Search results

Understanding the outcomes of co-creation (CC) in healthcare is increasingly gaining multidisciplinary scientific interest. Although more and more service management scholars have pointed out the benefits of cross-fertilization between the various research fields, the literature on this topic is still scattered and poorly integrated. This study aims to summarize and integrate multiple strands of extant knowledge CC by identifying the outcomes of health CC and the determinants of these outcomes and their relationships.

A structured literature review was conducted per PRISMA guidelines. A total of 4,189 records were retrieved from the six databases; 1,983 articles were screened, with 161 included in the qualitative thematic analysis.

This study advances a comprehensive framework for healthcare CC based on a thorough analysis of the outcomes and their determinants, that is, antecedents, management activities and institutional context. Extant research rarely evaluates outcomes from a multidimensional and systemic perspective. Less attention has been paid to the relationship among the CC process elements.

This study offers an agenda to guide future studies on healthcare CC. Highlighting some areas of integration among different disciplines further advances service literature.

The framework offers an operational guide to better shape managerial endeavors to facilitate CC, provide direction and assess multiple outcomes.

This is the first extensive attempt to synthesize and integrate multidisciplinary knowledge on CC outcomes in healthcare settings by adopting a systematic perspective on the overall process.

Research has examined how new ventures strengthen local economic outcomes; however, limited research examines health-oriented ventures and their impact on social outcomes, including health outcomes. Increased VC investment in healthcare service start-ups signals more activity toward this end, and the need for further academic inquiry. We examine the relationship between these start-ups and county-level health outcomes, health factors, and hospital utilization.

Data on start-ups funded via institutional venture capital from PitchBook were merged with US county-level outcomes from the County Health Rankings and Area Health Resources Files for 2010 to 2019. We investigated how the number of VC-funded healthcare service start-ups, as well as a subset defined as innovative, were associated with county-level health measures. We used panel models with two-way fixed effects and Propensity Score Matched (PSM), controlling for demographics and socioeconomic factors.

Each additional VC-funded healthcare service start-up was related to a significant 0.01 percentage point decrease in diabetes prevalence (p < 0.01), a decrease of 1.54 HIV cases per 100,000 population (p < 0.1), a 0.02 percentage point decrease in obesity rates (p < 0.01), and a 0.03 percentage point decrease in binge drinking (p < 0.01). VC-funded healthcare service start-ups were not related to hospital utilization.

This work expands our understanding of how industry-specific start-ups, in this case healthcare start-ups, relate to positive social outcomes. The results underscore the importance of evidence-based evaluation, the need for expanded outcome measures for VC investment, and the possibilities for integration of healthcare services and entrepreneurship ecosystems.

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.

This study examines the theoretical and empirical relationships between LM&SS, human resource management (HRM), climate for LM&SS and outcomes (employee well-being and performance) in hospitals. As part of this research, the authors examine the interplay between “hard” and “soft” practices for LM&SS and “soft” HR practices.

A cross-sectional, multisite survey study covering all internal service units at all eight Dutch university hospitals was conducted (42 units, N = 218 supervisors, N = 1,668 employees), and multivariate multilevel regression analyses were performed.

A systems approach involving “soft” LM&SS practices that are specifically HR-related has a positive effect (β is 0.46) on a climate for LM&SS. A climate for LM&SS is not related to perceived performance or employee health. It is, however, positively related to employee happiness and trusting relationships (both βs are 0.33). We did not find that a climate for LM&SS had a mediating effect.

This study shows that a balanced approach involving both “hard” and “soft” factors is crucial to achieving the desired breadth and depth of LM&SS adoption at the macro, meso, and micro levels. The authors found that a climate for LM&SS positively affects employee well-being in hospitals.

In their attempt to create mutual gains for both their organization and their employees, hospitals that adopt LM&SS should foster a climate for LM&SS by embracing a balanced approach consisting of both “hard” and “soft” practices, thereby internalizing LM&SS at the macro, meso, and micro levels.

