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BackgroundeHealth applications are constantly increasing and are frequently considered to constitute a promising strategy for cost containment in health care, particularly if the applications aim to support older persons. Older persons are, however, not the only major eHealth stakeholder. eHealth suppliers, caregivers, funding bodies, and health authorities are also likely to attribute value to eHealth applications, but they can differ in their value attribution because they are affected differently by eHealth costs and benefits. Therefore, any assessment of the value of eHealth applications requires the consideration of multiple stakeholders in a holistic and integrated manner. Such a holistic and reliable value assessment requires a profound understanding of the application’s costs and benefits. The first step in measuring costs and benefits is identifying the relevant costs and benefit categories that the eHealth application affects. ObjectiveThe aim of this study is to support the conceptual phase of an economic evaluation by providing an overview of the relevant direct and indirect costs and benefits incorporated in economic evaluations so far. MethodsWe conducted a systematic literature search covering papers published until December 2019 by using the Embase, Medline Ovid, Web of Science, and CINAHL EBSCOhost databases. We included papers on eHealth applications with web-based contact possibilities between clients and health care providers (mobile health apps) and applications for self-management, telehomecare, telemedicine, telemonitoring, telerehabilitation, and active healthy aging technologies for older persons. We included studies that focused on any type of economic evaluation, including costs and benefit measures. ResultsWe identified 55 papers with economic evaluations. These studies considered a range of different types of costs and benefits. Costs pertained to implementation activities and operational activities related to eHealth applications. Benefits (or consequences) could be categorized according to stakeholder groups, that is, older persons, caregivers, and health care providers. These benefits can further be divided into stakeholder-specific outcomes and resource usage. Some cost and benefit types have received more attention than others. For instance, patient outcomes have been predominantly captured via quality-of-life considerations and various types of physical health status indicators. From the perspective of resource usage, a strong emphasis has been placed on home care visits and hospital usage. ConclusionsEconomic evaluations of eHealth applications are gaining momentum, and studies have shown considerable variation regarding the costs and benefits that they include. We contribute to the body of literature by providing a detailed and up-to-date framework of cost and benefit categories that any interested stakeholder can use as a starting point to conduct an economic evaluation in the context of independent living of older persons.

BackgroundDespite the high potential of big data, their applications in health care face many organizational, social, financial, and regulatory challenges. The societal dimensions of big data are underrepresented in much medical research. Little is known about integrating big data applications in the corporate routines of hospitals and other care providers. Equally little is understood about embedding big data applications in daily work practices and how they lead to actual improvements for health care actors, such as patients, care professionals, care providers, information technology companies, payers, and the society. ObjectiveThis planned study aims to provide an integrated analysis of big data applications, focusing on the interrelations among concrete big data experiments, organizational routines, and relevant systemic and societal dimensions. To understand the similarities and differences between interactions in various contexts, the study covers 12 big data pilot projects in eight European countries, each with its own health care system. Workshops will be held with stakeholders to discuss the findings, our recommendations, and the implementation. Dissemination is supported by visual representations developed to share the knowledge gained. MethodsThis study will utilize a mixed-methods approach that combines performance measurements, interviews, document analysis, and cocreation workshops. Analysis will be structured around the following four key dimensions: performance, embedding, legitimation, and value creation. Data and their interrelations across the dimensions will be synthesized per application and per country. ResultsThe study was funded in August 2017. Data collection started in April 2018 and will continue until September 2021. The multidisciplinary focus of this study enables us to combine insights from several social sciences (health policy analysis, business administration, innovation studies, organization studies, ethics, and health services research) to advance a holistic understanding of big data value realization. The multinational character enables comparative analysis across the following eight European countries: Austria, France, Germany, Ireland, the Netherlands, Spain, Sweden, and the United Kingdom. Given that national and organizational contexts change over time, it will not be possible to isolate the factors and actors that explain the implementation of big data applications. The visual representations developed for dissemination purposes will help to reduce complexity and clarify the relations between the various dimensions. ConclusionsThis study will develop an integrated approach to big data applications that considers the interrelations among concrete big data experiments, organizational routines, and relevant systemic and societal dimensions. International Registered Report Identifier (IRRID)DERR1-10.2196/16779

Faced by a severe shortage of nurses and increasing demand for care, hospitals need to optimally determine their staffing levels. Ideally, nurses should be staffed to those shifts where they generate the highest positive value for the quality of healthcare. This paper develops an approach that identifies the incremental benefit of staffing an additional nurse depending on the patient mix. Based on the reasoning that timely fulfillment of care demand is essential for the healthcare process and its quality in the critical care setting, we propose to measure the incremental benefit of staffing an additional nurse through reductions in time until care arrives (TUCA). We determine TUCA by relying on queuing theory and parametrize the model with real data collected through an observational study. The study indicates that using the TUCA concept and applying queuing theory at the care event level has the potential to improve quality of care for a given nurse capacity by efficiently trading situations of high versus low workload. [ABSTRACT FROM AUTHOR]

