Workshop, hackathon, interviews: successful exchange with relevant stakeholders

In total we interviewed 38 clinicians and managers from hospital and community-based settings. The interviews focussed on how to successfully implement the decision tools at an organisational level.

The preliminary results show that there are important factors at different levels. There are different roles and responsibilities among team members that are important for the use of decision tools in practice. Enthusiasm among healthcare professionals to implement decision tools can be driven by the fact that the platform is evidence-based and that patients can benefit from support in the decision-making process. Financial incentives might be necessary to motivate organisations to implement decision tools. However, some participants believe that these incentives are unnecessary because patient health is what matters the most.

In order to test whether the domains identified in the initial interviews are applicable in other healthcare settings, we conducted a focus group with participants from other COMPAR-EU countries (Belgium, the Netherlands, Greece).

In addition, we conducted a workshop with other relevant stakeholders such as mHealth start-ups, pharma, researchers, management organisations, to explore how decision tools can be embedded in the value chain and how we can market them to different end users. We gathered different outputs related to the resources required, key activities, communication channels and costs. We were happy to see that participants were excited about the various features of the platform and also showed great interest in participating in our next event: the COMPAR-EU Hackathon in September. There we want to develop specific use-cases in detail to ensure the sustainability of the platform.

These, together with the outcomes of the workshop, interviews and focus group, will be included in the core business plan for the decision-making tools of the COMPAR-EU project.

Click here to sign up for the COMPAR-EU hackathon.

Paula Zietzsch

Paula Zietzsch is a Manager at the Research & Innovation department at OptiMedis. She is responsible for project organisation within OptiMedis and implementation of patient decision aids into the routine clinical settings. She holds a Master´s degree in Health Economics and Health Care Management from University of Hamburg

Full steam ahead – the EPF team prepares for a successful end of COMPAR-EU (2018-2022)

With the end of the COMPAR-EU project foreseen for end of the year, the European Patients’ Forum (EPF) team has begun its preparations for further outreach efforts, and a big event to close our collaboration on this project at the end of the year.

Audio-visual assets to support our uptake

As part of its involvement in the COMPAR-EU project, the European Patients’ Forum (EPF) has produced a series of lay summaries – on COPD, Heart Failure, Obesity, Type 2 Diabetes core outcome sets, and on the Taxonomy process, among others. Producing lay summaries is an important step to share project results in accessible language for patients and non-expert audiences.

To facilitate their visibility and uptake, the EPF team has focused the last months on preparing the lay summaries for wide dissemination. First, EPF has begun working with professional translation services to translate 8 lay summaries into 6 languages of the Patient Panel – Maltese, Romanian, Hungarian, Italian, Polish, and Gaelic (Irish)/Catalan. Translating these documents into more languages (German, French, Spanish, and others)  helps to ensure that they will be available to wider multilingual audiences.

At the same time, EPF held a tendering process for design materials at the end of 2021 and has begun working with the selected designer on various tools. This collaboration will allow us to transform the lay summaries into attractive and easy to use documents and leaflets, which will support their uptake by lay-users including patients as well as the wider public. Additionally, we will also produce 4 short videos to promote the project and its main results. This work will be merged with our translations, to create visually attractive and multilingual documents which can be used by stakeholders outside of the immediate ecosystem of the project partners, and with the aim of reaching audiences such as various European patient organisations, national level organisations, individual patients and other public audiences.

Final Advocacy Event in Brussels, November 2022

EPF would be leading the Closing Advocacy Conference in November 2022 in Brussels, Belgium. This Advocacy Event will be a bouquet of stakeholders and perspectives. Over the summer, EPF and other COMPAR-EU partners will be working towards building the agenda of the event, identifying and inviting speakers from across Europe and with deep understanding and interest in self-management (interventions). Currently, the plan is to have a two-half days event with 60 to 80 participants onsite and virtual element.

What to expect in 2023 from the project?

As previously communicated, the official end of the Project is planned for December 31st 2022. However, this does not mean that the dissemination of achievements and outcomes of COMPAR-EU will cease. Two activities will be of particular importance for the sustainability and scalability of the project beyond this date.

Self-Management Europe Initiative – find out more about the initiative here – this partnership will be a crucial part when it comes to continue working on the topic of self-management. In a nutshell, It is an exploitation initiative of the COMPAR-EU project with the aim of developing the potential of people, professionals, organisations, systems, and communities for creating a society that strengthens empowerment and self-management in people with chronic diseases.

The Interactive COMPAR-EU Platform must be fully operational and functional and available to all interested parties and hopefully, 2023 will be its pivotal year. As a reminder, among many other materials – all lay summaries should be available to the public in 10 different languages.


Estefania Cordero

Estefania is EPF’s Communications & Outreach Officer. Previously she has worked at the European Commission’s DG Research & Innovation in activities to promote social sciences integration and behavioural change in the Horizon research programmes. She has also worked as a Health Policy Researcher at Hanover Communications, a consultancy.


Lyudmil Ninov

Lyudmil Ninov is EPF´s Senior Programme Officer. He joined EPF in April 2017. His focus is mainly on the PRO-STEP tender project, Summer Training for Young Patients Advocates 2017 & 2018, Horizon 2020 projects proposals/calls, COMPAR-EU and CHRODIS projects and providing support to other EPF projects and team members.

COMPAR-EU Evidence to Decision frameworks (EtDs): a tool for stakeholders

One of these outputs are the Evidence to Decision (EtD) frameworks. Four COMPAR-EU panels used these frameworks to formulate recommendations about self-management interventions (SMIs) for patients living with Type 2 diabetes mellitus (T2DM), obesity, chronic obstructive pulmonary disease (COPD), and heart failure. The project will make available these EtD frameworks as interactive tools to support healthcare professionals, patients, and policymakers, among others, to make informed decisions on the use and implementation of SMIs.

What are these frameworks all about?

EtD frameworks help panels use evidence in a structured and transparent way to make informed decisions in the context of clinical recommendations, coverage decisions, and health system or public health recommendations and decisions. The frameworks include key background information, criteria for making a decision, and a conclusion section.

The background section provides details of the question addressed, including the population, interventions, and main outcomes of interests, as well as the setting, and the perspective (individual or population perspective).

The criteria section includes all the important factors that should be considered for formulating the recommendation (e.g. desirable and undesirable effects, the certainty of evidence, how patients value outcomes, cost-effectiveness, or acceptability).

Illustration: Sarah Rosenbaum, Norwegian Institute of Public Health

The COMPAR-EU panels have made judgements about each criteria taking into consideration a synthesis of the best available research evidence, tabulated as Summary of Findings tables. Finally, taking into consideration all the judgements, they formulated 40 recommendations across conditions, and proposed implementation considerations and research priorities. Recommendations address both SMIs in general (versus usual care), several selected individual SMIs, defined according to their components (specific support techniques, type of providers, specific delivery methods, etc).

The panel judgments for each criterion and the recommendations were made during the more than 15 online meetings that have taken place, and online before meetings, using the Panel Voice extension from GRADEpro. Panel meetings included the presentation of the project, thorough discussions of the evidence from the systematic reviews conducted, and structured and transparent discussions, using the EtD frameworks.

How can stakeholders use the EtD frameworks?

The EtD frameworks are interactive tools to support decision-making on the use and implementation of SMIs, and as such, stakeholders, including clinicians, researchers, and policymakers, among others, can access these resources for the four conditions.

Clinicians will be able to review the frameworks in case they want to better understand the rationale of the recommendations, including the research evidence, additional considerations, and the judgments made by the panel. Furthermore, if relevant, they can use the decision aid module to inform and discuss with patients about their options (see previous blog on this topic). Researchers will be able to access previous research in a summarised format, and be aware of the gaps in current literature (detailed in the research priorities section) or the need of update of the research evidence. policymakers will also be able to review them and decide if they should adopt (use as described) or adapt a given recommendation to their setting.


Jessica Beltran

Jessica holds a Medical Degree (Universidad Cayetano Heredia, Peru) and a Master’s degree in Epidemiological Research (Universidad Cayetano Heredia, Peru). Currently, she is a Researcher at the Iberoamerican Cochrane Centre. Her work focuses on conducting evidence synthesis to support informed decision-making and guideline development.

Headroom analysis as a method to estimate the potential for a cost-effective implementation of self-management interventions

The COMPAR-EU project aims to rank the most (cost-)effective interventions for self-management. To estimate the cost-effectiveness of self-management interventions (SMIs) health economic models were used to predict the lifetime health benefits and (healthcare) costs for a scenario assuming one-time implementation of a SMI in comparison with a scenario assuming care as usual.

One of the important components of a cost-effectiveness analysis is the cost of the intervention. However, in some cases these costs are not readily available. As an alternative to standard cost-effectiveness analysis a headroom analysis can then be performed. In a headroom analysis (1), a headroom is estimated indicating how much an intervention or treatment may maximally cost to be considered cost-effective given the health benefits associated to the intervention and a threshold for the cost associated to these health benefits. Health benefits are often expressed in quality-adjusted life-years (QALYs). A threshold for the cost per QALY reflects the maximum cost society is willing to pay to gain one additional QALY.

