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Healthcare Robots! – #HITsm Chat Topic

Posted on January 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.

We’re excited to share the topic and questions for this week’s #HITsm chat happening Friday, 2/3 at Noon ET (9 AM PT). This week’s chat will be hosted by Mr RIMP (@MrRimp, Robot-In-My-Pocket), mascot of the first ever #HIMSS17 Innovation Makerspace! (Booth 7785) (with assistance from @wareflo) We’ll be discussing the topic “Healthcare Robots!” and so it seems appropriate to have a robot hosting the chat.

In a first, #HIMSS17 has a #makerspace (Booth 7785), in the HIMSS17 Innovation Zone. It has robots! They are rudimentary, but educational and fun. One of those robots is @MrRIMP, for Robot-In-My-Pocket. Here is an YouTube interview with @MrRIMP. As you can tell, little Mr. R. has a bit of an attitude. He also wrote the questions below and will moderate tweets about them during the #HITsm tweetchat.

From the recent “How medical robots will change healthcare” (@PeterBNichol), there are three main areas of robotic health:

1. Direct patient care robots: surgical robots (used for performing clinical procedures), exoskeletons (for bionic extensions of self like the Ekso suit), and prosthetics (replacing lost limbs).  Over 500 people a day loses a limb in America with 2 million Americans living with limb loss according to the CDC.

2. Indirect patient care robots: pharmacy robots (streamlining automation, autonomous robots for inventory control reducing labor costs), delivery robots (providing medical goods throughout a hospital autonomously), and disinfection robots (interacting with people with known infectious diseases such as healthcare-associated infections or HAIs).

3. Home healthcare robots: robotic telepresence solutions (addressing the aging population with robotic assistance).

Before the #HITsm tweetchat I hope you’ll watch Robot & Frank, about a household robot and an increasingly infirm retiree (86% on Rotten Tomatoes, available on YouTube, Amazon, Itunes, Vudu, and Google for $2.99) I’ll also note a subcategory to the direct care robots: pediatric therapy robots. Consider, for example, New Friends 2016, The Second International Conference on Social Robots in Therapy and Education. I, Mr. RIMP, have a special interest in this area.

Join us as we discuss Healthcare Robots during the February 3rd #HITsm chat. Here are the questions we’ll discuss:

T1: What is your favorite robot movie? Why? How many years in the future would you guess it will take to achieve similar robots? #HITsm

T2: Robots promise to replace a lot of human labor. Cost-wise, humanity-wise, will this be more good than bad, or more bad than good? #HITsm

T3: Have you played with, or observed any “toy” robots. Impressed? Not impressed? Why? #HITsm

T4: IMO, “someday” normal, everyday people will be able design and program their own robots. What kind of robot would you design for healthcare? #HITsm

T5: Robots and workflow? Connections? Think about healthcare robots working *together* with healthcare workers. What are potential implications? #HITsm

Bonus: Isn’t @MrRIMP (Robot-In-My-Pocket) the cutest, funniest, little, robot you’ve ever seen? Any suggestions for the next version (V.4) of me? #HITsm

Here’s a look at the upcoming #HITsm chat schedule:
2/10 – Maximizing Your HIMSS17 Experience – Whether Attending Physically or Virtually
Hosted by Steve Sisko (@HITConfGuy and @shimcode)

2/17 – Enough talk, lets #GSD (Get Stuff Done)
Hosted by Burt Rosen (@burtrosen) from @healthsparq

2/24 – HIMSSanity Recovery Chat
With #HIMSS17 happening the week of this chat, we’ll take the week off from a formal chat. However, we encourage people that attended HIMSS or watched HIMSS remotely to share a “Tweetstorm” that tells a #HIMSS17 story, shares insights about a topic, rants on a topic of interest, or shows gratitude. Plus, it will be fun to test out a new form of tweetstorm Twitter chat. We’ll post more details as we get closer.

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.

Health IT Leaders Struggle With Mobile Device Management, Security

Posted on January 30, 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.

A new survey on healthcare mobility has concluded that IT leaders aren’t thrilled with their security arrangements, and that a significant minority don’t trust their mobile device management solution either. The study, sponsored by Apple device management vendor Jamf, reached out to 550 healthcare IT leaders in the US, UK, France, Germany and Australia working in organizations of all sizes.

Researchers found that 83% or organizations offer smartphones or tablets to their providers, and that 32% of survey respondents hope to offer mobile devices to consumers getting outpatient care over the next two years.  That being said, they also had significant concerns about their ability to manage these devices, including questions about security (83%), data privacy (77%) and inappropriate employee use (49%).

The survey also dug up some tensions between their goals and their capacity to support those goals. Forty percent of respondents said staff access to confidential medical records while on the move was their key reason for their mobile device strategy. On the other hand, while 84% said that their organization was HIPAA-compliant, almost half of respondents said that they didn’t feel confident in their ability to adapt quickly to changing regulations.

To address their concerns about mobile deployments, many providers are leveraging mobile device management platforms.  Of those organizations that either have or plan to put an MDM solution in place, 80% said time savings was the key reason and 79% said enhanced employee productivity were the main benefits they hoped to realize.

Those who had rolled out an MDM solution said the benefits have included easier access to patient data (63%), faster patient turnaround (51%) and enhanced medical record security (48%). At the same time, 27% of respondents whose organizations had an MDM strategy in place said they didn’t feel especially confident about the capabilities of their solution.

In any event, it’s likely that MDM can’t solve some of the toughest mobile deployment problems faced by healthcare organizations anyway.

Health organizations that hope to leverage independently-developed apps will need to vet them carefully, as roughly one-quarter of these developers didn’t have privacy policies in place as of late last year. And the job of selecting the right apps is a gargantuan one. With the volume of health apps hitting almost 260,000 across the Google and Apple app marketplaces, it’s hard to imagine how any provider could keep up.

So yes, the more capabilities MDM systems can offer, the better. But choosing the right apps with the right pedigree strikes me as posing an even bigger challenge.

Exchange Value: A Review of Our Bodies, Our Data by Adam Tanner (Part 3 of 3)

Posted on January 27, 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 previous part of this article raised the question of whether data brokering in health care is responsible for raising or lower costs. My argument that it increases costs looks at three common targets for marketing:

  • Patients, who are targeted by clinicians for treatments they may not need or have thought of

  • Doctors, who are directed by pharma companies toward expensive drugs that might not pay off in effectiveness

  • Payers, who pay more for diagnoses and procedures because analytics help doctors maximize charges

Tanner flags the pharma industry for selling drugs that perform no better than cheaper alternatives (Chapter 13, page 146), and even drugs that are barely effective at all despite having undergone clinical trials. Anyway, Tanner cites Hong Kong and Europe as places far more protective of personal data than the United States (Chapter 14, page 152), and they don’t suffer higher health care costs–quite the contrary.

Strangely, there is no real evidence so far that data sales have produced either harm to patients or treatment breakthroughs (Conclusion, 163). But the supermarket analogy does open up the possibility that patients could be induced to share anonymized data voluntarily by being reimbursed for it (Chapter 14, page 157). I have heard this idea aired many times, and it fits with the larger movement called Vendor Relationship Management. The problem with such ideas is the close horizon limiting our vision in a fast-moving technological world. People can probably understand and agree to share data for particular research projects, with or without financial reimbursement. But many researchers keep data for decades and recombine it with other data sets for unanticipated projects. If patients are to sign open-ended, long-term agreements, how can they judge the potential benefits and potential risks of releasing their data?

Data for sale, but not for treatment

In Chapter 11, Tanner takes up the perennial question of patient activists: why can drug companies get detailed reports on patient conditions and medications, but my specialist has to repeat a test on me because she can’t get my records from the doctor who referred me to her? Tanner mercifully shields here from the technical arguments behind this question–sparing us, for instance, a detailed discussion of vagaries in HL7 specifications or workflow issues in the use of Health Information Exchanges–but strongly suggests that the problem lies with the motivations of health care providers, not with technical interoperability.

And this makes sense. Doctors do not have to engage in explicit “blocking” (a slippery term) to keep data away from fellow practitioners. For a long time they were used to just saying “no” to requests for data, even after that was made illegal by HIPAA. But their obstruction is facilitated by vendors equally uninterested in data exchange. Here Tanner discards his usual pugilistic journalism and gives Judy Faulkner an easy time of it (perhaps because she was a rare CEO polite enough to talk to him, and also because she expressed an ethical aversion to sharing patient data) and doesn’t air such facts as the incompatibilities between different Epic installations, Epic’s tendency to exchange records only with other Epic installations, and the difficulties it introduces toward companies that want to interconnect.

