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