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Real-world Health AI Applications in 2018 and Further

Posted on August 29, 2018 I Written By

The following is a guest blog post by Inga Shugalo, Healthcare Industry Analyst at Itransition.

In contrast to legacy systems that are just algorithms performing strict tasks, artificial intelligence can extend the task itself, creating new insights from the information fed to it. Current healthcare AI is powerful enough to undertake such complex challenges as automated diagnosis, medical image analysis, virtual patient assistance, and risk analysis, supporting health specialists in making more swift and informed decisions.

In 2016, Frost & Sullivan predicted the healthcare AI market to reach $6.6 billion by 2021. Meanwhile, 2017’s Accenture report estimates AI saving $150 billion annually for the U.S. healthcare economy by 2026. “At hyper-speed, AI is re-wiring our modern conception of healthcare delivery,” researchers from Accenture say.

Standing in the middle of 2018, the industry already hints on its course regarding further AI expansion. Spoiler alert: as well as with blockchain AR, VR, and any other kind of innovative custom medical software, the adoption challenges persist.

Current and prospective AI directions in healthcare

Diagnosis support

One of the most fascinating and valuable directions for AI to evolve is its ability to help providers diagnose patients more accurately and at a higher pace. We are thrilled to see how 2018 erupts with many healthcare organizations adopting artificial intelligence and creating unprecedented cases of assisted diagnostics with it.

Geisinger specialists applied AI to analyze CT scans of patients’ heads and detect intracranial hemorrhage early. Intracranial hemorrhage is a life-threatening form of internal bleeding, affecting about 50,000 patients per year, with 47% dying within 30 days.

Geisinger was able to automatically pinpoint and prioritize the cases of intracranial hemorrhage, focusing the attention of radiologists on them and thus allowing for timely interventions. This approach reduced the time to diagnosis by 96%.

Mayo Clinic currently uses IBM Watson’s superpowers to match patients with fitting clinical trials. The clinic’s officials stated that only 5% of patients enrolled in trials in the U.S., which significantly hinders clinical research and innovation in cancer therapies. On the other side, manual patient-trial matching is a time-exhausting process.

Watson runs this process on the background, comparing the patients’ conditions with available trials and suggesting the appropriate trials for providers and patients to consider including in a treatment plan. Since its implementation in 2016, Watson was able to deliver about an 80% increase in enrollment to Mayo’s trials for breast cancer.

Patient risk analysis

“…Healthcare is one of the most important fields AI is going to transform,” Google CEO Sundar Pichai noted during the Google I/O 2018 keynote. Last year, the event presented Google AI, a “collection of our teams and efforts to bring the benefits of AI to everyone.”

In 2018, Google uses their AI to tap into critical patient risks, such as mortality, readmission, and prolonged LOS. Cooperating with UC San Francisco, The University of Chicago Medicine, and Stanford Medicine, they analyzed over 46 billion anonymized retrospective EHR data points collected from over 216 thousand adult patients hospitalized for at least 24 hours at two US academic medical centers.

The deep learning model built by researchers reviewed each patient’s chart as a timeline, from its creation to the point of hospitalization. This data allowed clinicians to make various predictions on patient health outcomes, including prolonged length of stay, 30-day unplanned readmission, upcoming in-hospital mortality, and even a patient’s final discharge diagnosis. Remarkably, the model achieved an accuracy level that significantly outperformed traditional predictive models.

According to Pichai, “If you go and analyze over 100,000 data points per patient, more than any single doctor could analyze, we can actually quantitatively predict the chance of readmission 24 to 48 hours earlier than traditional methods. It gives doctors time to act.”

Of course, researchers don’t claim that their approach is ready for implementation in clinical settings, but they are looking forward to collaborating with providers to test this model further. Hopefully, we will see field trials and, who knows, even early adoption in 2019.

