Understanding Digital Imaging Strategy In Healthcare And Its Future Direction…

Future interoperability standards, solution architecture and principles guidance underpin strategy for digital diagnostic imaging.

They can be summarised by the following requirements:

• Safe And Effective: Digital systems’ primary purpose is to support safer and more effective delivery of clinical and social care support to the population
• Common Services: ICS (Integrated Care Systems) partner organisations will develop, or procure, clinical systems collaboratively to remove duplication of effort and inappropriate localisation
• Cloud Native: All system developments will aim to utilise cloud-based designs as the desired approach
• Internet Facing: All system developments will aim to ensure that internet-facing technologies are used where possible to ensure access to system outputs is not unfairly distributed
• Standards Based: All systems will utilise recognised standards (e.g. IHE, XDS, NHS specific) to ensure commonality of understanding across those systems
• Open And Interoperable: All systems will be capable of interoperating with other related systems using open APIs (Application Programming Interface) and other shared interoperable standards
• Secure And Confidential: All systems will be governed using ‘privacy by design’ principles to ensure that patient and citizen confidentiality is properly considered at all times
• Nationally Compatible: All systems will be capable of integrating, or interoperating, with key systems
• Joint Communication: New launches affect all imaging across a wide digital diagnostics territory

 

What Do The Digital Imaging Requirements Actually Mean?

And how do these requirements affect you in real terms?

 

Central Image Repositories — ‘Multi-Ology, Vendor Agnostic, Standards Based Cloud’

The basic premise of a centralised (cloud) imaging repository is to include all imaging and reports/results storage (multi-ologies), diagnostic data outputs from information reporting systems, as well as a centralised diagnostic viewing platform. All of these utilise IHE profiles and open standards, with no vendor lock-in, or proprietary systems.

The resultant is a modular, vendor-agnostic (‘best of breed’) solution whereby Integrated Care Systems (ICS) can continue to independently procure their vendors of choice. Systems supplied must simply be capable of being installed on the ICS-provided public cloud infrastructure (e.g. AWS), adhering to IHE interoperability standards, with all data remaining in the control and ownership of the ICS.

Each ICS digital platform shall aim to use a single-system multi-tenancy approach meaning that a single instance of the software and its supporting infrastructure can serve multiple customers. Each customer will share the software application and common standards.

This allows ICS organisations to share computing resources in the ICS public cloud where appropriate, while maintaining the integrity of local knowledge and processes, and the security of patient confidentiality.

The benefits of this approach mean that ICS entities retain autonomy by adherence to central repository IHE standards while maintaining cross-ICS interoperability and market competitiveness.

 

The Importance Of Standardisation

Each imaging network maturity matrix describes steps to achieve optimal ICS imaging operational workflow alignment. Networks that are maturing should have common business processes across all trusts in the network e.g. scanning protocols, slot durations, reporting templates, and alert management.

The issue of non-standardisation can be highlighted via a simple example of one site calling/coding a ‘CT Head’ scan and another site coding it a ‘CT Brain’ scan.

Similarly, a common examination is a ‘CT Thorax, Abdomen, and Pelvis’ (TAP). However, some sites call/code it as a ‘Chest, Abdomen, and Pelvis’ (CAP). Furthermore, some sites count it as 2 body parts in their BI/stats (abdomen and pelvis as one part, due to overlapping the same area), while other sites count it as 3 body parts.

This results in the distortion of stats and misrepresentation in comparisons. There are numerous examples similar to these in many different areas of imaging. More complex examples extend to exam protocols. For example, interventional procedures are sometimes different, and also care pathways differing between sites/ICSs.

However, the point is, that to enable cross-site working, there needs to be industry-wide standards set. The work of standardisation is beyond the scope of the current strategy and needs to be clinically led by the regional Digital Advisory Board (DAB) and ICS Clinical Reference Groups (CRG) with various modality or specialist-based sub-groups set up to define standards across the regions. Input from BI and radiographer/PACS managers groups are also vital for the process.

Traditionally, one of the biggest issues with patient data sharing has been primary ID. A Patient with primary ID ‘123456’ in ICS A, could be a different patient in ICS B.

Primary IDs in use include NHS number, hospital ID, CRIS ID and MSI (HL&SC). We regularly see the use of different IDs as primary. For example, with imaging network A using RIS ID as primary, whilst imaging network B is using the NHS number.

For imaging sharing to work and flow across a region/ICS, there needs to be oversight and an understanding of how each system uses patient IDs. There are intelligent systems available (within existing PACS systems) to enable matching by looking at demographic similarities (rather than specific to one primary ID) however the rules need to be set as to what and how a match occurs. Incorrect matching can have far-reaching implications back upstream to root systems including Electronic Patient Records (EPR) and Patient Administration Systems (PAS).

Another resultant issue is data quality and duplicates of imaging studies. For example, in an imaging network with 11 sites, over the years as patients have travelled between trusts, copies of their images travelled with them.

Copies then get extra ‘bits’ added on (e.g. annotations or reconstructed series e.g. 3D) when reviewed at the next trust. But—which is the master copy?

Is the original copy, or the copy that has bits added regarded as the ‘master copy’?

The answer is currently in fact both—but there needs to be a policy to find a more efficient way of saving (and streaming) the study as one (rather than storing it twice and wasting space and energy) while at the same time clearly defining any additions and when they were added (and by whom).

A lot of the above work will require the input and collaboration of the various PACs (and RIS) providers who can often be market rivals and see opportunities to overcharge. Hence, any focus group needs to vigorously manage its involvement.

Therefore, in summary, while perhaps beyond the scope of this blog, the recommendation is the establishment of a data quality and imaging standards group for imaging studies across the region to facilitate ease of image sharing and reduce study migration and subsequent duplication.

Inter ICS Operability — Image Sharing

Imaging sharing is a critical element of the digital maturity matrix in any ICS. Generally, this will already be enabled within each ICS. However, enabling image sharing regionally (i.e. across ICSs/between imaging networks) is a significant challenge.

This is due to a number of factors including network connectivity, the use of different systems/providers, and interoperability standards. Standardisation is also a key element for interoperability. Once connectivity and standards are in place, there are a number of ways to image share (often combined) including;

 

Cross Platform Workflow

Cross-platform workflow is a same-provider solution to connect multiple (same provider) PACS instances allowing streaming of reports and images without the need to download.

Also relevant for cross imaging network training and REALM (Standards for Radiology Events and Learning) meetings. With all sites eventually adopting PACS-driven reporting workflow, it also supports cross-site reporting capabilities. This enables local user authentication which provides “trusted” authentication within the remote instance.

 

Direct DICOM

Support for an “extended search” to 3rd party systems (different providers) through configuration of point-to-point Digital Imaging and Communications in Medicine (DICOM) connection—for images only, not reports.

And consideration on whether open access (DICOM QR) or pushed access (via DICOM teleradiology) should be adopted. This is where provider engagement and their willingness to work with other providers is key.

 

XDS

Cross-Enterprise Document Sharing (XDS) is an industry interoperability ‘IHE profile’ (standard) that is seen as a key solution to inter-ICS connectivity sharing of images (on demand).

In practice, a user in each ICS would only see images for that patient within their ICS. However, with XDS inter-site connectivity, an ‘expanded search’ functionality button would search the other ICSs for any further imaging for that patient (including radiographer comments e.g. ‘anaphylactoid reaction with immediate medical support’ on a previous contrast scan) which would subsequently be viewable cross ICSs.

For XDS to work, each ICS needs 2 elements; an XDS repository and an XDS registry. The registry is the ‘index’ of studies/patients while the repository points to relevant images and reports.

Usually, there will only be one XDS registry and repository in an ICS. Several PACS can exist within an ICS as long as they register their information with the central XDS registry.

XDS’s can then interconnect via XDA gateways. XCA (Cross Community Access) is another IHE profile that enables several XDS networks to be connected to one another.

Perhaps the most viable solution for inter-ICS image sharing is by fully enabling XDS and ‘extended search’ functionality. The benefits of this would be images on demand across the regions (specialist MDTs e.g. paediatrics) and reduced admin function (sending images between ICS (e.g. IEP).

It’s important to note limitations of XDS connectivity (which often users fail to grasp) which include;

1. XDS doesn’t enable cross-site reporting (the standard hasn’t been developed yet) however, is highly useful for vetting

2. XDS only streams and caches the images temporarily and images aren’t permanently merged into a single patient folder (this is due to different hospital numbering systems in different ICSs). Users sometimes expect the images to ingest from the other ICS (but they don’t)

One other point to note is that each PACS system needs to be XDA enabled in that they can export (push) and ingest (pull) images. This needs to be the case for both versions of each PACS (i.e. the web version and the reporter version).

XDS(i) again is a further IHE interoperability profile that extends XDS to share images, diagnostic reports, and related information across a group of care sites.³

XDS(i) continues to be slowly adopted across the NHS in England, it is worth conducting a feasibility study to understand the benefits and how XDS(i) could be integrated into the existing mature architecture.

 

Future PACS IT & Network direction

The big issue, in general, is that national policy around Community Commissioning Groups (CCGs) promoted independence and hence competition.

This resulted in isolated development of systems with different vendors. However, as we’ve seen here, the most recent policy (Richards reports and national digital imaging strategy docs) promotes collaborative working across trusts and the creation of imaging networks through ICSs.

The obvious problem is now joining these different and independent systems. An example of legacy topology is below with a future state aspiration included for comparison.

This is an example of an old-style disjointed regional imaging topology;

 

Whereas future direction is as follows;

 

Reliance on legacy, labour intense intermediary technology such as the Image Exchange Portal (IEP) or an in-house web portal to facilitate access/sharing between ICSs is completely inadequate and requires more modern sharing methods. Knowing what images are available, assimilating the images quickly, and ease of access for clinicians are of paramount importance.

Given the scale of network/bandwidth requirement for imaging diagnostics, it is not unrealistic to expect a dedicated network for imaging systems, removing bottlenecks and competition for bandwidth.

An alternative is interconnectivity between each imaging network’s dedicated network, essentially creating a ‘virtual’ dedicated imaging network.

A realistic bandwidth expectation is somewhere between 1Gb and 10Gb for dedicated links to form a sharing network between sites for both the RIS and PACS cloud.

A dedicated network/bandwidth strategy eliminates the dependency on bandwidth and the problem of ‘slow systems’ (e.g. competing for bandwidth with MS Teams meetings in the NHS), while also being scalable for the addition of other ‘ologies’ (e.g. digital pathology and ophthalmology).

Ultimately future state topology is full ICS interconnectivity with a regional approach as opposed to ICS or individual trust silos. The above future direction revolves around a central core (dedicated) imaging network with direct connections between ICS networks.

The end goal is to create a virtual (all) imaging network. Hence once the inter-ICS network connectivity is available, the various inter-ICS initiatives described later in this blog can be initiated.

 

Future PACS And RIS System Direction…

‘A PACS is a PACS is a PACS’…

PACSs (Picture Archiving and Communication System) are relatively straightforward, being well established and standardised (e.g. DICOM) with most trusts having single, mature, and market-leading systems.

Some trusts consolidate their PACS (and RIS) systems. A challenge is to engage the system providers to interoperate and align with shared policy. Any subsequent procurement should include a regional digital imaging strategy alignment.

There may be a point in the future, if contract renewals align (time and date wise), whereby a regional approach to procurement is possible. However, it shouldn’t be an issue with a sound argument to have different providers to prevent a monopoly and keep each system provider ‘on their toes’ through healthy competition when it comes to system development and innovation.

On the other hand, the same approach for RIS (Radiology Information Systems) may not be best. The RIS has been the cornerstone of trust radiology for decades. However, there is a school of thought that the future of RIS systems is limited.

‘The RIS is dead, long live the RIS!’

 

Essentially an RIS currently has two main functions;

1. Booking/scheduling/appointments

2. Reporting

 

We are seeing the booking element being taken over by RIS ‘modules’ of EPRs (we see this with EPIC’s radiant module), and the reporting element is being replaced by PACS-based reporting.

So RIS is getting squeezed out from both sides, with very few staff needing to interact directly with the RIS. However, RIS systems have been around for so long that their functionality is extensively developed to deal with the intricacies of reporting workflow and dedicated patient management—systems like EPR haven’t yet developed their ‘RIS modules’ to adequately replace RIS.

The future direction of RIS is less certain and will be best monitored over the coming years. However, a key priority should be to establish a regional RIS group to plan a roadmap and future direction for RIS going forward.

Cross-Enterprise Reporting — Levelling Up Health Inequality

Cross-site reporting is another key objective of any ICS. It enables shared reporting resources and promotes health equality by getting the right image to the most appropriate specialist, regardless of patient postcode.

There are a number of challenges to overcome. These include reporter passports (both technically and HR-wise). Technical ability is being authenticated at one hospital and reporting/accessing another hospital’s system (perhaps in a different ICS).

