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Archive for the ‘Medical Imaging Technology, Image Processing/Computing, MRI, CT, Nuclear Medicine, Ultra Sound’ Category


These twelve artificial intelligence innovations are expected to start impacting clinical care by the end of the decade.

Reporter: Gail S. Thornton, M.A.

 

This article is excerpted from Health IT Analytics, April 11, 2019.

 By Jennifer Bresnick

April 11, 2019 – There’s no question that artificial intelligence is moving quickly in the healthcare industry.  Even just a few months ago, AI was still a dream for the next generation: something that would start to enter regular care delivery in a couple of decades – maybe ten or fifteen years for the most advanced health systems.

Even Partners HealthCare, the Boston-based giant on the very cutting edge of research and reform, set a ten-year timeframe for artificial intelligence during its 2018 World Medical Innovation Forum, identifying a dozen AI technologies that had the potential to revolutionize patient care within the decade.

But over the past twelve months, research has progressed so rapidly that Partners has blown up that timeline. 

Instead of viewing AI as something still lingering on the distant horizon, this year’s Disruptive Dozen panel was tasked with assessing which AI innovations will be ready to fundamentally alter the delivery of care by 2020 – now less than a year away.

Sixty members of the Partners faculty participated in nominating and narrowing down the tools they think will have an almost immediate benefit for patients and providers, explained Erica Shenoy, MD, PhD, an infectious disease specialist at Massachusetts General Hospital (MGH).

“These are innovations that have a strong potential to make significant advancement in the field, and they are also technologies that are pretty close to making it to market,” she said.

The results include everything from mental healthcare and clinical decision support to coding and communication, offering patients and their providers a more efficient, effective, and cost-conscious ecosystem for improving long-term outcomes.

In order from least to greatest potential impact, here are the twelve artificial intelligence innovations poised to become integral components of the next decade’s data-driven care delivery system.

NARROWING THE GAPS IN MENTAL HEALTHCARE

Nearly twenty percent of US patients struggle with a mental health disorder, yet treatment is often difficult to access and expensive to use regularly.  Reducing barriers to access for mental and behavioral healthcare, especially during the opioid abuse crisis, requires a new approach to connecting patients with services.

AI-driven applications and therapy programs will be a significant part of the answer.

“The promise and potential for digital behavioral solutions and apps is enormous to address the gaps in mental healthcare in the US and across the world,” said David Ahern, PhD, a clinical psychologist at Brigham & Women’s Hospital (BWH). 

Smartphone-based cognitive behavioral therapy and integrated group therapy are showing promise for treating conditions such as depression, eating disorders, and substance abuse.

While patients and providers need to be wary of commercially available applications that have not been rigorously validated and tested, more and more researchers are developing AI-based tools that have the backing of randomized clinical trials and are showing good results.

A panel of experts from Partners HealthCare presents the Disruptive Dozen at WMIF19.
A panel of experts from Partners HealthCare presents the Disruptive Dozen at WMIF19.

Source: Partners HealthCare

STREAMLINING WORKFLOWS WITH VOICE-FIRST TECHNOLOGY

Natural language processing is already a routine part of many behind-the-scenes clinical workflows, but voice-first tools are expected to make their way into the patient-provider encounter in a new way. 

Smart speakers in the clinic are prepping to relieve clinicians of their EHR burdens, capturing free-form conversations and translating the content into structured documentation.  Physicians and nurses will be able to collect and retrieve information more quickly while spending more time looking patients in the eye.

Patients may benefit from similar technologies at home as the consumer market for virtual assistants continues to grow.  With companies like Amazon achieving HIPAA compliance for their consumer-facing products, individuals may soon have more robust options for voice-first chronic disease management and patient engagement.

IDENTIFYING INDIVIDUALS AT HIGH RISK OF DOMESTIC VIOLENCE

Underreporting makes it difficult to know just how many people suffer from intimate partner violence (IPV), says Bharti Khurana, MD, an emergency radiologist at BWH.  But the symptoms are often hiding in plain sight for radiologists.

Using artificial intelligence to flag worrisome injury patterns or mismatches between patient-reported histories and the types of fractures present on x-rays can alert providers to when an exploratory conversation is called for.

“As a radiologist, I’m very excited because this will enable me to provide even more value to the patient instead of simply evaluating their injuries.  It’s a powerful tool for clinicians and social workers that will allow them to approach patients with confidence and with less worry about offending the patient or the spouse,” said Khurana.

REVOLUTIONIZING ACUTE STROKE CARE

Every second counts when a patient experiences a stroke.  In far-flung regions of the United States and in the developing world, access to skilled stroke care can take hours, drastically increasing the likelihood of significant long-term disability or death.

