Aurora clinical emr is used by leading hospitals in America and Europe for technological efficiency, data analytics and medical necessity. It also provides a 360 degree view of patient data which includes phenotype and genotype data, test and lab results, family histories and pedigrees.
To make the data usable for research without identifiers or dates, Aurora analysts created queries to extract data, merge data across tables, exclude data from certain patient groups as required by the IRB (further description in the Legal Framework for Granting Access section), and de-identify.
Aurora Clinical EMR is a complete EHR (Electronic Health Records) software system that provides medical practices with technological efficiency, data analytics, and performance. It caters to larger hospitals and practices and provides a 360-degree view of patient data in various forms like phenotype, genotype, family history, tests, pedigrees, and text reports.
Its customization capabilities are unique and are designed to meet individual practice needs and standards of procedure. This makes it a preferred choice for hospitals in US and Europe.
Moreover, the Aurora software offers PM for practice management, billing systems, and a comprehensive data warehouse. It also supports mobile accessibility and a convenient patient portal.
The team at MIT worked with the data analysts at Aurora to create a mapping process that would ensure stable identifiers for the various entities within the research data (e.g., patients, providers, and encounters). The data analysts used a random number generating method to generate surrogate mapping IDs for every entity, which were then merged into a two-column table.
Aurora Clinical EMR is designed to be flexible and accessible, allowing quick and easy changes to meet specific needs. In addition, it provides comprehensive data pertaining to phenotype, genotype, family history, test results, and pedigrees.
For IVF, Aurora offers a 360-degree view of patient data to help improve treatment decisions. The system enables users to collect, analyze and present the gathered information in a single platform.
To gain an understanding of how providers interpreted data, the MIT team had direct discussions with health care providers. This helped researchers to understand how data entered into the EMR is interpreted for various purposes, including insurance billing and radiology scan ordering.
The researchers also worked with data analysts and research analysts at Aurora, who were able to think about the data in new ways and make suggestions for improving its use. These relationships were important because the team needed to trust that the analysts had a strong, professional interest in working with de-identifiable data for research.
Aurora clinical emr, previously known as Beacon Specialty, is an Electronic Health Records (EHR) software system produced by Boston Advanced Analytics and caters to large hospitals and practices. It aims to help medical practices enhance their technological efficiency, data analytics, and performance.
Its flexibility allows it to be easily customized to suit the standards of a medical practice and a patient’s preference. It also enables easy access to all the information garnered through labs, diagnostic equipment and pharmacies.
In addition, it helps in gaining test results spanning days, months or even years. This enables better analysis of patient records, thereby improving healthcare services and outcomes.
The MIT team had experience working with confidential and protected health information for research, including HIPAA regulations, which governed their data use agreements and IRB review process. They also had a history of clear communication and strong relationships, which helped them build trust and commitment to project success.
In addition to data-sharing, the MIT team provided a number of valuable insights through research, observations and discussions with Aurora’s data and clinical experts. These included a deeper understanding of the meaning of data entry fields and definitions than could have been gained through the documentation alone, as well as a more detailed understanding of the underlying coding structure that underlies the EMR.
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Aurora clinical emr offers scalable features that can accommodate large practices’ needs. Whether you need to manage a patient database with millions of records or add self-scheduling and automated communication features, our cloud-first technology handles massive amounts of data without compromising uptime, accessibility or speed.
MIT team members worked closely with providers and analysts at Aurora to identify, prepare, interpret, and link data for this research. Through in-person site visits and regular weekly meetings, the MIT team was able to develop strong trusting relationships that enabled open dialog about data questions.
As with other research projects, the process of locating and interpreting data for this study was complex and required substantial input from both Aurora analysts and MIT data analysts. These data were drawn from multiple tables and systems that are not designed for research, which resulted in a significant amount of time spent on both sides of the data relationship to make this information usable for this research in de-identifiable form.