EHRs “on steroids”

HIMSS18 was all about artificial intelligence and machine learning. Surprisingly, all major EHR vendors – Allscripts, Cerner, athenahealth, Epic, and eClinicalWorks – came up with a promise to include AI into upcoming iterations of their platforms.

At the event, Epic announced a new partnership with Nuance to integrate their AI-powered conversational virtual assistant into the Epic EHR workflow. Particularly, the assistant will enable health specialists to access patient information and lab results, record patient vitals as well as check schedules and manage patient appointments using voice.

Similarly, eClinicalWorks puts AI into work on voice control but also prioritizes telemedicine, pop health, and clinical decision support. According to the company’s CEO Girish Navani, “We spent the last decade putting data in EHRs. The next decade is about intelligence and creating inferences that improve care outcomes. We can have the computer do things for the clinician to make them aware of actions they can take.” The new EHR’s launch is expected in late 2018 or early 2019.

Athenahealth also added a virtual assistant into their EHRs to improve mobile connectivity and welcomes NoteSwift’s AI-based Samantha technology to enhance clinical workflows by introducing robust automation. Samantha can grasp free-text and natural language, process information, structure it, assign ICD-10, SNOMED or CPT codes, prepare e-prescriptions and orders.

Pre-existing challenges for healthcare AI adoption

Gartner predicted that 50% of organizations will miss AI and data literacy skills to gain business value by 2020. Certainly, a lot of healthcare organizations will get in this 50%, and there are two reasons for that.

Regulations and security concerns are the main pre-existing challenges that delay practically any technology adoption in healthcare and entail an array of new challenges along with them.

First, an AI application or device has to be approved by the FDA. The catch is that the existing process focuses on the hardware or the way that algorithms work, but not the data it should or would interact with.

Speaking of data, another challenge is security breaches. Safeguarding sensitive information is a must for healthcare because patient data is a constant target for identity theft and reimbursement fraud. In Accenture’s new report, nearly 25% of healthcare execs admitted experiencing “adversarial AI behaviors, like falsified location data or bot fraud.” While this doesn’t mean AI threatens patient data, such claims do increase the concerns related to its adoption.

Still, artificial intelligence is growing in healthcare and will continue to do so. Maybe not at rocket speed, but the most recent cases show consistent improvements in major care delivery gaps. Healthcare AI’s future appears bright.

About Inga Shugalo
Inga Shugalo is a Healthcare Industry Analyst at Itransition. She focuses on Healthcare IT, highlighting the industry challenges and technology solutions that tackle them. Inga’s articles explore diagnostic potential of healthcare IoT, opportunities of precision medicine, robotics and VR in healthcare and more.

AI-Based Tech Could Speed Patient Documentation Process

Posted on August 27, 2018 I Written By

Anne Zieger is veteran healthcare consultant and analyst with 20 years of industry experience. Zieger formerly served as editor-in-chief of FierceHealthcare.com and her commentaries have appeared in dozens of international business publications, including Forbes, Business Week and Information Week. She has also contributed content to hundreds of healthcare and health IT organizations, including several Fortune 500 companies. Contact her at @ziegerhealth on Twitter or visit her site at Zieger Healthcare.

A researcher with a Google AI team, Google Brain, has published a paper describing how AI could help physicians complete patient documentation more quickly. The author, software engineer Peter Lui, contends that AI technology can speed up patient documentation considerably by predicting its content.

On my initial reading of the paper, it wasn’t clear to me what advantage this has over pre-filling templates or even allowing physicians to cut-and-paste text from previous patient encounters. Still, judge for yourself as I outline what author Liu has to say, and by all means, check out the write-up.

In its introduction, the paper notes that physicians spend a great deal of time and energy entering patient notes into EHRs, a process which is not only taxing but also demoralizing for many physicians. Choosing from just one of countless data points underscoring this conclusion, Liu cites a 2016 study noting that physicians spend almost 2 hours of administrative work for every hour of patient contact.

