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Open Source Software and the Path to EHR Heaven (Part 2 of 2)

Posted on September 20, 2018 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 segment of this article explained the challenges faced by health care organizations and suggested two ways they could be solved through free and open source software. We’ll finish the exploration in this segment of the article.

Situational awareness would reduce alert fatigue and catch errors

Difficult EHR interfaces are probably the second most frustrating aspect of being a doctor today: the first prize goes to the EHR’s inability to understand and adapt to the clinician’s workflow and environment. This is why the workplace redounds with beeps and belches from EHRs all day, causing alert fatigue and drowning out truly serious notifications. Stupid EHRs have an even subtler and often overlooked effect: when regulators or administrators require data for quality or public health purposes, the EHR is often “upgraded” with an extra field that the doctor has to fill in manually, instead of doing what computers do best and automatically replicating data that is already in the record. When doctors complain about the time they waste in the EHR, they often blame the regulators or the interface instead of placing their finger on the true culprit, which is the lack of awareness in the EHR.

Open source can ease these problems in several ways. First, the customizability outlined in the first section of this article allows savvy users to adapt it to their situations. Second, the interoperability from the previous section makes it easier to feed in information from other parts of the hospital or patient environment, and to hook in analytics that make sense of that information.

Enhancements from outside sources could be plugged in

The modularity of open source makes it easier to offer open platforms. This could lead to marketplaces for EHR enhancements, a long-time goal of the open SMART standard. Certainly, there would have to be controls for the sake of safety: an administrator, for instance, could limit downloads to carefully vetted software packages.

At best, storage and interface in an EHR would be decoupled in separate modules. Experts at storage could optimize it to improve access time and develop new options, such as new types of filtering. At the same time, developers could suggest new interfaces so that users can have any type of dashboard, alerting system, data entry forms, or other access they want.

Bugs could be fixed expeditiously

Customers of proprietary software remain at the mercy of the vendors. I worked in one computer company that depended on a very subtle feature from our supplier that turned out not to work as advertised. Our niche market, real-time computing, needed that feature to achieve the performance we promised customers, but it turned out that no other company needed it. The supplier admitted the feature was broken but told us point-blank that they had no plans to fix it. Our product failed in the marketplace, for that reason along with others.

Other software users suffer because proprietary vendors shift their market focus or for other reasons–even going out of business.

Free and open source software never ossifies, so long as users want it. Anyone can hire a developer to fix a bug. Furthermore, the company fixing it usually feeds the fix back into the core project because they want it to be propagated to future versions of the software. Thus, the fixes are tested, hardened, and offered to all users.

What free and open source tools are available?

Numerous free and open source EHRs have been developed, and some are in widespread use. Most famously is VistA, the software created at the Department of Veterans Affairs, and used also by the Indian Health Service and other government agencies, has a community chaperone and has been adopted by the country of Jordan. VistA was considered by the Department of Defense as well, but ultimately rejected because the department didn’t want to invest in adding some missing features.

Another free software EHR, OpenMRS, supports health care in Kenya, Haiti, and elsewhere. OpenEMR is also deployed internationally.

What free and open source software has accomplished in these settings is just a hint of what it can do for health care across the board. The problem holding back open source is simple neglect: as VistA’s experience with the DoD showed, institutions are unwilling to support open source, even through they will pay 10 or 100 times as much on substandard proprietary software. Open Health Tools, covered in the article I just linked to, is one of several organizations that shriveled up and disappeared for lack of support. Some organizations gladly hop on for a free ride, using the software without contributing either funds or code. Others just ignore open source software, even though that means their own death: three hospitals have recently declared bankruptcy after installing proprietary EHRs. Although the article focuses on the up-front costs of installing the EHRs, I believe the real fatal blow was the inability of the EHRs to support efficient, streamlined health care services.

We need open source EHRs not just to reduce health care costs, but to transform health. But first, we need a vision of EHR heaven. I hope this article has taken us at least into the clouds.

Open Source Software and the Path to EHR Heaven (Part 1 of 2)

Posted on September 19, 2018 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.

