Improving Detection of Client Complexity in the Community (Impact): A Study Protocol of a Pragmatic Randomized Controlled Trial

Boak, Jennifer; Blackberry, Irene; Rasekaba, Tshepo

doi:10.3390/mps4040070

Cited by 2 publications

(3 citation statements)

References 26 publications

(33 reference statements)

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“…Whiteford and colleagues, however, recognised this limitation and added a dimension from a needs perspective, to include 'requiring multi-agency support to maximise their health, housing, social participation and personal functioning' (Whiteford et al, 2016). There have also been efforts put into measuring client complexity to inform caseload management (Boak et al, 2021;Korasz et al, 2018;Shukor et al, 2019), but again, these were done in the context of adult mental health services. This lack of consensus has resulted in oversimplified tools for service planning and evaluation.…”

Section: Discussionmentioning

confidence: 99%

Capturing the clinical complexity in young people presenting to primary mental health services: A data-driven approach

Gao,

Telford,

Filia

et al. 2023

Preprint

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Background: The specific and multifaceted service needs of young people have driven the development of youth-specific integrated primary mental health care models, such as the internationally pioneering headspace services in Australia. Although these services were designed for early intervention, they often need to cater for young people with severe conditions and complex needs, creating challenges in service planning and resource allocation. There is, however, a lack of understanding and consensus on the definition of complexity in such clinical settings. Methods: This retrospective study involved analysis of headspace’s clinical minimum data set from young people accessing services in Australia between July 1, 2018, and June 30, 2019. Based on consultations with experts, complexity factors were mapped from a range of demographic information, symptom severity, diagnoses, illness stage, primary presenting issues, and service engagement patterns. Consensus clustering was used to identify complexity subgroups based on identified factors. Multinomial logistic regression was then used to evaluate whether these complexity subgroups were associated with other risk factors.Results: A total of 81,622 episodes of care from 76,021 young people across 113 services were analysed. Around 20% of young people clustered into a "high complexity" group, presenting with a variety of complexity factors, including severe disorders, a trauma history, and psychosocial impairments. Two moderate complexity groups were identified representing "distress complexity" and "psychosocial complexity" (about 20% each). Compared with the "distress complexity" group, young people in the “psychosocial complexity" group presented with a higher proportion of education, employment, and housing issues in addition to psychological distress, and had lower levels of service engagement. The distribution of complexity profiles also varied across different headspace services.Discussion: The proposed data-driven complexity model offers valuable insights for clinical planning and resource allocation. The identified groups highlight the importance of adopting a holistic and multidisciplinary approach to address the diverse factors contributing to clinical complexity. The large number of young people presenting with moderate-to-high complexity to headspace early intervention services emphasises the need for systemic change in youth mental health care to ensure the availability of appropriate and timely support for all young people.

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Section: Discussionmentioning

confidence: 99%

Capturing the clinical complexity in young people presenting to primary mental health services: A data-driven approach

Gao,

Telford,

Filia

et al. 2023

Preprint

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“…Detailed ImPaCt study methods have been described in the published protocol (Boak et al, 2021). At the time of this study there were no other pragmatic randomised controlled trials of complexity tools for older people in Australia.…”

Section: Methodsmentioning

confidence: 99%

“…Sample size was determined using initial pilot and feasibility data. A post-hoc sample size calculation was conducted on the first n=84 assessments, resulting in a minimum 138 client participants (files) required to detect a significant difference in the proportions of levels of complexity rating between the control (23.1%) vs. intervention (5.7%), odds ratio = 4.97, power = 0.8 and p < 0.05 (Boak et al, 2021).…”

Section: Sample Size and Randomisationmentioning

confidence: 99%

Improving quality of care through detection for care complexity amongst community-dwelling older people receiving community nursing support: The ImPaCt study Randomised Controlled Trial

Boak

2024

Preprint

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This is a preprint and has not been peer-reviewed. Data may be preliminary.Reporting Method : The study adhered to the updated guidelines for reporting parallel group randomised trials. Reporting was conducted according to the CONSORT checklist (Schulz et al., 2010).Patient or Public Contribution: This study was focussed on the nurses' perspective of client complexity and how this complexity is detected. During the protocol development phase, a presentation about the study was made to a consumer group from the lead researcher's workplace. This presentation was well received with five consumers in attendance. The protocol was also presented to the Board of the lead researcher's workplace. Four board members were in attendance and showed positive interest. Implications for the profession and patient careThis study underscores the merit of incorporating a care complexity assessment tool into community nursing care for older people, amplifying decision-making regarding patient complexity. Future studies should explore clients' perspectives, the PCI's effect on and implications for hospital (re)admissions and longevity at home.

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Improving Detection of Client Complexity in the Community (Impact): A Study Protocol of a Pragmatic Randomized Controlled Trial

Cited by 2 publications

References 26 publications

Capturing the clinical complexity in young people presenting to primary mental health services: A data-driven approach

Capturing the clinical complexity in young people presenting to primary mental health services: A data-driven approach

Improving quality of care through detection for care complexity amongst community-dwelling older people receiving community nursing support: The ImPaCt study Randomised Controlled Trial

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