However, it might be possible to reduce the number of hours doctors spend on this dreary task. Google Brain has been working on technologies which can speed up the process of documentation, including a new medical language modeling approach. Liu and his colleagues are also looking at how to represent an EHR’s mix of structured and unstructured text data.

The net of all of this? Google Brain has been able to create a set of systems which, by drawing on previous patient records can predict most of the content a physician will use next time they see that patient.

The heart of this effort is the MIMIC-III dataset, which contains the de-identified electronic health records of 39,597 patients from the ICU of a large tertiary care hospital. The dataset includes patient demographic data, medications, lab results, and notes written by providers. The system includes AI capabilities which are “trained” to predict the text physicians will use in their latest patient note.

In addition to making predictions, the Google Brain AI seems to have been able to pick out some forms of errors in existing notes, including patient ages and drug names, as well as providing autocorrect options for corrupted words.

By way of caveats, the paper warns that the research used only data generated within 24 hours of the current note content. Liu points out that while this may be a wide enough range of information for ICU notes, as things happen fast there, it would be better to draw on data representing larger windows of time for non-ICU patients. In addition, Liu concedes that it won’t always be possible to predict the content of notes even if the system has absorbed all existing documentation.

However, none of these problems are insurmountable, and Liu understandably describes these results as “encouraging,” but that’s also a way of conceding that this is only an experimental conclusion. In other words, these predictive capabilities are not a done deal by any means. That being said, it seems likely that his approach could be valuable.

I am left with at least one question, though. If the Google Brain technology can predict physician notes with great fidelity, how does that differ than having the physician cut-and-paste previous notes on their own?  I may be missing something here, because I’m not a software engineer, but I’d still like to know how these predictions improve on existing workarounds.

HPV Surveillance Project Reminds Us Why HIEs Still Matter

Posted on August 24, 2018 I Written By

Anne Zieger is veteran healthcare consultant and analyst with 20 years of industry experience. Zieger formerly served as editor-in-chief of FierceHealthcare.com and her commentaries have appeared in dozens of international business publications, including Forbes, Business Week and Information Week. She has also contributed content to hundreds of healthcare and health IT organizations, including several Fortune 500 companies. Contact her at @ziegerhealth on Twitter or visit her site at Zieger Healthcare.

When healthcare organizations use EHR data to improve care or streamline processes, it seems like an obvious way to go. There are many benefits to doing so – certainly far more than I could cover in a single story—and odds of finding better ways to leverage such data further keep increasing over time.

Given the attention commercial EHR data use gets, it’s easy to forget the role of such data in improving public health. Yes, medical practices need to meet criteria that converge with public health objectives, such as managing diabetes and its side effects. And of course, population health management efforts directly mirror and sometimes overlap with public health goals. But it’s seldom the work of which rockstars are made.

However, given that the bulk of efforts have typically been spearheaded by government agencies or independent non-profits in the past, it’s a good idea to keep track of what they’re doing, especially if you’re wondering what else you can do with patient health data. It’s even more important to remember that even a cache of regional health data can be very valuable in supporting community health.

I was thinking about this recently when the following story turned up in my inbox.  On the surface, it’s not a big deal, but it’s the kind of cooperative effort that can improve community health in ways that work for everyone in healthcare.

This story looks at the kind of data harvesting exercise that flies under the radar of most providers. It describes an HPV surveillance effort, the HPV Vaccine Impact Monitoring Project (HPV-IMPACT), which is sponsored by the CDC and implemented by the Center for Community Health and Prevention at the University of Rochester.

The HPV-IMPACT project is relying in part on data by the Rochester RHIO, which is sharing anonymized patient health information collected between 2008 to 2014. The researchers are also using data from California, Connecticut, Oregon and Tennessee.

The goal of HPV-IMPACT is to identify trends such as changes in the percentage of women screened for HPV, the implications for different age groups and overall test outcomes. Once they complete this analysis, research will use it to determine whether HPV incidence rates can be attributed to vaccine use or alternatively, decreases in detection.

