to examine the state of research into adoption of machine learning systems within the health sector, to identify themes that have been studied and observe the important gaps in the literature that can inform a research agenda going forward.

A systematic literature strategy was utilized to identify and analyze scientific papers between 2012 and 2022. A total of 28 articles were identified and reviewed.

The outcomes reveal that while advances in machine learning have the potential to improve service access and delivery, there have been sporadic growth of literature in this area which is perhaps surprising given the immense potential of machine learning within the health sector. The findings further reveal that themes such as recordkeeping, drugs development and streamlining of treatment have primarily been focused on by the majority of authors in this area.

The search was limited to journal articles published in English, resulting in the exclusion of studies disseminated through alternative channels, such as conferences, and those published in languages other than English. Considering that scholars in developing nations may encounter less difficulty in disseminating their work through alternative channels and that numerous emerging nations employ languages other than English, it is plausible that certain research has been overlooked in the present investigation.

This review provides insights into future research avenues for theory, content and context on adoption of machine learning within the health sector.

The study aims to explore how segmentation as a methodology can be adapted to the healthcare context to provide a more nuanced understanding of the served population and to facilitate the design of patient-centric services.

The study was based on a collaborative project with a national healthcare organization following the principles of action design research. The study describes the quantitative segmentation performed during the project, followed by a qualitative interview study of how segments correspond with patient behaviors in an actual healthcare setting, and service design workshops facilitated by segments. A number of design principles are outlined based on the learnings of the project.

The segmentation approach increased understanding of patient variability within the service provider organization and was considered an effective foundation for modular service design. Patient characteristics and life circumstances were related to specific patterns of health behaviors, such as avoidance or passivity, or a persistent proactivity. These patterns influenced the patients' preferred value co-creation role and what type of support patients sought from the care provider.

The proposed segmentation approach is immediately generalizable to further healthcare contexts and similar services: improved understanding of patients, vulnerable patients in particular, improves the fit and inclusivity of services.

The segmentation approach to service design was demonstrated to be effective in a large-scale context. The approach allows service providers to design service options that improve the fit with individual patients' needs for support and autonomy. The results illuminate how patient characteristics influence health and value co-creation behaviors.

The purpose of this investigation is to gain insight into parents' perceptions of benefits vs burdens (value) of educational and healthcare service received for their child with ASD. Parents are the main integrators of long-term educational and healthcare service for their child with ASD.

Design/methodology/approach included (1) a sentiment analysis of discussion forum posts from an autism message board using a rule-based sentiment analysis tool that is specifically attuned to sentiments expressed in social media and (2) a qualitative content analysis of one-on-one interviews with parents of children diagnosed with ASD, complemented with interviews with experienced educators and clinicians.

Findings reveal the link between customized service integration and long-term benefits. Both parents and service providers emphasize the need to integrate healthcare and educational service to create holistic long-term care for a child with ASD. Parents highlight the benefits of varied services, but availability or cost are burdens if the service is not publicly provided, or covered by insurance. Service providers' lack of experience with ASD and people's ignorance of the challenges of ASD are burdens.

Ensuring health outcomes for a child with ASD requires an integrated service system and long-term, customer-centric service process because the scope of service covers the child's entire childhood. Customized educational and healthcare service must be allocated and budgeted early in order to reach the goal of a satisfactory service output for each child.

This is the first service research to focus on parents' challenges with obtaining services for their child with ASD. This paper provides service researchers and managers insight into parents' perceptions of educational and healthcare service value (i.e. benefits vs. burdens) received for their child with ASD. These insights into customer-centric perceptions of value may be useful to research and may help service providers to innovate and provide integrated service directly to parents, or indirectly to service providers, who serve children with ASD.

The primary objective of this paper is to show a systematic and methodological approach for the digitalization of critical clinical pathways (CPs) within the healthcare domain.

The methodology entails the integration of service design (SD) and action research (AR) methodologies, characterized by iterative phases that systematically alternate between action and reflective processes, fostering cycles of change and learning. Within this framework, stakeholders are engaged through semi-structured interviews, while the existing and envisioned processes are delineated and represented using BPMN 2.0. These methodological steps emphasize the development of an autonomous, patient-centric web application alongside the implementation of an adaptable and patient-oriented scheduling system. Also, business processes simulation is employed to measure key performance indicators of processes and test for potential improvements. This method is implemented in the context of the CP addressing transient loss of consciousness (TLOC), within a publicly funded hospital setting.

