Free Hospital EMR and EHR Newsletter Want to receive the latest news on EMR, Meaningful Use, ARRA and Healthcare IT sent straight to your email? Join thousands of healthcare pros who subscribe to Hospital EMR and EHR for FREE!

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.

Healthcare Prominently Featured at Information Builders Summit

Posted on June 6, 2018 I Written By

Colin Hung is the co-founder of the #hcldr (healthcare leadership) tweetchat one of the most popular and active healthcare social media communities on Twitter. Colin speaks, tweets and blogs regularly about healthcare, technology, marketing and leadership. He is currently an independent marketing consultant working with leading healthIT companies. Colin is a member of #TheWalkingGallery. His Twitter handle is: @Colin_Hung.

It was a pleasant surprise to see healthcare clients prominently featured at the 2018 Information Builders Summit (#IBSummit) in Orlando FL. Best known for their work in financial services, government and retail, Information Builders has recently carved out healthcare as an industry of focus. That focus was on full display with presentations from: Floyd Healthcare, St. Luke’s University Health Network, Markham Stouffville Hospital, and the Healthcare Association of New York State.

According to experts at GE Healthcare, the average US hospital generates in excess of 50 Petabytes (PB) of data each year. That’s inclusive of all images, lab results, EHR data, financial information, and every other bit of operational as well as clinical information. To help put that amount of data in perspective:

  • 1GB = 7min of HDTV video [1]
  • 1TB = 1024 GB = 130,000 digital photos
  • 1PB = 1024 TB = 3.4 years worth of HDTV video, or about the size of the movie Avatar
  • 50PB = The entire written works of mankind from the beginning of recorded history in all languages [2]

With this much data, it’s no surprise that many companies are putting energy behind Big Data and Machine Learning (ML) initiatives to help wring value from this growing mountain of information. Companies like IBM Watson, Health Catalyst, Caradigm and Optum all offer advanced data analytics platforms that use various forms of ML to discern patterns within healthcare data. However, most healthcare organizations do not have the technology infrastructure, funds or executive buy-in to adopt these heavy-weight solutions.

Luckily, Information Builders (IB) offers healthcare organizations a way to ease into advanced analytics that does not require the hiring of a data scientist as step one.

According to Grace Auh, Manager of Business Intelligence & Analytics at Markham Stouffville Hospital (located north of Toronto, Ontario), IB provided a smooth on ramp to data analytics. “Instead of trying to go from zero to 100 KPH (MPH for those in the US) in a single step, we adopted IB’s webFOCUS tool to whet the appetite of internal stakeholders” said Auh. “We started with ED pay-for-performance metrics that are tied to reimbursement bonuses here in Ontario. We created a series of reports that executives could drill-down into for deeper analysis. We update the clinical data monthly and the financial data quarterly.”

Auh and the team at Markham Stouffville opted for simple reports/charts rather than fancy data visualization in order to help gain executive buy-in. By keeping things simple, Auh was able to quickly convince executives that the data within the IB reports were indeed accurate (something that had been a challenge with previous data initiatives).

“The goal,” explained Auh. “Is to have a fully integrated and real-time system that is the single source of truth for the hospital. We want to empower program and hospital leaders to self-serve their data needs. It’s our job to build the platform so that they can get the data they want in the format they need it whenever they want. It’s got to be clean, simple, complete and easy to consume. We even want physicians to start using it.”

Floyd Healthcare, an independently-owned community hospital network in Georgia, had a similar goal.

“We have a vision to roll out our dashboards to directors, supervisors and even front-line staff,” said Drew Dempsey, Director of Planning & Business Intelligence at Floyd Medical Center. “We already have a data-driven culture at Floyd because of our lean six-sigma work. The appetite for metrics is high and our level of data maturity grows each day. The data we are able to get through IB is helping us achieve our goals and drive operational efficiencies.”

