Search

Per governare i tempi di attesa delle prestazioni è imprescindibile utilizzare i dati amministrativi nella pianificazione dell'offerta e nel governo della domanda di servizi. L'articolo illustra l'esperienza della ricerca-intervento realizzata dall'Azienda Sanitaria dell'Alto Adige per la stima dei fabbisogni futuri e l'analisi retrospettiva dei consumi della specialistica ambulatoriale. Viene presentato il modello quantitativo predittivo sviluppato dall'azienda per la definizione del fabbisogno di prestazioni con dettaglio per singola prestazione, e vengono esposte le scelte strategiche aziendali per ridurre i tempi di attesa. Rispetto ad alcune prestazioni di particolare rilevanza sono invece proposte analisi retrospettive per verificare l'appropriatezza dei consumi, con particolare riferimento agli indici di consumo per area di residenza, MMG, o fasce di età; alla mobilità intraprovinciale; alle modalità di accesso alle prestazioni; ai codici di priorità. Tale approccio si presta a esser facilmente applicabile ad altre aziende sul territorio italiano data la semplicità dello strumento e la crescente disponibilità e granularità di dati sul consumo di prestazioni sanitarie a disposizione delle aziende.

Resource modeling aims to explicitly quantify the effects of adopting new health care technologies in settings with capacity-related constraints. The aim of this analysis was to use resource modeling to explore the effects of the uptake of first-line treatment with daratumumab on wait lists and wait times in patients with untreated multiple myeloma. Two formulations were compared: the standard IV formulation (DARA-IV) and a recently approved SC formulation (DARA-SC).First, semi-structured interviews at six oncologic centers were used to retrieve data on the management of patients given a DARA-IV regimen. Second, a discrete event simulation (DES) model was built to estimate the effects on resource consumption, wait lists, and wait times in scenarios with different incident numbers of patients treated with either DARA-IV or DARA-SC.In all of the simulated scenarios with more incident patients initiated on first-line treatment with DARA-IV, the actual capacity of infusion chairs was not enough to meet the demand, leading to increases in wait times and wait lists. In the highest-demand scenario, 17 more infusion chairs per center would be required to avoid such increases. Treatment with DARA-SC would allow centers to meet the demand with their actual capacity.DES modeling can effectively be used to formally explore the effects of different formulations on the use of limited resources, wait lists, and wait times at the facility level. Based on the findings from this analysis, DARA-SC may free up resources and prevent short- and long-term costs to infusion centers.

Health economists have written extensively on the design and implementation of coverage with evidence development (CED) schemes and have proposed theoretical frameworks based on cost-effectiveness modeling and value of information analysis. CED may aid decision-makers when there is uncertainty about the (cost-)effectiveness of a new health technology at the time of reimbursement. Medical devices are potential candidates for CED schemes, as regulatory regimes do not usually require the same level of efficacy and safety data normally needed for pharmaceuticals. The purpose of this research is to assess whether the actual practice of CED for medical devices in Europe meets the theoretical principles proposed by health economists and whether theory and practice can be more closely aligned. Based on decision-makers' perceptions of the challenges associated with CED schemes, plus examples from the schemes themselves, we discuss a series of proposals for assessing the desirability of schemes, their design, implementation, and evaluation. These proposals, while reflecting the practical challenges with developing CED programs, embody many of the principles suggested by economists and should support decision-makers in dealing with uncertainty about the real-world performance of devices.

The blended learning (BL) approach to training health care professionals is increasingly adopted in many countries because of high costs and disruption to service delivery in the light of severe human resource shortage in low resource settings. The Covid-19 pandemic increased the urgency to identify alternatives to traditional face-to-face (f2f) education approach. A four-day f2f antenatal care (ANC) and postnatal care (PNC) continuous professional development course (CPD) was repackaged into a 3-part BL course; 1) self-directed learning (16 hours) 2) facilitated virtual sessions (2.5 hours over 3 days) and 3) 2-day f2f sessions. This study assessed the feasibility, change in healthcare providers’ knowledge and costs of the BL package in Nigeria, Tanzania, and Kenya. A mixed methods design was used. A total of 89 healthcare professionals, were purposively selected. Quantitative data was collected through an online questionnaire and skills assessments, analyzed using STATA 12 software. Qualitative data was collected through key informant interviews and focus group discussions, analysed using thematic analysis.Majority of participants (86%) accessed the online sessions using a mobile phone from home and health facilities. The median (IQR) time of completing the self-directed component was 16 hours, IQR (8, 30). A multi-disciplinary team comprising of 42% nurse-midwives, 28% doctors, 20% clinical officers and 10% other healthcare professionals completed the BL course. Participants liked the BL approach due to its flexibility in learning, highly educative/relevant content, mixing of health worker cadres and CPD points. Aspects that were noted as challenging were related to personal log-in details and network connectivity issues during the self-directed learning and facilitated virtual sessions respectively. The blended learning approach to ANC-PNC in-service training was found to be feasible, cost saving compared to the face-to-face approach and acceptable to health care professionals in LMICs.

Experiences with coverage with evidence development (CED) schemes are fairly limited in Central and Eastern European (CEE) countries, which are usually late adopters of new health technologies. Our aim was to put forward recommendations on how CEE health technology assessment bodies and payer organizations can apply CED to reduce decision uncertainty on reimbursement of medical devices, with a particular focus on transferring the structure and data from CED schemes in early technology adopter countries in Western Europe. Structured interviews on the practices and feasibility of transferring CED schemes were conducted and subsequently, a draft tool for the systematic classification of decision alternatives and recommendations was developed. The decision tool was reviewed in a focus group discussion and validated within a wider group of CEE experts in a virtual workshop. Transferability assessment is needed in case of (1) joint implementation of a CED scheme; (2) transferring the structure of an existing CED scheme to a CEE country; (3) reimbursement decisions that are linked to outcomes of an ongoing CED scheme in another country and (4) real-world evidence transferred from completed CED schemes. Efficient use of available resources may be improved by adequately transferring evidence and policy tools from early technology adopter countries.