Introduction: Traditional payment models reward volume rather than value. Moving away from reimbursing separate providers to network-level reimbursement is assumed to support structural changes in health care organizations that are necessary to improve patient care. This scoping review evaluates the performance of care networks that have adopted network-level payment models. Methods: A scoping review of the empirical literature was conducted according to the five-step York framework. We identified indicators of performance, categorized them in four categories (quality, utilization, spending and other consequences) and scored whether performance increased, decreased, or remained stable due to the payment model. Results: The 76 included studies investigated network-level capitation, disease-based bundled payments, pay-for-performance and blended global payments. The majority of studies stem from the USA. Studies generally concluded that performance in terms of quality and utilization increased or remained stable. Most payment models were associated with improved spending performance. Overall, our review shows that network-level payment models are moderately successful in improving network performance. Discussion/conclusion: As health care networks are increasingly common, it seems fruitful to continue experimenting with reimbursement models for health care networks. It is also important to broaden the scope to not only scrutinize outcomes, but also the contexts and mechanisms that lead to certain outcomes. [ABSTRACT FROM AUTHOR]

Background: Inspired by the newpublic management movement, many public sector organizations have implemented business-like performance measurement systems (PMSs) in an effort to improve organizational efficiency and effectiveness. However, a large stream of the accounting literature has remained critical of the use of performance measures in the public sector because of the inherent difficulty in measuring output and the potential adverse effects of performance measurement. Although we acknowledge that PMSs may indeed sometimes yield adverse effects, we highlight in this study that the effects of PMSs depend on the way in which they are used. Purpose: The aim of this study was to investigate various uses of PMSs among hospital managers and their effects on hospital outcomes, including process quality, degree of patient-oriented care, operational performance, and work culture. Methodology: We use a survey sent to 432 Dutch hospital managers (19.2% response rate, 83 usable responses). For our main variables, we rely on previously validated constructs where possible, and we conduct ordinary least squares regressions to explore the relation between PMS use and hospital outcomes. Results: We find that the way in which PMSs are used is associated with hospital outcomes. An exploratory use of PMS has a positive association with patient-oriented care and collective work culture. Furthermore, the operational use of PMSs is positively related to operational performance but negatively related to patient-oriented care. There is no single best PMS use that positively affects all performance dimensions. Practice Implications: The way in which managers use PMSs is related to hospital outcomes. Therefore, hospital managers should critically reflect on how they use PMSs and whether their type of use is in line with the desired hospital outcomes. [ABSTRACT FROM AUTHOR]

Our study contributes to the scholarly debate whether organizational units should have a narrow focus and admit a homogeneous patient cluster or whether they should admit a pool of patient clusters. We investigate whether the benefits of increased volume through pooling patients outweigh the disadvantages of increased heterogeneity and pursue our analysis in the context of neonatal care. Our empirical studies relies on 4020 patient episodes collected in 18 German neonatal intensive care units and we distinguish between two patient clusters that differ with respect to the inherent medical risk and operational heterogeneity. Cluster 1 consists of very‐low birth weight (VLBW) infants with increased risk of complications but similar service trajectories and lower operational heterogeneity. Cluster 2 contains non‐VLBW infants with lower risk of complications but more diversity in disease patterns and higher operational heterogeneity. Our analysis shows that cluster volume, that is, the unit's absolute patient volume in a cluster, is positively related to process outcomes as indicated by decreasing length of stay. This relationship is found for both clusters. Regarding focus, we do not find any evidence of positive effects. In fact, we even find that cluster focus, that is, the unit's relative volume of the cluster, is detrimentally related to process outcomes for non‐VLBW patients with lower risk of complications and more operational heterogeneity. This indicates that organizational units providing services for complex patients should not have a narrow focus, but should rather provide services for related patient clusters in order to achieve higher volume levels within the unit. [ABSTRACT FROM AUTHOR]

International Registered Report Identifier (irrid): RR2-10.2196/16779. [ABSTRACT FROM AUTHOR]

International Registered Report Identifier (irrid): RR2-10.2196/16779., (©Hilco J van Elten, Sandra Sülz, Erik M van Raaij, Rik Wehrens. Originally published in the Journal of Medical Internet Research (https://www.jmir.org), 22.02.2022.)