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!

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.

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.

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.

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 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.

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. https://doi.org/10.1186/s13012-018-0731-z.
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. https://doi.org/10.2217/cer-2016-0002.
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. https://doi.org/10.1177/2381468320915906.
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. https://doi.org/10.1016/j.zefq.2017.05.024.
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. https://doi.org/10.1186/s13012-016-0480-9.
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. https://doi.org/10.1186/s13012-019-0941-z.
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. https://doi.org/10.1186/1748-5908-8-35.
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. https://doi.org/10.1111/j.1369-7625.2011.00760.x.

A societal perspective includes the impact of a disease on informal care, that is care given by people other than healthcare professionals. Informal care includes, but is not limited to, care and support given by family and friends. Especially chronically ill older adults need day-to-day help with personal care, such as dressing and eating, practical household help, such as shopping, and many other activities essential to their health and quality of life.

We are familiar with the costs associated with a doctor, a nurse, or other healthcare workers providing professional care to older adults.

In part, that’s because information on informal care use is often sparse if not missing at all, thus in order to predict these costs we need to study its relationship with known predictors of health care use.

Different studies evaluated the relationship between proximity to death and health care expenditures. These studies found that proximity to death is a much better predictor of health care expenditures than age.[1] This is supported by the finding that health losses are more pronounced in the last years of life [2], and similarly severe disability is centered in the final phase of life. [3] Within COMPAR-EU, we therefore aimed to predict informal care use based on age and proximity to death.

We used data from the Survey of Health, Ageing and Retirement in Europe (SHARE) release 7.0.0. [4] SHARE is a longitudinal, multidisciplinary, and cross-national survey, which aims to collect data on health, socio-economic status along with social and family networks of non-institutionalized people aged over 50 in 21 European countries and Israel. With these data we predicted informal care use based on age, gender and proximity to death. Our findings show that the weekly use of informal care increased with proximity to death from 19% to 53% among those who died in the same year of the interview. Also, the number of hours of informal care per day increased from 2.0 to 4.7 in the last year of life.

In an aging population, where interventions potentially prolong life, severe disability is rather postponed to the last years of life. Therefore, proximity to death could be considered as a proxy of disability, which is an important determinant of informal care use. The overall aim of COMPAR-EU is to identify, compare and rank the most cost-effective self-management interventions for adults suffering from type 2 diabetes, obesity, chronic obstructive pulmonary disease, and heart failure. By using proximity to death to predict informal care use, we are able to take into account the impact that self-management interventions might have on the costs of informal care when they accomplish to prolong life.

References

1. Seshamani M, Gray A. The impact of ageing on expenditures in the National Health Service.
2. Gheorghe M, Picavet S, Verschuren M, et al. Health losses at the end of life: a Bayesian mixed beta regression approach. J R Stat Soc Ser A (Statistics Soc 2017;180:723–49. doi:10.1111/RSSA.12230
3. De Meijer C, Koopmanschap M, d’Uva TB, et al. Determinants of long-term care spending: Age, time to death or disability? J Health Econ 2011;30:425–38. doi:10.1016/J.JHEALECO.2010.12.010
4. Börsch-Supan A, Team on behalf of the SCC, Brandt M, et al. Data Resource Profile: The Survey of Health, Ageing and Retirement in Europe (SHARE). Int J Epidemiol 2013;42:992–1001. doi:10.1093/IJE/DYT088

EPF leads the work of COMPAR-EU on eliciting patients’ priorities and preferences. In this role, EPF ensures that patient’s views, experience, gender, and socio-economic dimensions are accounted for and also guarantees meaningful patient involvement across various project outputs, tasks and activities. EPF is closely working together with all COMPAR-EU partners in order to embed and promote what matters to patients the most. To inform this work, EPF has set up a dedicated Patient Panel back in 2019. Since then, the Patient Panel regularly meets and through its work advises with first-hand experience and expertise on various project outputs. Furthermore, EPF representatives are part of the COMPAR-EU IT Platform Task Force and in constant coordination with other project partners in order to bring the patient perspective. Finally, in order to make the results of the project more accessible to non-expert audiences, EPF produces lay language summaries of key project documents and these will be translated in various European languages towards mid-2022.

