The effect of telemedicine in critically ill patients: systematic review and meta-analysis

Abstract

Introduction: Telemedicine extends intensivists' reach to critically ill patients cared for by other physicians. Our objective was to evaluate the impact of telemedicine on patients' outcomes.

Methods: We searched electronic databases through April 2012, bibliographies of included trials, and indexes and conference proceedings in two journals (2001 to 2012). We selected controlled trials or observational studies of critically ill adults or children, examining the effects of telemedicine on mortality. Two authors independently selected studies and extracted data on outcomes (mortality and length of stay in the intensive care unit (ICU) and hospital) and methodologic quality. We used random-effects meta-analytic models unadjusted for case mix or cluster effects and quantified between-study heterogeneity by using I² (the percentage of total variability across studies attributable to heterogeneity rather than to chance).

Results: Of 865 citations, 11 observational studies met selection criteria. Overall quality was moderate (mean score on Newcastle-Ottawa scale, 5.1/9; range, 3 to 9). Meta-analyses showed that telemedicine, compared with standard care, is associated with lower ICU mortality (risk ratio (RR) 0.79; 95% confidence interval (CI), 0.65 to 0.96; nine studies, n = 23,526; I2 = 70%) and hospital mortality (RR, 0.83; 95% CI, 0.73 to 0.94; nine studies, n = 47,943; I² = 72%). Interventions with continuous patient-data monitoring, with or without alerts, reduced ICU mortality (RR, 0.78; 95% CI, 0.64 to 0.95; six studies, n = 21,384; I² = 74%) versus those with remote intensivist consultation only (RR, 0.64; 95% CI, 0.20 to 2.07; three studies, n = 2,142; I2 = 71%), but effects were statistically similar (interaction P = 0.74). Effects were also similar in higher (RR, 0.83; 95% CI, 0.68 to 1.02) versus lower (RR, 0.69; 95% CI, 0.40 to 1.19; interaction, P = 0.53) quality studies. Reductions in ICU and hospital length of stay were statistically significant (weighted mean difference (telemedicine-control), -0.62 days; 95% CI, -1.21 to -0.04 days and -1.26 days; 95% CI, -2.49 to -0.03 days, respectively; I2 > 90% for both).

Conclusions: Telemedicine was associated with lower ICU and hospital mortality among critically ill patients, although effects varied among studies and may be overestimated in nonrandomized designs. The optimal telemedicine technology configuration and dose tailored to ICU organization and case mix remain unclear.

Figure 1

Flowchart of study selection for the systematic review.

Figure 2

Effect of telemedicine on ICU mortality (upper panel) and hospital mortality (lower panel). The pooled risk ratio with 95% confidence interval (CI) was calculated by using a random-effects model. Weight refers to the contribution of each study to the overall estimate of treatment effect.

Figure 3

Effect of telemedicine on ICU length of stay (upper panel) and hospital length of stay (lower panel) in days. The pooled weighted mean difference with 95% confidence interval (CI) was calculated by using a random-effects model. Weight refers to the contribution of each study to the overall estimate of treatment effect.

Figure 4

Subgroup analyses of effect of telemedicine on ICU mortality based on study quality (higher quality in upper panel and lower quality in lower panel). Pooled risk ratios were calculated by using a random-effects model. Weight refers to the contribution of each study to each subgroup's estimate of treatment effect. The interaction P value for the difference between risk ratios is 0.53.

Figure 5

Subgroup analyses of effect of telemedicine on ICU mortality based on intensity of the intervention (continuous patient-data monitoring, with or without computer-generated alerts (active or high-intensity passive systems), in upper panel, and neither present (low-intensity passive systems) in lower panel). Pooled risk ratios were calculated by using a random-effects model. Weight refers to the contribution of each study to each subgroup's estimate of treatment effect. The interaction p value for the difference between risk ratios is 0.74.