Do you feel your electronic health record (EHR) is heaven or hell? The vast majority of clinicians–and many patients, too, who interact with the EHR through a web portal–see it as the latter. In this article, I’ll describe an EHR heaven and how free and open source software can contribute to it. But first an old joke (which I have adapted slightly).

A salesman for an EHR vendor dies and goes before the Pearly Gates. Saint Peter asks him, “Would you like to go to heaven or hell?”

Surprised, the salesman says, “I didn’t know I had a choice.”

Saint Peter suggests, “How about this. We’ll show you heaven and hell, and then you can decide.”

“Sounds fair,” says the EHR salesman.

First they take him to heaven. People wearing white robes are strumming harps and singing hymns, and it goes on for a long time, till they take him away.

Next they take him to hell. And it’s really cool! People are clinking wine glasses together and chatting about amusing topics around the pool.

When the EHR salesman gets back to the Pearly Gates, he says to Saint Peter, “You know, this sounds really strange, but I choose hell.”

Immediately comes a clap of thunder. The salesman is in a fiery pit being prodded with pitchforks by dreadful demons.

“Wait!” he cries out. “This is not the hell I saw!”

One of the demons answers, “They must have shown you the demo.”

Most hospitals and clinicians are currently in EHR hell–one they have freely chosen, and one paid for partly by government Meaningful Use reimbursements. So we all know what EHR hell look like. What would EHR heaven be? And how does free and open source software enable it? The following sections of this article list the traits I think clinicians would like to see.

Interfaces could be easily replaced and customized

The greatest achievement of the open source movement, in my opinion, has been to strike an ideal balance between “let a hundred flowers bloom” experimentation and choosing the best option to advance the field. A healthy open source project encourages branching, which lets any individual or team with the required expertise change a product to their heart’s content. Users can then try out different versions, and a central committee vets the changes to decide which version is most robust.

Furthermore, modularization on various levels (programming modules, hooks, compile-time options, configuration tools) allows multiple versions to co-exist, each user choosing the options right for their environment. Open source software tends to be modular for several reasons, notably because it is developed by many different individuals and teams who want control over their small parts of the system.

With easy customization, a hospital or clinic can mandate that certain items be highlighted and that safe workflow rules be followed when entering or retrieving data. But the institution can also offer leeway for individual clinicians and patients to arrange a dashboard, color scheme, or other aspect of the environment to their liking.

Many of the enablers for this kind of agile, user-friendly programming are technical. Modularity is built into programming languages, while branching is standard in version control systems. So why can’t proprietary vendors do what open source communities routinely do? A few actually do, but most are constrained in ways that prevent such flexibility, especially in electronic health records:

  • Most vendors are dragging out the lifetime of nearly 40-year old technology, with brittle languages and tools that put insurmountable barriers in the way of agile work styles. They are also stuck with monolithic systems instead of modular ones.
  • The vendors’ business model depends on this monolithic control. To unbundle components, allow mix-and-match installations, and allow third parties to plug in new features would challenge the prices they charge.
  • The vendors are fundamentally unprepared for empowered users. They may vet features with clinically trained consultants and do market research, but handling power over the system to users is not in their DNA.

Data could be exchanged in a standard format without complex transformations

Data sharing is the lifeblood of modern computing; you can’t get much done on a single computer anymore. Data sharing lies behind new technologies ranging from the Internet of Things to real-time ad generation (the reason you’ll see a link to an article about “Fourteen celebrities who passed out drunk in public” when you’re trying to read a serious article about health IT). But it’s so rare in health care–where it’s uniquely known as “interoperability”–that every year, reformers call it the most critical goal for health IT, and the Office of the National Coordinator has repeatedly narrowed its Meaningful Use and related criteria to emphasize interoperability.

Open source software can share data with other systems as a matter of course. Data formats are simple, often text-based, and defined in the code in easy-to-find ways. Open source programmers, freed from the pressures on proprietary developers to reinvent wheels and set themselves apart from competitors, like to copy existing data formats. As a stark example of open source’s advantages, consider the most recent version of the Open Document Format, used by LibreOffice and other office suites. It defines an entire office suite in 104 pages. How big is the standards document for the Microsoft OOXML format, offering roughly equivalent functionality? Currently, 6,755 pages–and many observers say even that is incomplete. In short, open source is consistently the right choice for data exchange.

What would the adoption of open source do to improve health care, given that it would solve the interoperability problem? Records could be stored in the cloud–hopefully under patient control–and released to any facility treating the patient. Research would blossom, and researchers could share data as allowed by patients. Analytical services could be plugged in to produce new insights about disease and treatment from the records of millions of people. Perhaps interoperability could also contribute to solving the notorious patient matching problem–but that’s a complicated issue that I have discussed elsewhere, touching on privacy issues and user control outside the scope of this article.

The next segment of this article will list three more benefits of free and open source software, along with an assessment of its current and future prospects.

Three Pillars of Clinical Process Improvement and Control

Posted on February 21, 2018 I Written By

The following is a guest blog post by Brita Hansen, MD, Chief Medical Officer at LogicStream Health.

In a value-based care environment, achieving quality and safety measures is a priority. Health systems must have the capabilities to measure a process following its initial implementation. The reality, however, is that traditional improvement methods are often plagued with lagging indicators that provide little (if any) insight into areas requiring corrective actions. Health systems have an opportunity to make a significant impact on patient care by focusing on three pillars of clinical process improvement and control: quality and safety, appropriate utilization and clinician engagement.

Quality and Safety

Data in a health system’s electronic health record (EHR) typically is not easily accessible. Providers struggle to aggregate the data they need in a timely manner, often with limited resources, thereby hindering efforts to measure process efficacy and consistency. To achieve sustainable quality improvements, clinical leaders must equip their teams with advanced software solutions capable of delivering highly-actionable insights in near-real-time, thereby allowing them to gain a true understanding of clinical processes and how to avoid clinical errors and care variations.

Clinicians need instant insights into what clinical content in their EHR is being used; by whom; and how it affects patient care. This data empowers providers with the ability to continuously analyze and address care gaps and inefficient workflows.

For example, identifying inappropriate uses of Foley catheters that lead to catheter associated urinary tract infections (CAUTI) allows clinical leaders make targeted improvements to the care process or to counsel individual clinician outliers on appropriate best practices. This will, in turn, reduce CAUTI rates. To most effectively improve clinical processes, clinicians need software tools that enable them to examine those processes in their entirety, including process steps within the EHR, patient data and the actions of individual clinicians or groups as they interact with the care process every day.

Only with instant insight into how the care process is being followed can clinicians see in real-time what is happening and where to intervene, make the necessary changes in the EHR workflow, then measure and monitor the effects over time to improve care delivery in a sustainable way.

Appropriate Utilization

Verifying appropriate utilization of best practices also plays a critical role in optimizing clinical processes. Yet healthcare organizations often lack the ability to identify and correct the use of obsolete tests, procedures and medications. When armed with dynamic tools that quickly and easily allow any individual to understand the exact location of ordering opportunities for these components, an organization can evaluate its departments, clinicians, and patient populations for ineffective ordering patterns and areas that require greater compliance. By assessing areas in need of intervention, organizations can notify clinicians of the most up-to-date best practices that, when integrated into clinical workflows, will improve care and yield significant cost savings. Through targeted efforts to ensure proper usage of high-cost and high-volume medications, lab tests and other orderables, for example, health systems can achieve significant savings while improving the quality of care delivery.

The benefits of such an approach are reflected in one health system’s implementation of clinical process improvement and control software, which allowed them to more effectively manage the content in their EHR, including oversight of order sets. Specifically, the organization focused on reviewing the rate of tests used diagnose acute myocardial infarctions (heart attacks). It discovered that physicians were regularly ordering an outdated Creatine kinase-MB (CKMB) lab test along with a new, more efficient test for no other reason than it was pre-checked on numerous order sets.

Although the test itself was inexpensive, the high order rate led to massive waste and increased the cost of care. Leveraging the software enabled the organization to quickly identify the problem, then significantly reduce costs and save resources by eliminating an unnecessary test that otherwise would have remained hidden within the EHR.

Clinician Engagement

Enhancing clinician engagement is key to addressing dissatisfaction and burnout, often traced to alert fatigue and a lack of order set optimization within an EHR. The typical health system averages 24 million alert firings per year. Confronted with a high volume of unnecessary warnings, clinicians ignore alerts 49 percent to 96 percent of the time, resulting in poor compliance with care protocols. EHRs often contain an overwhelming number of order sets that can lead to confusion about best practices for patient care and a frustrating amount of choice to navigate. To increase engagement, alerts must be designed to send the right information, to the right person, in the right format, through the right channel, at the right time in the workflow; and order sets should be streamlined and make it easy for clinicians to follow the up-to-date best clinical practices.

For example, one hospital utilized EHR-generated alerts targeting potential cases of sepsis. These alerts, however, were rarely acted upon as they were not specific enough and fired inappropriately at such exhaustive rates clinicians grew to simply ignore them, creating a clear case of alert fatigue. By fine-tuning alerts and adjusting the workflow to ensure alerts were sent to the right clinician at the optimal time, the hospital was able to achieve and maintain nearly full compliance with its initiative. As early detection and treatment of sepsis increased, the hospital also reduced length of stay in its intensive care unit. Data-driven targeted interventions were developed to address outliers whose actions were driving unnecessary variation in the process.

Ultimately, when the three pillars—quality and safety, appropriate utilization and clinician engagement—are used as the building blocks for standardizing and controlling vital clinical processes, multiple objectives can be realized. Empowered with technology that supports these factors, healthcare organizations can truly achieve sustainable, proactive clinical process improvement and control.

Dr. Brita Hansen is a hospitalist at Hennepin County Medical Center in Minneapolis and Assistant Professor of Medicine at the University of Minnesota School of Medicine. Dr. Hansen also serves as Chief Medical Officer of LogicStream Health.

Analytics Take an Unusual Turn at PeraHealth

Posted on August 17, 2017 I Written By

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

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

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

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

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

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

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

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

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

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

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

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

More About Artificial Intelligence in Healthcare – #HITsm Chat Topic

Posted on August 8, 2017 I Written By

John Lynn is the Founder of the HealthcareScene.com blog network which currently consists of 10 blogs containing over 8000 articles with John having written over 4000 of the articles himself. These EMR and Healthcare IT related articles have been viewed over 16 million times. John also manages Healthcare IT Central and Healthcare IT Today, the leading career Health IT job board and blog. John is co-founder of InfluentialNetworks.com and Physia.com. John is highly involved in social media, and in addition to his blogs can also be found on Twitter: @techguy and @ehrandhit and LinkedIn.

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

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

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

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

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

Reference Materials

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

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

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

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

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

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

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

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

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

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

Making Precision Medicine a Reality with SAP Healthcare and Mercy

Posted on February 23, 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 this Healthcare Scene interview, I sit down with Curtis Dudley and Dr. David Delaney to talk about their precision medicine work at Mercy using SAP HANA to improve quality outcomes and reduce delivery costs using perioperative analytics. We also dive into why Mercy chose to use a third party analytics software instead of their Epic EHR. Plus, we talk about where Mercy and SAP plan to take these healthcare analytics platforms next and how they plan to share the work they’ve done with other hospital systems. We know you’ll enjoy this look into precision medicine at work:

Here are a few more details for our panelists:

  • Curtis Dudley, Vice President of Performance Solutions at Mercy
  • David Delaney, MD is Chief Medical Officer of SAP Public Services and Healthcare Industries
  • John Lynn, Founder of HealthcareScene.com

In the “after party” we dove into more of the technical details of what’s required to roll out a healthcare analytics platform. We dug into Mercy’s approach to exporting data from their EHR and other data sources into SAP HANA and when they choose to just store pointers to the data instead of exporting all the data. We also talk about whether healthcare analytics is really available for the smaller health systems or if it really only works for larger health systems.

If you want to learn more about SAP’s work with Mercy hospital system, both Mercy and SAP Healthcare will be at HIMSS 2016.

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 Shifting Health Care IT Markets

Posted on November 5, 2015 I Written By

John Lynn is the Founder of the HealthcareScene.com blog network which currently consists of 10 blogs containing over 8000 articles with John having written over 4000 of the articles himself. These EMR and Healthcare IT related articles have been viewed over 16 million times. John also manages Healthcare IT Central and Healthcare IT Today, the leading career Health IT job board and blog. John is co-founder of InfluentialNetworks.com and Physia.com. John is highly involved in social media, and in addition to his blogs can also be found on Twitter: @techguy and @ehrandhit and LinkedIn.

I’m at the end of my Fall Healthcare IT Conference season (although I’m still considering attending RSNA for my first time) and besides being thankful to be done with all the travel, I’m also taking a second to think about what I’ve learned over the past couple months as I’ve traveled to a wide variety of conferences.

While the EHR market has been hot for so many years, I’m seeing a big shift in purchasing to three areas: Analytics/Population Health, Revenue Cycle Management, and Privacy/Security. This isn’t a big surprise, but the EHR market has basically matured and now even EHR vendors are looking at new ways to market their products. These are the three main areas where I see the market evolving.

Analytics and Population Health
I could have easily added the other buzzword “patient engagement” to this category as well. There’s a whole mixture of technologies and approaches for this category of healthcare IT. In fact, it’s where I see some of the most exciting innovations in healthcare. Most of it is driven by some form of value based reimbursement or organizations efforts to prepare for the shift to value based reimbursement. However, there’s also a great interest by many organizations to try and extract value from their EHR investment. Many are betting on these tools being able to help them realize value from their EHR data.

Revenue Cycle Management
We’re seeing a whole suite of revenue cycle solutions. For many years we’ve seen solutions that optimized an organization’s relationships with payers. Those are still popular since it seems like most organizations never really fix the problem so their need for revenue cycle management is cyclical. Along with these payer solutions, we’re seeing a whole suite of products and companies that are focused on patient payment solutions. This shift has been riding the wave of high deductible plans in healthcare. As an organization’s patient pay increases, they’re looking for better ways to collect the patient portion of the bill.

Privacy and Security
There have been so many health care breaches, it’s hard to even keep up. Are we becoming numb to them? Maybe, but I still see many organizations investing in various privacy and security programs and tools whenever they hear about another breach. Plus, the meaningful use requirement to do a HIPAA Risk Assessment has built an entire industry focused on those risk assessments. You can be sure the coming HIPAA audits will accelerate those businesses even more.

What other areas are you seeing become popular in health care IT?

Analytics Integration Back to EHR Can’t Disrupt the Workflow

Posted on November 3, 2015 I Written By

John Lynn is the Founder of the HealthcareScene.com blog network which currently consists of 10 blogs containing over 8000 articles with John having written over 4000 of the articles himself. These EMR and Healthcare IT related articles have been viewed over 16 million times. John also manages Healthcare IT Central and Healthcare IT Today, the leading career Health IT job board and blog. John is co-founder of InfluentialNetworks.com and Physia.com. John is highly involved in social media, and in addition to his blogs can also be found on Twitter: @techguy and @ehrandhit and LinkedIn.

One of the challenges we face with healthcare analytics is getting the right information to the right care provider at the right time. In many cases that means presenting the analytics information to the doctor or nurse in the EHR at the point of care. It’s hard enough to know which data to present to which person and at what point in the care process. However, EHR vendors have made this integration even more difficult since it’s not easy to interface the healthcare analytics insights into the EHR workflow. The integrations that I’ve seen are crude at best.

That’s absolutely where we need to go though. There are very few situations where you can disrupt the healthcare providers workflow and send them to another system. I love the second screen concept as much as the next, but that’s not reasonable for most organizations.

I did recently talk to a BI Manager from a hospital who talked about the way they’ve integrated some of their analytics into the EHR workflow of their doctors. What they were doing was basic at best, but did illustrate an important point of learning: inform, don’t interrupt.

The concept of informing the doctor and not interrupting the doctor is a good one. While there are likely a few cases where you’d want to interrupt the doctor, it’s more common that you want to inform the doctor of some insight on the patient as opposed to interrupting the workflow. Doctors love having the right information at their fingertips. Interrupting their workflow (especially when it was unnecessary) causes alert fatigue.

