Initiating a Standardized Regional Referral and Counter-Referral System in Guatemala: A Mixed-Methods Study
نویسندگان
چکیده
Background:Few data exist for referral processes in resource-limited settings. We utilized mixed-methods to evaluate the impact of a standardized algorithm and training module developed for locally identified needs in referral/counter-referral procedures between primary health centers (PHCs) and a Guatemalan referral hospital. Methods: PHC personnel and hospital physicians participated in surveys and focus groups pre-implementation and 3, 6, and 12 months post-implementation to evaluate providers' experience with the system. Referred patient records were reviewed to evaluate system effectiveness. Results: A total of 111 initial focus group participants included 96 (86.5%) from PHCs and 15 from the hospital. Of these participants, 53 PHC physicians and nurses and 15 hospital physicians initially completed written surveys. Convenience samples participated in follow-up. Eighteen focus groups achieved thematic saturation. Four themes emerged: effective communication; provision of timely, quality patient care with adequate resources; educational opportunities; and development of empowerment and relationships. Pre- and post-implementation surveys demonstrated significant improvement at the PHCs (P < .001) and the hospital (P = .02). Chart review included 435 referrals, 98 (22.5%) pre-implementation and 337 (77.5%) post-implementation. There was a trend toward an increased percentage of appropriately referred patients requiring medical intervention (30% vs 40%, P = .08) and of patients requiring intervention who received it prior to transport (55% vs 73%, P = .06). Conclusions: Standardizing a referral/counter-referral system improved communication, education, and trust across different levels of pediatric health care delivery. This model may be used for extension throughout Guatemala or be modified for use in other countries. Mixed-methods research design can evaluate complex systems in resource-limited settings.
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