Tanner does not address a revolution in data storage that many patient advocates have called for, which would at one stroke address both the Chapter 11 problem of patient access to data and the book’s larger critique of data selling: storing the data at a site controlled by the patient. If the patient determined who got access to data, she would simply open it to each new specialist or team she encounters. She could also grant access to researchers and even, if she chooses, to marketers.

What we can learn from Chapter 9 (although Tanner does not tell us this) is that health care organizations are poorly prepared to protect data. In this woeful weakness they are just like TJX (owner of the T.J. Maxx stores), major financial institutions, and the Democratic National Committee. All of these leading institutions have suffered breaches enabled by weak computer security. Patients and doctors may feel reluctant to put data online in the current environment of vulnerability, but there is nothing special about the health care field that makes it more vulnerable than other institutions. Here again, storing the data with the individual patient may break it into smaller components and therefore make it harder for attackers to find.

Patient health records present new challenges, but the technology is in place and the industry can develop consent mechanisms to smooth out the processes for data exchange. Furthermore, some data will still remain with the labs and pharmacies that have to collect it for financial reasons, and the Supreme Court has given them the right to market that data.

So we are left with ambiguities throughout the area of health data collection. There are few clear paths forward and many trade-offs to make. In this I agree ultimately with Tanner. He said that his book was meant to open a discussion. Among many of us, the discussion has already started, and Tanner provides valuable input.

Exchange Value: A Review of Our Bodies, Our Data by Adam Tanner (Part 2 of 3)

Posted on January 26, 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 previous part of this article summarized the evolution of data brokering in patient information and how it was justified ethically and legally, partly because most data is de-identified. Now we’ll take a look at just what that means.

The identified patient

Although doctors can be individually and precisely identified when they prescribe medicines, patient data is supposedly de-identified so that none of us can be stigmatized when trying to buy insurance, rent an apartment, or apply for a job. The effectiveness of anonymization or de-identification is one of the most hotly debated topics in health IT, and in the computer field more generally.

I have found a disturbing split between experts on this subject. Computer science experts don’t just criticize de-identification, but speak of it as something of a joke, assuming that it can easily be overcome by those with a will to do so. But those who know de-identification best (such as the authors of a book I edited, Anonymizing Health Data) point out that intelligent, well-designed de-identification databases have been resistant to cracking, and that the highly publicized successes in re-identification have used databases that were de-identified unprofessionally and poorly. That said, many entities (including the South Korean institutions whose practices are described in Chapter 10, page 110 of Tanner’s book) don’t call on the relatively rare experts in de-identification to do things right, and therefore fall into the category of unprofessional and poor de-identification.

Tanner accurately pinpoints specific vulnerabilities in patient data, such as the inclusion of genetic information (Chapter 9, page 96). A couple of companies promise de-identified genetic data (Chapter 12, page 130, and Conclusion, page 162), which all the experts agree is impossible due to the wide availability of identified genomes out in the field for comparison (Conclusion, page 162).

Tanner has come down on the side of easy re-identification, having done research in many unconventional areas lacking professional de-identification. However, he occasionally misses a nuance, as when describing the re-identification of people in the Personal Genome Project (Chapter 8 page 92). The PGP is a uniquely idealistic initiative. People who join this project relinquish interest in anonymity (Chapter 9, page 96), declaring their willingness to risk identification in pursuit of the greater good of finding new cures.

In the US, no legal requirement for anonymization interferes with selling personal data collected on social media sites, from retailers, from fitness devices, or from genetic testing labs. For most brokers, no ethical barriers to selling data exist either, although Apple HealthKit bars it (Chapter 14 page 155). So more and more data about our health is circulating widely.

