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[Awareness and use of mechanism for clinical coordination between levels in two health care networks in Pernambuco State, Brasil].

Cecylia Roberta Ferreira de OliveiraIsabella Chagas SamicoMarina Ferreira de Medeiros MendesIngrid VargasMaría-Luisa Vázquez
Published in: Cadernos de saude publica (2020)
This article assesses awareness and use of mechanisms for clinical coordination between service levels in two health care networks in the Pernambuco State, Brasil. It is a descriptive, cross-sectional, survey-based study. We interviewed 381 doctors from the public primary health care and specialized health care networks in the cities of Caruaru and Recife (Sanitary Districts III and VII). We used a structured questionnaire (COORDENA) in order to assess awareness, frequency and characteristics of the use of the following mechanisms: referral and reply letters, discharge summary, phone and notes (mutual adaptation mechanisms), Health Ministry protocols and joint clinical sessions (standardization mechanisms). We analyzed the data using simple frequencies, means and percentages. In general, primary health care doctors are more familiar with the mechanisms, and use them more frequently, than specialized health care doctors. In the comparison between networks, Recife had better results. Referral and reply letters were the most used (61.4%) and joint clinical sessions were the least used (8.6%), in addition to the existence of informal mechanisms (phone 58%, notes 56.6%, WhatsApp 2.6%). Underutilization of mechanisms, divergences in information sent and received between primary health care and specialized health care professionals and inadequate mechanism use suggest communication failures among professionals and service levels. The findings reveal a need for investments that enable awareness, communication and collaboration between professionals, contributing to a better coordination between the different services levels.
Keyphrases
  • healthcare
  • mental health
  • primary care
  • health information
  • public health
  • risk assessment
  • cross sectional
  • climate change
  • artificial intelligence