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Here are six areas of guidance for a successful solution evaluation.

The funds will be used by the Seqster to accelerate the adoption of their interoperability technology for clinical trials, patient engagement and outcomes.  

Four experts in diagnostic imaging IT offer CIOs, CMOs and other leaders some best practices for making a complex technology work best for their own healthcare organizations.

Remaining tasks include additional development of interfaces between the existing EHR, Cerner’s platform and the general IT infrastructure of the VA, which could run through the end of April, according to a news report.

Healthcare providers and device manufacturers are both responsible for putting mitigations in place to address patient safety risks, says FDA's Suzanne Schwartz.

A new study, conducted by Korean academic hospitals and Lunit, a medical AI company specializing in developing AI solutions for radiology and oncology, demonstrated the benefits of AI-aided breast cancer detection from mammography images. The study was published online on 6 February 2020, in Lancet Digital Health and features large-scale data of over 170,000 mammogram examinations from five institutions across South Korea, USA, and the UK, consisting of Asian and Caucasian female breast images. TOP FINDINGS One of the major findings showed that AI, in comparison to the radiologists, displayed better sensitivity in detecting cancer with mass (90% vs 78%) and distortion or asymmetry (90% vs 50%). The AI was better in the detection of T1 cancers, which is categorized as early-stage invasive cancer. AI detected 91% of T1 cancers and 87% of node-negative cancers, whereas the radiologist reader group detected 74% for both. Another finding was a significant improvement in the performance of radiologists, before and after using AI. According to the study, the AI alone showed 88.8% sensitivity in breast cancer detection, whereas radiologists alone showed 75.3%. When radiologists were aided by AI, the accuracy increased by 9.5% to 84.8%. An important factor in diagnosing mammograms is breast density and dense breast tissues, mostly from the Asian population, make it harder to interpret as dense tissue is more likely to mask cancers in mammograms. According to the study’s findings, the diagnostic performance of AI was less affected by breast density, whereas radiologists' performance was prone to density, showing higher sensitivity for fatty breasts at 79.2% compared to dense breasts at 73.8%. When aided by AI, the radiologists’ sensitivity when interpreting dense breasts increased by 11%. THE LARGER TREND Findings from a study published in Nature indicated that Google’s AI model spotted breast cancer in de-identified screening mammograms with greater accuracy, with fewer false positives and false negatives than experts, HealthCareITNews reported. Lunit recently raised a $26M Series C funding from Korean and Chinese investors, which the company said was its biggest funding round, according to a DealStreetAsia report in January.  ON THE RECORD “It is an unprecedented quantity of data with accurate ground truth--especially the 36,000 cancer cases, which is seven times larger than the usual number of datasets from resembling studies conducted previously,” said Hyo-Eun Kim, the first author of the study and Chief Product Officer at Lunit.  Prof. Eun-Kyung Kim, the corresponding author of the study and a breast radiologist at Yonsei University Severance Hospital, said: “One of the biggest problems in detecting malignant lesions from mammography images is that to reduce false negatives—missed cases—radiologists tend to increase recalls, casting a wider safety net, which brings an increased number of unnecessary biopsies.” “It requires extensive experience to correctly interpret breast images, and our study showed that AI can help find more breast cancer with lesser recalls, also detecting cancers in its early stage of development.” 

To prevent cybersecurity incidents, it is important to recognize the enormity and complexity of the problem, and to catalog various threats and vulnerabilities, as one infosec expert will show at HIMSS20.

The cloud technology vendor has made new CDC-recommended tools available to ambulatory and hospital clients, to help with faster diagnosis and continuous monitoring.

AdventHealth, the faith-based Florida health system that runs 50 hospitals and more than 1,200 outpatient settings in nine states nationwide, has decided to deploy a new electronic health record and revenue cycle management system from Epic – replacing its existing Cerner technology. According to reports from KCTV and Kansas City Business Journal, AdventHealth – which was known as Adventist Health System until this past year, and is one of the country's biggest non-profit systems – says the transition will begin next month and could take three to five years. "The shift is expected to take up to five years and Cerner is committed to working closely with AdventHealth to continue delivering superior healthcare technology solutions throughout the transition," said Cerner officials in a written statement. AdventHealth has been a Cerner customer since 2002, and with the technology some of its hospitals have reached Stage 7 on the HIMSS Analytics EMR Adoption Model. The choice to move instead to rival Epic was a business decision, driven by the need to improve provider and patient experience, AdventHealth President and CEO Terry Shaw told KCTV. "Our journey to become a consumer-focused clinical company requires a fully connected network throughout our entire enterprise," said Shaw. "Connecting our network with a robust, integrated health record platform will give our caregivers access to the clinical information they need at the point of care and ultimately advance our consumer promises through a more seamless experience for those we serve." Twitter: @MikeMiliardHITN Email the writer: mike.miliard@himssmedia.com Healthcare IT News is a publication of HIMSS Media.