A null meta-analytic result is neither proof of no effect nor license for the field to continue unchanged. Both interpretations remain plausible: that DHIs are conceptually sound but poorly executed and that they are genuinely ineffective for this population. The available evidence cannot adjudicate between them. What it does allow is a clearer diagnosis of why the evidence remains uninformative.

The 95% prediction interval for anxiety (−1.46 to 0.79) is the most consequential figure in the paper. Its upper boundary indicates the possibility of clinically meaningful harm. However, Zhang et al [1] report that no adverse outcomes were prospectively monitored across any of the 17 included trials—a striking omission given that all intervened in a population already at elevated risk for PTSD, depression, and complicated grief. The same adverse-event reporting standards applied to pharmaceutical RCTs should apply here. An undetected signal cannot be assumed benign; it may be neutral, heterogeneous across subgroups, or—in a subset of vulnerable families—harmful.

The heterogeneity is not merely statistical. A 14-minute virtual reality exposure [2] is being pooled with a 5-week internet-based cognitive behavioral writing program [3]. These are fundamentally different interventions, illustrating the absence of a coherent intervention taxonomy. This is not a criticism of the meta-analysis itself, but a call for theory-driven subgroup classification before the next generation of trials is designed.

Taken together, these issues point to four structural barriers that currently prevent the field from generating conclusive evidence.

Future trials require interventions grounded in behavioral theory, delivered longitudinally, with pre-specified engagement thresholds, prospective adverse-event monitoring, and follow-up windows matched to the outcomes being targeted. Equity must be a design requirement, not an afterthought: older adults, non-English-speaking families, and low-income caregivers bear a disproportionate PICS-F burden yet face the greatest barriers to DHI access [7]. If equity considerations are not embedded in eligibility criteria and delivery design, effective interventions will reach only the most resourced families, deepening the disparities they might otherwise address.

Table 1 traces the evolution of DHI design toward a third generation defined by artificial intelligence (AI)–driven personalization, adaptive delivery, and bidirectional clinician–family communication. These tools represent a genuine conceptual advance over what has been tested—but not yet a proven one. The Society of Critical Care Medicine has articulated the case for family-centered ICU care [8]; building the evidence base to deliver it is the work that remains.

What Zhang et al actually demonstrate is not that DHIs for ICU families do not work. It is that we do not yet know whether they work, in whom, at what dose, or through what mechanism. Answering these questions will require a fundamentally different approach to trial design than the field has so far adopted.