Telehealth Strategies to Support Referral Management to Secondary Care in Brazil: A Cost-Effectiveness Analysis

Abstract

Objectives

This study aimed to assess the cost-effectiveness of a remotely operated referral management system (RORMS) compared with a conventional referral management system (CRMS) in Brazil.

Methods

This is a model-based cost-effectiveness analysis under the perspective of the Unified Healthcare System (Sistema Único de Saúde [SUS]) in Brazil. A Markov microsimulation model was developed to compare costs and referral outcomes of the RORMS and the CRMS. Model consisted of 4 states representative of sequential stepwise assessments of referral suitability, 3 states representative of referral outcomes, and 1 exit model state. Target population represented cases being referred from primary healthcare units to specialized care in SUS. Model inputs related to costs and effectiveness in the RORMS arm were obtained from the data set of a RORMS between July and December 2019. Model inputs for the CRMS model arm were obtained from administrative data sets of 2 Brazilian localities for the year 2019. Relative effect size of RORMS in comparison with CRMS in SUS was obtained from published studies. Effectiveness outcome was unnecessary referrals averted. The incremental cost-effectiveness ratio was calculated for the base case. Probabilistic sensitivity analysis was conducted.

Results

In the base-case analyses, RORMS dominated CRMS, with expected cost-savings from $50.42 to $80.62 per unnecessary referral averted. RORMS was the dominant strategy in 83.7% of 100 000 simulations in the probabilistic sensitivity analysis. In 16.2% of simulations, incremental cost-effectiveness ratio was between $0 and $222 per unnecessary referral averted.

Conclusions

Model-based simulations indicate that the RORMS is likely to be cost saving in comparison with the CRMS.

Authors

Daniela V. Pachito André Luis F. de Azeredo-da-Silva Patrícia Roberta B.P. de Oliveira Ângela Maria Bagattini Josué Basso Luana G. Gehres Érica de B. Mallmann Átila S. Rodrigues Rachel Riera Sabrina D. Gadenz

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