8 Stages to EI | SurgicalAR | Hospital Price Growth

“Well, first of all I had no idea who he is. I’ve never watched that show. But secondly, it was like, ‘This is really weird!’ Thirdly, it was just a shrug: ‘We’re not going to do that.’”

Epic CEO, Judy Faulkner, on her three stages of learning about Jim Cramer’s suggestion that Apple should buy Epic. We didn’t think so either.

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The Imaging Wire

The 8 Stages to EI
HIMSS Analytics unveiled its eight-stage adoption model intended to help measure and guide healthcare providers’ progress towards full enterprise imaging adoption. The Digital Imaging Adoption Model for Enterprise Imaging (DIAM – EI) expands DIAM from its previous radiology focus (available since 2016) to support all imaging, with the goals of improving workflows, safety, and care. The development of DIAM EI should help the many healthcare organizations still working towards full enterprise imaging adoption, while also benefiting the enterprise imaging players who could see their target market grow in size and motivation as a result of a prescribed framework like this.

Medivis unveiled its SurgicalAR augmented reality surgical planning platform at HIMSS last week, which allows physicians to access and view 3D medical images/data through a Microsoft HoloLens-based system, achieving what they claim is “the holy grail” of patient rendering. SurgicalAR will initially be used for surgery planning, although Medivis plans to eventually support surgery navigation, with that next phase likely funded by the $2.3 million seed investment that it also announced at HIMSS. Medivis’ technology seems impressive, but it’s perhaps more notable for its contribution towards what could be a key technology shift in radiology, as the last few years brought an influx of studies and forecasts around AR in radiology, followed by an increase in AR-related launches over the last six months.

Hospital Price Growth
Research published in Health Affairs reveals that hospital-based prices grew much faster than physician prices between 2007 and 2014. Hospital price growth over the eight years significantly outpaced physician prices for both inpatient care (+42% vs. +18%) and hospital-based outpatient care (+25% vs. +6%), prompting the group to suggest that efforts to reduce healthcare spending should primarily focus on hospital prices “including antitrust enforcement, administered pricing, the use of reference pricing, and incentivizing . . . more cost-efficient referrals.” The article didn’t touch on the role of hospital consolidation on pricing trends, but industry commentary positioned these two trends as directly related, suggesting that the restricted competition and greater inefficiencies that came from consolidation had a direct hand in hospital pricing growth.

Imaging Financials Remain Positive
The second round of medical imaging company financials from the October-December 2018 period revealed solid performances from Fujifilm, Shimadzu, and Mednax, following a largely positive first round of financials from most other players earlier this month.

Ovarian Cancer Predictor
Researchers at the Imperial College London and the University of Melbourne developed a machine learning algorithm that uses CT images to predict ovarian cancer patients’ survival rates and response to treatments more accurately than current solutions. The researchers used the machine learning tool, TEXLab 2.0, to identify the aggressiveness of tumors in CT scans and tissue samples from 364 women with ovarian cancer, examining each tumor for characteristics that have a major impact on survival (structure, shape, size and genetic makeup) to produce a Radiomic Prognostic Vector (RPV) score. The RPV scores were then compared with current methods for ovarian cancer evaluations/predictions and found that their approach was up to four times more accurate. The team believes that their RPV score may prove to be an important biomarker for predicting response to treatment (e.g. high RPV = chemo resistance, poor surgical outcomes), potentially allowing physicians to make better and more personalized treatment decisions.

The Wire

  • IBM Watson Health announced a 10-year, $50 million investment to fund joint research collaboration projects with Brigham and Women’s Hospital and Vanderbilt University Medical Center intended to advance AI in healthcare. The research will particularly focus on using EHR and claims data to address significant public health issues (e.g. patient safety, precision medicine, and health equity) and explore the physician and patient experience with AI.

  • A team of 18 radiologists read, cleaned, and annotated 30,000 frontal chest radiographs from a 112k-image NIH dataset, making the cleaned/labeled studies publicly available to support the future development of pneumonia-detection machine learning algorithms. This is believed to be the first effort of its kind and it’s a great example of the type of one-to-many labeling efforts that could help add efficiency to a very “human” (read: inefficient) step in the machine learning process. That said, this doesn’t appear to be the last effort of its kind, as the RSNA-involved team is seeking volunteers to explore new localization methods using future data sets.

  • Zebra Medical Vision received three grants from the Israeli government to provide AI solutions at three of the country’s largest healthcare providers: Ichilov Hospital (supporting radiology worklist prioritization), Maccabi Healthcare Services (providing a breast cancer “second reader” solution to reduce misdiagnosis), and Clalit Health Services (providing a solution for early osteoporosis and heart disease detection).

  • New research from Denmark highlights the high diagnostic and cost-saving potential of point-of-care ultrasound (POCUS), once again referring to POCUS as the “the future stethoscope” and outlining a wide range of applications that POCUS could support. The researchers cited some impressive accuracy results from existing POCUS studies, while emphasizing POCUS’ advantages around ease of training, ease of use, procedure efficiency, and ability to eliminate additional higher-cost testing. However, the team did call for POCUS decision-making guidelines and the need for studies on the long-term effects of point-of-care ultrasound exposure to determine POCUS’ role in primary care.

The Resource Wire

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