Link-me: Protocol for a randomised controlled trial of a systematic approach to stepped mental health care in primary care

Clinical decision support methods for children and youths with mental health disorders in primary care

BACKGROUND

Mental health disorders among children and youths are common and often have negative consequences for children, youths, and families if unrecognized and untreated. With the goal of early recognition, primary care physicians (PCPs) play a significant role in the detection and referral of mental disorders. However, PCPs report several barriers related to confidence, knowledge, and interdisciplinary collaboration. Therefore, initiatives have been taken to assist PCPs in their clinical decision-making through clinical decision support methods (CDSMs).

OBJECTIVES

This review aimed to identify CDSMs in the literature and describe their functionalities and quality.

METHODS

In this review, a search strategy was performed to access all available studies in PubMed, PsychINFO, Embase, Web of Science, and COCHRANE using keywords. Studies that involved CDSMs for PCP clinical decision-making regarding psychosocial or psychiatric problems among children and youths (0-24 years old) were included. The search was conducted according to PRISMA-Protocols.

RESULTS

Of 1,294 studies identified, 25 were eligible for inclusion and varied in quality. Eighteen CDSMs were described. Fourteen studies described computer-based methods with decision support, focusing on self-help, probable diagnosis, and treatment suggestions. Nine studies described telecommunication methods, which offered support through interdisciplinary (video) calls. Two studies described CDSMs with a combination of components related to the two CDSM categories.

CONCLUSION

Easy-to-use CDSMs of good quality are valuable for advising PCPs on the detection and referral of children and youths with mental health disorders. However, valid multicentre research on a combination of computer-based methods and telecommunication is still needed.

Economic evaluation of a Decision Support Tool to guide intensity of mental health care in general practice: the Link-me pragmatic randomised controlled trial

Background

This paper reports on the cost-effectiveness evaluation of Link-me – a digitally supported, systematic approach to triaging care for depression and anxiety in primary care that uses a patient-completed Decision Support Tool (DST).

Methods

The economic evaluation was conducted alongside a parallel, stratified individually randomised controlled trial (RCT) comparing prognosis-matched care to usual care at six- and 12-month follow-up. Twenty-three general practices in three Australian Primary Health Networks recruited 1,671 adults (aged 18 – 75 years), predicted by the DST to have minimal/mild or severe depressive or anxiety symptoms in three months. The minimal/mild prognostic group was referred to low intensity services. Participants screened in the severe prognostic group were offered high intensity care navigation, a model of care coordination. The outcome measures included in this evaluation were health sector costs (including development and delivery of the DST, care navigation and other healthcare services used) and societal costs (health sector costs plus lost productivity), psychological distress [Kessler Psychological Distress Scale (K10)] and quality adjusted life years (QALYs) derived from the EuroQol 5-dimension quality of life questionnaire with Australian general population preference weights applied. Costs were valued in 2018–19 Australian dollars (A$).

Results

Across all participants, the health sector incremental cost-effectiveness ratio (ICER) of Link-me per point decrease in K10 at six months was estimated at $1,082 (95% CI $391 to $6,204) increasing to $2,371 (95% CI $191 to Dominated) at 12 months. From a societal perspective, the ICER was estimated at $1,257/K10 point decrease (95% CI Dominant to Dominated) at six months, decreasing to $1,217 (95% CI Dominant to Dominated) at 12 months. No significant differences in QALYs were detected between trial arms and the intervention was dominated (less effective, more costly) based on the cost/QALY ICER.

Conclusions

The Link-me approach to stepped mental health care would not be considered cost-effective utilising a cost/QALY outcome metric commonly adopted by health technology assessment agencies. Rather, Link-me showed a trend toward cost-effectiveness by providing improvement in mental health symptoms, measured by the K10, at an additional cost.

Trial registration

Australian and New Zealand Clinical Trials Registry, ANZCTRN 12617001333303.

Clinical efficacy of a Decision Support Tool (Link-me) to guide intensity of mental health care in primary practice: a pragmatic stratified randomised controlled trial

BACKGROUND

The volume and heterogeneity of mental health problems that primary care patients present with is a substantial challenge for health systems, and both undertreatment and overtreatment are common. We developed Link-me, a patient-completed Decision Support Tool, to predict severity of depression or anxiety, identify priorities, and recommend interventions. In this study, we aimed to examine if Link-me reduces psychological distress among individuals predicted to have minimal/mild or severe symptoms of anxiety or depression.

METHODS

In this pragmatic stratified randomised controlled trial, adults aged 18-75 years reporting depressive or anxiety symptoms or use of mental health medication were recruited from 23 general practices in Australia. Participants completed the Decision Support Tool and were classified into three prognostic groups (minimal/mild, moderate, severe), and those in the minimal/mild and severe groups were eligible for inclusion. Participants were individually and randomly assigned (1:1) by a computer-generated allocation sequence to receive either prognosis-matched care (intervention group) or usual care plus attention control (control group). Participants were not blinded but intervention providers were only notified of those allocated to the intervention group. Outcome assessment was blinded. The primary outcome was the difference in the change in scores between the intervention and control group, and within prognostic groups, on the 10-item Kessler Psychological Distress Scale at 6 months post randomisation. The trial was registered on the Australian and New Zealand Clinical Trials Registry, ACTRN12617001333303.

OUTCOMES

Between Nov 21, 2017, and Oct 31, 2018, 24 616 patients were invited to complete the eligibility screening survey. 1671 of these patients were included and randomly assigned to either the intervention group (n=834) or the control group (n=837). Prognosis-matched care was associated with greater reductions in psychological distress than usual care plus attention control at 6 months (p=0·03), with a standardised mean difference (SMD) of -0·09 (95% CI -0·17 to -0·01). This reduction was also seen in the severe prognostic group (p=0·003), with a SMD of -0·26 (-0·43 to -0·09), but not in the minimal/mild group (p=0·73), with a SMD of 0·04 (-0·17 to 0·24). In the complier average causal effect analysis in the severe prognostic group, differences were larger among those who received some or all aspects of the intervention (SMD range -0·58 to -1·15). No serious adverse effects were recorded.

INTERPRETATION

Prognosis-based matching of interventions reduces psychological distress in patients with anxiety or depressive symptoms, particularly in those with severe symptoms, and is associated with better outcomes when patients access the recommended treatment. Optimisation of the Link-me approach and implementation into routine practice could help reduce the burden of disease associated with common mental health conditions such as anxiety and depression.

FUNDING

Australian Government Department of Health.

Using Digital Technologies to Facilitate Care Coordination Between Youth Mental Health Services: A Guide for Implementation

Enhanced care coordination is essential to improving access to and navigation between youth mental health services. By facilitating better communication and coordination within and between youth mental health services, the goal is to guide young people quickly to the level of care they need and reduce instances of those receiving inappropriate care (too much or too little), or no care at all. Yet, it is often unclear how this goal can be achieved in a scalable way in local regions. We recommend using technology-enabled care coordination to facilitate streamlined transitions for young people across primary, secondary, more specialised or hospital-based care. First, we describe how technology-enabled care coordination could be achieved through two fundamental shifts in current service provisions; a model of care which puts the person at the centre of their care; and a technology infrastructure that facilitates this model. Second, we detail how dynamic simulation modelling can be used to rapidly test the operational features of implementation and the likely impacts of technology-enabled care coordination in a local service environment. Combined with traditional implementation research, dynamic simulation modelling can facilitate the transformation of real-world services. This work demonstrates the benefits of creating a smart health service infrastructure with embedded dynamic simulation modelling to improve operational efficiency and clinical outcomes through participatory and data driven health service planning.