Electronic health records (EHRs) hold promise to improve productivity, quality, and outcomes; however, using EHRs can be cumbersome, disruptive to workflow, and off-putting to patients and clinicians. One proposed solution to this problem is the use of medical scribes to generate data for research and real-time analysis.
Aims:
The deployment and adoption of electronic medical records in order to improve the quality and delivery of patient care has recently been receiving growing attention. Due to the complexity and time constraints associated with documenting a clinical encounter, the majority of patient information is still stored in free-text documents as opposed to discrete coded data elements. This has created a paradox for many institutions because, while the data are in electronic format, access to specific information of interest is not readily available through automated means.
Data mining techniques have sought to address components of this problem, but the methodology usually involves experts with advanced technical skills. Additionally, humans are still often needed to review a document to ensure accurate data abstraction. Search engines have become highly popular among regular computer users and their utility for identifying appropriate medical literature has been increasingly discussed. While some attempts have been made to apply the power of search engines to the electronic medical record, the widespread adoption of such technology has not yet been realized. Inefficient navigation in electronic health records has been shown to increase users’ cognitive load, which may increase potential for errors, reduce efficiency, and increase fatigue. However, navigation has received insufficient recognition and attention in the electronic health record (EHR) literature as an independent construct and contributor to overall usability.
Methods and applications:
There is growing interest in using data captured in electronic health records (EHRs) for patient registries. Both EHRs and patient registries capture and use patient-level clinical information, but conceptually, they are designed for different purposes. A patient registry is defined as “an organized system that uses observational study methods to collect uniform data (clinical and other) to evaluate specified outcomes for a population defined by a particular disease, condition, or exposure and that serves one or more predetermined scientific, clinical, or policy purposes.”
An EHR is an electronic system used and maintained by healthcare systems to collect and store patients’ medical information. EHRs are used across clinical care and healthcare administration to capture a variety of medical information from individual patients over time, as well as to manage clinical workflows. EHRs contain different types of patient-level variables, such as demographics, diagnoses, problem lists, medications, vital signs, and laboratory data. According to the National Academies of Medicine, an EHR has multiple core functionalities, including the capture of health information, orders and results management, clinical decision support, health information exchange, electronic communication, patient support, administrative processes, and population health reporting.
In summary, registries are patient-centered, purpose-driven, and designed to derive information on defined exposures and health outcome. In contrast, EHRs are visit-centered and transactional. Despite these differences, EHRs capture a wealth of data that is relevant to patient registries. EHRs also may assist in certain functions that a patient registry requires (e.g., data collection, data cleaning, data storage), and a registry may augment the value of the information collected in an EHR (e.g., comparative safety, effectiveness and value, population management, quality reporting).
This project application will enable clinicians and researchers to:
Artificial intelligence to extract information from electronic health records
A review has been written by OSRC team and negotiations with partners is ongoing to establish partnership and launch funding.
Currently, the most important data sources for medical big data include administrative databases, online clinical trials registries, electronic medical records (EMR), medical images, and omics data but EMR still unique data source.
Artificial intelligence to extract information from blood investigations.
This project is in the design phase.
Contact us if you have some knowledge/experience about the subject and wish to get involved.
Artificial intelligence to decipher chronic abdominal pain through investigating patterns of contact to health care sectors (primary, secondary and tertiary) and long follow up.
This project is in the design phase.
Contact us if you have some knowledge/experience about the subject and wish to get involved.
Natural language processing (NLP) is a subfield that emphasizes building a computer’s ability to understand human language and is crucial for large scale analyses of content such as electronic medical record (EMR) data, especially physicians’ narrative documentation. To achieve human-level understanding of language, successful NLP systems must expand beyond simple word recognition to incorporate semantics and syntax into their analyses.
Rather than relying on codified classifications such as ICD codes, NLP enables machines to infer meaning and sentiment from unstructured data (e.g. prose written in the history of present illness or in a physician’s assessment and plan). NLP allows clinicians to write more naturally rather than having to input specific text sequences or select from menus to allow a computer to recognize the data. NLP has been utilized for large scale database analysis of the EMR to detect adverse events and postoperative complications from physician documentation, and many EMR systems now incorporate NLP – for example, to achieve automated claims coding – into their underlying software architecture to improve workflow or billing.
Natural language processing (NLP) can be used to improve interaction between the clinicians and their patients.
How?
In LMICs context, mobile phones can generate text messages, to be analysed by NLP and sored out to help clinicians follow up their patients even in remote locations.
Science fiction? No. Join our research team and hear more about this and other projects.