With all these data sets floating around–some supposedly anonymized, some tightly tied to your identity–is anonymization dead? Every anonymized data set already contains a few individuals who can be theoretically re-identified; determining this number is part of the technical process of de-identification? Will more and more of us fall into this category as time goes on, victims of advanced data mining and the “mosaic effect” (combining records from different data sets)? This is a distinct possibility for the future, but in the present, there are no examples of re-identifying data that is anonymized properly–the last word properly being all important here. (The authors of Anonymizing Health Data talk of defensible anonymization, meaning you can show you used research-vetted processes.) Even Latanya Sweeney, whom Tanner tries to portray in Chapter 9 as a relentless attacker who strips away the protections of supposedly de-identified data, believes that data can be shared safely and anonymously.

To address people’s fretting over anonymization, I invoke the analogy of encryption. We know that our secret keys can be broken, given enough computing power. Over the decades, as Moore’s Law and the growth of large computing clusters have increased computing power, the recommended size of keys has also grown. But someday, someone will assemble the power (or find a new algorithm) that cracks our keys. We know this, yet we haven’t stopped using encryption. Why give up the benefits of sharing anonymized data, then? What hurts us is the illegal data breaches that happen on average more than once a day, not the hypothetical re-identification of patients.

To me, the more pressing question is what the data is being used for. No technology can be assessed outside of its economic and social context.

Almighty capitalism

One lesson I take from the creation of a patient data market, but which Tanner doesn’t discuss, is its existence as a side effect of high costs and large inefficiencies in health care generally. In countries that put more controls on doctors’ leeway to order drugs, tests, and other treatments, there is less wiggle room for the marketing of unnecessary or ineffective products.

Tanner does touch on the tendency of the data broker market toward monopoly or oligopoly. Once a company such as IMS Health builds up an enormous historical record, competing is hard. Although Tanner does not explore the affect of size on costs, it is reasonable to expect that low competition fosters padding in the prices of data.

Thus, I believe the inflated health care market leaves lots of room for marketing, and generally props up the companies selling data. The use of data for marketing may actually hinder its use for research, because marketers are willing to pay so much more than research facilities (Conclusion, pages 163-164).

Not everybody sells the data they collect. In Chapter 13, Tanner documents a complicated spectrum for anonymized data, ranging from unpublicized sales to requiring patient consent to forgoing all data sales (for instance, footnote 6 to Chapter 13 lists claims by Salesforce.com and Surescripts not to sell patient information). Tenuous as trust in reputation may seem, it does offer some protection to patients. Companies that want to be reputable make sure not to re-identify individual patients (Chapter 7, page 72, Chapter 9, pages 88-90, and Chapter 9, page 99). But data is so valuable that even companies reluctant to enter that market struggle with that decision.

The medical field has also pushed data collectors to make data into a market for all comers. The popular online EHR, Practice Fusion, began with a stable business model offering its service for a monthly fee (Chapter 13, page 140). But it couldn’t persuade doctors to use the service until it moved to an advertising and data-sharing model, giving away the service supposedly for free. The American Medical Association, characteristically, has also found a way to extract profit from sale of patient data, and therefore has colluded in marketing to doctors (Chapter 5, page 41, and Chapter 6, page 54).

Thus, a Medivo executive makes a good argument (Chapter 13, page 147) that the medical field benefits from research without paying for the dissemination of data that makes research possible. Until doctors pony up for this effort, another source of funds has to support the collection and research use of data. And if you believe that valuable research insights come from this data (Chapter 14, page 154, and Conclusion, page 166), you are likely to develop some appreciation for the market they have created. Another obvious option is government support for the collection and provision of data for research, as is done in Britain and some Nordic countries, and to a lesser extent in the US (Chapter 14, pages 158-159).

But another common claim, aired in this book by a Cerner executive (Chapter 13, page 143) is that giving health data to marketers reduces costs across the system, similarly to how supermarkets grant discounts to shoppers willing to have their purchases tracked. I am not convinced that costs are reduced in either case. In the case of supermarkets, their discounts may persuade shoppers to spend more money on expensive items than they would have otherwise. In health care, the data goes to very questionable practices. These become the topic of the last part of this article.

Exchange Value: A Review of Our Bodies, Our Data by Adam Tanner (Part 1 of 3)

Posted on January 25, 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.

A lot of people are feeling that major institutions of our time have been compromised, hijacked, or perverted in some way: journalism, social media, even politics. Readers of Adam Tanner’s new book, Our Bodies, Our Data: How Companies Make Billions Selling Our Medical Records, might well add health care data to that list.