Using IB’s new Omni-HealthData platform, Dempsey and his team put together a surgical volume dashboard for their CEO. It showed surgeries by speciality, by surgeon and by location. This type of report was a regular part of executive meetings. It used to take days to compile this information by hand and required 120 PowerPoint slides to present it to the level of detail needed for the meeting. The entire report is now automated within Omni and offers executives multiple ways to slice the data.

“We used to spend a lot of time compiling data,” recalled Dempsey. “But now with Information Builders we are able to spend more time analyzing and interpreting the data – a far better use of everyone’s time. We build everything once and it gets used many times.”

The team at Floyd is now working to expand into other reports that provide Service Line and Operational leaders with clinical as well as financial reports that will allow them to make better strategic decisions. From there they plan to tackle revenue cycle reporting, quality metrics, population health indicators and PCMH reporting.

It would be fair to say that Floyd and Markham Stouffville are both fairly early in their analytics journey with IB. St. Luke’s University Health Network, however, is highly advanced in their use of IB’s tools for clinical and operational insight. A ten hospital system centered in Bethlehem PA with over 300 sites of care, St. Luke’s is a top performer on the Truven Top 100 (now IBM Watson Top 100) hospital analytics list.

St. Luke’s codeveloped the Omni-HealthData platform in cooperation with the team at IB. Many of the out-of-the-box report objects and visualizations are the refinement of the reports that St. Luke’s created for their internal users. These reports include:

  • Department/Service Line Performance
  • Patient Safety Indicators
  • In-patient Quality Metrics (ALOS, SSIs)
  • Marketing Analytics
  • Value-based Contract Metrics

In total there are over 90 self-service reports (called applications in IB vernacular) available.

“We borrowed proven tactics from the retail industry,” explained Dan Foltz, Managing Director at Parnassus Consulting, who helped St. Luke’s with their IB implementation. “With IB we were able to do targeted patient outreach based on cohorts of interest. Using data from multiple systems we were able to determine which patients might benefit from education and special programs. For example, the hospital wanted to make early stage Parkinsons patients aware of a deep brain stimulation program. We were able to achieve an 80-90% uptake – something unheard of in healthcare. It was amazing.”

The St. Luke’s electronic data warehouse consolidates information from six main (and silo’d) systems:

  1. Find-a-doc
  2. Allscripts
  3. McKesson
  4. EPIC
  5. Enrollment
  6. Credentialling

Over the next few years they plan to consolidate all their source systems into the warehouse and use their IB portal to provide insights. They currently have 40 data sources integrated within IB.

You can read more about the St. Luke’s implementation of IB in this success story.

I came away from IBSummit impressed by the success that Information Builders has helped its healthcare clients achieve. Every healthcare client that I spoke to raved about how the IB team helped them avoid project traps like diving too deeply into data specifics, losing sight of overall strategic goals, and not gaining sufficient executive buy-in.

“We’re sticking to what has made us successful in so many other industries,” said Jake Freivald, Information Builder’s Vice President of Product Marketing (Healthcare). “We are here to help healthcare organizations collect information faster & easier, and providing tools that allow them to present that information in valuable ways. The one thing we see our healthcare clients needing is more help in the data consolidation step. That’s where we are focusing more attention.”

It will be interesting to revisit IB’s early-stage healthcare clients at next year’s Summit to see how much progress they have made.

Hospital Using AI To Handle Some Tasks Usually Done By Doctors And Nurses

Posted on May 30, 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.

One of the UK’s biggest facilities has announced plans to delegate some tasks usually performed by doctors and nurses to AI technology. Leaders there say these activities can range from diagnosing cancer to triaging patients.

University College London Hospitals (UCLH) has signed up for a three-year partnership with the Allen Turing Institute designed to bring machine learning to bear on care, a project which could ultimately spark additional AI projects across the entire National Health Service. The NHS is the body which governs all healthcare in the UK’s universal health system.

UCLH is making a big bet on artificial intelligence, investing what UK newspaper The Guardian describes as a “substantial” sum to develop the infrastructure for the effort.

