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Searching EMR For Risk-Related Words Can Improve Care Coordination

Posted on September 18, 2017 I Written By

Anne Zieger is a healthcare journalist who has written about the industry for 30 years. Her work has appeared in all of the leading healthcare industry publications, and she's served as editor in chief of several healthcare B2B sites.

Though healthcare organizations are working on the problem, they’re still not as good at care coordination as they should be. It’s already an issue and will only get worse under value-based care schemes, in which the ability to coordinate care effectively could be a critical issue for providers.

Admittedly, there’s no easy way to solve care coordination problems, but new research suggests that basic health IT tools might be able to help. The researchers found that digging out important words from EMRs can help providers target patients needing extra care management and coordination.

The article, which appears in JMIR Medical Informatics, notes that most care coordination programs have a blind spot when it comes to identifying cases demanding extra coordination. “Care coordination programs have traditionally focused on medically complex patients, identifying patients that qualify by analyzing formatted clinical data and claims data,” the authors wrote. “However, not all clinically relevant data reside in claims and formatted data.”

For example, they say, relying on formatted records may cause providers to miss psychosocial risk factors such as social determinants of health, mental health disorder, and substance abuse disorders. “[This data is] less amenable to rapid and systematic data analyses, as these data are often not collected or stored as formatted data,” the authors note.

To address this issue, the researchers set out to identify psychosocial risk factors buried within a patient’s EHR using word recognition software. They used a tool known as the Queriable Patient Inference Dossier (QPID) to scan EHRs for terms describing high-risk conditions in patients already in care coordination programs.

After going through the review process, the researchers found 22 EHR-available search terms related to psychosocial high-risk status. When they were able to find nine or more of these terms in the patient’s EHR, it predicted that a patient would meet criteria for participation in a care coordination program. Presumably, this approach allowed care managers and clinicians to find patients who hadn’t been identified by existing care coordination outreach efforts.

I think this article is valuable, as it outlines a way to improve care coordination programs without leaping over tall buildings. Obviously, we’re going to see a lot more emphasis on harvesting information from structured data, tools like artificial intelligence, and natural language processing. That makes sense. After all, these technologies allow healthcare organizations to enjoy both the clear organization of structured data and analytical options available when examining pure data sets. You can have your cake and eat it too.

Obviously, we’re going to see a lot more emphasis on harvesting information from structured data, tools like artificial intelligence and natural language processing. That makes sense. After all, these technologies allow healthcare organizations to enjoy both the clear organization of structured data and analytical options available when examining pure data sets. You can have your cake and eat it too.

Still, it’s good to know that you can get meaningful information from EHRs using a comparatively simple tool. In this case, parsing patient medical records for a couple dozen keywords helped the authors find patients that might have otherwise been missed. This can only be good news.

Yes, there’s no doubt we’ll keep on pushing the limits of predictive analytics, healthcare AI, machine learning and other techniques for taming wild databases. In the meantime, it’s good to know that we can make incremental progress in improving care using simpler tools.

Analytics Take an Unusual Turn at PeraHealth

Posted on August 17, 2017 I Written By

Andy Oram is an editor at O'Reilly Media, a highly respected book publisher and technology information provider. An employee of the company since 1992, Andy currently specializes in open source, software engineering, and health IT, but his editorial output has ranged from a legal guide covering intellectual property to a graphic novel about teenage hackers. His articles have appeared often on EMR & EHR and other blogs in the health IT space. Andy also writes often for O'Reilly's Radar site (http://oreilly.com/) and other publications on policy issues related to the Internet and on trends affecting technical innovation and its effects on society. Print publications where his work has appeared include The Economist, Communications of the ACM, Copyright World, the Journal of Information Technology & Politics, Vanguardia Dossier, and Internet Law and Business. Conferences where he has presented talks include O'Reilly's Open Source Convention, FISL (Brazil), FOSDEM, and DebConf.

Data scientists in all fields have learned to take data from unusual places. You’d think that monitoring people in a hospital for changes in their conditions would be easier than other data-driven tasks, such as tracking planets in far-off solar systems, but in all cases some creativity is needed. That’s what PeraHealth, a surveillance system for hospital patients, found out while developing alerts for clinicians.

It’s remarkably hard to identify at-risk patients in hospitals, even with so many machines and staff busy monitoring them. For instance, a nurse on each shift may note in the patient’s record that certain vital signs are within normal range, and no one might notice that the vital signs are gradually trending worse and worse–until a crisis occurs.

PeraHealth identifies at-risk patients through analytics and dashboards that doctors and nurses can pull up. They can see trends over a period of several shifts, and quickly see which patients in the ward are the most at risk. PeraHealth is a tool for both clinical surveillance and communication.

Michael Rothman, co-founder and Chief Science Officer, personally learned the dangers of insufficient monitoring in 2003 when a low-risk operation on his mother led to complications and her unfortunate death. Rothman and his brother decided to make something positive from the tragedy. They got permission from the hospital to work there for three weeks, applying Michael’s background in math and data analysis (he has worked in the AI department of IBM’s Watson research labs, among other places) and his brother’s background in data visualization. Their goal, arguably naive: to find a single number that summarizes patient risk, and expose that information in a usable way to clinicians.

Starting with 70 patients from the cardiac unit, they built a statistical model that they tested repeatedly with 1,200 patients, 6,000 patients, and finally 25,000 patients. At first they hoped to identify extra data that the nurse could enter into the record, but the chief nurse laid down, in no uncertain terms, that the staff was already too busy and that collecting more data was out of the question. It came time to get creative with data that was already being collected and stored.

The unexpected finding was that vital signs were not a reliable basis for assessing a patient’s trends. Even though they’re “hard” (supposedly objective) data, they bounce around too much.

Instead of relying on just vital signs, PeraHealth also pulls in nursing assessments–an often under-utilized source of information. On each shift, a nurse records information on a dozen different physical systems as well as essential facts such as whether a patient stopping eating or was having trouble walking. It turns out that this sort of information reliably indicates whether there’s a problem. Many of the assessments are simple, yes/no questions.

Rothman analyzed hospital data to find variables that predicted risk. For instance, he compared the heart rates of 25,000 patients before they left the hospital and checked who lived for a year longer. The results formed a U-shaped curve, showing that heart rates above a certain level or below a certain level predicted a bad outcome. It turns out that this meaure works equally well within the hospital, helping to predict admission to the ICU, readmission to the ICU, and readmission after discharge.

The PeraHealth team integrated their tool with the hospital’s EHR and started producing graphs for the clinicians in 2007. Now they can point to more than 25 peer-reviewed articles endorsing their approach, some studies comparing before-and-after outcomes, and others comparing different parts of the hospital with some using PeraHealth and others not using it. The service is now integrated with major EHR vendors.

PeraHealth achieved Rothman’s goal of producing a single meaningful score to rate patient risk. Each new piece of data that goes into the EHR triggers a real-time recalculation of the score and a new dot on a graph presented to the nurses. In order to save the nurses from signing into the EHR, PeraHealth put a dashboard on the nurse’s kiosk with all the patients’ graphs. Color-coding denotes which patients are sickest. PeraHealth also shows which patients to attend to first. In case no one looks at the screen, at some hospitals the system sends out text alerts to doctors about the most concerned patients.

PeraHealth is now expanding. In an experiment, they did phone interviews with people in a senior residential facility, and identified many of those who were deteriorating. So the basic techniques may be widely applicable to data-driven clinical decision support. But without analytics, one never knows which data is most useful.