Companies collecting our data–when they are not ruthlessly trying to keep their practices secret–hammer us with claims that this data will improve care and lower costs. Anecdotal evidence suggests it does. But the way this data is used now, it serves the business agendas of drug companies and health care providers who want to sell us treatments we don’t need. When you add up the waste of unnecessary tests and treatments along with the money spent on marketing, as well as the data collection that facilitates that marketing, I’d bet it dwarfs any savings we currently get from data collection.

How we got to our current data collection practices

Tanner provides a bit of history of data brokering in health care, along with some intriguing personalities who pushed the industry forward. At first, there was no economic incentive to collect data–even though visionary clinicians realized it could help find new diagnoses and treatments. Tanner says that the beginnings of data collection came with the miracle drugs developed after World War II. Now that pharmaceutical companies had a compelling story to tell, ground-breaking companies such as IMS Health (still a major player in the industry) started to help them target physicians who had both the means of using their drugs–that is, patients with the target disease–and an openness to persuasion.

Lots of data collection initiatives started with good intentions, some of which paid off. Tanner mentions, as one example, a computer program in the early 1970s that collected pharmacy data in the pursuit of two laudable goals (Chapter 2, page 13): preventing patients from getting multiple prescriptions for the same drug, and preventing adverse interactions between drugs. But the collection of pharmacy data soon found its way to the current dominant use: a way to help drug companies market high-profit medicines to physicians.

The dual role of data collection–improving care but taking advantage of patients, doctors, and payers–persists over the decades. For instance, Tanner mentions a project by IMS Health (which he treats pretty harshly in Chapter 5) collecting personal data from AIDS patients in 1997 (Chapter 7, page 70). Tanner doesn’t follow through to say what IMS did with the AIDS data, but I am guessing that AIDS patients don’t offer juicy marketing opportunities, and that this initiative was aimed at improving the use and effectiveness of treatments for this very needy population. And Chapter 7 ends with a list of true contributions to patient health and safety created by collecting patient data.

Chapter 6 covers the important legal battles fought by several New England states (including the scrappy little outpost known for its worship of independent thinking, New Hampshire) to prevent pharmacies from selling data on what doctors are prescribing. These attempts were quashed by the well-known 2011 Supreme Court ruling on Vermont’s law. All questions of privacy and fairness were submerged by considering the sale of data to be a matter of free speech. As we have seen during several decisions related to campaign financing, the current Supreme Court has a particularly expansive notion of what the First Amendment covers. I just wonder what they will say when someone who breaks into the records of an insurer or hospital and steals several million patient records pleads free speech to override the Computer Fraud and Abuse Act.

Tanner has become intrigued, and even enamored, by the organization Patient Privacy Rights and its founder, Deborah Peel. I am closely associated with this organization and with Peel as well, working on some of their privacy summits and bringing other people into their circle. Because Tanner airs some criticisms of Peel, I’d like to proffer my own observation that she has made exaggerated and unfair criticisms of health IT in the past, but has moderated her views a great deal. Working with experts in health IT sympathetic to patient privacy, she has established Patient Privacy Rights during the 2010 decade as a responsible and respected factor in the health care field. So I counter Tanner’s repeated quotes regarding Peel as “crazy” (Chapter 8, page 83) by hailing her as a reputable and crucial force in modern health IT.

Coincidentally, Tanner refers (Chapter 8, page 79) to a debate that I moderated between IMS representative Kim Gray and Michelle De Mooy (available in a YouTube video). The discussion started off quite tame but turned up valuable insights during the question-and-answer period (starting at 38:33 in the video) about data sharing and the role of de-identification.

While the Supreme Court ruling stripped doctors of control over data about their practices–a bit of poetic irony, perhaps, if you consider their storage of patient data over the decades as an unjust taking–the question of patient rights was treated as irrelevant. The lawyer for the data miners said, “The patients have nothing to do with this” (Chapter 6, page 57) and apparently went unchallenged. How can patients’ interest in their own data be of no concern? For that question we need to look at data anonymization, also known as de-identification. This will begin the next section of our article.

Key Components of #HealthIT Strategy and Disaster Recovery – #HITsm Chat Topic

Posted on January 24, 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, 1/27 at Noon ET (9 AM PT). This week’s chat will be hosted by Bill Esslinger (@billesslinger) from @FogoDataCenters on the topic of “Key Components of Health IT Strategy and Disaster Recovery“.

Medical records are worth more on the Black Market than credit cards. The value is greater because a medical record contains multiple credentials that can be used by hackers more than once or twice. A medical record contains not only a social security number but additional qualifying information, allowing thieves to penetrate layers of data, and conduct multiple acts of fraud before the data is even missing.

As healthcare organizations embark on the improved use of data sets, from analytics to precision medicine and value based care, Cybersecurity rises to the number one concern for CIO’s.

How secure is your cloud based data strategy?

Consideration must be given to the different models of service

With each delivery model: Infrastructure as a Service (IaaS), Platform as a Service (PaaS) and Software as a Service (SaaS), comes a new set of requirements and responsibilities. The key considerations for deployment and ongoing data management include on-demand 24/7 access to critical healthcare information, support for big data and small data sets, traceability, HIPAA compliance and a thorough understanding of the healthcare environment from both a security and a legal perspective.

Join us as we discuss Key Components of #HealthIT Strategy and Disaster Recovery during the January 27th #HITsm chat.

T1: How can we prepare for the unexpected in data security? #HITsm

T2: Are we making Cybersecurity a priority in risk management? #HITsm

T3: Is Your Prevention Strategy Scalable for a Ransomware Attack? #HITsm

T4: What are the top threats regarding healthcare data today? #HITsm

T5: What Service Levels are Necessary for Redundancy in Data, Power, Cooling, and Connectivity? #HITsm

Bonus: Do you worry about the security of your health information? Why or why not? #HITsm

About Fogo Data Centers
Fogo Data Centers are SSAE16, SOCII, and HIPAA compliant as well as PCI compliant. Each site provides redundancies across all support systems. Our centers of excellence provide flexible and scalable solutions to protect your critical data and applications. Colocation at a Fogo Data Centers can ease the cost of building your own facility and maintaining your own on-site dedicated servers. Properties feature full perimeter fencing with an electric gate requiring keycard access and audio/video check-in.

Our hashtag is #KnowYourCloud. We stand ready 24/7, with years of experience, integrity and legal know-how, to protect data and securely manage your cloud strategy. In the event of a disaster or incident the Fogo team can have your facility back-up and running within hours. Call us today or take a look at our facility page to learn more.

Here’s a look at the upcoming #HITsm chat schedule:

2/3 – Healthcare Robots!
Hosted by Mr RIMP (@MrRimp, Robot-In-My-Pocket), mascot of the first ever #HIMSS17 Innovation Makerspace! (Booth 7785) (with assistance from @wareflo)

2/10 – Maximizing Your HIMSS17 Experience – Whether Attending Physically or Virtually
Hosted by Steve Sisko (@HITConfGuy and @shimcode)

2/17 – Enough talk, lets #GSD (Get Stuff Done)
Hosted by Burt Rosen (@burtrosen) from @healthsparq

2/24 – HIMSSanity Recovery Chat
With #HIMSS17 happening the week of this chat, we’ll take the week off from a formal chat. However, we encourage people that attended HIMSS or watched HIMSS remotely to share a “Tweetstorm” that tells a #HIMSS17 story, shares insights about a topic, rants on a topic of interest, or shows gratitude. Plus, it will be fun to test out a new form of tweetstorm Twitter chat. We’ll post more details as we get closer.

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.

E-Patient Update:  You Need Our Help

Posted on January 20, 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.

I just read the results of a survey by Black Book Research suggesting that many typical consumers don’t trust, like or understand health IT.

The survey, which reached out to 12,090 adult consumers in September 2016, found that 57% of those interacting with health IT at hospitals or medical practices were skeptical of its benefit. Worse, 87% said they weren’t willing to share all of their information.

Up to 70% of consumers reported that they distrusted patient portals, medical apps and EMRs. Meanwhile, while many respondents said they were interested in using health trackers, 94% said that their physicians weren’t willing or able to synch wearables data with their EMR.

