Author Archives: Simon Harris

User-Specificity Key for Cloud Adoption in Imaging IT?

A Tipping Point for Cloud Adoption in Imaging IT

Written by Steve Holloway

The quaint English phrase “Good things come to those who wait” is apt advice to industry stakeholders bullish on the adoption of cloud technology for imaging IT. After a decade of wholesale change to imaging IT, there is little evidence to show cloud adoption has progressed towards the mainstream. Yet, a growing array of cloud-based imaging IT solutions are now available and cloud is again being widely debated. Could this latest breed of products signal the start of mainstream adoption?

Different Users Have Very Different Needs

One size fits all” has rarely worked well in imaging IT. In fact, almost every imaging IT deployment today is different, nuanced by the unique complexity of pre-existing infrastructure, legacy software, organisational complexity, scale and user needs. This is too often overlooked by vendors and providers, leading to long, complex deployments and expensive professional services bills. Past products have also fallen foul of this critical fact and failed to cater for specific user groups.

The driving factors for selecting a cloud solution for imaging IT vary significantly between user groups. A large academic hospital will in general look at cloud as a means to improved accessibility to imaging data, with the ability to rapidly scale up new deployments and upgrades, while maintaining security and data ownership. Cloud adoption is not, as is regularly misunderstood, a cost-saving exercise for imaging IT in this scenario; frankly, the savings pale into insignificance in comparison to recent investment in electronic medical records (EMR) or cost-savings potential with better care pathway management. In contrast, a small community hospital usually wants to better manage cost, limit exposure to on-site hardware downtime and improve flexibility of use.

Small Providers to Drive Adoption?

At last it appears the industry is coming to terms with this and new cloud IT imaging products are being targeted to specific user groups. For small clinics and hospitals, this is more commonly in the form of SaaS-based products, with more predictable cost, integrated maintenance and support and full off-site storage. Moreover, the push towards modular imaging IT software will help to spur this change, leading to a common feature set of image ingestion, storage, workflow and viewing, in a secure, thin-client, hosted environment. Adoption to date has been slow –with the “trickle-down” effect of new technology from deployments at larger institutions. However, given the growing abundance of lower-cost solutions targeting this market, the adoption of hosted cloud for imaging IT solutions should outstrip that of the larger provider segment.

For larger providers and networks, tied to large capital budgets and long-term infrastructure investment, the managed service approach is a harder sell. Here the onus is on workflow capability and, above all, speed. Cloud is viewed as an enabler, to help develop inter-disciplinary information sharing, improve data access and improve the roll-out and scaling up of new software and features.

However, data ownership and security are also of tantamount importance to the provider and willingness to allow data “off-site” is uncommon. Consequently, vendors are now offering imaging IT software that better fits the “private cloud” model, either in a managed (third party administration and operation) or non-hosted model (software is located and managed by the health provider in its own data centre, but is accessible via a proprietary private cloud network). Cloud adoption will certainly ramp up in the larger provider market but with more focus on managed or non-hosted private clouds and far less third-party hosting.

External Forces

So, while user demand varies significantly and availability of user-specific cloud imaging IT solutions is improving, there are a few other factors further pushing the development of cloud technology towards a mainstream adoption tipping point. The focus on integrated care between disciplines and providers means data interoperability is top of many health providers’ agendas, while more common use of mobile technology is demanding data access from any networked location or device. Furthermore, a greater push for patient access and ownership of data is focusing the industry to utilise cloud as a means increasing the use of patient-provider data portals (enabling wider provider choice) and in improving security and data confidentiality. Add to this a global shortage of radiologists, stimulating growth for teleradiology and remote reading, and the digitalisation of pathology images, demanding more cost-effective storage options, and the case of cloud imaging IT becomes far stronger.

In the mid- to long-term, the influence of deep learning and artificial intelligence will also play an industry defining part in driving cloud adoption. Decision Support Tools and Computer Aided Diagnosis (CADx) software will be based on deep learning platforms that will need widespread access to large volumes of imaging data to be able to “learn” from it. Therefore, the common model today of on premise imaging IT and data-storage will be a major barrier to deep-learning unless cloud technology is embraced.

Taken together, these factors point to a need for more widespread adoption of cloud technology for imaging IT. Of course, barriers such as security concerns and data ownership still exist, but the direction and demands of future healthcare appear set to abolish them soon. So, while the tipping point for cloud-enabled imaging IT is probably still a few years-off, those “good things” are just around the corner.

Deep Learning in Medical Imaging: A $300M Market by 2021

15 February 2017

Deep Learning in Medical Imaging A $300M Market by 2021

Deep learning will increasingly be used in the interpretation of medical images to address many long-standing industry challenges. This will lead to a $300 million market by 2021, according to a new report by Signify Research, an independent supplier of market intelligence and consultancy to the global healthcare technology industry.

In most countries, there are not enough radiologists to meet the ever-increasing demand for medical imaging. Consequently, many radiologists are working at full capacity. The situation will likely get worse, as imaging volumes are increasing at a faster rate than new radiologists entering the field. Even when radiology departments are well-resourced, radiologists are under increasing pressure due to declining reimbursement rates and the transition from volume-based to value-based care delivery. Moreover, the manual interpretation of medical images by radiologists is subjective, often based on a combination of experience and intuition, which can lead to clinical errors.

A new breed of image analysis software that uses advanced machine learning methods, e.g. deep learning, is tackling these problems by taking on many of the repetitive and time-consuming tasks performed by radiologists. There is a growing array of “intelligent” image analysis products that automate various stages of the imaging diagnosis workflow. In cancer screening, computer-aided detection can alert radiologists to suspicious lesions. In the follow-up diagnosis, quantitative imaging tools provide automated measurements of anatomical features. At the top-end of the scale of diagnostic support, computer-aided diagnosis provides probability-driven, differential diagnosis options for physicians to consider as they formulate their diagnostic and treatment decisions.

“Radiology is evolving from a largely descriptive field to a more quantitative discipline. Intelligent software tools that combine quantitative imaging and clinical workflow features will not only enhance radiologist productivity, but also improve diagnostic accuracy,” said Simon Harris, Principal Analyst at Signify Research and author of the report.

However, it is early days for deep learning in medical imaging. There are only a handful of commercial products and it is uncertain how well deep learning will cope with variations in patient demographics, imaging protocols, image artefacts, etc. Many radiologists were left underwhelmed by early-generation computer-aided detection, which used traditional machine learning and relied heavily on feature engineering. They remain sceptical of machine learning’s abilities, despite the leap in performance of today’s deep learning solutions, which automatically learn about image features from radiologist-annotated images and a “ground-truth”. Furthermore, the “black box” nature of deep learning and the lack of traceability as to how results are obtained could lead to legal implications. Whilst none of these problems are insurmountable, healthcare providers are likely to take a ‘wait and see’ approach before investing in deep learning-based solutions.

“Deep learning is a truly transformative technology and the longer-term impact on the radiology market should not be under-estimated. It’s more a question of when, not if, machine learning will be routinely used in imaging diagnosis”, Harris concluded.

About the Report

“Machine Learning in Medical Imaging – 2017 Edition” provides a data-centric and global outlook on the current and projected uptake of machine learning in medical imaging. The report blends primary data collected from in-depth interviews with healthcare professionals and technology vendors, to provide a balanced and objective view of the market.

About Signify Research

Signify Research is an independent supplier of market intelligence and consultancy to the global healthcare technology industry. Our major coverage areas are Healthcare IT, Medical Imaging and Digital Health. Our clients include technology vendors, healthcare providers and payers, management consultants and investors. Signify Research is headquartered in Cranfield, UK. To find out more:, T: +44 (0) 1234 436 150,

Initial Takeaways from PHM Vendor 2016 Financial Results

Initial Takeaways from PHM Vendor 2016 Financial Results

Written by Alex Green

Many of the larger EHR vendors that are active in population health management have recently released their Q4 2016 and full year results, with more to follow shortly. Signify Research examines what we can take away so far.

Cerner Corporation

  • Q4 2016: Revenue up 7% on Q4 2015
  • Full year 2016: Revenue grew 8% compared to 2015 to $4.8 billion
  • Population Health: Revenue grew 13% in 2016 compared to 2015

The top level: For a relatively mature company, 8% growth for the year would seem healthy. However, it is much less than the 30% revenue growth seen in 2015 and it was also down on the double-digit guidance the that company gave at the start of the financial year.

Much of this short fall was a result of a decline in Cerner’s Systems Sales, which fell from $1.28 billion in 2015 to $1.27 billion in 2016, with licensed software and technology resale taking the brunt of the decline. However, this was partially offset by a 30% increase in SaaS software sales.

PHM driving SaaS Growth: The biggest driver of the increase in Cerner’s SaaS business was population health management (PHM). Overall the PHM business of Cerner was up 13% on the 2015 figure of $214 million. Some good news but it also isn’t without caution.

The PHM business grew at a significantly faster rate for Cerner in 2015 (21%) and as a share of the overall business, PHM remains at 5%, unchanged for the last three years and in fact slightly down on the 2014 figure.

Time to deliver on long term PHM strategy: In its investor communiques during 2016, Cerner continued to push the message that it saw PHM as a significant growth driver for its overall business in the medium term, targeting PHM driving approximately 20% of revenues in 2025. If this is to remain realistic it needs to see an upturn in its PHM business growth soon.

The company does believe that the market is approaching an inflection point for population health, as the transition from fee-for-service to at-risk models accelerates, a view that Signify Research agrees strongly with. One ingredient that has been lacking in the Cerner PHM strategy so far has been a strong advisory portfolio to address client implementation and process reengineering demands that accompany a PHM rollout. Some of its competitors, particularly those not from an EHR background such as Health Catalyst, have done a better initial job of this. Therefore, the company’s recent commitment to expand this area of its solution should certainly assist with growth towards the longer-term target for PHM.

Athena Health

  • Q4 2016: Revenue up 12% on Q4 2015
  • Full year 2016: Revenue grew 17% compared to 2015 to $1.08 billion
  • Population Health: 2.2 million lives covered with population health solution
  • Patient Engagement: 57,861 providers using patient engagement solution

Core business: Athena Health achieved overall revenues of $1.08 billion in 2016, up from $925 million in 2015. This represents 17% growth, down a little on the previous year and at the bottom end of the guidance given out at the start of 2016.

Most of the company’s revenues are from its core ambulatory EHR & revenue cycle management businesses, both of which grew strongly in terms of provider customers. In 2016 its athenaCollector revenue cycle management user base grew 16% to 87,691 providers and its althenaClinicals EHR user base grew 26% to 41,340 providers. However, for both products, the growth rate was lower than 2015, re-enforcing the fact that for Athena to continue to see growth at rates similar to previous years, it needs to move beyond its traditional customer base.

Addressing PHM though patient engagement: Athena Health is very aware of this need to expand beyond its traditional ambulatory markets and products and it sees PHM as an opportunity to aid executing on this strategy. However, to date what it labels as its PHM business is relatively small, representing less than 1% of the company revenues.

However, when you include Athena’s patient engagement product line, athenaCommunicator, as part of the PHM market, then its already a significant player, with 64,763 providers using its patient engagement solution at the end of 2016, up 23% on the previous year.

