Community care navigation intervention for people who are at risk of unplanned hospital presentations.

BACKGROUND

Care navigation is a type of care co-ordination used to manage people with chronic conditions with the goal of reducing unplanned hospital presentations and improving patient care and outcomes. Care navigation involves individual case management by a trained professional who is not involved in the person's direct care. Care navigation has been used in various healthcare settings, adopted as a single or multi-component intervention by different health services. However, little is known about its effect on unplanned hospital presentations and patient-reported outcome measures (PROMs).

OBJECTIVES

Primary: to assess the effects of care navigation, delivered in the community, on hospital presentations and patient-reported outcome measures in people at risk of unplanned hospital presentations. Secondary: to assess whether the effects of community care navigation differ according to the type of clinician delivering the intervention and the populations receiving the intervention.

SEARCH METHODS

We used CENTRAL, MEDLINE, four other databases and two clinical trial registers, together with reference checking, citation searching and contact with study authors to identify the studies included in this review. The latest search date was October 2024.

SELECTION CRITERIA

We included randomised controlled trials (RCTs) and cluster-RCTs that recruited people who were at risk of hospital admission and utilised care navigation delivered in the community as an intervention. The comparison was usual care.

DATA COLLECTION AND ANALYSIS

Two review authors independently extracted data from the included studies, evaluated study quality, and judged the certainty of the evidence using the GRADE approach. We performed a meta-analysis of the results where possible, and a narrative synthesis of the remainder of the results. We present results in a summary of findings table, showing effect sizes for all outcomes.

MAIN RESULTS

We included 19 studies (36,745 participants), all conducted in high-income countries. Eighteen were RCTs. Of these, four studies were pragmatic non-blinded RCTs that randomised participants prior to obtaining consent. One study was a cluster-RCT. Follow-up ranged from one to 24 months. All studies included various healthcare professionals as care navigators: registered nurses in seven studies, social workers in five, and community health workers in one. In six studies, a multidisciplinary team delivered the care navigation intervention. The studies investigated the effects of community care navigation interventions in a variety of groups, including older people, those with chronic diseases (such as heart failure, chronic obstructive pulmonary disease, diabetes, mental health problems, cancer, alcohol and other drug use), people with complex psychosocial needs, high readmission risk and frequent emergency department users. All studies compared the intervention to usual care. Across the five risk of bias domains and where outcomes were reported, we deemed three of 42 study results to have 'some concerns' in at least one domain. Overall risk of bias across all domains ranged from 'low risk' in results reported in two studies to 'some concerns' or 'high risk' of bias across all other results. Overall, when inconsistency was also considered, we judged the certainty of the evidence to be very low or moderate. There may be little to no difference in unplanned hospital admission rates within one month (30 days) between community care navigation and usual care, but the evidence is very uncertain (risk ratio (RR) 0.95, 95% confidence interval (CI) 0.79 to 1.14; P = 0.59; 5 studies, 3488 participants; very low-certainty evidence). However, community care navigation likely results in a reduction in unplanned hospital admission rates within 12 months (365 days) compared to usual care (RR 0.87, 95% CI 0.77 to 0.97; P = 0.01; 3 studies, 795 participants; moderate-certainty evidence). Community care navigation probably results in little to no difference in emergency department presentation rates within one month (30 days) compared to usual care (RR 1.09, 95% CI 0.92 to 1.29; P = 0.30; 3 studies, 4087 participants; moderate-certainty evidence) and in emergency department presentation rates within 12 months (365 days) (RR 0.99, 95% CI 0.91 to 1.08; P = 0.88; 2 studies, 873 participants; moderate-certainty evidence). None of the studies measured hospital presentations within three months (90 days). Eight studies reported different types of PROMs, collecting results at different time points. We narratively synthesised these results in the main text of the review, but could not determine the impact of community care navigation on PROMs due to the very low-certainty evidence. Community care navigation increases the proportion of patients having hospital outpatient appointments within one month (30 days) (RR 1.07, 95% CI 1.01 to 1.13; P = 0.02; 2 studies, 2178 participants; high-certainty evidence) compared to usual care, which may indicate that the intervention shifts patient care towards community services. We could not determine the impact of community care navigation on general practitioner (GP) visits, treatment satisfaction and quality of care due to the low- or very low-certainty evidence. No included study measured adverse events.

AUTHORS' CONCLUSIONS: Community care navigation for people at risk of unplanned hospital presentations is likely to reduce hospital admission rates within 12 months (365 days) and increase outpatient appointments within one month (30 days) compared to usual care, with moderate to high certainty of evidence. Results showed little to no effect on hospital admissions within one month (30 days) or on emergency department presentation rates compared to usual care. The evidence is very uncertain about the effect of community care navigation on health-related quality of life and quality of care. More robust studies are required to produce greater evidence certainty. Study risk of bias can be improved if future studies use traditional RCT designs and implement strategies to reduce dropout rates and reduce missing follow-up data.