| Educational guide School of Engineering |
english |
| Biomedical Data Science (2022) |
 Subjects |
| BIOMEDICAL DATA CHALLENGES |
| Contents |
| IDENTIFYING DATA | 2023_24 |
| Subject | BIOMEDICAL DATA CHALLENGES | Code | 17705106 | |||||
| Study programme |
|
Cycle | 2nd | |||||
| Descriptors | Credits | Type | Year | Period | ||||
| 3 | Compulsory | First | 1Q |
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| Competences | Learning outcomes | Contents |
| Planning | Methodologies | Personalized attention |
| Assessment | Sources of information | Recommendations |
| Topic | Sub-topic |
| Biomedical data challenges to manage global health | 1.- Health concept. Health as a citizen right. Health as key factor in the economic development 2.- Sustainable development goals of the UN for 2030 World Health Organization (WHO) and the role of Health authorities in the member states. 3.- Data challenges for a global health scope. Morbidity and mortality of human population. Differences between developed and underdeveloped countries. Chronic conditions. From disease statistics towards Precision Medicine. 4.- The WHO-FIC (Family of International Classifications): its role and ongoing activities on coding and classification |
| Biomedical data challenges to manage healthcare organizations | 1.- Strategic challenges of collaboration in healthcare organizations and the role of biomedical science in their solving. - Challenges of collaboration at the level of organization. - Challenges of collaboration at the level of stakeholders: - Challenges of collaboration at macro level and macro factors, that affect healthcare organizations - Biomedical data technologies and methods that can help to deal with challenges of collaboration 2.- Business process reengineering in public and private healthcare organizations based on data analytics: problems and decisions - Business process reengineering in healthcare organizations. - Business Process Models and Notations and its implementation in healthcare organizations - Using LEAN-approach in business process reengineering in healthcare organizations. |
| Biomedical data challenges to share data and information among organizations | 1.- Standardization of medical language: problems in medical terminology, generalities of terminological resources in biomedicine 2.- SNOMED CT: logical model, concept model, reference sets, drug modelling 3.- International Classification of Diseases (ICD): ICD-10/ICD-10-CM, ICD-11, DRG, Minimum Basic Data Set 4.- LOINC: general overview, coding. 5.- Semantic interoperability: definition, difficulties with the sharing of health information, health information standards (EN/ISO 13606, OpenEHR, FHIR) |
| Biomedical data challenges on quality assessment of data | 1.- What is data quality?. You have data, but it's not usable yet. You transform data into information. Furthermore, you obtain knowledge. In addition, you make an informed decision. 2.- Characteristics of Data Quality. Accuracy. Completeness. Relevance Consistency. Accessibility. Timeliness 3.- Data Quality Analysis. Data Quality Management. Data Quality Metrics. Missing values. Inconsistent values. Wrong information due to data errors (manual/automated). Wrong metadata information 4. Data Quality Tools. Software tools for biomedical data quality analyzing. SPSS, R, R-studio, Statistics, MathCAD, Mathematics etc. Examples, datasets for studies. |
