Detecção das desordens potencialmente malignas de lábio e cavidade oral (DBPM): modelo de informação para a atenção primária em saúde

Abstract

For the effectiveness of early detection of oral cancer (OC), greater investments in public policies to strengthen Primary Health Care (PHC) actions are necessary, including the use of information and communication technologies to support clinical decision-making. Information models (IM) can guide clinical assessment in health as well as the development of interoperable electronic health information systems, particularly in PHC, where they can contribute to more accurate referrals to other levels of health care. Objective: To propose an IM for the detection of potentially malignant oral disorders (PMOD) in PHC. Method: This is a qualitative study with a descriptive approach, using action research, associated with the Delphi Method. The study was organized into three stages: 1) identification of the minimum set of clinical criteria (MSCC) to support the differential diagnosis of PMOD; 2) validation of the MSCC and establishment of a Delphi Panel; 3) structuring of the IM. Results: A preliminary version of the CMCC was prepared by a specialist, submitted to pre-testing in the format of an electronic questionnaire on the REDCapⓇ Platform, generating a final version that was submitted to validation by the Delphi method, by 21 panelists (specialist dentists with experience in the diagnosis of PMDD). A total of 90% (n = 19/21) of the panelists responded to the first round and 86% (n = 18/21) responded to the second. All changes suggested in each round were analyzed, considering the Content Validity Index (CVI) ≥ 0.83. The Delphi Panel was completed in two rounds. The validated version of the CMCC resulted in a set of 30 questions, distributed in seven sections. From this CMCC, an MI was proposed in the format of a table, containing 80 data elements in the rows, structured in seven sections: anamnesis/sociodemographic information (n=9); anamnesis/history and lifestyle habits (n=16); clinical presentation of the lesion (n=18); clinical diagnosis (n=2); histopathological diagnosis (n=2); clinical conduct (n=1) and differential diagnosis (n=34). In the columns, the elements are arranged according to level, occurrence, description, type of data and standard codes. Conclusions: The MSCC developed and validated for the differential clinical diagnosis of PMOD represents an important step in obtaining the fundamental data elements for the definition of the IM and for the construction of an interoperable Computational Model for PHC. This could contribute to advances in the digital transformation of health within the context of oral health, with a potential impact on the quality of health care.