While the dawn of the EHR promised streamlined, accelerated healthcare delivery, the technology can also include burdensome alerts and documentation requirements that lead to clinician...
While the promises of precision medicine in healthcare are seemingly boundless, the industry has had to overcome several challenges to integrating these approaches with everyday clinical care.
From...
Machine learning tools can predict the likelihood that a patient with a urinary tract infection (UTI) won’t respond to antibiotics, which could reduce antibiotic resistance in outpatient clinical...
Tufts Medical Center has announced a partnership with Olive to use artificial intelligence to streamline COVID-19 testing operations and improve the care experience for both patients and...
Using a limited amount of data and basic feature engineering, a predictive analytics model could effectively reduce the rate of MRI appointment no-shows, according to a study published in the American...
In the era of value-based healthcare, digital innovation, and big data, clinical decision support systems have become vital for organizations seeking to improve care delivery.
Clinical decision support (CDS) tools have the ability to...
Thirty-five percent of younger healthcare professionals are overwhelmed by digital patient data or are unsure about how to use patient data and analytics to inform care, according to a global survey...
Providers are inundated with large amounts of data that is daunting to comb through to find actionable outcomes.
Artificial intelligence (AI) technology is frequently seen as the solution to...
Amazon Web Services (AWS) has launched a new machine learning service called Amazon Transcribe Medical, which will automatically convert physician consultations and dictated notes from speech to...
At the core of any meaningful healthcare intervention is medical data. A patient’s health history, prescription information, and demographic data are the building blocks of quality care delivery,...
As healthcare has become increasingly digital and patient outcomes have become more intertwined with provider rewards, physicians have turned to clinical decision support (CDS) tools to help them...
Using real-time data to improve patient safety and clinical decision support can lead to workflow and data quality issues, according to a study published in JMIR.
The digitization of healthcare...
While chemotherapy is an effective form of cancer treatment, it can also come with significant side effects. Providers must synthesize a wealth of big data to ensure patients are receiving the best...
HEALTHeLINK, the health information exchange (HIE) of Western New York, is continuing its efforts to improve population health, streamline clinical workflows, and boost data quality in the communities...
User-centered design in health IT tools is a relatively new phenomenon, but one that is growing increasingly common, especially as end-users continue to gripe about convoluted workflows and...
To succeed with electronic clinical quality measures (eCQMs), organizations should optimize their data quality, improve data governance efforts, and enhance clinician education, according to a recent...
Incorporating persuasive design concepts into primary care electronic health records (EHRs) increased same-day data entry by 10 percent per physician, demonstrating the potential for persuasive design...
Meaningful care coordination and more efficient clinical workflows depend on seamless data exchange between primary care providers, specialists, and their patients.
While much of the industry has...
Using EHR data, organizations may be able to create predictive analytics models that accurately identify the risk of a patient appointment no-show, according to a new study published in...
The rise of big data in the healthcare industry is setting the stage for natural language processing (NLP) and other artificial intelligence tools to assist with improving the delivery of care....
