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Heathcare AI Maturity Index

Posted on October 16, 2018 I Written By

John Lynn is the Founder of the HealthcareScene.com blog network which currently consists of 10 blogs containing over 8000 articles with John having written over 4000 of the articles himself. These EMR and Healthcare IT related articles have been viewed over 16 million times. John also manages Healthcare IT Central and Healthcare IT Today, the leading career Health IT job board and blog. John is co-founder of InfluentialNetworks.com and Physia.com. John is highly involved in social media, and in addition to his blogs can also be found on Twitter: @techguy and @ehrandhit and LinkedIn.

Everywhere you turn in healthcare you’re seeing AI. I know some people would argue with how many companies define AI. In fact, there’s no doubt that AI has started to be used for everything from simple analytics to machine learning to neural networks to true artificial intelligence. I don’t personally get worked up in the definitions of various words since I think all of these things can and will benefit healthcare. Regardless of definition, what’s clear is that this broad definition of AI is going to have a big impact on healthcare.

In a recent tweet from David Chou, he shared a really interesting look at AI adoption in healthcare as compared with other industries. The healthcare AI maturity index also looks at where healthcare’s AI trajectory is headed in the next 3 years. Check out the details in the chart below:

There are a couple of things that concern me about this chart. First, it shows that healthcare is behind other industries when it comes to AI adoption. That’s not too surprising since healthcare is usually pretty risk averse with new technology. The “First Do No Harm” is an important part of the healthcare culture that scares many away from technology like AI. The only question is will this culture prevent helpful new AI technologies from coming to healthcare.

Many people would look at the chart and see that it projects a lot of growth in healthcare AI investment. That’s a good thing, but it also is a common pattern in healthcare. Lots of anticipation and hope that never fully realizes. Will we see the same in healthcare AI?

What’s been your experiences with AI in healthcare? Where do you see AI having the most impact right now? What companies are doing AI that’s going to impact your hospital or health system? Share your thoughts in the comments or on social media with @healthcarescene.

AI Project Set To Offer Hospital $20 Million In Savings Over Three Years

Posted on October 4, 2018 I Written By

Anne Zieger is veteran healthcare branding and communications expert with more than 25 years of industry experience. and her commentaries have appeared in dozens of international business publications, including Forbes, Business Week and Information Week. She has also worked extensively healthcare and health IT organizations, including several Fortune 500 companies. She can be reached at @ziegerhealth or www.ziegerhealthcare.com.

While they have great potential, healthcare AI technologies are still at the exploration stage in most healthcare organizations. However, here and there AI is already making a concrete difference for hospitals, and the following is one example.

According to an article in Internet Health Management, one community hospital located in St. Augustine, Florida expects to save $20 million dollars over the next the three years thanks to its AI investments.

Not long ago, 335-bed Flagler Hospital kicked off a $75,000 pilot project dedicated to improving the treatment of pneumonia, sepsis and other high mortality conditions, building on AI tools from vendor Ayasdi Inc.

Michael Sanders, a physician who serves as chief medical informatics officer for the hospital, told the publication that the idea was to “let the data guide us.” “Our ability to rapidly construct clinical pathways based on our own data and measure adherence by our staff to those standards provides us with the opportunity to deliver better care at a lower cost to our patients,” Sanders told IHM.

The pilot, which took place over just nine weeks, reviewed records dating back five years. Flagler’s IT team used Ayasdi’s tools to analyze data held in the hospital’s Allscripts EHR, including patient records, billing, and administrative data. Analysts looked at data on patterns of care, lengths of stay and patient outcomes, including the types of medications docs and for prescribing and when doctors were ordering CT scans.

After analyzing the data, Sanders and his colleagues used the AI tools to build guidelines into the Allscripts EHR, which Sanders hoped would make it easy for physicians to use them.

The project generated some impressive results. For example, the publication reported, pathways for pneumonia treatment resulted in $1,336 in administrative savings for a typical hospital stay and cut down lengths of stay by two days. All told, the new approach cut administrative costs for pneumonia treatment by $800,000.

Now, Flagler plans to create pathways to improve care for sepsis, substance abuse, heart attacks, and other heart conditions, gastrointestinal disorders and chronic conditions such as diabetes.

Given the success of the project, the hospital expects to expand the scope of its future efforts. At the outset of the project, Sanders had expected to use AI tools to take on 12 conditions, but given the initial success with rolling out AI-based pathways, Sanders now plans to take on one condition each month, with an eye on meeting a goal of generating $20 million in savings over the new few years, he told IHM.

Flagler is not the first, nor will it be the last, hospital to streamline care using AI. For another example, check out the efforts underway at Montefiore Health, which seems to be transforming its entire data infrastructure to support AI-based analytics efforts.

Three Hot Healthcare AI Categories

Posted on September 26, 2018 I Written By

Anne Zieger is veteran healthcare branding and communications expert with more than 25 years of industry experience. and her commentaries have appeared in dozens of international business publications, including Forbes, Business Week and Information Week. She has also worked extensively healthcare and health IT organizations, including several Fortune 500 companies. She can be reached at @ziegerhealth or www.ziegerhealthcare.com.

The way people talk about AI, one might be forgiven for thinking that it can achieve magical results overnight. Unfortunately, the reality is that it’s much easier to talk about AI application than execute them.

However, there are a few categories of AI development that seems to be emerging as possible game-changers for the healthcare business. Here’s five that have caught my eye.

Radiology: In theory, we’ve been able to analyze digital radiology images for quite some time. The emergence of AI tools has supercharged the discussion, though. The growing list of vendors competing for business in this nascent market is real.

Examples include Aidence, whose Veye Chest automates analysis and reporting of pulmonary modules, aidoc, which finds acute abnormalities in imaging and adds them to the radiologist’s worklist; CuraCloud, which helps with medical imaging analysis and NLP for medical data and more. (For a more comprehensive list, check out this Medium article.)

I’d be remiss if I didn’t also mention a partnership between Facebook and the NYU School of Medicine focused on speeding up MRI imaging dramatically.

Vendors and industry talking heads have been assuring radiologists that such tools will reduce their workload while leaving diagnostic in clinical decisions in their hands. So far, it seems like they’re telling the truth.

Physician documentation: The notion of using AI to speed up the physician documentation process is very hot right now, and for good reason. The advent of EHRs has added new documentation work to physicians’ already-full plate, and some are burning out. Luckily, new AI applications may be able to de-escalate this crisis.

For example, consider applications like NoteSwift’s Samantha, an EHR virtual assistant which structures transcription content and inputs it directly into the EHR. There’s also Robin, also which “listens” to discussions in the clinic rooms, drafts clinical documentation using Conversational Speech Recognition. After review, Robin also submits final documentation directly to an EMR.

Other emerging companies offering AI-driven documentation products apps including Sophris Health, Saykara, and Suki, all of which offer some isotype of virtual assistant or medical scribe functions. Big players like Nuance and MModal are working in this space as well. If you want to find more vendors – and there’s a ton emerging out there – just Google the terms “virtual physician assistant” or “AI medical scribe.” You’ll be swamped with possibilities.

My takeaway here is that we’re getting steadily closer to a day in which simply approve documentation, click a button and populate the EHR automatically. It’s an exciting possibility.

Medical chatbots: This category is perhaps a little less mature than the previous two, but a lot is going on here. While most deployments are experimental, it’s beginning to look like chatbots will be able to do everything from triage to care management, individual patient screenings and patient education. Microsoft recently highlighted how companies can easily create healthcare chatbots on Microsoft Azure. That should open up a variety of use cases.

The hottest category in medical chatbots seems to be preliminary diagnosis. Examples include Sensely, whose virtual medical assistant avatar uses AI to suggest diagnoses based on patient symptoms, along with competitors like Babylon Health, another chatbot which offing patient screenings and tentative diagnoses and Ada, whose smartphone app offers similar options.

Other medical chatbots are virtual clinicians, such as Florence, which reminds patients to take the medication and tracks key patient health metrics like body weight and mood, while still others focus on specific medical issues. This category includes Cancer Chatbot, a resource for cancer patients,  caregivers, friends and family, and Safedrugbot, which helps doctors who need data about use of drugs during breastfeeding.

While many of these apps are in beta or still sorting out their role, they’re becoming more capable by the day and should soon be able to provide patients with meaningful medical advice. They may also be capable of helping ACOs and health systems manage entire populations by digging into patient records, digesting patient histories and using this data to monitor conditions and send specialized care reminders.

This list is far from comprehensive. These are just a few categories of AI-driven healthcare applications poised to foster big changes in healthcare – especially the nature of the health IT infrastructure. There’s a great deal more to learn about what works. Still, we’re just steps away from seeing AI-based technologies hit the industry hard. In the meantime, it might be smart to consider taking some of these for a test run.

Montefiore Health Makes Big AI Play

Posted on September 24, 2018 I Written By

Anne Zieger is veteran healthcare branding and communications expert with more than 25 years of industry experience. and her commentaries have appeared in dozens of international business publications, including Forbes, Business Week and Information Week. She has also worked extensively healthcare and health IT organizations, including several Fortune 500 companies. She can be reached at @ziegerhealth or www.ziegerhealthcare.com.

I’ve been doing a lot of research on healthcare AI applications lately. Not surprisingly, while people find the abstract issues involved to be intriguing, most would prefer to hear news of real-life projects, so I’ve been on the lookout for good examples.

One interesting case study, which appeared recently in Health IT Analytics, comes from Montefiore Health System, which has been building up its AI capabilities. Over the past three years, it has created an AI framework leveraging a data lake, infrastructure upgrades and predictive analytics algorithms. The AI is focused on addressing expensive, dangerous health issues, HIA reports.

“We have created a system that harvests every piece of data that we can possibly find, from our own EMRs and devices to patient-generated data to socio-economic data from the community,” said Parsa Mirhaji, MD, PhD, director of the Center for Health Data Innovations at Montefiore and the Albert Einstein College of Medicine, who spoke with the publication.

Back in 2015, Mirhaji kicked off a project bringing semantic data lake technology to his organization. The first pilot using the technology was designed to find patients at risk of death or intubation within 48 hours. Now, clinicians can also see red flags for admitted patients with increased risk of mortality 3 to 5 days in advance.

In 2017, the health system also rolled out advanced sepsis detection tools and a respiratory failure detection algorithm called APPROVE, which identifies patients at a raised risk of prolonged ventilation up to 48 hours before onset, HIA reported.

The net result of these efforts was dubbed PALM, the Patient-centered Analytical  Learning Machine. PALM “represents a very new way of interacting with data in healthcare,” Miraji told HIA.

What makes PALM special is that it speeds up the process of collecting, curating, cleaning and accessing metadata which must be conducted before the data can be used to train AI models. In most cases, the process of collecting data for AI use is largely manual, but PALM automates this process, Miraji told the publication.

This is because the data lake and its graph repositories can find relationships between individual data elements on an on-the-fly basis. This automation lets Montefiore cut way down on labor needed to get these results. Miraji noted that ordinarily, it would take a team of data analysts, database administrators and designers to achieve this result.

PALM also benefits from a souped-up hardware architecture, which Montefiore created with help from Intel and other technology partners. The improved architecture includes the capacity for more system memory and processing power.

The final step in optimizing the PALM system was to integrate it into the health system’s clinical workflow. This seems to have been the hardest step. “I will say right away that I don’t think we have completely solved the problem of integrating analytics seamlessly into the workflow,” Miraji admitted to HIA.

Problems We Need To Address Before Healthcare AI Becomes A Thing

Posted on September 7, 2018 I Written By

Anne Zieger is veteran healthcare branding and communications expert with more than 25 years of industry experience. and her commentaries have appeared in dozens of international business publications, including Forbes, Business Week and Information Week. She has also worked extensively healthcare and health IT organizations, including several Fortune 500 companies. She can be reached at @ziegerhealth or www.ziegerhealthcare.com.

Just about everybody who’s anybody in health IT is paying close attention to the emergence of healthcare AI, and the hype cycle is in full swing. It’d be easier to tell you what proposals I haven’t seen for healthcare AI use than those I have.

Of course, just because a technology is hot and people are going crazy over it doesn’t mean they’re wrong about its potential. Enthusiasm doesn’t equal irrational exuberance. That being said, it doesn’t hurt to check in on the realities of healthcare AI adoption. Here are some issues I’m seeing surface over and over again, below.

The black box

It’s hard to argue that healthcare AI can make good “decisions” when presented with the right data in the right volume. In fact, it can make them at lightning speed, taking details into account which might not have seemed important to human eyes. And on a high level, that’s exactly what it’s supposed to do.

The problem with this, though, is that this process may end up bypassing physicians. As things stand, healthcare AI technology is seldom designed to show how it reached its conclusions, and it may be due to completely unexpected factors. If clinical teams want to know how the artificial intelligence engine drew a conclusion, they may have to ask their IT department to dig into the system and find out. Such a lack of transparency won’t work over the long term.

Workflow

Many healthcare organizations have tweaked their EHR workflow into near-perfect shape over time. Clinicians are largely satisfied with work patterns and patient throughput is reasonable. Documentation processes seem to be in shape. Does it make sense to throw an AI monkeywrench into the mix? The answer definitely isn’t an unqualified yes.

In some situations, it may make sense for a provider to run a limited test of AI technology aimed at solving a specific problem, such as assisting radiologists with breast cancer scan interpretations. Taking this approach may create less workflow disruption. However, even a smaller test may call for a big investment of time and effort, as there aren’t exactly a ton of best practices available yet for optimizing AI implementations, so workflow adjustments might not get enough attention. This is no small concern.

Data

Before an AI can do anything, it needs to chew on a lot of relevant clinical data. In theory, this shouldn’t be an issue, as most organizations have all of the digital data they need.  If you need millions of care datapoints or several thousand images, they’re likely to be available. The thing is, they may not be as usable as one might hope.

While healthcare providers may have an embarrassment of data on hand, much of it is difficult to filter and mine. For example, while researchers and some isolated providers are using natural language processing to dig up useful information, critics point out that until more healthcare info is indexed and tagged there’s only so much it can do. It may take a new generation of data processing and indexing tech to prepare the data before we have the right data to feed an AI.

These are just a few practical issues likely to arise as providers begin to use AI technologies; I’m sure there are many others you might be able to name. While I have little doubt we can work our way through such issues, they aren’t trivial, and it could take a while before we have standardized approaches in place for addressing them. In the meantime, it’s probably a good idea to experiment with AI projects and prepare for the day when it becomes more practical.

Facebook Partners With Hospital On AI-based MRI Project

Posted on August 23, 2018 I Written By

Anne Zieger is veteran healthcare branding and communications expert with more than 25 years of industry experience. and her commentaries have appeared in dozens of international business publications, including Forbes, Business Week and Information Week. She has also worked extensively healthcare and health IT organizations, including several Fortune 500 companies. She can be reached at @ziegerhealth or www.ziegerhealthcare.com.

I’ve got to say I’m intrigued by the latest from Facebook, a company which has recently been outed as making questionable choices about data privacy. Despite the kerfuffle, or perhaps because of it, Facebook is investing in some face-saving data projects.

Most recently, Facebook has announced that it will collaborate with the NYU School of Medicine to see if it’s possible to speed up MRI scans.  The partners hope to make MRI scans 10 times faster using AI technology.

The NYU professors, who are part of the Center for Advanced Imaging Innovation and Research, will be working with the Facebook Artificial Intelligence Research group. Facebook won’t be bringing any of its data to the table, but NYU will share its imaging dataset, which consists of 10,000 clinical cases and roughly 3 million images of the knee, brain and liver. All of the imaging data will be anonymized.

In taking up this effort, the researchers are addressing a tough problem. As things stand, MRI scanners work by gathering raw numerical data and turning that data into cross-sectional images of internal body structures. As with any other computing platform, crunching those numbers takes time, and the larger the dataset to be gathered, the longer the scan takes.

Unfortunately, long scan times can have clinical consequences. While some patients can cope with being in the scanner for extended periods, children, those with claustrophobia and others for whom lying down is painful might have trouble finishing the scanning session.

But if MRI scanning times can be minimized, more patients might be candidates for such scans. Not only that, physicians may be able to use MRI scans in place of X-ray and CT scans, both of which generate potentially harmful ionizing radiation.

Researchers hope to speed up the scanning process by modifying it using AI. They believe it may be possible to capture less data, speeding up the process substantially, while preserving or even enhancing the rich content gathered by an MRI machine. To do this, they will train artificial neural networks to recognize the underlying structure of the images and fill in visual information left out of the faster scanning process.

The NYU research team admits that meeting its goal will be very difficult. These neural networks would have to generate absolutely accurate images, and it’s not clear how possible this is as of yet. However, if the researchers can reconstruct high-value images in a new way, their work could have an impact on medicine as a whole.

Healthcare AI Adoption Curve – Where Is Your Hospital At?

Posted on July 30, 2018 I Written By

John Lynn is the Founder of the HealthcareScene.com blog network which currently consists of 10 blogs containing over 8000 articles with John having written over 4000 of the articles himself. These EMR and Healthcare IT related articles have been viewed over 16 million times. John also manages Healthcare IT Central and Healthcare IT Today, the leading career Health IT job board and blog. John is co-founder of InfluentialNetworks.com and Physia.com. John is highly involved in social media, and in addition to his blogs can also be found on Twitter: @techguy and @ehrandhit and LinkedIn.


The above image is the best one I’ve seen when it comes to the adoption and integration of AI into healthcare. Of course, this same chart has been used to describe the integration of technology into healthcare in general. The reason this chart is so relevant is that very few healthcare organizations have reached the point where they are an IT enabled business with IT embedded in business with hybrid, cross-functional roles. If this is true for technology in general, AI is still way out there.

In fact, the one complaint I have about this chart is that it’s missing a bubble that should say “What’s AI?” Ok, that’s a little bit of an exaggeration, but not much for many healthcare organizations. They’d more appropriately ask “How can I use AI in healthcare?” but it’s about the same point. Most aren’t there yet, but they’re going to have to get there. AI is coming and in a big way.

The good news is that most of the AI a healthcare organization will use will be embedded in the IT systems they purchase. This is why it’s so important that healthcare organizations have good vendor partners. Healthcare organizations aren’t going to enable this AI future. They’re going to partner with vendors who bring the AI to bear for them. When David Chou shared the image above, he asked the right question “What is your role as the CIO for the adoption of AI?” How many of you know the answer to that question?

If you’re not sure the answer, check out this other image and tweet that David Chou shared about using AI for automation:

I agree 100% with David Chou that if you want to start thinking about how to utilize AI, then start with repetitive tasks which can and should be automated. Take the mundane out of your healthcare providers lives. That will create some early AI wins that will help you to be able to build an AI driven culture in your organization.

Clinicians Say They Need Specialized IT To Improve Patient Safety

Posted on July 24, 2018 I Written By

Anne Zieger is veteran healthcare branding and communications expert with more than 25 years of industry experience. and her commentaries have appeared in dozens of international business publications, including Forbes, Business Week and Information Week. She has also worked extensively healthcare and health IT organizations, including several Fortune 500 companies. She can be reached at @ziegerhealth or www.ziegerhealthcare.com.

Hospitals are loaded down with the latest in health IT and have the bills to prove it. But according to a new survey, they need to invest in specialized technologies to meet patient safety goals, as well as providing more resources and greater organizational focus.

Health Catalyst recently conducted a national survey of physicians, nurses and health executives to gather their thoughts on patient safety issues. Among its main findings was that almost 90% of respondents said that their organizations were seeing success in improving patient safety. However, about the same percentage said there was room for improving patient safety in their organization.

The top obstacle they cited as holding them back from the patient safety goals was having effective information technology, as identified by 30% of respondents. The same number named a lack of technologies offering real-time warnings of possible patient harm.

These were followed by lack of staffing and budget resources (27%), organizational structure, culture priorities (19%), a lack of reimbursement for safety initiatives (10%) and changes in patient population practice setting (9%).

Part of the reason clinicians aren’t getting as much as they’d like from health IT is that many healthcare organizations rely largely on manual methods to track and report safety events.

The top sources of data for patient safety initiatives respondents used for safety initiatives voluntary reporting (82%). Hospital-acquired infection surveys (67%), manual audits (58%) and retrospective coding (29%). Such reporting is typically based on data sets which are at least 30 days old, and what’s more, collecting and analyzing the data can be time and resource-consuming.

Not surprisingly, Health Catalyst is launching new technology designed to address these problems. Its Patient Safety Monitor™ Suite: Surveillance Module uses protective and text analytics, along with concurrent critical reviews of data, to find and prevent patient safety threats before they result in harm.

The announcement also falls in line with the organization’s larger strategic plans, as Health Catalyst has applied to the AHRQ to be certified as a Patient Safety Organization.

The company said that he had spent more than $50 million to create the Surveillance module, whose technology includes the use of predictive analytics models and AI. It expects to add new AI and machine learning capabilities to its technology in the future which will be used to propose strategies to eliminate patient safety risks.

And more is on the way. Health Catalyst is working with its clients to add new features to the Suite including risk prediction, improvement tracking and decision support.

I’m not sure if it’s typical for PSOs to bringing their own specialized software to the job, but either way, it should give Health Catalyst a leg up. I have little doubt that doing better predictive analytics and offering process recommendations would be useful.

Rate Of Healthcare Ransomware Attacks Falls In First Half of 2018

Posted on July 12, 2018 I Written By

Anne Zieger is veteran healthcare branding and communications expert with more than 25 years of industry experience. and her commentaries have appeared in dozens of international business publications, including Forbes, Business Week and Information Week. She has also worked extensively healthcare and health IT organizations, including several Fortune 500 companies. She can be reached at @ziegerhealth or www.ziegerhealthcare.com.

Most research I’ve read lately suggests that the rate of healthcare cyberattacks is at an all-time high, and that ransomware is leading the parade.

But is that really true? Maybe not. A new security report has concluded that the rate of ransomware attacks on healthcare organizations actually fell during the first half of this year, and what’s more, that such attacks trended lower during the same period.

The study, which comes from security firm CryptoniteNXT, notes that cybercriminals target healthcare because they can fetch great prices for the data by reselling it on the dark web. Also, given the complexity of healthcare networks and the high number of vulnerabilities in those networks, thieves see providers as a fat and easy target.

However, when it comes to ransomware, the landscape may be changing. CryptoniteNXT found that the number of ransomware attacks impacting over 500 patient records dropped from 19 major data breaches in the first half of 2017 to 8 major breaches in the first half of 2018. That’s an impressive 57% decrease.

The biggest reported records IT/hacker-driven breach hit LifeBridge Health, affecting 538,127 individuals. Other organizations targeted included academic medical centers, medical practices, ambulatory surgical centers, health plans and government agencies.

Meanwhile, the rate of ransomware attacks as a percentage of IT/hacking events has fallen substantially, from 30.16% during the first half of 2017 to 13.6% during the first half of this year.

On the other hand, the volume of patients affected has climbed. Roughly 1.9 million patient records were breached in the first half of this year, compared with 1.7 million records the first half of 2017 and 1.8 million records the second half of that year, it concludes.

Also, the report notes that ransomware attackers are far from done with the industry. The authors say that ransomware will still pose a “formidable threat” to healthcare organizations and that new variants such as AI-based malware will pose a major threat to healthcare organizations for the next couple of years.

To fend off hacking attacks, CryptoniteNXT recommends adopting new best practices such as moving target cyber defense and network micro-segmentation, which can address the inherent weakness of TCP/IP networks.

3 Key Steps to Driving your Revenue Strategy

Posted on July 9, 2018 I Written By

The following is a guest blog post by Brad Josephson is the Director of Marketing and Communications at PMMC.

For healthcare providers struggling to accurately collect reimbursement, developing a revenue strategy based off a foundation of accuracy is the most efficient way to ensure revenue integrity throughout the revenue cycle.

Currently, many hospitals operate under multiple systems running for their different departments within the organization. This type of internal structure can threaten the accuracy of the analytics because data is forced to come into multiple systems, increasing the chances that the data will be misrepresented.

By maintaining revenue integrity, not only does it give hospitals assurance that the data they’ve collected is current and accurate, but it also provides invaluable leverage with the payer when it comes time to (re)negotiating payer contracts.

Let’s begin by starting from the ground up…

Here are the 3 steps needed for maintaining revenue integrity:

  • Creating a foundation backed by accurate analytics
  • Breaking down the departmental siloes
  • Preparing ahead of time for consumerism and price transparency

Accuracy Drives Meaningful Analytics

The first step toward maintaining revenue integrity is to assess whether your data is accurate. We know that accurate data drives meaningful analytics, essentially functioning as the engine of the revenue cycle.

And what happens when you stop taking care of the engine regularly and it no longer works properly? It not only costs you a lot of money to repair the engine, but you may also have to pay for other parts of the car that were damaged by the engine failure.

What if, however, you were able to visualize pie charts and bar graphs on your car’s dashboard that showed the current health of the engine to inform you when it requires a maintenance check?

You would be better informed about the current state of your engine and have a greater urgency to get the car repaired.

This same principle applies to healthcare organizations looking to increase the accuracy of their data to drive meaningful analytics. While some organizations struggle to draw valuable insight from pieces of raw data, data visualization tools are more efficient because it allows the user to see a complete dashboard with a drill-down capability to gain a deeper and clearer understanding of the implications of their data analytics.

Data visualization allows healthcare providers to quickly identify meaningful trends. Here are the 4 key benefits of implementing data visualization:

  • Easily grasp more information
  • Discover relationships and patterns
  • Identify emerging trends faster
  • Directly interact with data

Figure 1: Payer Dashboard

Removing Departmental Siloes  

While data visualization does generate helpful insight into current and future trends, it begins with storing the data in one integrated system so that different departments can easily communicate regarding the data.

System integration is crucial to maintaining revenue integrity because it dramatically lowers the likelihood of data errors, missed reimbursement, and isolated decisions that don’t look at the full revenue picture. Here is a list of other issues associated with organizations running revenue siloes:

  • No consistent accuracy metrics driving performance and revenue.
  • Different data sources and systems drive independent and isolated decisions without known impact on the rest of the revenue cycle.
  • Departments cannot leverage analytics and insight into contract and payer performance.

In the spirit of the recent international World Cup games, think of revenue siloes like playing for a professional soccer team.

Similar to the structure of a hospital’s revenue team, soccer teams are large organizations that need to be able to clearly communicate with each other quickly in order to make calls on-the-spot. These quick decisions can be the difference in turning the ball over to the other team or scoring a goal in the final minutes so it’s crucial that everyone knows their role on the team.

If other players don’t understand the plays that are being called, however, then mistakes will be made that could cost them the game. Each player on the team needs to study the same playbook so they stay on the same page and decrease the chances that a costly mistake will be made.

A hospital’s Managed Care department works in a similar way. If Managed Care is preparing to renegotiate payer contracts, they need to fully understand and have insight into underpayment and denial trends across multiple payers.

Preparing Now for Consumerism and Price Transparency

Now that we know the reimbursement rate is accurate, how do we communicate an accurate price to patients in order to encourage upfront payment?

Studies have shown that by increasing accuracy in pricing estimates, it increases the likelihood that patients pay upfront, which can help your organization lower bad debt.

In an effort to migrate to a more patient-centric approach, these accurate online estimates also enable hospitals to address the patient’s fear of the unknown with healthcare of ‘how much is this procedure going to cost?’ By giving the patient more control over their financial responsibility, hospitals can become a leader in pricing transparency for their entire community while expanding on their market share.

At the end of the day, what this all comes down to is maintaining accuracy to help drive your revenue strategy. By integrating all data into a single system, the hospital is positioned to identify trends more quickly while increasing the accuracy of their patient estimates, ultimately driving your revenue strategy to new heights.

With many healthcare organizations still making the transition away from the traditional fee-for-service model, now is the time to prepare for consumerism and value-based care. Take some time to evaluate where your organization currently stands in the local market as well as any pricing adjustments that need to be made.

About Brad Josephson
Brad Josephson is the Director of Marketing and Communications at PMMC, a provider of revenue cycle software and contact management services for healthcare providers. Brad received a Bachelor of Arts, Public Relations and Marketing Degree from Drake University. He has worked at PMMC for over three years and has a deep knowledge of hospital revenue cycle management tools which improves the financial performance of healthcare organizations.