In the COMPAR-EU project data on the characteristics and health benefits of SMIs were obtained from published data. The cost of SMIs is determined by factors such as the type of healthcare provider involved, the time spend per patient and the mode of delivery. The majority of published studies on self-management did not provide enough detailed information on these factors to be able to estimate the cost of SMIs. Therefore, a headroom analysis was conducted to estimate what SMIs may maximally cost to be considered cost-effective given its health benefits and a certain threshold for cost-effectiveness. Headrooms were estimated for two different threshold values: €20,000 per QALY gained, as this is a figure that is often used in the context of preventive interventions, and €50,000 per QALY gained, as this value is more often used for curative interventions in for instance COPD and heart failure patients. Overall, headrooms for SMIs varied across diseases and countries and were estimated to range from €0 to €2,400 and from €200 to €8,000 at a threshold of €20,000 and €50,000, respectively.

A lower headroom for a particular SMI implies that the SMI needs to be delivered at lower cost in order to achieve cost-effectiveness. As such, headroom estimates are relevant for policymakers and health care providers as they give guidance to when (and when not) to consider SMIs a tool to gain health at reasonable costs, and in what disease areas and patient groups it might be more efficient to invest in SMIs.


Martine Hoogendoorn

Martine Hoogendoorn is a Senior Researcher at iMTA with more than 15 years experience in modelling the disease COPD. She holds a Master´s degree in Human Nutrition from Wageningen University and a PhD in Health Economics from the Erasmus University Rotterdam. She has extensive experience in disease modelling using different types of models (e.g. cohort, patient-level, Markov, DES).

de_Groot_ Saskia

Saskia de Groot

Saskia is a Medior Researcher at iMTA. She holds a Master´s degree in Health Economics, Policy & Law from the Erasmus University Rotterdam, a Master´s degree in Clinical Epidemiology from the Netherlands Institute for Health Science of the Erasmus Medical Center and a PhD in Health Economics from the Erasmus University Rotterdam.


[1] Girling A, Lilford R, Cole A, Young T. Headroom approach to device development: current and future directions. Int J Technol Assess Health Care. 2015 Jan;31(5):331-8.

Qualitative interviews to facilitate a smooth transition from the evidence of self-management interventions to practice

During digitization in the healthcare system and the continuously advancing process toward patient-centred care, the responsibility of patients for their own health is becoming increasingly important. Particularly in the case of chronic diseases, it is important that patients are enabled to actively participate in the management of the disease [1]. This is where self-management interventions (SMIs) play a significant role. SMIs aim to equip patients and informal caregivers if appropriate to actively participate in the management of their disease [2].

SMIs have good evidence and are very well researched. We are developing the COMPAR-EU interactive platform with different types of decision-making tools that summarize this evidence about SMI in a structured and transparent way. The decision tools can support decision-making on SMIs and so integrating the evidence into practice for different end users such as patients, clinicians, policymakers or guideline developers.

Many well evidenced and exhaustively developed decision-making tools and interventions do not deliver their potential impact because their use requires changes in clinical workflows as well as organizational structure. In addition to this there is insufficient support for these changes [3]. This leads to the question how decision-making tools can facilitate and disseminate the use of the most effective SMIs into the real life? How can it be made visible at the right time and what incentives are there for healthcare professionals and managers to use decision-making tools and the other way around SMIs?

Interviews with managers and clinicians

These are the questions we want to explore with qualitative implementation interviews with managers and health care professionals at the organisational level in COMPAR-EU countries. We will use their insights to build and refine business plans for the various provider and health system context. This means that a manager or clinician working in any relevant provider can look at the business plan and identify any key actions they should take which would help themselves and their colleagues make use of the decision tools produced by COMPAR-EU.

Collecting information on different health systems represented in COMPAR-EU countries illustrates that Germany and Spain provide a good representation of health system feature, in particular due to the differences in organisational enablers in insurance-based system than purely public system. Therefore, we have conducted a total of 40x qualitative interviews with managers and clinicians in Spain and Germany. The participants were recruited from different settings (primary care practice, hospital or special care practice) so we can obtain the implementation factors of innovations from different perspectives. We developed a semi-structured interview guide with reference to the TICD framework and a realist review by Joseph-Williams et. al (2020).

