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To examine whether nurse-led stroke aftercare is beneficial for long-term psychosocial outcome of community-dwelling persons with stroke. Comparative effectiveness research design in which a prospective stroke aftercare cohort (n = 87) was compared to care-as-usual (n = 363) at six- and 12-months post stroke. Changes over time in cognitive and emotional problems experienced in daily life, fatigue and stroke impact on daily life were examined for stroke aftercare only. Multilevel modelling was used to compare stroke aftercare to care-as-usual concerning anxiety and depression symptoms, social participation and quality of life, over time. Sample characteristics did not differ between cohorts except for stroke type and on average, more severe stroke in the stroke aftercare cohort (p < 0.05). Following stroke aftercare, anxiety and emotional problems decreased significantly (p < 0.05), whereas care-as-usual remained stable over time in terms of anxiety. No significant changes over time were observed on the other outcome domains. Nurse-led stroke aftercare showed to be beneficial for emotional well-being in comparison to care-as-usual. Providing psychoeducation and emotional support seem effective elements but adding other therapeutic elements such as self-management strategies might increase the effectiveness of nurse-led stroke aftercare. Routine stroke follow-up care should pay attention to psychosocial and emotional outcome in a systematic manner, in addition to secondary prevention. Healthcare professionals such as (specialized) nurses are needed to appropriately address the hidden cognitive and emotional consequences of stroke. Providing psychoeducation and emotional support in stroke aftercare diminish insecurities and worries in community-dwelling persons with stroke, leading to better outcomes. [ABSTRACT FROM AUTHOR]

Introduction: This systematic review aims to identify what rehabilitation care networks have been described for patients with chronic musculoskeletal pain (CMP), and their impact on the Quadruple Aim outcomes (health; health care costs; quality of care experienced by patients; work satisfaction for health care professionals)1. Methods: Studies with original descriptions of interventions in Dutch, English, or German published between 1/1/1994 and 11/4/2019 were identified in PubMed, CINAHL, Web of Science, and PsycInfo. They were included if the main population comprised patients with CMP, intervention was implemented in primary care, or a combination of primary care and other health care settings, with a rehabilitation aim and an interdisciplinary care network. After data extraction, it became evident that interventions, outcome measures, and study designs were too heterogeneous to justify meta-analysis. Results: Of the 34 included interventions (49 articles), 21 consisted of a collaboration within primary care. Results on impact were presented in 19 randomized, 12 non-randomized, and 7 qualitative studies. The collaborations ranged from performing an assessment to giving a complete interdisciplinary treatment in primary care. Discussion: There is a wide variety of content, collaboration, and evaluation methods of interventions. It seems that patient-centered interdisciplinary interventions are more effective than usual care. Further initiatives should be performed for interdisciplinary interventions within and across health care settings and evaluated with mixed methods on all Quadruple Aim outcomes. Process evaluation: Interventions were often not described in full detail and/or the health care settings left unclear, potentially resulting in erroneous exclusions or classification of studies. [ABSTRACT FROM AUTHOR]

Introduction: eHealth could support cost-effective interdisciplinary primary care for patients with chronic musculoskeletal pain1,2. This study aims to explore the feasibility of the eCoach-Pain, comprising a tool measuring pain complexity, diaries, pain education sessions, monitoring options, and chat function. Methods: Feasibility was evaluated (June-December 2020) by assessing learnability, usability, desirability, adherence to the application, and experiences from patients and general practitioners, practice nurses mental health, and physiotherapists. Primary healthcare professionals (PHCPs) participated in a focus group. Patient data was collected by evaluation questionnaires, individual interviews, and eCoach-Pain-use registration. Results: Six PHCPs from two settings participated and recruited 29 patients (72% female, median age 50.0 years (IQR = 24.0)). Patients used the eCoach during the entire treatment phase (on average 107.0 days (IQR = 46.0); 23 patients completed the pain complexity tool and used the educational sessions, and 12 patients the chat function. Patients were satisfied with the eCoach-Pain (median grade 7.0 (IQR = 2.8) on a 0-10 scale) and made some recommendations for better fit with patient-specific complaints. Discussion: The eCoach-Pain is of added value to PHCPs' treatment, and it has treatment benefits for patients. The implementation strategy is important for successful use of the eCoach-Pain. It is recommended to improve this strategy and involve a case-manager per patient. Process evaluation: There was a small sample of patients and PHCPs that participated and the limited use of the eCoach-Pain (selection bias). Not all patients performed all measurements, the completeness of available data per measurement differed (information bias). [ABSTRACT FROM AUTHOR]

Introduction: Despite growing numbers of patients facing chronic multimorbidity with complex care needs, current integrated primary care approaches for chronic disease are still highly standardized and single disease-focused. To facilitate a more person-centered approach, a risk stratification tool distinguishing patient subgroups with homogeneous care needs could be valuable. The aim of the present study was to explore the degree to which routinely registered patient characteristics can be used to stratify chronically ill into care utilization subgroups. Methods: We conducted a two-year retrospective cohort study using routinely-registered chronically ill patient characteristics from Electronic Health Records EHRs total population of 481,842 patients, 129 Dutch general practices. Care utilization was used as a proxy for care needs and operationalized as the number of duration-weighted consultations per patient. Cumulative care utilization was divided into three equal parts i.e. care utilization subgroups. Chi-square, ANOVA and post-hoc tests p < 0.05 were used to test whether subgroups of care utilization differed in terms of age, sex, socio-economic status, multimorbidity, number and nature of chronic conditions. Results: A division of the cohort 66 practices, n= 85,457 into three subgroups with equal care utilization i.e. each consuming one-third of cumulative weighted consultations showed a 'lowutilization' subgroup covering almost two-third of patients 63.8%, while the 'mediumutilization' and 'high-utilization' subgroups represented significantly smaller proportions of the cohort 24.5% and 11.7%, respectively. Compared to the low and middle group, this last subgroup contains relatively older patients, more females, lower socio-economic status and higher multimorbidity and combinations of physical and mental chronic conditions. Discussions: The significant differences in patient characteristics between the three care utilization subgroups are in line with findings from previous studies. Enriched with other relevant patient characteristics such as personal income and family size, the care utilization subgroups could stimulate to consider person-centred rather than disease-specific characteristics in aligning care to the needs of chronically ill. An individual consultation could for instance be built around the patient, having multiple conditions, instead of offering a fragmented consultation for every condition. Conclusions: The present study shows that care utilization subgroups, distinct in patient characteristics, can be developed from patient characteristics routinely registered in EHRs. Lessons learned: It was demonstrated that even a simple stratification of chronically ill based on care utilization shows great variety of patient characteristics between the subgroups. These insights are promising for the development of a risk stratification tool to support person-centered, integrated primary care. Limitations: This study lacks the inclusion of social patient characteristics not routinely registered in EHRs and data on care utilization outside primary care. Besides, care utilization was used as a proxy of care needs. Suggestions for future research: A more sophisticated analysis, like Latent Class Analysis, could be employed to examine latent classes of care needs amongst chronically ill. Moreover, a predictive analysis technique, such as machine learning, is needed to create insight into the use of patient characteristics in allocating chronically ill to a matching care needs subgroup. [ABSTRACT FROM AUTHOR]