Artificial intelligence has the potential to close the gaps in access to high-quality imaging studies that can identify the type of stroke and the location of the clot or bleed.  Research teams are currently working on AI-driven tools that can automate the detection of stroke and support decision-making around the appropriate treatment for the individual’s needs.  

In rural or low-resource care settings, these algorithms can compensate for the lack of a specialist on-site and ensure that every stroke patient has the best possible chance of treatment and recovery.

AI revolutionizing stroke care

Source: Getty Images

REDUCING ADMINISTRATIVE BURDENS FOR PROVIDERS

The costs of healthcare administration are off the charts.  Recent data from the Center for American progress states that providers spend about $282 billion per year on insurance and medical billing, and the burdens are only going to keep getting bigger.

Medical coding and billing is a perfect use case for natural language processing and machine learning.  NLP is well-suited to translating free-text notes into standardized codes, which can move the task off the plates of physicians and reduce the time and effort spent on complying with convoluted regulations.

“The ultimate goal is to help reduce the complexity of the coding and billing process through automation, thereby reducing the number of mistakes – and, in turn, minimizing the need for such intense regulatory oversight,” Partners says.

NLP is already in relatively wide use for this task, and healthcare organizations are expected to continue adopting this strategy as a way to control costs and speed up their billing cycles.

UNLEASHING HEALTH DATA THROUGH INFORMATION EXCHANGE

AI will combine with another game-changing technology, known as FHIR, to unlock siloes of health data and support broader access to health information.

Patients, providers, and researchers will all benefit from a more fluid health information exchange environment, especially since artificial intelligence models are extremely data-hungry.

Stakeholders will need to pay close attention to maintaining the privacy and security of data as it moves across disparate systems, but the benefits have the potential to outweigh the risks.

“It completely depends on how everyone in the medical community advocates for, builds, and demands open interfaces and open business models,” said Samuel Aronson, Executive Director of IT at Partners Personalized Medicine.

“If we all row in the same direction, there’s a real possibility that we will see fundamental improvements to the healthcare system in 3 to 5 years.”

OFFERING NEW APPROACHES FOR EYE HEALTH AND DISEASE

Image-heavy disciplines have started to see early benefits from artificial intelligence since computers are particularly adept at analyzing patterns in pixels.  Ophthalmology is one area that could see major changes as AI algorithms become more accurate and more robust.

From glaucoma to diabetic retinopathy, millions of patients experience diseases that can lead to irreversible vision loss every year.  Employing AI for clinical decision support can extend access to eye health services in low-resource areas while giving human providers more accurate tools for catching diseases sooner.

REAL-TIME MONITORING OF BRAIN HEALTH

The brain is still the body’s most mysterious organ, but scientists and clinicians are making swift progress unlocking the secrets of cognitive function and neurological disease.  Artificial intelligence is accelerating discovery by helping providers interpret the incredibly complex data that the brain produces.

From predicting seizures by reading EEG tests to identifying the beginnings of dementia earlier than any human, artificial intelligence is allowing providers to access more detailed, continuous measurements – and helping patients improve their quality of life.

Seizures can happen in patients with other serious illnesses, such as kidney or liver failure, explained, Bandon Westover, MD, PhD, executive director of the Clinical Data Animation Center at MGH, but many providers simply don’t know about it.

“Right now, we mostly ignore the brain unless there’s a special need for suspicion,” he said.  “In a year’s time, we’ll be catching a lot more seizures and we’ll be doing it with algorithms that can monitor patients continuously and identify more ambiguous patterns of dysfunction that can damage the brain in a similar manner to seizures.”

AUTOMATING MALARIA DETECTION IN DEVELOPING REGIONS

Malaria is a daily threat for approximately half the world’s population.  Nearly half a million people died from the mosquito-borne disease in 2017, according to the World Health Organization, and the majority of the victims are children under the age of five.

Deep learning tools can automate the process of quantifying malaria parasites in blood samples, a challenging task for providers working without pathologist partners.  One such tool achieved 90 percent accuracy and specificity, putting it on par with pathology experts.

This type of software can be run on a smartphone hooked up to a camera on a microscope, dramatically expanding access to expert-level diagnosis and monitoring.

AI for diagnosing and detecting malaria

Source: Getty Images

AUGMENTING DIAGNOSTICS AND DECISION-MAKING

Artificial intelligence has made especially swift progress in diagnostic specialties, including pathology. AI will continue to speed down the road to maturity in this area, predicts Annette Kim, MD, PhD, associate professor of pathology at BWH and Harvard Medical School.

“Pathology is at the center of diagnosis, and diagnosis underpins a huge percentage of all patient care.  We’re integrating a huge amount of data that funnels through us to come to a diagnosis.  As the number of data points increases, it negatively impacts the time we have to synthesize the information,” she said.

AI can help automate routine, high-volume tasks, prioritize and triage cases to ensure patients are getting speedy access to the right care, and make sure that pathologists don’t miss key information hidden in the enormous volumes of clinical and test data they must comb through every day.

“This is where AI can have a huge impact on practice by allowing us to use our limited time in the most meaningful manner,” Kim stressed.

PREDICTING THE RISK OF SUICIDE AND SELF-HARM

Suicide is the tenth leading cause of death in the United States, claiming 45,000 lives in 2016.  Suicide rates are on the rise due to a number of complex socioeconomic and mental health factors, and identifying patients at the highest risk of self-harm is a difficult and imprecise science.

Natural language processing and other AI methodologies may help providers identify high-risk patients earlier and more reliably.  AI can comb through social media posts, electronic health record notes, and other free-text documents to flag words or concepts associated with the risk of harm.

Researchers also hope to develop AI-driven apps to provide support and therapy to individuals likely to harm themselves, especially teenagers who commit suicide at higher rates than other age groups.

Connecting patients with mental health resources before they reach a time of crisis could save thousands of lives every year.

REIMAGINING THE WORLD OF MEDICAL IMAGING

Radiology is already one of AI’s early beneficiaries, but providers are just at the beginning of what they will be able to accomplish in the next few years as machine learning explodes into the imaging realm.

AI is predicted to bring earlier detection, more accurate assessment of complex images, and less expensive testing for patients across a huge number of clinical areas.

But as leaders in the AI revolution, radiologists also have a significant responsibility to develop and deploy best practices in terms of trustworthiness, workflow, and data protection.

“We certainly feel the onus on the radiology community to make sure we do deliver and translate this into improved care,” said Alexandra Golby, MD, a neurosurgeon and radiologist at BWH and Harvard Medical School.

“Can radiology live up to the expectations?  There are certainly some challenges, including trust and understanding of what the algorithms are delivering.  But we desperately need it, and we want to equalize care across the world.”

Radiologists have been among the first to overcome their trepidation about the role of AI in a changing clinical world, and are eagerly embracing the possibilities of this transformative approach to augmenting human skills.”

“All of the imaging societies have opened their doors to the AI adventure,” Golby said.  “The community very anxious to learn, codevelop, and work with all of the industry partners to turn this technology into truly valuable tools. We’re very optimistic and very excited, and we look forward to learning more about how AI can improve care.”

Source:

https://healthitanalytics.com/news/top-12-artificial-intelligence-innovations-disrupting-healthcare-by-2020

 

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Applying AI to Improve Interpretation of Medical Imaging

Author and Curator: Dror Nir, PhD

 

 

images

The idea that we can use machines’ intelligence to help us perform daily tasks is not an alien any more. As consequence, applying AI to improve the assessment of patients’ clinical condition is booming. What used to be the field of daring start-ups became now a playground for the tech-giants; Google, Amazon, Microsoft and IBM.

Interpretation of medical-Imaging involves standardised workflows and requires analysis of many data-items. Also, it is well established that human-subjectivity is a barrier to reproducibility and transferability of medical imaging results (evident by the reports on high intraoperative variability in  imaging-interpretation).Accepting the fact that computers are better suited that humans to perform routine, repeated tasks involving “big-data” analysis makes AI a very good candidate to improve on this situation.Google’s vision in that respect: “Machine learning has dozens of possible application areas, but healthcare stands out as a remarkable opportunity to benefit people — and working closely with clinicians and medical providers, we’re developing tools that we hope will dramatically improve the availability and accuracy of medical services.”

Google’s commitment to their vision is evident by their TensorFlow initiative. “TensorFlow is an end-to-end open source platform for machine learning. It has a comprehensive, flexible ecosystem of tools, libraries and community resources that lets researchers push the state-of-the-art in ML and developers easily build and deploy ML powered applications.” Two recent papers describe in length the use of TensorFlow in retrospective studies (supported by Google AI) in which medical-images (from publicly accessed databases) where used:

Prediction of cardiovascular risk factors from retinal fundus photographs via deep learning, Nature Biomedical Engineering, Authors: Ryan Poplin, Avinash V. Varadarajan, Katy Blumer, Yun Liu, Michael V. McConnell, Greg S. Corrado, Lily Peng, and Dale R. Webster

As a demonstrator to the expected benefits the use of AI in interpretation of medical-imaging entails this is a very interesting paper. The authors show how they could extract information that is relevant for the assessment of the risk for having an adverse cardiac event from retinal fundus images collected while managing a totally different medical condition.  “Using deep-learning models trained on data from 284,335 patients and validated on two independent datasets of 12,026 and 999 patients, we predicted cardiovascular risk factors not previously thought to be present or quantifiable in retinal images, such as age (mean absolute error within 3.26 years), gender (area under the receiver operating characteristic curve (AUC) = 0.97), smoking status (AUC = 0.71), systolic

blood pressure (mean absolute error within 11.23 mmHg) and major adverse cardiac events (AUC = 0.70).”

 

Screenshot 2019-05-28 at 10.07.21Screenshot 2019-05-28 at 10.09.40

Clearly, if such algorithm would be implemented as a generalised and transferrable medical-device that can be used in routine practice, it will contribute to the cost-effectiveness of screening programs.

 

End-to-end lung cancer screening with three-dimensional deep learning on low-dose chest computed tomography, Nature Medicine, Authors: Diego Ardila, Atilla P. Kiraly, Sujeeth Bharadwaj, Bokyung Choi, Joshua J. Reicher, Lily Peng, Daniel Tse , Mozziyar Etemadi, Wenxing Ye, Greg Corrado, David P. Naidich and Shravya Shetty.

This paper is in line of many previously published works demonstrating how AI can increase the accuracy of cancer diagnosis in comparison to current state of the art: “Existing challenges include inter-grader variability and high false-positive and false-negative rates. We propose a deep learning algorithm that uses a patient’s current and prior computed tomography volumes to predict the risk of lung cancer. Our model achieves a state-of-the art performance (94.4% area under the curve) on 6,716 National Lung Cancer Screening Trial cases, and performs similarly on an independent clinical validation set of 1,139 cases.”

Screenshot 2019-05-28 at 10.22.06Screenshot 2019-05-28 at 10.23.48

The benefit of using an AI based application for lung cancer screening (If and when such algorithm is implemented as a generalised and transferable medical device) is well summarised by the authors: “The strong performance of the model at the case level has important potential clinical relevance. The observed increase in specificity could translate to fewer unnecessary follow up procedures. Increased sensitivity in cases without priors could translate to fewer missed cancers in clinical practice, especially as more patients begin screening. For patients with prior imaging exams, the performance of the deep learning model could enable gains in workflow efficiency and consistency as assessment of prior imaging is already a key component of a specialist’s workflow. Given that LDCT screening is in the relatively early phases of adoption, the potential for considerable improvement in patient care in the coming years is substantial. The model’s localization directs follow-up for specific lesion(s) of greatest concern. These predictions are critical for patients proceeding for further work-up and treatment, including diagnostic CT, positron emission tomography (PET)/CT or biopsy. Malignancy risk prediction allows for the possibility of augmenting existing, manually created interpretation guidelines such as Lung-RADS, which are limited to subjective clustering and assessment to approximate cancer risk.

BTW: The methods section in these two papers is detailed enough to allow any interested party to reproduce the study.

For the sake of balance-of-information, I would like to note that:

  • Amazon is encouraging access to its AI platform Amazon SageMaker “Amazon SageMaker provides every developer and data scientist with the ability to build, train, and deploy machine learning models quickly. Amazon SageMaker is a fully-managed service that covers the entire machine learning workflow to label and prepare your data, choose an algorithm, train the model, tune and optimize it for deployment, make predictions, and take action. Your models get to production faster with much less effort and lower cost.” Amazon is offering training courses to help programmers get proficiency in Machine-Learning using its AWS platform: “We offer 30+ digital ML courses totaling 45+ hours, plus hands-on labs and documentation, originally developed for Amazon’s internal use. Developers, data scientists, data platform engineers, and business decision makers can use this training to learn how to apply ML, artificial intelligence (AI), and deep learning (DL) to their businesses unlocking new insights and value. Validate your learning and your years of experience in machine learning on AWS with a new certification.”
  • IBM is offering a general-purpose AI platform named Watson. Watson is also promoted as a platform to develop AI applications in the “health” sector with the following positioning: “IBM Watson Health applies data-driven analytics, advisory services and advanced technologies such as AI, to deliver actionable insights that can help you free up time to care, identify efficiencies, and improve population health.”
  • Microsoft is offering its AI platform as a tool to accelerate development of AI solutions. They are also offering an AI school : “Dive in and learn how to start building intelligence into your solutions with the Microsoft AI platform, including pre-trained AI services like Cognitive Services and Bot Framework, as well as deep learning tools like Azure Machine Learning, Visual Studio Code Tools for AI and Cognitive Toolkit. Our platform enables any developer to code in any language and infuse AI into your apps. Whether your solutions are existing or new, this is the intelligence platform to build on.”

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The role of PET/CT in diagnosing giant cell arteritis (GCA) and assessing the risk of ischemic events

 

Reporter: Aviva Lev-Ari, PhD, RN

 

 

May 20, 2019 — PET/CT images are offering evidence of a link between vascular patterns at the time of diagnosis for giant cell arteritis (GCA) and a patient’s risk of an ischemic event, Spanish researchers explained in a study published online on 12 May in the European Journal of Nuclear Medicine and Molecular Imaging.

The group found that patients with inflammation in vertebral arteries, which causes blood vessels to narrow, were five times more likely to develop ischemic symptoms. The information may be particularly helpful because GCA is difficult to diagnose in its early stages.

“Bearing in mind these results and our findings, we consider that the vertebral arteries should be carefully studied in patients with suspected GCA, not only to support the diagnosis but also to assess the risk of development of ischemic events,” wrote lead author Dr. Jaume Mestre-Torres and colleagues from Hospital Vall d’Hebron in Barcelona.

GCA’s challenges

Giant cell arteritis is an inflammatory disease that causes the large blood vessels to narrow and restrict blood flow. The affliction is typically seen in the temporal arteries and the aorta in adults older than 50. Currently, there is little information on how the disease develops, although there are indications that it may be linked to genetics.

The challenge for clinicians is that there are “no specific clinical symptoms that lead to the diagnosis of GCA, but headache and ischemic symptoms such as jaw claudication and transient visual loss or permanent visual loss may raise suspicion [of the disease],” the authors noted.

Results

In assessing visual loss, the team found no significant differences between patients with vertebral artery involvement and permanent visual loss (61.5%) and patients with vertebral artery issues and no permanent visual loss (58.8%) (p = 0.88). Interestingly, the presence of intrathoracic large-vessel vasculitis tended to protect against a patient’s likelihood of permanent visual loss.

In addition, “all patients with vertebral involvement but no aortic involvement showed ischemic manifestations at disease onset,” the researchers noted. “In contrast, none of the patients with aortic involvement but no vertebral hypermetabolism showed ischemic symptoms.”

SOURCE

https://www.auntminnieeurope.com/index.aspx?sec=sup&sub=mol&pag=dis&ItemID=617395

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That is the question…

Anyone who follows healthcare news, as I do , cannot help being impressed with the number of scientific and non-scientific items that mention the applicability of Magnetic Resonance Imaging (‘MRI’) to medical procedures.

A very important aspect that is worthwhile noting is that the promise MRI bears to improve patients’ screening – pre-clinical diagnosis, better treatment choice, treatment guidance and outcome follow-up – is based on new techniques that enables MRI-based tissue characterisation.

Magnetic resonance imaging (MRI) is an imaging device that relies on the well-known physical phenomena named “Nuclear Magnetic Resonance”. It so happens that, due to its short relaxation time, the 1H isotope (spin ½ nucleus) has a very distinctive response to changes in the surrounding magnetic field. This serves MRI imaging of the human body well as, basically, we are 90% water. The MRI device makes use of strong magnetic fields changing at radio frequency to produce cross-sectional images of organs and internal structures in the body. Because the signal detected by an MRI machine varies depending on the water content and local magnetic properties of a particular area of the body, different tissues or substances can be distinguished from one another in the scan’s resulting image.

The main advantages of MRI in comparison to X-ray-based devices such as CT scanners and mammography systems are that the energy it uses is non-ionizing and it can differentiate soft tissues very well based on differences in their water content.

In the last decade, the basic imaging capabilities of MRI have been augmented for the purpose of cancer patient management, by using magnetically active materials (called contrast agents) and adding functional measurements such as tissue temperature to show internal structures or abnormalities more clearly.

 

In order to increase the specificity and sensitivity of MRI imaging in cancer detection, various imaging strategies have been developed. The most discussed in MRI related literature are:

  • T2 weighted imaging: The measured response of the 1H isotope in a resolution cell of a T2-weighted image is related to the extent of random tumbling and the rotational motion of the water molecules within that resolution cell. The faster the rotation of the water molecule, the higher the measured value of the T2 weighted response in that resolution cell. For example, prostate cancer is characterized by a low T2 response relative to the values typical to normal prostatic tissue [5].

T2 MRI pelvis with Endo Rectal Coil ( DATA of Dr. Lance Mynders, MAYO Clinic)

  • Dynamic Contrast Enhanced (DCE) MRI involves a series of rapid MRI scans in the presence of a contrast agent. In the case of scanning the prostate, the most commonly used material is gadolinium [4].

Axial MRI  Lava DCE with Endo Rectal ( DATA of Dr. Lance Mynders, MAYO Clinic)

  • Diffusion weighted (DW) imaging: Provides an image intensity that is related to the microscopic motion of water molecules [5].

DW image of the left parietal glioblastoma multiforme (WHO grade IV) in a 59-year-old woman, Al-Okaili R N et al. Radiographics 2006;26:S173-S189

  • Multifunctional MRI: MRI image overlaid with combined information from T2-weighted scans, dynamic contrast-enhancement (DCE), and diffusion weighting (DW) [5].

