Paragon Consulting Partners

Healthcare IT Solutions and Results

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Laurie Lafleur

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Data normalization is an essential activity whenever two or more previously independent facilities are migrated into single, consolidated data repository, as it preserves the integrity of incoming data by avoiding broken links between patient records and problematic collisions between device and study identifiers. 

Jude Mosley, Manager of Information Technology, Imaging at Sentara Healthcare knows this better than anyone. She and her team face this very challenge regularly whenever a new hospital or imaging center is integrated into the 5-hospital network in Norfolk, Virginia. 

Hey, that’s my jacket!

The first challenge is ensuring patient jackets from differing systems are reconciled correctly so that no information was lost or attached erroneously to the wrong patient. Using a master patient index (MPI), Sentara is able to match patients between the source and destination systems and update all incoming studies with the correct patient identifier upon ingestion. This results in a single, consistent set of MRNs across the Sentara network, and eliminates future risk that an old MRN could be mistakenly used. 

The next challenge is ensuring study accession numbers remain unique, and that the migration process doesn’t introduce duplicate accession numbers for different patients. To accomplish this, Sentara employed normalization policies that pre-fixed the accession number and updated the location attribute to a 3-character code representing the source facility for all incoming studies. Not only did this avoid potential collisions between accession numbers, it also added a level of transparency to the migrated data that allowed Sentara to quickly and easily identify its original source. 

The Big Bang theory of modalities

While the migration process provides an excellent opportunity to normalize data on-the-fly, it is often necessary to enforce data normalization policies at the modalities themselves. At Sentara, normalization at the modality level is a well-defined process. With each new modality introduction, update, or site acquisition Sentara ensures that every affected modality adheres to a consistent set of pre-defined policies regarding AE title and station naming conventions. When new modalities are installed, or vendor updates are applied, Sentara requires the vendors to be aware of and sign off on the required data policies to ensure they’re properly applied and thoroughly tested after the installation is complete to maintain adherence.

For larger-scale activities, like a new site acquisition and migration, the PACS team prepares a comprehensive list of all modalities and their new AE titles, station names, and IP addresses, and orchestrates a big-bang update process. While this is no small undertaking, through experience Sentara has refined this process to run like a well-oiled machine. Once complete, Sentara has improved the consistency and supportability of their now-larger infrastructure, and once again ensured that data arrives in a predictable and consistent manner. 

A shining example of normalcy

This case provides excellent examples of how data normalization can address the integration challenges faced by expanding health systems. Not only does it mitigate risk by avoiding data loss and collisions during the migration process, it also measurably improves data quality and reduces support costs in the future by improving the consistency and predictability of systems and data across the entire network.

Our next blog will be taking a more clinical focus, looking at how data normalization can be leveraged to uncover deep insights across population-wide data sources. Sound interesting? Subscribe to our blog to be notified when the next post becomes available!


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Normalization has become an essential activity for any data-driven organization. While it does require an investment in time and resources to define and apply the policies, structures, and values you will find that it is well worth the effort. Not only will you see measurable improvements in the quality and efficiency of clinical workflow, stakeholder satisfaction, and your bottom line – you will also unlock the untapped potential of what could prove to be one of your organization’s biggest assets – your data! That being said, let’s take a look at the various methods that can be used to make sure your data falls in line and conforms to your new norms.

What does normal look like anyway?

The first step is identifying and defining the attributes to be normalized. This begins with a close look at your organization’s key challenges and goals. Having trouble with data integrity, fragmentation, and collisions? Take a look at how unique ID’s are assigned, reconciled, and managed at the facility, system, device, and patient levels. Hearing lots of complaints from your clinical team about broken and inefficient workflow? Consider looking at the variations in procedure, exam, and diagnostic naming conventions across your enterprise. Once the troublesome attributes have been defined key stakeholders should be engaged to define what the ‘gold standard’ values should be, and map these to the ‘dirty’ values that are plaguing your system.

Keeping it clean on-the-fly

Once the normalized values have been defined transient systems, such as DICOM and HL7 routers, or destination systems, such as EHR/EMRs, Picture Archiving and Communication Systems (PACS) or Vendor Neutral Archives (VNA), can often be configured to inspect the data as it arrives to dynamically identify and adjust any inconsistencies and ensure what is stored and shared adheres to normalization policies. This is accomplished through a normalization or ‘tag morphing’ rules engines that are able to inspect incoming data on-the-fly, identify deviations from normalization policies using pattern matching algorithms, and apply the appropriate transformations based on the pre-defined set of consistent values. This ensures incoming data is clean, consistent, and reliable – regardless of its original source or format.

As well, it enables rapid integration of outside facilities and systems resulting from mergers or acquisitions as new systems and modalities can be integrated quickly without requiring updates to hanging protocols, routing and retention policies, reporting systems, etc.

Finally, it mitigates the impact of any unforeseen changes that may occur due to vendor updates at the modality, which can sometimes alter attribute values such as series descriptions. This is most common among complex multi-slice acquisition devices, and ultimately results in broken hanging protocols and frustrated radiologists.

Garbage in, garbage out

In some cases, it may be necessary to enforce data normalization policies at the modalities themselves. This is especially important if receiving systems do not provide robust tag morphing capabilities, leaving you without the ability to enforce normalization policies on-the-fly. As well, if you find that your technologists are frequently required to manually enter or adjust data values at the modality, then added variability and potential for error in the resulting data sets are more likely to occur. This may not always be caught by a discerning rules engine. In either case, why not take the opportunity to ensure your modalities are sending clean data from the get-go? As the old adage says: garbage in, garbage out.

When you’re going where the grass is greener

If you’re considering a retiring and replacing ageing systems, data migrations present an excellent opportunity to clean and normalize existing data as it moves between systems, providing immediate benefits like better access and filtering of relevant priors, improved reading efficiency through consistent and reliable hanging protocols, and the ability to incorporate historic data into analytics, AI, and deep learning applications.

As well, it positions you to minimize the effort involved in any future system replacement or migration activity by simplifying the configuration of any system features that rely on specific attribute values or structures to function effectively. For instance, hanging protocols are not typically transferrable between vendors’ systems and therefore need to be re-created whenever a PACS replacement or viewer change occurs. The consistency of normalized data facilitates rapid configuration of protocols within a new system, as the complexity associated with configuring multiple protocols for each distinct layout, or the viewer provided lexicons required to accommodate or ‘map’ the various combinations and permutations of inconsistent protocol, study, or series information is eliminated. The same holds true for other attribute-driven features including but not limited to reading worklists, routing or forwarding rules, information lifecycle management policies, and analytics and deep learning.

That sounds like a lot of work…

More often than not the perceived effort of defining such practices and data structures seems overwhelming to many already busy IT departments and project teams. This often results in a choice to forego data normalization activities in favour of saving some time and effort, or simply due to lack of financial and human resources.

While it may be true that data normalization is no small task, the benefits far outweigh the cost of the initial investment, and many organizations are now realizing the strategic value of data normalization initiatives. The heaviest lift, by far, is the process of gathering and analyzing existing data and distilling it into normalized data structures and values, which is a one-time effort that will yield immediate and recurring dividends by creating an actionable data repository that supports clinical and business continuous improvement initiatives.

By now you might be thinking, “this sounds good in theory, but it’s pretty anecdotal. Where’s the real-life evidence that data normalization is worth the effort?”

Our next posts will include real-world examples of how some luminary healthcare organizations have leveraged data normalization to achieve a variety of measurable benefits. Subscribe to our blog to be notified when the next post becomes available!


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Why Be Normal?

There are many times when it’s important to be unique and stand out. For example, when attending a job interview, showing off your costume for a Halloween competition, or when you’re auditioning for American Idol. The creation of healthcare data, however, is not one of them.

Unfortunately, unique is often the default state of imaging and other health data as it is generated across modalities, systems, departments, and facilities – where the presence of diverse vendors and local policies result in bespoke data management practices and attribute values such as procedure or study names, series descriptors, disease characteristics, and in which format, tag, or sequence data is stored. Such inconsistency in data structure and content leads to a number of workflow and operational challenges, and significantly reduces the value of underlying health data, for instance:

  1. It complicates the creation and maintenance of reliable and consistent hanging protocols that are required for efficient reading workflow, forcing a never ending and hard to manage set of rules that require a complex set of rules to maintain consistent hanging protocols
  2. It limits the ability to effectively curate and analyze data for clinical and business improvement purposes
  3. It inhibits effective artificial intelligence (AI) and machine learning algorithm training
  4. It results in difficult and costly migration implications when considering system retirement or replacement

Data normalization – the process of defining standards and applying them to the structure and content of health data – overcomes these challenges by ensuring incoming data arrives in a consistent and predictable manner. The resulting clean, standardized data can be leveraged to:

  1. Inform continuous improvement initiatives to improve workflow efficiency, quality, and cost
  2. Better support interoperability between existing applications, and simplifies implementation and integration of new enterprise imaging systems
  3. Reduce the cost and complexity of future data migration projects
  4. Allow data to be more easily be inspected and mined to unlock valuable insights at departmental, organizational, and population levels

The value of being normal(ized)

Whether undertaken as part of a larger Enterprise Imaging initiative, or a standalone project, data normalization has the potential to yield a huge return on investment. Not only can it realize measurable improvements in the quality and efficiency of clinical workflow, stakeholder satisfaction, and your bottom line, it can also unlock the untapped value of what could prove to be one of your organization’s biggest assets – your data.

You may be wondering – this all sounds good in theory, but how exactly can data be normalized and what are the practical business and clinical applications? To address these questions, we will be posting a series of articles that will explore the methods of data normalization and dive deeper into the clinical, operational, and fiscal use cases and benefits for data normalization at the enterprise, departmental, and modality level.

Subscribe to our blog to be notified when a new post is available and learn more about how data normalization can better support workflow optimization, interoperability, data curation and migration, artificial intelligence and machine learning, and data analysis and intelligence.

 

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Establishing a well-represented and empowered governance committee up-front is critical to the success of any Enterprise Imaging project. By providing a sense of direction, fostering collaboration, and monitoring project progress the governance committee ensures each Enterprise Imaging initiative addresses the organization’s top needs and priorities, on time and within budget.

You might be wondering ‘how do we establish good governance?’. Read the following 6 best practices for establishing a strong governance foundation, to ensure your next enterprise imaging venture starts off on the right foot, or check out our animated video on YouTube.

1. Take a left turn at Albuquerque

Good governance provides a sense of direction by setting and communicating the vision to ensure continued alignment across all initiatives. This helps to avoid distractions and provides a clear set of guidelines when assessing scope and priorities. It is important to engage leadership across stakeholder groups and specialties, such as physicians and other clinical roles, as well as IT, Operations, and Finance

2. One bite at a time

Next, determine priorities – what is burning, and what can wait? Identify which departments, workflows, and systems will have the biggest impact on quality of care and business priorities. As well, identify any other peripheral departments and ‘ologies that should be considered. Determining chronology is a very political exercise and must be careful considered, as factors such as referral revenue, tenure, and highly marketed specialties carry additional influence.

3. Put your heads together

Be sure to include representation and buy-in from key stakeholders across the organization, including business, technical, and clinical function to ensure a complete and successful project. Avoid missed requirements that could disrupt both timelines and budget, minimize conflict by fostering collaboration and engagement, and determine standards early and enforce what data qualifies to be enfolded into the data platform.

4. Measure twice, cut once

Set clear and measurable success criteria and monitor progress. Agree up front on what success looks like, and hold stakeholders accountable for milestone delivery. It is important not to miss your milestones unless a critical event has occurred, losing trust is a very difficult road to rebuild.

5. Expect the unexpected

Even the best laid plans sometimes go awry; a good governance model ensures there are mechanisms in place to address unforeseen obstacles and shifting priorities. The Governance team MUST be engaged and active throughout the Enterprise Imaging lifecycle as things change.

6. Lift the blindfold

Establish clear and consistent communication between governance committee, project teams, and affected stakeholders to provide transparency into project objectives and progress. This helps to keep everyone on the same page – both within and outside the core governance team. As well, it helps everyone in the organization to feel included and important, and avoids feelings of uncertainty and frustration when such transparency is not provided. Communication to stakeholders should include strategy, progress, scope changes with efficacious feedback loop mechanisms.

Following these best practices will help to ensure your next Enterprise Imaging initiative is setup for success, and will ultimately lead to a smoother project roll-outs and easier change management.

Contact us to learn more about how we can help your organization tackle its next Enterprise Imaging initiative, including establishing governance and strategy, workflow analysis and requirements definition, vendor selection and RFP, deployment and project management, and beyond. If you enjoyed this post subscribe to our blog to be notified when we post new articles.