No doubt you have to be careful with how you inform the doctor as well. The insights you offer the doctor better be actionable and useful or they’ll become blind to that as well. That’s the challenge we face with healthcare analytics. How do we take the data and make it useful to the providers? The first step is going to be creating a pathway of communication from the analytics into the EHR. Everything else will evolve from that connection.

Will Your Healthcare Analytics Solution Scale?

Posted on October 26, 2015 I Written By

John Lynn is the Founder of the HealthcareScene.com blog network which currently consists of 10 blogs containing over 8000 articles with John having written over 4000 of the articles himself. These EMR and Healthcare IT related articles have been viewed over 16 million times. John also manages Healthcare IT Central and Healthcare IT Today, the leading career Health IT job board and blog. John is co-founder of InfluentialNetworks.com and Physia.com. John is highly involved in social media, and in addition to his blogs can also be found on Twitter: @techguy and @ehrandhit and LinkedIn.

One of the big themes being talked about at the Healthcare IT Transformation Assembly this week and particularly during my Care Performance Transformation roundtable with Midas+ has been around healthcare analytics and the solutions that will help a hospital utilize their data for population health, value based reimbursement, and improved care. This has made for an interesting discussion for me after having attended SAP Teched last week where SAP talked about the need for the right healthcare data solution that can scale to the needs of healthcare.

At both of these events it became very clear that the future of healthcare is being built on the back of healthcare data. The quantity and quality of healthcare data is expanding rapidly. There’s a lot of healthcare data being generated within the 4 walls of every healthcare organization. There’s a lot of healthcare data being generated outside of the healthcare setting. Plus, we’re just barely getting started with all of the data that’s needed for all the -omics (Genomics and Proteomics). Getting a handle on this data and ensuring the data can be trusted is of paramount concern for healthcare leaders.

What seems to be playing out is healthcare organizations are having to choose to invest in both point solutions and larger healthcare analytics solutions. Unfortunately there doesn’t seem to be one catch all solution that will solve all of a healthcare organization’s data transformation needs. None of the current solutions scale across all types of data and solve all of the current healthcare requirements. Although, some could eventually grow into that role.

In today’s discussion in particular, a number of hospital CIOs made clear that they had no choice but to have a variety of care transformation and healthcare analytics solutions. There wasn’t one integrated solution they could purchase and be done. In many ways it reminds me of the early days of PM, HIS, LIS, and EHR purchasing. Most purchased them separately because there wasn’t one integrated solution. However, over time people moved to buying one integrated system across PM, EHR, LIS, etc as the software become integrated and mature. Will we see the same thing happen with our healthcare analytics solutions?

While we’ve seen the move to more integrated healthcare IT solutions, we’re also seeing a move away from that now as well. Every EHR vendor is working on APIs to allow third party companies to integrate new solutions with the EHR. There’s a realization that it would be nice if the EHR could do everything in one nicely integrated solution, but it won’t. It’s a cycle that we see in software. I imagine we’ll see that same cycle with healthcare analytics solutions as well.

Top 10 Healthcare CIO Budget Priorities

Posted on September 22, 2015 I Written By

John Lynn is the Founder of the HealthcareScene.com blog network which currently consists of 10 blogs containing over 8000 articles with John having written over 4000 of the articles himself. These EMR and Healthcare IT related articles have been viewed over 16 million times. John also manages Healthcare IT Central and Healthcare IT Today, the leading career Health IT job board and blog. John is co-founder of InfluentialNetworks.com and Physia.com. John is highly involved in social media, and in addition to his blogs can also be found on Twitter: @techguy and @ehrandhit and LinkedIn.

For those on the email list that can’t see the image that Charles Webster, MD shared, here are the list of top technology priorities:
1. BI/Analytics
2. CRM
3. Digitalization/Digital Marketing
4. Legacy Modernization
5. Industry-Specific Applications
6. Enterprise Applications
7. Infrastructure and Data Center
8. Application Development
9. Architecture
10. BPM
11. Cloud
12. Collaboration

Sure makes the life of a CIO look pretty easy, doesn’t it? (That was my sarcasm font in case you don’t have that font installed on your computer)

As I chew on this list, I’m processing Will Weider, CIO at Ministry Health Care’s response to me asking him what would he consider the 3 key focus areas for healthcare CIO’s: