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Searching EMR For Risk-Related Words Can Improve Care Coordination

Posted on September 18, 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.

Though healthcare organizations are working on the problem, they’re still not as good at care coordination as they should be. It’s already an issue and will only get worse under value-based care schemes, in which the ability to coordinate care effectively could be a critical issue for providers.

Admittedly, there’s no easy way to solve care coordination problems, but new research suggests that basic health IT tools might be able to help. The researchers found that digging out important words from EMRs can help providers target patients needing extra care management and coordination.

The article, which appears in JMIR Medical Informatics, notes that most care coordination programs have a blind spot when it comes to identifying cases demanding extra coordination. “Care coordination programs have traditionally focused on medically complex patients, identifying patients that qualify by analyzing formatted clinical data and claims data,” the authors wrote. “However, not all clinically relevant data reside in claims and formatted data.”

For example, they say, relying on formatted records may cause providers to miss psychosocial risk factors such as social determinants of health, mental health disorder, and substance abuse disorders. “[This data is] less amenable to rapid and systematic data analyses, as these data are often not collected or stored as formatted data,” the authors note.

To address this issue, the researchers set out to identify psychosocial risk factors buried within a patient’s EHR using word recognition software. They used a tool known as the Queriable Patient Inference Dossier (QPID) to scan EHRs for terms describing high-risk conditions in patients already in care coordination programs.

After going through the review process, the researchers found 22 EHR-available search terms related to psychosocial high-risk status. When they were able to find nine or more of these terms in the patient’s EHR, it predicted that a patient would meet criteria for participation in a care coordination program. Presumably, this approach allowed care managers and clinicians to find patients who hadn’t been identified by existing care coordination outreach efforts.

I think this article is valuable, as it outlines a way to improve care coordination programs without leaping over tall buildings. Obviously, we’re going to see a lot more emphasis on harvesting information from structured data, tools like artificial intelligence, and natural language processing. That makes sense. After all, these technologies allow healthcare organizations to enjoy both the clear organization of structured data and analytical options available when examining pure data sets. You can have your cake and eat it too.

Obviously, we’re going to see a lot more emphasis on harvesting information from structured data, tools like artificial intelligence and natural language processing. That makes sense. After all, these technologies allow healthcare organizations to enjoy both the clear organization of structured data and analytical options available when examining pure data sets. You can have your cake and eat it too.

Still, it’s good to know that you can get meaningful information from EHRs using a comparatively simple tool. In this case, parsing patient medical records for a couple dozen keywords helped the authors find patients that might have otherwise been missed. This can only be good news.

Yes, there’s no doubt we’ll keep on pushing the limits of predictive analytics, healthcare AI, machine learning and other techniques for taming wild databases. In the meantime, it’s good to know that we can make incremental progress in improving care using simpler tools.

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.

Tips on Implementing Text Analytics in Healthcare

Posted on July 6, 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.

Most of us would agree that extracting clinical data from unstructured physician notes would be great. At present, few organizations have deployed such tools, nor have EMR vendors come to the rescue en masse, and the conventional wisdom holds that text analytics would be crazy expensive. I’ve always suspected that digging out and analyzing this data may be worth the trouble, however.

That’s why I really dug a recent article from HealthCatalyst’s Eric Just, which seemed to offer some worthwhile ideas on how to use text analytics effectively. Just, who is senior vice president of product development, made a good case for giving this approach a try. (Note: HealthCatalyst and partner Regenstrief Institute offer solutions in this area.)

The article includes an interesting case study explaining how healthcare text analytics performed head-to-head against traditional research methods.

It tells the story of a team of analysts in Indiana that set out to identify peripheral artery disease (PAD) patients across two health systems. At first gasp, things weren’t going well. When researchers looked at EMR and claims data, they found that failed to identify over 75% of patients with this condition, but text analytics improved their results dramatically.

Using ICD and CPT codes for PAD, and standard EMR data searches, team members had identified less than 10,000 patients with the disorder. However, once they developed a natural language processing tool designed to sift through text-based data, they discovered that there were at least 41,000 PAD patients in the population they were studying.

To get this kind of results, Just says, there are three key features a medical text analytics tool should have:

  • The medical text analytics software should tailor results to a given user’s needs. For example, he notes that if the user doesn’t have permission to view PHI, the analytics tool should display only nonprivate data.
  • Medical text analytics tools should integrate medical terminology to improve the scope of searches. For example, when a user does a search on the term “diabetes” the search tool should automatically be capable of displaying results for “NIDDM,” as this broadens the search to include more relevant content.
  • Text analytics algorithms should do more than just find relevant terms — they should provide context as well as content. For example, a search for patients with “pneumonia,” done with considering context, would also bring up phrases like “no history of pneumonia.” A better tool would be able to rule out phrases like “no history of pneumonia,” or “family history of pneumonia” from a search for patients who have been treated for this illness.

The piece goes into far more detail than I can summarize here, so I recommend you read it in full if you’re interested in leveraging text analytics for your organization.

