scholarly journals Ensuring access to medicines at a fair price? Innovative contracting experiences in France

2020 ◽  
Vol 30 (Supplement_5) ◽  
Author(s):  
I Alaoui ◽  
C Izambert ◽  
A Toullier
Keyword(s):  
Public Investment ◽  
Access To Medicines ◽  
Added Value ◽  
Fair Price ◽  
Real World Data ◽  
Expensive Drug ◽  
Patient Organizations ◽  
Performance Contracts ◽  
Performance Based Contracts ◽  
Innovative Contracting

Abstract Issue Innovative contracting models are developed to ease price-setting negotiations in case an extremely expensive drug has not proven sufficient efficiency in clinical trials. As disruptive HIV treatments are expected in the near future, French patient organizations evaluated the ability of these innovative contracts to ensure accessible medicines at a fair price. Description Performance-based schemes condition prices paid by the State to the efficiency of the medicine observed through real-world data. In France, thirteen performance-based contracts have been concluded between 2008 and 2015. They are presented as a triple solution: innovative treatments are available to patients, manufacturers access markets, and states ensure healthcare within limited budgets. Establishing the added value of these models implies determining if they allow rapid access to treatments with substantial savings for payers, while ensuring rigorous price and cost transparency. Results Performance-based contracts indeed ensure patient access to treatments, but other mechanisms (such as temporary use authorizations) already serve this purpose. Regarding expenditure reduction however, these schemes have not proven their worth. The Court of Auditors' evaluation showed they do not generate substantial savings, as final prices correspond to those that would have applied with the European price guarantee. Lastly, as contracts are protected by business secrecy, the public cannot access neither to actual prices negotiated by payers, nor the amount of public investment that have been used for the research and development of the drug. Lessons The derogatory nature of performance contracts invites us to consider them on a case-by-case basis if ensuring access to a specific innovation is necessary. These contracts are certainly innovative, but they cannot be presented as technologies providing access at a fair price. Finally, their contractual and derogatory nature raises serious transparency issues. Key messages Performance-based contracts should be considered as alternatives to existing administrative channels provided that they lead to substantial savings and are drawn up in full transparency. Patient organizations need to assess innovative schemes such as performance-based contracting to ensure access to treatments without undermining historical struggles for fair and transparent pricing.

scholarly journals Real-world evidence: perspectives on challenges, value, and alignment of regulatory and national health technology assessment data collection requirements

2021 ◽  
Vol 37 ◽  
Author(s):  
Hannah Sievers ◽  
Angelika Joos ◽  
Mickaël Hiligsmann
Keyword(s):  
Health Technology Assessment ◽  
Technology Assessment ◽  
Real World ◽  
Health Technology ◽  
Practical Experience ◽  
Added Value ◽  
Real World Data ◽  
Conflicting Demands ◽  
Real World Evidence ◽  
Semistructured Interviews

Abstract Objective This study aims to assess stakeholder perceptions on the challenges and value of real-world evidence (RWE) post approval, the differences in regulatory and health technology assessment (HTA) real-world data (RWD) collection requirements under the German regulation for more safety in drug supply (GSAV), and future alignment opportunities to create a complementary framework for postapproval RWE requirements. Methods Eleven semistructured interviews were conducted purposively with pharmaceutical industry experts, regulatory authorities, health technology assessment bodies (HTAbs), and academia. The interview questions focused on the role of RWE post approval, the added value and challenges of RWE, the most important requirements for RWD collection, experience with registries as a source of RWD, perceptions on the GSAV law, RWE requirements in other countries, and the differences between regulatory and HTA requirements and alignment opportunities. The interviews were recorded, transcribed, and translated for coding in Nvivo to summarize the findings. Results All experts agree that RWE could close evidence gaps by showing the actual value of medicines in patients under real-world conditions. However, experts acknowledged certain challenges such as: (i) heterogeneous perspectives and differences in outcome measures for RWE generation and (ii) missing practical experience with RWD collected through mandatory registries within the German benefit assessment due to an unclear implementation of the GSAV. Conclusions This study revealed that all stakeholder groups recognize the added value of RWE but experience conflicting demands for RWD collection. Harmonizing requirements can be achieved through common postlicensing evidence generation (PLEG) plans and joint scientific advice to address uncertainties regarding evidence needs and to optimize drug development.

