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Pricing Transparency and Provider Quality: Insights from Utah HIMSS

Posted on September 10, 2018 I Written By

Healthcare as a Human Right. Physician Suicide Loss Survivor. Janae writes about Artificial Intelligence, Virtual Reality, Data Analytics, Engagement and Investing in Healthcare. twitter: @coherencemed

Working to improve Health IT has been a major focus of Utah HIMSS this year. I am honored to serve as part of the Utah HIMSS Board. Utah HIMSS hosts educational events and luncheons for members. On August 29, 2018  the meeting focused on Pricing Transparency and Provider Quality. Health Informatics is positioned to help reduce waste in healthcare and providing better care for patients.

Bob White works with Select Health, one of the major insurance providers in the state, which is a subsidiary of Intermountain Healthcare. He was able to talk about payment models and value based care work within the Select Health group. Providing more visibility into the cost for patients and physicians has been a major focus on Select Health and payer provider entities have a unique market position. They want the cost of care delivery to be lower since they are paying the cost. Point of service adjudication requires that a lot of workflows need to be coordinated before the patient leaves the office.

Bob asked: How often do we feel like we don’t have complete information to know what is going on and what your options are?

One of the most notable things that he spoke about was the lack of adoption. They have great visibility but not everyone knows where to find that information. Some of the employees at Selecthealth have high deductible plans and in effect, become self-pay members. Becoming more educated consumers is a huge part of what Select Health has done with their pricing transparency.

Katie Harwood from the University Of Utah discussed their pricing transparency tool. The University of Utah is one of the first systems in the country to create an online interactive tool to help predict cost to patients. Patients can look up what a procedure might cost and enter information about their copay and caps. Most importantly, the cost estimator included the cost of facility and cost of provider, so patients don’t get stuck with unexpected out of network bills.

The most common search? Vaginal delivery without complications. I was thrilled to hear them speak because I’m pregnant and my provider is with the University of Utah Health. I got a cost estimate on my second visit to the OB and I was pleasantly surprised that they gave that information.  I was able to pay for what (might be) the cost of my maternity care. Being able to plan ahead is very valuable. The University of Utah has invested in creating bundled payment models to improve care coordination and as a patient, having that information has improved my healthcare experience.

While in development, the University of Utah wanted to add appointment scheduling for patients. Harwood mentioned this created a larger data matching challenge, as it was difficult to match exact providers with procedures. Insurance companies are trying to make it easier for patients to schedule and understand what their costs will be, and physician directories create unique challenges. What if you were a surgeon who performed a total knee replacement but you didn’t have the information connected with the correct insurance company for you to appear in the online scheduling tool?

Interestingly, many people go to the cost estimator tool enter “I don’t know” for some of their search criteria such as deductible and copay. Bridging the consumer gap to give even better information and creating the most accurate scheduling possible starts with efforts to create great health IT tools and adjusting them according to user behavior.

Holly Rimmasch from Health Catalyst was able to ask great questions and mentioned a program that Health Catalyst is doing to promote women in health IT. She served as a moderator and has an extensive background with pricing. They have promoted women in Health IT in the Utah area, including providing student scholarships for their Healthcare Analytics Summit in September.  A key question that Holly has focused on is “Are we making a difference in both quality and costs?”  “Does it translate into cost savings for those that are paying?” Part of her work involves bringing data sources together (clinical, financial, claims, etc.) to create transparency to services and care being provided and at what cost.  Over the last 6 years, Holly has been involved in developing a more accurate activity-based costing system. Accurate costing leads to more accurate pricing and more accurate pricing leads to improved price transparency. I am looking forward to learning more about what Health Catalyst does for improving Healthcare IT in Utah.

Norm Thurston is a Utah State Representative and I was surprised how much I enjoyed his presentation and I will tell you why. Norm Thurston has a background in statistics and I felt confident that the Utah legislature was getting good information about improving healthcare. Representative Thurston spoke about the availability of state data to see things like prescribing trends and billing trends among physicians. He asked Bob White about upcoding- and how the government of Utah looks at billing data to make that information more transparent for payers and providers. The checks and balances of legislators asking about trends based on data aren’t something I see every day in healthcare. Data backed inquiry can improve prescribing. Utah has had a decrease in opioid deaths in the last year, and the healthcare system and state efforts have actively used data to improve the numbers. Utah has historically been a state with a problem and has actively worked to improve rates of opioid deaths. One of the audience comments that I enjoyed was a question from Todd Allen, MD about how they evaluate the statistical significance of prescribing and billing differences. How do we know if using this drug or billing code 75% of the time has better outcomes that in the hospital where it is used less than 65% of the time? Having visibility and data is part of the equation for improving healthcare outcomes, and another part is interpreting the data and deciding best practices.