The methodology integrating SD and AR enables the detection of pivotal bottlenecks within diagnostic CPs and proposes optimal corrective measures to ensure uninterrupted patient care, all the while advancing the digitalization of diagnostic CP management. This study contributes to theoretical discussions by emphasizing the criticality of process optimization, the transformative potential of digitalization in healthcare and the paramount importance of user-centric design principles, and offers valuable insights into healthcare management implications.

The study’s relevance lies in its ability to enhance healthcare practices without necessitating disruptive and resource-intensive process overhauls. This pragmatic approach aligns with the imperative for healthcare organizations to improve their operations efficiently and cost-effectively, making the study’s findings relevant.

This survey explores the application of real options theory to the field of health economics. The integration of options theory offers a valuable framework to address these challenges, providing insights into healthcare investments, policy analysis and patient care pathways.

This research employs the real options theory, a financial concept, to delve into health economics challenges. Through a systematic approach, three distinct models rooted in this theory are crafted and analyzed. Firstly, the study examines the value of investing in emerging health technology, factoring in future advantages, associated costs and unpredictability. The second model is patient-centric, evaluating the choice between immediate treatment switch and waiting for more clarity, while also weighing the associated risks. Lastly, the research assesses pandemic-related government policies, emphasizing the importance of delaying decisions in the face of uncertainties, thereby promoting data-driven policymaking.

Three different real options models are presented in this study to illustrate their applicability and value in aiding decision-makers. (1) The first evaluates investments in new technology, analyzing future benefits, discount rates and benefit volatility to determine investment value. (2) In the second model, a patient has the option of switching treatments now or waiting for more information before optimally switching treatments. However, waiting has its risks, such as disease progression. By modeling the potential benefits and risks of both options, and factoring in the time value, this model aids doctors and patients in making informed decisions based on a quantified assessment of potential outcomes. (3) The third model concerns pandemic policy: governments can end or prolong lockdowns. While awaiting more data on the virus might lead to economic and societal strain, the model emphasizes the economic value of deferring decisions under uncertainty.

This research provides a quantified perspective on various decisions in healthcare, from investments in new technology to treatment choices for patients to government decisions regarding pandemics. By applying real options theory, stakeholders can make more evidence-driven decisions.

Decisions about patient care pathways and pandemic policies have direct societal implications. For instance, choices regarding the prolongation or ending of lockdowns can lead to economic and societal strain.

The originality of this study lies in its application of real options theory, a concept from finance, to the realm of health economics, offering novel insights and analytical tools for decision-makers in the healthcare sector.

The purpose of the article is to assess the effectiveness, compliance, adoption and lessons learnt from the pilot implementation of a data integration solution between an acute care hospital information system (HIS) and a long-term care (LTC) home electronic medical record through a case report.

Utilization statistics of the data integration solution were captured at one-month post implementation and again one year later for both the emergency department (ED) and LTC home. Clinician feedback from surveys and structured interviews was obtained from ED physicians and a multidisciplinary LTC group.

The authors successfully exchanged health information between a HIS and the electronic medical record (EMR) of an LTC facility in Canada. Perceived time savings were acknowledged by ED physicians, and actual time savings as high as 45 min were reported by LTC staff when completing medication reconciliation. Barriers to adoption included awareness, training efficacy and delivery models, workflow integration within existing practice and the limited number of facilities participating in the pilot. Future direction includes broader staff involvement, expanding the number of sites and re-evaluating impacts.

A data integration solution to exchange clinical information can make patient transfers more efficient, reduce data transcription errors, and improve the visibility of essential patient information across the continuum of care.

Although there has been a large effort to integrate health data across care levels in the United States and internationally, the groundwork for such integrations between interoperable systems has only just begun in Canada. The implementation of the integration between an enterprise LTC electronic medical record system and an HIS described herein is the first of its kind in Canada. Benefits and lessons learnt from this pilot will be useful for further hospital-to-LTC home interoperability work.