Show pageBacklinksCite current pageExport to PDFBack to top This page is read only. You can view the source, but not change it. Ask your administrator if you think this is wrong. ====== Standardization ====== {{rss>https://pubmed.ncbi.nlm.nih.gov/rss/search/1jiITQk_mbfdbTsY_DwJAzbRdpJEwVJLQpS10Yz7rkWyy_1ku5/?limit=15&utm_campaign=pubmed-2&fc=20250317185710}} Standardization is the process of implementing and developing technical [[standard]]s. Standardization can help to maximize compatibility, interoperability, safety, repeatability, or quality. It can also facilitate the commoditization of formerly custom processes. In social sciences, including economics, the idea of standardization is close to the solution for a coordination problem, a situation in which all parties can realize mutual gains, but only by making mutually consistent decisions. This view includes the case of "spontaneous standardization processes", to produce de facto standards. ---- In healthcare and health informatics, standardisation means: Using common formats, terminologies, and data models (e.g., SNOMED CT, HL7 FHIR) Ensuring that data collected in different places or systems can be understood, shared, and reused without ambiguity Improving efficiency, safety, and quality of care Example: In [[Electronic Health Record]] systems, standardisation allows a blood pressure reading entered in one hospital to be automatically interpreted correctly by another system, thanks to shared coding like LOINC. ====== Electronic Health Record (EHR) Standardization ====== **Definition:** EHR standardization refers to the process of applying common structures, terminologies, and communication protocols to electronic health records to ensure consistency and interoperability across healthcare systems. **Key Elements:** * Terminologies: [[SNOMED CT]], [[LOINC]], [[ICD-10]] * Data exchange formats: [[HL7 FHIR]], CDA * Data models: [[OMOP]], [[openEHR]] * Structured documentation: templates for notes, diagnostics, procedures **Goals:** - Facilitate sharing of health data across institutions - Improve clinical decision-making and patient safety - Enable research, audits, and public health initiatives - Support AI and analytics by providing high-quality, structured data standardization.txt Last modified: 2025/05/18 15:06by administrador