Data Mining Techniques for Communities’ Detection in Dynamic Social Networks

Data Mining ◽  
2013 ◽  
pp. 719-733
Author(s):  
Céline Robardet
Keyword(s):  
Dynamic Networks ◽  
Dynamic Network ◽  
Added Value ◽  
Strong Interactions ◽  
Time Stamp ◽  
Efficient Computation ◽  
Real World Data ◽  
Positive Side ◽  
Dynamic Social Networks ◽  
Temporal Events

Social network analysis studies relationships between individuals and aims at identifying interesting substructures such as communities. This type of network structure is intuitively defined as a subset of nodes more densely linked, when compared with the rest of the network. Such dense subgraphs gather individuals sharing similar property depending on the type of relation encoded in the graph. In this chapter we tackle the problem of identifying communities in dynamic networks where relationships among entities evolve over time. Meaningful patterns in such structured data must capture the strong interactions between individuals but also their temporal relationships. We propose a pattern discovery method to identify evolving patterns defined by constraints. In this paradigm, constraints are parameterized by the user to drive the discovery process towards potentially interesting patterns, with the positive side effect of achieving a more efficient computation. In the proposed approach, dense and isolated subgraphs, defined by two user-parameterized constraints, are first computed in the dynamic network restricted at a given time stamp. Second, the temporal evolution of such patterns is captured by associating a temporal event types to each subgraph. We consider five basic temporal events: the formation, dissolution, growth, diminution and stability of subgraphs from one time stamp to the next one. We propose an algorithm that finds such subgraphs in a time series of graphs processed incrementally. The extraction is feasible thanks to efficient pruning patterns strategies. Experimental results on real-world data confirm the practical feasibility of our approach. We evaluate the added-value of the method, both in terms of the relevancy of the extracted evolving patterns and in terms of scalability, on two dynamic sensor networks and on a dynamic mobility network.

scholarly journals MG-RAST version 4—lessons learned from a decade of low-budget ultra-high-throughput metagenome analysis

2017 ◽  
Vol 20 (4) ◽  
pp. 1151-1159 ◽  
Author(s):  
Folker Meyer ◽  
Saurabh Bagchi ◽  
Somali Chaterji ◽  
Wolfgang Gerlach ◽  
Ananth Grama ◽  
...  
Keyword(s):  
Performance Optimization ◽  
High Performance ◽  
Lessons Learned ◽  
Added Value ◽  
Data Sets ◽  
Real World Data ◽  
Metagenome Analysis ◽  
Trade Offs ◽  
And Performance ◽  
The Right

Abstract As technologies change, MG-RAST is adapting. Newly available software is being included to improve accuracy and performance. As a computational service constantly running large volume scientific workflows, MG-RAST is the right location to perform benchmarking and implement algorithmic or platform improvements, in many cases involving trade-offs between specificity, sensitivity and run-time cost. The work in [Glass EM, Dribinsky Y, Yilmaz P, et al. ISME J 2014;8:1–3] is an example; we use existing well-studied data sets as gold standards representing different environments and different technologies to evaluate any changes to the pipeline. Currently, we use well-understood data sets in MG-RAST as platform for benchmarking. The use of artificial data sets for pipeline performance optimization has not added value, as these data sets are not presenting the same challenges as real-world data sets. In addition, the MG-RAST team welcomes suggestions for improvements of the workflow. We are currently working on versions 4.02 and 4.1, both of which contain significant input from the community and our partners that will enable double barcoding, stronger inferences supported by longer-read technologies, and will increase throughput while maintaining sensitivity by using Diamond and SortMeRNA. On the technical platform side, the MG-RAST team intends to support the Common Workflow Language as a standard to specify bioinformatics workflows, both to facilitate development and efficient high-performance implementation of the community’s data analysis tasks.

scholarly journals Predicting individual risk for COVID19 complications using EMR data

2020 ◽  
Author(s):  
Yaron Kinar ◽  
Alon Lanyado ◽  
Avi Shoshan ◽  
Rachel Yesharim ◽  
Tamar Domany ◽  
...  
Keyword(s):  
High Risk ◽  
Real World ◽  
Large Scale ◽  
Predictive Analytics ◽  
Epidemiological Data ◽  
Added Value ◽  
Individual Risk ◽  
Real World Data ◽  
Mesh Terms ◽  
Age And Sex

AbstractBackgroundThe global pandemic of COVID-19 has challenged healthcare organizations and caused numerous deaths and hospitalizations worldwide. The need for data-based decision support tools for many aspects of controlling and treating the disease is evident but has been hampered by the scarcity of real-world reliable data. Here we describe two approaches: a. the use of an existing EMR-based model for predicting complications due to influenza combined with available epidemiological data to create a model that identifies individuals at high risk to develop complications due to COVID-19 and b. a preliminary model that is trained using existing real world COVID-19 data.MethodsWe have utilized the computerized data of Maccabi Healthcare Services a 2.3 million member state-mandated health organization in Israel. The age and sex matched matrix used for training the XGBoost ILI-based model included, circa 690,000 rows and 900 features. The available dataset for COVID-based model included a total 2137 SARS-CoV-2 positive individuals who were either not hospitalized (n = 1658), or hospitalized and marked as mild (n = 332), or as having moderate (n = 83) or severe (n = 64) complications.FindingsThe AUC of our models and the priors on the 2137 COVID-19 patients for predicting moderate and severe complications as cases and all other as controls, the AUC for the ILI-based model was 0.852[0.824–0.879] for the COVID19-based model – 0.872[0.847–0.879].InterpretationThese models can effectively identify patients at high-risk for complication, thus allowing optimization of resources and more focused follow up and early triage these patients if once symptoms worsen.FundingThere was no funding for this studyResearch in contextEvidence before this studyWe have search PubMed for coronavirus[MeSH Major Topic] AND the following MeSH terms: risk score, predictive analytics, algorithm, predictive analytics. Only few studies were found on predictive analytics for developing COVID19 complications using real-world data. Many of the relevant works were based on self-reported information and are therefore difficult to implement at large scale and without patient or physician participation.Added value of this studyWe have described two models for assessing risk of COVID-19 complications and mortality, based on EMR data. One model was derived by combining a machine-learning model for influenza-complications with epidemiological data for age and sex dependent mortality rates due to COVID-19. The other was directly derived from initial COVID-19 complications data.Implications of all the available evidenceThe developed models may effectively identify patients at high-risk for developing COVID19 complications. Implementing such models into operational data systems may support COVID-19 care workflows and assist in triaging patients.

Real-World Data Analytics Fit for Regulatory Decision-Making

2018 ◽  
Vol 44 (2-3) ◽  
pp. 197-217 ◽  
Author(s):  
Sebastian Schneeweiss ◽  
Robert J. Glynn
Keyword(s):  
Decision Making ◽  
Real World ◽  
Randomized Clinical Trials ◽  
Routine Care ◽  
Added Value ◽  
Real World Data ◽  
Healthcare Database ◽  
World Data ◽  
Medical Products ◽  
Regulatory Decision

Healthcare database analyses (claims, electronic health records) have been identified by various regulatory initiatives, including the 21st Century Cures Act and Prescription Drug User Fee Act (“PDUFA”), as useful supplements to randomized clinical trials to generate evidence on the effectiveness, harm, and value of medical products in routine care. Specific applications include accelerated drug approval pathways and secondary indications for approved medical products. Such real-world data (“RWD”) analyses reflect how medical products impact health outside a highly controlled research environment. A constant stream of data from the routine operation of modern healthcare systems that can be analyzed in rapid cycles enables incremental evidence development for regulatory decision-making.Key evidentiary needs by regulators include 1) monitoring of medication performance in routine care, including the effectiveness, safety and value; 2) identifying new patient strata in which a drug may have added value or unacceptable harms; and 3) monitoring targeted utilization. Four broad requirements have been proposed to enable successful regulatory decision-making based on healthcare database analyses (collectively, “MVET”): Meaningful evidence that provides relevant and context-informed evidence sufficient for interpretation, drawing conclusions, and making decisions; valid evidence that meets scientific and technical quality standards to allow causal interpretations; expedited evidence that provides incremental evidence that is synchronized with the decision-making process; and transparent evidence that is audible, reproducible, robust, and ultimately trusted by decision-makers.Evidence generation systems that satisfy MVET requirements to a high degree will contribute to effective regulatory decision-making. Rapid-cycle analytics of healthcare databases is maturing at a time when regulatory overhaul increasingly demands such evidence. Governance, regulations, and data quality are catching up as the utility of this resource is demonstrated in multiple contexts.

Knowledge and Skills to Lead Effective Patient Organizations

2020 ◽  
pp. 66-92
Keyword(s):  
Capacity Building ◽  
Business Model ◽  
Training Programs ◽  
Added Value ◽  
Patient Organization ◽  
Knowledge And Skills ◽  
Patient Capacity ◽  
Patient Organizations ◽  
Business Model Canvas

The potential role of patients' organizations in healthcare, in order to be effective, needs to be sustained by appropriate knowledge and skills. The chapter has the scope to review the key requirements for the management of such organizations. After a background introduction about added value of patient capacity building, it proposes the ‘antenna skill framework', a visual and practical illustration summarizing the necessary knowledge and skills for patient organizations' management. A generic patient organization is represented as an antenna progressively picking up many kinds of knowledge and skills: about the institutional framework, disease-related, technical, and managerial. Particular attention is devoted to management and administration, proposing a business model canvas tailored to patient organizations. In conclusion, the insightful tools proposed in the chapter can foster policymakers, universities, and other educational operators to conceive suitable training programs to form capable patient managers.

scholarly journals Comparative Sectoral Efficiency in the Fight Against Poverty and Unemployment in Burkina Faso

2018 ◽  
Vol 5 (2) ◽  
pp. 185
Author(s):  
Patrice Rélouendé Zidouemba
Keyword(s):  
Economic Growth ◽  
Burkina Faso ◽  
Poverty Reduction ◽  
Public Investment ◽  
Public Capital ◽  
Added Value ◽  
African Countries ◽  
Capital Productivity ◽  
Sub Saharan ◽  
The Impact

In this paper, we construct an economy-wide recursive dynamic model for Burkina Faso to explore the impact of scaling up public capital in different aggregate sectors. While several researchers emphasize the importance for sub-Saharan African countries of giving higher priority to agriculture to stimulate economic growth and reduce poverty, some authors state that non-agricultural sectors should now receive special attention following the success achieved in some countries in South Asia. These countries have indeed applied a different paradigm: a program of economic growth and poverty reduction based on non-agricultural sectors. This study aims to provide insights into this debate. It draws from the public capital productivity literature to postulate the positive productive externalities of public investment. The results show that, with the same amount of public investment, financed by the same source, public investment in agriculture yields positive impacts that are significantly higher than those yielded by investments in non-agricultural sectors (industry and services). Added value growth in non-agricultural sectors is higher under public investment in agriculture than in non-agricultural sectors.

Early Access to Medicines Scheme: real-world data collection

2019 ◽  
Vol 24 (12) ◽  
pp. 2231-2233
Author(s):  
Hok Pang ◽  
Meng Wang ◽  
Christopher Kiff ◽  
Mira Soni ◽  
Dara Stein ◽  
...  
Keyword(s):  
Data Collection ◽  
Real World ◽  
Access To Medicines ◽  
Real World Data ◽  
World Data ◽  
Early Access

scholarly journals EU Health Policy, Coherence, Stakeholder Diversity and Their Impact on the EMA

2017 ◽  
Vol 2 (Suppl. 1) ◽  
pp. 1-11
Author(s):  
Denis Horgan ◽  
Alastair Kent
Keyword(s):  
Well Being ◽  
Access To Medicines ◽  
Added Value ◽  
European Medicines Agency ◽  
Policy Coherence ◽  
Regulatory Standards ◽  
Added Uncertainty ◽  
Health And Well Being ◽  
The Eu

Innovation is a major pillar in bringing new, targeted medicines to patients. In the health arena, this means the translation of knowledge into what we can call “value.” The latter covers the value to patients but must also take into account value to healthcare systems, society and, of course, manufacturers. The EU has recognised that innovations in healthcare can contribute to the health and well-being of citizens and patients through access to new products, services and treatments with added value. It is also aware that in order to stimulate development, there is a need to facilitate the translation of scientific advances into innovative medicinal products that meet regulatory standards, accelerate patients' access to new therapies and are affordable to Member States' health systems. Early dialogue between technology developers, regulators, health technology assessment and, where relevant, pricing bodies will promote innovation and quicker access to medicines at affordable prices, for the benefit of patients. But while uncertainties in healthcare policy still exist, a request by the European Ombudsman to the European Medicines Agency to provide more information about its early dialogue procedures questions the above “early dialogue” principal. It raises the issue of what the EU aims to do with its health regulation in bringing innovation to the patient. Is this added uncertainty about the hereto trusted role of the EMA a welcome development? Not necessarily.

Sign in / Sign up

Export Citation Format

Share Document