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Artificial Medical Intelligence Announces Automatic Generation of Medical Problem Lists; Integrates with Hospital EMRs; Meets Meaningful Use Criteria

Artificial Medical Intelligence (AMI) and Robert Wood Johnson University Hospital (RWJUH) today announced a process that automatically generates medical problem lists from AMI’s EMscribe® Computer Assisted Coding (CAC) data abstraction engine. RWJUH is currently using the EMscribe solution to successfully generate problem lists from its Allscripts SCM EMR system.

A problem list is a vital part of a patient's medical chart that details the most important medical information. As the health care industry transitions to Electronic Medical Records (EMRs), the problem list is recognized as one of the core meaningful use criteria that require standardization in content and structure in order to be compatible for health information exchange (HIE).

Currently health care organizations either manually enter patient problem list information or may not have a system in place at all. AMI’s EMscribe addresses this issue by utilizing its innovative Natural Language Processing (NLP) technology to read patient health records for appropriate medical terminology, generates a problem list based on this information and automatically populates a healthcare organization’s EMR system. It allows clinicians to capture the true clinical intent that leverages their existing medical documentation through immediate access to the identified patient problems.

RWJUH is unique in applying EMscribe’s data abstraction engine in this capacity and is realizing the benefits of enhanced clinical workflow while aiding in the achievement of meaningful use compliance. Clinical documentation comprising the patient record from multiple sources is analyzed automatically by EMscribe "behind the scenes.” RWJUH designed and developed a custom workspace in the Allscripts SCM EMR where clinicians can selectively "drag and drop" suggested diagnoses into the patient's active, chronic, or resolved medical problem list.

“Utilizing EMscribe’s NLP to generate medical problem lists improves our hospital’s efficiency and clinical care, and most importantly, helps manage populations of patients while they are hospitalized,” said Joshua Bershad, MD, senior vice president and chief medical officer at RWJUH.

Jordan D. Ruch, administrative director of Clinical Information Systems at RWJUH added, “In addition to supporting Meaningful Use, it allows our providers to easily manage the problem list in real-time with automated suggestions from the source medical record. With a discrete, up-to-date problem list, we are able to build robust patient-specific clinical decision support.”

According to Stuart Covit, COO AMI, “The beauty of EMscribe’s CAC platform is that it is driven by Natural Language Processing, the most efficient medical record information abstraction engine available. This means that it can go well beyond the scope of medial coding and help provide solutions like generating problem lists by simply abstracting data from documented patient charts. The EMscribe platform is innovative technology that helps healthcare organizations with customized applications to keep pace with the rapidly changing healthcare environment.”

AMI’s EMscribe with problem list capabilities is available immediately. Interested parties can contact the company here.


Robert Wood Johnson University Hospital (RWJUH) is a 600-bed academic medical center which is the flagship Cancer Hospital of Rutgers Cancer Institute of New Jersey and the principal hospital of Rutgers Robert Wood Johnson Medical School in New Brunswick, NJ. Robert Wood Johnson is an innovative leader in advancing state-of-the-art care. Its Centers of Excellence include cardiovascular care from minimally invasive heart surgery to transplantation, cancer care, and women’s and children’s care including The Bristol-Myers Squibb Children’s Hospital at Robert Wood Johnson University Hospital ( The hospital is also a Level 1 Trauma Center and serves as a national resource in its ground-breaking approaches to emergency preparedness. The hospital has earned significant national recognition for clinical quality and patient safety. RWJUH has been ranked among the best hospitals in America by U.S.News & World Report six times and has been selected by the publication as a high performing hospital in several specialties. The Bristol-Myers Squibb Children’s Hospital has also ranked among the nation’s Best Children’s Hospitals by U.S.News & World Report for two consecutive years. The American College of Surgeons’ Commission on Cancer has rated RWJUH among the nation's best comprehensive cancer centers and the hospital’s Comprehensive Stroke Center is certified by the Joint Commission to provide complex stroke care. The Leapfrog Group rated RWJUH as one of the 50 exceptional U.S. hospitals, as published in Consumers Digest magazine, and has given the hospital an “A” grade for safety and quality. Harvard University researchers, in a study commissioned by The Commonwealth Fund, identified RWJUH as one of the top 10 hospitals in the nation for clinical quality. RWJUH is also a four-time recipient of the prestigious Magnet Award for Nursing Excellence. Visit us online at

About AMI

Artificial Medical Intelligence, Inc. (AMI) is a software developer of health information technology solutions since its incorporation in 2002. AMI initially targeted the labor intensive medical coding process, with its premier offering, EMscribe®, a proprietary Natural Language Processing (NLP), Computer Assisted Coding (CAC) solution. EMscribe was first commercialized in 2005, providing a software platform for the medical coding of free text electronic documents for the inpatient and outpatient hospital environment. Through continued version updates, the EMscribe software became comprehensive for all medical record processing by provisioning for all medical record systems including ICD-10 CM/PCS, DSM, LOINC, PQRS and E & M. AMI has also diversified its solution to accommodate, clinics and physician practices. Additionally, the solution was augmented to provide a platform for numerous other medical language capabilities touching on revenue cycle, clinical documentation improvement and clinical decision making enhancements vital for the efficient information gathering and support of the medical record for the healthcare industry.

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