Automating the processing of medical reports at Plexus Law

Emerson wanted to provide a connected environmental analyzer to their customers. They wanted to leverage IoT technologies to provide a software solution that was easy to use, real-time and centralized. Compliance with pollution control board guidelines and the ability to remotely calibrate and troubleshoot these devices was the primary objective. Requirements - Centralized Remote Monitoring. - IoT Based Smart Environmental Analyzers. - Remote Calibration and Troubleshooting. - User Friendly Application. - Reporting & Dashboards. - Compliance with pollution control board guidelines.

Pharmaceutical production is subject to a strict set of enforced rules that must be adhered to and compliance to these standards is critically necessary. Due to the efforts of WIN 911’s strategic partner Micromedia, Lilly was able to adopt an alarm notification infrastructure that integrated smoothly with their existing workflows and emergency hardware and protocols. These raw energy sources enable the industrial process to function: electricity, WIN-911 Software | 4020 South Industrial Drive, Suite 120 | Austin, TX 78744 USA industrial steam, iced water, air mixtures of varying quality. Refrigeration towers, boilers and wastewater are monitored by ALERT. Eli Lilly identified 15000 potential variables, but limitations compelled them to chisel the variable list down to 300. This allowed all major alarms to be covered including pressure, discharge, quantity of waste water discharged,temperature, carbon dioxide content, oxygen & sulphur content, and the water’s pH.

There are several factors simultaneously driving integration of AI in radiology. Firstly, in many countries around the world there is a discrepancy between the number of doctors trained in radiology and the rising demand for diagnostic imaging. This leads to greater demands for work efficiency and productivity. For example, the number of radiology specialists (consultant work- force) in England went up 5% between 2012 and 2015, while in the same period the number of CT and MR scans increased by 29 and 26 percentage points respectively. In Scotland, the gap widened even further (The Royal College of Radiologists 2016). Today, the average radiologist is interpreting an image every three to four seconds, eight hours a day (Choi et al. 2016).Secondly, the image resolution of today’s scanners is continuously improving – resulting in an ever greater volume of data. Indeed, the estimated overall medical data volume doubles every three years, making it harder and harder for radiologists to make good use of the available information without extra help from computerized digital processing. It is desirable, both in radiological research and in clinical diagnostics, to be able to quantitatively analyze this largely unexploited wealth of data and, for example, utilize new measurable imaging biomarkers to assess disease progression and prognosis (O’Connor et al. 2017). Experts see considerable future potential in the transformation of radiology from a discipline of qualitative interpretation to one of quantita- tive analysis, which derives clinically relevant information from extensive data sets (“radiomics”). “Images are more than pictures, they are data,” American radiologist Robert Gillies and his colleagues write (Gillies et al. 2016). Of course, this direction for radiology will require powerful, automated procedures, some of which at least will come under the field of artificial intelligence.

Dr. Partho Sengupta needed a way to accurately identify disease patterns resulting from echocardiograms in order to improve diagnostics and save more lives. Specifically, he wanted to distinguish between two disparate diseases: cardiomyopathy, which directly impacts the heart muscle and often leads to heart failure, and pericarditis, which acts as if the heart is involved but doesn’t actually affect the heart. While both diseases present with similar heart conditions, the treatments are vastly different. For pericarditis, the treatment may include medication and, rarely, surgery. However, if the diagnosis is cardiomyopathy the patient undergoes medical management (i.e. a pacemaker) or in extreme cases, a heart transplant. Misdiagnosis of these disease conditions can put the patient’s life at risk and be very expensive for the hospital. Dr. Sengupta, therefore, looked to Saffron’s Natural Intelligence Platform to help his team increase the diagnosis accuracy of these medical conditions.

Technology to support vessels in the commercial fishing industry had not changed significantly since the advent of radar. Over the past 40 years commercial fishing in the United States has continued to be one of the most active, yet regulated industries. The combination of federal regulations and lack of technology created an opportunity for Faria Watchdog to go-to-market with a solution.

The Solidarity and Social Investment Fund (FOSIS), a service of the Chilean Government, was faced with the challenge of standardizing its administrative processes to comply with the new regulatory framework established by the Law of Digital Transformation of the State (LTDE). The LTDE focuses on updating administrative procedures and document management in public administration through digital transformation. It also mandates that all communication related to administrative procedures between different public bodies must be carried out electronically. FOSIS had been using the AuraQuantic platform for over 14 years to automate different internal processes. However, with the introduction of the new LTDE framework, the institution needed to launch an ambitious project to align with the new regulations.