It has been a year since we released (in GA) our last TA API (v3.0). After five previews of adding features, responsible AI, incorporating customer feedback, UX feedback, and optimizations; in July 2021 we announced GA (General Availability) of Text Analytics v3.1. With this release, starting July 2021 customers can use Text Analytics for health, Opinion Mining, PII and Analyze as GA offerings. Text Analytics for health is a feature of the Text Analytics API service that extracts and labels relevant medical information from unstructured texts such as doctor's notes, discharge summaries, clinical documents, and electronic health records. Millions of Text Records were processed during the preview in the last year.
Epic Systems, America's largest electronic health records company, maintains medical information for 180 million U.S. patients (56% of the population). Using the slogan, "with the patient at the heart," it has a portfolio of 20 proprietary artificial intelligence (AI) algorithms designed to identify different illnesses and predict the length of hospital stays. As with many proprietary algorithms in medicine and elsewhere, users have no way of knowing whether Epic's programs are reliable or just another marketing ploy. The details inside the black boxes are secret and independent tests are scarce. One of the most important Epic algorithms is for predicting sepsis, the leading cause of death in hospitals.
Clinical Trials are the mandatory path for developing and bringing a new drug or vaccine to the market. Unfortunately, according to a study conducted by MIT, 86 percent of the drugs will fail during this process. This very high failure rate not only has consequences on the Pharmaceutical companies' bottom line, but it precludes potentially safe and efficacious drugs from reaching patients that could benefit from them. Recruitment is one of the main bottlenecks, is time-consuming, and very expensive. According to Chunhua Weng from Columbia University (New York), "Recruitment is the number one barrier to clinical research."
A federal rule that requires health care providers to offer patients free, convenient and secure electronic access to their personal medical records went into effect earlier this year. However, providing patients with access to clinician notes, test results, progress documentation and other records doesn't automatically equip them to understand those records or make appropriate health decisions based on what they read. "Medicalese" can trip up even the most highly educated layperson, and studies have shown that low health literacy is associated with poor health outcomes. University of Notre Dame researcher John Lalor, an assistant professor of information technology, analytics and operations at the Mendoza College of Business, is part of a team working on a web-based natural language processing system that could increase the health literacy of patients who access their records through a patient portal. NoteAid, a project based at the University of Massachusetts Amherst, conveniently translates medical jargon for health care consumers.
Several artificial intelligence algorithms developed by Epic Systems, the nation's largest electronic health record vendor, are delivering inaccurate or irrelevant information to hospitals about the care of seriously ill patients, contrasting sharply with the company's published claims, a STAT investigation found. Employees of several major health systems said they were particularly concerned about Epic's algorithm for predicting sepsis, a life-threatening complication of infection. The algorithm, they said, routinely fails to identify the condition in advance, and triggers frequent false alarms. Some hospitals reported a benefit for patients after fine-tuning the model, but that process took at least a year. Unlock this article by subscribing to STAT and enjoy your first 30 days free!
Amazon HealthLake is a new, HIPAA-eligible service designed to store, transform, query, and analyze health data at scale. You can bring your healthcare data into Amazon HealthLake using Fast Healthcare Interoperability Resources (FHIR) R4 APIs. If you don't have your data in FHIR R4, Amazon has collaborated with industry experts to build Amazon HealthLake connectors to help you with custom file and HL7 to FHIR R4 mappings. This post highlights one of those partners, Redox, and their Amazon HealthLake Connector. Developers at over 300 companies use the Redox platform to exchange data with more than 1,700 healthcare provider organizations.
The advent of Electronic Health Record systems and their accompanying documentation has created a deep fissure within the medical community. Epidemic-level numbers show that more and more physicians report feeling burnt out and depressed. The overall rate of work-life happiness reported by healthcare providers dropped below 50% thanks to the pandemic. Numbers released in Medscape's 2021 physician lifestyle report state that 43% of all physicians report feeling burnt out. Of those burnt-out physicians, 58% say they feel that way due to the long list of bureaucratic tasks like note taking and EHR documentation.
Projected a few years ago to be a $150 billion industry by 2026, Artificial Intelligence (AI) systems are radically transforming industries around the world and healthcare is no exception to this development. New AI applications are being developed and experimented with to streamline administrative and medical processes, enhance clinical decision making and support, manage long-term care - all of which are showing great promise. AI in healthcare refers to the use of complex algorithms designed to mimic human cognition and perform certain tasks in an automated fashion at a fraction of the time and cost. Simply put, when data is injected into the platform, algorithms, and machine learning solutions kick in, working with the data, using deep data analytics, and delivering outcomes and reports which would be as accurate if not more than human interventions. From making more accurate diagnoses, finding links between genetic codes to powering surgical robots, maximising administrative efficiency, and understanding how patients will respond to treatment plans, there are limitless opportunities to leverage AI in healthcare. Using machine learning in precision medicine can help predict what treatment protocols are likely to succeed based on a patient's attributes, treatment history, and context, allowing more accurate and impactful interventions at the right moment in a patient's care.
Electronic Health Record (EHR) has initiated the onset of transformation in healthcare by digitizing valuable patient information. Still, about 70% of human-generated data like clinical notes and audio interview transcripts, which is a part of EHR remain unused. This unstructured data, if successfully processed, could turn into an information gold mine. They carry valuable information that could potentially help healthcare providers in making the right decisions and even foresee complications that may occur during the course of a procedure. While the value of structured data in EHRs are well known, research shows that the real-world data captured in the unstructured format too holds immense value.
With the continuous evolvement of Artificial Intelligence, the world is being benefited to the utmost level, as the applications of Artificial Intelligence is unremitting. This technology can be operated in any sector of industry, including the healthcare industry.The advancement of technology and the AI (Artificial Intelligence), as a part of modern technology have resulted in the formation of a digital macrocosm. Machine learning technologies as a part of Artificial Intelligence, can be applied to the electronic health records, with the help of this the clinical professionals can hunt for proper, error-free, confirmation-based statistics that has been cured by medical professionals. Further Artificial Intelligence technology with the help of electronic health records is used in healthcare industry that allows the cardiologists to recognize critical cases first and give diagnosis with accuracy and potentially avoiding errors. Making a start from Natural Language process, Algorithms and medical coding, imaging and diagnosis, there is a long way for the Artificial Intelligence to be capable of innumerable activities and to help medical professionals in making superior decisions.