UCLH officials believe — like other health organizations around the world — that machine learning algorithms may someday diagnose disease, identify people at risk for serious illness and more. Examples of related projects abound. Just one case in point is a project begun in 2016 by New York-based Mount Sinai Hospital, which launched an effort using AI to predict which patients might develop congestive heart failure and offer better care to those who have already done so.

Professor Bryan Williams, director of research at University College London Hospitals NHS Foundation Trust, said the move will be a “game changer” which could have a major impact on patient outcomes. “On the NHS, we are nowhere near sophisticated enough,” Williams told The Guardian. “We’re still sending letters out, which is extraordinary.”

UCLH’s first AI effort, which is already underway, is intended to identify patients likely to miss appointments. Using existing data, including demographic factors such as age and address plus outside factors like weather conditions, researchers there have been able to predict with 85% accuracy whether the patient will show up for outpatient visits and MRIs.

Another planned project includes improving the performance of the hospital’s emergency department, which, like many NHS hospitals, isn’t meeting government performance targets such as maximum four-hour wait times. “[This is] an indicator of some of the other things in the entire chain concerning the flow of acute patients in and out of the hospital,” UCLH chief executive Professor Marcel Levi told the newspaper.

The hospital envisions solving its wait-time problem with machine learning. Drawing on data taken from thousands of patients, machine learning algorithm might be able to determine whether a patient with abdominal pain suffers from severe problem like intestinal perforation or a systemic infection, then fast-track those patients. This kind of triage is generally performed by nurses in hospitals around the world.

That being said, the partners agree that machine learning performance must be incredibly accurate before it has any major role in care. At that point, it will be ready to support clinicians, not undercut them. According to Professor Chris Holmes of The Alan Turing Institute, the whole idea is to let doctors do what they do best: “We want to take out the more mundane stuff that’s purely information driven and allow time for things the human expert is best at.”

5 Ways Allscripts Will Help Fight Opioid Abuse In 2018

Posted on May 22, 2018 I Written By

The following is a guest blog post by Paul Black, CEO of Allscripts, a proud sponsor of Health IT Expo.

Prescription opioid misuse and overdoses are on the rise. The Centers for Disease Control and Prevention (CDC) reports that more than 40 Americans die every day from prescription opioid overdose. It also estimates that the economic impact in the United States is $78.5 billion a year, including the costs of healthcare, lost productivity, addiction treatment and criminal justice involvement.

The opioid crisis has taken a devastating toll on our communities, families and loved ones. It is a complex problem that will require a lot of hard work from stakeholders across the healthcare continuum.

We all have a part to play. At Allscripts, we feel it is our responsibility to continuously improve our solutions to help providers address public health concerns. Our mission is to design technology that enables smarter care, delivered with greater precision, for better outcomes.

Here are five ways Allscripts plans to help clinicians combat the opioid crisis in 2018:

1) Establish a baseline. Does your patient population have a problem with opioids?

Before healthcare organizations can start addressing opioid abuse, they need to understand how the crisis is affecting their patient population. We are all familiar with the national statistics, but how does the crisis manifest in each community? What are the specific prescribing practices or overdose patterns that need the most attention?

Now that healthcare is on a fully digital platform, we can gain insights from the data. Organizations can more precisely manage the needs of each patient population. We are working with clients to uncover some of these patterns. For example, one client is using Sunrise™ Clinical Performance Manager (CPM) reports to more closely examine opioid prescribing patterns in emergency rooms.

2) Secure the prescribing process. Is your prescribing process safe and secure?

Electronic prescribing of controlled substances (EPCS) can help reduce fraud. Unfortunately, even though the technology is widely available, it is not widely adopted. Areas where clinicians regularly use EPCS have seen significantly less prescription fraud and abuse.

EPCS functionality is already in place across our EHRs. While more than 90% of all pharmacies are EPCS-enabled, only 14% of controlled substances are prescribed electronically. We’re making EPCS adoption one of our top priorities at Allscripts, and we continue to discuss the benefits with policymakers.

3) Provide clinical decision support. Are you current with evidence-based best practices?

We are actively pursuing partnerships with health plans, pharmaceutical companies and third-party content providers to collaborate on evidence-based prescribing guidelines. These guidelines may suggest quantity limits, recommendations for fast-acting versus extended-release medications, protocols for additional and alternative therapies, and expanded educational material and content.

We’ll use the clinical decision support technologies we already have in place to present these assessment tools and guidelines at the time needed within clinical workflows. Our goal is to provide the information to providers at the right time, so that they can engage in productive conversations with patients, make informed decisions and create optimal treatment plans.

4) Simplify access to Prescription Drug Monitoring Programs (PDMPs). Are you avoiding prescribing because it’s too hard to check PDMPs?

PDMPs are state-level databases that collect, monitor and analyze e-prescribing data from pharmacies and prescribers. The CDC Guidelines recommend clinicians should review the patient’s history of controlled substance prescriptions by checking PDMPs.

PDMPs, however, are not a unified source of information, which can make it challenging for providers to check them at the point of care. The College of Healthcare Information Management Executives (CHIME) has called for better EHR-PDMP integration, combined with data-driven reports to identify physician prescribing patterns.

In 2018, we’re working on integrating the PDMP into the clinician’s workflow for every patient. The EHR will take PDMP data and provide real-time alert scores that can make it easier to discern problems at the point of care.

5) Predict risk. Can big data help you predict risk for addiction?

Allscripts has a team of data scientists dedicated to transforming data into information and actionable insights. These analysts combine vast amounts of information from within the EHR, our Clinical Data Warehouse – data that represents millions of patients – and public health mechanisms (such as PDMPs).

We use this “data lake” to develop algorithms to identify at-risk patients and reveal prescription patterns that most often lead to abuse, overdose and death. Our research on this is nascent, and early insights are compelling.

The opioid epidemic cannot be solved overnight, nor is it something any of us can address alone. But we are enthusiastic about the teamwork and efforts of our entire industry to address this complex, multi-faceted epidemic.

Hear Paul Black discuss the future of health IT beyond the EHR at this year’s HIT Expo.

Effort Focuses On Better Ways For Hospitals To Detect Drug Diversion

Posted on May 17, 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.

Using a combination of machine learning technology and advanced analytics, a healthcare vendor has been working to find better ways to spot drug diversion in U.S. hospitals. The work done by the firm, Invistics, is funded by an NIH research grant.

The project has taken aim at a ripe target. According to a 2017 study by Porter Research, 96% of healthcare professionals who responded said that drug diversion happened often in their business. Also, sixty-five percent of respondents said that most diversion never gets detected. Clearly, there’s a hole you could drive a truck through in the drug dispensing process.

During the first stage of the research, Invistics worked with a pilot hospital to find opioid and drug theft across the entire facility. To get the job done, the vendor aggregated data from across the pilot hospital’s systems, including medical records, employee time clocks, wholesale purchasing, inventory and dispensing cabinets.

By leveraging data across several departments, Invistics got a much clearer view of potential problems than other efforts have in the past. The initiative was completely successful, with the technology picking out 100% of drug diversion happening within the project’s parameters, the company said. Since the completion of Phase I of the grant, Invistics has rolled out the solution at several other hospitals.

When it comes to avoiding opioid abuse, far morer attention has been focused on patterns of opioid prescribing, with the assumption that the opioid addiction epidemic can be stemmed at the source. For example, we recently covered a study looking at post hospital-discharge opioid use which centered on predicting which patients would be on chronic opioid therapy after discharge and planning for that discharge appropriately.

There’s no question that such research has a place in the battle against opioid misuse and abuse. After all, it seems likely that at least some needless addictive patterns stem from physician prescribing habits. It also makes sense that states are revising their guidelines for opioid prescribing, though to my knowledge these changes are being based more on ideology than rigorous research.

On the other hand, drug diversion creates a pipeline between drug supplies and drug abusers which must be addressed directly if the opioid abuse war is to be won. I for one was interested to learn about a solution that addresses this piece of the puzzle.