• News
    • Featured Articles
    • Product News
    • E-News
  • Magazine
    • About us
    • Digital edition
    • Archived issues
    • Media kit
    • Submit Press Release
  • White Papers
  • Events
  • Suppliers
  • E-Alert
  • Contact us
  • Subscribe newsletter
  • Search
  • Menu Menu
International Hospital
  • AI
  • Cardiology
  • Oncology
  • Neurology
  • Genetics
  • Orthopaedics
  • Research
  • Surgery
  • Innovation
  • Medical Imaging
  • MedTech
  • Obs-Gyn
  • Paediatrics

Archive for category: Featured Articles

Featured Articles

High-end MRI scanner adapts automatically to individual anatomical and physiological characteristics

, 26 August 2020/in Featured Articles /by 3wmedia

Magnetom Vida, the new high-end 3-Tesla MRI scanner with BioMatrix
technology from Siemens Healthineers, was launched to the public at University Hospital Tübingen, where the first system is installed. It has been undergoing clinical tests in the hospital’s Department for Diagnostic and Interventional Radiology since December 2016.

Magnetom Vida is the first scanner equipped with BioMatrix, a brand-new, innovative scanner technology that addresses inherent anatomical and physiological differences among individual patients, as well as variability among users. Magnetom Vida and BioMatrix allow users to meet the growing demand for MR imaging, perform the full range of routine as well as complex examinations, and deliver robust results for every patient. Furthermore, the scanner also makes MRI more cost-effective by reducing rescans and increasing productivity. High-precision imaging means that radiologists can deliver essential and robust information to choose the right treatment for each patient every time. Siemens Healthineers, in collaboration with its customers, is playing an important role in taking healthcare forward in the development of precision medicine.

Siemens Healthineers has been developing this disruptive and innovative BioMatrix technology for over five years. Its introduction represents a further advance in MRI imaging as well as the next level of automation and patient centricity.

High image quality and efficient workflows – regardless of user or patient
Due to high levels of exam variability, MRI is often considered to be one of the most complex medical imaging modalities. Physiological and anatomical differences between patients as well as different experiences levels in users contribute to this unwanted variability. This frequently is a source of errors, rescans, and inefficient workflows in MR imaging, making it all the more important that MRI scanners deliver reliable and reproducible image data irrespective of the patient being examined or the person operating the system. This issue is precisely addressed with the new BioMatrix technology.

BioMatrix sensors in the table automatically track a patient’s respiratory pattern, giving users insights into a patient’s individual ability to hold his or her breath during the scan. This allows the user to select the optimal exam strategy, while also saving time during the examination. BioMatrix tuners can help avoid rescans, which represent a major burden on productivity as well as a driver of additional costs in radiology. In cervical spine examinations, for example, this feature uses intelligent coil technology to automatically set the optimal scan parameters based on the individual patient anatomy, all without any additional user interaction. BioMatrix tuners also improve the quality and reproducibility of whole-body diffusion. Precise control of scan parameters in real-time to match the individual patient anatomy makes it possible to avoid distortions, which can render diffusion imaging non-diagnostic, especially in 3 Tesla MRI. Innovative interfaces also help ensure a consistently high examination quality, accelerating workflows, and improving quality of care. BioMatrix Interfaces accelerate the scanning process by up to 30 percent. Automated patient positioning based on intelligent body models automatically moves the patient table to the correct scan position. An intuitive touchscreen user interface integrated onto the scanner allows for one-touch positioning. A new, easy-to-move motorized patient table further simplifies examinations, especially for adipose, immobile, and trauma patients.

Magnetom Vida is the first system to be equipped with the new BioMatrix technology, designed to tackle the challenges of variability and thereby, reduce unwanted variability in MRI examinations. It will help users achieve fewer rescans, predictable scheduling, and consistent, high-quality personalized examination results.
The ability to provide consistent and reproducible quality regardless of the individual patient and user will help reduce rescans, which can be a great financial burden for healthcare institutions. As publications have shown, rescans can account for up to €100,000 per year and system in additional costs.