 

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A few years ago we signed up for what promised to be an exciting adventure: Extreme Fitness Vacation in the Grand Canyon: Hike from the Rim to the River and Back in One Day! Sounds fun, right? If you think so, or if you’re just curious what this has to do with project management, read on!

This excursion was led by a gym owner, manager, personal trainer, and beefcake in his own right. The trip was seemingly well planned; our routes were outlined and our host promised to take care of all the incidentals – including a relaxing lunch at the bottom of the canyon before beginning the arduous hike back up. What could possibly go wrong? Unfortunately, lots.

To start, it turns out this particular hike (or at least the upwards part of the journey) is actually noted to be one of the 10 most dangerous hikes in North America – not for the faint of heart, and certainly not for the faint-of-fitness. This little tidbit was not known to our excursion leader, who himself was somewhat of a hiking newb, and thus there were no clear fitness or hiking experience guidelines or requirements laid out for those who were interested in signing up. All that was required, as it turned out, was the ability to pay the less-than-modest fee. This resulted in a rag-tag group of pleasant, yet ill-equipped and ill-informed individuals joining the excursion.

The temperature in the Canyon at that time of year (June) often exceeds 110F (or 43C for my Canadian friends), which means a few things: first, you’ll need lots of water and electrolytes (critical to muscle function) because you’re going to sweat. A lot. Second, you won’t feel hungry during the hike, but trust me, your body will be. This means you need to bring lots of light snacks to eat along the way – even when the last thing you feel like doing is eating – to ensure your energy levels are maintained to support your level of physical exertion.

To make a long story short, because of his lack of experience and knowledge of the physical and environmental demands of a Grand Canyon hike, our host made a few critical errors:

  1. He did not bring adequate or appropriate snacks for the number of people in his group (seriously ‘Arnold’s extreme protein muscle fuel bars’ are really the last thing you want to be trying to digest under these conditions)
  2. He assumed there was water along the trail – which would have been great because it meant we wouldn’t have to carry it ourselves! However, water was only available halfway up the return trail, so for 75% of the hike there were no viable re-fill options for empty bottles and thus many among us ran out of water…quickly.
  3. He failed to provide or educate participants on the importance of electrolytes, which are lost with sweat and must be replenished to ensure your leg muscles function well and long enough to haul your butt out of the canyon (as many-a-sign said, down is optional – up is mandatory)
  4. Lunch was promised at the bottom of the canyon, but he didn’t pack anything as he expected he could purchase sandwiches at a river side cafe. The problem? There was no such cafe, and nowhere to purchase sandwiches (unless you want to travel another mile or so off-route to the famed Phantom Ranch, and you’ve booked ahead…)

The result? Heat exhaustion, severe muscle cramping, and even delirium were experienced by some. I’m happy to report we all made it back out without serious injuries – no thanks to our leader, who himself could barely walk due to leg cramps near the end.

So what does hiking the Grand Canyon and Healthcare IT project management have in common? Simple. In this example our fearless (to a fault) leader acted as the project manager. He defined the scope of the excursion, the paths we would take, and the schedule we would adhere to. He was an expert in health and fitness, so the participants trusted his judgement and his plan – and thus followed him on what turned out to be a dangerous and misguided adventure. What he wasn’t, however, was an expert on the specific challenges presented by a Grand Canyon hike and therefore was ill-equipped to identify and navigate the unique terrain and conditions the group encountered.

Proving that even the best laid plans go awry, this (albeit extreme) story provides the perfect parallel for healthcare IT project management. Certified project management professionals (PMPs) are intimately aware of all associated best practices and methodologies required to steer general project initiative and are an important part of any project team. However, unless they also have the subject matter expertise that comes from hands-on experience in clinical and technical healthcare settings they are not equipped with the knowledge or tools needed to craft the most efficient and effective plan or steer the project flawlessly to success.

In addition to knowing how to run a project a PMP who is also a true subject matter expert brings immeasurable added value by additionally understanding the needs of clinical stakeholders, how data flows and systems interact, and knowing what pitfalls may lie ahead – thus allowing them to take proactive steps to help ensure their healthcare IT projects run smoothly, and are delivered on time and within budget.

For more information on our health IT project management methodology download the Six Steps For Effective Health IT Project Management Infographic.

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