Figure 1 The overall process of qualitative implementation study

Directed qualitative content analysis

Currently, the interviews need to be analysed. We will use directed qualitative content analysis (QCA). This approach allows combining the development of deductive as well as inductive codes, and so allowing the use of existing evidence. A guide for analysis will be provided, based on the work of Hsieh and Shannon [4], Hamilton [5], and Gale et al. [6]. A codebook will be developed by OptiMedis based on the TICD framework [7] (inductive) and interviews conducted (deductive).

Challenges of the cross-national research

As the translation of the interview guide in different languages is considered to be challenging task [8], we had several meetings to adjust and translate the interview guide to ensure the cultural meaning of the questions and the shared understanding between both teams, as the interviewers from different countries may have different views and experience.

Additionally, we agreed to conduct the interviews in the local language of the respective country to avoid any language difficulties for both the participants and the interviewers. After pretesting of the developed coding system, we will discuss and add possible additional themes if needed. Similar as in other studies [9] [10], each country team will analyse the data in the native language as long as possible and not translate all interview data into one language. This form of rapid qualitative analysis yields similar results compared to traditional qualitative analysis and is therefore a useful tool for this analysis [11].

Business plans with key actions for implementation

In summary, the interviews aim to identify drivers and barriers for the use of decision tools on the COMPAR-EU platform capturing the evidence of SMIs in practice. These results will serve to develop business plans for organisations who will actually be making use of the decision-making tools. These business plans will focus on how to implement evidence based decision aids about SMI at the organizational level into the healthcare system.

Nina Sofie Krah_Quadrat

Nina Sofie Krah

Nina has a background in Health Economics & Health Care Management as well as Ethics in Health Care and works at OptiMedis as a working student. With a keen interest in the use of evidence in the health care system, her master’s thesis explores the conditions under which self-management interventions can be meaningfully implemented into clinical workflow.


Paula Zietzsch

Paula has a background in Health Economics and works at OptiMedis in different national and EU projects with a particular interest in shared decision-making. As a Research & Innovation Manager, she focuses on implementing evi-dence-based interventions in routine clinical care.


[1] Aujoulat I, Marcolongo R, Bonadiman L, Deccache A. Reconsidering patient empowerment in chronic illness: a critique of models of self- efficacy and bodily control. Soc Sci Med. 2008;66(5):1228-1239.

[2] Tattersall RL. TThe expert patient: a new approach to chronic disease management for the twenty-first century. Clin Med (Lond). 2002;2(3):227-229.

[3] Orrego C, Ballester M, Heymans M, et al; the COMPAR-EU Group. Talking the same language on patient empowerment: Development and content validation of a taxonomy of self-management interventions for chronic conditions. Health Expect. 2021;00:1–13.

[4] Hsieh H-F, Shannon SE. Three Approaches to Qualitative Content Analysis . Qual Health Res 2005;15:1277–88.

[5] Hamilton AB, Finley EP. Qualitative methods in implementation research: An introduction. Psychiatry Res 2019;280.

[6] Gale RC, Wu J, Erhardt T, Bounthavong M, Reardon CM, Damschroder LJ, et al. Comparison of rapid vs in-depth qualitative analytic methods from a process evaluation of academic detailing in the Veterans Health Administration. Implement Sci 2019;14:1–12.

[7] Flottorp SA, Oxman AD, Krause J, Musila NR, Wensing M, Godycki-Cwirko M, et al. A checklist for identifying determinants of practice: A systematic review and synthesis of frameworks and taxonomies of factors that prevent or enable improvements in healthcare professional practice. Implement Sci 2013;8:1–11.

[8] McGreevy J, Orrevall Y, Belqaid K, Bernhardson BM. Reflections on the process of translation and cultural adaptation of an instrument to investigate taste and smell changes in adults with cancer. Scand J Caring Sci. 2014 Mar;28(1):204-11. DOI: 10.1111/scs.12026.

[9] Woolhead G, Tadd W, Boix-Ferrer JA, Krajcik S, Schmid-Pfahler B, Spjuth B, Stratton D, Dieppe P; Dignity, and Older Europeans (DOE) project. “Tu” or “Vous?” A European qualitative study of dignity and communication with older people in health and social care settings. Patient Educ Couns. 2006 Jun;61(3):363-71. doi: Epub 2005 Jun 20. PMID: 15970421.

[10] Knutsen, I., Foss, C., Todorova, E., Roukova, P., Kennedy, A., Portillo, M., . . . Rogers, A. (2015). Negotiating diet in networks: A cross-European study of the experiences of managing Type 2 diabetes. Qualitative Health Research. 1-12.