Source AJR: http://www.ajronline.org/content/196/6/W715/F3

  • Blood oxygen level-dependent (BOLD) MRI: Assessing tissue oxygenation. Tumors are characterized by a higher density of micro blood vessels. The images that are acquired follow changes in the concentration of paramagnetic deoxyhaemoglobin [5].

In the last couple of years, medical opinion leaders are offering to use MRI to solve almost every weakness of the cancer patients’ pathway. Such proposals are not always supported by any evidence of feasibility. For example, a couple of weeks ago, the British Medical Journal published a study [1] concluding that women carrying a mutation in the BRCA1 or BRCA2 genes who have undergone a mammogram or chest x-ray before the age of 30 are more likely to develop breast cancer than those who carry the gene mutation but who have not been exposed to mammography. What is published over the internet and media to patients and lay medical practitioners is: “The results of this study support the use of non-ionising radiation imaging techniques (such as magnetic resonance imaging) as the main tool for surveillance in young women with BRCA1/2 mutations.”.

Why is ultrasound not mentioned as a potential “non-ionising radiation imaging technique”?

Another illustration is the following advert:

An MRI scan takes between 30 to 45 minutes to perform (not including the time of waiting for the interpretation by the radiologist). It requires the support of around 4 well-trained team members. It costs between $400 and $3500 (depending on the scan).

The important question, therefore, is: Are there, in the USA, enough MRI  systems to meet the demand of 40 million scans a year addressing women with radiographically dense  breasts? Toda there are approximately 10,000 MRI systems in the USA. Only a small percentage (~2%) of the examinations are related to breast cancer. A

A rough calculation reveals that around 10000 additional MRI centers would need to be financed and operated to meet that demand alone.

References

  1. Exposure to diagnostic radiation and risk of breast cancer among carriers of BRCA1/2 mutations: retrospective cohort study (GENE-RAD-RISK), BMJ 2012; 345 doi: 10.1136/bmj.e5660 (Published 6 September 2012), Cite this as: BMJ 2012;345:e5660 – http://www.bmj.com/content/345/bmj.e5660
  1. http://www.auntminnieeurope.com/index.aspx?sec=sup&sub=wom&pag=dis&itemId=607075
  1. Ahmed HU, Kirkham A, Arya M, Illing R, Freeman A, Allen C, Emberton M. Is it time to consider a role for MRI before prostate biopsy? Nat Rev Clin Oncol. 2009;6(4):197-206.
  1. Puech P, Potiron E, Lemaitre L, Leroy X, Haber GP, Crouzet S, Kamoi K, Villers A. Dynamic contrast-enhanced-magnetic resonance imaging evaluation of intraprostatic prostate cancer: correlation with radical prostatectomy specimens. Urology. 2009;74(5):1094-9.
  1. Advanced MR Imaging Techniques in the Diagnosis of Intraaxial Brain Tumors in Adults, Al-Okaili R N et al. Radiographics 2006;26:S173-S189 ,

http://radiographics.rsna.org/content/26/suppl_1/S173.full

  1. Ahmed HU. The Index Lesion and the Origin of Prostate Cancer. N Engl J Med. 2009 Oct; 361(17): 1704-6

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Digital Therapeutics: A Threat or Opportunity to Pharmaceuticals


Digital Therapeutics: A Threat or Opportunity to Pharmaceuticals

Reporter and Curator: Dr. Sudipta Saha, Ph.D.

 

Digital Therapeutics (DTx) have been defined by the Digital Therapeutics Alliance (DTA) as “delivering evidence based therapeutic interventions to patients, that are driven by software to prevent, manage or treat a medical disorder or disease”. They might come in the form of a smart phone or computer tablet app, or some form of a cloud-based service connected to a wearable device. DTx tend to fall into three groups. Firstly, developers and mental health researchers have built digital solutions which typically provide a form of software delivered Cognitive-Behaviour Therapies (CBT) that help patients change behaviours and develop coping strategies around their condition. Secondly there are the group of Digital Therapeutics which target lifestyle issues, such as diet, exercise and stress, that are associated with chronic conditions, and work by offering personalized support for goal setting and target achievement. Lastly, DTx can be designed to work in combination with existing medication or treatments, helping patients manage their therapies and focus on ensuring the therapy delivers the best outcomes possible.

 

Pharmaceutical companies are clearly trying to understand what DTx will mean for them. They want to analyze whether it will be a threat or opportunity to their business. For a long time, they have been providing additional support services to patients who take relatively expensive drugs for chronic conditions. A nurse-led service might provide visits and telephone support to diabetics for example who self-inject insulin therapies. But DTx will help broaden the scope of support services because they can be delivered cost-effectively, and importantly have the ability to capture real-world evidence on patient outcomes. They will no-longer be reserved for the most expensive drugs or therapies but could apply to a whole range of common treatments to boost their efficacy. Faced with the arrival of Digital Therapeutics either replacing drugs, or playing an important role alongside therapies, pharmaceutical firms have three options. They can either ignore DTx and focus on developing drug therapies as they have done; they can partner with a growing number of DTx companies to develop software and services complimenting their drugs; or they can start to build their own Digital Therapeutics to work with their products.