This is one of the first studies to examine in-depth the impact of “hard” and “soft” LM&SS on both employee well-being (subdivided into different components) and performance in healthcare, as well as the role of “soft” HRM in this relationship. Linking LM&SS, HRM and outcomes to a climate for LM&SS is relatively a new approach and has led to a deeper understanding of the mechanisms underpinning the internalization of LM&SS in healthcare.

Previous research provides adequate evidence on performance management (PM) for hospitals and healthcare providers; however, less is known about their individual and PM appraisal process. Additionally, there is limited research exploring PM in the Middle Eastern context. This study investigates PM practices in the Middle Eastern healthcare industry.

This study adopts the qualitative research methodology through semi-structured interviews of healthcare professionals in Kingdom of Saudi Arabia and the United Arab Emirates. Thematic analysis was adopted for analyzing this qualitative data.

The main findings have uncovered different facets of appraisal challenges for both the appraiser (i.e. manager) and the appraisee (i.e. employee). These challenges include communication deficits, lack of goal setting standards and regular meeting updates in order to ensure employee satisfaction and motivation in the workplace.

This study has significant implications for policymakers in Middle Eastern hospitals in terms of implementing PM for their staff. Moreover, future studies can conduct in-depth analysis and provide comparison between public and private sectors in the Gulf countries.

This study is one of the first to portray challenges involved in conducting PM in the Middle East healthcare sector specifically in the UAE and Kingdom of Saudi Arabia (KSA), both from the perspectives of the appraiser and appraisee.

The purpose of this study is to examine the role of artificial intelligence (AI) in transforming the healthcare sector, with a focus on how AI contributes to entrepreneurship and value creation. This study also aims to explore the potential of combining AI with other technologies, such as cloud computing, blockchain, IoMT, additive manufacturing and 5G, in the healthcare industry.

Exploratory qualitative methodology was chosen to analyze 22 case studies from the USA, EU, Asia and South America. The data source was public and specialized podcast platforms.

The findings show that combining technologies can create a competitive advantage for technology entrepreneurs and bring about transitions from simple consumer devices to actionable healthcare applications. The results of this research identified three main entrepreneurship areas: 1. Analytics, including staff reduction, patient prediction and decision support; 2. Security, including protection against cyberattacks and detection of atypical cases; 3. Performance optimization, which, in addition to reducing the time and costs of medical procedures, includes staff training, reducing capital costs and working with new markets.

This study demonstrates how AI can be used with other technologies to cocreate value in the healthcare industry. This study provides a conceptual framework, “AI facilitators – AI achievers,” based on the findings and offer several theoretical contributions to academic literature in technology entrepreneurship and technology management and industry recommendations for practical implication.

The purpose of this paper is to better understand how and why adoption and implementation of healthcare IT innovations occur. The authors examine two IT applications, computerised physician order entry (CPOE) and picture archiving and communication systems (PACS) at the meso and micro levels, within the context of the National Programme for IT in the English National Health Service (NHS).

To analyse these multi-level dynamics, the authors blend Rogers’ diffusion of innovations theory (DoIT) with Webster’s sociological critique of technological innovation in medicine and healthcare systems to illuminate a wider range of interacting factors. Qualitative data collected between 2004 and 2006 uses semi-structured, in-depth interviews with 72 stakeholders across four English NHS hospital trusts.

Overall, PACS was more successfully implemented (fully or partially in three out of four trusts) than CPOE (implemented in one trust only). Factors such as perceived benefit to users and attributes of the application – in particular speed, ease of use, reliability and flexibility and levels of readiness – were highly relevant but their influence was modulated through interaction with complex structural and relational issues.

Results reveal that combining contextual system level theories with DoIT increases understanding of real-life processes underpinning implementation of IT innovations within healthcare. They also highlight important drivers affecting success of implementation, including socio-political factors, the social body of practice and degree of “co-construction” between designers and end-users.

The originality of the study partly rests on its methodological innovativeness and its value on critical insights afforded into understanding complex IT implementation programmes.

Clinical notes typically contain medical jargons and specialized words and phrases that are complicated and technical to most people, which is one of the most challenging obstacles in health information dissemination to consumers by healthcare providers. The authors aim to investigate how to leverage machine learning techniques to transform clinical notes of interest into understandable expressions.

The authors propose a natural language processing pipeline that is capable of extracting relevant information from long unstructured clinical notes and simplifying lexicons by replacing medical jargons and technical terms. Particularly, the authors develop an unsupervised keywords matching method to extract relevant information from clinical notes. To automatically evaluate completeness of the extracted information, the authors perform a multi-label classification task on the relevant texts. To simplify lexicons in the relevant text, the authors identify complex words using a sequence labeler and leverage transformer models to generate candidate words for substitution. The authors validate the proposed pipeline using 58,167 discharge summaries from critical care services.

The results show that the proposed pipeline can identify relevant information with high completeness and simplify complex expressions in clinical notes so that the converted notes have a high level of readability but a low degree of meaning change.

The proposed pipeline can help healthcare consumers well understand their medical information and therefore strengthen communications between healthcare providers and consumers for better care.

An innovative pipeline approach is developed to address the health literacy problem confronted by healthcare providers and consumers in the ongoing digital transformation process in the healthcare industry.

The paper aims to investigate the practices facilitating the transformation of healthcare services, understanding the resulting outcomes in terms of well-being and uplifting changes. a systematic literature review (SLR) focusing on analyzing the healthcare sector under the transformative service research (TSR) theoretical domain is conducted to achieve this goal.

Employing a structured SLR developed based on the PRISMA protocol (Pickering and Byrne, 2014; Pickering et al., 2015) and using Scopus and WoS databases, the study identifies and analyzes 49 papers published between 2021 and 2022. Content analysis is used to classify and analyze the papers.

The SLR reveals four transformative practices (how) within the healthcare sector under the TSR domain, each linked to specific well-being outcomes (what). The analysis shows that both practices and outcomes are mainly patient-related. An integrative framework for transformative healthcare service is presented and critically examined to identify research gaps and define the trajectory for the future development of TSR in healthcare. In addition, managerial implications are provided to guide practitioners.

This research is among the first to analyze TSR literature in the context of healthcare. The study critically examines the TSR’s impact on the sector’s transformation, providing insights for future research and offering a roadmap for healthcare practitioners to facilitate uplifting changes.

This paper aims to present the Expero4care model. Considering the growing need for a training evaluation model that does not simply fix processes, the Expero4care model represents the first attempt of a “quality model” dedicated to the learning outcomes of healthcare trainings.

Created as development of the Expero model (Cervai et al., 2013), the Expero4care model has been tailored for workplace learning in the healthcare sector and tested in six organizations across Europe. The model has been validated through the review of an international team of experts and its approval as QualiCert standard.

Expero4care allows the evaluation of the quality of learning outcomes focusing on competences, impact in the workplace, transferability, participation and credits. The evaluation process involves different categories of stakeholders (learners, trainers, colleagues, managers, internal or external bodies that can benefit the training’s results, i.e. final users of the service, such as patients and citizens), and it is based on a systematic data collection and comparison among expectations and perceptions. The implementation of the Expero4care model gives the opportunity to start a continuous improvement process of the trainings in the healthcare service.

Expero4care has been tested in both university courses and organizational trainings dedicated to professionals in the healthcare sector. The initial sample is not wide enough to cover all the countries and the types of trainings, so a larger implementation is needed to validate its appropriateness.

Expero4care is the first model created specifically for organizations providing training in the healthcare sector. The implementation of the Expero4care model – adaptable to different kind of organizations and trainings – means that it is possible to highlight the value of the training considering its impact on the workplace and on the citizens.

As the most commonly used tools to assess the quality of trainings consist of questionnaires submitted to participants at the end of the training and considering that quality models have not been utilized to analyse learning outcomes in healthcare, Expero4care represents the first quality model dedicated to training in healthcare service.