Professor Konstantin Nikolaou, Medical Director of the Department of Diagnostic and Interventional Radiology at University Hospital Tübingen considers Magnetom Vida to be part of the general trend toward precision medicine: “To provide our patients with individual therapies, we need every piece of information available. When it comes to imaging, this means that we need robust, standardized, and reproducible image data that are always of the same quality regardless of the patient or user. Only then we can compare results and link them with additional information, such as data from laboratory medicine or genetics,” says Nikolaou, referring to the clinical validation of the new MRI scanner in his department. “Magnetom Vida gives us this data quality and comprehensive image information so that we can choose the right kind of personalized therapy and evaluate it – to see, for instance, how a patient responds to chemotherapy before tumour removal. This MRI scanner along with BioMatrix technology is the perfect fit for our current medical approaches, and is helping us on our way to quantitative radiology,” says Nikolaou.

Faster scans with very high patient comfort
Magnetom Vida has another major advantage: “We can examine sick patients faster with Magnetom Vida,” says Professor Mike Notohamiprodjo who, as head of MRI at University Hospital Tübingen, works intensively with the new scanner. “The scanner offers the highest degree of patient comfort with the performance of a research system, which speeds up our workflows,” he says. As examinations in Tübingen show, the new scanner decreases measurement times for musculoskeletal and prostate imaging compared to previous MRI systems. What is more, it does so with significantly improved image quality: “The signal-to-noise ratio in the clinical images is up to 30 percent higher than with systems from the previous generation,” says Notohamiprodjo.

While this is partly due to BioMatrix technology, it is also a result of the diverse insights that developers at Siemens Healthineers gathered from intense fundamental research and close customer collaborations. Key learnings from the development of a 7-Tesla research MRI system translated into a new 3-Tesla magnet design. Magnetom Vida’s all-new system architecture offers extremely high performance and unmet long-term stability – without requiring any more space than previous clinical systems. The new scanner’s 60/200 XT gradient system provides over 2.7 megawatts of power, making it the most powerful commercially available gradients in a 70-centimeter bore scanner. And, thanks to a very large field of view (55x55x50 cm), Magnetom Vida can also cover larger body regions in one step, such as full coverage abdominal exams.

The result is a great increase in productivity for routine examinations of the brain, spine, and joints – from correct patient positioning at the touch of a button to transferring the clinical images to the PACS archiving system. This is made possible by the GO technologies, which automate and simplify workflows from the start of the scan right through to the quality control of the image data. A new user interface allows not only for automated acquisition and processing, but also for more advanced post-processing applications to run at the scanner. With spine examinations, for instance, GO technologies reduce the time needed by about a fifth. This means that a department could carry out four additional spine examinations per day and per system. Given the decline in reimbursement rates, this is of great value to many radiological institutes.

Broader patient groups and new clinical growth areas
The system also allows customers to access additional clinical growth fields – for instance, by serving patient groups that were previously deemed unsuitable for MRI due to issues such as cardiac arrhythmias, excess weight, or health problems that prevent them from actively supporting the scan. With the introduction of Magnetom Vida, Siemens Healthineers expands its Compressed Sensing applications – which can make MRI scans up to ten times faster – to cover more body regions. It features Compressed Sensing Cardiac Cine, which allows free-breathing cardiology examinations (even when using contrast medium for comprehensive tissue characterization). Now, Compressed Sensing Grasp-Vibe, which enables dynamic, free-breathing liver examinations in one comprehensive scan by the push of button and for every patient, is also available. Until today, in contrast, dynamic liver imaging required four steps with exhausting breath-holds and complex timing. Grasp-Vibe technology also makes the post-processing of liver images significantly faster. During the studies he carried out in Tübingen, Professor Notohamiprodjo found that post-processing times fell from 20 to just four minutes.

Magnetom Vida even simplifies whole-body scans, which are currently particularly challenging, because they have to cover multiple scan sections and demand highly trained users. A new special technology, the Whole-Body Dot Engine, allows these difficult scans to be carried out in predictable time slots, as short as 25 minutes, with very high quality. This is accomplished through intelligent automation. The planning and execution of the scan requires only a few simple clicks. Providing high-quality diffusion weighted imaging is important for whole body exams; Magnetom Vida, with its BioMatrix Tuner technology, can deliver this distortion-free. Combined also with its strong 60/200 gradients and a large homogeneous field of view, Magnetom Vida makes whole-body examinations simple to perform, reproducibly, and with very high-quality. This is a major advantage, particularly when treating oncology patients, such as those with multiple myeloma, where guidelines have recently been moving toward whole-body MRI scans for therapy control.