Wellpepper and SimplifiMed Meet the Patients Where They Are Through Modern Interaction Techniques

Posted on August 9, 2017 I Written By

Andy Oram is an editor at O'Reilly Media, a highly respected book publisher and technology information provider. An employee of the company since 1992, Andy currently specializes in open source, software engineering, and health IT, but his editorial output has ranged from a legal guide covering intellectual property to a graphic novel about teenage hackers. His articles have appeared often on EMR & EHR and other blogs in the health IT space. Andy also writes often for O'Reilly's Radar site (http://oreilly.com/) and other publications on policy issues related to the Internet and on trends affecting technical innovation and its effects on society. Print publications where his work has appeared include The Economist, Communications of the ACM, Copyright World, the Journal of Information Technology & Politics, Vanguardia Dossier, and Internet Law and Business. Conferences where he has presented talks include O'Reilly's Open Source Convention, FISL (Brazil), FOSDEM, and DebConf.

Over the past few weeks I found two companies seeking out natural and streamlined ways to connect patients with their doctors. Many of us have started using web portals for messaging–a stodgy communication method that involves logins and lots of clicking, often just for an outcome such as message “Test submitted. No further information available.” Web portals are better than unnecessary office visits or days of playing phone tag, and so are the various secure messaging apps (incompatible with one another, unfortunately) found in the online app stores. But Wellpepper and SimplifiMed are trying to bring us a bit further into the twenty-first century, through voice interfaces and natural language processing.

Wellpepper’s Sugarpod

Wellpepper recently ascended to finalist status in the Alexa Diabetes Challenge, which encourages research into the use of Amazon.com’s popular voice-activated device, Alexa, to improve the lives of people with Type 2 Diabetes. For this challenge, Wellpepper enhanced its existing service to deliver messages over Amazon Echo and interview patients. Wellpepper’s entry in the competition is an integrated care plan called Sugarpod.

The Wellpepper platform is organized around a care plan, and covers the entire cycle of treatment, such as delivering information to patients, managing their medications and food diaries, recording information from patients in the health care provider’s EHR, helping them prepare for surgery, and more. Messages adapt to the patient’s condition, attempting to present the right tone for adherent versus non-adherent patients. The data collected can be used for analytics benefitting both the provider and the patient–valuable alerts, for instance.

It must be emphasized at the outset that Wellpepper’s current support for Alexa is just a proof of concept. It cannot be rolled out to the public until Alexa itself is HIPAA-compliant.

I interviewed Anne Weiler, founder and CEO of Wellpepper. She explained that using Alexa would be helpful for people who have mobility problems or difficulties using their hands. The prototype proved quite popular, and people seem willing to open up to the machine. Alexa has some modest affective computing features; for instance, if the patient reports feeling pain, the device will may respond with “Ouch!”

Wellpepper is clinically validated. A study of patients with Parkinson’s showed that those using Wellpepper showed 9 percent improvement in mobility, whereas those without it showed a 12% decline. Wellpepper patients adhered to treatment plans 81% of the time.

I’ll end this section by mentioning that integration EHRs offer limited information of value to Wellpepper. Most EHRs don’t yet accept patient data, for instance. And how can you tell whether a patient was admitted to a hospital? It should be in the EHR, but Sugarpod has found the information to be unavailable. It’s especially hidden if the patient is admitted to a different health care providers; interoperability is a myth. Weiler said that Sugarpod doesn’t depend on the EHR for much information, using a much more reliable source of information instead: it asks the patient!

SimplifiMed

SimplifiMed is a chatbot service that helps clinics automate routine tasks such as appointments, refills, and other aspects of treatment. CEO Chinmay A. Singh emphasized to me that it is not an app, but a natural language processing tool that operates over standard SMS messaging. They enable a doctor’s landline phone to communicate via text messages and route patients’ messages to a chatbot capable of understanding natural language and partial sentences. The bot interacts with the patients to understand their needs, and helps them accomplish the task quickly. The result is round-the-clock access to the service with no waiting on the phone a huge convenience to busy patients.

SimplifiMed also collects insurance information when the patient signs up, and the patient can use the interface to change the information. Eventually, they expect the service to analyze patient’s symptom in light of data from the EHR and help the patient make the decision about whether to come in to the doctor.

SMS is not secure, but HIPAA does not get violated because the patient can choose what to send to the doctor, and the chatbot’s responses contain no personally identifiable information. Between the doctor and the SimplifiMed service, data is sent in encrypted form. Singh said that the company built its own natural language processing engine, because it didn’t want to share sensitive patient data with an outside service.

Due to complexity of care, insurance requirements, and regulations, a doctor today needs support from multiple staff members: front desk, MA, biller, etc. MACRA and value-based care will increase the burden on staff without providing the income to hire more. Automating routine activities adds value to clinics without breaking the bank.

Earlier this year I wrote about another company, HealthTap, that had added Alexa integration. This trend toward natural voice interfaces, which the Alexa Diabetes Challenge finalists are also pursuing, along with the natural language processing that they and SimplifiMed are implementing, could put health care on track to a new era of meeting patients where they are now. The potential improvements to care are considerable, because patients are more likely to share information, take educational interventions seriously, and become active participants in their own treatment.

More About Artificial Intelligence in Healthcare – #HITsm Chat Topic

Posted on August 8, 2017 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.

We’re excited to share the topic and questions for this week’s #HITsm chat happening Friday, 8/11 at Noon ET (9 AM PT). This week’s chat will be hosted by Prashant Natarajan (@natarpr) on the topic of “More About Artificial Intelligence in Healthcare.” Be sure to also check out Prashant’s HIMSS best selling book Demystifying Big Data and Machine Learning for Healthcare to learn about his perspectives and insights into the topic.

Healthcare transformation requires us to continually look at new and better ways to manage insights – both within and outside the organization today. Increasingly, the ability to glean and operationalize new insights efficiently as a byproduct of an organization’s day-to-day operations is becoming vital to hospitals and health systems ability to survive and prosper. One of the long-standing challenges in healthcare informatics has been the ability to deal with the sheer variety and volume of disparate healthcare data and the increasing need to derive veracity and value out of it.

The potential for big data in healthcare – especially given the trends discussed earlier is as bright as any other industry. The benefits that big data analytics, AI, and machine learning can provide for healthier patients, happier providers, and cost-effective care are real. The future of precision medicine, population health management, clinical research, and financial performance will include an increased role for machine-analyzed insights, discoveries, and all-encompassing analytics.

This chat explores participants thoughts and feelings about the future of artificial intelligence in the healthcare industry and how healthcare organizations might leverage artificial intelligence to discover new business value, use cases, and knowledge.

Note: For purpose of this chat, “artificial intelligence” can mean predictive analytics, machine learning, big data analytics, natural language processing and contextually intelligent agents.

Reference Materials

Questions we will explore in this week’s #HITsm chat include:
T1: What words or short phrases convey your current thoughts & feelings about ‘artificial intelligence’ in the healthcare space? #HITsm #AI

T2: What are big & small steps healthcare can take to leverage big data & machine learning for population health & personalized care? #HITsm

T3: Which areas of healthcare might be most positively impacted by artificial intelligence? #HITsm #AI

T4: What are some areas within healthcare that will likely NOT be improved or replaced by artificial intelligence? #HITsm #AI

T5: What lessons learned from early days of ‘advanced analytics’ must not be forgotten as use of artificial intelligence expands? #HITsm #AI

Bonus: How is your organization preparing for the application and use of artificial intelligence in healthcare? #HITsm #AI

Upcoming #HITsm Chat Schedule
8/18 – Diversity in HIT
Hosted by Jeanmarie Loria (@JeanmarieLoria) from @advizehealth

8/25 – Consumer Data Liquidity – The Road So Far, The Road Ahead
Hosted by Greg Meyer (@Greg_Meyer93)

We look forward to learning from the #HITsm community! As always, let us know if you’d like to host a future #HITsm chat or if you know someone you think we should invite to host.

If you’re searching for the latest #HITsm chat, you can always find the latest #HITsm chat and schedule of chats here.

A Hospital CIO Perspective on Precision Medicine

Posted on July 31, 2017 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.

#Paid content sponsored by Intel.

In this video interview, I talk with David Chou, Vice President, Chief Information and Digital Officer with Kansas City, Missouri-based Children’s Mercy Hospital. In addition to his work at Children’s Mercy, he helps healthcare organizations transform themselves into digital enterprises.

Chou previously served as a healthcare technology advisor with law firm Balch & Bingham and Chief Information Officer with the University of Mississippi Medical Center. He also worked with the Cleveland Clinic to build a flagship hospital in Abu Dhabi, as well as working in for-profit healthcare organizations in California.

Precision Medicine and Genomic Medicine are important topics for every hospital CIO to understand. In my interview with David Chou, he provides the hospital CIO perspective on these topics and offers insights into what a hospital organization should be doing to take part in and be prepared for precision medicine and genomic medicine.

Here are the questions I asked him, if you’d like to skip to a specific topic in the video or check out the full video interview embedded below:

What are you doing in your organization when it comes to precision medicine and genomic medicine?

Tips on Implementing Text Analytics in Healthcare

Posted on July 6, 2017 I Written By

Anne Zieger is a healthcare journalist who has written about the industry for 30 years. Her work has appeared in all of the leading healthcare industry publications, and she's served as editor in chief of several healthcare B2B sites.

Most of us would agree that extracting clinical data from unstructured physician notes would be great. At present, few organizations have deployed such tools, nor have EMR vendors come to the rescue en masse, and the conventional wisdom holds that text analytics would be crazy expensive. I’ve always suspected that digging out and analyzing this data may be worth the trouble, however.

That’s why I really dug a recent article from HealthCatalyst’s Eric Just, which seemed to offer some worthwhile ideas on how to use text analytics effectively. Just, who is senior vice president of product development, made a good case for giving this approach a try. (Note: HealthCatalyst and partner Regenstrief Institute offer solutions in this area.)

The article includes an interesting case study explaining how healthcare text analytics performed head-to-head against traditional research methods.

It tells the story of a team of analysts in Indiana that set out to identify peripheral artery disease (PAD) patients across two health systems. At first gasp, things weren’t going well. When researchers looked at EMR and claims data, they found that failed to identify over 75% of patients with this condition, but text analytics improved their results dramatically.

Using ICD and CPT codes for PAD, and standard EMR data searches, team members had identified less than 10,000 patients with the disorder. However, once they developed a natural language processing tool designed to sift through text-based data, they discovered that there were at least 41,000 PAD patients in the population they were studying.

To get this kind of results, Just says, there are three key features a medical text analytics tool should have:

  • The medical text analytics software should tailor results to a given user’s needs. For example, he notes that if the user doesn’t have permission to view PHI, the analytics tool should display only nonprivate data.
  • Medical text analytics tools should integrate medical terminology to improve the scope of searches. For example, when a user does a search on the term “diabetes” the search tool should automatically be capable of displaying results for “NIDDM,” as this broadens the search to include more relevant content.
  • Text analytics algorithms should do more than just find relevant terms — they should provide context as well as content. For example, a search for patients with “pneumonia,” done with considering context, would also bring up phrases like “no history of pneumonia.” A better tool would be able to rule out phrases like “no history of pneumonia,” or “family history of pneumonia” from a search for patients who have been treated for this illness.

The piece goes into far more detail than I can summarize here, so I recommend you read it in full if you’re interested in leveraging text analytics for your organization.

But for what it’s worth, I came away from the piece with the sense that analyzing your clinical textual information is well worth the trouble — particularly if EMR vendors being to add such tools to their systems. After all, when it comes to improving outcomes, we need all the help we can get.

What’s a Patient?

Posted on May 10, 2017 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.

For quite a while I’ve been pushing the idea that healthcare needs to move beyond treating patients. Said another way, we need to move beyond just helping people who have health problems which are causing them to complain and move into treating patients that otherwise feel healthy.

Said another way, Wanda Health once told me “The definition of a healthy patient is someone who’s not been studied long enough.”

If you look long enough and hard enough, we all have health issues or we’re at risk for health issues. There’s always something that could be done to help all of us be healthier. That’s a principle that healthcare hasn’t embraced because our reimbursement models are focused on treating a patients’ chief complaint.

In another conversation with NantHealth, they suggested the idea that we should work towards knowing the patient so well that you know the treatment they need before you even physically see the patient.

These two ideas go naturally together and redefine our current definition of patient. In the above context, all of us would be considered patients since I have little doubt that all of us have health issues that could be addressed if we only knew the current state of our health better.

While NantHealth’s taken a number of stock hits lately for overpromising and under delivering, the concept I heard them describe is one that will become a reality. It could be fair to say that their company was too early for such a big vision, but it’s inspiring to think about creating technology and collecting enough data on a patient that you already know how to help the patient before they even come into the office. That would completely change the office visit paradox that we know today.

This is an ambitious vision, but it doesn’t seem like a massive stretch of the imagination either. That’s what makes it so exciting to me. Now imagine trying to do something like this in the previous paper chart world. Yeah, it’s pretty funny to just even think about it. Same goes with what we call clinical decision support today.

Healthcare Ransomware

Posted on May 8, 2017 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.

Health Data Management has a nice article up with insights on healthcare ransomware from GreyCastle Security’s CEO, Reg Harnish. Reg made a great case for why healthcare is seeing so much ransomware:

He contends that healthcare isn’t any more vulnerable to ransomware than other industries. But Harnish observes that—given the value of patient data and medical records—providers are the focus of cyber criminals who are targeting them with file-encrypting malware.

“You take their data away, and it literally threatens lives, patient safety and patient care, so they are much more likely to pay a ransom,” he adds.

I think healthcare organizations do respond differently to ransomware than other organizations and that makes them more vulnerable to an attack since many healthcare organizations feel it’s their obligation to maintain patient safety and that the ransom is worth the money so they can do no harm to patients.

Reg also addressed whether paying the ransom in a ransomware incident was a good idea (it’s not):

On the question of whether or not organizations should give in to the demands of cyber criminals using ransomware, Harnish says that GreyCastle never recommends paying a ransom. “There’s no guarantee that the ransom will work,” he warns. “If you pay the ransom, you may not get decryption keys. And even if you do get decryption keys, they may not be the right ones.”

Further, Harnish cautions that those organizations that pay a ransom then get put on a list of victims who have complied with ransomware demands. As a result, he says they are much more likely to be targeted again as a “paying” customer. “None of our clients have ever paid a ransom,” he adds.

I agree that in 98% of cases, paying the ransomware is a bad idea. Plus, every healthcare organization that pays the ransomware makes it worse for other healthcare organizations. Instead, the key is to have a great backup and disaster recovery strategy if and when ransomware occurs in your organization.

As Reg also points out, ransomware most often comes into your organization through your users. So, it’s worth the investment to educate your end users on possible hacking/ransomware attempts. Education isn’t perfect, but it can help decrease your chances of a ransomware incident.

#TransformHIT Think Tank Hosted by DellEMC

Posted on April 5, 2017 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.


DellEMC has once again invited me back to participate at the 6th annual #TransformHIT Healthcare Think Tank event happening Tuesday, April 18, 2017 from Noon ET (9 AM PT) – 3 PM ET (Noon PT). I think I’ve been lucky enough to participate 5 of the 6 years and I’ve really enjoyed every one of them. DellEMC does a great job bringing together really smart, interesting people and encourages a sincere, open discussion of major healthcare IT topics. Plus, they do a great job making it so everyone can participate, watch, and share virtually as well.

This year they asked me to moderate the Think Tank which will be a fun new adventure for me, but my job will be made easy by this exceptional list of people that will be participating:

  • John Lynn (@techguy)
  • Paul Sonnier (@Paul_Sonnier)
  • Linda Stotsky (@EMRAnswers)
  • Joe Babaian (@JoeBabaian)
  • Dr. Joe Kim (@DrJosephKim)
  • Andy DeLaO (@cancergeek)
  • Dan Munro (@danmunro)
  • Dr. Jeff Trent (@TGen)
  • Shahid Shah (@ShahidNShah)
  • Dave Dimond(@NextGenHIT)
  • Mike Feibus (@MikeFeibus)

This panel is going to take on three hot topics in the healthcare industry today:

  • Consumerism in Healthcare
  • Precision Medicine
  • Big Data and AI in Healthcare

The great thing is that you can watch the whole #TransformHIT Think Tank event remotely on Livestream (recording will be available after as well). We’ll be watching the #TransformHIT tweet stream and messages to @DellEMCHealth during the event as well if you want to ask any questions or share any insights. We’ll do our best to add outside people’s comments and questions into the discussion. The Think Tank is being held in Phoenix, AZ, so if you’re local there are a few audience seats available if you’d like to come watch live and meet any of the panelists in person. Just let me know in the comments or on our contact us page and I can give you more details.

If you have an interest in healthcare consumerism, precision medicine, or big data and AI in healthcare, then please join us on Tuesday, April 18, 2017 from Noon ET (9 AM PT) – 3 PM ET (Noon PT) for the live stream. It’s sure to be a lively and interesting discussion.
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IBM Watson Partners With FDA On Blockchain-Driven Health Sharing

Posted on January 16, 2017 I Written By

Anne Zieger is a healthcare journalist who has written about the industry for 30 years. Her work has appeared in all of the leading healthcare industry publications, and she's served as editor in chief of several healthcare B2B sites.

IBM Watson Health has partnered with the FDA in an effort to create scalable exchange of health data using blockchain technology. The two will research the exchange of owner-mediated data from a variety of clinical data sources, including EMRs, clinical trial data and genomic health data. The researchers will also incorporate data from mobiles, wearables and the Internet of Things.

The initial project planned for IBM Watson and the FDA will focus on oncology-related data. This makes sense, given that cancer treatment involves complex communication between multispecialty care teams, transitions between treatment phases, and potentially, the need to access research and genomic data for personalized drug therapy. In other words, managing the communication of oncology data is a task fit for Watson’s big brain, which can read 200 million pages of text in 3 seconds.

Under the partnership, IBM and the FDA plan to explore how the blockchain framework can benefit public health by supporting information exchange use cases across varied data types, including both clinical trials and real-world data. They also plan to look at new ways to leverage the massive volumes of diverse data generated by biomedical and healthcare organizations. IBM and the FDA have signed a two-year agreement, but they expect to share initial findings this year.

The partnership comes as IBM works to expand its commercial blockchain efforts, including initiatives not only in healthcare, but also in financial services, supply chains, IoT, risk management and digital rights management. Big Blue argues that blockchain networks will spur “dramatic change” for all of these industries, but clearly has a special interest in healthcare.  According to IBM, Watson Health’s technology can access the 80% of unstructured health data invisible to most systems, which is clearly a revolution in the making if the tech giant can follow through on its potential.

According to Scott Lundstrom, group vice president and general manager of IDC Government and Health Insights, blockchain may solve some of the healthcare industry’s biggest data management challenges, including a distributed, immutable patient record which can be secured and shared, s. In fact, this idea – building a distributed, blockchain-based EMR — seems to be gaining traction among most health IT thinkers.

As readers may know, I’m neither an engineer nor a software developer, so I’m not qualified to judge how mature blockchain technologies are today, but I have to say I’m a bit concerned about the rush to adopt it nonetheless.  Even companies with a lot at stake  — like this one, which sells a cloud platform backed by blockchain tech — suggest that the race to adopt it may be a bit premature.

I’ve been watching tech fashions come and go for 25 years, and they follow a predictable pattern. Or rather, they usually follow two paths. Go down one, and the players who are hot for a technology put so much time and money into it that they force-bake it into success. (Think, for example, the ERP revolution.) Go down the other road, however, and the new technology crumbles in a haze of bad results and lost investments. Let’s hope we go down the former, for everyone’s sake.