Free EMR 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 EMR and HIPAA for FREE!!

The Future Of…Healthcare Innovation

Posted on March 17, 2015 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 post is part of the #HIMSS15 Blog Carnival which explores “The Future of…” across 5 different healthcare IT topics.

Innovation is a fascinating concept. Historians and philosophers have been thinking and investigating the key to innovation forever. I’m not sure anyone has ever found the true secret sauce to innovation. Every innovation I’ve ever seen has been a mix of timing, luck, and hard work.

Some times the timing is not right for a product and therefore it fails. The product might have been great, but the timing wasn’t right for it to be rolled out. Innovation always requires a little luck. Maybe it was the chance meeting with an investor that helps take and idea to the next level. Maybe it’s the luck of getting the right exposure that catapults your idea into a business. Maybe it’s the luck of the right initial end users which shape the direction of the product. Every innovation has also required hard work. In fact, the key to ensuring you’re ready for luck to be heaped upon you or to test if your timing is right is to put in the work.

The great thing is that it’s a brilliant time to be working on innovations in healthcare. We’re currently at the beginning of a confluence of healthcare innovations. Each one on its own might seem like a rather small innovation, but taken together they’re going to provide amazing healthcare innovations that shape the future of healthcare as we know it.

Let me give a few examples of the wave of innovations that are happening. Health sensors are exploding. Are ability to know in real time how well our body is performing is off the charts. There are sensors out there for just about every measurable aspect of the human body. The next innovation will be to take all this sensor data and collapse it down into appropriate communication and actions.

Another example, is the innovations in genomic medicine. The cost and speed required to map your genome is collapsing faster than Moore’s law. All of that genomic data is going to be available to innovators who want to build something on top of it.

3D printing is progressing at light speed. Don’t think this applies to healthcare? Check out this 3D printed prosthetic hand or this 3D printed heart. If you really want your mind blown, check out people’s work to provide blood to 3D printed organs.

If you think we’ve gotten value out of healthcare data, you’re kidding yourself. There are so many innovations in healthcare data that are sitting there waiting in healthcare data hoards. We just need to tap into that data and start sharing those findings with a connected healthcare system.

The mobile device is an incredible innovation just waiting for healthcare. We are all essentially walking around with a computer in our pocket now. We’ve already started to see the innovations this will provide healthcare, but it’s only just the beginning. This computer in our pocket will become the brain and communication hub for our healthcare needs.

I’m sure you can think of other innovations that I haven’t mentioned including robotics, health literacy, healthcare gaming, etc. What’s most exciting to me about the future of healthcare innovation is that each of these innovations will combine into a unforeseen innovation. The most powerful innovations in healthcare will not be a single innovative idea. Instead, it will come from someone who combines multiple innovations into one beautiful package.

The most exciting part of innovation is that it’s usually unexpected and surprising. I love surprises. What do you see as the future building blocks of innovation in healthcare?

De-Identification of Data in Healthcare

Posted on January 14, 2015 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.

Today I had a chance to sit down with Khaled El Emam, PhD, CEO and Founder of Privacy Analytics, to talk about healthcare data and the de-identification of that healthcare data. Data is at the center of the future of healthcare IT and so I was interested to hear Khaled’s perspectives on how to manage the privacy and security of that data when you’re working with massive healthcare data sets.

Khaled and I started off the conversation talking about whether healthcare data could indeed be de-identified or not. My favorite Patient Privacy Rights advocate, Deborah C. Peel, MD, has often made the case for why supposedly de-identified healthcare data is not really private or secure since it can be re-identified. So, I posed that question to Khaled and he suggested that Dr. Peel is only telling part of the story when she references stories where healthcare data has been re-identified.

Khaled makes the argument that in all of the cases where healthcare data has been reidentified, it was because those organizations did a poor job of de-identifying the data. He acknowledges that many healthcare organizations don’t do a good job de-identifying healthcare data and so it is a major problem that Dr. Peel should be highlighting. However, just because one organization does a poor job de-identifying data, that doesn’t mean that proper de-identification of healthcare data should be thrown out.

This kind of reminds me of when people ask me if EHR software is secure. My answer is always that EHR software can be more secure than paper charts. However, it depends on how well the EHR vendor and the healthcare organization’s staff have done at implementing security procedures. When it’s done right, an EHR is very secure. When it’s done wrong, and EHR could be very insecure. Khaled is making a similar argument when it comes to de-identified health data.

Khaled did acknowledge that the risks are never going to be 0. However, if you de-identify healthcare data using proper techniques, the risks are small enough that they are similar to the risks we take every day with our healthcare data. I think this is an important point since the reality is that organizations are going to access and use healthcare data. That is not going to stop. I really don’t think there’s any debate on this. Therefore, our focus should be on minimizing the risks associated with this healthcare data sharing. Plus, we should hold organizations accountable for the healthcare data sharing their doing.

Khaled also suggested that one of the challenges the healthcare industry faces with de-identifying healthcare data is that there’s a shortage of skilled professionals who know how to do it properly. I’d suggest that many who are faced with de-identifying data have the right intent, but likely lack the skills needed to ensure that the healthcare data de-identification is done properly. This isn’t a problem that will be solved easily, but should be helped as data security and privacy become more important.

What do you think of de-identification in healthcare? Is the way it’s being done a problem today? I see no end to the use of data in healthcare, and so we really need to make sure we’re de-identifying healthcare data properly.

Healthcare Interoperability Series Outline

Posted on November 7, 2014 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.

Interoperability is one of the major priorities of ONC. Plus, I hear many doctors complaining that their EHR doesn’t live up to its potential because the EHR is not interoperable. I personally believe that healthcare would benefit immeasurably from interoperable healthcare records. The problem is that healthcare interoperability is a really hard nut to crack

With that in mind, I’ve decided to do a series of blog posts highlighting some of the many challenges and issues with healthcare interoperability. Hopefully this will provide a deeper dive into what’s really happening with healthcare interoperability, what’s holding us back from interoperability and some ideas for how we can finally achieve interoperable healthcare records.

As I started thinking through the subject of Healthcare Interoperability, here are some of the topics, challenges, issues, discussions, that are worth including in the series:

  • Interoperability Benefits
  • Interoperability Risks
  • Unique Identifier (Patient Identification)
  • Data Standards
  • Government vs Vendor vs Healthcare Organization Efforts and Motivations
  • When Should You Share The Data and When Not?
  • Major Complexities (Minors, Mental Health, etc)
  • Business Model

I think this is a good start, but I’m pretty sure this list is not comprehensive. I’d love to hear from readers about other issues, topics, questions, discussion points, barriers, etc to healthcare interoperability that I should include in this discussion. If you have some insights into any of these topics, I’d love to hear it as well. Hopefully we can contribute to a real understanding of healthcare interoperability.

A Little #AHIMACon14 Twitter Roundup

Posted on September 29, 2014 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’m in San Diego today at the AHIMA Annual Convention. It’s a great event that brings together some really passionate and wonderful Health Information Management professionals. There’s been some interesting Twitter activity at the event. Here’s a roundup of some of the interesting tweets:

Some really great insights. I’d love to hear your thoughts on the tweets above.

Modeling Health Data Architecture After DNS

Posted on September 12, 2014 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 was absolutely intrigued by the idea of structuring the healthcare data architecture after DNS. As a techguy, I’m quite familiar with the structure of DNS and it has a lot of advantages (Check out the Wikipedia for DNS if you’re not familiar with it).

There are a lot of really great advantages to a system like DNS. How beautiful would it be for your data to be sent to your home base versus our current system which requires the patient to go out and try and collect the data from all of their health care providers. Plus, the data they get from each provider is never in the same format (unless you consider paper a format).

One challenge with the idea of structuring the healthcare data architecture like DNS is getting everyone a DNS entry. How do you handle the use case where a patient doesn’t have a “home” on the internet for their healthcare data? Will the first provider that you see, sign you up for a home on the internet? What if you forget your previous healthcare data home and the next provider provides you a new home. I guess the solution is to have really amazing merging and transfer tools between the various healthcare data homes.

I imagine that some people involved in Direct Project might suggest that a direct address could serve as the “home” for a patient’s health data. While Direct has mostly been focused on doctors sharing patient data with other doctors and healthcare providers, patients can have a direct address as well. Could that direct address by your home on the internet?

This will certainly take some more thought and consideration, but I’m fascinated by the distributed DNS system. I think we healthcare data interoperability can learn something from how DNS works.

Value of Data, EMR Jobs, and EMR vs EHR

Posted on July 27, 2014 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 agree with Wen that the EMR and claims data needs to be cleaned up. I think it gives the wrong message to say it’s not meaningful though. Once it’s cleaned up, it has a lot of value.


How many of you have applied for a job because you saw it posted on Twitter? I’m really interested in this since I do a lot of health IT job posts on Twitter. We see quite a bit of traffic from Twitter to our healthcare IT job board, but I haven’t added a good way to track who signs up and applies for jobs. That’s next.


I love how academic Practice Fusion tries to make the discussion. I thought I made the discussion of EMR vs EHR much simpler.

IMS IPO and Health Data Privacy

Posted on January 7, 2014 I Written By

The following is a guest post by Dr. Deborah Peel, Founder of Patient Privacy Rights. There is no bigger advocate of patient privacy in the world than Dr. Peel. I’ll be interested to hear people comments and reactions to Dr. Peel’s guest post below. I look forward to an engaging conversation on the subject.

Clearly the way to understand the massive hidden flows of health data are in SEC filings.

For years, people working in the healthcare and HIT industries and government have claimed PPR was “fear-mongering”, even while they ignored/denied the evidence I presented in hundreds of talks about dozens of companies that sell health data (see slides up on our website)

But IMS SEC filings are formal, legal documents and IMS states that it buys “proprietary data sourced from over 100,000 data suppliers covering over 780,000 data feeds globally”. It buys and aggregates sensitive “prescription” records, “electronic medical records”, “claims data”, and more to create “comprehensive”, “longitudinal” health records on “400 million” patients.

* All purchases and subsequent sales of personal health records are hidden from patients. Patients are not asked for informed consent or given meaningful notice.
* IMS Health Holdings sells health data to “5,000 clients”, including the US Government.

These statements show the GREAT need for a comprehensive health data map—–and that it will include potentially a billion places that Americans’ sensitive health data flows.

In what universe is our health data “private and secure”?

Physician Focus, Data as King, and Real Time EHR Data

Posted on December 1, 2013 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’m a little torn on this tweet. While I agree that there is too much administrative overhead in healthcare that distracts from patients and lifelong learning, I also think that things like EMR could contribute to both. A well implemented EMR software can help doctors focus on patients and help the doctor learn. This is certainly not the way most doctors look at EMR. Is this an EMR image problem or EMR software that’s not living up to its potential?


Of course, you have to take this tweet with a grain of salt since it comes from our very own Big Data Geek, Mandi Bishop. However, it’s an interesting topic of discussion. How important is the EMR data in healthcare today?


This tweet is related to the healthcare data tweet above. We all know that the EHR data isn’t perfect. Although, it’s worth noting that the paper chart wasn’t perfect either. However, I was more interested in the idea of real-time EHR data. I don’t think we’re there yet, but I’m interested to see how we could get there.

Visualization of Healthcare Data, DocGraph, and Open Source — #HITsm Chat Highlights

Posted on February 9, 2013 I Written By

Katie Clark is originally from Colorado and currently lives in Utah with her husband and son. She writes primarily for Smart Phone Health Care, but contributes to several Health Care Scene blogs, including EMR Thoughts, EMR and EHR, and EMR and HIPAA. She enjoys learning about Health IT and mHealth, and finding ways to improve her own health along the way.

Topic One: How can we leverage referral and collaboration information in #HealthIT software? What is DocGraph good for?

Topic Two: Generally, what are the best examples of data visualization of healthcare data that you have seen or heard of?

Topic Three: What other open doctor data should we merge with DocGraph? #HealthIT

Topic Four: What open data or open source software do you use regularly as a #HIT professional? #HealthIT

Topic Five: What open data or open source software do you wish existed? #HealthIT

Is Healthcare Big Data Biased?

Posted on November 30, 2012 I Written By

Mandi Bishop is a healthcare IT consultant and a hardcore data geek with a Master's in English and a passion for big data analytics, who fell in love with her PCjr at 9 when she learned to program in BASIC. Individual accountability zealot, patient engagement advocate, innovation lover and ceaseless dreamer. Relentless in pursuit of answers to the question: "How do we GET there from here?" More byte-sized commentary on Twitter: @MandiBPro.

Have you ever wondered whether YOUR healthcare data is included in the “big data” everyone’s talking about? After all, healthcare big data analytics are going to change the world; shouldn’t those changes be representative of the population they will impact?

To answer that question, we have to identify the sources of the healthcare big data being used to effect change, and consider the likelihood that your data may have been captured and consumed by one of the reporting organizations. So let’s start with the “capture” part of that equation.

Have you received some type of healthcare service this year? That includes, but is not limited to: hospital visit, physical therapy, doctor visit, chiropractor visit, urgent care visit, e-visit or phone consultation, health risk assessment or health fair.

Have you purchased or requested any regulated healthcare product this year, such as prescription drugs?

Do you have private health insurance?

Are you enrolled in Medicare or Medicaid?

If yes to any of the above, and the last question, in particular, YES, your data is included in the “big data” analytics currently shaping policy. It is likely that each billable product and service is attached to your Electronic Health Record, available for review and reporting by each involved party from your PCP (Primary Care Provider) to your friendly insurance call center agent. Your individual collection of data points are aggregated into a larger population, and sliced and diced to provide insights into groundbreaking research efforts. Congratulations! But does that inclusion mean that the conclusions driven by healthcare big data are representative?

By nature, the relevance of data-driven insights increases in proportion to the size of the population – and data points – included. But what if the outliers for the general population are the norm for your data set? Are your conclusions skewed?

What if you represent a population segment that is recognized as underserved? Consider the following, from the first Health Disparities and Inequalities Report, prepared in 2011 by the CDC (Centers for Disease Control): “Increasingly, the research, policy, and public health practice literature report substantial disparities in life expectancy, morbidity, risk factors, and quality of life, as well as persistence of these disparities among segments of the population…defined by race/ethnicity, sex, education, income, geographic location, and disability status.”

If your access to healthcare is limited by any of the factors indicated above, your data may not be captured unless/until there is an acute episode which requires medical intervention. In the report, the CDC acknowledges the challenge of capturing national data to support health initiatives for these populations; it is widely accepted as a barrier to healthcare equality that must be overcome.

What if you’re healthy? I’ll use myself as an example. I don’t go to the doctor unless it’s urgent, and I haven’t visited my PCP in over a year. I’ve injured my shoulder and my back over the past year, both of which required MRI and CAT scans to diagnose severity; however, I do not follow any medically supervised treatment plan for rehabilitation. I don’t take any routine prescription medication. I’m an exercise enthusiast who works out intensely 5-6 days/week, and I sleep 8-9 hours a night. Yes, I do sleep that much. And no, me putting all this information into a blog does not constitute the data being captured for use in healthcare big data analytics. Because I haven’t needed to go to my PCP lately, don’t take routine prescription medication, and am not of age for Medicare or income level for Medicaid, the only current healthcare data available for analysis for me is orthopedic in nature and revolves around imaging data, not traditional clinical measures. Someone like me who had NOT experienced an acute care episode would have no current data available for consumption and reporting as part of a larger population.

Could it be that much, if not most, healthcare big data cited for research purposes is comprised primarily of a triangle of outlier population segments: 1) oldest, 2) poorest, and 3) sickest?

Perhaps. So, when reading on the advances in healthcare big data analytics, ask yourself whether that “big data” means “YOUR data”.

PS – For those of you curious about defining “big data” in healthcare, read Dr. Graham Hughes blog post for SAS, “How Big Is Big Data In Healthcare?”, detailing the nuances of the term as it relates to data size, complexity, and usage. Also, I’d like to thank the good folks at Vanderbilt University for compiling a fairly comprehensive list of healthcare data resources; it has been highly educational. Finally, if you’d like to read the complete CDC report, you can find it here.