 

Digital Therapeutics will have knock-on effects in health industries, which may be as great as the introduction of therapeutic apps and services themselves. Together with connected health monitoring devices, DTx will offer a near constant stream of data about an individuals’ behavior, real world context around factors affecting their treatment in their everyday lives and emotional and physiological data such as blood pressure and blood sugar levels. Analysis of the resulting data will help create support services tailored to each patient. But who stores and analyses this data is an important question. Strong data governance will be paramount to maintaining trust, and the highly regulated pharmaceutical industry may not be best-placed to handle individual patient data. Meanwhile, the health sector (payers and healthcare providers) is becoming more focused on patient outcomes, and payment for value not volume. The future will say whether pharmaceutical firms enhance the effectiveness of drugs with DTx, or in some cases replace drugs with DTx.

 

Digital Therapeutics have the potential to change what the pharmaceutical industry sells: rather than a drug it will sell a package of drugs and digital services. But they will also alter who the industry sells to. Pharmaceutical firms have traditionally marketed drugs to doctors, pharmacists and other health professionals, based on the efficacy of a specific product. Soon it could be paid on the outcome of a bundle of digital therapies, medicines and services with a closer connection to both providers and patients. Apart from a notable few, most pharmaceutical firms have taken a cautious approach towards Digital Therapeutics. Now, it is to be observed that how the pharmaceutical companies use DTx to their benefit as well as for the benefit of the general population.

 

References:

 

https://eloqua.eyeforpharma.com/LP=23674?utm_campaign=EFP%2007MAR19%20EFP%20Database&utm_medium=email&utm_source=Eloqua&elqTrackId=73e21ae550de49ccabbf65fce72faea0&elq=818d76a54d894491b031fa8d1cc8d05c&elqaid=43259&elqat=1&elqCampaignId=24564

 

https://www.s3connectedhealth.com/resources/white-papers/digital-therapeutics-pharmas-threat-or-opportunity/

 

http://www.pharmatimes.com/web_exclusives/digital_therapeutics_will_transform_pharma_and_healthcare_industries_in_2019._heres_how._1273671

 

https://www.mckinsey.com/industries/pharmaceuticals-and-medical-products/our-insights/exploring-the-potential-of-digital-therapeutics

 

https://player.fm/series/digital-health-today-2404448/s9-081-scaling-digital-therapeutics-the-opportunities-and-challenges

 

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The Role of Advanced Imaging in Structural Heart Interventions – Watch a Video

Reporter: Aviva Lev-Ari, PhD, RN

 

 

VIDEO: The Role of Advanced Imaging in Structural Heart Interventions

Robert Quaife, M.D., director of advanced cardiac imaging,…

WATCH VIDEO

 

VIDEOS | CATH LAB NAVIGATION AIDS | JANUARY 08, 2019

VIDEO: The Role of Advanced Imaging in Structural Heart Interventions

Robert Quaife, M.D., director of advanced cardiac imaging, University of Colorado Hospital, explains why advanced imaging techniques are required to tackle complex transcatheter procedures and structural heart interventions. The University of Colorado Hospital helped develop the Philips EchoNavigator live image fusion technology, and this video offers an overview of how it came to be and where the technology is going.

Watch the related VIDEO: Evolution of Transcatheter Mitral Valve Repair at the University of Colorado, which shows exaplmes of the navigation technology is use during a MitraClip procedure.

 

Additional videos and coverage of the University of Colorado Hospital

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2019 Trends in Cardiology

Reporter: Aviva Lev-Ari, PhD, RN

 

BLOG | DAVE FORNELL, DAIC EDITORDECEMBER 11, 2018

A 40,000 Foot View of Trends in Cardiology

A 40,000 Foot View of Trends in Cardiology

 

I was recently asked about my thoughts on the big picture, over arching trends effecting cardiology. Here is the outline I gave them.

 

Cardiology Cost Drivers

Reimbursements from Centers for Medicare and Medicaid Services (CMS) and insurance providers drive trends for the adoption of new technologies. However, new technologies that can show empirical evidence for being able to improve outcomes at lower costs are being moved up for better payments. CMS and other insurers are also using a carrot and stick approach with increased use of CMS bundled payments. These give a flat fee for diagnosing and treating a heart attack or heart failure, rather than hospitals being paid for all the tests and procedures they did. This approach makes the hospitals want to find new ways to be more cost effective to increase their bottom lines to capture more of the bundled payment as revenue.