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.

Health IT Consulting Demand To Explode This Year

Posted on August 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.

As payment models shift from fee-for-service to value-based care, hospitals are having to adopt new technologies and tweak existing ones. The thing is, it takes a mighty team of IT pros to make all this happen. In some cases, a provider has enough resources to handle this kind of big transition, but most need some help, especially when they’re handling major infrastructure improvements or even switching out technologies.

This seems to be at least part of what’s driving a dramatic increase in spending on health IT consulting, according to a new study from Black Book Research. The study drew on input from 1,586 professionals with knowledge of the US health IT industry.

Black Book concluded that health IT management consulting spending has grown from $20 billion in 2016 to $45 billion last year. Not only that, the firm expects to see this number climb to nearly $53 billion for 2018. That’s a massive increase, particularly given that providers were already spending heavily on consultants as they beat their enterprise EHRs into shape.

According to the analyst firm, 64% of last year’s spending paid for implementation of software, information systems, systems integration and optimization and support for mergers and acquisitions. This summary covers a lot of ground, but it’s hardly surprising given the drastic changes underway.

Going forward, respondents expect three key forces to drive healthcare consulting spend, including a lack of highly-skilled IT professionals (cited by 81% of respondents), adoption of cloud technology in healthcare (74%) and growing industry digitalization (71%). (I’d also expect to see investment in new organizational infrastructures — for, let’s say, ACOs)  — will continue to increase in importance as well.)

Providers responding to the study said that they expect to hire health IT consultants for EHR and RCM system optimization (61%) and to offer expertise in software training and implementation (46%) next year. Other areas providers hope to address include value-based care (39%), cloud infrastructure (36%), compliance issues (33%) and a grab bag of big data, decision support and analytics projects (31%).

The vast majority of respondents (84%) said they expect to enter into a wide range of consulting agreements to include work with single-shop consultants, single freelancers, group purchasing organizations, HIT vendors, networks of freelancers, boutique advisory firms and traditional major consultancies, Black Book reported. In other words, it’s all hands on deck!

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.

Connecting the Data: Three Steps to Meet Digital Transformation Goals

Posted on July 16, 2018 I Written By

The following is a guest blog post by Gary Palgon, VP Healthcare and Life Sciences Solutions at Liaison Technologies.

A white paper published by the World Economic Forum in 2016 begins with the statement, “Few industries have the potential to be changed so profoundly by digital technology as healthcare, but the challenges facing innovators – from regulatory barriers to difficulties in digitalizing patient data – should not be underestimated.”

That was two years ago, and many of the same challenges still exist as the digital transformation of healthcare continues.

In a recent HIMSS focus group sponsored by Liaison, participants identified their major digital transformation and interoperability goals for the near future as:

  • EMR rollout and integration
  • Population health monitoring and analytics
  • Remote clinical encounters
  • Mobile clinical applications

These goals are not surprising. Although EMRs have been in place in many healthcare organizations for years, the growth of health systems as they add physicians, clinics, hospitals and diagnostic centers represents a growing need to integrate disparate systems. The continual increase in the number of mobile applications and medical devices that can be used to gather information to feed into EMR systems further exacerbates the challenge.

What is surprising is the low percentage of health systems that believe that they are very or somewhat well-prepared to handle these challenges – only 35 percent of the HIMSS/Liaison focus group members identified themselves as well-prepared.

“Chaos” was a word used by focus group participants to describe what happens in a health system when numerous players, overlapping projects, lack of a single coordinator and a tendency to find niche solutions that focus on one need rather than overall organizational needs drive digital transformation projects.

It’s easy to understand the frustration. Too few IT resources and too many needs in the pipeline lead to multiple groups of people working on projects that overlap in goals – sometimes duplicating each other’s efforts – and tax limited staff, budget and infrastructure resources. It was also interesting to see that focus group participants noted that new technologies and changing regulatory requirements keep derailing efforts over multi-year projects.

Throughout all the challenges identified by healthcare organizations, the issue of data integrity is paramount. The addition of new technologies, including mobile and AI-driven analytics, and new sources of information, increases the need to ensure that data is in a format that is accessible to all users and all applications. Otherwise, the full benefits of digital transformation will not be realized.

The lack of universal standards to enable interoperability are being addressed, but until those standards are available, healthcare organizations must evaluate other ways to integrate and harmonize data to make it available to the myriad of users and applications that can benefit from insights provided by the information. Unlocking access to previously unseen data takes resources that many health organizations have in short supply. And the truth is, we’ll never have the perfect standards as they will always continue to change, so there’s no reason to wait.

Infrastructure, however, was not the number one resource identified in the HIMSS focus group as lacking in participants’ interoperability journey. In fact, only 15 percent saw infrastructure as the missing piece, while 30 percent identified IT staffing resources and 45 percent identified the right level of expertise as the most critical needs for their organization.

As all industries focus on digital transformation, competition for expert staff to handle interoperability challenges makes it difficult for healthcare organizations to attract the talent needed. For this reason, 45 percent of healthcare organizations outsource IT data integration and management to address staffing challenges.

Health systems are also evaluating the use of managed services strategies. A managed services solution takes over the day-to-day integration and data management with the right expertise and the manpower to take on complex work and fluctuating project levels. That way in-house staff resources can focus on the innovation and efficiencies that support patient care and operations, while the operating budget covers data management fees – leaving capital dollars available for critical patient care needs.

Removing day-to-day integration responsibilities from in-house staff also provides time to look strategically at the organization’s overall interoperability needs – coordinating efforts in a holistic manner. The ability to implement solutions for current needs with an eye toward future needs future-proofs an organization’s digital investment and helps avoid the “app-trap” – a reliance on narrowly focused applications with bounded data that cannot be accessed by disparate users.

There is no one answer to healthcare’s digital transformation questions, but taking the following three steps can move an organization closer to the goal of meaningful interoperability:

  • Don’t wait for interoperability standards to be developed – find a data integration and management platform that will integrate and harmonize data from disparate sources to make the information available to all users the way they need it and when they needed.
  • Turn to a data management and integration partner who can provide the expertise required to remain up-to-date on all interoperability, security and regulatory compliance requirements and other mandatory capabilities.
  • Approach digital transformation holistically with a coordinated strategy that considers each new application or capability as data gathered for the benefit of the entire organization rather than siloed for use by a narrowly-focused group of users.

The digital transformation of healthcare and the interoperability challenges that must be overcome are not minor issues, nor are they insurmountable. It is only through the sharing of ideas, information about new technologies and best practices that healthcare organizations can maximize the insights provided by data shared across the enterprise.

About Gary Palgon
Gary Palgon is vice president of healthcare and life sciences solutions at Liaison Technologies, a proud sponsor of Healthcare Scene. In this role, Gary leverages more than two decades of product management, sales, and marketing experience to develop and expand Liaison’s data-inspired solutions for the healthcare and life sciences verticals. Gary’s unique blend of expertise bridges the gap between the technical and business aspects of healthcare, data security, and electronic commerce. As a respected thought leader in the healthcare IT industry, Gary has had numerous articles published, is a frequent speaker at conferences, and often serves as a knowledgeable resource for analysts and journalists. Gary holds a Bachelor of Science degree in Computer and Information Sciences from the University of Florida.

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.

Healthcare Interoperability Insights

Posted on June 29, 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.

I came across this great video by Diameter Health where Bonny Roberts talked with a wide variety of people at the interoperability showcase at HIMSS. If you want to get a feel for the challenges and opportunities associated with healthcare interoperability, take 5 minutes to watch this video:

What do you think of these healthcare interoperability perspectives? Does one of them stand out more than others?

I love the statement that’s on the Diameter Health website:

“We Cure Clinical Data Disorder”

What an incredible way to describe clinical data today. I’m not sure the ICD-10 code for it, but there’s definitely a lot of clinical data disorder. It takes a real professional to clean the data, organize the data, enrich the data, and know how to make that data useful to people. IT’s not a disorder that most people can treat on their own.

What’s a little bit scary is that this disorder is not going to get any easier. More data is on its way. Better to deal with your disorder now before it becomes a full on chronic condition.

Financial Perspectives from the HFMA Annual Conference

Posted on June 26, 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.

I always enjoy attending the HFMA Annual Conference (Formerly known as ANI) which brings together healthcare CFOs and others in the healthcare financial management community. Or as someone once told me, this use to be a conference of CPAs. In spite of its roots, there was an interesting mix of people at HFMA including health IT professionals, HIM professionals, and of course CFOs at the conference.

In one of my interviews at the conference, I sat down with Dan Berger, Director of Healthcare at AxiaMed. We had a wide-ranging conversation about healthcare payments and payment processing, but he struck me pretty hard when he talked about what would happen if a hospital or health systems payment processing went down. We talk a lot about EHR downtime and encourage healthcare organizations to have downtime procedures, but we don’t talk about payment downtime.

