Who Answers the Call? Massive Trial Reveals Why the Sickest Patients Slip Through Care Coordination’s Fingers

Reaching the patients who need the most help is proving to be one of the hardest problems in American healthcare, and a sweeping new analysis has now quantified just how steep the challenge is. In a study published in BMC Health Services Research, researchers led by Anusha Fatehpuria and Tannaz Moin of the David Geffen School of Medicine at UCLA examined a national, telephone-based care coordination intervention aimed at so-called high-need, high-cost beneficiaries — the small fraction of insured Americans who consume an outsized share of healthcare resources while juggling multiple chronic illnesses, behavioral health conditions, and unmet social needs. The findings reveal a sobering funnel: of 46,483 eligible beneficiaries randomized to the intervention, only 59 percent could ever be reached by phone, and fewer than half of those successfully contacted went on to complete even a single care coordination activity.

The study drew on administrative claims data spanning 2014 to 2022 from a large commercial insurer, giving the team an unusually rich window into who these patients are and how they interact with the health system. To qualify as high-need, high-cost, beneficiaries had to sit in the top 5 percent of healthcare spending in the prior year and be projected to remain there in the following year — a definition that captures people with complex, expensive, and often unstable medical lives. The intervention itself was a randomized controlled trial registered as NCT04415515, in which registered nurses attempted telephone outreach to coordinate care for these vulnerable patients. Because the program was national in scope, the researchers could probe whether geography, insurance plan design, and clinical history shaped the odds of contact and engagement.

The statistical machinery behind the analysis was deliberately twofold. The team used multivariate linear regression alongside lasso regression, a machine-learning technique that shrinks the coefficients of weak predictors toward zero and thereby isolates the variables that genuinely matter. Lasso regression also allowed the investigators to test two-way combinations of variables — interactions such as whether a particular comorbidity mattered more in one age group or region than another — which traditional models often miss. The predictors fed into the models included age, sex, geographic region, plan type, duration of enrollment in the insurance plan, a catalogue of comorbid conditions, and whether the patient had been hospitalized or treated in an emergency department in the three months before randomization.

One of the most counterintuitive findings concerns age. Younger beneficiaries were harder to contact and harder to engage than older ones, a pattern that inverts the common assumption that digital-native, working-age adults would be the easiest to pull into a phone-based program. The authors suggest that younger high-cost patients may be balancing employment, caregiving, and other demands that make unsolicited phone calls easy to ignore, or may simply be less receptive to a healthcare system reaching out by telephone. Whatever the mechanism, the implication is clear: a one-size-fits-all outreach script will leave younger complex patients behind.

Recent hospitalization told an equally striking story. Beneficiaries who had an inpatient admission within three months before randomization were more difficult to contact and engage, despite being, on paper, among the patients with the most obvious need for coordinated follow-up care. Hospital discharge is a chaotic moment — recovery at home, new medication regimens, follow-up appointments, and often lingering symptoms — and the study suggests that a nurse’s phone call can get lost in that turbulence. Longer duration of plan enrollment also predicted worse contact and engagement, hinting that patients with long, complicated insurance histories may have developed skepticism toward outreach efforts or accumulated administrative barriers that newer enrollees lack.

Comorbidity profiles painted a nuanced picture of who picks up the phone. Arthritis, HIV, and osteoporosis were each associated with successful contact, while congestive heart failure and substance abuse predicted less successful contact, as did living in the northeastern United States. When it came to actual engagement — completing at least one care coordination activity — arthritis, chronic obstructive pulmonary disease, and liver disease were associated with success. The divergence between the contact and engagement predictors is itself informative: the conditions that help a nurse reach a patient are not necessarily the ones that help that patient commit to the program. Regional variation also matters, and the authors note that the northeast’s lower contact rates may reflect anything from local phone habits to differences in healthcare infrastructure across states.

The lasso regression added a layer of granularity that the headline numbers conceal. Certain two-way variable-level combinations — specific pairings of characteristics such as a comorbidity crossed with a demographic or plan feature — emerged as predictors of engagement, suggesting that the pathways into care coordination are not uniform but instead depend on how a patient’s attributes interact. For program designers, this is both a warning and an opportunity. It warns that aggregate statistics can mask subgroups of patients who are systematically missed, and it offers a roadmap: predictive models trained on claims data could flag, in advance, which beneficiaries are least likely to answer or engage, allowing outreach teams to tailor their approach before the first call is ever placed.

The scale of the gap is worth dwelling on. If 41 percent of the highest-cost, highest-need patients in a national program cannot be reached at all, and nearly half of those reached never complete a single coordination activity, then the effective reach of the intervention shrinks to roughly 28 percent of the intended population. Every unreached patient represents missed opportunities: unmanaged medication conflicts, duplicated tests, avoidable emergency department visits, and preventable readmissions. Care coordination programs have shown promise in reducing costs and improving outcomes for complex populations, but this study demonstrates that their success is gated at the very first step — the human act of answering a phone call and agreeing to a conversation.

The authors conclude that recruitment and engagement of high-need, high-cost patients must be tailored rather than uniform, and their predictor catalogue offers a concrete starting point for that tailoring. Outreach strategies might need to differ for younger patients, for those recently discharged from the hospital, for people with substance abuse or heart failure, and for residents of particular regions — perhaps through multiple contact channels, different timing, or trusted messengers beyond the standard nurse call. The study was supported by a cooperative agreement from the Centers for Disease Control and Prevention, and while the findings and conclusions are those of the authors, their implications ripple across insurers, policymakers, and health systems nationwide. As healthcare continues to pour resources into managing its most expensive patients, this research delivers a blunt message: before any intervention can work, someone has to pick up the phone — and far too many of the people who need it most never do.

Subject of Research: Predictors of patient contact and engagement in a national telephone-based care coordination intervention for high-need, high-cost health insurance beneficiaries

Article Title: Predictors of contact and engagement in a national care coordination intervention for high-need, high-cost beneficiaries

Article References: Fatehpuria, A., Harwood, J. M., Tseng, C.-H., Duru, O. K., Mangione, C. M., Onufrak, S. J., & Moin, T. (2026). Predictors of contact and engagement in a national care coordination intervention for high-need, high-cost beneficiaries. BMC Health Services Research. https://doi.org/10.1186/s12913-026-15662-0

Image Credits: AI Generated

DOI: 10.1186/s12913-026-15662-0

Keywords: care coordination, high-need high-cost patients, patient engagement, telephone intervention, health services research, predictive factors, randomized controlled trial, lasso regression, chronic disease, health insurance, hospital readmission, CDC