Activities, Achievements and Ambitions of the Patient Panel in 2021

This year so far, members of the COMPAR-EU Patient Panel, together with EPF representatives, joined forces and focused their efforts on four key aspects related to patient involvement, co-design and patient engagement:

• Four webinars – regular online meetings where members of the Patient Panel provide their input onto project’s activities
• COMPAR-EU IT Platform Task Force – two members of the Patient Panel became a vital part of the Task Force dedicated to the development, design, implementation and of the Platform
• Work on various project outputs and their translation into lay language continued
• Kicked-off the process of translating the lay summaries into 8 European languages

A Quote from the Members of the Patient Panel

Where to now – what is ahead for the Patient Panel in 2021?

Looking ahead, EPF is planning a range of activities focused on involving and empowering patients in 2021. EPF will continue to hold its monthly webinars with the Patient Panel to validate project materials and consult on planned activities. As with previous years, EPF will continue to adapt key project materials into lay language, but this year EPF will be coordinating the translation of these lay materials into the working languages of the project. Together with the Patient Panel, we will be producing one-page infographics and short white board videos to support the dissemination and communication of these materials. Lastly, as part of its work to ensure accessibility and sustainability, EPF will continue to contribute to the COMPAR-EU IT Platform testing, validation and review. In June, during the first Patient Panel Workshop for 2021, there will be a dedicated session to discuss a key output of the project – the Decision Aid Tool. During the session, survey results (done earlier in June) will be presented, followed by conducting semi-structured interviews with some members of the Patient Panel.

Stay tuned for more news in the fall of 2021.

Patient decision aids (PDAs) are tools that are designed to help people participate in decision making about different care options. They provide information about the options and outcomes and help patients make informed decisions based on their values and preferences. They are designed to complement, rather than replace, counselling from a clinician. Self-management interventions are complex interventions that can be made up of many different components. Within the COMPAR-EU project we aim to provide PDAs to facilitate the self-management decision-making process for both patients and health care professionals. However, we are aware that we will be faced with a number of factors that will influence their real implementation.

While many clinicians feel they involve patients in their own care and they do not understand why their self-management recommendations are not being followed, patients do not feel involved or listened to and think things are being done “to” them rather that “with” them. Besides, clinicians are unaware of the values and preferences of patients whereas patients are unaware of the options and evidence that might influence their health outcomes. The result of this is that decisions and plans are less successful than they could be, and the level of satisfaction is low, resulting in wasted time, resources and opportunities (Lewis-Barnet, 2016).

Shared decision-making is part of patient-centred care whereby patients and clinicians decide on an intervention together. This is particularly relevant when the decision is preference-sensitive and when there is not too much evidence about the intervention. One way to promote this collaborative approach is by using a PDA, which provides information about the available options, benefits and risk of each option and ways to incorporate patients’ values and preferences.

Barriers to adoption

Although PDAs have been proven to be effective in involving patients in shared decision-making and improving their decision quality (Stacey, 2017), the adoption and implementation of PDAs remains poor (Lin, 2013). There are many factors that influence the implementation of PDAs in real practice.

A systematic review of health professionals’ perceptions highlighted that the most often reported barriers for implementing PDAs were time constraints, lack of applicability due to patient characteristics and the clinical situation (Légaré, 2008). In terms of attitudes and roles, some clinicians find the idea of partnership working disconcerting. Others feel that they already do it despite the evidence to the contrary. Furthermore, clinicians perceive poor interpersonal skills as a barrier to shared decision-making. Other issues relate to organizational and system level characteristics (Scholl, 2018). Organizational level factors include organizational leadership, culture, resources, and priorities, as well as teams and workflows (Scholl, 2018). System level characteristics that have been described as influencing factors are policies, clinical guidelines, incentives, culture, education, and licensing (Scholl, 2018).

The main barriers for PDA implementation as experienced by older patients are related to poor health and/or cognitive or physical impairments (Pel-Little, 2021). Socio-cultural barriers such as language barriers and clinician paternalism, and lack of resources in terms of infrastructure or technology development are potential influencing factors to be considered that may vary across countries.

Faciliators to adoption

Among the facilitators to implement PDAs in routine care, we should consider clinicians’ motivation, introduction of a system to identify eligible patients to use the PDAs ahead of clinical consultations, positive impact on the clinical process and patients’ outcomes (Légaré, 2008). Clinicians need a supporting organizational context and good communication skills to provide the individualized approach for patient care that using a PDA requires (Pel-Little, 2021).

