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Editor's e-Note
Used correctly, artificial intelligence can be a game-changer in the health care industry. One such example is illustrated in this month’s E-News Exclusive.

At the University of Pittsburgh Medical Center, researchers have developed an algorithm to improve hospital care. The article takes a deep dive into just how this is being accomplished, with numbers to back up the claim.

Lee DeOrio, editor
e-News Exclusive
Data Tool Streamlines Hospital Transfers for Improved Patient Care

With the aid of sophisticated machine learning, researchers at University of Pittsburgh Medical Center (UPMC) and the University of Pittsburgh School of Medicine demonstrated that a tool they developed can rapidly predict mortality for patients facing transfer between hospitals in order to access higher-acuity care. This research, published in PLOS One, could help physicians, patients, and their families avoid unnecessary hospital transfers and low-value treatments, while better focusing on the goals of care expressed by patients.

Each year, nearly 1.6 million patients—or as much as 3.5% of all inpatient admissions—are transferred from one hospital to another to access specialized care for complex conditions. “However, securing these services often requires burdensome travel and reduced community support for patients and their families. While some may recover from the acute illness, many others realize little benefit in terms of improved outcomes,” says Daniel E. Hall, MD, corresponding author of the study, as well as medical director of high-risk populations and outcomes at the UPMC Wolff Center and an associate professor of surgery at Pitt’s School of Medicine.

Full story »
Other News
Former NY Hospital Employee Charged With HIPAA Violation
New York–based Huntington Hospital discovered an employee improperly accessed patients’ protected health information; the employee was terminated and charged with a criminal HIPAA violation, according to HealthITSecurity.

Data Science Proved What Pittsburgh’s Black Leaders Knew: Racial Disparities Compound COVID Risk
Data analysts from Pittsburgh found that people of color were at higher risk of COVID infection, severe disease, and death, reports Kaiser Health News.
Products & Services
Coding Platform Specifically Designed for EDs
Nym Health, a leader in autonomous medical coding, announces the expansion of its explainable AI and clinical language understanding technology platform’s capabilities to address the growing need for scalable revenue cycle management (RCM) solutions within urgent care settings. Nym’s autonomous medical coding platform simultaneously provides a fuller, deeper, and better understanding of patient visits and reduces the administrative burden on health care providers. The company’s RCM solution transforms provider narratives in the free text within patient charts, turning them into accurate and compliant ICD-10-CM and CPT reimbursement codes in a matter of seconds, without any human intervention. Nym’s proven technology delivers comprehensive, audit-ready, traceable codes for full transparency and processes more than 3 million charts annually in more than 90 emergency medicine care settings nationwide. Learn more »

Dolbey Adds Denial Management Module
For many, denial management is time-consuming and costly, and without proper management, denials can create significant stress in the revenue cycle. Dolbey’s denial management module, Fusion Reclaim, assists and tracks the entire denial process. Fusion Reclaim ingests the payer’s denial and aids in identifying the reason and categorizing the denials by queuing them up for review by hospital staff. If the denial is rebutted, Fusion Reclaim assists in submitting the rebuttal by tracking its progress and status. Reporting is available throughout the entire process, as well as artificial intelligence–assisted intelligence to monitor future claims to avoid denials and identify areas of education for billing and revenue cycle staff. Fusion Reclaim can be added to Dolbey’s award-winning, No. 1 Best in KLAS computer-assisted coding software, Fusion CAC. Learn more »

OneRecord Introduces API
OneRecord, a digital health company focused on bringing patients access to their personal health data, unveils the new, powerful OneRecord API, built to provide health care developers and organizations a streamlined solution to access consented patient health care data from EHRs and payers’ claims data, among a variety of other sources. OneRecord API is designed “by developers, for developers” to seamlessly access patients’ medical data and claims data via the latest technology to accelerate advancements in patient-centered care, including meeting the requirements of the 21st Century Cures Act’s Interoperability and Patient Access Final Rule. With a simple way to access health care data consented by the patient, OneRecord API enriches partner workflows with on-demand access to their users’ data, replacing traditional record retrieval processes which are costly and typically take weeks or months. OneRecord API emerges in response to countless inquiries from public and private sector health care stakeholders for a patient-meditated FHIR solution to gain better access to health care data. OneRecord’s straightforward solution enables developers to build patient-facing tools that can exchange health data between their platforms and the leading EHRs, including Epic, Cerner, Allscripts, athenahealth, Meditech, NextGen, Medhost, CPSI/Evident, eClinicalworks, and more, covering more than 80% of the inpatient and ambulatory provider market for access to data. Learn more »
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In this e-Newsletter
Recently in For The Record
KPIs Shift With the Times
A recent survey sheds lights on how health care organizations are changing their views on key performance indicators. Read more »

Making Sense of Social Determinants of Health
The industry must develop trustworthy data governance processes that enable widespread use. Read more »

IPPS: What’s New and What’s Ahead
Things are moving at a frenetic pace as coding departments adapt to a myriad of changes. Read more »

Person Matching Supports Data Exchange
A giant insurer’s idea to better connect its consumers across various health plans may lead to better health outcomes. Read more »
Industry Insight
Artificial Intelligence, Genomic Surveillance Combine to Detect Infectious Disease Outbreaks

By coupling machine learning with whole genome sequencing, University of Pittsburgh School of Medicine and Carnegie Mellon University scientists greatly improved the quick detection of infectious disease outbreaks within a hospital setting over traditional methods for tracking outbreaks.

The results, published in the journal Clinical Infectious Diseases, indicate a way for health systems to identify and then stop hospital-based infectious disease outbreaks in their tracks, cutting costs and saving lives.

“The current method used by hospitals to find and stop infectious disease transmission among patients is antiquated. These practices haven’t changed significantly in over a century,” says senior author Lee Harrison, MD, a professor of infectious diseases at Pitt’s School of Medicine and epidemiology at Pitt’s Graduate School of Public Health. “Our process detects important outbreaks that would otherwise fly under the radar of traditional infection prevention monitoring.”

The Enhanced Detection System for Healthcare-Associated Transmission (EDS-HAT) couples the recent development of affordable genomic sequencing with computer algorithms connected to the vast trove of data in EHRs. When the sequencing detects that any two or more patients in a hospital have near-identical strains of an infection, machine learning quickly mines those patients’ EHRs for commonalities—whether that be close proximity of hospital beds, a procedure using the same equipment, or a shared health care provider—alerting infection preventionists to investigate and halt further transmission.

Read more »
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