Magnetom Vida offers not only numerous clinical advances, but also a number of improvements in energy consumption. These help to lower the total cost of ownership of the system over its entire life-cycle. Technologies such as Eco-Power provide an intelligent control of power-hungry components by switching them off when they are not needed for longer periods of time. The result is a MR scanner that consumes 30 percent less energy than the industry average for 3-Tesla scanners, as reported by the European Coordination Committee of the radiological, electromedical and healthcare IT industry (COCIR). 

https://interhospi.com/wp-content/uploads/sites/3/2020/08/IH232_Siemens_BioMatrix-_-Magnetom-Vida-MRI.jpg 533 800 3wmedia https://interhospi.com/wp-content/uploads/sites/3/2020/06/Component-6-–-1.png 3wmedia2020-08-26 14:17:292021-01-08 12:30:33High-end MRI scanner adapts automatically to individual anatomical and physiological characteristics

Big data and imaging – algorithms and analytics aid clinical decision making

, 26 August 2020/in Featured Articles /by 3wmedia

A fluid, game-changing combination of mathematical tools and Big Data seems ready to disrupt the field of radiology. However, it also promises to pave the way for what may turn out to be potentially-dramatic advances in healthcare.
There is some irony here. Data was once seen as a liability, to maintain and pay for. It is now being considered a potentially major asset. The key to this turnaround in perspectives lies in increasingly sophisticated, deep learning algorithms, advanced analytics and artificial intelligence which interpret the Big Data and make it usable.

Explosion in image numbers and volume
There is no hyperbole in the use of the term Big Data, as far as radiology is concerned. In recent years, there has been a veritable explosion in the stock of medical images. Emergency room radiologists often examine up to 200 cases a day, and each patient’s imaging studies can be around 250 GB of data. At the upper end, a ‘pan scan’ CT of a trauma patient can render 4,000 images. Currently, about 450-500 petabytes of medical imaging data are generated per year, but this is accelerating. Decisions are made on the basis of small parts of imaging data, the proverbial tip of the iceberg. Much of the information in this data has still to be deciphered and used.

Medical imaging and disease
Medical imaging provides important information on anatomy and organ function as well as detecting diseases states. Its analysis covers a gamut of areas from image acquisition and compression, to  transmission, enhancement, segmentation, de-noising and reconstruction. 
Technology has enabled often-dramatic leaps in image resolution, size and availability. Sophisticated picture archiving and communications systems (PACS) have allowed for the merger of patient images from different modalities and their integration with other patient data for analysis and use in a clinical setting.

Limits to vision – from digital to analogue
So far, radiology information to identify disease or other clinical conditions is presented in the form of images. Although scanners digitize data into pixels, this is reconstructed into shapes and shades or colours for display in a form that can be understood by the human brain. 
This is where the ‘tip of the iceberg’ statement above comes into play. Medical scanners encode an image pixel in 56 bits, equivalent to 72,000 trillion ‘shades’. However, the scanner reduces the data amount to 16 bits, just 65,536 shades, for the human eye. As a result, 40 bits of information is lost, in just one pixel.
At some point in the future, it seems likely that radiologists use numbers rather than images to numerically define and detect patterns of diseases. The process may in fact have already begun.

Imaging analytics and deep learning
Such trends are being fuelled by rapid advances in imaging analytics. Smart, deep learning (DL) algorithms, which analyse pixels and other digital data bytes within an image, have the capacity to detect specific patterns associated with a pathology and provide conclusions in terms of a numerical metric.
One example of the use of numbers as a diagnostic definition concerns the use of algorithms in CT images to calculate bone density. The result is compared to a reference number, which au tomatically trigger alerts on low bone density. Avoiding the need for another dedicated examination, a physician can determine if a patient needs calcium supplements or another preventative measure.
Such algorithms also learn over time, and become better at what they do, resulting in even greater speed and more confidence in the future. Such a process has been driven by the steady acceleration, over the years, in computer processing speed. Indeed, while training an algorithm at the turn of the century took 2-3 months, the same results can now be achieved and iterated within minutes.

