With the new Pandemic, that the healthcare industry is in crisis is beyond dispute. According to the American Hospital Association, health systems are losing 51% of their revenues, mostly due to the cancellation of elective surgeries1. While the influx of COVID-19 patients has provided some revenue, it has not nearly closed the gap.
The world has turned upside down in the last 30 days. I share the experience of the real-life impact of the crisis at Silicon Valley, specifically in Santa Clara County, which has become the epicenter of the COVID-19 crisis in California.
The healthcare industry has an abiding interest in dose limits. These limits form the norm for health safety in the use of radiation for diagnostic procedures. With a pre-established exposure boundary, a patient receives radiation dose within a range of parameters during diagnostic examinations, general screening procedures, or therapy.
Could untangling patterns in utilization and examination lifecycles of the past lead us to a better understanding of the current state? Could it also help us prevent an imminent deterioration of patient care? Can a diagnostic unit transform itself from a nimble operation to a center of excellence in a healthcare network?
Most healthcare imaging facilities are not able to report metrics on their diagnostics equipment usage. From my discussions with healthcare leaders, there seems to be a common thread unifying their concern – identifying and isolating bottlenecks that are deterring optimal utilization of the big iron machines.
Despite an array of monitoring systems from CMMS to EVMs, there are wide-ranging areas of pain points that are yet to be addressed, including optimal shift operations, profiling scheduling patterns, and segmenting examination throughput.
Not only does analytics from every diagnostic examination offer cost containment insights, but it can also uncover referral inconsistencies, plug scheduling delays, and describe a successful approach to evaluate a technician’s productivity.
The aim of this blog is to highlight the dynamics that are opening up innovative opportunities for radiology leaders and, to provide insights into the underlying digital connections that are creating new sources of value and how to use them.
Deep machine data analytics is getting more sophisticated each year and is now becoming more pervasive in radiology facilities used as an optimization tool instead of relying on plain vanilla billing spreadsheets to get precise information on the usage of the machines, for instance, are our machines underutilized or over-utilized? If they are, then what action can I take to get the most of the MRIs or CT Scanners?
For Glassbeam, the RSNA Annual Meeting is the top conference on our field calendar that gets us the most excited. Once again, this year’s show, held during Dec 1-5 at McCormick Place in Chicago, lived up to that expectation. The showcase of innovations from technology companies, in-depth demos at the booths, an entire floor for Artificial Intelligence vendors, and a range of radiology topics focused on expert learning sessions, and so much more made RSNA Chicago a great way to end our roadshows for the year 2019.
We at Glassbeam always strive to expand our portfolio of supported products by listening to our customers and understanding their areas of acute pain. As we rolled out our utilization analytics solution in Clinsights™, we spoke to various radiology groups to understand what gaps still need to be addressed. One recurring issue that came up is the ability to understand the reject ratio for technologists, particularly in the Digital Radiography department.