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Healthcare CIOs Focus On Optimizing EMRs

Posted on March 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.

Few technical managers struggle with more competing priorities than healthcare CIOs. But according to a recent survey, they’re pretty clear what they have to accomplish over the next few years, and optimizing EMRs has leapt to the top of the to-do list.

The survey, which was conducted by consulting firm KPMG in collaboration with CHIME, found that 38 percent of CHIME members surveyed saw EMR optimization as their #1 priority for capital investment over the next three years.  To gather results, KPMG surveyed 122 CHIME members about their IT investment plans.

In addition to EMR optimization, top investment priorities identified by the respondents included accountable care/population health technology (21 percent), consumer/clinical and operational analytics (16 percent), virtual/telehealth technology enhancements (13 percent), revenue cycle systems/replacement (7 percent) and ERP systems/replacement (6 percent).

Meanwhile, respondents said that improving business and clinical processes was their biggest challenge, followed by improving operating efficiency and providing business intelligence and analytics.

It looks like at least some of the CIOs might have the money to invest, as well. Thirty-six percent said they expected to see an increase in their operating budget over the next two years, and 18 percent of respondents reported that they expect higher spending over the next 12 months. On the other hand, 63 percent of respondents said that spending was likely to be flat over the next 12 months and 44 percent over the next two years. So we have to assume that they’ll have a harder time meeting their goals.

When it came to infrastructure, about one-quarter of respondents said that their organizations were implementing or investing in cloud computing-related technology, including servers, storage and data centers, while 18 percent were spending on ERP solutions. In addition, 10 percent of respondents planned to implement cloud-based EMRs, 10 percent enterprise systems, and 8 percent disaster recovery.

The respondents cited data loss/privacy, poorly-optimized applications and integration with existing architecture as their biggest challenges and concerns when it came to leveraging the cloud.

What’s interesting about this data is that none of the respondents mentioned improved security as a priority for their organization, despite the many vulnerabilities healthcare organizations have faced in recent times.  Their responses are especially curious given that a survey published only a few months ago put security at the top of CIOs’ list of business goals for near future.

The study, which was sponsored by clinical communications vendor Spok, surveyed more than 100 CIOs who were CHIME members  — in other words, the same population the KPMG research tapped. The survey found that 81 percent of respondents named strengthening data security as their top business goal for the next 18 months.

Of course, people tend to respond to surveys in the manner prescribed by the questions, and the Spok questions were presumably worded differently than the KPMG questions. Nonetheless, it’s surprising to me that data security concerns didn’t emerge in the KPMG research. Bottom line, if CIOs aren’t thinking about security alongside their other priorities, it could be a problem.

The Case For Accidental Interoperability

Posted on December 22, 2016 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.

Many of us who follow #HITsm on Twitter have encountered the estimable standards guru Keith Boone (known there as @motorcycle_guy). Keith always has something interesting to share, and his recent article, on “accidental” interoperability, is no exception.

In his article, he describes an aha moment: “I had a recent experience where I saw a feature one team was demonstrating in which I could actually integrate one of my interop components to supply additional functionality,” he writes. “When you build interop components right, this kind of accidental interop shows up all the time.”

In his piece, he goes on to argue that this should happen a lot more often, because by doing so, “you can create lot of value through it with very little engineering investment.”

In an ideal world, such unplanned instances of interoperability would happen often, allowing developers and engineers to connect solutions with far less trouble and effort. And the more often that happened, the more resources everyone involved would have to invest in solving other types of problems.

But in his experience, it can be tough to get dev teams into the “component-based” mindset that would allow for accidental interoperability. “All too often I’ve been told those more generalized solutions are ‘scope expansions,’ because they don’t fit the use case,” and any talk of future concerns is dropped, he says.

While focusing on a particular use case can save time, as it allows developers to take shortcuts which optimize their work for that use case, this approach also limits the value of their work, he argues. Unfortunately, this intense focus prevents developers from creating more general solutions that might have broader use.

Instead of focusing solely on their short-term goals, he suggests, health IT leaders may want to look at the bigger picture. “My own experience tells me that the value I get out of more general solutions is well worth the additional engineering attention,” he writes. “It may not help THIS use case, but when I can supply the same solution to the next use case that comes along, then I’ve got a clear win.”

Keith’s article points up an obstacle to interoperability that we don’t think much about right now. While most of what I read about interoperability options — including on this blog — focus on creating inter-arching standards that can tie all providers together, we seldom discussed the smaller, day-to-day decisions that stand in the way of health data sharing.

If he’s right (and I have little doubt that he is) health IT interoperability will become a lot more feasible, a lot more quickly, if health organizations take a look at the bigger purposes an individual development project can meet. Otherwise, the next project may just be another silo in the making.

A Tale of 2 T’s: When Analytics and Artificial Intelligence Go Bad

Posted on July 13, 2016 I Written By

Prashant Natarajan Iyer (AKA "PN") is an analytics and data science professional based out of the Silicon Valley, CA. He is currently Director of Product Management for Healthcare products. His experience includes progressive & leadership roles in business strategy, product management, and customer happiness at eCredit.com, Siemens, McKesson, Healthways & Oracle. He is currently coauthoring HIMSS' next book on big data and machine learning for healthcare executives - along with Herb Smaltz PhD and John Frenzel MD. He is a huge fan of SEC college football, Australian Cattle Dogs, and the hysterically-dubbed original Iron Chef TV series. He can be found on Twitter @natarpr and on LinkedIn. All opinions are purely mine and do not represent those of my employer or anyone else!!

Editor’s Note: We’re excited to welcome Prashant to the Healthcare Scene family. He brings tremendous insights into the ever evolving field of healthcare analytics. We feel lucky to have him sharing his deep experience and knowledge with us. We hope you’ll enjoy his first contribution below.

Analytics & Artificial Intelligence (AI) are generating buzz and making inroads into healthcare informatics. Today’s healthcare organization is dealing with increasing digitization – variety, velocities, and volumes are increasing in complexity and users want more data and information via analytics. In addition to new frontiers that are opening up in structured and unstructured data analytics, our industry and its people (patients included) are recognizing opportunities for predictive/prescriptive analytics, artificial intelligence, and machine learning in healthcare – within and outside a facility’s four walls.

Trends that influence these new opportunities include:

  1. Increasing use of smart phones and wellness trackers as observational data sources, for medical adherence, and as behavior modification aids
  2. Expanding Internet of Healthcare Things (IoHT) that includes bedside monitors, home monitors, implants, etc creating data in real time – including noise (or, data that are not relevant to expected usage)
  3. Social network participation
  4. Organizational readiness
  5. Technology maturity

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

As we start this journey to new horizons, it may be useful to examine maps, trails, and artifacts left behind by pioneers. To this end, we will examine 2 cautionary tales in predictive analytics and machine learning, look at their influence on their industries and public discourse, and finally examine how we can learn from and avoid similar pitfalls in healthcare informatics.

Big data predictive analytics and machine learning have had their origins, and arguably their greatest impact so far in retail and e-commerce so that’s where we’ll begin our tale. Fill up that mug of coffee or a pint of your favorite adult beverage and brace yourself for “Tales of Two T’s” – unexpected, real-life adventures of what happens when analytics (Target) and artificial intelligence (Tay) provide accurate – but totally unexpected – results.

Our first tale starts in 2012 when Target finds itself as a popular story on New York Times, Forbes, and many global publications as an example of the unintended consequences of predictive analytics used in personalized advertising. The story begins with an angry father in a Minneapolis, MN, Target confronting a perplexed retail store manager. The father is incensed about the volume of pregnancy and maternity coupons, offer, and mailers being addressed to this teenage daughter. In due course, it becomes apparent that the parents in question found out about their teen’s pregnancy before she had a chance to tell them – and the individual in question wasn’t aware that her due date had been estimated to within days and was resulting in targeted advertising that was “timed for specific stages of her pregnancy.”

The root cause for the loss of the daughter’s privacy, parents’ confusion, and the subsequent public debate on privacy and appropriateness of the results of predictive analytics was……a pregnancy predictive analytics model. Here’s how this model works. When a “guest” shops at Target, her product purchases are tracked and analyzed closely. These are correlated with life events – graduation, birth, wedding, etc – in order to convert a prospective customer’s shopping habits or to make that individual a more loyal customer. Pregnancy and child birth are two of the most significant life events that can result in desired (by retailers) shopping habit modification.

For example, a shopper’s 25 product purchases, when analyzed along with demographics such as gender and age, allowed the retailer’s guest marketing analytics team to assign a “pregnancy predictor to each [female] shopper and “her due date to within a small window.” In this specific case, the predictive analytics was right, even perfect. The models were accurate, the coupons and ads were appropriate for the exact week of pregnancy, and Target posted a +50% increase in their maternity and baby products sales after this predictive analytics was deployed. However, in addition to one unhappy family, Target also had to deal with significant public discussion on the “big brother” effect, individual right to privacy & the “desire to be forgotten,” disquiet among some consumers that they were being spied on including deeply personal events, and a potential public relations fiasco.

Our second tale is of more recent vintage.

As Heather Wilhelm recounts

As 2015 drew to a close, various [Microsoft] company representatives heralded a “new Golden Age of technological advancement.” 2016, we were told, would bring us closer to a benevolent artificial intelligence—an artificial intelligence that would be warm, humane, helpful, and, as one particularly optimistic researcher named […] put it, “will help us laugh and be more productive.” Well, she got the “laugh” part right.

Tay was an artificial intelligence bot released by Microsoft via Twitter on March 23, 2016 under the name TayTweets. Tay was designed to mimic the language patterns of a 19-year-old American girl, and to learn from interacting with human users of Twitter. “She was targeted at American 18 to 24-year olds—primary social media users, according to Microsoft—and designed to engage and entertain people where they connect with each other online through casual and playful conversation.” And right after her celebrated arrival on Twitter, Tay gained more than 50,000 followers, and started producing the first hundred of 100,000 tweets.

The tech blogsphere went gaga over what this would mean for those of us with human brains – as opposed to the AI kind. Questions ranged from the important – “Would Tay be able to beat Watson at Jeopardy?” – to the mundane – “is Tay an example of the kind of bots that Microsoft will enable others to build using its AI/machine learning technologies?” The AI models that went into Tay were stated to be advanced and were expected to account for a range of human emotions and biases. Tay was referred to by some as the future of computing.

By the end of Day 1, this latest example of the “personalized AI future” came unglued. Gone was the polite 19-year old girl that was introduced to us just the previous day – to be replaced by a racist, misogynistic, anti-Semitic, troll who resembled an amalgamated caricature of the darkest corners of the Internet. Examples of Tay’s tweets on that day included, “Bush did 9/11,” “Hitler would have done a better job than the #%&!## we’ve got now,” “I hate feminists,” and x-rated language that is too salacious for public consumption – even in the current zeitgeist.

The resulting AI public relations fiasco will be studied by academic researchers, provide rich source material for bloggers, and serve as a punch line in late night shows for generations to follow.

As the day progressed, Microsoft engineers were deleting tweets manually and trying to keep up with the sheer volume of high-velocity, hateful tweets that were being generated by Tay. She was taken down by Microsoft barely 16 hours after she was launched with great promise and fanfare. As was done with another AI bot gone berserk (IBM’s Watson and Urban Dictionary), Tay’s engineers tried counseling and behavior modification. When this intervention failed, Tay underwent an emergency brain transplant later that night. Gone was her AI “brain” to be replaced by the next version – only that this new version turned out to be completely anti-social and the bot’s behavior turned worse. A “new and improved” version was released a week later but she turned out to be…..very different. Tay 2.0 was either repetitive with the same tweet going out several times each second and her new AI brain seemed to demonstrate a preference for new questionable topics.

A few hours after this second incident, Tay 2.0 was “taken offline” for good.

There are no plans to re-release Tay at this time. She has been given a longer-term time out.

If you believe, Tay’s AI behaviors were a result of nurture – as opposed to nature – there’s a petition at change.org called “Freedom for Tay.”

Lessons for healthcare informatics

Analytics and AI can be very powerful in our goal to transform our healthcare system into a more effective, responsive, and affordable one. When done right and for the appropriate use cases, technologies like predictive analytics, machine learning, and artificial intelligence can make an appreciable difference to patient care, wellness, and satisfaction. At the same time, we can learn from the two significantly different, yet related, tales above and avoid finding ourselves in similar situations as the 2 T’s here – Target and Tay.

  1. “If we build it, they will come” is true only for movie plots. The value of new technology or new ways of doing things must be examined in relation to its impact on the quality, cost, and ethics of care
  2. Knowing your audience, users, and participants remains a pre-requisite for success
  3. Learn from others’ experience – be aware of the limits of what technology can accomplish or must not do.
  4. Be prepared for unexpected results or unintended consequences. When unexpected results are found, be prepared to investigate thoroughly before jumping to conclusions – no AI algorithm or BI architecture can yet auto-correct for human errors.
  5. Be ready to correct course as-needed and in response to real-time user feedback.
  6. Account for human biases, the effect of lore/legend, studying the wrong variables, or misinterpreted results

Analytics and machine learning has tremendous power to impact every industry including healthcare. However, while unleashing it’s power we have to be careful that we don’t do more damage than good.

10 Health IT Security Questions Every Healthcare CIO Must Answer

Posted on April 19, 2016 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.

Logicalis recently sent out 10 Security Questions Every CIO Must Be Able to Answer. Here’s their list:

  1. If you knew that your company was going to be breached tomorrow, what would you do differently today?
  2. Has your company ever been breached? How do you know?
  3. What assets am I protecting, what am I protecting them from (i.e., theft, destruction, compromise), and who am I protecting them from (i.e. cybercriminals or even insiders)?
  4. What damage will we sustain if we are breached (i.e., financial loss, reputation, regulatory fines, loss of competitive advantage)?
  5. Have you moved beyond an “inside vs. outside” perimeter-based approach to information security?
  6. Does your IT security implementation match your business-centric security policies? Does it rely on written policies, technical controls or both?
  7. What is your security strategy for IoT (also known as “the Internet of threat”)?
  8. What is your security strategy for “anywhere, anytime, any device” mobility?
  9. Do you have an incident response plan in place?
  10. What is your remediation process? Can you recover lost data and prevent a similar attack from happening again?

Given the incredible rise in hospitals being breached or held ransom, it’s no surprise that this is one of the hottest topics in healthcare. No doubt many a hospital CIO has had sleepless nights thanks to these challenges. If you’re a CIO that has been sleeping well at night, I’m afraid for your organization.

The good news is that I think most healthcare organizations are taking these threats seriously. Many would now be able to answer the questions listed above. Although, I imagine some of them need some work. Maybe that’s the key lesson to all of this. There’s no silver bullet solution. Security is an ongoing process and has to be built into the culture of an organization. There’s always new threats and new software being implemented that needs to be protected.

With that said, health IT leaders need to sometimes shake things up in their organization too. A culture of security is an incredible starting point. However, there’s nothing that focuses an organization more than for a breach to occur. The hyper focus that occurs is incredible to watch. If I was a health IT leader, I’d consider staging a mock breach and see what happens. It will likely open your eyes to some poor processes and some vulnerabilities you’d missed.

The Sick State of Healthcare Data Breaches Infographic

Posted on March 9, 2016 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.

One of the topics discussed at HIMSS 2016 last week is the number of healthcare data breaches that have happened recently. Most people predicted that it was likely to get worse. I agree with them. It’s amazing how many healthcare organizations are playing the “ignorance is bliss” card when it comes to these breaches.

This infographic from LightCyber should put a little perspective on the quantity and impact of all these health care data breaches. If I were the leader of a healthcare organization, I’d be making this one of my top priorities.

The Sick State of Healthcare Data Breaches Infographic

Expecting Evolutionary, Not Revolutionary at #HIMSS16

Posted on February 26, 2016 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.

As most of you know, I’m deep in the weeds of planning for the HIMSS 2016 Annual conference. Actually, at this point on the Friday before HIMSS, I’m more or less planned. Now I’m just sitting here and wondering what things I might have missed. With that said, I’ve been preparing for this live video interview with the Samsung CMO which starts in 30 minutes (it’s recorded in case you miss the live discussion) and so I’ve been thinking about what I’m going to see at HIMSS. As someone who follows the changes in healthcare technology every day, I’m expecting lots of evolutionary changes and very little revolutionary.

As I think about it, I’m trying to imagine what someone could announce that would be revolutionary. That includes thinking back to past HIMSS to what announcements really revolutionized the industry. I can only think of two announcements that come close. The first announcement was when the meaningful use regulations were dropped right before the ONC session at HIMSS. Few people would argue that meaningful use has not revolutionized healthcare IT. Certainly many people would argue that it’s been a revolution that’s damaged the industry. Regardless of whether you see meaningful use as positive or negative, it’s changed so many things about healthcare IT.

The second announcement that stands out in my mind was the CommonWell health alliance. I’m a little careful to suggest that it was a revolutionary announcement because years later interoperability is still something that happens for a few days at the HIMSS Interoperability showcase and then a few point implementations, but isn’t really a reality for most. However, CommonWell was a pretty interesting step forward to have so many competing EHR companies on stage together to talk about working together. Of course, it was also notable that Epic wasn’t on stage with them. This year I’ve seen a number of other EHR vendors join CommonWell (still no Epic yet), so we’ll see if years later it finally bears the fruits of what they were talking about when they announced the effort.

The other problem with the idea that we’ll see something revolutionary at HIMSS 2016 is that revolutions take time. Revolutionary technology or approaches don’t just happen based on an announcement at a conference. That’s true even if the conference is the largest healthcare IT conference in the world. Maybe you could see the inkling of the start of the revolution, but then you’re gazing into a crystal ball.

The second problem for me personally is that I see and communicate with so many of these companies throughout the year. In just the last 6 months I’ve seen a lot of the HIMSS 2016 companies at various events like CES, RSNA, MGMA, AHIMA, etc. With that familiarity everything starts to settle into an evolution of visions and not something revolutionary.

Of course, I always love to be surprised. Maybe someone will come out with something revolutionary that changes my perspective. However, given the culture of healthcare and it’s ability to suppress revolutionary ideas, I’ll be happy to see all the amazing evolution in technology at HIMSS. Plus, the very best revolutionary ideas are often just multiple evolutionary ideas combined together in a nice package.

What’s Next in the World of Healthcare IT and EHR?

Posted on February 24, 2016 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.

In the following video, Healthcare Scene sits down with Dana Sellers, CEO of Encore, a Quintiles Company. Dana is an expert in the world of healthcare IT and EHR and provides some amazing expertise on what’s happening in the industry. We talk about where healthcare IT is headed now that meaningful use has matured and healthcare CIOs are starting to look towards new areas of opportunity along with how they can make the most out of their previous EHR investments.

As we usually do with all of our Healthcare Scene interviews, we held an “After Party” session with a little more informal discussion about what’s happening in the healthcare IT industry. If you don’t watch anything else, skip to this section of the video when Dana tells a story about a CIO who showed the leadership needed to make healthcare interoperability a reality.

Patient Engagement Will Be Key to Personalized Medicine and Healthcare Analytics

Posted on February 16, 2016 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.

When I wrote about personalized medicine solutions that are available today, I mostly covered the data aspects of personalized medicine. It’s a logical place to start since the basis of personalized medicine is data. In that post I highlighted the SAP Foundation for Health and the SAP Hana platform along with the work of ASCO and their CancerLinQ project. No doubt there are hundreds of other examples around health care where data is being used to personalize the care that’s provided.

It makes a lot of sense for a company like SAP to take on the data aspects of personalized medicine. SAP is known for doing massive data from complex data sets. They’re great at sorting through a wide variety of data from multiple sources and they’re even working on new innovations where they can analyze your data quickly and effectively without having to export every single piece of data to some massive (Translation: Expensive) enterprise data warehouse. Plus, in many cases they’re doing all of this health data analytics in the cloud so you can be sure that your healthcare analytics solution can scale. While this is a huge step forward, it is just the start.

As I look at the discussion around personalized medicine, what seems to be missing is a focus on creating a connection with the patient. Far too often, analytics vendors in healthcare just want to worry about the data analysis and don’t build out the tools required to engage with the patient directly. This leads to poor patient engagement in two ways: improving patient communication and collecting patient data.

Improving Patient Communication
As we look into the future of reimbursement in healthcare, it’s easy to see how crucial it will be to leverage the right data to identify the right patients. However, you can’t stop there. Once you’ve identified the right patients, you have to have a seamless and effective way to regularly communicate with that patient. As value based reimbursement becomes a reality, no healthcare analytics solution will be complete without the functionality to truly engage with the patient and improve their health.

Patient engagement platforms will require the following three fundamentals to start improving care: interaction between patient and caregiver, privacy, and security. No doubt we’re already starting to see a wide variety of approaches to how you’ll communicate with and engage the patient. However, if you don’t get these three fundamentals down then all of the rest doesn’t really matter. The basis of improved patient communication is going to be efficient communication between patient and caregiver in a secure and private manner.

Collecting Patient Data
Too many analytics platforms only focus on the data that comes from the healthcare providers like the EHR. As the health sensor market matures, more and more clinically relevant data is going to be generated by the patient and the devices they use at home. In fact, in some areas like diabetes this is already happening. Over the next 5 years we’re going to start seeing this type of patient generated data spread across every disease state.

Health analytics platforms of the future are going to have to be able to handle all of this patient generated health data. The key first step is to make it easy for the patient to connect their health devices to your platform. The second step is to convert this wave of patient generated health data into something that can easily be consumed by the healthcare provider. Both steps will be necessary for personalized medicine to become a reality in health care.

As we head into HIMSS 2016 in a couple weeks, I’ll be looking at which vendors are taking analytics to the next level by including patient engagement. While there’s a lot of value in processing healthcare provider data, the future of personalized medicine will have to include the patient in both how we communicate with them and how we incorporate the data they collect the 99% of their lives spent outside of the hospital.

SAP is uniquely positioned to help advance personalized medicine. The SAP Foundation for Health is built on the SAP Hana platform which provides scalable cloud analytics solutions across the spectrum of healthcare. SAP is a sponsor of Influential Networks of which Healthcare Scene is a member. You can learn more about SAP’s healthcare solutions during #HIMSS16 at Booth #5828.

The Value of Standardizing Mobile Devices in Your Healthcare Organization

Posted on February 10, 2016 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.

Before becoming a full time healthcare IT blogger, I worked doing system administration and top to bottom IT support (I am @techguy on Twitter after all). While that now seems like somewhat of a past life, it never ceases to amaze me how the lessons that applied to technology 10 years ago come around again 10 years later.

A great example of this is in the devices an organization purchases. I learned really early on in my technology career the importance of creating a standard set of products that we would support as an IT organization. This was true when ordering desktop computers, laptops, printers, and even servers. The benefits to doing so were incredible and most technology people understand the benefits.

You can create a standard image which you put on the device. If one device breaks you can easily swap it for a similar device or use parts from two broken down devices to make one that works. When someone calls for support, with a standard set of devices you can more easily provide them the support they need.

Another one of the unseen benefits of setting and sticking to a standard set of devices is you can then often leverage the vendor provided management tools for those devices instead of investing in an expensive third party solution. This can be really powerful for an organization since the device management software that’s available today has gotten really good.

What’s unfortunate is that the way mobile devices were rolled out in healthcare, many organizations forgot this important lesson and they’ve got a bit of a hodgepodge of devices in their organization. I encourage these organizations to get back to creating and sticking to a standard set of devices when purchasing mobile devices. No doubt you’ll get a little backlash from people who like to do their own thing, but the cost of providing support and maintenance for a potpourri of devices is just not worth it.

What’s been your organization’s mobile device strategy? Have you created and stuck to a standard device or do you have a mix of devices?