Neural systems and algorithms

Technically, deep learning produces a direct mapping from raw inputs to outputs such as image classes. Many DL algorithms are inspired by biologic neural systems. They are different from traditional machine learning, which requires manual feature extraction from inputs, and face limitations to use in the face of the large volumes of information associated with Big Data.
Big Data’s virtuous circle
Many DL algorithms directly seek to harness Big Data in radiology. Gigantic (and fast-growing) image libraries are being accessed for investigation to develop, test, validate and continuously refine algorithms, with the aim of covering a whole range of pathologies.
For radiologists, analytic results from an examination can be comprehensively evaluated against similar data obtained over a long period of time and evaluated to suggest appropriate diagnosis in current scenarios.

Such a virtuous cycle of algorithms and Big Data have become the focus for a host of major medical technology vendors as well as start-ups. However, the key enabling players are radiology departments, who own the data repositories and are uniquely placed to curate the data, in other words, organize it from fragments and make it available for running analytical algorithms.
The above process has, in some senses, been jump-started by previous efforts to data mine reports from radiology departments as they transitioned from PACS to enterprise imaging. The next step in this Big Data-driven opportunity will consist of linking information in radiology reports to the pixels of medical images.

The pixel goldmine
Few doubt any more that pixels are a goldmine, holding wholly new insights into a medical image and how best they could be utilized, not just by radiologists but other clinicians offering patient care. Alongside data mined from electronic medical records, quantitative pixel-based analysis algorithms are increasingly likely to be used to find patterns in images.

Big Data-based screening algorithms, for example, can be used to highlight subtle, multi-dimensional changes in a nodule or a lesion. This can be followed by applications such as curved planar or 3D multi-planar reconstructions, or dynamic contrast enhancement (DCE) texture analysis on highly targeted data subsets, instead of making the time-consuming effort of querying a complete imaging dataset. 

Specific examples of such an approach might include diagnosis of lesions in the liver and identification of disease-free liver parenchyma. Another would be volume analysis of lung tumours and solitary pulmonary nodules to decide temporal evolution of lesion. Big data based pattern analysis modules can detect areas of opacities, honeycombing, reticular densities and fibrosis, and thereby provide a list of differentials, using computer aided diagnostic tools.
For tumours, in general, radiologists can run algorithms to check contrast enhancement characteristics, and such metrics can be compared to prior results as well as other pathology data to provide a specific differential list.

Decision support systems
One decision support system based on Big Data assists physicians in providing treatment planning for patients suffering from traumatic brain injury (TBI). The algorithm couples demographic data and medical records of the patient to specific features extracted from CT scans in order to predict intracranial pressure (ICP) levels.
Google’s entry into this field seeks to address real world limitations – not just in terms of human capacities but also trained medical personnel. Its first deep learning imaging algorithm sought to recognize diabetic retinopathy, the fastest growing cause of blindness in poor countries, where a shortage of specialists meant many patients lost their sight before diagnosis.

The promise of AI
Google’s algorithm is based on artificial intelligence (AI), seen as an especially promising catalyst for advances in such areas.
AI-based algorithms, for example,  can calculate the volume of bleed on the basis of multiple brain CT slices in stroke patients, with the size of bleed volume indicating urgency as well as care pathway. Another recent algorithm assesses recent infarcts on CT, which can be missed if they are hyper-acute (less than 8-12 hours old), and is therefore relevant to all patients with sudden onset weakness. The University of California in San Francisco has been testing an algorithm to identify pneumothorax in chest radiographs of surgery patients, before they exit the OR (operating room).  The aim is to not only avoid the huge costs of a collapsed lung but also ensure that the OR is freed from being used for an otherwise-avoidable procedure.
AI is also being considered for workflow management and triaging. In the near future, it is almost certain that images are screened as data is acquired by a scanner, to distinguish between ‘normal’ and ‘abnormal’ images, prioritize cases according to the likelihood of disease and alerting radiologists to conditions that require urgent attention. The results are tangible and impressive. One algorithm has helped physicians to shrink the time for cardiac diagnoses from 30 minutes to 15 seconds.

Certain vendors are leveraging AI to correlate findings on properties like morphology, cell density or physiological characteristics to expert radiologist’s reports, while taking additional clinical data such as biopsy results into account. Others use reasoning protocols as well as visual technologies such as virtual rendering to analyse medical images. This is then combined with data from a patient’s medical record to offer radiologists and clinicians decision-making support.

AI and the radiologist
So far, algorithms and emerging metrics are expected to be largely used as a complement to decisions made by radiologists.
However, at some point in the future, it seems plausible that radiologists no longer need to look at images at all. Instead, they would simply analyse outcomes of the algorithms.
Once again, AI is at play here. Apart from deep learning algorithms, radiology can claim to be witness to the first successes with the emerging science of ‘swarm’ AI, which helps form a diagnostic consensus by turning groups of human experts into super experts.  Swarm AI is directly based on nature, which sees species accomplishing more by participating in a flock, school or colony (a ‘swarm’) than they can individually. One report, published in ‘Public Library of Science (PLOS)’, stated that swarm intelligence could improve other types of medical decision-making, ”including many areas of diagnostic imaging.”
In December 2015, a study in ‘IET Systems Biology’ reported about a swarm intelligence algorithm which assisted “in the identification of metastasis in bone scans and micro-calcifications on mammographs.” The authors, from universities in the UK and India, also reported about the use of the algorithm in assessing CT images of the aorta and in chest X-ray. They proposed a hybrid swarm intelligence approach to detect tumour regions in an abnormal MR brain image.

The future: human-machine symbiosis

AI is unlikely to become a replacement for radiologists, but a tool to help them. According to Curt Langlotz, MD, PhD, professor of radiology and biomedical informatics at Stanford, the “human-machine system always performs better than either alone.”

https://interhospi.com/wp-content/uploads/sites/3/2020/08/IH167_Big-Data_Tosh_thematic_crop.jpg 591 800 3wmedia https://interhospi.com/wp-content/uploads/sites/3/2020/06/Component-6-–-1.png 3wmedia2020-08-26 14:17:292021-01-08 12:30:15Big data and imaging – algorithms and analytics aid clinical decision making

Arab Health 2018, 29 Jan – 1 Feb, Dubai

, 26 August 2020/in Featured Articles /by 3wmedia
https://interhospi.com/wp-content/uploads/sites/3/2020/08/47240_AH18_ADVERT_MEDIA_PARTNERS.jpg 2116 1500 3wmedia https://interhospi.com/wp-content/uploads/sites/3/2020/06/Component-6-–-1.png 3wmedia2020-08-26 14:17:292021-01-08 12:30:25Arab Health 2018, 29 Jan – 1 Feb, Dubai

IHF 2018: Better performance and quality through focused innovation

, 26 August 2020/in Featured Articles /by 3wmedia
https://interhospi.com/wp-content/uploads/sites/3/2020/08/IH171_IHF_page1.jpg 1500 1061 3wmedia https://interhospi.com/wp-content/uploads/sites/3/2020/06/Component-6-–-1.png 3wmedia2020-08-26 14:17:292021-01-08 12:30:10IHF 2018: Better performance and quality through focused innovation

Production of Thermosensitive Chart Recording Papers and Accessories 2018

, 26 August 2020/in Featured Articles /by 3wmedia
https://interhospi.com/wp-content/uploads/sites/3/2020/08/47208_IHE-LESSA_01.jpg 933 643 3wmedia https://interhospi.com/wp-content/uploads/sites/3/2020/06/Component-6-–-1.png 3wmedia2020-08-26 14:17:292021-01-08 12:30:18Production of Thermosensitive Chart Recording Papers and Accessories 2018

IHF Recognition Awards for 2016

, 26 August 2020/in Featured Articles /by 3wmedia
https://interhospi.com/wp-content/uploads/sites/3/2020/08/IH142_page1_IHF_WHHS-2017-Vol-53-No1v2.jpg 1500 1066 3wmedia https://interhospi.com/wp-content/uploads/sites/3/2020/06/Component-6-–-1.png 3wmedia2020-08-26 14:17:292021-01-08 12:30:32IHF Recognition Awards for 2016

2018 International Hospital Federation Awards

, 26 August 2020/in Featured Articles /by 3wmedia
https://interhospi.com/wp-content/uploads/sites/3/2020/08/IH166_IHF_P4.jpg 1425 1000 3wmedia https://interhospi.com/wp-content/uploads/sites/3/2020/06/Component-6-–-1.png 3wmedia2020-08-26 14:17:292021-01-08 12:30:122018 International Hospital Federation Awards

Production of thermosensitive chart recoding paper and accessories

, 26 August 2020/in Featured Articles /by 3wmedia
https://interhospi.com/wp-content/uploads/sites/3/2020/08/47207_IHE-LESSA.jpg 1500 1028 3wmedia https://interhospi.com/wp-content/uploads/sites/3/2020/06/Component-6-–-1.png 3wmedia2020-08-26 14:17:292021-01-08 12:30:22Production of thermosensitive chart recoding paper and accessories

The frontiers of radiology – connecting patients

, 26 August 2020/in Featured Articles /by 3wmedia

The march of healthcare technology is not always even. Benefits on one front can often be outweighed by problems on another. Radiology is no exception to this rule.
Like other medical professionals, radiologists have begun using portals and social media to connect to patients and join the move towards personal healthcare.

Websites and radiology
Today, websites staffed by imaging professionals seek to directly address the public about radiology. Such a trend is especially pronounced in the US. Examples include radiology Q&A portals at the University of Texas’ John P. and Kathrine G. McGovern Medical School, Northwest Radiology Consultants in Atlanta, Georgia, and a host of others. One of the best known is the RSNA/ACR public information website, RadiologyInfo.org, which offers a library of resources for patients including information on how various imaging procedures are performed. In Europe, the ESR has a Website page dedicated to ‘Radiation and Patients’ and an ‘Ask EuroSafe Imaging’ Q&A page, split into three sections (CT, interventional radiology and pediatric imaging), with answers provided by radiologists from across the continent.

Radiologists and social media
Radiologists have also sought to use social media to build and continuously strengthen interactive relationships with patients outside a formal hospital or physician office setting. Such approaches have spilt over into tackling concerns after widespread reports in the media about the ‘over-use’ of medical radiation. In the US, for example, the Health Physics Society has a site dedicated largely to addressing such risk perceptions in the general public. The UK too has seen such a step with the British Institute of Radiology and the Institute of Physics and Engineering in Medicine endorsing Ask for Evidence, as part of which a panel of radiologists and medical physicists respond to questions from the public on radiation safety.

Technology versus patient downtime
These are clearly significant and laudable developments. Informed patients are increasingly regarded to be better patients by several physicians. However, other recent developments in technology, above all electronic medical/health records (EMR/EHR), are placing a growing burden on clinicians to update medical documentation, in order to facilitate real-time sharing and reduce errors. This results in less time for patient care.  A key driver here, in the US, consists of federal government meaningful use (MU) requirements, which provides physicians with financial incentives to use EHRs.
For radiologists, these incentives are hardly negligible and range from 44,000 to 63,750 dollars (39,000 to 56,735 Euros) over a 5-or 6-year period via Medicare and Medicaid, respectively.
On the other hand, MU also requires 10% of patients viewing, downloading or transmitting their electronic health information, with over 40% of all imaging scans to be made accessible via certified EHR technology.
The above requirements are hardly a testimonial to efficiency. One study on MU published by the Radiological Society of North America (RSNA) in 2012 found that medical residents reported having to spend the bulk of their time updating charts and documentation, and that EHR adoption correlated directly to reduced time for direct patient care.

Radiology strives to remain at technology cutting edge
This is a profound challenge. Radiology has traditionally been the medical speciality at the cutting edge of technical advancement. It was radiology which first moved away from paper to digital technology. As a result, radiologists and industry are currently seeking to fast track solutions for increasing patient downtime and improving workflow.

Data use
In the first stage, the focus was on enhancing use of available data. Ironically, illustrating the unevenness and asynchronicity in the progress of technology, efforts were concentrated on getting more usable data out of electronic records, which did not always trickle down to radiologists. One reason was the lack of skills. Referring physicians often left responsibility to get approval for imaging to office staff, many of of who lacked the clinical knowledge required to seek such approval.

Automation: From CPOE to CDS
Soon after, the effort shifted to automation, especially in the shape of decision support (and so-called assistant clinical reasoning) tools. Such a process continues, with evolution from static to dynamic, patient-centred tools. A good example of this is the computerized order entry (CPOE) system. In 2012, a study in the ‘Journal of the American College of Radiology’ proved the clinical viability of combining radiology CPOE with imaging decision support, including pathways and algorithms, as well as classification for actionable findings.
One of the longest-used clinical decision support systems is ACR Assist from the American College of Radiology, which is designed to blend in seamlessly with radiology workflow. Clinical data is encoded in vendor-neutral ways, in order to quickly build commercial applications. The ACR has since created guidelines for radiologists and referring physicians to proceed after clinical findings. Others are also stepping in with new initiatives to enhance automation and decision support. Massachusetts General Hospital, for example, has developed Procedure Order Entry (PrOE), a surgical appropriateness system to help identify whether a procedure is necessary, and the implications of this for radiology are under active investigation. By utilizing evidence-based guidelines rather than have a less-informed entity authorize diagnostic imaging, CPOE in radiology not only enhances efficiency, but also the quality of care.

