Background: Generally, a significant portion of healthcare spending consists of out-of-pocket (OOP) expenses. Patients indicate that, in practice, there are often some OOP expenses, incurred when they receive medical care, which are unexpected for them and should have been taken into account when deciding on a course of action. Patients are often reliant on their GP and may, therefore, expect their GP to provide them with information about the costs of treatment options, taking into consideration their individual insurance plan. This also applies to the Netherlands, where OOP expenses increased rapidly over the years. In the current study, we observed the degree to which matters around patients' insurance and OOP expenses are discussed in the Netherlands, using video recordings of consultations between patients and GPs. Methods: Video recordings were collected from patient-GP consultations in 2015-2016. In 2015, 20 GPs and 392 patients from the eastern part of the Netherlands participated. In 2016, another eight GPs and 102 patients participated, spread throughout the Netherlands. The consultations were coded by three observers using an observation protocol. We achieved an almost perfect inter-rater agreement (Kappa = .82). Results: In total, 475 consultations were analysed. In 9.5% of all the consultations, issues concerning patients' health insurance and OOP expenses were discussed. The reimbursement of the cost of medication was discussed most often and patients' current insurance and co-payments least often. In some consultations, the GP brought up the subject, while in others, the patient initiated the discussion. Conclusions: While GPs may often be in the position to provide patients with information about treatment alternatives, few patients discuss the financial effects of their referral or prescription with their GP. This result complies with existing literature. Policy makers, GPs and insurers should think about how GPs and patients can be facilitated when considering the OOP expenses of treatment. There are several factors why this study, analysing video recordings of routine GP consultations in the Netherlands, is particularly relevant: Dutch GPs play a gatekeeper function; OOP expenses have increased relatively swiftly; and patients have both the right to decide on their treatment, and to choose a provider.
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This study analyses the determinants of cycling expenditure by means of a Tobit regression analysis, based on a dataset of 5,157 cyclists. Using a heterodox economic framework, 23 different variables are combined into two commonly used variable groups (socio-demographics, sports intensity variables) and two rarely investigated variables groups (socio-economic cycling capital, psychographics). With all variables included in the Tobit regression, gender, trip duration, frequency, number of cycling variants practiced, visiting cycling websites, and practicing road-cycling or mountain bike are positive determinants of cycling expenditure. A negative association is found with competitive riding and cycling drop out. Marketeers of cycling services and cycling apparel should meet the cyclists need for identification instead of focusing solely on socio-demographic factors.
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Aim: Aim of this study was to provide insight into the costs associated with severe mental illnesses from a societal perspective. Insight into expenses is of value to policymakers and mental health institutions that are dealing with ongoing budget cuts. A reliable cost estimate is also necessary to assess the cost-effectiveness of interventions and make decisions on reimbursement. Methods: Baseline costs were calculated for 188 individuals with severe mental illness (SMI) who wish to increase their societal participation defined as paid or unpaid work, education and meaningful daily activities. Costs were measured from a societal perspective by means of the TIC-P questionnaire and expressed in Euros.
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De technische en economische levensduur van auto’s verschilt. Een goed onderhouden auto met dieselmotor uit het bouwjaar 2000 kan technisch perfect functioneren. De economische levensduur van diezelfde auto is echter beperkt bij introductie van strenge milieuzones. Bij de introductie en verplichtstelling van geavanceerde rijtaakondersteunende systemen (ADAS) zien we iets soortgelijks. Hoewel de auto technisch gezien goed functioneert kunnen verouderde software, algorithmes en sensoren leiden tot een beperkte levensduur van de gehele auto. Voorbeelden: - Jeep gehackt: verouderde veiligheidsprotocollen in de software en hardware beperkten de economische levensduur. - Actieve Cruise Control: sensoren/radars van verouderde systemen leiden tot beperkte functionaliteit en gebruikersacceptatie. - Tesla: bij bestaande auto’s worden verouderde sensoren uitgeschakeld waardoor functies uitvallen. In 2019 heeft de EU een verplichting opgelegd aan automobielfabrikanten om 20 nieuwe ADAS in te bouwen in nieuw te ontwikkelen auto’s, ongeacht prijsklasse. De mate waarin deze ADAS de economische levensduur van de auto beperkt is echter nog onvoldoende onderzocht. In deze KIEM wordt dit onderzocht en wordt tevens de parallel getrokken met de mobiele telefonie; beide maken gebruik van moderne sensoren en software. We vergelijken ontwerpeisen van telefoons (levensduur van gemiddeld 2,5 jaar) met de eisen aan moderne ADAS met dezelfde sensoren (levensduur tot 20 jaar). De centrale vraag luidt daarom: Wat is de mogelijke impact van veroudering van ADAS op de economische levensduur van voertuigen en welke lessen kunnen we leren uit de onderliggende ontwerpprincipes van ADAS en Smartphones? De vraag wordt beantwoord door (i) literatuuronderzoek naar de veroudering van ADAS (ii) Interviews met ontwerpers van ADAS, leveranciers van retro-fit systemen en ontwerpers van mobiele telefoons en (iii) vergelijkend rij-onderzoek naar het functioneren van ADAS in auto’s van verschillende leeftijd en prijsklassen.
Granular materials (GMs) are simply a collection of individual particles, e.g., rice, coffee, iron-ore. Although straightforward in appearance, GMs are key to several processes in chemical-pharmaceutical, high-tech, agri-food and energy industry. Examples include laser sintering in additive manufacturing, tableting in pharma or just mixing of your favourite crunchy muesli mix in food industry. However, these bulk material handling processes are notorious for their inefficiency and ineffectiveness. Thereby, affecting the overall expenses and product quality. To understand and enhance the quality of a process, GMs industries utilise computer-simulations, much like how cars and aeroplanes have been designed and optimised since the 1990s. Just as how engineers utilise advanced computer-models to develop our fuel-efficient vehicle design, energy-saving granular processes are also developed utilising physics-based simulation-models, using a computer. Although physics-based models can effectively optimise large-scale processes, creating and simulating a fully representative virtual prototype of a GMs process is very iterative, computationally expensive and time intensive. On the contrary, given the available data, this is where machine learning (ML) could be of immense value. Like how ML has transformed the healthcare, energy and other top sectors, recent ML-based developments for GMs show serious promise in faster virtual prototyping and reduced computational cost. Enabling industries to rapidly design and optimise, enhancing real-time data-driven decision making. GranML aims to empower the GMs industries with ML. We will do so by (i) performing an in-depth GMs-ML literature review, (ii) developing open-access ML implementation guidelines; and (iii) an open-source proof-of-concept for an industry-relevant use case. Eventually, our follow-up mission is to build upon this vital knowledge by (i) expanding the consortium; (ii) co-developing a unified methodology for efficient computer-prototyping, unifying physics- and ML-based technologies for GMs; (iii) enhancing the existing computer-modelling infrastructure; and (iv) validating through industry focused demonstrators.