[11] Nevedal, A.L., Reardon, C.M., Opra Widerquist, M.A. et al. Rapid versus traditional qualitative analysis using the Consolidated Framework for Implementation Research (CFIR). Implementation Sci. 2021; 16(67).

Self-Management Europe has great ambitions!

In this way we want to create a network of researchers, health care professionals, developers, industry and other stakeholders whose common goal is to improve patient self-management and empowerment of patients with chronic diseases in Europe. SME is a spin-off of COMPAR-EU and initiated by four COMPAR-EU partners that all have a broad experience on self-management, patient empowerment and personalized health care: FAD, Nivel, OptiMedis and EPF.

SME aims to raise public, professional and political awareness of the critical role patients play in living with chronic disease by providing the most updated and innovative multidisciplinary knowledge about self-management interventions and empowerment and how to address these topics in Europe in policy and practice; SME wants to provide practical tools to encourage and support health care professionals to adopt SMI in their real life contexts; work with organizations and industry to develop approaches to incorporate SMI in practice; will organize training courses on implementing approaches which support patients’ self-management and empowerment, and make the expertise of experienced programs and interventions available to others.

During the last year we worked on a website and on a mission paper on SME (in progress). While we are looking for some structural funding to carry out the ambitious activities outlined above, we are giving small steps on some areas that, in our opinion, complement the work done in COMPAR-EU. The first dissemination activity of SME were two “ALERTS” for healthcare professionals, managers and other stakeholders looking for practical recommendations to implement practices that enhance self-management and patient empowerment. The first alert was on empowerment of patient to take an active role in health care, the second alert on health literacy.

Click here to read more about Self-Management Europe.


Monique Heijmans

Monique Heijmans is a Health Psychologist and works as a Senior Researcher at Nivel since 1998. She is an expert in the area of (determinants of) self-management and chronic illness and has extensive research experience in the area of psychosocial factors and their interaction with health behavior and health.

Available tools and challenges encountered when interpreting results from sparse networks of multicomponent interventions

Multi-component SMIs

SMIs are multicomponent interventions and they are determined by their characteristics (support techniques) and the context under which they are applied (e.g. type of encounter, mode of delivery, type of provider, intensity). It is our goal in COMPAR-EU to

  1. locate those components that work (or do not work) and
  2. explore how these components interact with each other.

To this end, we employed network meta-analyses models. The main benefits of network meta-analysis is that it synthesizes both direct and indirect evidence and results in more precise effect estimates compared to those of pairwise meta-analyses.

Sparse networks

SMIs are very heterogeneous interventions. They typically form sparse networks with each combination of components observed only in a few studies. An example of a network of SMIs depicting a network of 461 randomized clinical trials comparing a total of 97 distinct SMIs for the reduction in glycated haemoglobin (HbA1c) is shown in the Figure below. Nodes represent interventions (combination of components in this case) and edges represent direct evidence (trials directly comparing the interventions shown in the connecting nodes). Size of nodes and thickness of edges is proportional to the information the network provides for the respective intervention and comparison. Information flows across the network and each trial informs the entire network. Out of these 461 trials, 386 (84%) compare an SMI to usual care. This is why we note a large node for “usual care”. The remaining nodes are poorly connected to each other. A relative effect for a pair of SMIs (a line in the Figure) is informed mainly by those studies (usually one or two) that compare this pair. As a result, this NMA estimate depends heavily on what is observed on these trials and as this is the case for most pairs of SMIs, we infer that NMA results are heavily confounded with study characteristics. This compromises the main assumption in NMA; the distribution of effect modifiers is similar across treatment comparisons.

Consider there is a small trial comparing a SMI (e.g. comprising education and shared decision making techniques, done remotely by a non-professional) to “usual care”. This study has a large effect maybe because its population is severely ill or the population is a certain minority group or the duration and intensity of the SMI is very large or even for reasons such as fraud or poor methodology. The NMA effect for that intervention would be very large just like what we observed in the trial just because the rest of the network will not provide much information (if any at all) for this NMA effect. This result would be misleading as the remaining studies in the network have other characteristics. But what drives the effect of this SMI are the study characteristics and not the SMI per se.

Evaluating components’ effectiveness

The classical interpretation of NMA effects won’t be of much help in sparse networks of multi-component SMIs such as those we deal with in COMPAR-EU. Not only efficacy is confounded with study characteristics but we also get very imprecise NMA effects as there is little flow of information.