But for what it’s worth, I came away from the piece with the sense that analyzing your clinical textual information is well worth the trouble — particularly if EMR vendors being to add such tools to their systems. After all, when it comes to improving outcomes, we need all the help we can get.

Hands-On Guidance for Data Integration in Health: The CancerLinQ Story

Posted on June 15, 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.

Institutions throughout the health care field are talking about data sharing and integration. Everyone knows that improved care, cost controls, and expanded research requires institutions who hold patient data to safely share it. The American Society of Clinical Oncology’s CancerLinQ, one of the leading projects analyzing data analysis to find new cures, has tackled data sharing with a large number of health providers and discovered just how labor-intensive it is.

CancerLinQ fosters deep relationships and collaborations with the clinicians from whom it takes data. The platform turns around results from analyzing the data quickly and to give the clinicians insights they can put to immediate use to improve the care of cancer patients. Issues in collecting, storing, and transmitting data intertwine with other discussion items around cancer care. Currently, CancerLinQ isolates the data from each institution, and de-identifies patient information in order to let it be shared among participating clinicians. CancerLinQ LLC is a wholly-owned nonprofit subsidiary of ASCO, which has registered CancerLinQ as a trademark.

CancerLinQ logo

Help from Jitterbit

In 2015, CancerLinQ began collaborating with Jitterbit, a company devoted to integrating data from different sources. According to Michele Hazard, Director of Healthcare Solutions, and George Gallegos, CEO, their company can recognize data from 300 different sources, including electronic health records. At the beginning, the diversity and incompatibility of EHRs was a real barrier. It took them several months to figure out each of the first EHRs they tackled, but now they can integrate a new one quickly. Oncology care, the key data needed by CancerLinQ, is a Jitterbit specialty.

Jitterbit logo

One of the barriers raised by EHRs is licensing. The vendor has to “bless” direct access to EHR and data imported from external sources. HIPAA and licensing agreements also make tight security a priority.

Another challenge to processing data is to find records in different institutions and accurately match data for the correct patient.

Although the health care industry is moving toward the FHIR standard, and a few EHRs already expose data through FHIR, others have idiosyncratic formats and support older HL7 standards in different ways. Many don’t even have an API yet. In some cases, Jitterbit has to export the EHR data to a file, transfer it, and unpack it to discover the patient data.

Lack of structure

Jitterbit had become accustomed to looking in different databases to find patient information, even when EHRs claimed to support the same standard. One doctor may put key information under “diagnosis” while another enters it under “patient problems,” and doctors in the same practice may choose different locations.

Worse still, doctors often ignore the structured fields that were meant to hold important patient details and just dictate or type it into a free-text note. CancerLinQ anticipated this, unpacking the free text through optical character recognition (OCR) and natural language processing (NLP), a branch of artificial intelligence.

It’s understandable that a doctor would evade the use of structured fields. Just think of the position she is in, trying to keep a complex cancer case in mind while half a dozen other patients sit in the waiting room for their turn. In order to use the structured field dedicated to each item of information, she would have to first remember which field to use–and if she has privileges at several different institutions, that means keeping the different fields for each hospital in mind.

Then she has to get access to the right field, which may take several clicks and require movement through several screens. The exact information she wants to enter may or may not be available through a drop-down menu. The exact abbreviation or wording may differ from EHR to EHR as well. And to carry through a commitment to using structured fields, she would have to go through this thought process many times per patient. (CancerLinQ itself looks at 18 Quality eMeasures today, with the plan to release additional measures each year.)

Finally, what is the point of all this? Up until recently, the information would never come back in a useful form. To retrieve it, she would have to retrace the same steps she used to enter the structured data in the first place. Simpler to dump what she knows into a free-text note and move on.

It’s worth mentioning that this Babyl of health care information imposes negative impacts on the billing and reimbursement process, even though the EHRs were designed to support those very processes from the start. Insurers have to deal with the same unstructured data that CancerLinQ and Jitterbit have learned to read. The intensive manual process of extracting information adds to the cost of insurance, and ultimately the entire health care system. The recent eClinicalWorks scandal, which resembles Volkswagon’s cheating on auto emissions and will probably spill out to other EHR vendors as well, highlights the failings of health data.

Making data useful

The clue to unblocking this information logjam is deriving insights from data that clinicians can immediately see will improve their interventions with patients. This is what the CancerLinQ team has been doing. They run analytics that suggest what works for different categories of patients, then return the information to oncologists. The CancerLinQ platform also explains which items of data were input to these insights, and urges the doctors to be more disciplined about collecting and storing the data. This is a human-centered, labor-intensive process that can take six to twelve months to set up for each institution. Richard Ross, Chief Operating Officer of CancerLinQ calls the process “trench warfare,” not because its contentious but because it is slow and requires determination.

Of the 18 measures currently requested by CancerLinQ, one of the most critical data elements driving the calculation of multiple measures is staging information: where the cancerous tumors are and how far it has progressed. Family history, treatment plan, and treatment recommendations are other examples of measures gathered.