IPads and speed
One unexpected finding cited in the 2012 RSNA study on meaningful use was that residents using iPads were able to enter and update data more rapidly. Indeed, a majority of those surveyed found that iPads led to significant increases in work efficiency.
This was an opportune moment, given that a year previously, the US Food and Drug Administration had cleared the first mobile app to allow physicians to make diagnoses using iPads or iPhones.
Currently, radiology imaging applications for mobile platforms allow remote monitoring and control for a PACS administrator. Fuelled by standard tools such as DICOM viewers, these impact directly on quality control, data management and workflow efficiency.

The implications of teleradiology connectivity are especially dramatic in emergency settings. About five years ago, Mayo Clinic physicians deployed smartphones in order to assess their utility in a telemedicine stroke-management network which connected radiologists to neurologists and emergency physicians at a remote facility. The findings were encouraging, with over 90% of agreement on the key radiological findings. In the future, smartphone-based teleradiology systems are likely to become commonplace among first responders.

Image management and automation

Image management is also being used as a means to automate processes. The fast growth of technology has also necessitated unprecedented collaborations between specialists. Oncologists, for example, have been working with radiologists to analyse datasets for tumour detection and monitoring, and some studies report sharp reduction in the time required to study suspicious tissue.
On its part, Massachusetts General has also developed QPID (Queriable Patient Interface Dossier) to integrate electronic records and streamline providers’ abilities to access details in a patient’s medical history.
Other areas for attention include voice-enabled documentation, accompanied by structured reporting and data sets that pre-populate a radiology report. These not only reduce human error when inputting data but also enables radiologists to interpret and diagnose a study when a referring physician is most in need of the information – while meeting a patient.

From automation to deep machine learning
The greatest benefit of automation is to maximize the use of available data. This enhances the  ability to provide not just personal but precision medicine, too. When interfaced to an appropriate radiology-focused IT platform, individual radiologists and the broader radiology (as well as clinical) community will be empowered to benefit from feedback loops that reinforce positive lessons, de-emphasize negative ones and continuously build appropriateness and best-practice guidelines. Based on the templated information in a report, colleagues (real and virtual) would be able to rapidly offer second opinions and perspectives on how to best serve a specific patient-case.
Further down the road are deep machine learning tools which will provide sophisticated, structured and in-depth data on a patient, to enable increasingly informed decisions in the context of specific and individual challenges – influenced by factors ranging from pharmacogenomics to disease staging, age and lifestyle. Such knowledge, which would create highly actionable reports, are expected to dramatically impact upon patient outcomes.

Radiology and public perception
It is no secret that professional radiological societies strongly believe there is a need to improve patient (and public) perception of the role played by radiologists in healthcare, and that this necessitates closer contact with patients. Patients after all seldom choose a radiologist. This choice is made by a referring physician or health plan.
Though radiology is essential to patient care, radiological services often seem inconvenient, a threat to privacy, sometimes mysterious and scary. The connect between a radiologist and patient is intermediated by nurses and assistants (e.g. for injecting contrast material or preparing them for the imaging procedure), or by technologists seen as managers of machines. Various studies have shown that radiologists are not always present during performance of a study and seldom introduce themselves to a patient.
As a result, patients increasingly consider radiologists to be supervisors of a technological process. The clinician requesting the examination and receiving the radiology report is considered to be the one interpreting the study and making the decision.

Patient at the core
To sum up, the core value proposition in transforming and keeping radiology up to date involves the patient. Although the growing digitization of healthcare pushes radiologists away from patients, there is a need to make these interactions more prominent. Some radiologists warn that otherwise, there is a risk of their services becoming commoditized. For such a process, there is clearly a need to draw more patient data into decision-making.  One of the most ambitious efforts on this count was launched at the turn of the decade by RSNA, with funding from the National Institute of Biomedical Imaging and Bioengineering. The project, which promotes patient access to self-management tools, is known as Image Share, and consists of a secure network based on open-standards architecture. Images are exchanged between servers at radiology departments and imaging centres via the Cloud. A two-year pilot began in 2011 at Mount Sinai Medical Center in New York, followed by university hospitals in several states. In 2016, RSNA introduced a validation programme for the project, to test vendor system compliance with standards for exchange of medical images. To date, results have been satisfying.