The right direction: On the surface the Athena strategy towards PHM does seem somewhat disjointed. Patient engagement and care coordination are typically viewed as components of PHM. However, the Athena product offering keeps them separate and to some extent restricts their use by limiting implementation of certain PHM solutions, such as athenaCoordinator, to customers that also use athenaCollect or athenaClinicals. However, it’s overall strategy of expanding its offering beyond its core ambulatory claims and EHR customer base is the right one for the company.

Others Yet to Report

These two companies represent an important but relatively small share of the overall population health management market. Many other key players in the PHM market are still to announce their full year results.

For example, Allscripts is due to announce its results on 16th February. As with Cerner and Athena Health, results for Allscripts for the first three quarters of the year were mixed. It had seen growth in its PHM business of 4.4% but in a similar vein to Cerner, the share that PHM was taking of the overall business had remained relatively stagnant. Tomorrow will enable us to see whether the final quarter of the year has changed this.

Conifer Health Solutions is also due to announce its results (via its owner Tenet Health) later this month. Conifer had grown its business 13.6% during the first three quarters of the year. PHM still plays second fiddle to Conifer Health’s revenue cycle management business, in particular its captive business with Tenet. However, PHM is central to its growth strategy and the final quarter’s results should give some insight on how successful it’s been executing on this.

Analysis of All PHM Players 2016 performance in New Market Report from Signify Research Publishing Soon

A full analysis of all leading PHM players will be provided in Signify Research’s upcoming market reports ‘Population Health Management – North America Market Report 2017’, publishing in March 2017.

This will include analysis of the PHM businesses of Cerner, Allscripts, Medecision, GetWellNetwork, eClinicalWorks, AxisPointHealth, HealthCatalyst, Emmi Solutions, Meditech, AthenaHealth, Welltok, Verscend, Transcend Insights, Philips, Orion Health, Optum, NextGen Healthcare, YourCareUniverse, MedHost, McKesson, Lightbeam, InfluenceHealth, IBM,  I2I Population Heath, HealthDialog, GetRealHealth, GE Health, Forward Health, Evolent, Epic, Enli, Conifer Health, Caradigm, Aetna and The Advisory Group.

For further details please click here or contact

Machine learning in radiology targets efficiency

Machine learning in radiology targets efficiency

Written by Simon Harris for AuntMinnie Europe

Whilst artificial intelligence (AI) is unlikely to replace radiologists any time soon, a new breed of machine learning-based software applications is poised to take on many of their tedious, repetitive, and time-consuming tasks – improving productivity and freeing up more time to focus on value-added activities.

In most countries, radiologists are already operating at, or near, capacity; any further gains in efficiency is likely to be derived from the use of “intelligent” workflow software tools. Furthermore, radiology is evolving from a largely descriptive reporting model to a more quantitative discipline, placing added demands on radiologists. As a result, the need for workflow efficiency has never been greater. It’s time to cut through the AI hyperbole and take advantage of the many benefits that machine learning is bringing to radiology.

There is a growing array of intelligent image analysis products that automate various stages of the imaging diagnosis workflow. Whilst early generation computer-aided detection (CAD) products largely failed to meet expectations, the application of advanced machine-learning techniques such as deep learning will, in part, enable CAD products to evolve from purely detection systems to more advanced decision-support tools.

This is the opening extract of a feature article for AuntMinnie Europe.

To read the full article, please click here.

(Access to the article may require free membership to AuntMinnie Europe – it’s full of great content and insight so well worth signing up!)

Quantitative Imaging Market to Exceed $500M in 2021

Quantitative Imaging Software Market to Exceed $500 million in 2021

Written by Simon Harris

The market for Quantitative Imaging Software1 comprises a wide range of software tools that provide automated measurements of anatomical structures, for various body sites and across multiple modalities. These tools vary in complexity, from tools that provide size measurements, to tools that provide more advanced metrics, such as perfusion analysis and texture analysis.

The market is being driven by the evolution of radiology from a largely descriptive field to a more quantitative discipline. Quantitative imaging, also called radiomics, is the use of algorithmic tools to provide objective and repeatable measurements of imaging biomarkers, such as size, texture, calcification, location in the organ and rate of growth. These biomarkers are indications of disease characteristics and may be useful for predicting prognosis and therapeutic response. Quantitative imaging decreases the subjectivity associated with radiologist interpretation of medical images, leading to increased diagnostic and prognostic accuracy.

Quantitative imaging tools have been available for many years and are typically sold as applications for advanced visualisation platforms. There is a growing trend to combine quantitative imaging data with other relevant information, such as pathology reports and patient information extracted from an EHR. This relatively new class of products, called Decision Support Tools, is forecast to take-off in the coming years, with the first products now entering the market.

Several companies are developing Computer-aided Diagnosis (CADx) systems that provide the functionality of Decision Support Tools and provide interpretation of medical images, for example, a probability score for the presence of cancer. The first FDA approved CADx systems are expected to enter the market later this year. These first generation CADx systems will have narrow diagnostic capabilities and will be limited to specific parts of the body and specific modalities, e.g. diagnosis of breast cancer from MRI scans. The introduction of CADx systems with broad diagnostic capabilities, at an affordable price point, will be the trigger for more widespread uptake of CADx systems, but this is likely to be several years away.

This topic and other issues will be explored in full in Signify Research’s upcoming market report ‘Machine Learning in Radiology – World Market Report, publishing in January 2017. For further details please click here or contact


1 The Quantitative Imaging Software market comprises Quantitative Imaging Tools, Decision Support Tools and Computer-aided Diagnosis Systems.

Digging into GE & Philips Annual Results

Digging into GE & Philips Annual Results

Written by Steve Holloway

Two of the largest global health technology vendors released their Q4 2016 and full year 2016 results in the last week. We dig into the key takeaways for both GE Healthcare and Philips Health Tech divisions.


GE Healthcare

  • 4Q 2016: Health division posted revenue growth of 3%; operating profit up 10%
  • Full year 2016: Health division revenues posted growth of 4%; operating profit up 10%

The Signify View:

GE Healthcare posted a solid fourth quarter and full year results for 2016. Here’s some key highlights and clues as to future performance:

Lifesciences increasingly a strong growth driver: GE has been making significant inroads into the Lifesciences marketplace, showcased with 9% growth in Lifesciences revenues in Q4 2016. While no split is provided in the recent full year 2016 results, a 2015 company report shows Lifesciences accounted for close to one quarter of the $18 billion GE Healthcare business line (~23%). This push towards biotechnology and pharmaceuticals is clearly paying off and will also help position the Healthcare division for future growth markets, especially in areas such as predictive disease treatment, cell therapy, personalised medicine and genomics. Moreover, diversification into the more predictable Lifesciences sector has also helped the healthcare division to smooth out swings in its core capital-intensive diagnostic imaging, clinical care and healthcare informatics business lines.

Long-term play in BRIC+ regions is starting to pay off: GE Healthcare has made it a strategic priority to push into emerging healthcare markets in the BRIC+ regions, at significant cost. While this has delivered growth in some regions (such as China) other emerging markets can be fickle, with large swings in procurement demand for high-end products, adding further risk to this strategy. However, Q4 2016 results suggest this strategy is starting to pay dividends, with Healthcare division revenue in China growing 19% and Latin America 16% (in comparison to 6% growth in Europe and -1% in the US). This may also reflect GE’s strategy for “in-region, for-region” manufacturing, enabling it to compete in lower-cost product markets against local competitors.

Core device business is steady but not stellar: Posting a slim 2% growth for non-Lifescience segments (such as Imaging, Clinical Care and Health Informatics) in Q4 2016 is a reasonable performance (in comparison to a revenue decline in Q4 2015), especially with operating profit increasing by 10%. However, fourth quarter results are often a useful marker as to the robustness and future potential of capital-intensive markets such as Diagnostic Imaging and Healthcare Informatics. Therefore, slim revenue growth in Q4 suggests GE Healthcare has more work to do in 2017 in its traditional healthcare markets.


Philips Healthcare

  • 4Q 2016: Health Tech division posted revenue growth of 5%; adjusted EBITA at 15.3%, 1.9% up on Q4 2015
  • Full year 2016: Health division revenues posted growth of 5% on full year 2015

The Signify View:

Philips Healthcare posted healthy Q4 2016 and full year results for 2016. Here’s some key highlights and clues as to future performance:

Large, long-term deal strategy paying off: Philips has been one of the pioneers of pushing a managed service approach for hardware and software to health providers. With several long-term deals (often 10 years or more) including bundled imaging and clinical device hardware and health informatics software closed in 2016, Philips is becoming more entrenched with key large clients and protecting it’s installed base long-term, while ensuring more repeatable revenue generation. Furthermore, as the health informatics sector consolidates towards central health informatics platforms for healthcare providers, entrenched vendors with long-term bundled managed service deals will be prime position to capitalise.

Innovative health tech sectors yet to be realised: Philips has made a strategic move in recent years towards markets that combine technology with new care models. These newer products such as telehealth are part of Philips Healthcare’s Connected Care & Health Informatics business line. While it has built up a solid reputation as a leading vendor in this sector, this move has yet to provide strong growth. Accounting for 19% of healthcare revenues (when combined with Health Informatics), the Connected Care business posted comparable growth of 4% on 2015, the same rate as Philips Diagnosis and Treatment business (which accounts for 39% of revenues). Given that Connected Care is relatively small, yet covers an under-penetrated market with high-potential for growth, it would seem Philips has yet to fully capitalise here.

Mature markets positive; emerging markets steady; Western European orders take a dive: Unlike GE Healthcare (see above), Philips Healthcare posted solid comparable fourth quarter revenue growth of 4% and 5% in Western Europe and North America respectively. However, emerging “growth geographies” were far weaker than GE Healthcare, with Philips Healthcare comparable sales growth at only 5% for 4Q 2016. This is because Philips has invested less aggressively in emerging markets and has also been less focused on rolling-out “value” device products specifically for emerging markets, instead preferring to focus on higher-end devices in more established markets. Consequently, Philips portfolio is more exposed to swings in mature markets and less exposed to swings in emerging markets, such as the BRIC+ regions.

Given this, one concern is evident for Philips Health Tech division in 2017: the outlook for mature markets is increasingly uncertain, especially with US healthcare change imminent with Donald Trump as president and a worsening economic picture for Europe. This may also be exemplified in a steep drop-off (15% in Q4 2016) in order intake in Western Europe for the Diagnostic & Treatment and Connected Care & Health Informatics segments. So, while 2016 revenues have been solid in mature markets, 2017 could be a more challenging year for Philips Health Tech’s core business.

Time to Move Beyond Meaningful Use & MACRA

The Signify View: Time to Move Beyond Meaningful Use & MACRA

Written by Alex Green

Meaningful Use targets set by the Centers for Medicare and Medicaid Services (CMS) have served a purpose in rapidly bringing patient engagement and population health management (PHM) to the forefront of the US healthcare industry. However, providers, payers, employers and consumers have a huge amount more to gain if, and when, the use of these platforms moves beyond the box-ticking exercise of hitting Meaningful Use and MACRA targets.

Here’s our take on why:

The Signify View

The modern healthcare consumer in the US is used to having choices. In banking, retail, travel, and most other walks of life, product or service information is abundant and decisions are made quickly, based on price, convenience, reputation and quality. Increasingly, consumers are approaching healthcare with a similar attitude. They want to be able to compare the quality of the service they’ll receive, view feedback from other service users, manage appointments online, understand the cost implications of medical procedures, contribute their heath data to the decision-making process and be able to easily communicate electronically with providers.

At the same time health providers are under pressure to adapt to this new, consumer driven healthcare market environment. They need to be able to address the demands of this consumer-centric approach to healthcare and provide the improved cost transparency, greater convenience, better communication tools and a wider set of service information for the patient.

A Convergence of Demand

The solution from both perspectives, can be provided by population health management platforms and patient engagement platforms, the latter often being implemented as a component of the former. Patient engagement platforms provide both the consumer and provider with a vehicle for improving the communication channels.  They provide consumers with the information and the service access they need to obtain the information they’re demanding, while also giving providers a strategic tool to retain existing customers and roll out initiatives that result in much more comprehensive care management solutions for the population they’re serving.

However, many implementations of PHM and patient engagement platforms are not yet addressing these requirements, instead they are just being used to hit CMS targets around reimbursement.

Following purely a MACRA adherence strategy, a clinician can hit their CMS targets for patient access by ensuring that one of their patients interacts with their patient portal once during a three month period over 2017. Or similarly they can hit their patient education target by providing one patient over the three month period with targeted education material via their EHR. This will allow them to take a step towards receiving full reimbursement. But it doesn’t touch any of the issues outlined above and only scrapes the surface of what they otherwise could have achieved from the investment made in the enabling platform. A big missed opportunity!

Reducing Healthcare Costs

As well as having to address the increasingly consumer-centric nature of the healthcare industry, providers are also grappling with the move to value-based care and an overall agenda of better managing costs.

The strategy often employed to address this is via Accountable Care Organizations (ACOs). These can be physician led, hospital led, and even payer-led, but at their core is the objective of better managing the health of a whole population in order to control costs.

Population Health Management platforms are again the central tool employed to achieve this. These platforms provide:

  • The risk stratification solutions and analytics that can be used to target and pinpoint which patients within the population are those that are most likely to develop a long-term condition, or for those already managing long-term conditions which are most likely to be admitted or re-admitted to hospital.
  • The care management/care coordination tools that pull data together from disparate sources and provide multi-disciplinary care teams with a central tool to coordinate care across these populations.
  • The patient engagement tools that allow for providers and consumers to easily interact, share health data, provide reminders for screenings, allow for online appointment booking, provision remote health monitoring and allow for greater management of care costs.

The meaningful use, and future MACRA Advancing Care Information (ACI) targets around patient education, portal access, care transitions and patient generated health care started to encourage the use of population health management platforms as described above. But again, following a strategy based purely on hitting meaningful use or MACRA targets will not address this drive to manage the cost of providing healthcare for a given population. ACOs and providers need to instead embrace a much broader strategy when employing their PHM solution, one that goes well beyond these legislative targets.

Times Are Changing

Signify Research’s view is that ACOs, providers and payers do understand this, and they have now started to move on from their initial deployments. For many organisations the investment in technology has been made, it’s the re-engineering of processes that are now required in order to fulfil the technology’s potential. Over the coming years we’ll see greater use of the full range of features offered that address the demand driven by the consumerisation of health and the need to better manage health care costs.

This is good news for all. For the consumers demanding a healthcare experience that provides the choice, information, control and transparency they’re used to seeing elsewhere; for the providers and ACOs implementing strategies to support their move to better manage unforeseen financial risk, and of course to the vendors of these platforms.


New Market Report from Signify Research Publishing Soon

A full analysis of these issues will be provided in Signify Research’s upcoming market reports ‘Population Health Management – North America Market Report 2017’, publishing in March 2017, and ‘Population Health Management – EMEA, Asia & Latin America Market Report 2017’, publishing in June 2017. For further details please click here or contact

Hard Brexit Signals Rocky Road for Radiology

Hard Brexit Signals Rocky Road for Radiology

After the strongest hint yet from Prime Minister Theresa May that the U.K. will look to extract itself from the European Union (EU) single market (a so-called hard Brexit), it seems right to review early speculation on the outlook for medical imaging and healthcare IT.

Let’s focus on three factors: the U.K. National Health Service (NHS), regulatory aspects, and the market impact.

Radiology in U.K. NHS already suffering

Hard Brexit has few positives for the U.K. NHS, a system that’s already in some difficulty. Not only are current resources in radiology already at a breaking point, but also stricter immigration controls and a worsening economic picture could see the NHS imaging service facing a clinical brain drain.

This is the opening extract of Steve’s regular monthly market column for AuntMinnie Europe.  

To read the full article, please click here.

(Access to the article may require free membership to AuntMinnie Europe – it’s full of great content and insight so well worth signing up!) 

The PHM Competitive Environment

The Evolving Population Health Management Competitive Environment

The affordable care act and the emergence of accountable care organisations (ACOs) have brought population health management (PHM) to the forefront of the US healthcare IT industry agenda. This has brought with it a plethora of platform solutions aimed at addressing the need for population health management in its various forms. This white paper sets out to examine how the supplier base for population health management platforms in the US will develop over the coming years.

Click here to download the report.

Significant Barriers Still Exist for Risk-Sharing Contracting

The Signify View: Significant Barriers Still Exist for Risk-Sharing Contracting

Written by Steve Holloway

Despite the sudden surge of interest in risk-sharing contracts, a by-product of a recent wholesale legislative shift towards value-based care, multiple barriers remain that will limit widespread adoption, both for clinical content management and wider healthcare IT.

Here’s our take:

Providers’ ability to measure ROI is poor

Risk-sharing contracts take many forms, though the most common models rely on the ability of vendors and healthcare providers to define and measure agreed “key performance indices” (KPIs) targets. If a vendor’s technology or software solution fails to meet these agreed KPIs, financial penalties are incurred – exceed and bonuses are available. While this may seem attractive to health providers as a tangible way to hold vendors to account, there is a fundamental flaw: most health providers do not have robust systems in place for measuring KPIs, let alone return on investment (ROI). Without accurate and robust data, it may prove challenging for providers to hold their vendors to account when KPIs are missed.

It’s easy to forget that healthcare providers are relatively new to healthcare IT adoption. Ever changing legislation, tightening budgets and connecting a patchwork of disparate legacy IT systems have been primary concerns of late, leaving little focus and investment in quality performance metrics and analytics. Even radiology, an early adopter of IT and digitalised for almost two decades, is only beginning to implement analytics and dashboarding capability for KPI and ROI measurement.

If this remains the case, there will be two potential outcomes in the short to mid-term; providers will avoid risk-sharing contracts until they can improve their own KPI and ROI measurement, or professional services and consulting on KPI and ROI will be incorporated into risk-sharing contracts to improve reporting over the course of a contract term. The second option also has further complications in vendor self-interest, in that the same vendor could be advising the provider on how best to meet KPIs, while inherently also protecting itself from contractual penalties. One way around this is to bring in a third party integrator, consultant or specialist vendor to offer the advisory and optimisation services, though to date there have been few examples of these services rendered at scale.

Healthcare remains heavily capital expenditure focused

Despite the increasing focus on risk sharing and managed services contracts, most of healthcare remains based on annualised capital expenditure models. In public healthcare, the root lies in the process of governmental budgeting and rigid procurement frameworks, limiting providers’ ability to be more innovative in contracting. Imaging IT software and services may also be part of larger procurement deals for imaging modality hardware.

Admittedly, private healthcare providers have more flexibility for contract innovation and are likely to be more open to sharing risk with technology vendors, especially if a case can be made for making future expenditure more predictable. That said, few have been willing so far to stray from the traditional business model. To put this in context, we should look at managed services adoption; the benefits of a managed services contract are generally clearer and simpler to implement than risk-sharing contracts, yet to date uptake of managed services has been very low. This suggests the more complex risk-sharing has a long way to go before it becomes a common approach.

The interoperability chasm has only shrunk slightly

Healthcare providers are under increasing pressure to connect disparate health IT systems to drive the interoperability of patient and health data. Using imaging as an example, providers increasingly want to share standardised and unstructured image data within and between networks, spurring the advent of enterprise imaging platforms and clinical content management solutions. While some progress is being made, interoperability of images between common software such as EMR and imaging IT still has significant challenges and barriers.

Put in context of risk-sharing contracts, this is a major concern for providers. Firstly, without full and centralised access to the correct data in a structured format, gaining confident and accurate insights for KPI measurement is very difficult. Furthermore, there is today a far greater focus on care quality and coordination between disciplines and providers. Without improvements in interoperability and the interfacing of current provider systems on complex patient care, especially comorbid pathways, the value of risk-sharing contracts for providers will be significantly reduced.

Some way to go

It’s true that in some instances risk-sharing could be successfully implemented today, but these are relatively few. For imaging IT and clinical content management, scale is a major factor. Small private radiologist reading groups or imaging centres may look to adopt this model to make operations more predictable. These use-cases are relatively simple in purpose and do not have the complex issues associated with multi-department, multi-site hospital providers, not to mention the scale of risk.

When the above is considered with the healthcare environment of changing legislation and tightening healthcare budgets, the argument for risk-sharing contracts will not be enough to sway most healthcare providers in the short to mid-term. The rewards are simply not yet well known to counter the severe cost of failure.

Machine Learning in Radiology – Vendors Must Prove The ROI

Machine Learning in Medical Imaging – Vendors Must Prove The ROI

Written by Simon Harris

Machine learning was undoubtedly one of the hottest topics in radiology last year, with a steady stream of academic research papers highlighting how machine learning, particularly deep learning, can outperform traditional algorithms or manual processes in certain use-cases. Investment in machine learning start-ups also continued, with several companies attracting early stage funding. To date, more than $100m has been invested in start-ups that are developing AI solutions for radiology. Furthermore, commercial activity gained pace, with at least 20 companies exhibiting AI-based products at the RSNA conference towards the end of the year, although most were prototypes and only a handful had regulatory clearance. 2017 should see commercial activity ramp-up, FDA approvals permitting.

Whilst the enthusiasm for machine learning is certainly justified, it inevitably raises expectations, potentially to unrealistic levels. To counter this, machine learning companies must clearly articulate the value proposition of their solutions and demonstrate a clear return on investment (ROI) to healthcare providers. With healthcare budgets under pressure globally, vendors should demonstrate both improved clinical outcomes and a tangible ROI to stand the best chance of success. As a bare minimum, vendors must prove that any quality improvements from using their cognitive tools do not negatively impact clinician productivity.

Using cognitive tools for repetitive and time consuming tasks enables radiologists to focus on value-added tasks or to perform extra reads. Machine learning can be used to extract relevant information from an EHR or lab report to automatically populate the radiologist’s report. The same cognitive tool can present the radiologist with treatment outcomes from similar cases, to aid with diagnosis and treatment planning. In this example, the combination of increased radiologist productivity and improved clinical outcomes makes for a compelling argument for healthcare providers to invest in machine learning.

A commercially available example of how automated tools can lead to productivity gains is 4D Flow from Arterys. 4D Flow uses cloud-based image processing technology to provide visualization and quantification of blood flow on cardiac MRI studies. With 4D Flow, cardiac MRI exam times can be significantly reduced from typically 60 to 90 minutes to around 10 minutes, which increases the efficiency and throughput of the hospital’s MRI service. Additionally, automated segmentation eliminates the need for radiologists to calculate measurements between areas of the heart. Cardiologists will need to switch from using echocardiograms, but the benefit of more accurate flow data that can be tracked over time should be a motivator. Arterys also recently received FDA 510(k) clearance and CE Mark approval for its Cardio DL™ product that provides automated, editable ventricle segmentations based on conventional cardiac MRI images, eliminating the need for manual segmentation. Cardio DL™ is also cloud-based and leverages deep learning technology.

Another example is iCAD’s PowerLook® Tomo Detection, a Computer Aided Detection (CADe) system for breast tomosynthesis that’s built on deep learning technology. Each image in a tomosynthesis data set is analysed to detect potential areas of interest and the system blends those areas onto a synthetic 2D image so that they are visible on a single image of the breast. Based on initial trials, the company claims that the additional reading time associated with breast tomosynthesis over 2D mammography is reduced by an average of 29.2%, with no change in radiologist performance.

In the above examples, the ROI from using cognitive analytical tools is largely derived from radiologist (and technician) productivity gains. However, bigger opportunities are to be found higher-up the healthcare value chain. For example, cognitive tools can review existing scans to identify incidental findings that have not been followed-up and that could represent missed billing opportunities for the healthcare provider. Moreover, predictive analytics can identify at-risk patients to enable early intervention and to avoid costly readmissions, both leading to a reduction in treatment costs.

Whether machine learning is positioned as a productivity play, a cost-saving play or a revenue-generating play, one thing is clear – vendors must prove the ROI. Unsubstantiated claims of how machine learning can outperform or replace radiologists may attract the news headlines but do little to support the efficacy of the technology. Vendors must adopt a more responsible, fact-based approach to marketing their solutions if machine learning in radiology is to avoid the “trough of disillusionment”.


New Market Report from Signify Research Publishing Soon
This and other issues will be explored in full in Signify Research’s upcoming market report ‘Machine Learning in Radiology – World Market Report, publishing in January 2017. For further details please click here or contact

Signify Research @ HIMSS 2017


19 – 23 FEB 2017

Signify Research @ HIMSS 2107

The Signify Research Analyst team will be attending the 2017 HIMSS Annual Conference & Exhibition in Orlando.

HIMSS is a world leading event which brings together 40,000+ health IT professionals, clinicians, executives and vendors from around the world. During the event, leading edge technology will be showcased by the healthcare IT industry. As expected, we will be providing updates, news and analyst insight from the conference via Twitter, LinkedIn and

We welcome the opportunity to meet vendors, providers and industry stakeholders for:

  • Discussion on specific market topics and trends
  • Briefing on our most recent market findings and data
  • Provide a vendor briefing to our analyst team on your latest products
  • An introduction to the Signify Research team and our market intelligence solutions
  • Interest in press and media collaboration
  • Participation in our  syndicated market reports, customer insights and vendor evaluation research

Please contact us at, or find more details on our “Contact Us” page.

We look forward to seeing you in Orlando.

Back to the future: Top themes from RSNA 2016

Back to the future: Top themes from RSNA 2016?

Greeted with fiercely mild weather for late November/early December, the 102nd RSNA show already had the feel of being a bit different. Of course, there was the packed program of fascinating research and exhibition halls bursting with advanced imaging hardware and cutting-edge IT solutions. But there was also the growing feeling, perhaps spurred by the theme “Beyond Imaging,” that radiology is in a mode of transition.

This was perfectly exemplified near the entrance to exhibition hall B, where a radiology printed textbook stall was positioned next to an artificial intelligence software vendor. Amongst the glitz of RSNA, it’s easy to forget that much on show is a long way from having an immediate impact on the daily working practice of most radiologists, but it is a great chance to gauge where radiology is heading.

This is the opening extract of Steve’s regular monthly market column for AuntMinnie Europe.  

To read the full article, please click here. 

(Access to the article may require free membership to AuntMinnie Europe – it’s full of great content and insight so well worth signing up!) 

RSNA 2016 Post Show Report

RSNA 2016: Post Show Report

This show report presents the views from SIgnify Research’s leadership team attending the 102nd Scientific Assembly and Annual Meeting of the Radiological Society of North America (RSNA).  The show report contains three insights covering Imaging IT, Deep Learning in Imaging and Ultrasound.

Click here to download the report.

RSNA Imaging IT: Innovation Under The Surface

RSNA Imaging IT: Innovation Under The Surface

Written by Steve Holloway


Holy Smoke IT’s Everywhere
Probably the standout from RSNA is just how big imaging informatics and clinical data management has become at RSNA. Both technical exhibit halls were packed with imaging IT solutions, coming at the market from all angles.

While the big issue impacting almost every conversation was artificial intelligence (see our other RSNA round-up on this), it is becoming increasingly evident how many vendors currently have or want a piece of the imaging informatics pie. To add some structure to the plethora of solutions on show, we’ve categorised our takeaways into the three core functions of imaging informatics: view, manage and store.

Viewing technology has undergone a major change in recent years, most notably in merging with advanced visualisation and expanding its reach out to a larger volume of clinical disciplines. Moreover, as enterprise imaging and EMR systems have rolled out across health systems, a new array of users has emerged with unique needs, demanding more and more from viewing technology.

Therefore, viewing technology has had to evolve, and this was evident on the RSNA technical exhibit show floor;

  • Thin-client, mobile-enabled, server-side rendering: is rapidly becoming the standard for viewing technology, allowing almost-instant web and mobile access for large imaging studies in any location within a given authorised network.
  • User and application interface morphing: another major play from multiple vendors was the adaptability of viewer interfaces, toolsets and even user-preferences, based on the type of image or information displayed and physician using the viewer. While this capability has been available for a while in some platforms, it is increasingly being pushed as a vital component, especially for high-volume reporting.
  • Pre-load of context information: improvements in management and storage of historical prior studies and data into centralised, accessible repositories, has also led to automated, intelligent pre-fetching of related data that may be relevant to the reading or reviewing physician. This enables the diagnostician to quickly access previous patient data that may add context to their diagnosis within the same viewer. While this capability has huge potential for diagnostics reading and clinical review, it remains dependent on the underlying management and workflow capability, an area that needs considerable work to get to true interoperability.

Management of imaging and associated clinical data has remained in flux, with vendors from viewing, archiving and storage, EMR and enterprise content management all vying to disrupt the traditional PACS market. There was also notable presence of a range of middleware specialists in DICOM-routing, migration, worklist and decision support tools. This suggests “D econstructed PACS” is not a marketing fad and is a viable alternative for providers that have the resources and capability to manage a migration to a multi-vendor best-of-breed solution.

However, it was also abundantly clear that enterprise imaging remains the core focus for most vendors and providers, unsurprising perhaps considering many incumbent PACS vendors are taking this route. Expansion of departmental PACS capability to a more enterprise platform approach has short- and mid-term benefits for most providers, allowing ingestion of non-imaging content, a single vendor relationship and avoiding complicated and lengthy PACS migration is clear. That said, many of these solutions do little to enable true interoperability of imaging data, something that over time will be of growing importance with the advent of artificial intelligence and population health management.

One positive from the show was the high profile of the RSNA Image Sharing Validation programme, an accreditation scheme for vendors that pass a set of interoperability criteria for the sharing of images and imaging reports. While this is a great start in highlighting the issue that has plagued PACS for decades, there is clearly more that needs to be done on the issue of interoperability both for imaging content and all health data.

Unsurprisingly, almost every vendor involved with image informatics now offers some form of Vendor Neutral Archive (VNA), though few solutions are capable of true agnosticism to data type, clinical application or interfacing vendor.

Consequently, it’s clear that a two-tier market is emerging:

  • VNA: solutions that are essentially storage and archiving solutions to replace the “A” of PACS, with limited capability for non-DICOM content and which are usually deployed as a means of connecting disparate PACS or supporting enterprise imaging platforms.
  • Independent Clinical Archives (ICA): Fully content- and vendor-agnostic storage and content management platforms based on rigorous, standards-based repositories that exhibit true interoperability of content and support not only imaging content, but all structured (EMR) and unstructured data across health systems.

This distinction, while not immediately clear from strolling the exhibition hall, will, over time, become increasingly important as the demands of ingesting new clinical specialities stretches the capability of these solutions. Further muddying the issue was the raft of vendors in both categories also offering an array of workflow, management, clinical and viewing modules with their storage solutions, thereby appealing to providers across deconstructed PACS and enterprise imaging models. While disruption of the incumbent PACS market has been vital in driving the market towards improvements in interoperability and removing proprietary data blocking, the scramble of marketing jargon and merging of capability and features has made the proposition for health providers increasingly challenging and confusing.

Evolving Business Models Point to Cloud
Perhaps most notable was the increasing presence of new innovative business models across the imaging informatics sector. While today still a fundamentally capital-heavy IT sector, healthcare is tentatively moving to embrace managed services, a move that will undoubtedly also drive the market towards cloud IT technology.

Many vendors now offer some variation of their traditional capital expenditure model, though uptake to date has been minimal. Yet with major US and international initiatives putting greater emphasis on value-based care and a growing appetite for risk-sharing between providers and vendors, momentum for alternate business models is increasing substantially.

Therefore, expect adoption of hybrid and hosted imaging informatics to become increasingly common, despite the obvious concerns that exist around security and data ownership. Why so bullish?

Because the future direction of radiology, aptly caught in the show theme “Beyond Imaging” will demand a more flexible, interoperable and affordable approach to image and health data management. The constant query and amalgamation of data for artificial intelligence will require another level of processing capability, and the flexibility for image and data exchange to maximise radiologist reading resource will also require it. Finally, the spiralling cost of management and storage for the exponentially growing data is unsupportable without it. Get ready for a whole lot more discussion on the role of cloud IT in imaging informatics.

New Service from Signify Research: Clinical Content Management IT – 2017
This and other issues will be explored in full in Signify Research’s upcoming intelligence service ‘Clinical Content Management IT – World, with first delieverable due in February 2017. For further details please click here or contact

RSNA: Deep Learning Takes Centre Stage, but Beware the Hype

RSNA Goes AI: Deep Learning Takes Centre Stage, but Beware the Hype

Written by Simon Harris

Artificial Intelligence was undoubtedly one of the key themes at this year’s RSNA, featuring prominently on the exhibition floor and in several scientific sessions. At least 20 companies displayed products featuring AI technologies and a handful more used AI as a key part of their marketing messages, even if the use-case wasn’t entirely clear.

The use of artificial intelligence in medical imaging is not a new trend. The first generation of computer-assisted detection (CADe) products entered the market in the late 90s and used machine learning techniques such as shallow neural networks and support vector machines. What’s new is the increasing use of deep learning techniques, and in particular, convolutional neural networks.

With traditional machine learning, the algorithms are hand-crafted, meaning that the programmer essentially hard-codes the system to look for specific features. This is a time-intensive process that requires extensive clinical domain knowledge. Moreover, the performance of the algorithm is limited by the underlying rules and statistical modelling; hence the high number of false-positives generated by early CADe systems.

Deep learning techniques feature much larger (typically 10 layers or more) neural networks and the algorithms are trained using large sets of images. This requires considerably more computational power than traditional machine learning, which has been enabled by the introduction in recent years of affordable GPU-accelerated computing, which allows the algorithms to run much faster than with CPUs alone. By feeding the algorithms with radiologist annotated images and a “ground-truth”, the system automatically learns about the image features, rather than being programmed what to look for.  As such, deep learning methods typically produce faster and more accurate results over traditional hand-coded classification techniques.

So how is deep learning being applied in radiology? From walking the exhibition floor at RSNA there were two key themes, as discussed below.

Next Generation CADe

Deep learning has the potential to significantly enhance the performance of existing CADe systems, by offering improved sensitivity without burdening radiologists with a high rate of false-positives. Increasingly, CADe systems will supplement detection with automatic quantification of imaging biomarkers. Additionally, the results from computer-assisted detection (and quantification) can be presented alongside patient information extracted from an EHR, such as patient history, laboratory results and prior studies, to provide the clinician with an imaging decision support tool (see next section).

Moreover, deep learning offers improved support in the detection of co-morbidities and incidental findings. For example, research published earlier this year by researchers at Icahn School of Medicine at Mount Sinai in New York City found that existing mammograms can be used to detect calcified plaques in breast tissue, which can lead to heart attack or stroke. In commercial healthcare systems, such as the US, this may help to ensure that opportunities to bill for additional procedures are not missed. The combination of improved accuracy and enhanced functionality will make next generation CADe systems a far more compelling proposition than earlier systems.

iCAD made a big play on deep learning at RSNA, with a large part of its booth dedicated to PowerLook® Tomo Detection, a CADe system for breast tomosynthesis that’s built on deep learning technology. Each image in a tomosynthesis data set is analysed to detect potential areas of interest and the system blends those areas onto a synthetic 2D image so that they are visible on a single image of the breast. Based on initial trials, the company claims that the additional reading time associated with breast tomosynthesis over 2D mammography is significantly reduced by using its CADe software, by an average of 29.2%. iCAD received CE Mark certification for PowerLook Tomo Detection in April 2016 and is in active dialogue with the US FDA regarding pre-market approval.

Riverain Technologies, which is best known for its image analysis tools for nodule detection in chest x-rays, used RSNA 2016 for the commercial launch of its ClearRead CT Suite, comprising ClearRead CT Vessel Suppress and ClearRead CT Detect, which aids in the detection of nodules in chest CT scans. The vessel suppression tool features deep learning technology. Riverain received FDA 510(k) clearance for ClearRead CT in September.

From CADe to CADx and Imaging Decision Support

The theme of this year’s RSNA was Beyond Imaging, to reflect the broadening role that radiologists are playing in the larger medical community. The theme also reflects how radiologists will increasingly be able to leverage non-imaging data extracted from EHRs and other sources to assist in making diagnostic decisions. In addition to patient data, imaging decision support tools can provide radiologists with other supporting information, such as the treatment outcomes of patients who presented with similar conditions.  Beyond Imaging also captures how radiology is evolving from a largely qualitative to an increasingly quantitative discipline, with the increasing use of automated quantification tools to provide accurate and repeatable metrics of lesions and tumours, for example.

The first generation of imaging decision support and computer-assisted diagnosis (CADx) products are starting to enter the market and a handful were on show at RSNA. RADLogics presented its Virtual Resident™ decision support solution, based on its AlphaPoint™ cloud-based image analysis platform. The platform incorporates machine learning algorithmic tools for automatic analysis of X-ray and CT images. The results are combined with the patient’s medical record information into a preliminary report, in much the same way that a resident prepares information for a radiologist to review.

HealthMyne used RSNA to preview its QIDS software platform which provides radiologists with a quantitative imaging dashboard, including time-sequenced Epic EHR information. Laboratory results, treatment details, and the health status for each patient are viewable in a timeline-based longitudinal representation. As an example, a longitudinal representation could feature a plot of tumour size relative to the duration of a course of radiotherapy, with icons to denote the dates of follow-up CT scans from which tumour size was determined. Scans can then be examined by clicking on the icon and opening a viewer. QIDS retrieves prior studies, performs image registration and localization of previously identified lesions. The analytics software, which is not built on deep learning, also provides information such as tumour size, Lung-RADS categories for use in lung cancer screening, and other quantitative metrics. The product will be fully launched in January 2017.

Quantitative Insights (QI) Inc. showed its QuantX breast imaging workstation. Alongside a multi-modality viewer, QuantX provides automatic detection and quantification on MRI images for the characterization of breast lesions, to assist in breast cancer diagnosis. QuantX features a breast imaging decision support system with direct correlation to a database of lesions with known pathology, based on biopsy results. The system generates a QI Score™ to represent the probability of malignancy. QI has submitted a de novo 510(k) application to the FDA and believes that a decision is imminent. Should the company be successful, QuantX will be the first CADx product cleared by the FDA.

IBM gave demos of several Watson-powered initiatives, under both the Merge Healthcare and Watson Health Imaging brands. Examples included a solution for aggregating and filtering electronic health records and technology for automated analysis of cardiac ultrasounds and improved diagnosis of aortic stenosis. The most impressive demo was for a decision support tool code-named Avicenna, which automatically detects and quantifies anatomical features and abnormalities (the demos used a CT scan), and extracts relevant information from a patient’s electronic health record. Avicenna has a cognitive ‘reasoning’ capability that considers the imaging and non-imaging information to suggest possible diagnoses. Big Blue was tight lipped about the release date for Avicenna, but it will likely need another year at least, and most likely two years, to complete clinical trials and obtain regulatory approval. IBM’s first cognitive solution for radiology to hit the market will be a Cognitive Peer Review Tool, intended to help healthcare professionals reconcile differences between a patient’s clinical evidence and the data in that patient’s EHR, which is due to be released in 1Q 2017.

Separate the Hype from Reality

In addition to the above examples, several start-ups, including Enlitic, Zebra Medical, Lunit and Vuno, used RSNA to showcase how they are applying deep learning to medical imaging. For example, Enlitic gave a demo of a chest x-ray triage product and a solution for lung cancer screening, both powered by deep learning. Enlitic is in the process of gathering clinical validation for its products and does not yet have regulatory clearance to sell.

However, some of the other start-ups were less forthcoming regarding their product development plans, with one company’s booth no more than a display carrying the company’s logo. Many radiologists remain sceptical of the capabilities of artificial intelligence and some see it as threat. Moreover, many remember the limitations of early generation mammography CADe systems. Vendors need to complete and promote clinical studies to validate their claims, otherwise marketing soundbites may impede the acceptance of deep learning in radiology. More customer education is required so that the conversations at next year’s RSNA move on from “what’s deep learning?” to “tell me how can deep learning help me do my job better”.


New Market Report from Signify Research Publishing Soon
This and other issues will be explored in full in Signify Research’s upcoming market report ‘Machine Learning in Radiology – World Market Report, publishing in January 2017. For further details please click here or contact

RSNA Ultrasound: Premium Market Heats Up

RSNA Ultrasound: Premium Market Heats Up

Written by Steve Holloway

At first glance of the RSNA 2016 exhibition floor, one may have observed little has changed in ultrasound, apart from the re-emergence of live ultrasound scanning demonstrations after a 27-year hiatus. However, if you managed to get away from talking about artificial intelligence and the future role of radiology, innovation in ultrasound was clearly on show, albeit more subtly.

A new premium system baseline
The “flagship” premium category continues to evolve and break new ground for ultrasound clinical capability. While there were no market-defining features on show, it’s clear a new baseline has been established for system capability in the premium radiology category.

This was most evident from the array of premium systems now boasting a combination of shearwave and conventional elastography, real-time fusion and contrast-enhanced ultrasound (CEUS). This was particularly pertinent given the scrum of vendors showcasing liver CEUS, only approved in the US by the FDA earlier this year. Vendors clearly saw a big opportunity for demonstrating the combined diagnostic power of fusion imaging, quantitative shearwave elastography and real-time fusion for liver lesion assessment.

Throw in an array of nuanced improvements in workflow, widescreen displays and various other features, and it is becoming clear that the minimum criteria to qualify for “flagship” radiology ultrasound has been raised again.

Breast & MSK Everywhere
As has been evident in the past few shows, breast ultrasound continued to expand its reach across the booths of ultrasound exhibitors, albeit in a variety of forms. Focus continued to revolve around the lengthy time for conventional breast ultrasound scanning, with both automated breast ultrasound (ABUS) and whole-breast ultrasound (WBUS) seen as a potential time saving solution – most vendors in ABUS and WBUS claim scan times for similar results in the range of 1-5minutes, versus approximately 30 minutes for a conventional scan. While this is important for workflow and as ABUS/WBUS becomes more commonly used, the lack of an adaptive screening pathway based on breast-density pre-screening appears to still be some way off, suggesting large-scale use will be limited for the near future.

Also evident was the growing use of ultrasound for musculoskeletal (MSK) applications. Seen initially by many in the industry as the fourth true point-of-care (POC) ultrasound application after anaesthesiology, emergency medicine and critical care, MSK use is increasingly straddling the POC/traditional divide, highlighted in a variety of cart-based, pedestal and compact ultrasound systems. This suggest two distinct MSK markets are emerging; lower-capability, compact ultrasound for true POC imaging on the sport field or primary clinic, and more advanced diagnostic second or third generation users in specialist centres or the hospital, requiring higher capability systems. Quick to profit from this maturinguse-case, the focus on MSK by traditional cart-based system providers was far more pronounced than usual at the show.

Transducer innovation, app-based ultrasound & OB/GYN market heats up
Other notable trends from the show included:

  • Growing focus on innovation in transducer technology, especially around high-frequency capability and wide-bandwidth solutions, designed to offer greater flexibility of use, or reduction in different types of transducer required
  • System-in-a-transducer ultrasound was also on show again, both from Philips Healthcare’s Lumify and Clarius Mobile Health. Use of tablets as wi-fi remote-controls for premium ultrasound systems was also showcased with several premium cart-based systems, as a workflow benefit.
  • The OB/GYN market, traditionally dominated by GE Healthcare, Samsung Medison, Hitachi-Aloka and Mindray, has also been targeted by Toshiba Medical (traditionally a general imaging and cardiology ultrasound specialist), with the launch of a dedicated system for women’s health, Aplio i800WHC.

Advanced visualisation: Why so many viewers?

Advanced Visualisation: Why so many viewers?

November 21, 2016 – As many of us gear up for the annual pilgrimage to the RSNA meeting in Chicago, it’s safe to expect artificial intelligence and big data will be front and center of the exhibition. Perhaps less expected will be the abundance of visualization software on display from a growing selection of exhibitors.