HR wise, providing legal protection assurance in case of discrepancies in reports. For example, if reporter A reports for hospital B and a discrepancy ensues, which hospital is liable?

The solution has been provided in the form of a ‘reporter passport’ in regions (e.g. EMRAD) and is more within the scope of workforce strategy. Similar challenges include vetting at one institute but performing at another, as well as the availability of radiographer (scan) comments and a full clinical history.

Imaging Business Intelligence — Data Analytics

Healthcare data is expanding rapidly. According to estimates by the International Data Corporation (IDC), in 2018 there were 1,218 exabytes of healthcare data globally, with this predicted to grow by 36% by 2025 (compound annual growth rate).⁴

The average healthcare organisation is estimated to hold over 8 petabytes of data. Diagnostic imaging generates 90% of the total healthcare data worldwide, and more than 97% of it is unanalysed or unused (global stat, from a US-based company).⁵

Similar trends exist in imaging, with Greater Manchester PACS recent imaging system procurement demonstrating a 23% increase in imaging data consistently across the previous 5 years, hence scoping their Vendor Neutral Archive (VNA) accordingly.

The resultant is a huge amount of data to process and analyse, not least because it’s continuing to grow in its variety (meaning queries need to be run across multiple data sources), but also due to its volume, and its velocity (every time a patient interacts with a service more data is generated).

Healthcare data is particularly valuable when it is combined and analysed. This generates insights at a population level and can inform individual treatment plans for patients. However, managing this combination and analysis of so much data is challenging.

Given CCG policy of competitiveness (up until recently Brother QL-600), it’s quite apparent that Business Intelligence (BI) information has been extracted in insolation (i.e. per trust) from RIS, and submitted to national collection points. This has resulted in four problems which quickly became apparent with the inset of COVID-19:

1. Data quality and consistency (multiple different extractors in each ICS pulling data/codes in different ways), some pull directly from RIS in radiology, others from an external data warehouse

2. Data being passive (past events) rather than proactive/forward facing

3. Absence of ICS level and subsequent regional diagnostic dashboards

4. Duplication of effort, with each trust being responsible for submitting their data set, despite sharing a PACS/RIS infrastructure.

Individual ICSs are rapidly developing ICS-level diagnostic dashboards with real-time BI. However, again, the challenge is consistency and standardisation ‘comparing apples with apples’ across the region to develop a meaningful, live, forward-facing and interactive regional imaging dashboard.

In some Imaging Networks (INs), COVID-19 highlighted the lack of an IN-level tool and also reliance on national submissions that were very passive with BI representations, only as current as six weeks – three months previous.

During COVID-19, a lot of patients cancelled or delayed their appointments to stay out of hospitals which meant there was an impending ‘tsunami’ of delayed appointments—which every imaging manager was acutely aware of. However, it was extremely difficult to put a metric on the scale/demonstrate.

An absence of proactive and live/current metrics as well as proactive modelling became apparent. Also in some cases, the RIS was not in a fit state to deliver (hardware and bandwidth issues meant regional stats were impossible to execute).

One of the best ways to tackle complicated data integration is through data lakes and data warehouses. A data lake is a centralised, curated, and secured repository that stores all your data, both in its original form and prepared for analysis.

A data lake will enable a region/ICS to break down data silos and combine different types of analytics, such as data warehousing, big data processing, or operational analytics, to gain insights and guide better business decisions.

For example, population health management and making predictions about at-risk groups and future service needs. Ultimately, data lakes can help hospitals and healthcare organisations turn data into insights and maintain business continuity while preserving patient privacy.

Data lakes can pull in structured, semi-structured and unstructured data for analysis—this is particularly useful for imaging datasets which can consist of large volumes of unstructured data, which otherwise would be resource intensive to analyse.

Data lakes have a significant advantage over data warehouses, as data does not need to be cleansed or structured in advance, and the data lake can change its structure and size as the needs for it change. This means a region/ICS will be able to store all of its data without careful design or the need-to-know what questions we might need answers for in the future.

Data Lake/Warehouse

A data lake/warehouse approach means INs will be able to harness more outcome data from more sources, increasing the effectiveness of algorithms to deliver greater benefits to patients.

