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.

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.

Reference

[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.

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.

References

[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. https://doi.org/10.1016/j.socscimed.2007.11.034.

[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. https://doi.org/10.7861/clinmedicine.2-3-227.

[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. https://doi.org/10.1111/hex.13303.

[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. https://doi.org/10.1016/j.psychres.2019.112516.

[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. https://doi.org/10.1186/s13012-019-0853-y.

[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. https://doi.org/10.1186/1748-5908-8-35.

[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: https://doi.org/10.1016/j.pec.2005.04.014. 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. https://doi.org/10.1177/1049732315610318.

[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).