Once the staple diet of diagnostic and advanced visualization (AV) specialists, viewing software is of great importance to both clinicians and vendors alike. Today, many radiologists use integrated case-load worklists and diagnostic tools in their primary viewer, and the lucky ones have a suite of AV tools too. For most, the biggest change in recent years has been integrating AV tools into a single viewing platform, saving some walking between workstations. No big surprises here.

So why the sudden expansion of viewing software?

This is the opening extract of Steve’s regular monthly market column for AuntMinnie Europe.  

To read the full article, please click here. 

(Access to the article may require free membership to AuntMinnie Europe – it’s full of great content and insight so well worth it!!) 

Defining the Opportunity: Machine Learning in Radiology

Defining the Opportunity for Machine Learning in Radiology

Read time: 4 minutes 30 seconds

The application of machine learning in radiology is evolving at a rapid pace and whilst fully automated diagnostic systems are still several years away, there is a growing number of machine learning tools available now that are helping radiologists to improve their efficiency and to make better diagnoses. This Analyst Insight from Signify Research describes the current applications of machine learning in radiology and points to how the technology will increasingly be applied in four distinct areas, as shown the diagram below.

Computer Aided Diagnosis

Computer-aided detection (CADe) systems are intended to identify a variety of cancers such as breast cancer, prostate cancer, and lung lesions. They are most commonly used to detect microcalcifications and masses on screening mammograms. Despite concerns regarding the benefits of CADe and the high rate of false positives and false negatives, the market has grown steadily over the last two decades, most notably in the US where more than 90% of mammograms are interpreted using CADe. This has largely been driven by the availability of reimbursement for the use of CADe in breast screening. It is far less commonly used for detecting other cancers, where reimbursement for using CADe is currently not available.

Most CADe systems are rules-based and programmed to identify specific features. However, machine learning techniques, particularly deep learning using convolutional neural networks, are increasingly being applied. Deep learning has the potential to improve the accuracy of CADe, particularly for soft-tissue analysis. Moreover, as deep learning systems can be trained to identify features using large datasets, the algorithm development times are massively reduced compared with the traditional approach of ‘manually-crafting’ algorithms.

The benefits for radiologists are likely to be enhanced product performance (i.e. fewer false alerts), a faster pace of product innovation and a wider choice of products, particularly for the detection of lung, colon and prostate cancers, where there are currently only a handful of products on the market. For breast cancer detection, there will likely be a wider selection of CADe solutions for use with different modalities, e.g. breast MRI and breast ultrasound, in addition to digital mammography. In breast tomosysnthesis, deep learning can be applied to reduce the additional reading time associated with 3D images compared with 2D mammograms. For example, in March this year iCAD released PowerLook Tomo Detection which is built on deep learning technology. The system extracts areas of interest from the 3D planes and blends them onto a single 2D image. iCAD claims that its new system can reduce the interpretation time for breast tomosynthesis by an average of 29.2%.

Lastly, the increasing use of machine learning will enable CADe to gradually evolve from purely detection systems, to more advanced decision support and computer-aided diagnosis, as described in the following sections.

Quantitative Analysis Tools

Quantitative analysis tools are essentially workflow tools that provide radiologists with automatic detection and measurements of imaging features (biomarkers) to assist with diagnosis, such as lung density, breast density, analysis of coronary and peripheral vessels, etc. Much like with CADe, machine learning is increasingly being applied and the benefits to radiologists are much the same – improved accuracy, enhanced functionality and an increasing choice of products due to the faster algorithm development times. Most vendors currently offer a small number of quantification tools for very specific tasks, but the faster time to market associated with machine learning algorithms will enable vendors to offer an expanded ‘toolkit’, with solutions for multiple applications across multiple modalities.

Zebra Medical Vision, an Israeli start-up applying machine learning to medical imaging, has announced several algorithms for its Imaging Analytics platform. Its algorithm for lung applications analyses Chest CT scans to detect emphysematous regions in the lungs, and quantifies the volume of emphysema in comparison to the overall lung volume. Another example is 4D Flow from Arterys, which uses a cloud-based image processing technology to provide visualization and quantification of blood flow on cardiac MRI studies. 4D Flow utilizes machine learning analytics for automatic identification and segmentation and Arterys is validating the use of deep learning algorithms.

Decision Support Tools

Decision support tools provide detection and quantification, alongside supporting information extracted from an EHR, pathology reports and other patient records, to assist with diagnosis. Other features may include:

  • Registration algorithms, to pinpoint areas of interest in one study and have those areas linked to the same points in previous scans,
  • Automatic population of radiology reports with quantitative data,
  • Predictive analytics to identify high risk patients and enable early treatment
  • Treatment planning to determine the best course of treatment for an individual, by reviewing the outcomes of previous treatment pathways for patients with similar conditions.

Decision support tools do not provide automated diagnosis (see next section) and instead are intended to help radiologists improve their efficiency, while also improving accuracy and consistency. Early detection of high risk patients and improved treatment planning can also lead to cost savings for health providers and improved quality of care.

Today there are relatively few imaging decision support tools on the market, but this is likely to be one of the main applications for machine learning in radiology in the coming years. At this year’s RSNA meeting, HealthMyne will release its Quantitative Imaging Decision Support (QIDS) platform which combines imaging data with electronic medical record, radiation therapy and other clinical information to provide clinical decision support in the primary read. Another example is AlphaPoint™ from RADLogics, which was launched in early 2016. AlphaPoint is a cloud-based platform that incorporates machine learning algorithmic tools for automatic analysis of images and merges the results with the patient’s medical record information in a preliminary report.

IBM Watson Health is currently developing a radiology assistant product, code-named Qibo, that reviews imaging studies and patient records to produce a summary of the most important information.

Computer Aided Diagnosis

Computer aided diagnosis (CADx) systems are intended to provide information beyond detection and quantification by also providing interpretation of the scan, for example by providing a probability score for the presence of cancer. These systems are heavily regulated by the FDA and as far as Signify Research is aware, there are no commercially available CADx systems for clinical use.

Several companies are developing fully automated diagnostic systems and probably the most ambitious is IBM Watson Health, whose project is code-named Avicenna, after an 11th century philosopher who wrote an influential medical encyclopedia.  The key difference between Avicenna and existing decision support tools, as described in the previous section, is that Avicenna has a “reasoning” system that makes use of multiple data sources, such as scans, patient records and data from similar cases, to suggest diagnoses and possible treatment paths.  IBM has shown previews of Avicenna, but has not indicated when it will be commercialized.  Other companies developing CADx solutions include Aidence, Enlitic and CureMetrix, to name a few.

To summarize, we expect that 2017 will be a pivotal year for machine learning in medical imaging as the FDA is expected to approve the first tranche of detection and quantification tools based on deep learning. Although the clinical benefits of these systems are somewhat unproven, the early results from validation trials are encouraging. We also expect that several companies will release decision support tools which will help with market education as to the benefits of these systems. In the US, the trend to value-based care will be a major driver for their uptake. Looking beyond 2017, the rapid advancements in artificial intelligence technology, primarily driven by technology giants such as Apple, Google, Facebook and IBM, suggest that the importance of machine learning in radiology will only increase over time. That said, there are still legal and ethical considerations that need to be addressed, notwithstanding proving the efficacy of the technology, before computer-aided diagnosis becomes mainstream.


New Market Report from Signify Research Publishing Soon
This and other issues will be explored in full in Signify Research’s upcoming market report ‘Diagnostic Analytics in Radiology – World Market Report, publishing in January 2017. For further details please click here

Analysis of Potential AGFA Takeover by CompuGroup

The Signify View: Analysis of Outcome as AGFA Mulls Takeover by CompuGroup

  • CompuGroup Medical has made a public non-binding indication of interest to take over $2.6B AGFA business; no deal has been confirmed
  • The two companies have some cross over in medical Electronic Patient Record (EPR) software
  • A deal would allow CompuGroup Medical to tap into AGFA’s established customer base for imaging IT and X-ray equipment in Western Europe and US
  • Significant question mark remains over the future of the AGFA Graphics business line and strategic fit between the two firms

The Signify View
Here’s our key takeaways:

1. AGFA’s established customer base in Western European and the US is a big draw for CompuGroup Medical
The biggest positive for CompuGroup Medical is access to an established customer base in Western Europe and the US. CompuGroup’s core market focus and successes have been in Eastern and Central Europe to date, though recent acquisitions have expanded this reach.
In contrast, AGFA has spent decades building a strong reputation as a clinical specialist in medical imaging, mostly focused on digital radiography X-ray and imaging IT software. AGFA’s sizeable healthcare customer base would therefore make an attractive target for CompuGroup’s EPR products. In Western Europe, a market relatively immature in EPR adoption, this offer significant growth potential.
In contrast, the US market for EPR in hospitals is intensively competitive and dominated by major players such as Epic Systems and Cerner, limiting growth opportunity. However, the large and fragmented ambulatory and outpatient EPR market may be more attractive and could well suit CompuGroup’s products.

2. AGFA agreement would be an “all-in” move on healthcare
AGFA’s Healthcare unit accounted for only 41.5% of its $2.6 billion revenue in 2015, with Graphics and Speciality Products making up the remainder. AGFA may well be looking for some assurances on the future of the non-healthcare business lines. However, CompuGroup is a pure healthcare focused company, so the Graphics business is unlikely to be viewed as an essential asset, and could be targeted for potential sell-off.
If so, any deal acceptance from AGFA will essentially be an “all-in” move to focus on healthcare, mirroring the recent position taken by Philips, one of its major competitors north of the border. This will be viewed as a risk, given that the AGFA Healthcare business unit revenues have been stagnant in the last few years. However, when viewed in the wider market context, the healthcare sector is already a target for some of the largest technology and IT firms, such as Google, Apple, IBM and Microsoft. This confidence and investment therefore suggest it would be a risk worth taking.

3. Strategic mid- to long-term fit questionable
On balance the deal appears more favourable to CompuGroup than AGFA. It offers market access to more mature and profitable markets and will add a strong brand focused on clinical excellence in medical imaging and imaging IT software, providing further credibility to its growing market presence in Europe.
However, in context of the wider market, the deal would not solve the biggest strategic challenges for AGFA.

As a well-respected mid-sized firm entrenched in medical imaging hardware and software, the focus for AGFA should remain in driving clinical excellence in its core field of diagnostic imaging. Expansion to cover EPR and a new Eastern European customer base could drive some short-term revenue growth for both its software and hardware product lines. However, the joint EPR of an AGFA- CompuGroup deal would unlikely concern the major EPR or enterprise health IT vendors, so over time would offer little in terms of long-term growth potential. Moreover, most market growth and disruption is in the enterprise health platform segment, especially for population health management, analytics platforms and cognitive computing, a challenge for a firm of AGFA’s size to compete in.
Instead, AGFA should look to expand its reach within the clinical realm, focusing on applications where its expertise in clinical software and workflow can have most impact, especially in applications relatively new to digitalisation, such as Pathology. It should also look more to associate and partner in provision of new business models, service, cloud technology, cyber security and cognitive computing (much like its recent association with IBM Watson Imaging collaborative).
So, unless CompuGroup is willing to pay a premium for AGFA, the likelihood of a deal given the challenge of the Graphics business and poor long-term strategic fit of the healthcare assets is relatively low.

Impact of Trump Presidency on Patient Engagement

The Signify View: Impact of Trump Presidency on Patient Engagement Market

One of the defining themes of the Trump presidential campaign was the pledge to “completely repeal Obamacare”. Since winning the election there have been numerous signs from the president-elect that indicate his definition of “completely” may not quite be the one we all expected or understood. Whether the Affordable Care Act (ACA) is fully repealed or whether ultimately it’s amended but maintained, there will be significant ramifications for patient engagement platform market.

The Signify View
So why is the ACA important for the patient engagement market?

  • The ACA was designed to provide access to care for all Americans, improve population health outcomes, and decrease healthcare costs.
  • ACA has been successful in significantly reducing the uninsured rate in the US
  • That has brought with it additional demand on many healthcare services, such as primary care physicians, and emergency departments – which does not meet the final objective of decreasing healthcare cost.
  • This is a clear indication for the importance of patient engagement, something that has not been fully utilized by the initial ACA roll-out. Patient education is essential to better manage health cost, especially in ensuring patients appropriately use health services and self-manage chronic conditions.
  • This has now brought patient engagement into focus as a central tool in addressing some of the cost and demand elements driven by the ACA, leading ultimately to the implementation of the reimbursement targets and measures put in place for meaningful use and now MACRA.

So if the ACA is completely repealed isn’t this bad news for patient engagement platform suppliers?

Trump’s Plans Will Need Patient Engagement
Post-election, all the signs are that parts of the ACA will remain, with Trump himself now toning down his position from “completely repeal Obamacare” to “Either Obamacare will be amended or repealed and replaced”. Whatever the final outcome, there are a number of reasons why Signify Research expects patient engagement platforms will still be key. These are explored below.

Reduce Healthcare Costs
One of the central goals in repealing or changing the ACA is to reduce costs. “We have to repeal it and replace it with something absolutely much less expensive.” being the president-elect’s mantra. Doing this while not completely pulling the rug out from under 20 million people who now have health insurance coverage as a result of the ACA, will be an impossible challenge without measures to encourage certain patient behaviours.
These behaviours, along with the drive to improve efficiency in patient management, include:

  • Improving patients’ knowledge, skills, ability and willingness to proactivity manage their own health.
  • Provide interventions designed to increase activation and promote positive patient behaviour.
  • Reduce the burden on physicians and hospitals when managing a condition.
  • Supporting the move to value-based care, which Signify Research expects to still be a central theme going forward.

It’s therefore the Signify View that patient engagement platforms will be used as a supporting tool in the new administration’s drive to reduce the overall cost burden of healthcare in the US.

Enable Patient-centered Healthcare
The Trump administration has stated on is transition website that creating a patient-centered healthcare system is a key goal of their strategy. As we all know, patient-centered means patients being central in the decision-making processes governing their healthcare. Patient engagement platforms will be an essential tool in this process as they allow patients to efficiently access the educational resources, personal medical data, financial tools and clinician support required in the decision-making process.

Health Savings Accounts
Finally, as was stated throughout the presidential campaign, the Trump solution will incorporate Health Savings Accounts (HSAs). HSAs should drive increased consumer demand for transparency in care costs and care quality – and patient engagement platforms can be a key tool in supporting this transparency.

For these reasons, the outlook for patient engagement platforms remains very positive. The current uncertainty around the future of the ACA does mean that the path forward is a little less clear, but it’s one we firmly believe will still have patient engagement at its core.

New Market Report from Signify Research Publishing Soon
This and other issues will be explored in full in Signify Research’s upcoming market report ‘Patient Engagement Platforms & Portals – World Market Report 2017’, publishing in February 2017. For further details please click here or contact

Is CMS Still Serious About Patient Engagement?

The Signify View: Is CMS Still Serious About Patient Engagement?

  • On 14th October 2016, the Centers for Medicare & Medicaid Services (CMS) released its Final Rule implementing the Medicare physician payment reforms enacted as part of the Medicare Access and CHIP Reauthorization Act of 2015 (MACRA)
  • The Advancing Care Information (ACI) performance category is one of four key performance categories included within MACRA, and is a key driver for the patient engagement platform market in the US
  • In CMS’ original draft proposal, published in May 2016, there were 11 required measures that made up ACI, many of which would cement patient engagement functionality as a central component of Certified Electronic Health Record Technology (CEHRT).
  • In the Final Rule the number of required measures has been reduced to five. The requirements around patient engagement being the ones that have taken the brunt of the reduction, as illustrated below.

The Signify View
Four of the measures that have been dropped from the required list are measures that had patient engagement at their heart. These were:
• CEHRT driven patient-specific education
• Patient view, download or transmit (VDT) CEHRT health data to a 3rd party
• Patient-provider secure eMessaging via CEHRT
• Patient-generated healthcare data (PGHD) incorporated into CEHRT
The remaining ones that have a focus on patient-provider communication relate more to simple portal access and ePrescriptions, which represent only the tip of the iceberg in terms of the possibilities that patient engagement platforms can offer. The other remaining required measures all focus on ensuring that data exchange between different providers/public health authorities is well supported, so have a limited impact on driving patient engagement solutions.

So on the face of it, it would appear that the CMS’ drive to put patient engagement at the heart of MACRA has been significantly watered down with this Final Rule.

This is further reinforced when considered that a transition year has also been introduced, that results in only four of the five required measures being reported on in 2017, that the reporting period be only 90 days instead of a full year, and that for many of the measures the provider is only required to report on one unique patient to hit the target!

Devil is in the Detail
However, as you dig into the detail on the ACI measures, it becomes apparent that the structure of the measures will mean that MACRA will still act as a significant driver for the patient engagement platform market.

For one, the ‘required’ measures will contribute to only a maximum of 50% of the base score a provider needs to hit to receive the full reimbursement quota related to ACI. In order to push this to the upper ceiling of 100% (more is technically possible), the provider also needs to be addressing a selection of other ‘optional’ and ‘bonus’ measures. The aforementioned patient engagement measures that were dropped from the ‘required’ list make up four of these twelve ‘optional’ and ‘bonus’ measures that providers can choose from.

The reality is that many providers will still report on these other four measures and build in platforms to their CEHRT that provide the functionality to do so. And as the provider will already have invested in a patient engagement platform in order to meet the ‘required’ measures, Signify Research believes that in the majority of cases the provider will continue to focus on the patient engagement-centric measures, such as secure messaging and VDT to 3rd parties, when selecting the optional measures to report on. Similarly, the 90-day reporting period and single patient reporting element are also expected to have only a minimum impact on the platform market. Healthcare providers still need to be building the platforms and functionality into their CEHRT solutions to address this requirement, and once done are likely to then continue to maximise the use of these platforms.

Therefore, ultimately the measures will still have the effect of driving provider uptake of patient engagement platforms; albeit potentially with lower licencing revenues than the original ruling would have resulted in.

New Market Report from Signify Research Publishing Soon
This and other issues will be explored in full in Signify Research’s upcoming market report ‘Patient Engagement Platforms & Portals – World Market Report 2017, publishing in February 2017. For further details please click here or contact

Siemens Healthineers IBM Watson Health Alliance

Siemens Healthineers – IBM Watson Health Strategic Alliance

  • Companies have signed a 5-year “strategic alliance” focused on provision of population health management (PHM) services
  • Deal will allow Siemens access to bundle Watson Health PHM products and services with Siemens capital equipment and service deals
  • In addition, Siemens will offer value-based care consulting services
  • Intent of both firms to jointly develop PHM products and services in the future

The Signify View
Siemens Healthineers is the latest major health technology firm to announce an involvement with IBM Watson Health, following AGFA Healthcare being part of the IBM Watson “Medical Imaging Collaborative”, while Medtronic and Johnson & Johnson have partnerships for PHM.
The deal focuses on the growing and increasingly broad PHM market, in which legislators, health providers and healthtech vendors are seeking to shift the focus of healthcare from reactionary to preventative care. While the exact nature and details of the deal have not been publicised, it is a significant move for both companies.

Here are our key takeaways:

Win-win for Siemens Healthineers?
For Siemens Healthineers, the deal offers a number of benefits.

1. The addition of IBM Watson Care Manager product will allow Siemens to offer greater analytics and performance data capabilities to work with Siemens imaging hardware and service, especially premium and high-end segment customers. Differentiation and value-add will be key in the $25 billion global imaging market which has seen intensified competition and price pressure from suppliers in China and Korea.

2. The deal means Siemens doesn’t need to develop its own cognitive allowing a faster time to market for products and services based on cognitive platforms.

3. The alliance will allow Siemens Healthineers to capitalise on the biggest demand from its large customer base today, “value-based care”. Legislators and healthcare providers in North America and Western Europe are pushing to improve healthcare provision by focusing on preventative, personalised healthcare. By using cognitive PHM solutions from IBM Watson, Siemens Healthineers can collect, measure and identify high-risk and chronic patients and offer consulting services on how best to manage care to improve patient outcomes while managing healthcare costs.

4. It will also allow Siemens to continue to adapt its business model towards managed services for imaging equipment (as seen in their recent $154M 10-year deal with William Osler Health System in Canada), a growing market trend being pushed hard by major competitor Philips Healthcare.

Above all, the deal also signals a marked shift for the recently spun-out, newly named Siemens Healthineers. In the past, a strategic alliance would have been unlikely to have occurred when embedded within the industrial conglomerate. However, now it appears the healthcare unit is more progressive and adaptive to market changes.

IBM Watson Continues Surge into Healthcare
For IBM Watson, the alliance with an established global leader in medical technology also has strategic importance for its push into healthcare.

1. Partnership with Siemens Healthineers will add further credibility to the IBM Watson surge into healthcare. Siemens is one of the largest and most respected imaging providers globally, well renowned for technological advancement and high-end medical imaging capability. For IBM Watson, a relatively new market entrant, the deal will open-up an array of opportunities to get Watson Health Care Manager into leading healthcare provider institutions, building installed base far quicker than would have been possible working alone.

2. Increased roll-out of the Watson Health product to Siemens’ customer base also means more data to “feed” the Watson cognitive platform, a vital commodity as competition intensifies and more cognitive computing firms enter the healthcare market.

3. The deal also has significant revenue generating potential for IBM Watson Health. By continuing down the alliance and partnership route, IBM has far greater reach into the healthcare customer base than by going it alone. This is vital for the Watson Health platform “fee-per-use” business model, as building usage volume is directly linked to profitability. In the short to mid-term, IBM gets significant exposure and access to a larger pool of potential customers willing to pay for cognitive computing capability, creating some return on investment for its significant investment in healthcare so far.