Developing a data lake and supporting analytic tools can:

• Move data easily and allow INs to import any amount of data, including in real-time, from multiple sources
• Securely store and catalogue data. Allow INs to store structured and unstructured data, and gives the ability to understand what data is in the lake through cataloguing and indexing it
• Perform analytics. Enable analysis using a wide range of tools and frameworks, without having to move data to a separate analytics system
• Utilise machine learning. Facility to deploy machine learning applications to generate insight, for example, building models to forecast diagnoses or outcomes

This is a first step to benefits for patients and staff of having access to Population Health Management (PHM) platforms and will include pathology and genomic data in the future.

Initially, PHM platforms can be used for:

• Capacity and demand planning—one of the national criteria that emerging imaging networks must achieve
• Align data quality within the systems
• Removing duplicate requests across an IN, to ensure resources are used appropriately

Clinical leadership (CRG)

As mentioned, many positive outcomes depend on standardisation across regions. Whether that is peer review, exam codes or scan protocols, it needs to be clinically led and agreed through imaging networks with a mature clinical and operational governance model. This will require the support of a fully established network leadership team managing and overseeing the disposal day-to-day management of the imaging network.

Many imaging networks are now in place since their first inception in 2018 and with the more recent formation of ICSs, they now have a functioning clinical network. Regional imaging steering groups help coordinate imaging network planning activities, and collaborative workstreams are already in place for workforce and to support production of this digital imaging strategy.

Scheduling and Vetting

The imaging network maturity matrix describes how imaging networks that are thriving should be able to demonstrate cross-network vetting and scheduling, including Community Diagnostic Centres (CDCs) where appropriate, and supporting patient choice.

The clinical benefits from cross-enterprise scheduling and vetting include reducing inequalities in access to diagnostics across the network by better matching of capacity and demand. This enables better use of resources resulting in faster diagnosis and turnaround times.

The ability to schedule across providers supports direct access to specialist services where appropriate, and the ability to offer imaging closer to home coupled with patient choice of provider if preferred.

The latest government initiatives around Community Diagnostic Centres (CDCs) require digital solutions to enable these to work as network resources, and not just an extension of local trust imaging capacity. CDC’s will also incorporate Rapid Diagnostic Centres (RDCs).

The rollout of Rapid Diagnostic Centres (RDCs) across England is an ambitious five-year programme, which started in 2019, designed to speed up diagnosis of cancer and other serious conditions. Rapid Diagnostic Centre pathways make sure everyone with suspected cancer gets the right tests at the right time in as few visits as possible.

Technically, facilitating CDCs and RDCs is another challenge. A CDC will take referrals from multiple localities and subsequently send patients to several hospitals within an area (usually based on specialism). The facilities will be based in the community, and scheduling, vetting and reporting will need to be coordinated and centralised, therefore remote access to IT systems will be a key enabler.

Hence, three critical elements for CDCs to work optimally are robust scheduling and vetting, remote (equivalent performance access) access, and cross-site working.

Several companies including Soliton, WellBeing, InfoFlex, xWave, and AptVision have exciting new products that essentially sit across the top of different RIS, PACS and e-referral systems to present a single online booking system for both GP referral and patients alike. This is particularly relevant for the rapid progression of CDCs. Benefits include improved patient experience and reduced administrative workload to unify appointment management for any number of sites and ‘ologies’.

 

Dose Monitoring

Patient safety, particularly with regard to exposure to ionising radiation has always been a major concern in radiology. The use of high-dose, multi-slice modalities like Computerised Tomography (CT) and Positron Emission Tomography (PET) scanning in recent years has grown the risk dramatically.

This is set to continue with preventative screening programmes (mainly through the use of CT). The main source of the problem is that different scanners can emit different levels of radiation (for the same exam) based on different calibrations and scan protocols. And medical physics staffing resources are severely stretched at present too.

One way to mitigate the increased risk is to monitor radiation dose output across a wide area and compare the outputs from different locations (for the same work). In parallel, modern PACS are capable of absorbing dose information for each exam in their DICOM headers and sophisticated dose monitoring software can extract that information and present it as a comparative analysis.

Accumulative dose is also important to monitor. With increased specialisation of care, patients are more likely to move between hospitals. Hence a full picture of dose accumulated between scan locations has been difficult.

However, with specialist software, joining up imaging networks (ICSs) and cross-site coordination, it is now possible to monitor patient accumulative dose and offer a comparison between dose outputs right down to scanner level.

Standardising radiation dose output across the region has the potential to dramatically increase patient and operator safety through a net reduction in overall radiation dosage.

