Integrated Medical Records (IMR) is a middleware, being developed at IBM Haifa, that can be used to integrate and correlate medical records from diverse sources and transform data into knowledge. Today's medical arena is faced with the challenge of providing patients with improved care, reduced costs, and more efficient use of medical records. The only way to meet this challenge is to create a technology that allows patient information from different sources to be conveniently accessed and shared by different organizations, while maintaining patient privacy and information security. IMR is part of the SHAMAN system which is developed by the Haifa and Watson research labs.
Electronic health records (EHR) are defined as digitally stored healthcare information about an individual's lifetime, with the purpose of supporting patient care, education, and research. These records include data on observations, laboratory tests, diagnostic imaging reports, treatments, therapies, drugs administered, patient identification information, legal permissions, and so on. The IMR middleware transforms medical records from human-readable to machine-processable and facilitates the extraction of electronic health records, or parts of it according to some classification.
The IMR project leverages several technologies that exist within the Services and CRM Technology department and the Knowledge Management department in Haifa. A powerful engine combines several technologies and enables:
- Correlation of information from different sources within the hospital or from other healthcare centers.
- Correlation of anonymous patient information with articles, research papers, and journals that are publicly available.
- Correlation of information based on classifications, such as demographic information (genetics, age, sex, etc.), allergies, diseases, findings, and medical history.
- Query of the data repository for information extraction, such as "find all patients whose age is between 30 - 40, female, had over five pregnancies, and have cancer in their family history"
- Correlation of information from drug catalogs with information about allergies, demographic information, and physical characteristics such as weight.
- Hide the patient's identification to provide anonymous patient records for research and education.