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Healthcare systems are transforming from the traditional volume-based model of healthcare to a value-based model of healthcare. Value generation in healthcare is about emphasising the health outcomes achieved by patients and organisations while maintaining an optimal relationship with costs. This scoping review aimed to identify the key elements and outcomes of implementing value-based healthcare (VBHC). The review process included studies published from 2013 to 2023 in four different databases (SpringerLink, PubMed, ProQuest and Scopus). Of the 2801 articles retrieved from the searches, 12 met the study’s inclusion criteria. A total of 11 studies referred to value as the relationship between the outcomes achieved by patients and the costs of achieving those outcomes. Most of the studies highlighted the presence of leadership, the organisation of care into integrated care units, the identification and standardisation of outcome measures that generate value for the patient, and the inclusion of the patient perspective as the most prominent key elements for optimal VBHC implementation. Furthermore, some benefits were identified from VBHC implementation, which could shed light for future implementation actions. Therefore, the VBHC model is a promising approach that may contribute to an improvement in the efficiency and sustainability of healthcare.

Introduction Value‐based healthcare (VBHC) focusses on increasing value for patients. Hospitals aim to implement VBHC via value improvement (VI) teams for medical conditions. To determine the patient's perspective on value, collective patient participation is important in these teams. We therefore evaluated the current state of patient participation in VI teams and share lessons learned. Methods This mixed‐methods study was conducted at seven collaborating hospitals in the Netherlands. A questionnaire (the public and patient engagement evaluation tool) was tailored to the study's context, completed by VI team members (n = 147 from 76 different VI teams) and analysed with descriptive statistics. In addition, 30 semistructured interviews were held with VI team members and analysed through thematic analysis. Data were collected between February 2022 and January 2023 and were triangulated by mapping the quantitative results to the interview themes. Results Thirty‐eight of the 76 included VI teams reported using a form of patient participation. Many respondents (71%) indicated a lack of a clear strategy and goal for patient participation. Multiple VI team members believed that specific knowledge and skills are required for patients to participate in a VI team, but this led to concerns regarding the representativeness of participating patients. Furthermore, while patients indicated that they experienced some level of hierarchy, they also stated that they did not feel restricted hereby. Lastly, patients were satisfied with their participation and felt like equal VI team members (100%), but they did mention a lack of feedback from the VI team on their input. Conclusion The results imply the lack of full implementation of patient participation within VI teams. Guidelines should be developed that provide information on how to include a representative group of patients, which methods to use, how to evaluate the impact of patient participation, and how to give feedback to participating patients. Patient and Public Contribution Two patient advisors were part of the research team and attended the research team meetings. They were involved as research partners in all phases of the study, including drafting the protocol (e.g., drafting interview guides and selecting the measurement instrument), interpreting the results and writing this article.

Background Quebec’s healthcare system faces significant challenges due to labour shortage, particularly in long-term care facilities (CHSLDs). The aging population and increasing demand for services compound this issue. Teleconsultation presents a promising solution to mitigate labour shortage, especially in small CHSLDs outside urban centers. This study aims to evaluate the cost and cost savings associated with teleconsultation in CHSLDs, utilizing the Time-Driven Activity-Based Costing (TDABC) model within the framework of Value-Based Healthcare (VBHC). Methods This study focuses on CHSLDs with fewer than 50 beds in remote regions of Quebec, where teleconsultation for nighttime nursing care was implemented. Time and cost data were collected from three CHSLDs over varying periods. The TDABC model, aligned with VBHC principles, was applied through five steps, including process mapping, estimating activity times, calculating resource costs, and determining total costs. Results Teleconsultation increased the cost per minute for nursing care compared to traditional care, attributed to additional tasks during remote consultations and potential technical challenges. However, cost savings were realized due to reduced need for onsite nursing staff during non-eventful nights. Overall, substantial savings were observed over the project duration, aligning with VBHC’s focus on delivering high-value healthcare. Conclusions This study contributes both theoretically and practically by demonstrating the application of TDABC within the VBHC framework in CHSLDs. The findings support the cost savings from the use of teleconsultation in small CHSLDs. Further research should explore the long-term sustainability and scalability of teleconsultation across different CHSLD sizes and settings within the VBHC context to ensure high-value healthcare delivery.

