My previous blogs based on my local experiences outside KI education. This time I’m bringing this article which I am feeling is the right time for both for upcoming health informatics students and for students who are looking for their Master thesis.
Informatics within the field of public health is a developing, evolving, and growing industry. According to HealthcareIT news, “the demand for Health informatics job market to grow at twice the rate of employment, but there are not enough qualified workers within this field.”
What is Health informatics?
Health informatics is the study of resources and methods for the management of health information. Health informatics involves systems such as electronic health records (EHR), health information exchange (HIE) standards and portable medical data collection devices.
Who will be interested?
- Nurses, Doctors and other healthcare provider
- Information Technologies
- Software developers
- Research lab assistants
What are the current research topics in Health Informatics
Electronic Health Records
Electronic health records, personal health records, and electronic medical records are interrelated areas of research investigated by our faculty. The research spans usability, interoperability, and integration of systems. Specific areas include decision support and intelligent capabilities of systems.
Health Data Analytics
Data analytics is the future of health and health care. It is no longer possible for people to navigate the complexities of health and medical disciplines without data analytics. Our faculty interests span a wide range of topics including comparative effectiveness, causality, knowledge discovery from data, predictive modeling, and anomaly detection. In data analytic efforts, we closely collaborate with experts in health services research, health administration, and health policy.
Data Mining and Machine Learning in Health Care
Data mining is a process of discovering new interesting patterns and regularities in data. Data mining methods applied in health care help clinicians and administrators extend their knowledge and help decision making by sifting through large amounts of data. Machine learning is a related field that concerns creating learning capabilities in computers. By observing the environment, interacting with users, or analyzing past data, computer algorithms learn how to solve problems and aid decision makers by providing decision support.
Ontology-based Data Integration
The ability to resolve semantic conflicts between heterogeneous information systems is one of the major challenges in the data integration field. Our research aims to achieve semantic interoperability across systems using an ontology-based system.
Consumer Health Informatics and patient Generated Data
The focus of this project is to understand consumer health communities, as well patient generated data and its uses within and outside healthcare systems. This topic spans a wide range of issues from policy, privacy and security, through potential uses and requirements, to technical issues in Big Data analytics.
Technology adoption and Health care Management
Research in this area seeks to understand the factors that affect technology adoption. Here the focus is mainly on health care organizations and the users, mostly health care professionals, of new health information technologies. The research draws from different fields such as communication science and management to understand factors that impede technology adoption in the health care industry.
Text Mining and natural Language Processing in Health Care
Clinical notes hold valuable information about patients and healthcare. Health professionals prefer clinical notes because of tradition and the advantages of a natural language including convenience, flexibility, and richness. Those properties of natural language create challenges for machines that process clinical notes. The research in clinical text mining focuses on: 1) the problems of semantic similarity for information extraction, and 2) translating narrative texts into structured format using standard vocabularies including HL7 CDA.
Research focuses on decision models under mixed uncertainty: risk, ambiguity and ignorance. For many complex decisions especially in health care, the quantification of relevant uncertainty in terms of probability is unreliable and even impossible. In such situations, recommendations from traditional decision models using probability are not accepted by decision makers. Our research aims to develop a unified model for decisions under uncertainty that can deal not only with risk but also with ambiguity and ignorance that decision makers encounter in their practice.
Stay tuned more information for the health informatics program in KI and the opportunities.