 

Heart failure makes up about a third or more of the costs to Medicare. This has caused CMS to look closely at what is driving costs, and really high readmission rates are mainly to blame. There are penalties or no reimbursements for patients who come back for repeat treatments because they were not managed properly the first time. New technologies to address heart failure and other chronic diseases are of major interest to DAIC readers. Many of these include information technology (IT) solutions, rather than treatment device technologies.

 

Other conditions like atrial fibrillation (AF) also drive up costs, so vendors are attempting to find better ways to diagnose and treat this condition. Current treatments are only effective in the first attempt in about 60 percent of patients.

 

Consolidation of Hospitals and Outside Physicians

This is a continuing trend where single hospitals or smaller hospital systems are being bought up by bigger fish to create economy of scale with larger healthcare systems. These often cover specific geographic areas and often cast a wide net to include some luminary hospitals, smaller community hospitals, immediate care centers and minute clinics inside drug partner pharmacies. Duplicate staff and services are sometimes eliminated after mergers and consolidation. Outside physicians, including cardiologists and radiologists, are also being brought into the fold as employees of the health systems, rather than the old model as outside contractors who have access to the hospital’s amenities.

 

While there is fear about consolidation, it can also offer advantages in many cases. This includes faster access to the newest technologies and devices through the system’s luminary hospitals, which can train staff at other hospitals, and more complex cases can be referred to the larger hospital. Read about this in more detail in the article “Hospital Consolidation May Increase Access to TAVR, New Cardiac Technologies.”
Trends in Cardiovascular Technologies

Any techniques and technologies that can improve outcomes, cut costs, reduce hospital length of stay or prevent readmissions can capture hospital and cardiologist attention in today’s healthcare environment. There has been a massive movement over the past two decades away from traditional open heart or vascular surgical procedures to catheter-based interventional procedures. This includes improvements in the durability and complexity of percutaneous coronary intervention (PCI), reopening chronic total occlusions (CTOs)endovascular aortic repair (EVAR), expanded interest in treating peripheral artery disease (PAD), and structural heart cases that used to be the realm of the cardiac surgeon.

 

There is a major revolution and rapid uptake in transcatheter valve technologies to replace open heart surgery. Structural heart procedures to repair or replace failing heart valves have had positive clinical trial after positive trial over the last several years. Several key cardiac surgeons in the field say catheter based interventions will likely be the way of the future and surgical case volumes will see stead declines over the next decade.

 

The Role of Information Technology and AI in Cardiology

IT solutions are now increasingly being leveraged in more sophisticated ways since most hospitals have converted to integrated electronic medical records (EMRs) over the past decade. These allow all patient and departmental data to be accessible in one location. Analytics software is now being used to mine this data to identify workflow inefficiencies and areas to cut costs or improve charge capture. Clinical decision support (CDS) software to help hospitals and doctors better meet guideline-based care in all specialties is being introduced to help clinicians make better care decisions. This includes choosing appropriate tests and procedures in an effort to reduce costs or avoid tests that will not be reimbursed.

 

Artificial intelligence (AI) will be taking over many of the manual tasks for monitoring data and to answer questions more quickly. AI will also be used to alert administrators or doctors when it autonomously identifies a problem. Applications to watch also include AI to monitor population health in the background. This can identify patients at risk for various cardiovascular diseases before they present with any symptoms. The software also can identify patients who need extra care and counseling because of the high likelihood they will not be compliant with discharge orders and be readmitted. AI also will offer a second set of eyes on cardiac imaging to help identify anomalies or greatly reduce time by performing all the measurements automatically without human intervention.

 

This use of IT also includes patient portals to engage with patients and allow better access to their records and care. This is already starting to filter down to apps on smart phones to improve care, compliance with doctor’s orders and to aid diagnosis of conditions before they become problematic, such as heart failure and AF.

 

Cardiac Imaging Trends

Cardiac ultrasound (echo) remains the No.1 imaging modality in cardiology because of its broad availability, low cost and no radiation. However, computed tomography (CT) is poised to become the front-line imaging test for acute chest pain patients in the emergency department. It is also the gold standard for structural heart procedure planning, and the number of these cases is rapidly rising. CT fractional flow reserve (CT-FFR) technology is widely expected to become the main test for chest pain in the next decade, since it has the potential to save both time and money. CT-FFR also will become the primary gate-keeper to the cath lab to significantly lower, or possibly eliminate, the need for diagnostic catheter angiograms.

 

Cardiac MRI has seen numerous advances in recent years that cut imaging times by 50 percent and automate quantification, cutting the time to read and process these exams. MRI is expected to see and increase for cardiac exams in the coming years. MRI and CT-FFR may greatly reduce the number of nuclear exams, which are currently the gold standard for cardiac perfusion imaging.

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