On the surface, these stats are discouraging. At a minimum, they suggest that getting patients and doctors on the same page about health IT continues to be an uphill battle. But there’s a powerful tactic providers can use which – to my knowledge – hasn’t been tried with consumers.

Introducing the consumer health IT champion

As you probably know, many providers have recruited physician or nurse “champions” to help their peers understand and adjust to EMRs. I’m sure this tactic hasn’t worked perfectly for everyone who’s tried it, but it seems to have an impact. And why not? Most people are far more comfortable learning something new from someone who understands their work and shares their concerns.

The thing is, few if any providers are taking the same approach in rolling out consumer health IT. But they certainly could. I’d bet that there’s at least a few patients in every population who like, use and understand consumer health technologies, as well as having at least a sense of why providers are adopting back-end technology like EMRs. And we know how to get Great-Aunt Mildred to consider wearing a FitBit or entering data into a portal.

So why not make us your health IT champions? After all, if you asked me to, say, hold a patient workshop explaining how I use these tools in my life, and why they matter, I’d jump at the chance. E-patients like myself are by our nature evangelists, and we’re happy to share our excitement if you give us a chance. Maybe you’d need to offer some HIT power users a stipend or a gift card, but I doubt it would take much to get one of us to share our interests.

It’s worth the effort

Of course, most people who read this will probably flinch a bit, as taking this on might seem like a big hassle. But consider the following:

  • Finding such people shouldn’t be too tough. For example, I talk about wearables, mobile health options and connected health often with my PCP, and my enthusiasm for them is a little hard to miss. I doubt I’m alone in this respect.
  • All it would take to get started is to get a few of us on board. Yes, providers may have to market such events to patients, offer them coffee and snacks when they attend, and perhaps spend time evaluating the results on the back end. But we’re not talking major investments here.
  • You can’t afford to have patients fear or reject IT categorically. As value-based care becomes the standard, you’ll need their cooperation to meet your goals, and that will almost certainly include access to patient-generated data from mobile apps and wearables. People like me can address their fears and demonstrate the benefits of these technologies without making them defensive.

I hope hospitals and medical practices take advantage of people like me soon. We’re waiting in the wings, and we truly want to see the public support health IT. Let’s work together!

Healthcare Industry Leads In Blockchain Deployment

Posted on January 19, 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.

A new study by Deloitte concludes that healthcare and life sciences companies stand out as planning the most aggressive blockchain deployments of any industry. That being said, healthcare leaders are far from alone in paying close attention to blockchain, which seems to be coming into its own as corporate technology.

According to Deloitte, 39% of senior executives at large US companies had little or no knowledge of blockchain technology, but the other 61% reported their blockchain knowledge level as broad to expert. The execs who were well-informed about blockchain told Deloitte that it would be crucial for both their company and industry. In fact, 55% of the knowledgeable group said their company would be at a competitive disadvantage if they failed to adopt blockchain, and 42% believed it would disrupt their industry.

Given this level of enthusiasm, it’s not surprising that respondents have begun to invest in blockchain internally. Twenty-eight percent of respondents said their company had invested $5 million of more in blockchain tech to date, and 10% reported investing $10 million or more. Not only that,  25% of respondents expected to invest more than $5 million in blockchain technology this year.

While the level of blockchain interest seems to be pronounced across industries studied by Deloitte, healthcare and life science companies lead the pack when it came to deployment, with 35% of industry respondents saying that their company expects to put blockchain into production during 2017.

All that being said, aggressive deployment may or may not be a good thing just yet. According to research by cloud-based blockchain company Tierion, the majority of blockchain technology isn’t ready for deployment, though worthwhile experiments are underway.

Tierion argues that analysts and professional experts are overselling blockchain, and that most of blockchain technology is experimental and untested. Not only that, its research concludes that at least one healthcare application – giving patients the ability to manage their health data – is rather risky, as blockchain security is shaky.

It seems clear that health IT leaders will continue to explore blockchain options, given its tantalizing potential for sharing data securely and flexibly. And as the flurry of interest around ONC’s blockchain research challenge demonstrates, many industry thought leaders take this technology seriously. If the winning submissions are any indication, blockchain may support new approaches to health data interoperability, claims processing, medical records, physician-patient data sharing, data security, HIEs and even the growth of accountable care.

