Dataset Information

Identifying high-need patients with multimorbidity from their illness perceptions and personal resources to manage their health and care: a longitudinal study.

ABSTRACT: BACKGROUND:A proactive person-centred care process is advocated for people with multimorbidity. To that aim, general practitioners may benefit from support in the identification of high-need patients, i.e. patients who are high or suboptimal users of health services and/or have a poor quality of life. To develop such support, we examined whether knowledge about patients' illness perceptions and personal resources to manage their health and care is useful to identify high-need patients among multimorbid general practice populations. METHODS:Survey data, collected in 2016 and 2017, of 601 patients with two or more chronic diseases (e.g. COPD, diabetes, Parkinson's disease) registered with 40 general practices in the Netherlands were analysed by logistic regression analysis to predict frequent contact with the general practice, contact with general practice out-of-office services, unplanned hospitalisations and poor health related quality of life. Patients' illness perceptions and personal resources (education, health literacy, mastery, mental health status, financial resources, social support) were included as predictors. RESULTS:The four outcomes were only weakly associated among themselves (Phi .07-.19). Patients' illness perceptions and personal resources were of limited value to predict potentially suboptimal health service use, but they were important predictors of health related quality of life. Patients with a poor health related quality of life could be identified by their previously reported illness perceptions (attributing many symptoms to their chronic conditions (B?=?1.479, P

SUBMITTER: Rijken M

PROVIDER: S-EPMC7191697 | biostudies-literature | 2020 Apr

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Identifying high-need patients with multimorbidity from their illness perceptions and personal resources to manage their health and care: a longitudinal study.

Rijken Mieke M Valderas José Maria JM Heins Marianne M Schellevis Francois F Korevaar Joke J

BMC family practice 20200429 1

<h4>Background</h4>A proactive person-centred care process is advocated for people with multimorbidity. To that aim, general practitioners may benefit from support in the identification of high-need patients, i.e. patients who are high or suboptimal users of health services and/or have a poor quality of life. To develop such support, we examined whether knowledge about patients' illness perceptions and personal resources to manage their health and care is useful to identify high-need patients am ...[more]

PMID: 32349683

Similar Datasets

Project description:BackgroundIllness perceptions are beliefs about the cause, nature and management of illness, which enable patients to make sense of their conditions. These perceptions can predict adjustment and quality of life in patients with single conditions. However, multimorbidity (i.e. patients with multiple long-term conditions) is increasingly prevalent and a key challenge for future health care delivery. The objective of this research was to develop a valid and reliable measure of illness perceptions for multimorbid patients.MethodsCandidate items were derived from previous qualitative research with multimorbid patients. Questionnaires were posted to 1500 patients with two or more exemplar long-term conditions (depression, diabetes, osteoarthritis, coronary heart disease and chronic obstructive pulmonary disease). Data were analysed using factor analysis and Rasch analysis. Rasch analysis is a modern psychometric technique for deriving unidimensional and intervally-scaled questionnaires.ResultsQuestionnaires from 490 eligible patients (32.6% response) were returned. Exploratory factor analysis revealed five potential subscales 'Emotional representations', 'Treatment burden', 'Prioritising conditions', 'Causal links' and 'Activity limitations'. Rasch analysis led to further item reduction and the generation of a summary scale comprising of items from all scales. All scales were unidimensional and free from differential item functioning or local independence of items. All scales were reliable, but for each subscale there were a number of patients who scored at the floor of the scale.ConclusionsThe MULTIPleS measure consists of five individual subscales and a 22-item summary scale that measures the perceived impact of multimorbidity. All scales showed good fit to the Rasch model and preliminary evidence of reliability and validity. A number of patients scored at floor of each subscale, which may reflect variation in the perception of multimorbidity. The MULTIPleS measure will facilitate research into the impact of illness perceptions on adjustment, clinical outcomes, quality of life, and costs in patients with multimorbidity.

Project description:ObjectivePayers, providers and policymakers in the USA are interested in developing interventions that reduce preventable or modifiable healthcare use among high-need, high-cost (HNHC) patients. This study seeks to describe how and why complex interventions for HNHC patients lead to more appropriate use of healthcare services.DesignA realist review which develops programme theories from causal explanations generated and articulated through the creation of context-mechanism-outcome configurations.MethodsElectronic databases (including PubMed and Embase) and gray literature from January 2000 to March 2021 were searched. All study designs were included if the article provided data to develop our programme theories. Included studies were conducted in the USA and focused on interventions for adult, HNHC patients.ResultsData were synthesised from 48 studies. Identifying HNHC patients for inclusion in interventions requires capturing a combination of characteristics including their prior use of healthcare services, complexity of chronic disease(s) profile, clinician judgment and willingness to participate. Once enrolled, engaging HNHC patients in interventions requires intervention care providers and patients to build a trusting relationship. Tailored, individualised assistance for medical and non-medical needs, emotional support and self-management education empowers patients to increase their participation in managing their own care. Engagement of care providers in interventions to expand support of HNHC patients is facilitated by targeted outreach, adequate staffing support with shared values and regular and open communication.ConclusionsBuilding relationships with HNHC patients and gaining their trust is a key component for interventions to successfully change HNHC patients' behaviors. Identifying HNHC patients for an intervention can be best achieved through a multipronged strategy that accounts for their clinical and psychosocial complexity and prior experiences with the healthcare system. Successful interventions recognise that relationships with HNHC patients require the sustained engagement of care providers. To succeed, providers need ongoing emotional, financial, logistical and practical resources.Prospero registration numberCRD42020161179.

Project description:AimCalculating a modelled workload based on objective measures. Exploring the relation between this modelled workload and workload as perceived by nurses, including the effects of specific job demands, job resources and personal resources on the relation.DesignAcademic hospital in the Netherlands. Six surgical wards, capacity 15-30 beds. Data collected over 15 consecutive day shifts.MethodsModelled workload is calculated as a ratio of required care time, based on patient characteristics, baseline care time and time for non-patient related activities, and allocated care time, based on the amount of available nurses. Both required and allocated care time are corrected for nurse proficiency. Five dimensions of perceived workload were determined by questionnaires. Both the modelled and the perceived workloads were measured on a daily basis. Linear mixed effects models study the longitudinal relation between this modelled and workload as perceived by nurses and the effects of personal resources, job resources and job demands. ANOVA and post-hoc tests were used to identify differences in modelled workload between wards.ResultsModelled workload varies roughly between 70 and 170%. Significant differences in modelled workload between wards were found but confidence intervals were wide. Modelled workload is positively associated with all five perceived workload measures (work pace, amount of work, mental load, emotional load, physical load). In addition to modelled workload, the job resource support of colleagues and job demands time spent on direct patient care and time spent on registration had the biggest significant effects on perceived workload.ConclusionsThe modelled workload does not exactly predict perceived workload, however there is a correlation between the two. The modelled workload can be used to detect differences in workload between wards, which may be useful in distributing workload more evenly in order prevent issues of over- and understaffing and organizational justice. Extra effort to promote team work is likely to have a positive effect on perceived workload. Nurse management can stimulate team cohesion, especially when workload is high. Registered nurses perceive a higher workload than other nurses. When the proportion of direct patient care in a workday is higher, the perceived workload is also higher. Further research is recommended. The findings of this research can help nursing management in allocating resources and directing their attention to the most relevant factors for balancing workload.

Dataset Information

Identifying high-need patients with multimorbidity from their illness perceptions and personal resources to manage their health and care: a longitudinal study.

Publications

Identifying high-need patients with multimorbidity from their illness perceptions and personal resources to manage their health and care: a longitudinal study.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets