Abstract
Coding of medical data is a very important previous step for many activities in Health Care management, since it is the basis for several activities ranging from hospital reimbursement to clinical research. Literature identifies some issues related to coding clinical data, which derives in inadequate levels of quality leading to some in acceptable situations in health care organizations, impacting even to their sustainability. To alleviate these undesirable effects, we posse that the standardization of some best practices around clinical coding can lead to a better performance of the clinical coding process. One of the most relevant concerns in the process is the quality of the data used at the various stages of the data life cycle from its generation by clinicians up to the usage and exploitation of the data once coded. The main contribution of this work is twofold: on a hand to identify which are the best practices related to clinical coding, and on the other hand to investigate how these best practices can be enriched with some other related to data quality management and data governance. As a result, we produced CODE.CLINIC, a framework that can be used to support institutions to better code their medical data. This framework consists of two main components: a Process Reference Model (PRM) and a Process Assessment Model (PAM). In this paper we are going to first introduce the CODE.CLINIC PRM, which gather 16 process grouped in 4 blocks.
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Notes
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MAMDv3.0 can be downloaded for free from https://mamd.dqteam.es.
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Acknowledgements
We would like to first thank to Associação dos Médicos Auditores e Codificadores Clínicos for the valuable support during the AR Definition Cycle.
This investigation is partially supported by the Grant PID2020-112540RB-C42, AETHER-UCLM (A smart data holistic approach for context-aware data analytics), funded by MCIN/AEI/10.13039/501100011033/; The project “Clikode - Automatic Processing of Clinical Coding, (3I) Innovation, Research of AI models for hospital coding of Procedures and Diagnoses”, POCI-05-5762-FSE-000230, is financed by Portugal 2020, through the European Social Fund, within the scope of COMPETE 2020 (Operational Programme Competitiveness and Internationalization of Portugal 2020), and the project ADAGIO: Alarcos’ DAta Governance framework and systems generation (SBPLY/21/180501/000061), funded by the Consejería de Educación, Cultura y Deportes of the Junta de Comunidades de Castilla-La Mancha (Spain).
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Caballero, I., Souza, J., Lopes, F., Santos, J.V., Freitas, A. (2022). Towards a Process Reference Model for Clinical Coding. In: Vallecillo, A., Visser, J., Pérez-Castillo, R. (eds) Quality of Information and Communications Technology. QUATIC 2022. Communications in Computer and Information Science, vol 1621. Springer, Cham. https://doi.org/10.1007/978-3-031-14179-9_13
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