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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?

Scenarios for Health Care Reform (Part 2 of 2)

Posted on May 18, 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.

The first part of this article suggested two scenarios that could promote health care reform. We’ll finish off the scenarios in this part of the article.

Capitalism Disrupts Health Care

In the third scenario, reform is stimulated by an intrepid data science firm that takes on health care with greater success than most of its predecessors. After assembling an impressive analytics toolkit from open source software components–thus simplifying licensing–it approaches health care providers and offers them a deal they can’t refuse: analytics demonstrated to save them money and support their growth, all delivered for free. The data science firm asks in return only that they let it use deidentified data from their patients and practices to build an enhanced service that it will offer paying customers.

Some health care providers balk at the requirement to share data, but their legal and marketing teams explain that they have been doing it for years already with companies whose motives are less commendable. Increasingly, the providers are won over. The analytics service appeals particularly to small, rural, and safety-net providers. Hammered by payment cuts and growing needs among their populations, they are on the edge of going out of business and grasp the service as their last chance to stay in the black.

Participating in the program requires the extraction of data from electronic health records, and some EHR vendors try to stand in the way in order to protect their own monopoly on the data. Some even point to clauses in their licenses that prohibit the sharing. But they get a rude message in return: so valuable are the analytics that the providers are ready to jettison the vendors in a minute. The vendors ultimately go along and even compete on the basis of their ability to connect to the analytics.

Once stability and survival are established, the providers can use the analytics for more and more sophisticated benefits. Unlike the inadequate quality measures currently in use, the analytics provide a robust framework for assessing risk, stratifying populations, and determining how much a provider should be rewarded for treating each patient. Fee-for-outcome becomes standard.

Providers make deals to sign up patients for long-term relationships. Unlike the weak Medicare ACO model, which punishes a provider for things their patients do outside their relationship, the emerging system requires a commitment from the patient to stick with a provider. However, if the patient can demonstrate that she was neglected or failed to receive standard of care, she can switch to another provider and even require the misbehaving provider to cover costs. To hold up their end of this deal, providers find it necessary to reveal their practices and prices. Physician organizations develop quality-measurement platforms such as the recent PRIME registry in family medicine. A race to the top ensues.

What If Nothing Changes?

I’ll finish this upbeat article with a fourth scenario in which we muddle along as we have for years.

The ONC and Centers for Medicare & Medicaid Services continue to swat at waste in the health care system by pushing accountable care. But their ratings penalize safety-net providers, and payments fail to correlate with costs as hoped.

Fee-for-outcome flounders, so health care costs continue to rise to intolerable levels. Already, in Massachusetts, the US state that leads in universal health coverage, 40% of the state budget goes to Medicaid, where likely federal cuts will make it impossible to keep up coverage. Many other states and countries are witnessing the same pattern of rising costs.

The same pressures ride like a tidal wave through the rest of the health care system. Private insurers continue to withdraw from markets or lose money by staying. So either explicitly or through complex and inscrutable regulatory changes, the government allows insurers to cut sick people from their rolls and raise the cost burdens on patients and their employers. As patient rolls shrink, more hospitals close. Political rancor grows as the public watches employer money go into their health insurance instead of wages, and more of their own stagnant incomes go to health care costs, and government budgets tied up in health care instead of education and other social benefits.

Chronic diseases creep through the population, mocking crippled efforts at public health. Rampant obesity among children leads to more and earlier diabetes. Dementia also rises as the population ages, and climate change scatters its effects across all demographics.

Furthermore, when patients realize the costs they must take on to ask for health care, they delay doctor visits until their symptoms are unbearable. More people become disabled or perish, with negative impacts that spread through the economy. Output decline and more families become trapped in poverty. Self-medication for pain and mental illness becomes more popular, with predictable impacts on the opiate addiction crisis. Even our security is affected: the military finds it hard to recruit find healthy soldiers, and our foreign policy depends increasingly on drone strikes that kill civilians and inflame negative attitudes toward the US.