In some ways, this may be an appropriate response to downtime. If the EHR is down, that could impact patient care and literally patients lives. So, EHR downtime should be important. However, from a financial perspective payment processing downtime is a really big deal for healthcare organizations as well. The problem is that no patient will complain if you can’t collect their payment. The patients won’t go to the news with stories of payment processing issues. However, your business office will definitely feel it if the cash stops flowing.

This example is a simple reminder of how healthcare is a business. You see that in full view when you’re at a conference like HFMA’s annual conference. In some ways that’s a good thing since healthcare organizations have to be financially sound if they want to fulfill their missions. However, sometimes that can be taken too far as some people treat patients as a number on a spreadsheet.

I have seen some hope here at the conference. There are quite a few companies working hard to personalize the payment experience, to make pricing and payment information available to patients in ways it hasn’t been available before, and efforts to improve things like legible bills. These are small things, but they make a big difference to a patient.

I was also impressed with a number of companies that were using financial data to understand the patients better and when combined with other data can really personalize the care a patient is provided. A great example of this is Clarify Health Solutions which is making patient financial data useful and optimizing the patient journey. This is challenging stuff, but the data is getting there and companies are starting to see success and build up data that can be used by any healthcare organization.

What’s become more and more apparent to me is how challenging all of these healthcare problems are and how many people have to be influenced for change to happen. The wide variety of stakeholders that can hijack a great project is amazing. Dan Berger from AxiaMed who I mention at the start of this article commented on how payment processing used to be largely owned by the business office. He went on to share that now he’s seeing the CISO get involved and even the CIO. In many cases the CISO has veto power over vendors that don’t meet a healthcare organization’s security needs. Given all the security issues healthcare faces that’s generally a good thing. However, these types of group decision making do make the process of adding new innovations to your organization more complicated.

Your Big Data Assumptions May Be Flat-Out Wrong

Posted on June 21, 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.

It’s an article of faith in healthcare circles that leveraging big data stores can improve patient care. But what if this cherished assumption is flat-out wrong?

A new study published in the Proceedings of the National Academy of Sciences suggests that big data number-crunching might actually undermine providers’ ability to improve patient health.

To conduct the study, researchers from UC Berkeley, Drexel University and the University of Groningen compared data collected on hundreds of people, including both individuals with psychiatric disorders and healthy individuals. They found that group results didn’t capture some wide variations in symptoms from person to person.

Researchers concluded that big data analyses are a poor substitute for working with individuals, noting that these analyses are “worryingly imprecise” and that the variance between individuals is four times larger than those captured by big data. In other words, it concludes that big data analyses minimize differences between patients dramatically.

The authors said that it doesn’t work to generalize conclusions about individuals, whose emotions, behavior and physiology can vary greatly.

“Diseases, mental disorders, emotions, and behaviors are expressed within individual people, over time,” said study lead author Aaron Fisher, an assistant professor of psychology at UC Berkeley in a prepared statement. “A snapshot of many people at one moment in time can’t capture these phenomena.”

At this point, you’re probably thinking that this is terrible news. But Fisher believes that there are practical ways to address the problem. “Modern technologies allow us to collect many observations per person relatively easily, and modern computing makes the analysis of these data [points]  possible in ways that were not possible in the past,” Fisher said.

I don’t know about you, but I doubt that gathering loads of individual patient data will be as easy as Fisher suggests. Our current methods for documenting patient encounters in EHRs already impose significant burdens on physicians. Asking them to do more probably won’t fly, at least for the near term.

Not only that, there’s the question of how to work with this new data. We’d all like to see patients get highly individualized care, but current systems used by providers probably aren’t up to the task just yet.

I guess the bottom line here is that while Fisher et al are on to something, it will probably be a long time before healthcare organizations get there. In the meantime, it’s good to see that researchers are challenging our assumptions and keeping us on our toes.

The Truth about AI in Healthcare

Posted on June 18, 2018 I Written By

The following is a guest blog post by Gary Palgon, VP Healthcare and Life Sciences Solutions at Liaison Technologies.

Those who watched the television show, “The Good Doctor,” in its first season got to see how a young autistic surgeon who has savant syndrome faced challenges in his everyday life as he learns to connect with people in his world. His extraordinary medical skill and intuition not only saves patients’ lives but also creates bridges with co-workers.

During each show, there is at least one scene in which the young doctor “visualizes” the inner workings of the patient’s body – evaluating and analyzing the cause of the medical condition.

Although all physicians can describe what happens to cause illness, the speed, detail and clarity of the young surgeon’s ability to gather information, predict reactions to treatments and identify the protocol that will produce the best outcome greatly surpasses his colleagues’ abilities.

Yes, this is a television show, but artificial intelligence promises the same capabilities that will disrupt all of our preconceived notions about healthcare on both the clinical and the operational sides of the industry.

Doctors rely on their medical training as well as their personal experience with hundreds of patients, but AI can allow clinicians to tap into the experience of hundreds of doctors’ experiences with thousands of patients. Even if physicians had personal experience with thousands of patients, the human mind can’t process all of the data effectively.

How can AI improve patient outcomes as well as the bottom line?

We’re already seeing the initial benefits of AI in many areas of the hospital. A report by Accenture identifies the top three uses of AI in healthcare as robot-assisted surgery, virtual nursing assistants and administrative workflow assistance. These three AI applications alone represent a potential estimated annual benefit of $78 billion for the healthcare industry by 2026.

The benefits of AI include improved precision in surgery, decreased length of stay, reduction in unnecessary hospital visits through remote assessment of patient conditions, and time-saving capabilities such as voice-to-text transcription. According to Accenture, these improvements represent a work time savings of 17 percent for physicians and 51 percent for registered nurses – at a critical time when there is no end in sight for the shortages of both nurses and doctors.

In a recent webinar discussing the role of AI in healthcare, John Lynn, founder of HealthcareScene.com, described other ways that AI can improve diagnosis, treatment and patient safety. These areas include dosage error detection, treatment plan design, determination of medication adherence, medical imaging, tailored prescription medicine and automated documentation.

One of the challenges to fully leveraging the insights and capabilities of AI is the volume of information accumulated in electronic medical records that is unstructured data. Translating this information into a format that can be used by clinical providers as well as financial and administrative staff to optimize treatment plans as well as workflows is possible with natural language processing – a branch of AI that enables technology to interpret speech and text and determine which information is critical.

The most often cited fear about a reliance on AI in healthcare is the opportunity to make mistakes. Of course, humans make mistakes as well. We must remember that AI’s ability to tap into a much wider pool of information to make decisions or recommend options will result in a more deeply-informed decision – if the data is good.

The proliferation of legacy systems, continually added applications and multiple EMRs in a health system increases the risk of data that cannot be accessed or cannot be shared in real-time to aid clinicians or an AI-supported program. Ensuring that data is aggregated into a central location, harmonized, transformed into a usable format and cleaned to provide high quality data is necessary to support reliable AI performance.

While AI might be able to handle the data aggregation and harmonization tasks in the future, we are not there yet. This is not, however, a reason to delay the use of AI in hospitals and other organizations across the healthcare spectrum.

Healthcare organizations can partner with companies that specialize in the aggregation of data from disparate sources to make the information available to all users. Increasing access to data throughout the organization is beneficial to health systems – even before they implement AI tools.

Although making data available to all of the organization’s providers, staff and vendors as needed may seem onerous, it is possible to do so without adding to the hospital’s IT staff burden or the capital improvement budget. The complexities of translating structured and unstructured data, multiple formats and a myriad of data sources can be balanced with data security concerns with the use of a team that focuses on these issues each day.

While most AI capabilities in use today are algorithms that reflect current best practices or research that are programmed by healthcare providers or researchers, this will change. In the future, AI will expand beyond algorithms, and the technology will be able to learn and make new connections among a wider set of data points than today’s more narrowly focused algorithms.

Whether or not your organization is implementing AI, considering AI or just watching its development, I encourage everyone to start by evaluating the data that will be used to “run” AI tools. Taking steps now to ensure clean, easy-to-access data will not only benefit clinical and operational tasks now but will also position the organization to more quickly adopt AI.

About Gary Palgon
Gary Palgon is vice president of healthcare and life sciences solutions at Liaison Technologies, a proud sponsor of Healthcare Scene. In this role, Gary leverages more than two decades of product management, sales, and marketing experience to develop and expand Liaison’s data-inspired solutions for the healthcare and life sciences verticals. Gary’s unique blend of expertise bridges the gap between the technical and business aspects of healthcare, data security, and electronic commerce. As a respected thought leader in the healthcare IT industry, Gary has had numerous articles published, is a frequent speaker at conferences, and often serves as a knowledgeable resource for analysts and journalists. Gary holds a Bachelor of Science degree in Computer and Information Sciences from the University of Florida.