If nothing else, 2017 should see the development of some new and interesting healthcare blockchain applications, and probably the investment of record new amounts of capital to build them. In other words, whether blockchain is mature enough for real time deployment or not, it’s likely to offer an intriguing show.

Top 3 Tips for Taking on Digital Health

Posted on January 18, 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.

The following is a guest blog post by Brittany Quemby, Marketing Strategist of Stericycle Communication Solutions as part of the Communication Solutions Series. Follow & engage with them on Twitter: @StericycleComms
Brittany Quemby - Stericycle
The other day I deleted several apps from my mobile phone. One I had downloaded when I was traveling, one took up too many gigs on my phone, and the last was one I downloaded to track specific health activities last January probably hoping to achieve one of my many New Year’s resolutions.  This happens to me all the time – I download an app or tool, use it once or twice, realize I don’t have any use for it or haven’t used it in 3 months and end up deleting to free up space on my phone.

This got me thinking about digital technology in the healthcare industry. Unfortunately, every day there is a slew of digital health tools developed that take a lot of time, money and effort and then go unused by the user for a variety of reasons. I picture something like a digital health tool graveyard that exists somewhere in the cloud.

After I got the mental image of a technology version of the Lion King’s Elephant Graveyard out of my head, I began to ask myself why so many digital heath technologies went stale. What needed to change? The time, money, and beautiful design that is put into development won’t draw patients by the masses.  The thing about digital health is that there has to be something in it to evoke a user’s actions.  Below are 3 important strategies I believe we need to all keep in mind when taking on digital health:

1. What does the patient EXPECT?

It’s no surprise that patients want technology incorporated into their healthcare.  However, it’s essential to couple the right technology with appropriate expectation of the user.  What you THINK a patient expects, might not always turn out to be the case.  According to a recent study by business and technology consulting firm West Monroe Partners, 91 percent of healthcare customers say they would take advantage of mobile apps when offered.  However, according to an Accenture report, 66% of the largest 100 US hospitals have consumer-facing mobile apps, 38% of which have been developed for their patients, and only 2% of patients are actively using these apps. When users are met with digital health technology that lacks the expected user experience, they are left feeling disappointed, unfulfilled, and begin looking elsewhere for services.

2. What does the patient WANT?

Patients are longing for a consumer experience when it comes to their healthcare.  New research shows that “patients today are choosing their providers, in part, based on how well they use technology to communicate with them and manage their health,” says Joshua Newman, M.D., chief medical officer, Salesforce Healthcare and Life Sciences.  Patients crave technology, customization and convenience.  There is no doubt that digital health tools satisfy the convenience factor.  However, they are nothing without a customized experience. Limiting your interactions with patients to an out-of-the-box, one-way digital communication strategy can be disadvantageous and could mean you aren’t reaching patients at all.  Digital health that is personalized, optimized, and sent through multi modalities allows you to be sure that you are engaging your patient in a way they want.

3. Where does the patient GO?

It’s no surprise that patients expect a consumer experience when it comes to interacting with their healthcare provider. But mastering digital health must include more than just mobile apps and the doctor’s office.  A digital health strategy that connects with patients across the entire continuum of care will optimize their experience and satisfaction.  In a recent study by West Monroe Partners called No More Waiting Room: The Future of the Healthcare Customer Experience, Will Hinde, Senior Director says “we’re starting to see more providers incorporate the digital experience with their office visit, by shifting to more online scheduling of appointments, paperless office interactions, following up via email, portals, and mobile apps and taking steps towards greater cost and quality transparency.”  Connecting with patients outside of the doctor’s office and in places where they frequent most allows for better changes of engagement, leading to greater experiences.

Tackling digital health can be daunting and unsuccessful if it’s looked at solely from the angle that technology is king. Looking at it from the lens of the patient becomes less intimidating and more likely that your digital health efforts don’t end up in the Elephant Graveyard.

The Communication Solutions Series of blog posts is sponsored by Stericycle Communication Solutions, a leading provider of high quality call center & telephone answering servicespatient access services and automated communication technology. Stericycle Communication Solutions combines a human touch with innovative technology to deliver best-in-class communication services.  Connect with Stericycle Communication Solutions on social media: @StericycleComms