I think that, after considering this scenario, most of us would prefer one of the previous three I laid out in this article. If health care continues to be a major political issue for the next election, experts should try to direct discussion away from the current unproductive rhetoric toward advocacy for solutions. Some who read this article will hopefully feel impelled to apply themselves to one of the positive scenarios and bring it to fruition.

Scenarios for Health Care Reform (Part 1 of 2)

Posted on May 16, 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.

All reformers in health care know what the field needs to do; I laid out four years ago the consensus about patient-supplied data, widespread analytics, mHealth, and transparency. Our frustration comes in when trying to crack the current hide-bound system open and create change. Recent interventions by US Republicans to repeal the Affordable Care Act, whatever their effects on costs and insurance coverage, offer no promise to affect workflows or treatment. So this article suggests three potential scenarios where reform could succeed, along with a vision of what will happen if none of them take hold.

Patients Forge Their Own Way Forward

In the first scenario, a tiny group of selfer-trackers, athletes, and empowered patients start a movement that ultimately wins over hundreds of millions of individuals.

These scattered enthusiasts, driven to overcome debilitating health problems or achieve extraordinary athletic feats, start to pursue self-tracking with fanaticism. Consumer or medical-grade devices provide them with ongoing data about their progress, and an open source platform such as HIE of One gives them a personal health record (PHR).

They also take charge of their interactions with the health care system. They find that most primary care providers aren’t interested in the data and concerns they bring, or don’t have time to process those data and concerns in the depth they need, or don’t know how to. Therefore, while preserving standard relationships with primary care providers and specialists where appropriate, the self-trackers seek out doctors and other providers to provide consultation about their personal health programs. A small number of providers recognize an opportunity here and set up practices around these consultations. The interactions look quite different from standard doctor visits. The customers, instead of just submitting themselves to examination and gathering advice, steer the conversation and set the goals.

Power relationships between doctors and customers also start to change. Although traditional patients can (and often do) walk away and effectively boycott a practice with which they’re not comfortable, the new customers use this power to set the agenda and to sort out the health care providers they find beneficial.

The turning point probably comes when someone–probabaly a research facility, because it puts customer needs above business models–invents a cheap, comfortable, and easy-to-use device that meets the basic needs for monitoring and transmitting vital signs. It may rest on the waist or some other place where it can be hidden, so that there is no stigma to wearing it constantly and no reason to reject its use on fashion grounds. A beneficent foundation invests several million dollars to make the device available to schoolchildren or some other needy population, and suddenly the community of empowered patients leaps from a miniscule pool to a mainstream phenomenon.

Researchers join the community in search of subjects for their experiments, and patients offer data to the researchers in the hope of speeding up cures. At all times, the data is under control of the subjects, who help to direct research based on their needs. Analytics start to turn up findings that inform clinical decision support.

I haven’t mentioned the collection of genetic information so far, because it requires more expensive processes, presents numerous privacy risks, and isn’t usually useful–normally it tells you that you have something like a 2% risk of getting a disease instead of the general population’s 1% risk. But where genetic testing is useful, it can definitely fit into this system.

Ultimately, the market for consultants that started out tiny becomes the dominant model for delivering health care. Specialists and hospitals are brought in only when their specific contributions are needed. The savings that result bring down insurance costs for everyone. And chronic disease goes way down as people get quick feedback on their lifestyle choices.

Government Puts Its Foot Down

After a decade of cajoling health care providers to share data and adopt a fee-for-outcome model, only to witness progress at a snail’s pace, the federal government decides to try a totally different tack in this second scenario. As part of the Precision Medicine initiative (which originally planned to sign up one million volunteers), and leveraging the ever-growing database of Medicare data, the Office of the National Coordinator sets up a consortium and runs analytics on top of its data to be shared with all legitimate researchers. The government also promises to share the benefits of the analytics with anyone in the world who adds their data to the database.

The goals of the analytics are multi-faceted, combining fraud checks, a search for cures, and everyday recommendations about improving interventions to save money and treat patients earlier in the disease cycle. The notorious 17-year gap between research findings and widespread implementation shrinks radically. Now, best practices are available to any patient who chooses to participate.

As with the personal health records in the previous scenario, the government database in this scenario creates a research platform of unprecedented size, both in the number of records and the variety of participating researchers.

To further expand the power of the analytics, the government demands exponentially greater transparency not just in medical settings but in all things that make us sick: the food we eat (reversing the rulings that protect manufacturers and restaurants from revealing what they’re putting in our bodies), the air and water that surrounds us, the effects of climate change (a major public health issue, spreading scourges such as mosquito-borne diseases and heat exhaustion), disparities in food and exercise options among neighborhoods, and more. Public awareness leads to improvements in health that lagged for decades.

In the next section of this article, I’ll present a third scenario that achieves reform from a different angle.

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.

Accelerating Decision-Making in Healthcare: How Health Systems Choose Innovative Solutions – #HITsm Chat Topic

Posted on May 9, 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, 5/12 at Noon ET (9 AM PT). This week’s chat will be hosted by Bruce Brandes from Lucro Solutions on the topic of “Accelerating Decision-Making in Healthcare: How Health Systems Choose Innovative Solutions”.

To be competitive in today’s digital healthcare economy, providers, hospitals and health plans need to leverage existing and emerging products offered by a growing base of vendors. But the healthcare market is saturated with products and services with new vendors entering the market seemingly every day. The people involved with identifying and selecting appropriate products and services become overwhelmed by all that’s available. Cutting through the noise and separating the wheat from the chaff often results in scars when vendors oversell their offerings and ultimately under-deliver on their promises.

To be able to execute faster on innovation in healthcare, those charged with identifying, selecting and implementing healthcare products and services must filter the universe of shiny things and make better decisions faster. Mistakes can be costly. In the instance of certain products or services, mistakes could be fatal; literally.

In this tweetchat, we’ll explore topics related to the discovery, organization, collaboration and prioritization of healthcare products and services offered by vendors based on defined strategic initiatives and the healthcare organizations that purchase, implement and use them.

Join us on Friday May 12th at 12:00pm ET as we discuss the following questions on #HITsm:

The Questions
T1: How do you define and align organizational priorities for which you seek #HealthIT solutions? #HITsm

T2: How can you filter the “noise” to discover and evaluate #healthIT vendors relevant to your priorities? #HITsm

T3: What’s more efficient way to collaborate w/ trusted colleagues to support your decisions regarding #HealthIT products/services? #HITsm

T4: How do you leverage the scale and experience of your organization to drive standardization and “de-risk” decisions? #HITsm

T5: What type of vendor content, materials & actions help you reach a decision in the most expedient manner? #HITsm

Bonus: What are the advantages and disadvantages of your RFI / RFP process? #HITsm

Upcoming #HITsm Chat Schedule
5/19 – Patient Education Using Healthcare Social Media
Hosted by Anne Zieger (@annezieger)

5/26 – TBD
Hosted by Chad Johnson (@OchoTex)

We look forward to learning from the #HITsm community! As always let us know if you have ideas for how to make #HITsm better.

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

Precision Health 101: Understanding the Keys to Value – #HITsm Chat Topic

Posted on May 2, 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, 5/5 at Noon ET (9 AM PT). This week’s chat will be hosted by Bob Rogers (@ScientistBob) from @IntelHealth on the topic of “Precision Health 101: Understanding the Keys to Value”.

Precision health starts with personalized diagnosis and precision treatment planning. That’s why leading edge health systems are making health more precise at the individual patient level. This approach is a dramatic shift from the “one-size-fits-all” model of treatment and has shifted the conversation to the uniqueness of each person, down to “DNA fingerprint” that is fueling an individual’s disease.

In this Twitter chat, join the discussion with Bob Rogers, chief data scientist at Intel Corporation, about the definition of precision health, where it’s being utilized today, and what healthcare CIOs should be thinking about now to make personalized care a reality.

Join us on Friday May 5th at 12:00pm ET as we discuss the following questions on #HITsm:

The Questions
T1: What is your definition of precision health? #HITsm

T2: Where are you seeing precision health thriving? #HITsm

T3: What’s holding back our efforts in precision health? What changes need to be made? #HITsm

T4: What should an organization be doing to prepare for and participate in precision health?  #HITsm

T5: What benefits would a patient see from precision health? #HITsm

Bonus: How will data and analytics impact precision health? #HITsm

Upcoming #HITsm Chat Schedule
5/12 – Accelerating Decision-Making in Healthcare: How Health Systems Choose Innovative Decisions
Hosted by Bruce Brandes from Lucro Solutions

5/19 – Patient Education Using Healthcare Social Media
Hosted by Anne Zieger (@annezieger)

5/26 – TBD
Hosted by Chad Johnson (@OchoTex)

We look forward to learning from the #HITsm community! As always let us know if you have ideas for how to make #HITsm better.

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

Will Data Aggregation For Precision Medicine Compromise Patient Privacy?

Posted on April 10, 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.

Like anyone else who follows medical research, I’m fascinated by the progress of precision medicine initiatives. I often find myself explaining to relatives that in the (perhaps far distant) future, their doctor may be able to offer treatments customized specifically for them. The prospect is awe-inspiring even for me, someone who’s been researching and writing about health data for decades.

That being the case, there are problems in bringing so much personal information together into a giant database, suggests Jennifer Kulynych in an article for OUPblog, which is published by Oxford University Press. In particular, bringing together a massive trove of individual medical histories and genomes may have serious privacy implications, she says.

In arguing her point, she makes a sobering observation that rings true for me:

“A growing number of experts, particularly re-identification scientists, believe it simply isn’t possible to de-identify the genomic data and medical information needed for precision medicine. To be useful, such information can’t be modified or stripped of identifiers to the point where there’s no real risk that the data could be linked back to a patient.”

As she points out, norms in the research community make it even more likely that patients could be individually identified. For example, while a doctor might need your permission to test your blood for care, in some states it’s quite legal for a researcher to take possession of blood not needed for that care, she says. Those researchers can then sequence your genome and place that data in a research database, and the patient may never have consented to this, or even know that it happened.

And there are other, perhaps even more troubling ways in which existing laws fail to protect the privacy of patients in researchers’ data stores. For example, current research and medical regs let review boards waive patient consent or even allow researchers to call DNA sequences “de-identified” data. This flies in the face of conventional wisdom that there’s no re-identification risk, she writes.

On top of all of this, the technology already exists to leverage this information for personal identification. For example, genome sequences can potentially be re-identified through comparison to a database of identified genomes. Law enforcement organizations have already used such data to predict key aspects of an individual’s face (such as eye color and race) from genomic data.

Then there’s the issue of what happens with EMR data storage. As the author notes, healthcare organizations are increasingly adding genomic data to their stores, and sharing it widely with individuals on their network. While such practices are largely confined to academic research institutions today, this type of data use is growing, and could also expose patients to involuntary identification.

Not everyone is as concerned as Kulynych about these issues. For example, a group of researchers recently concluded that a single patient anonymization algorithm could offer a “standard” level of privacy protection to patient, even when the organizations involved are sharing clinical data. They argue that larger clinical datasets that use this approach could protect patient privacy without generalizing or suppressing data in a manner that would undermine its usefulness.

But if nothing else, it’s hard to argue Kulynych’s central concern, that too few rules have been updated to reflect the realities of big genomic and medical data stories. Clearly, state and federal rules  need to address the emerging problems associated with big data and privacy. Otherwise, by the time a major privacy breach occurs, neither patients nor researchers will have any recourse.

#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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How Do We Include Every Generation in our HIT? – #HITsm Chat Topic

Posted on March 14, 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, 3/17 at Noon ET (9 AM PT). This week’s chat will be hosted by Erica Johansen with Splash Media (@thegr8chalupa and @SplashMedia). We’ll be discussing the topic “How Do We Include Every Generation in our HIT?”