While this kind of project is bread-and-butter research, something that won’t ever make headlines in medical journals, it deserves some thought.

With things being as they are, it’s easy to dismiss HIEs as parts of a broken national interoperability effort. Hey, I’ve been as guilty of this as anyone. For many years, I waited for the HIE model today, in part because it just didn’t seem to be a sustainable business model, but at least some just kept on chuggin’.

As it turns out, regional HIEs aren’t abandonware — they just have their own niche. This kind of story reminds me that even limited health data collection efforts can make a difference. Keep up the good work, folks.

The Latest Look At How Physicians Share PHI Electronically

Posted on August 22, 2018 I Written By

Anne Zieger is veteran healthcare consultant and analyst with 20 years of industry experience. Zieger formerly served as editor-in-chief of FierceHealthcare.com and her commentaries have appeared in dozens of international business publications, including Forbes, Business Week and Information Week. She has also contributed content to hundreds of healthcare and health IT organizations, including several Fortune 500 companies. Contact her at @ziegerhealth on Twitter or visit her site at Zieger Healthcare.

Over the last several years, I’ve read many a report on physicians’ sharing of health data. The key metrics most observers use to measure these efforts are how often physicians send and receive data and what type of data they’re sending.

I’m not so sure that this measurement offers the best look at health data sharing. I’m more interested in what doctors do with the information than what they shared and received. My guess is that these reports measure PHI coming and going because it’s simply more practical and does offer at least some insight.

In that spirit, I present to you some numbers from the CDC’s National Health Statistics Reports. That data comes from the 2015 National Electronic Health Records Survey, a nationally-representative survey of nonfederal office-based physicians. The study estimates the types PHI doctors electronically sent, searched for, received and integrated.

Survey results included the following:

  • Among physicians who sent PHI electronically, the most common types of data sent were referrals (67.9%), laboratory results (67.2%) and medication lists (65.1%). The least commonly observed types were summary of care records (51.5%), registry data (55.9%) and imaging reports (56.6%).
  • When these physicians received PHI, the most common types the study found were laboratory results (78.8%), imaging (60.8%) and medication lists (54.4%). The types seen least often included ED notifications (34.5%), hospital discharge summaries (42.5%) and registry data (43.2%).
  • For physicians who integrated PHI electronically, the most commonly observed types were laboratory results (73.2%), imaging reports (49.8%) and hospital discharge summaries (48.7%). PHI least commonly integrated included registry data (30.9%), problem lists (32.7%) and medication allergy lists (36.1%).
  • The most common reasons physicians searched for PHI electronically were to find medication lists (90.2%), medication allergy lists (88.2%) and hospital discharge summaries (80.4%), followed by imaging reports (58.9%), laboratory results (48.5%) and problem lists (41.2%).

The CDC analysis of this data notes that it might be smart to articulate the differences between primary care PHI exchange and specialist PHI exchange. It rightfully points out that research which breaks down such data not only by specialty, but also office setting, practice size and EHR vendor would be a good idea.

These aren’t the only issues left unaddressed, though. What strikes me about this data is that there’s little symmetry between what doctors send and what they receive. There’s also little overlap between the sharing stats and those regarding what they integrate. Their priorities when searching for information seem to be on their own track as well.

What does this mean? It’s hard to tell. But I think someone should look at the differences in how doctors participate in various forms of electronic exchange of PHI. These differences probably say something, and it would be nice to know what it is.

 

 

Physician Revolt Against EHRs – Unlikely to Happen

Posted on August 20, 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.

Physicians hate EHRs.

Yes, there are a few exceptions, but it’s pretty rare to find a physician that loves their EHR. There are a fair number of them that are apathetic towards their EHR, but there are a lot of doctors who hate them.

How much do they hate them? That’s hard to say, but it seems clear that they don’t hate them enough to really change things. Sure, they’ll leave some comments on message boards, send out some tweets or write some blogs, but they don’t seem ready to take it to the board (even when they’re the board). The most common path is doctors hate the EHR when it’s first implemented and then they learn the EHR software and become apathetic.

Clay Forsberg recently laid out the strategy for doctors who hate their EHR and want change:

Clay makes a great point. He then extends the discussion with these tweets:

The real problem here is that EHRs are the epitome of “meh.” They get in the way, but it’s hard to draw a specific line between EHR software and deaths or really poor quality care. They cause some time issues with multiple logins and lots of clicks, but they also save time in other ways. They have some bad workflows, but they make some workflows better.

EHRs are just good enough to avoid a revolt.

Plus, a doctor replying to Clay Forsberg’s tweet above identified another issue:

Doctors definitely don’t want to risk their livelihood, but I think even more than that they don’t think that complaints about the EHR are going to have any impact. This is particularly true in large health systems. As Clay Forsberg points out, one voice will likely fall on deaf ears. It would take a coordinated effort to really effect change.

I’d also add that the problem I’ve seen with those doctors that are complaining about EHR software aren’t doing it in a productive manner. It’s almost like these people are arguing that we should go back to paper. Let’s be honest. That’s not going to happen. Plus, they don’t acknowledge how much they hated paper either. Think about something as simple as a missing chart and that usually refreshes some of the memories. Let alone the stacks of paper charts on physician’s desks that still needed to be completed.

Don’t get me wrong. I’m not suggesting that EHRs couldn’t do a lot more to make physicians’ lives easier. There’s also a ton of poorly optimized EHR implementations that are driving doctors crazy. Those are fixable even if many doctors don’t realize that there are solutions out there. It’s important to realize that both are issues, but are addressed very differently.

At the end of the day, doctors can complain about EHR software until their blue in the face, but EHRs aren’t going anywhere. We’re not going back to paper and I don’t see an alternative to them coming soon. That said, a physician revolt against EHRs would make them better and that would be a great thing for everyone involved. I just don’t see enough doctors ready to revolt. Do you? If so, I’d love to hear what they’re doing.

The Independent Small Healthcare Provider is In Trouble

Posted on August 17, 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.

This week I attended the Digital Health Investment Summit hosted by KLAS. They brought together a great group of people from the healthcare provider, vendor, payer, and investor space. We had some really great conversations and I heard a lot of great perspectives. However, if there was one theme I had coming out of the event it was this:

I’d take this statement one step further. I don’t think I heard one thing said at this event that indicated that there’s something that was going to be beneficial to the small practice. Everything that was said was building towards the need for big data, comprehensive solutions, and tools, relationships, and technology that was largely only going to be available to large health systems.

As one person on Twitter pointed out, this is unfortunate because there have been a lot of studies that have shown how the small ambulatory practices are much more cost effective than the corporate ones. Plus, patients often love that personal relationship as well. However, patients also love the advanced services and personalization that future healthcare IT can provide when you have the right scale.

This reminds me of what we’ve seen in the banking industry. There used to be a lot of local banks. Now, they’re all but disappearing as the large corporate banks take over. Sure, there are some credit unions and a few local banks left, but it’s really hard for these to compete with the kind of services the large corporate banks can provide. The same is likely to happen in healthcare and it seems to be happening fast. One health system executive told me at the event, “Practices are coming to us saying they want to be part of us. We can’t keep them away.”

I’ve mentioned before that the one hope for small practices is that large health IT companies that provide some of these advanced services could make them available to small practices. Basically, a healthcare IT company could provide a service to enough small practices that all the small practices together share in the costs associated with the advanced services like genomics for example. To go back to the banking industry, that’s what we’ve seen. A small bank or credit union doesn’t create their own online banking tech with the scan technology for mobile deposits. However, they can use a third party to be able to provide those services.

The only problem with this view is that most entrepreneurs look at the small practices as the worst market. Talking to the investors this weekend and other startups, almost none of them are targeting the small practice as their market. There are a lot of reasons for this. It’s notoriously hard to get to the decision makers at a small practice. We can thank pharma sales reps for this. Plus, small practices are quite cheap when it comes to investing in technology. All of this makes for a really challenging sales process for companies that want to sell into the small practice. The large health systems have a long sales cycle, but at least the reward is big when you make the sale.

While there are so many things going against the small practice. I still hope they find a way to survive. I also think it will be interesting to see how super groups do in this environment. If you have all the gastroenterologists in your area in a group, that can give you a lot of power to resist the health system taking you over. A fair number of direct primary care practices seem to be doing well also and could very well survive as well.

Watching some of the past senate hearings for various legislation, you can see how the government wants to do things to keep the small practice alive. Will what they do be enough? I don’t think many of us want to put our faith in government in this regard, but they may surprise us. There are some interesting rules and regulations for rural that has allowed many of them to survive.

I’d love to hear opposing viewpoints. Do you see reasons why the small practice can survive? What does this mean for healthcare? Should we be doing more to support small practices? If so, what can we do? I think it’s clear that most would like to keep small healthcare providers around, but it’s hard to see how they’ll be able to compete as technology continues to evolve.

Walgreen’s Perspectives on Patient Engagement at #DHIS18

Posted on August 15, 2018 I Written By

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

The past 2 days I’ve been attending the Digital Health Investor Summit that’s hosted by KLAS. It was a classy event and the people they had in attendance were phenomenal. I’ll be offering up a number of insights I got from the event across the Healthcare Scene network of blogs, but a couple slides from Chet Robson really stood out for me today. Chet is the Medical Director, Clinical Programs & Quality at Walgreens.

The slides that Chet Robson shared were around some views on patient engagement. Or as he framed it: patient engagement, patient activation, patient involvement, patient participation, patient adherence, patient compliance, patient empowerment, or patient experience. I love that we have so many terms for the same concept.

Here’s the first chart he shared for patient engagement:

The 3 dimensions in the chart listed above seemed like a good framework for patient engagement. So, I was glad when Chet then shared this slide:

I think that more things could be added to the above expectations. However, it’s a really good start. Imagine if all of healthcare implemented these principles.

As timing would have it, I’ve actually done 3 appointments at Walgreens in the last month. Without going into all the details of why, I’m happy to say that Walgreens delivered on these expectations. The visits were easy to schedule, quick and painless, and the experience was great. My only complaint was that the appointment process wasn’t clear. I wasn’t sure if you could only schedule certain appointments or if you could also do walk-ins. The answer is that it’s best to have an appointment. Otherwise, when you walk in, the computer will have you schedule an appointment and unless you’re lucky, you’ll likely be waiting for a bit. However, this is a minor learned thing that can easily be fixed.

What do you think of looking at patient experience from a behavioral, cognitive, and emotional dimension?

Let Vendors Lead The Way? Are You Nuts?

Posted on August 13, 2018 I Written By

Anne Zieger is veteran healthcare consultant and analyst with 20 years of industry experience. Zieger formerly served as editor-in-chief of FierceHealthcare.com and her commentaries have appeared in dozens of international business publications, including Forbes, Business Week and Information Week. She has also contributed content to hundreds of healthcare and health IT organizations, including several Fortune 500 companies. Contact her at @ziegerhealth on Twitter or visit her site at Zieger Healthcare.

Every now and then, a vendor pops up and explains how the next-gen EHR should work. It’s easy to ask yourself why anyone should listen, given that you’re the one dishing out the care. But bear with me. I’ve got a theory working here.

First of all, let’s start with a basic assumption, that EHRs aren’t going to stay in their current form much longer. We’re seeing them grow to encompass virtually every form of medical data and just about every transaction, and nobody’s sure where this crazy process is going to end.

Who’s going to be our guide to this world? Vendors. Yup, the people who want to sell you stuff. I will go out on a limb and suggest that at this point in the health data revolution, they’re in a better position to predict the future.

Sure, that probably sounds obnoxious. While vendors may employ reputable, well-intended physicians, the vast majority of those physicians don’t provide care themselves anymore. They’re rusty. And unless they’re in charge of the company they serve, their recommendations may be overruled by people who have never touched a patient.

On the flip side, though, vendor teams have the time and money to explore emerging technologies, not just the hip stuff but the ones that will almost certainly be part of medical practice in the future. The reality is that few practicing physicians have time to keep up with their progress. Heck, I spend all day researching these things, and I’m going nuts trying to figure out which tech has gone from a nifty idea to a practical one.

Given that vendors have the research in hand, it may actually make sense to let them drive the car for a while. Honestly, they’re doing a decent job of riding the waves.

In fact, it seems to me that the current generation of health data management systems are coming closer to where they should be.  For example, far more of what I’d call “enhanced EHR” systems include care management tools, integrating support for virtual visits and modules that help practices pull together MIPS data. As always, they aren’t perfect – for example, few ambulatory EHRs are flexible enough to add new functions easily — but they’re getting better.

I guess what I’m saying is that even if you have no intention of investing in a given product, you might want to see where developers’ ideas are headed. Health data platforms are at an especially fluid stage right now, tossing blockchain, big data analytics, AI and genomic data together and creating new things. Let’s give developers a bit of slack and see what they can do to tame these beasts.

eClinicalWorks Faces Additional Fine For Violating Terms Of Fraud Settlement

Posted on August 10, 2018 I Written By

Anne Zieger is veteran healthcare consultant and analyst with 20 years of industry experience. Zieger formerly served as editor-in-chief of FierceHealthcare.com and her commentaries have appeared in dozens of international business publications, including Forbes, Business Week and Information Week. She has also contributed content to hundreds of healthcare and health IT organizations, including several Fortune 500 companies. Contact her at @ziegerhealth on Twitter or visit her site at Zieger Healthcare.

In mid-2017, the news broke that EHR vendor eClinicalWorks had agreed to pay $155 million to settle a whistleblower lawsuit brought by a former employee. The government had accused the company of doctoring its code to cover the fact that its platform couldn’t pass certification testing,

Following the agreement with the government, eCW was hit with two class-action lawsuits related to the certification fraud, one filed by a group of clinicians over funds lost due to the certification and another by patients who say that data display errors may have affected their care.

Unfortunately for eCW, its legal troubles aren’t over. The vendor is now on the hook for a fine it incurred for failing to comply with the Corporate Integrity Agreement it signed as part of its settlement deal. The $132,500 fine probably won’t have a massive impact on the company, but it’s a reminder of how much trouble the certification problem continues to cause.

In signing the CIA, which will be in place for five years, eCW agreed to a number of things, including that it would adhere to software standards and practices, identify and address patient safety and certification issues and meet obligations to existing and future customers. eCW also promised to report patient safety issues in a timely manner.

Apparently, it didn’t do so, and that triggered the penalty stipulated in the CIA. Among the terms buried in the hefty CIA document is that the vendor would be fined $2,500 for each day eCW failed to establish and implement patient safety issues as reportable events. Somehow, the vendor let this go for almost two months. Bummer.

Of course, eCW leaders must be reeling. This has to have been the most painful year in the company’s history, without a doubt. Customers are understandably quite angry with eCW, and some of them are suing. Patients are suing. Its reputation has taken a major hit.

The financial implications of the settlement are staggering too. Very few companies could cover a $155 million payout without a struggle, and even if a business liability insurer is covering the loss, the settlement can’t be good for its relationships with financial institutions. It’s a mess I’d wish on no one.

On the other hand, am I being too harsh when I suggest that under the circumstances, letting a reporting problem go for 53 days doesn’t speak well of eCW’s recovery? Yes, I’m sure that keeping up with CIA requirements has been pretty burdensome, but we’re talking about survival here.

I’m not going to hazard a guess as to whether eCW is on the skids or just struggling to recover from a massive blow to its fundament. But geez, folks. Let’s hope you get on top of these issues soon. Violating the terms of the CIA within year two of the five-year agreement doesn’t exactly inspire confidence.

Nationwide Healthcare Interoperability Isn’t Happening

Posted on August 8, 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’ve got interoperability on the mind today. I think it’s probably because of all the tweets that are coming out on the #InteropForum hashtag from the ONC Interoperability Forum in DC. I would have liked to attend, but I’m grateful that so many people are sharing what’s happening. That said, I must admit that I’m tired of a lot of the tweets that aren’t grounded in reality and that call for things that are never going to happen or tweets that propose goals that aren’t meaningful (yes, I had to use that word).

The first reality that’s become clear to me is that nationwide interoperability of healthcare data isn’t going to happen.

It’s just not going to happen and in most cases it shouldn’t happen when you consider the costs and benefits. Sure, we are all traveling a lot more, but there are 45 or so states in the US where no healthcare organization has need for my health information. If they do, then there are ways they can get it, but they are rare. Even if I have a crazy medical incident in an unusual state, those care providers know how to take care of me even without all my health records. Doctors are always treating patients with limited information. If I’m a chronic patient where certain information would be important for me if I’m treated out of state by a doctor that doesn’t know me, there are hundreds of options for me to carry that information on my phone.

My point here is that there aren’t any massive economic incentives for there to nationwide sharing of health data. Don’t be confused though. I’m not saying that sharing health data is not beneficial. What I’m saying is that we don’t need to build a national framework of health data sharing. When people suggest we should make that a reality, they’re essentially dooming interoperability. Talk about biting off more than you can chew. It’s become quite clear to me that Nationwide Interoperability of health data isn’t going to happen.

I love this excerpt from Brian Mack’s blog post on the Great Lakes Health Connect (an HIE) blog:

The Trusted Exchange Framework and Common Agreement (TEFCA) released by the Office of the National Coordinator last January, was (it was thought) intended to bring clarity and define a path forward for national interoperability, but has instead just added more uncertainty and the promise of additional layers of bureaucracy.

Discussions around national healthcare interoperability just bring more uncertainty and more layers of bureaucracy. It’s a failed approach.

With that said, it’s also very clear that smaller scale interoperability is not only possible but a valuable thing for most in healthcare. This was highlighted by interoperability expert, Greg Meyer, when he tweeted:

It’s really great that Greg is trying to figure out how we can generalize these point to point interoperability solutions. That’s a smart approach. However, buried in this tweet in a way that most will miss is the fact that there are a lot of unique scenarios and solutions where healthcare interoperability has been successful. Healthcare interoperability is possible and many organizations are doing it. Just not on a national scale.

To continue Greg’s analogy, we need more of these interoperability “snowflakes” and we need those creating the snowflakes to share their successes. A blizzard of snowflakes is a powerful thing even though the individual snowflakes are small. As it stands today, a national approach to interoperability is more like spending millions and billions of dollars on a snow making machine and then never turning it on. I’d rather have a million snowflakes than a billion dollar machine that doesn’t produce any snow. </ end snowflake analogy>

Another example of healthcare interoperability in action was shared at the Healthcare IT Expo this year. Don Lee offered a great summary of UPMC’s success with interoperability and the parts of interoperability they have solved. There’s always still more work to do, but if every hospital was able to accomplish what UPMC has accomplished in regards to healthcare interoperability, then we could have a very different discussion around healthcare data sharing.

The only solution I see to healthcare interoperability is for healthcare organizations to make it a priority. As I said back in 2013, Interoperability Needs Action, Not Talk. The more small interoperability connections we make, the more we’ll understand our data, how to connect, and build relationships between organizations. All of that will be key to even starting to thinking about nationwide healthcare interoperability. Until then, let’s table the nationwide healthcare interoperability discussions.