Component network meta-analysis

Alternative evidence synthesis models have been developed that aim to estimate the effect of each component (component NMA -CNMA). Each component is included in many trials and all these trials will inform its relative effect. Hence, we get very precise effects. Unfortunately, problems do not just vanish into thin air

  1. We don’t know how components interact with each other (mathematically we assume that components have an additive effect, an assumption that is hard to test and/or defend). Even if we identify the “perfect” SMIs by combining those components with large effects, we do not know how these will perform in practice.
  2. The context and conditions under which a SMI is applied is of paramount importance. Hence, confounding may still be an issue, especially if a component is included in a few trials. Most probably the contextual factors would reveal themselves in large statistical heterogeneity compromising the validity of results and making interpretation difficult.

Visual inspection of NMA results.

A visual inspection of the NMA results may reveal important information regarding which components work and interact well with each other. For example, if most of the SMIs with large effects are applied face-to-face whereas those with small effects are applied remotely, this is an indication that face-to-face is working. This is not an easy task and we have developed a series of results and graphical methods to disentangle those components which are associated with large effects from those that are not.

When I realized that in COMPAR-EU we will be working with networks of hundreds of studies I felt I hit the jackpot. In practice, with so many known and unknown effect modifiers, components and nodes in the network, it is easy to downplay uncertainty and hard to separate signal from noise. A famous aphorism in statistics, attributed to British statistician George Box, is “All models are wrong, but some are useful”. The aphorism recognizes that we cannot perfectly model the complex systems of reality but we can still get some useful information. Our results, from many NMAs on many outcomes, include a wealth of information!


Dimitris Mavridis

Dimitris Mavridis is an Assistant Professor in statistics for the social sciences at the Department of Primary School Education at the University of Ioannina. He has published more than 40 papers relevant to network meta-analysis (NMA). His works include both methodological papers and applications of NMA in various fields.

Development and user-testing of a web-based patient decision aid for self-management interventions

Towards the end of the COMPAR-EU project – after completing network meta-analysis, assessing the certainty of the evidence, selecting the most promising interventions for the four different chronic conditions, and formulating recommendations on these interventions – we are now in the process of developing interactive web-based decision aids tools including all of the above information.

COMPAR-EU decision-aids will assist patients to decide on which self-management intervention best suits them. They will help them to compare different intervention options (e.g., how important are the potential desirable and undesirable effects), and prepare them to participate with their health professional in making an optimally informed decision.

We relied on the work conducted by leaders in the field of shared decision-making, including the Ker Unit at Mayo Clinic, and other emerging groups like the MAGIC consortium to identify the main aspects that need to be included and considered in their development. We also reviewed research synthesis in this field, and existing decision aids tools. Finally, after an in-depth brainstorming, we developed a first prototype.

To ensure the usability and understandability, we gathered feedback from a Task Force, a multidisciplinary group including patients, clinicians, methodologists, and other relevant stakeholders. We also assembled an external group of patients who have been involved in previous steps of the COMPAR-EU project. With the Task Force, we held regular meetings in which we presented the tools and their progress, whereas with the patients’ group we conducted a workshop in which we presented the prototype. Finally, we conducted semi-structured interviews with patients. At the moment, we are currently working on making all the necessary changes in the decision aid web-based tools and have an optimal version ready to conduct – in collaboration with OptiMedis – user-testing with patients and health professionals.

This user-testing will allow us to identify errors and areas of improvement by asking both, patients and health professionals, about their experience navigating the web-based decision aid tool. With their feedback, we will finalize the web-based decision aid tools, translate them into six different languages (English, French, German, Spanish, Dutch and Greek) and make them available in the COMPAR-EU platform during 2022.


Claudia Valli

Claudia Valli is a Researcher at the Biomedical Research Institute (Hospital Sant Pau) and a PhD Candidate in the Methodology of Biomedical Research and Public Health Programme (Univeristat Autonoma of Barcelona). Her work focuses on conducting clinical and nutritional systematic reviews and synthetising research evidence to support informed decision-making and guideline development.

Managing my Type 2 Diabetes: personal patient story of a successful journey

It´s time to change!

The COVID-19 pandemic scared me into action. Social media was invaded by posters and articles highlighting the increase in obesity during lockdown as people increasingly turned to food for comfort. I wondered what complications I was likely to get if my weight kept increasing. During the first two months of the pandemic and lockdown, I taught myself to make no-knead bread after following a YouTube video. Thus, I found myself endlessly making brown bread rolls, just in case we ran out. And I ate and ate.

I am becoming hugely interested in learning more about patient-driven self-management tools and processes.