Si l’on parle aujourd’hui de l’inondation de 1966 à Florence, on ne le doit qu’à l’entêtement des Florentins qui, bien qu’ayant déjà expérimenté trois fois « quelle était la force du vindicatif Arno, voulurent toutefois en garder auprès de leurs maisons une partie plus grande encore, en réduisant à bon terme, autour de 1324, la troisième et jusqu’alors plus ample enceinte de la cité ». La Bibliothèque nationale vue de la Piazza Poggi Cliché Adriano Bartolozzi, coll. Archives photographiques ...

Early Feasibility Studies (EFS) are among the pre-market clinical investigations allowed by the International Standard for Clinical investigation of medical devices (MD) for human subjects. The Food and Drug Administration (FDA) introduced an EFS program in the US in 2013. The European Union (EU) MD Regulation, that entered into force in May 2021, opened the possibility of EFS in the EU. However, European countries at present have no standardized procedural framework for EFS. In this paper, we address the desirability of a European EFS program.Characteristics of EFS conducted so far are reviewed, and perceptions of an expert, multidisciplinary panel of key stakeholders are explored regarding desirability and feasibility of a European EFS program and critical factors favoring or hampering its implementation.Implementing an EFS program in the EU would contribute to creating a favorable environment for early-stage clinical investigations, with positive effects on the quality and timeliness of clinical evidence for novel MDs, and attractiveness of the European system for pre- and post-market clinical research. Based on discussion with experts, also leveraging on the US experience, three dimensions should be considered for effective design and implementation: process, resources, and ethical issues.

Payers and manufacturers can disagree on the appropriate level of evidence that is required for new medical devices, resulting in high societal costs due to decisions taken with sub‐optimal information. A cost‐effectiveness model of a hypothetical total artificial heart was built using data from the literature and the (simulated) results of a pivotal study. The expected value of perfect information (EVPI) was calculated from both the payer and manufacturer perspectives, using net monetary benefit and the company's return on investment respectively. A function was also defined, linking effectiveness to market shares. Additional constraints such as a minimum clinical difference or maximum budget impact were introduced into the company's decisions to simulate additional barriers to adoption. The difference in the EVPI between manufacturers and payers varied greatly depending on the underlying decision rules and constraints. The manufacturer's EVPI depends on the probability of being reimbursed, the uncertainty on the (cost‐)effectiveness of the technology, as well as other parameters relating to initial investments, operating costs and market dynamics. The use of Value of information for both perspectives can outline potential misalignments and can be particularly useful to inform early dialogs between manufacturers and payers, or negotiations on conditional reimbursement schemes.

Health economic modeling of novel technology at the early stages of a product lifecycle has been used to identify technologies that are likely to be cost-effective. Such early assessments are challenging due to the potentially limited amount of data. Modelers typically conduct uncertainty analyses to evaluate their effect on decision-relevant outcomes. Current approaches, however, are limited in their scope of application and imposes an unverifiable assumption, i.e., uncertainty can be precisely represented by a probability distribution. In the absence of reliable data, an approach that uses the fewest number of assumptions is desirable. This study introduces a generalized approach for quantifying parameter uncertainty, i.e., probability bound analysis (PBA), that does not require a precise specification of a probability distribution in the context of early-stage health economic modeling. We introduce the concept of a probability box (p-box) as a measure of uncertainty without necessitating a precise probability distribution. We provide formulas for a p-box given data on summary statistics of a parameter. We describe an approach to propagate p-boxes into a model and provide step-by-step guidance on how to implement PBA. We conduct a case and examine the differences between the status-quo and PBA approaches and their potential implications on decision-making.

IntroductionAn iterative, life-cycle approach to the evaluation of healthcare technologies requires that clinical and economic evidence is collected since the initial stages of diffusion. Nevertheless, early cost-effectiveness models are challenging mainly due to the difficulties in estimating model parameters and faithfully characterizing parameter uncertainty. This is especially true with AI-based diagnostics, where attribution of effects on costs and patient-relevant outcomes is more challenging. Empirical applications of early-models are useful to identify the main challenges of iterative modelling and provide recommendations on best-practices. Here, we reported on a case study on a machine learning-derived hypotension predictive index (HPI), that predicts the onset of intraoperative hypotension and trigger corrective measures.MethodsA hybrid decision-tree/Markov model was developed comparing an HPI-based intervention protocol to standard-of-care intervention protocol during gynecological procedures. A short-term component of the model was populated using data from individual patients collected at one hospital in Italy. An historical control group was also defined using propensity score matching. Long-term costs and consequences of HPI were modelled using secondary data. A probabilistic version of the headroom approach was used to determine the maximum achievable price of HPI based on available evidence. Value of Information analysis was also conducted to identify the parameters that contribute the most to the overall uncertainty, and to identify optimal future study designs. Extensive deterministic and probabilistic sensitivity analyses were conducted to characterize the uncertainty over the cost-effectiveness of HPI.ResultsThe preliminary results of the analysis suggest that HPI has potential to improve patients’ outcomes and generate efficiency gains by reducing hypotension events and permanent complications, such as acute kidney injury. The link between reduction in hypotension and the rate of complications, or the long-term effects on healthcare costs and patients’ quality of life are the parameters that contribute the most to model uncertainty.ConclusionsEarly cost-effectiveness models are a valuable tool to inform further product development and evidence requirements, but characterization of uncertainty and transparency in modelling assumptions are key.