Background Clinicians’ experiences of providing care constitute an important outcome for evaluating care from a value-based healthcare perspective. Yet no currently available instruments have been designed and validated for assessing clinicians’ experiences. This research sought to address this important gap by developing and validating a novel instrument in a public health system in Australia. Methods A multi-method project was conducted using co-design with 12 clinician leaders from a range of NSW Health Local Health Districts to develop the Clinician Experience Measure (CEM). Validity and reliability analyses were conducted in two stages, first assessing face and content validity with a pool of 25 clinicians and then using psychometric analysis with data from 433 clinicians, including nurses, doctors and allied health and representing all districts within one jurisdiction in Australia. Results Data gathered from 25 clinicians via the face and content validity process indicated that the initial 31-items were relevant to the range of staff employed in the NSW state health system, with minor edits made to the survey layout and wording within two items. Psychometric analysis led to a rationalised 18-item final instrument, comprising four domains: psychological safety (4-items); quality of care (5-items); clinician engagement (4-items) and interprofessional collaboration (5-items). The 18-item four-factor model produced a good fit to the data and high levels of reliability, with factor loadings ranging from .62 to .94, with Cronbach’s alpha (range: .83 to .96) and composite reliability (range: .85 to .97). Conclusions The CEM is an instrument to capture clinicians’ experiences of providing care across a health system. The CEM provides a useful tool for healthcare leaders and policy makers to benchmark and assess the impact of value-based care initiatives and direct change efforts.

Objective. Clinician's experiences of providing care are identified as a key outcome associated with value-based healthcare (VBHC). In contrast to patient-reported experience measures, measurement tools to capture clinician's experiences in relation to VBHC initiatives have received limited attention to date. Progressing from an initial 18-item clinician experience measure (CEM), we sought to develop and evaluate the reliability of a set of 10 core clinician experience measure items in the CEM-10. Methods. A multi-method project was conducted using a consensus workshop with clinicians from a range of NSW Health local health districts to reduce the 18-item CEM to a short form 10-item core clinician experience measure (CEM-10). The CEM-10 was deployed with clinicians providing diabetes care, care for older adults and virtual care across all districts and care settings of New South Wales, Australia. Psychometric analysis was used to determine the internal consistency of the tool and its suitability for diverse clinical contexts. Results. Consensus building sessions led to a rationalised 10-item tool, retaining the four domains of psychological safety (two items), quality of care (three items), clinician engagement (three items) and interprofessional collaboration (two items). Data from four clinician cohorts (n = 1029) demonstrated that the CEM-10 four-factor model produced a good fit to the data and high levels of reliability, with factor loadings ranging from 0.77 to 0.92, with Cronbach's alpha (range: 0.79–0.90) and composite reliability (range: 0.80–0.92). Conclusions. The CEM-10 provides a core set of common clinician experience measurement items that can be used to compare clinician's experiences of providing care between and within cohorts. The CEM-10 may be supported by additional items relevant to particular initiatives when evaluating VBHC outcomes.

Objective. The aim of this study was to develop a concise, accessible definition of value-based health care (VBHC) and identify its main characteristics through a comprehensive analysis of existing literature. Methods. A scoping review methodology was employed to map definitions and characteristics of VBHC from nine databases, including JBI EBP Database, Cochrane Reviews, Embase, Ovid MEDLINE(R), APA PsycINFO, and others, from inception until November 2023. The scoping review aimed to clarify existing concepts and identify gaps in VBHC definitions and frameworks across various geographical contexts. Additionally, qualitative data on VBHC were analysed from the included studies using a word cloud generated via an online tool and a word frequency table generated from Excel. This dual analysis informed the creation of a simplified, data-driven definition of VBHC along with its key characteristics. Results. The word frequency analysis highlighted common themes, including \"care, 'outcomes, 'quality, 'efficiency,' and 'cost.' Based on these frequently mentioned terms, a simplified definition of VBHC was formulated, focusing on patient-centred care that aims to improve health outcomes relative to costs. Comparisons with existing literature revealed that while the derived definition is more accessible and concise, it lacks the depth of the academic definitions, which emphasise strategic implementation, interdisciplinary collaboration, and nuanced measurement of outcomes. Conclusion. This study provides a simplified, data-driven definition of VBHC that can facilitate understanding and implementation among practitioners and stakeholders. Integrating this accessible definition can bridge the gap between theory and practice, ultimately supporting better health outcomes and system sustainability.