Working in partnership with patients requires a change in attitudes skills and system to become part of normal practice. With COMPAR-EU we pretend to provide a clear understanding of what self-management interventions work and do not work. Incorporating PDAs as part of our goals considering all influential factors, we pretend to enhance their implementation in real practice.

What do you need to know about future costs and cost effectiveness analysis?

‘Future costs’ are costs that are incurred in life years gained if a preventive intervention or medical treatment postpones death. Imagine that because of a self-management intervention a person with diabetes is able to lower his/her weight and better control HbA1c, and thereby prevents a fatal heart attack and lives longer. Because of these survival benefits this person will consume medical care (and also other goods and services) in added life years. The costs of this consumption in added life years are called future costs.

In both of the published papers we lay out the methods that can be used, with aggregate population data, to estimate future costs for a particular intervention. These vary between the two countries due to different perspectives of healthcare decision makers and the data available.

Findings from these methods

Our findings show that future costs rise with age and with the amount of life-years gained from the intervention in question. Alongside our two papers we have published two separate online tools, both under the name ‘PAID’ which are freely available, that allow researchers doing cost-effectiveness analyses to download the future medical costs specific to their intervention. This removes several hurdles from the otherwise time-consuming process of including future unrelated medical costs and future non-medical costs in economic evaluation. This will lead to less biased estimates of cost-effectiveness and provide more reliable rankings of self-management interventions.

References

1. Kellerborg, K., Perry-Duxbury, M., de Vries, L., & van Baal, P. (2020). Practical Guidance for Including Future Costs in Economic Evaluations in The Netherlands: Introducing and Applying PAID 3.0. Value in Health, 23(11), 1453-1461.
2. Perry-Duxbury, M., Asaria, M., Lomas, J., & van Baal, P. (2020). Cured Today, Ill Tomorrow: A Method for Including Future Unrelated Medical Costs in Economic Evaluation in England and Wales. Value in Health, 23(8), 1027-1033.
3. de Vries L,M., van Baal P,H.M., Brouwer WBF. Future costs in cost-effectiveness analyses: Past, present, future. 2019;37:119-130.

Resources

Online Tools: https://www.imta.nl/paid

EPF leads the work of COMPAR-EU on eliciting patients’ priorities and preferences. In this role, EPF works to identify patient-prioritised outcomes as reported in the literature for each of the four focus chronic conditions; through the same lens, EPF ensures that patient’s views, gender, and socio-economic dimensions are accounted for and guarantees meaningful patient empowerment across various project outputs and tasks.

To inform this work, EPF regularly engages a dedicated Patient Panel that advise with first-hand experience and expertise. Furthermore, EPF representatives are part of the COMPAR-EU IT Platform Task Force and EPF members are updated monthly on the project’s progress. Finally, in order to make the results of the project more accessible to non-expert audiences, EPF produces lay language summaries of key project documents.

Activities and Achievements of the Patient Panel in 2020

Before looking ahead, let us summarize what was done last year. In 2020, members of the COMPAR-EU Patient Panel (PP), together with EPF representatives, joined forces and focused their efforts on three key areas related to patient involvement and engagement:

• Webinars and Workshops – regular webinars and two workshops where patient input onto project activity was taken on board;
• COMPAR-EU Platform Task Force – members of the PP became part of the Task Force dedicated to the development, design, and implementation of the final project output;
• Work on various project outputs and their translation into lay language continued.

EPF decides to join “Self-Management Europe” Research Centre

Over the years, EPF has done a tremendous amount of work in the field of self-management in chronic conditions. One of the best examples of this is the PRO-STEP project, an EPF-lead project that explored the added-value of self-management in chronic diseases in a context of promotion of self-care in European health systems, hence the decision of the EPF board to join this new initiative and continue the promotion and reinforcement of patient empowerment and self-management in Europe.

The year ahead – what is coming up next for EPF in 2021?

Looking to the year ahead, EPF is planning a range of activities focused on communicating with patients. Mainly, EPF will continue to hold its quarterly webinars with the patient panel in order to validate project materials and consult on planned activities. In previous years, EPF produced 5 lay summaries of key project outputs. This year, the focus will be not only on translating those documents into the working languages of the project, but also producing infographics and short videos to support the dissemination of these materials. Lastly, as part of its work to ensure accessibility and sustainability, EPF will continue to contribute to the COMPAR-EU IT Platform testing, validation and review, which it has been participating in from the start of the proces.