 

Summary Objectives

• Set out your current ‘state of the PACS nation’ across your imaging network and ICS
• Encapsulate current thinking and best practice to ensure a sustainable, sharing, interoperable, and advanced imaging technology with the capacity and capability to meet the current and future demand for imaging services across the ICS
• Set out the case for cross-site reporting, shared reporting resources, and promoting health equality by getting the right image to the most appropriate specialist regardless of patient postcode
• Similarly with cross-network vetting and scheduling, including CDCs where appropriate, and supporting patient choice.
• Establish the need for a regional RIS group to establish a roadmap and future direction for RIS going forward.
• Demonstrate the need for a regional core central business management model (at scale) for both in and out sourcing of radiology reporting
• Enable cross-site on-demand Multi-Disciplinary Team (MDT) functionality between ICSs (regional/ICS level MDT on-demand)
• Establish a regional imaging AI oversight board with appointed clinical and operation AI leads to focus on imaging AI applications with the co-ordination of shared learning and deployment at scale in a timely manner.
• Establish quality standards for image migration and duplication.
• Embrace the latest app technology that enables further integration with community and secondary care.
• Establish a data quality and imaging standards group for imaging studies across the region/ICS to facilitate ease of image sharing and reduce study migration and subsequent duplication.
• Development of a Radiology Reporting Collaborative (RRC) to tackle insourcing and outsourced reporting inefficiencies and expense
• Scale-up successful mini-projects including;
  • PACS based reporting
  • Out-of-Hours (virtual) reporting hubs
  • Imaging AI
  • Outsourced Radiology Reporting Collaborative (RRC).
• Support an ICS/regional imaging training academy with the latest imaging technology and support tools
• Set up an ICS/regional reporting hub with both physical locations and a virtual hub to enable users to work from home
• Develop meaningful, live, forward-facing, and interactive regional imaging dashboards
• Embrace the latest app technology (e.g. remote image capture and paperless handover between CDCs and trusts) that enables further application into both the community and secondary care.
• Push the marketplace to enhance their products to solve today’s problems with scheduling and reporting at scale.
• Demonstrate a leading-edge imaging network based on better patient outcomes and value for money.
• Standardise reporting economies and report lockouts across the region

Summary Benefits

• Clinical workflow efficiency gains will ultimately benefit patient care by providing faster turnaround and more accurate diagnosis (including cross-ICS, ‘multi-ology’ MDT on-demand)
• A reduction in health inequality by providing equity of access to specialist reporting (regardless of postcode) with the ability to offer imaging closer to home and patient choice of provider if preferred.
• Inter-ICS image sharing (via XDS) will enable images on demand across a region (specialist MDTs e.g. paediatrics) and dramatically reduce PACS administration functions (sending images between ICSs via IEP).
• The benefits of adopting the government cloud first policy are well documented and include;
• Not having ‘on-prem’ hardware to maintain
• Heightened levels of security, flexibility, and value for money
• Deployment (of software tools) at scale (support tools such as AI, advanced visualisation and remote access) as well as training and outcome monitoring
• Inclusion of other ‘ologies’ as they come on stream including digital pathology and ophthalmology to enable side-by-side and synchronised ‘multi-ology’ review platform
• Cross enterprise scheduling, vetting, and quality assurance resulting in improved patient experience and reduced administration workload to unify appointment management for any number of sites and ‘ologies’ (particularly beneficial to Community Diagnostic Centres)
• The current and continued exponential growth of imaging diagnostics and subsequent clinical capacity strain can be addressed by imaging AI. The policies outlined in this document (e.g. cloud-first, single repositories, and structured reporting) enable the significant opportunity for imaging AI adoption in a region
• Messenger (added value for critical alerts), chat and video calling functionality; diagnostic imaging can benefit greatly from multimedia engagement.
• A regional dose monitoring policy will result in dramatically increased patient and operator safety through a net reduction in radiation dosage.
• Supporting the regional workforce strategy by providing more technical and efficient ways of working (including remotely) and ultimately making imaging an attractive place to work
• Provide the platform and tools to address the imbalance between insourced and outsourced reporting while saving on significant outsourcing costs
• Benefit the imaging training academy with the latest imaging technology and support tools

 

About The Author

Pauric Greenan is an experienced PACS consultant that has been PACS lead on several high-profile PACS projects in both the UK and Ireland. Pauric is internationally available for PACS consulting. Contact him here for more details.

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