Paragon Consulting Partners

Healthcare IT Solutions and Results

Imaging

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AI so fast it has time to read your images and clean your floors.

This tongue-in-cheek statement isn’t unlike the many other bold claims that are out there. The recent hype surrounding AI in healthcare is boundless, with promises that it can exponentially increase productivity, improve accuracy, and slash operational costs at a scale that was previously unfathomable. As a result, many healthcare organizations are actively developing strategies to incorporate AI into their technology roadmaps and clinical workflows, hoping to immediately reap the promised benefits.

Unfortunately, in many cases, implementation of AI in clinical practice has fallen short of expectations, proving to be more complicated, expensive, and cumbersome than originally advertised. This is largely due to an overarching perception in the industry that off-the-shelf AI applications can be procured and integrated ‘out-of-the-box’ into a variety of existing technologies, such as EHRs, PACS, and other data sources. The reality, however, is that successful adoption and deployment of AI requires careful evaluation of the following key considerations: 

1. Finding the right AI fit for your organization

There is a plethora of different AI algorithms out there – each with their own distinct use cases and value propositions including but not limited to: 

  • Predictive analytics to profile your organization’s capacity and performance potential in line with demand and growth patterns; 
  • Image analysis to automatically detect, escalate, and monitor abnormalities or disease; 
  • And proactive recommendations to treat or mitigate disease based on family, clinical, and social and environmental factors. 

Determining which AI applications will provide the most ‘bang for your buck’ requires thoughtful evaluation and identification of your organization’s own unique challenges and objectives. As well, it’s important to consider how AI will integrate into your existing technologies and day-to-day operations. Applications that fragment workflow or introduce cumbersome steps rarely achieve successful adoption – especially among busy clinicians. Be sure that your AI roadmap prioritizes applications that will bring meaningful and measurable benefits to address your burning platforms, and ensure your AI vendor has designed integrations, interfaces, and feedback loops that deliver a seamless and efficient user experience.

2. AI is only as good as its underlying data

AI models typically have specific requirements regarding the structure, content, and format of the data they are analyzing. Unfortunately, most healthcare organizations have a unique data fingerprint, with diverse technology ecosystems, image acquisition techniques, clinical documentation practices, and population characteristics that can introduce variability and negatively impact the accuracy of AI algorithms in clinical practice.

Careful analysis of each algorithm’s data requirements alongside your own data warehouse is required to determine whether there are any content or formatting gaps that will need to be addressed, and/or whether changes will be required to HL7/FHIR, DICOM, XDS, or other interfaces.

As well, it’s essential that your AI vendor has a strategy in-place to validate and, if necessary, re-train their algorithms against your own unique datasets before going live to ensure quality and accuracy of results.

Finally, it’s common – even expected – for changes in data structure and semantics to shift over time due to a number of factors such as process changes, introduction of new or updated imaging modalities, or changes in patient population characteristics, etc. It’s therefore critical that AI vendors have processes in place to proactively identify and accommodate these changes on an ongoing basis to ensure continued accuracy and efficacy within the live clinical environment.

3. Striking a cost-value balance

One of the biggest barriers to adoption for AI today continues to be the financial reimbursement model – or lack thereof. AI introduces additional operational, and sometimes capital costs, which in most cases do not realize a full return through billable outcomes.

While there are a few examples where computer-generated findings qualify for reimbursements (i.e. CAD for mammography), CMS has yet to provide direct reimbursement models for providers to bill for AI-rendered diagnostic interpretations or reviews. This doesn’t mean AI is a money pit – rather ROI is measured based on improved workflow and provider efficiency and accuracy, which in turn increases capacity (revenue) and decreases resource utilization, risk, and other ‘waste’ (costs).

As well, AI can automate risk stratification, data correlation, and reporting to help providers qualify for additional reimbursements, incentives, or even grants related to specific disease profiles and patient populations – a process that would be all but impossible if done manually due to the sheer volume and variability of the underlying data. The cost-value balance is unique for each healthcare organization and depends upon the opportunities, challenges, and priorities identified above. In any case, be sure to challenge your AI vendor to provide quantifiable evidence that they will be able to deliver the ROI you’re expecting – whatever that may be. 

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Are you ready to integrate AI into your organization? We can help you separate truth from fiction and select a strategy, technology, and vendor that will best fit your organizational capabilities and needs. Contact us to setup a meeting at RSNA 2019.


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Beyond cloud technology, our highly secure data centres are hosted throughout the far reaches of outer space for maximum geographic coverage and performance at the speed of light.

It’s true – there are some big advantages to outsourcing your infrastructure and data management to a qualified ‘as-a-service’ provider. Cloud services have come a long way, offering highly scalable and secure infrastructure and data management solutions that can help operationalize budgets and reduce or eliminate the need for local hardware and IT services. However, there are pros and cons related to performance, flexibility, and cost that should be carefully considered when determining whether a move to the cloud is right for your organization, and if so, which models will deliver the best results and ROI:

  1. Differing service models: As if there weren’t enough acronyms in the healthcare world already – Cloud adds its own collection of ‘aaS’ (as-a-service) to the pile. It’s important to understand the primary service models offered by cloud vendors so you can choose the model that best fits your organizational needs. Infrastructure and Platform as-a-service (IaaS, PaaS) are more fundamental. IaaS provides the basic virtualized infrastructure for the storage and processing of data, whereas PaaS adds operating system, middleware, and runtime environments. Both can reduce the amount of physical hardware and IT expertise that is required on-premise. This model is great if your needs are focused on supporting data growth, data mining, or business continuity. Software-as-a-Service (SaaS) typically represents a more comprehensive service model, delivering subscription-based business solutions that are fully vendor hosted and managed. This fully operationalizes your IT budget, and greatly reduces your in-house IT burden, however the trade-off is typically a reduction in customization opportunities. Which model would work best for your organization entirely depends on the degree of control you wish to exercise over your own infrastructure, software configuration, and workflows.
  2. Maturity of your IT organization: The scale and maturity of your in-house infrastructure and depth of expertise held by your IT team are two key factors to consider when thinking about a move to the cloud. Have you made significant investments into your own data centres and leading-edge infrastructure? Do your operations require sophisticated and customized IT solutions to support complex and varied clinical and operational workflows? If the answer is no, cloud could certainly be a viable option. However, if you answered yes to one or both of these questions then a full-blown cloud solution may not be right for you. Instead, you may wish to consider a hybrid cloud solution that leverages your current investments and affords your IT team full autonomy over your system architecture and configuration while augmenting it with additional secure flexible storage and compute resources and replication alternatives. 
  3. Growth pace and predictability: Cloud environments are more dynamic and elastic than traditional on-premise hardware infrastructure – which is one if its biggest advantages. Cloud environments can flex and scale storage and compute resources to accommodate large and unpredictable swings in data and user volumes. If your organization’s infrastructure is struggling to keep pace with your growth or usage patterns, then cloud services might be right for you. However, factors such as location, available network bandwidth, and network latency can impact the performance of applications hosted in the cloud. As well, not all vendors have optimized their software solutions to operate efficiently in a cloud-hosted environment – which can not only effect performance but can also lead to surprisingly high service fees. For instance, ‘chatty’ applications that make superfluous round trips to cloud-based servers and databases can greatly increase network service fees, while their performance can be significantly and adversely impacted by network latency. Before boarding that rocket ship to the cloud, make sure you thoroughly vet your vendor’s technology architecture and test system performance and connectivity across representative use cases and locations to ensure it will meet or exceed your expectations.

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Are you in the market for a cloud-based imaging solution? We can help you separate truth from fiction and select a strategy, technology, and vendor that will best fit your organizational capabilities and needs. Contact us to setup a meeting at RSNA 2019.


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Tools you didn’t know you needed for imaging modalities that don’t even exist yet.

While it’s important (nay, essential) to keep an eye on the future and plan for new technologies, it’s equally important to ensure the technologies you have in place, or are considering introducing, will address your current challenges and bring immediate measurable return on investment in terms of care quality and efficiency, stakeholder satisfaction, and/or your financial bottom line. As such, it is necessary to perform a careful evaluation of your current-state workflow and technical ecosystem and design an Enterprise Imaging strategy that aligns with your near and long-term objectives, resource plan, and budget. The following considerations can assist you in your evaluation:

  1. Remember the Pareto Principle (80/20 rule): this states that in most cases 80% of your results will come from 20% of your activities. Or conversely, 80% of your problems stem from 20% of the root causes. In the imaging world this means incremental improvements should not be undervalued. Comprehensive workflow analysis can uncover inefficiencies, gaps, and opportunities for optimization that may not all require a heavy lift to address. Focusing on technologies that are equipped to optimize core workflows will often get you further than looking at bells and whistles that bring incremental value to only a few narrow use cases. 
  2. Don’t be blinded by the shiny objects: Speaking of bells and whistles, some vendor technologies appear to offer lots and lots of these and boast their ability to go broad and deep across the entire spectrum of imaging specialties – well beyond the traditional ‘ologies. What you have to determine is, while they may have lots of tools that tick many of your RFP boxes, how well do these tools work in reality? Do they adequately cover the breadth of functionality you require to truly integrate into or replace incumbent technologies? How reliable are they, and have they been proven in clinical practice? If not, are you willing to invest the time and resources to help your vendor overcome these hurdles and develop potentially disruptive technologies (because there are definitely pros and cons on each side of that fence)? Be sure to carefully evaluate the needs of your service lines and care providers, and consider your available resources when evaluating how many of these tools and features can be feasibly integrated into your workflow, which will make a real, measurable impact in your organization, and which ones are the ‘shiny objects’ to be avoided (at least for now). 
  3. Don’t get hit by the swinging pendulum: There’s been a lot of debate and shifting of opinions in the industry regarding which deployment model is best: best-of-breed, or single-vendor. While both have their merits, the real answer often lies somewhere in between. No one vendor yet provides all of the tools and features that will satisfy the bespoke needs of primary care providers, specialists, clinicians, patients, and other stakeholders across the care continuum. This means that in pretty much all cases you will be looking at some flavour of a multi-vendor solution. How much you can squeeze out of your primary vendor depends again on their capabilities, product maturity, and how these align with the unique needs of your particular organization. Try not to fall victim to the swinging ‘hype’ pendulum and force yourself into one model or the other – rather, take the time to properly assess your current and desired future states alongside the current and future capabilities of technology vendors, and look for a fit that will bring the most value today, while supporting your vision for tomorrow.

Are you in the market for an Enterprise Imaging or PACS replacement solution? We can help you separate truth from fiction and select a strategy, technology, and vendor that will best fit your organizational capabilities and needs. Contact us to setup a meeting at RSNA 2019.

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Your world isn’t limited to up, down, and sideways – so why is your imaging platform? Flexibly scale in all directions, like super-stretch Armstrong. 

Growing populations, ageing baby boomers, advances in imaging technologies, and ongoing mergers and acquisitions between health systems, community hospitals, and independent clinics have elevated the importance of system scalability in Enterprise Imaging and PACS replacement projects. Ensuring your next technology platform will be able to efficiently and cost-effectively accommodate your unique growth rate and style calls for careful evaluation of the following considerations:

  1. Vertical vs. horizontal scalability: Traditional (aka legacy) software is typically vertically scalable, which means it needs to be pre-provisioned to accommodate projected exam and user volumes as well as anticipated data usage patterns (for example by data analytics applications or artificial intelligence and machine learning algorithms, etc). When growth and demand exceed capacity things can get a little ugly; performance often slows over time and storage and hardware replacement, database migrations, downtime, and other invasive and costly interventions are often required. Not only is it difficult to accurately forecast growth rates in today’s rapidly evolving ecosystem, it’s nearly impossible when you start tossing in various non-DICOM service lines where image volumes, types, are influenced by the rate and degree of user adoption, which usually means new systems are either over or under provisioned – and in either case ends up costing more than it needs to. Be sure your next imaging platform can scale horizontally – meaning it can accommodate on-demand growth in real-time without requiring new infrastructure, hardware, or downtime.
  2. Vendor-prescribed vs. commodity hardware: You have likely made significant investments in your physical infrastructure over time, and therefore it’s also likely that you would like to realize as much value from these investments as possible. When shopping for a new Enterprise Imaging or PACS platform be sure the technology is hardware agnostic – meaning it can be deployed on any hardware infrastructure, allowing you to avoid purchasing all new (expensive) pre-prescribed hardware from your vendor and by leveraging as much of your existing storage, servers, and desktop workstations as possible (provided, of course, that they meet or exceed the vendor’s recommended specifications).
  3. Virtualization: Another way to ensure your infrastructure can flexibly and efficiently scale is to ensure your next imaging platform can be fully virtualized. In addition to avoiding vendor-prescribed hardware, virtualization reduces capital and operational IT costs and enables faster and more agile provisioning of applications and resources as system demand grow – all while providing more robust business continuity infrastructure and reducing (if not eliminating) downtime.

Are you in the market for an Enterprise Imaging platform? We can help you separate truth from fiction and select a strategy, technology, and vendor that will best fit your organizational capabilities and needs. Contact us to setup a meeting at RSNA 2019.

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Accurately predict future events to mitigate disease, catastrophes, and natural disasters using tarot-based analytics powered by magic 8-ball technology.

It’s true, today’s clinical and business intelligence can deliver deep insights that can be used for disease mitigation and operational optimization. However, the ability of your analytics software and programs to deliver on lofty marketing promises is only as good as your underlying data strategy. Here are a few considerations to keep in mind before embarking on an analytics journey:

  1. You can’t measure what you don’t know: The first, and most important step in building a successful analytics program is understanding your current state environment, gaps, and challenges. Defining meaningful key performance indicators (KPIs) is not always simple but will enable you to measure improvement over time and ensure you’re tracking towards your organizational goals. 
  2. Your results are only as good as the quality of your data: Getting meaningful and actionable insights requires aggregation of data across many disparate and heterogeneous locations, systems, and formats. Be sure to have a flexible and scalable data model, a data normalization strategy in place, and carefully and regularly evaluate the quality, consistency, and integrity of your data to ensure accurate and consistent results over time. 
  3. The single biggest problem in communication is the illusion that it has taken place: Having scads of clinical and operational metrics is awesome, but is essentially useless unless you can deliver those insights to the right people, at the right time, and in a format that can be easily consumed and acted upon. This starts with having clear organizational objectives and fostering a data-driven culture where information is distilled and shared according to the communication methods that work best for each of your key stakeholder groups. 

Are you in the market for an enterprise analytics solution? We can help you separate truth from fiction and select a strategy, technology, and vendor that will best fit your organizational capabilities and needs. Contact us to setup a meeting at RSNA 2019.

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