Background Healthcare organizations measure costs for business operations but do not routinely incorporate costs in decision-making on the value of care. Aim Provide guidance on how to use costs in value-based healthcare (VBHC) delivery at different levels of the healthcare system. Setting and Participants Integrated practice units (IPUs) for diabetes mellitus (DM) and for acute myocardial infarction (AMI) at the Leiden University Medical Center and a collaboration of seven breast cancer IPUs of the Santeon group, all in the Netherlands. Program Description and Evaluation VBHC aims to optimize care delivery to the patient by understanding how costs relate to outcomes. At the level of shared decision-making between patient and clinician, yearly check-up consultations for DM type I were analyzed for patient-relevant costs. In benchmarking among providers, quantities of cost drivers for breast cancer care were assessed in scorecards. In continuous learning, cost-effectiveness analysis was compared with radar chart analysis to assess the value of telemonitoring in outpatient follow-up. Discussion Costs vary among providers in healthcare, but also between provider and patient. The joint analysis of outcomes and costs using appropriate methods helps identify and optimize the aspects of care that drive desired outcomes and value.

Background Health care costs are rising rapidly in Western societies. Understanding the benefits and costs of care is crucial to maintaining or improving existing health care systems. We propose an instrument that provides a clear overview of both the costs and returns of a treatment to improve the quality of care while keeping the costs affordable. Methods First, a general value-based healthcare concept was developed as an efficacy index. Second, a Physiotherapy-specific Efficacy Index (PE- Index ) for musculoskeletal disorders was formulated based on pain and functional improvement, treatments, and episode duration. The PE- Index  discriminative value was assessed using a linear mixed model with physiotherapy practices as a random effect in real-world data from a national registry. Variation attributed to practices was quantified by an intraclass correlation coefficient. Separate linear mixed models and a radar plot (PE- Graph ) visualized individual PE- Index components. Lastly, stakeholders evaluated the PE- Index and PE- Graph for internal quality improvement and external transparency through surveys and advisory board meetings. Results In total, 95.805 episodes treated in 370 practices were included in the linear mixed models. The PE- Index demonstrated an adequate discriminative ability with an ICC of 0.118. Stakeholders agree that the PE- Index and the PE- Chart are appropriate for improvement of quality of care and enhancing the current system for external transparency. Nevertheless, because of concerns about a too hasty implementation and the risk of strategic gaming, both were not considered suitable for external transparency right now. Conclusions The PE- Index and PE- Graph are adequate instruments to discriminate between practices and can be used for internal quality improvement, however, are not yet suitable for external transparency purposes.

Background: Value‐based healthcare (VBHC) aims to improve the quality of healthcare delivery while reducing costs and also aims for outcomes that are of utmost importance from patients’ perspectives. Despite a growing interest in VBHC, a significant knowledge gap persists within the existing literature in the absence of a clear conceptualization of VBHC itself. Aim: The aim of the present study was to develop a comprehensive understanding of the concept of VBHC in order to arrive at a definition based on the evidence in the existing literature. Method: A concept analysis approach was used to identify the concept’s defining attributes, its antecedents, consequences, and its empirical referents. Results: The analysis of the concept yielded three defining attributes: monetary value of health service, quality of care, and patient‐centered care. The analysis also identified several crucial antecedents for transitioning traditional fee‐for‐service models to those focused on value; it also identified key interrelated consequences: improved patient outcomes, cost reduction, and increased patient satisfaction. Conclusion: The concept analysis of VBHC provides a comprehensive framework for understanding its key components and challenges. By aligning healthcare delivery with the values and needs of patients, VBHC represents a promising avenue toward achieving high‐quality, sustainable healthcare. The findings from this analysis call for a collaborative effort among healthcare leaders, researchers, and policymakers to further refine and implement VBHC models, ensuring healthcare systems are both patient‐centered and cost‐effective. These findings also have implications for nursing management.

Since the introduction of artificial intelligence (AI) in radiology, the promise has been that it will improve health care and reduce costs. Has AI been able to fulfill that promise? We describe six clinical objectives that can be supported by AI: a more efficient workflow, shortened reading time, a reduction of dose and contrast agents, earlier detection of disease, improved diagnostic accuracy and more personalized diagnostics. We provide examples of use cases including the available scientific evidence for its impact based on a hierarchical model of efficacy. We conclude that the market is still maturing and little is known about the contribution of AI to clinical practice. More real-world monitoring of AI in clinical practice is expected to aid in determining the value of AI and making informed decisions on development, procurement and reimbursement.