5 Reasons Why Radiology Needs Artificial Intelligence

5 Reasons Why Radiology Needs Artificial Intelligence

Artificial intelligence, such as neural networks, deep learning and predictive analytics, has the potential to transform radiology, by enhancing the productivity of radiologists and helping them to make better diagnoses. This short report from Signify Research presents 5 reasons why artificial intelligence will increasingly be used in radiology in the coming years and concludes with a list of the barriers that will first need to be overcome before mainstream adoption will occur. The reasons presented are:

1. Global shortage of radiologists
2. Enhanced productivity
3. Better diagnostic accuracy
4. Lower rates of misdiagnosis
5. Improved patient outcomes

Click here to download the report.

3 Things Every Radiologist Must Know About Analytics

3 Things Every Radiologist Must Know About Analytics

October 12, 2016 — Big data analytics has dominated the imaging market headlines in recent times. This emerging sector may appear tantalizing and visionary, but much of the hype is based on conceptual discussion, and there is a lack of substance and evidence about how big data analytics can be applied to mainstream use in radiology.

The other frustration in picking through the volumes of analytics-focused news is the scarcity of a clear definition for this emerging marketplace. All too often jargon replaces concise explanation of the wide array of types and use cases. Even worse, little is done to discuss the viability of implementation and the myriad of hurdles this sector must overcome.

So, it’s time to cut the hyperbole, pull out the key issues, and make some sensible guidance.

This is the opening extract of Steve’s regular monthly market column for AuntMinnie Europe.  

To read the full article, please click here. 

(Access to the article may require free membership to AuntMinnie Europe – it’s full of great content and insight so well worth it!!) 

Alex Green Joins Signify Research

11 OCTOBER 2016

Alex Green Joins Signify Research Leadership Team

Signify Research ( is pleased to announce that Alex Green has joined its leadership team as a Company Director and Principal Analyst. Alex will be responsible for Signify Research’s Digital Health market intelligence portfolio, initially developing its coverage of the patient engagement platforms and portals market.
Alex brings with him 20 years of technology market research experience. Most recently he served as Senior Research Director at IHS Inc., leading the IT & Networking research and analysis team within IHS’ Technology Media and Telecoms (TMT) business line. Prior to that he was a Research Director at IMS Research, overseeing a number of the company’s consumer electronics and communication technology research areas.
Alex also brings with him a wealth of experience from the healthcare sector having worked as a Business Analyst & IT Project Manager in a joint NHS/Local Government role. Within this role he was responsible for the implementation of EHR systems for social care, producing health service demand/capacity forecast models and developing process re-engineering strategies to maximize healthcare system capacity.
Alex will be joining company founder Simon Harris, and healthcare market analysis veteran Steve Holloway on the leadership team at Signify Research. “I’m extremely excited to be joining Signify Research” stated Green, “I’ll be joining a leadership team that has a combined experience of more than 50 years in technology market intelligence and one that includes some of the most highly respected analysts within the healthcare technology industry”.

Venture Capital Investment in Patient Engagement Platforms

Analysis of Venture Capital Investment in Patient Engagement Platforms

Earlier in 2016 the Centers for Medicare & Medicaid Services’ (CMS) introduced the Advancing Care Information (ACI) performance category as part of its Merit-based Incentive Payment System (MIPS). Its introduction brought patient engagement to the forefront of EHR implementation and provider re-imbursement policy.

This short report from Signify Research concludes that ACI’s impact on VC funding for patient engagement platform suppliers has been immediate.  Already 2016 has outperformed previous years in terms of VC funding and includes some of the largest deals to date.

Click here to download the report

Signify View: Wolters Kluwer To Aquire Emmi Solutions

Signify View: Wolters Kluwer Plan to Acquire Emmi Solutions Marks Tip of Iceberg in Patient Engagement M&A

Last week it was announced that US patient engagement platform supplier, Emmi Solutions, had entered into an agreement to become part of the Dutch information and services business, Wolters Kluwer.

• The deal, once complete, will result in Wolters Kluwer paying $170m in cash for Emmi.
• Although paying nearly six times the size of Emmi’s expected 2016 patient engagement business, the deal represents a great opportunity for Wolters Kluwer.
• Emmi is a leading brand in what is forecast to be a rapidly growing market, and one that will be central as the market implements recent legislation, moves toward value-based care and grapples with how to utilize patient-generated data.
• It also provides new channels for Wolters Kluwer to distribute its online content to healthcare professionals and patients.

The Signify View
This latest announcement is just the tip of the iceberg in terms of what we’re likely to see in terms of M&A activity in the patient engagement platform space in the next year or two. Signify Research is aware of 100+ firms that are offering products and services that fall under the, admittedly broad, scope of patient engagement. Many are specialists or start-ups that focus on just one part of the patient engagement ecosystem, be it offering an mHealth app that connects patients with practitioners, focussing on one therapeutic area such as long term diabetes management or targeting just one application such as healthcare financial management.

Market Won’t Sustain This Volume of Suppliers
Despite this being a fast growing market, it will not be one that can sustain this number of suppliers. Furthermore, payers and providers will increasingly demand single platforms that provide support and functionality across a wide spectrum of therapeutic areas, service delivery formats and platform functions. In addition, many of the larger healthcare IT companies are to some extent waiting in the wings in relation to addressing the patient engagement market, offering relatively rudimentary solutions that play second fiddle to their more lucrative EHR businesses. However, all are expected to make a strong play in this area ultimately. This is particularly so in the US where patient engagement has become a central component in relation to reimbursement for healthcare IT through the Advancing Care Information (ACI) performance category of the Merit-based Incentive Payment System (MIPS). For many of these leading EHR suppliers looking to address their customers’ ACI patient engagement requirements, acquisition will be the strategy employed.

M&A Activity Already Heating Up
These conditions all amount to an environment where M&A activity will be a central theme in patient engagement over coming years, be it as a strategy to enter the market, expand a portfolio, achieve scale or supplement an existing product set. On top of the Emmi acquisition, 2016 has already seen a number of other acquisitions in the patient engagement market. Some examples include:
• Athena Health’s acquisition of Austin, TX-based Patient IO in August 2016, which brought Patient IO’s care coordination platform into Athena Health’s patient engagement portfolio.
• MedData’s acquisition of Duet Health in May 2016, which added mHealth and patient education tools to MedData’s more operational patient engagement offering.
• Sharecare’s acquisition in September 2016 of virtual reality in healthcare supplier BioLucid, which brings with it the potential for immersive video functionality to be added to Sharecare’s patient engagement platform.
• Teledoc’s agreement in June 2016 to acquire HealthiestYou, allowing Teledoc to expand beyond telehealth and enter the patient engagement platform market.
• GCI and Safety Net Connect’s joint acquisition of vCareConnect, also last week, marking an entry for both acquirers into the patient engagement market.
• The proposed merger of Lincor Solutions and the Hills Health Solutions (HHS) business of Hills Limited to form Lincor Limited, announced in September 2016.

Will Attract Tech & Telecom Providers Not Traditionally Playing in Healthcare
Apple’s entry into the broader EHR portal market via its acquisition of Gliimpse in August 2016 illustrates that interest in the market goes well beyond those companies already serving healthcare providers and payers.
Despite the high volume of companies addressing this market, VC funding for new start-ups continues to come thick and fast. The first three-quarters of 2016 has already surpassed full year 2015 in terms of VC funding and is set to pass the $250 million mark before the year end.

New Market Report from Signify Research Publishing Soon
A full analysis of the patient engagement platform competitive market will be provided in Signify Research’s upcoming market report ‘Patient Engagement Platforms & Portals – World Market Report 2017’, publishing in February 2017. For further details please click here or contact

Signify View: Philips Qualcomm Deal

Signify View: Three Key Takeaways from Philips Healthcare – Qualcomm Life Partnership

Earlier this week, Philips Healthcare and Qualcomm Life Inc. announced a strategic deal to leverage components of each firms’ product offerings. Philips will benefit from enabling users of its HealthSuite cloud platform to connect to a wide array of devices that utilise Qualcomm’s 2net Platform connectivity. The company claims it will allow Philips devices and third-party devices secure connectivity, data capture and transmission to the HealthSuite platform.

For Qualcomm Life Inc, the deal allows the 2net platform to utilise secure storage and data management provided by the Philips HealthSuite platform, while also allowing Qualcomm Life customers to make use of the analytics and application building capability of the Philips platform.

The Signify View

1) This is a good move for Philips
Philips has a strong presence for medical devices in the hospital and ambulatory care setting and has been actively pursuing business in the homecare and telehealth space. They have also been industry pioneers of the “all-in-one” managed service approach to provision of equipment and IT for hospitals and health systems.

Tapping into Qualcomm Life’s well established medical-grade 2net Platform opens up a wider range of uses and options for Philips HealthSuite customers in terms of device compatibility. The 2net platform is also a leading solution for homecare and telehealth third party devices, a market Philips is keen to capitalise on. This also plays well into the “all-in-one” managed services model Philips has been pushing in recent years.

For Qualcomm Life, the HealthSuite platform has a large global installed base of devices and users, so the deal opens up a significant portion of Philips’ customer device data to Qualcomm, allowing better understanding and insight into medical device use, a potential boon for health connectivity in the hospital and ambulatory sectors, not to mention healthcare credibility in partnering with a leading industry brand.

2) Connectivity is a necessity in health data aggregation
There’s a good reason why many of the world’s largest tech firms are entering healthcare: health data is becoming big business. Google, Microsoft, IBM and many others have identified the untapped potential and are piling into big data analytics. Moreover, health providers are looking for ways to learn more from patient data, be it how to prevent disease, lower the cost of care and better manage population health.

This is where the value of integrating the Qualcomm Life 2net Platform is greatest for Philips. By enabling all 2net Platform compatible devices to be connected to HealthSuite, Philips is opening up access to a far greater pool of patient data, especially in the homecare and personal health & wellness market. By expanding the volume and variety of health data ingested into its platform, Philips can glean more insight about usage and demand, strategically valuable information for a vendor also offering a range of own-brand devices.

In addition, the larger the pool of data aggregated in HealthSuite, the more valuable the platform will become to users and potential strategic partners down the line, while also enabling their healthcare provider customers to benefit from greater functionality and data sources in HealthSuite.

3) The game has changed
Most striking when considering Philips’ recent market activity has been the push to become the central part of a healthcare solution “ecosystem”. Philips has realised the great value of its health provider customer base, clinical expertise and regulatory experience.
For a company like, a recent partner of Philips Healthcare in forming the HealthSuite platform, this is invaluable, both in terms of market entry and growth potential. GE Healthcare is already heading in a similar route with its HealthCloud, and other traditional medical device firms are also moving in this direction.

Strategic partnerships such as these also demonstrate that the business model for the largest healthcare technology vendors like Philips Healthcare, GE Healthcare and Siemens Healthineers has changed dramatically. A decade ago, the model was based on advanced R&D in hardware, tactical pricing, emerging market expansion and long-term lucrative maintenance and warranty service contracts.

Now it’s far more about ecosystem and partnership development, being the central connector of a co-operative, aggregating health data, utilising clinical and regulatory expertise. It also allows traditional medical device vendors to reign in some control over new market entrants, while their platform “ecosystems” remain right at the centre of the market and deeply entrenched in long-term, enterprise deals with healthcare providers.