Commentary on generational nuances have made their way into previous #HITsm chats and those comments usually sparked quite a discussion. So, we couldn’t think of a better way to give the people what they want!

This week, our chat will spark conversations on how generational perspectives are influencing healthcare technology, and additionally, how can we (as health IT leaders) can strive to incorporate and include diverse generational needs into the industry roadmap.

Be sure to join the #HITsm chat this Friday, March 17th, 2017 at 12:00pm ET.

The Topics
T1: What generation do you identify with & are there any stereotypes that you feel are inaccurate? #HITsm

T2: What #healthIT initiatives are setting up future generations for success? What initiatives are setting up for failure? #HITsm

T3: Is there a health concern might you share with someone in another generation? If so, how could #healthIT anticipate that concern? #HITsm

T4: If you could advise #healthIT leadership on behalf of your generation, what would you tell them? #HITsm

T5: In what ways can #healthIT cultivate meaningful engagement from every generation? #HITsm

Bonus: What healthcare technology that exists today holds untapped potential? #HITsm

Upcoming #HITsm Chat Schedule
3/24 – How Technology Helps and Hurts Healthy Behavior Change
Hosted by @MelissaxxMcCool

3/31 – AI and Cognitive Science
Hosted by @HBI_Solutions

4/7 – TBD

4/14 – TBD

4/21 – Innovation vs Incremental
Hosted by @Colin_Hung

We look forward to learning from the #HITsm community! As always let us know if you have ideas for how to make #HITsm better.

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

AMIA Asks NIH To Push For Research Data Sharing

Posted on January 23, 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.

The American Medical Informatics Association has is urging leaders at the NIH to take researchers’ data sharing plans into account when considering grant proposals.

AMIA is responding to an NIH Request for Information (topic: “Strategies for NIH Data Management, Sharing and Citation”) was published in November 2016. In the RFI, it asked for feedback on how digital scientific data generated by NIH-funded research should be managed and disclosed to the public. It also asked for input on how to set standards for citing shared data and software.

In its response, AMIA said that the agency should give researchers “institutional incentives” designed to boost data sharing and strengthen data management. Specifically, the trade group suggested that NIH make data sharing plans a “scoreable” part of grant applications.

“Data sharing has become such an important proximal output of research that we believe the relative value of a proposed project should include consideration of how its data will be shared,” AMIA said in its NIH response. “By using the peer-review process, we will make incremental improvements to interoperability, while identifying approaches to better data sharing practices over time.”

To help the agency implement this change, AMIA recommended that applicants earmark funds for data curation and sharing as part of the grants’ direct costs. Doing so will help assure that data sharing becomes part of research ecosystems.

AMIA also recommends that NIH offer rewards to scholars who either create or contribute to publicly-available datasets and software. The trade group argues that such incentives would help those who create and analyze data advance their careers. (And this, your editor notes, would help foster a virtuous cycle in which data-oriented scientists are available to foster such efforts.)

Right now, to my knowledge, few big data integration projects include the kind of front-line research data we’re talking about here.  On the other hand, while few community hospitals are likely to benefit from research data in the near term, academic medical organizations are having a bit more luck, and offer us an attractive picture of how things could be.

For example, look at this project at Vanderbilt University Medical Center which collects and manages translational and clinical research data via an interface with its EMR system.

At Vanderbilt, research data collection is integrated with clinical EMR use. Doctors there use a module within the research platform (known as REDCap) to collect data for prospective clinical studies. Once they get their research project approved, clinicians use menus to map health record data fields to REDCap. Then, REDCap automatically retrieves health record data for selected patients.

My feeling is that if NIH starts pushing grantees to share data effectively, we’ll see more projects like REDCap, and in turn, better clinical care supported by such research. It looks to me like everybody wins here. So I hope the NIH takes AMIA’s proposal seriously.