Although initiatives like this will continue to grow in importance, they are unlikely to do more than enhance the efficiency of radiologists – and their professional judgement – in improving patient care.

https://interhospi.com/wp-content/uploads/sites/3/2020/08/IH144_Radiology-tools_Tosh_thematic.jpg 570 800 3wmedia https://interhospi.com/wp-content/uploads/sites/3/2020/06/Component-6-–-1.png 3wmedia2020-08-26 14:17:292021-01-08 12:30:34The frontiers of radiology – connecting patients

The leader in medical imaging since 1982

, 26 August 2020/in Featured Articles /by 3wmedia
https://interhospi.com/wp-content/uploads/sites/3/2020/08/47177_Ampronix-International-November.jpg 1500 1060 3wmedia https://interhospi.com/wp-content/uploads/sites/3/2020/06/Component-6-–-1.png 3wmedia2020-08-26 14:17:292021-01-08 12:30:22The leader in medical imaging since 1982
Page 86 of 102«‹8485868788›»

Latest issue of International Hospital

April 2024

11 July 2025

New Harley Street diagnostics clinic opens with the support of equipment and finance from Siemens

9 July 2025

New stem cell bank enables global Alzheimer’s research

9 July 2025

DNA-guided prescribing shows major clinical impact in NHS trial

Digital edition
All articles Archived issues

Free subscription

View more product news

Get our e-alert

The medical devices information portal connecting healthcare professionals to global vendors

Sign in for our newsletter
  • News
    • Featured Articles
    • Product News
    • E-News
  • Magazine
    • About us
    • Archived issues
    • Media kit
    • Submit Press Release

Beukenlaan 137
5616 VD Eindhoven
The Netherlands
+31 85064 55 82
info@interhospi.com

PanGlobal Media IS not responsible for any error or omission that might occur in the electronic display of product or company data.

Scroll to top

This site uses cookies. By continuing to browse the site, you are agreeing to our use of cookies.

Accept settingsHide notification onlyCookie settings

Cookie and Privacy Settings



How we use cookies

We may ask you to place cookies on your device. We use cookies to let us know when you visit our websites, how you interact with us, to enrich your user experience and to customise your relationship with our website.

Click on the different sections for more information. You can also change some of your preferences. Please note that blocking some types of cookies may affect your experience on our websites and the services we can provide.

Essential Website Cookies

These cookies are strictly necessary to provide you with services available through our website and to use some of its features.

Because these cookies are strictly necessary to provide the website, refusing them will affect the functioning of our site. You can always block or delete cookies by changing your browser settings and block all cookies on this website forcibly. But this will always ask you to accept/refuse cookies when you visit our site again.

We fully respect if you want to refuse cookies, but to avoid asking you each time again to kindly allow us to store a cookie for that purpose. You are always free to unsubscribe or other cookies to get a better experience. If you refuse cookies, we will delete all cookies set in our domain.

We provide you with a list of cookies stored on your computer in our domain, so that you can check what we have stored. For security reasons, we cannot display or modify cookies from other domains. You can check these in your browser's security settings.

.

Google Analytics Cookies

These cookies collect information that is used in aggregate form to help us understand how our website is used or how effective our marketing campaigns are, or to help us customise our website and application for you to improve your experience.

If you do not want us to track your visit to our site, you can disable this in your browser here:

.

Other external services

We also use various external services such as Google Webfonts, Google Maps and external video providers. Since these providers may collect personal data such as your IP address, you can block them here. Please note that this may significantly reduce the functionality and appearance of our site. Changes will only be effective once you reload the page

Google Webfont Settings:

Google Maps Settings:

Google reCaptcha settings:

Vimeo and Youtube videos embedding:

.

Privacy Beleid

U kunt meer lezen over onze cookies en privacy-instellingen op onze Privacybeleid-pagina.

Privacy policy
Accept settingsHide notification only

Sign in for our newsletter

Free subscription