Theo van Achterberg

Background: Numerous transitional care innovations (TCIs) are being developed and implemented to optimize care continuity for older persons when transferring between multiple care settings, help meet their care needs, and ultimately improve their quality of life. Although the implementation of TCIs is influenced by contextual factors, the use of effective implementation strategies is largely lacking. Thus, to improve the implementation of TCIs targeting older persons receiving long-term care services, we systematically developed a set of viable strategies selected to address the influencing factors. Methods: As part of the TRANS-SENIOR research network, a stepwise approach following Implementation Mapping (steps 1 to 3) was applied to select implementation strategies. Building on the findings of previous studies, existing TCIs and factors influencing their implementation were identified. A combination of four taxonomies and overviews of change methods as well as relevant evidence on their effectiveness were used to select the implementation strategies targeting each of the relevant factors. Subsequently, individual consultations with scientific experts were performed for further validation of the process of mapping strategies to implementation factors and for capturing alternative ideas on relevant implementation strategies. Results: Twenty TCIs were identified and 12 influencing factors (mapped to the Consolidated Framework for Implementation Research) were designated as priority factors to be addressed with implementation strategies. A total of 40 strategies were selected. The majority of these target factors at the organizational level, e.g., by using structural redesign, public commitment, changing staffing models, conducting local consensus discussions, and organizational diagnosis and feedback. Strategies at the level of individuals included active learning, belief selection, and guided practice. Each strategy was operationalized into practical applications. Conclusions: This project developed a set of theory and evidence-based implementation strategies to address the influencing factors, along further tailoring for each context, and enhance the implementation of TCIs in daily practice settings. Such work is critical to advance the use of implementation science methods to implement innovations in long-term care successfully.

Introduction: This study investigated how patients with COVID‐19, telemonitoring (TM) teams, general practitioners (GPs) and primary care nurses in Belgium experienced remote patient monitoring (RPM) in 12 healthcare organizations, in relation to the patients' illness, health, and care needs, perceived quality of care, patient and health system outcomes, and implementation challenges.Design: A qualitative research approach was adopted, including focus group discussions and semi‐structured interviews.Methods: Four different groups of participants were interviewed, that is, patients (n = 17), TM teams (n = 27), GPs (n = 16), and primary care nurses (n = 12). An interview guide was drafted based on a literature review. Interviews were transcribed verbatim, and NVivo was used for managing and analyzing the data. The QUAGOL method was used to guide the data analysis process and was adapted for the purpose of a thematic content analysis.Results: All participants agreed that RPM‐reassured patients. The overall perceived value of RPM for individual patients depended on how well the intervention matched with their needs. Patients who did not have the necessary language (Dutch/French speaking) and digital skills, who did not have the right equipment (smartphone or tablet), or who missed the necessary infrastructure (no internet coverage in their region) were often excluded. Remote patient monitoring also reassured healthcare professionals as it gave them information on a disease they had little knowledge about. Professionals involved in RPM experienced a high workload. All TM teams agreed that quality of data was a key factor to ensure an adequate follow‐up, but they differed in what they found important. The logistic management of RPM was a challenge because of the contagious character of COVID‐19, and the need for an effective information flow between the hospital team and primary care providers. Participants missed clarification about who was accountable for the care for patients in the projects. Primary care nurses and GPs missed access to RPM data. All agreed that the funding they received was not sufficient to cover all the costs associated with RPM.Conclusion: Healthcare professionals and patients perceive RPM as valuable and believe that the concept will have its place in the Belgium health system. However, current RPM practice is challenged by many barriers, and the sustainability of RPM implementation is low.Clinical relevance: Remote patient monitoring (RPM) was perceived as a valuable intervention for patients with COVID‐19, but there were important concerns about unequal access to care. While the technology for RPM is available, the sustainability of implementation is low because of concerns with data quality, challenging logistics within projects, lack of data integration and communication, and a lack of an overarching guiding framework.

Background: Disability poses a burden for older persons, and is associated with poor outcomes and high societal costs. Prediction models could potentially identify persons who are at risk for disability. An up to date review of such models is missing. Objective: To identify models developed for the prediction of functional status in community dwelling older persons. Methods: A systematic review was performed including studies of older persons that developed and/or validated prediction models for the outcome functional status. Medline and EMBASE were searched, and reference lists and prospective citations were screened for additional references. Risk of bias was assessed using the PROBAST-tool. The performance of models was described and summarized, and the use of predictors was collated using the bag-of-words text mining procedure. Results: Forty-three studies were included and reported 167 evaluations of prediction models. The median c-statistic values for the multivariable development models ranged between 0.65 and 0.76 (minimum = 0.58, maximum = 0.90), and were consistently higher than the values of the validation models for which median c-statistic values ranged between 0.6 and 0.68 (minimum = 0.50, maximum = 0.81). A total of 559 predictors were used in the models. The five predictors most frequently used were gait speed (n = 47), age (n = 38), cognition (n = 27), frailty (n = 24), and gender (n = 22). Conclusions: No model can be recommended for implementation in practice. However, frailty models appear to be the most promising, because frailty components (e.g. gait speed) and frailty indexes demonstrated good to excellent predictive performance. However, the risk of study bias was high. Substantial improvements can be made in the methodology.

Background: Transitions in care for older persons requiring long-term care are common and often problematic. Therefore, the implementation of transitional care innovations (TCIs) aims to improve necessary or avert avoidable care transitions. Various factors were recognized as influencers to the implementation of TCIs. This study aims to gain consensus on the relative importance level and the feasibility of addressing these factors with implementation strategies from the perspectives of experts. This work is within TRANS-SENIOR, an innovative research network focusing on care transitions.Methods: A modified Delphi study was conducted with international scientific and practice-based experts, recruited using purposive and snowballing methods, from multiple disciplinary backgrounds, including implementation science, transitional care, long-term care, and healthcare innovations. This study was built on the findings of a previously conducted scoping review, whereby 25 factors (barriers, facilitators) influencing the implementation of TCIs were selected for the first Delphi round. Two sequential rounds of anonymous online surveys using an a priori consensus level of > 70% and a final expert consultation session were performed to determine the implementation factors’: i) direction of influence, ii) importance, and iii) feasibility to address with implementation strategies. The survey design was guided by the Consolidated Framework for Implementation Research (CFIR). Data were collected using Qualtrics software and analyzed with descriptive statistics and thematic analysis.Results: Twenty-nine experts from 10 countries participated in the study. Eleven factors were ranked as of the highest importance among those that reached consensus. Notably, organizational and process-related factors, including engagement of leadership and key stakeholders, availability of resources, sense of urgency, and relative priority, showed to be imperative for the implementation of TCIs. Nineteen factors reached consensus for feasibility of addressing them with implementation strategies; however, the majority were rated as difficult to address. Experts indicated that it was hard to rate the direction of influence for all factors.Conclusions: Priority factors influencing the implementation of TCIs were mostly at the organizational and process levels. The feasibility to address these factors remains difficult. Alternative strategies considering the interaction between the organizational context and the outer setting holds a potential for enhancing the implementation of TCIs.

Healthcare research funders may undertake various roles to facilitate implementation of research findings. Their ability to enact such roles depends on several factors, knowledge of implementation being one essential requirement. However, previous studies do not assess the type or level of knowledge about implementation that research funders possess. This paper therefore presents findings from a qualitative, inductive study of the implementation knowledge of research funders. Three aspects of this knowledge are explored, namely how research funders define implementation, their level of self-assessed implementation knowledge and the factors influencing their self-assessment of implementation knowledge. Research funders (n = 18) were purposefully selected from a sample of research funding organisations in Sweden (n = 10). In-depth semi-structured interviews were conducted, recorded and transcribed verbatim. An inductive method using a systematic coding procedure was employed to derive the findings. The research funders defined implementation as either an outcome or a process, with the majority believing that implementation of healthcare research results demands a process, although its complexity varied in the research funders’ view. They perceived their own level of implementation knowledge as either limited or substantial, with a majority regarding it as limited. Clinical research experience, clinical experience and task relevance were singled out as the clearest factors affecting the self-assessment of their own implementation knowledge. This study, the first to focus on implementation knowledge of research funders, demonstrates that they are a category of policy-makers who may possess knowledge, based on their previous professional experience, that is comparable to some important findings from implementation research. Consequently, the findings not only pinpoint the relevance of professional experience, but also reveal a lack of awareness and knowledge of the results of implementation research among research funders in charge of healthcare research.

Given the great symptom burden associated with chemotherapy on the one hand and generally poor self-management of symptoms by cancer patients on the other hand, our aim was to develop a nursing intervention to reduce symptom burden in adult cancer patients treated with chemotherapy and to support them in dealing with their various symptoms at home. Development of the intervention was guided by the Intervention Mapping Approach and included following steps: needs assessment, formulation of proximal programme objectives, selection of methods and strategies, production of programme components, and planning for implementation and evaluation of the intervention. A panel of multidisciplinary healthcare professionals (n = 12) and a panel of patients and family caregivers (n = 7) were actively involved developing the intervention at each stage. For the intervention, four patient performance objectives relating to self-management were advanced. Self-efficacy and outcome expectations were selected as key determinants of dealing with chemotherapy-related symptoms. As methods for supporting patients, motivational interviewing and tailoring were found to fit best with the change objectives and determinants. Existing patient information materials were re-designed after panel input to reinforce the new intervention approach. The intervention mapping approach, including active involvement of the intervention providers and receivers, informed the design of this nursing intervention with two or more contacts. Further evaluation is needed to gain insight into the potential effects, feasibility and mechanisms of this complex intervention.