My husband, on the other hand, started a diet and was soon shedding weight. He encouraged me to change my relationship to food. I thought about this and asked myself: “What do I most love to eat?” The instant answer was “Indian Food”. Because of the pandemic, we could no longer have our occasional meal in an Indian restaurant. So, I decided that, as I had learnt to bake bread daily, I could also learn to cook the Indian dishes that I most yearned for. And that’s what I started to do, from September 2020 to the present. YouTube is the school that helped me to become a chef.

As I am a vegetarian, I bought a steamer to cook 5 different types of fresh vegetables daily, adding spices according to Indian recipes that I have learnt to make – my food is extremely well balanced now. If I hanker after something sweet, I’ve found a recipe using chia seeds, raw cacao, and skimmed milk. After a few hours in the fridge, the mixture turns into a mousse – only a few calories and sugar-free. And the hankering is satisfied!

I now avoid the temptation of eating processed carbs and stick to low calory fresh veg which I steam and mix with herbs, spices and lentils to die for. I have learned to toast and grind my own spices. With advice from a dietitian, I was overjoyed to be encouraged to continue reducing my insulin intake. I used to take a total of 82 units of a mixed dose of Insulatard and Actrapid daily. I am now down to a combined dose of 8 and 6 in the morning and sometimes have none in the evening if I am on intermittent fasting. I check my blood sugar 4 times a day, and avoid bread, pasta and rice. The HbA1c blood test was recently down to 6. A huge difference! Hope I manage to continue on this promising pathway.

Through the European Patients’ Forum and COMPAR-EU, I am becoming hugely interested in learning more about patient-driven self-management tools and processes, exchanging good practices and knowledge with the other members of the Panel. The activities that the Panel is part of are vital to the success of COMPAR-EU project and empowerment patients. Finally, I am also interested in how patients who are managing their condition well can become mentors of other patients who are affected similarly. There are some plans for building a Toolkit for Mentors with useful tips and recommendations within the project.


Nora Macelli

Nora Macelli is CEO of the St Jeanne Antide Foundation (SJAF) in Malta, a registered social purpose NGO that provides a range of support services
for very vulnerable families at both community and national levels. Nora studied social work in India (MSW), specialized in community development and was a full-time volunteer community development worker there for six years and a volunteer with the United Nations Volunteers (UNV) for two years. With a colleague, she has edited two books in Maltese for family caregivers of mentally ill persons. She is especially interested in volunteer mentoring with persons with complex needs and peer mentoring by persons with the lived experience of domestic violence and family caregiving in the field of mental health.

Applying incentives to adopting shared decision-making with patient decision tools

Shared decision-making (SDM) is a well-known approach where clinicians and patients share evidence-based information about medical interventions and their risks and benefits, while taking into account the patient’s concerns and preferences. Tools such as patients’ decision aids (PDAs) have been developed to support SDM. Patients using PDAs improve their understanding of the treatment options, are more likely to participate in the decision-making process, and can as a result avoid unwanted treatment [1]. Despite the reported positive effects, decision-making tools are not often implemented in routine clinical care [2]. There is a clear need for sustainable incentives for both individuals [3] and organisations to engage in the adoption and use of a PDAs.

In the last several years, there has been growing interest by academics and policy-makers in advancing SDM in routine healthcare. Many countries are attempting to commit to an SDM approach and its inclusion in a number of clinical practice procedures by developing specific health policies [4]. Despite these efforts, the uptake in the real-world application stays poor[1].

There has been less focus on the characteristics of the healthcare system in which SDM with its decision aids are embedded

There are various factors influencing the implementation of SDM which have been conceptualized into the several models and frameworks. For instance, such factors could be categorized by the level of impact into four main groups: individual-level (patient, clinicians), interactional-level (patient-clinician), organizational-level and system-level factors [1]. Many efforts have already been made to study the interaction between clinicians and patients, but there has been less focus on the characteristics of the healthcare system in which the SDM and PDAs are embedded and which guide the work of the healthcare organizations [5]. To shed more light on this topic we focus on one of the system-level factors – incentives. Incentives are defined as external stimuli that serve as a motive for implementation [6]. We can differentiate between financial and non-financial incentives [7].

Providing financial and non-financial incentives

Financial incentives used for implementation of SDM and PDAs can be, for instance, payment models (i.e. bonuses, saving contracts, payment for activities etc.). These could be especially attractive to healthcare organizations. In practice, we can see that the payment models can influence the “amount of time a HCP has for a patient’s visit” and in turn the use of the PDAs tool itself [1]. In case of individuals like clinic staff working within the healthcare system, the research shows that monetary factors can discourage or ‘‘cheapen’’ desired behaviors that may be linked to more intrinsic motivations such as altruism or self-determination [3]. Furthermore, it has been shown that the financial incentives to promote an adoption of new approaches have a short-term positive effect, but they do not lead to sustained use when rewards are withdrawn [8].

Non-financial incentives, on the other hand, can be, for instance, the issuing of accreditation/certification criteria when applying SDM. The policy-makers could include the degree to which SDM with PDAs are included in the HCP´s workflow as a criterion for the accreditation [1]. This would make it possible for the organizations to differ from each other and be more motivated to adopt the SDM approach and its PDAs. In case of individuals, non-financial incentives are based as emphasized above on the intrinsic motivation of the individuals and other factors like “social/professional role and identity” or “belief about consequences” [6]. Possible examples of such incentives are: the clinic staff seeing the real-life and immediate positive effects on patient’s care [3] or continuing in educational activities by receiving education credits [7].

All in all, the implementation of SDM is frequently included in the healthcare policies of individual countries and seems to generally be encouraged. However, the level of utilization of the SDM approach and its tools in the routine practice stays poor. As the literature indicates, incentives are an important strategy at the system-level to facilitate the implementation of SDM with its PDAs. Nevertheless, it should be recognized that even successful behavioral changes can diminish once incentives are removed. There is therefore a need for such incentives for individuals and organizations to be sustainable.

My Post (6)

Paula Zietzsch

Paula has a background in Health Economics and works at OptiMedis in different national and EU projects with a particular interest in shared decision-making. As a Research & Innovation Manager, she focuses on implementing evidence-based interventions in routine clinical care.

  1. Scholl, Isabelle, Allison LaRussa, Pola Hahlweg, Sarah Kobrin, und Glyn Elwyn. „Organizational- and System-Level Characteristics That Influence Implementation of Shared Decision-Making and Strategies to Address Them — a Scoping Review“. Implementation Science 13, Nr. 1 (Dezember 2018): 40.
  2. Gayer, Christopher C, Matthew J Crowley, William F Lawrence, Jennifer M Gierisch, Bridget Gaglio, John W Williams, Evan R Myers, Amy Kendrick, Jean Slutsky, und Gillian D Sanders. „An Overview and Discussion of the Patient-Centered Outcomes Research Institute’s Decision Aid Portfolio“. Journal of Comparative Effectiveness Research 5, Nr. 4 (Juli 2016): 407–15.
  3. Kostick, Kristin M., Meredith Trejo, Robert J. Volk, Jerry D. Estep, und J.S. Blumenthal-Barby. „Using Nudges to Enhance Clinicians’ Implementation of Shared Decision Making With Patient Decision Aids“. MDM Policy & Practice 5, Nr. 1 (Januar 2020): 238146832091590.
  4. Härter, Martin, Nora Moumjid, Jacques Cornuz, Glyn Elwyn, und Trudy van der Weijden. „International accomplishments in policy, research and implementation.“ ZEFQ Z Evidenz Fortbild Qual G., Nr. 123–124 (o. J.): 1–5.
  5. Elwyn, Glyn, Dominick L. Frosch, und Sarah Kobrin. „Implementing Shared Decision-Making: Consider All the Consequences“. Implementation Science 11, Nr. 1 (Dezember 2015): 114.
  6. Munro, Sarah, Ruth Manski, Kyla Z. Donnelly, Daniela Agusti, Gabrielle Stevens, Michelle Banach, Maureen B. Boardman, u. a. „Investigation of Factors Influencing the Implementation of Two Shared Decision-Making Interventions in Contraceptive Care: A Qualitative Interview Study among Clinical and Administrative Staff“. Implementation Science 14, Nr. 1 (Dezember 2019): 95.
  7. Flottorp, Signe A, Andrew D Oxman, Jane Krause, Nyokabi R Musila, Michel Wensing, Maciek Godycki-Cwirko, Richard Baker, und Martin P Eccles. „A Checklist for Identifying Determinants of Practice: A Systematic Review and Synthesis of Frameworks and Taxonomies of Factors That Prevent or Enable Improvements in Healthcare Professional Practice“. Implementation Science 8, Nr. 1 (Dezember 2013): 35.
  8. Uy, Visith, Suepattra G. May, Caroline Tietbohl, und Dominick L. Frosch. „Barriers and Facilitators to Routine Distribution of Patient Decision Support Interventions: A Preliminary Study in Community-Based Primary Care Settings: Distribution of Patient Decision Support“. Health Expectations 17, Nr. 3 (Juni 2014): 353–64.