The data collection process has to start by determining how each practice defines a cancer patient. The CancerLinQ team builds this definition into its request for data. Sometimes they submit “pull” requests at regular intervals to the hospital or clinic, whereas other times the health care provider submits the data to them at a time of its choosing.

Some institutions enforce workflows more rigorously than others. So in some hospitals, CancerLinQ can persuade the doctors to record important information at a certain point during the patient’s visit. In other hospitals, doctors may enter data at times of their own choosing. But if they understand the value that comes from this data, they are more likely to make sure it gets entered, and that it conforms to standards. Many EHRs provide templates that make it easier to use structured fields properly.

When accepting information from each provider, the team goes through a series of steps and does a check-in with the provider at each step. The team evaluates the data in a different stage for each criterion: completeness, accuracy of coding, the number of patients reported, and so on. By providing quick feedback, they can help the practice improve its reporting.

The CancerLinQ/Jitterbit story reveals how difficult it is to apply analytics to health care data. Few organizations can afford the expertise they apply to extracting and curating patient data. On the other hand, CancerLinQ and Jitterbit show that effective data analysis can be done, even in the current messy conditions of electronic data storage. As the next wave of technology standards, such as FHIR, fall into place, more institutions should be able to carry out analytics that save lives.

Scenarios for Health Care Reform (Part 2 of 2)

Posted on May 18, 2017 I Written By

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

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

Capitalism Disrupts Health Care

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

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

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

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

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

What If Nothing Changes?

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

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

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

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

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

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

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

Scenarios for Health Care Reform (Part 1 of 2)

Posted on May 16, 2017 I Written By

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

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

Patients Forge Their Own Way Forward

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

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

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

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

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

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

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

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

Government Puts Its Foot Down

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

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

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

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

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

What’s a Patient?

Posted on May 10, 2017 I Written By

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

For quite a while I’ve been pushing the idea that healthcare needs to move beyond treating patients. Said another way, we need to move beyond just helping people who have health problems which are causing them to complain and move into treating patients that otherwise feel healthy.

Said another way, Wanda Health once told me “The definition of a healthy patient is someone who’s not been studied long enough.”

If you look long enough and hard enough, we all have health issues or we’re at risk for health issues. There’s always something that could be done to help all of us be healthier. That’s a principle that healthcare hasn’t embraced because our reimbursement models are focused on treating a patients’ chief complaint.

In another conversation with NantHealth, they suggested the idea that we should work towards knowing the patient so well that you know the treatment they need before you even physically see the patient.

These two ideas go naturally together and redefine our current definition of patient. In the above context, all of us would be considered patients since I have little doubt that all of us have health issues that could be addressed if we only knew the current state of our health better.

While NantHealth’s taken a number of stock hits lately for overpromising and under delivering, the concept I heard them describe is one that will become a reality. It could be fair to say that their company was too early for such a big vision, but it’s inspiring to think about creating technology and collecting enough data on a patient that you already know how to help the patient before they even come into the office. That would completely change the office visit paradox that we know today.

This is an ambitious vision, but it doesn’t seem like a massive stretch of the imagination either. That’s what makes it so exciting to me. Now imagine trying to do something like this in the previous paper chart world. Yeah, it’s pretty funny to just even think about it. Same goes with what we call clinical decision support today.

tranSMART and i2b2 Show that Open Source Software Can Fuel Precision Medicine

Posted on April 19, 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.

Medical reformers have said for years that the clinic and the research center have to start working closely together. The reformists’ ideal–rarely approached by any current institution–is for doctors to stream data about treatments and outcomes to the researchers, who in turn inject the insights that their analytics find back into the clinic to make a learning institution. But the clinicians and researchers have trouble getting on the same page culturally, and difficulties in data exchange exacerbate the problem.

On the data exchange front, software developers have long seen open source software as the solution. Proprietary companies are stingy in their willingness to connect. They parcel out gateways to other providers as expensive favors, and the formats often fail to mesh anyway (as we’ve always seen in electronic health records) because they are kept secret. In contrast, open source formats are out for everyone to peruse, and they tend to be simpler and more intuitive. As open source, the software can be enhanced by anyone with programming skill in order to work with other open source software.

Both of these principles are on display in the recent merger announced by two open source projects, the tranSMART Foundation and i2b2. As an organizational matter, this is perhaps a minor historical note–a long-awaited rectification of some organizational problems that have kept apart two groups of programmers who should always have been working together. But as a harbinger of progress in medicine, the announcement is very significant.

tranSMART logo

Here’s a bit about what these two projects do, to catch up readers who haven’t been following their achievements.

  • i2b2 allows doctors to transform clinical data into a common format suitable for research. The project started in 2004 in response to an NIH Roadmap initiative. It was the brainchild of medical researchers trying to overcome the frustrating barriers to extracting and sharing patient data from EHRs. The nugget from which i2b2 came was a project of the major Boston hospital consortium, Partners Healthcare. As described in another article, the project was housed at the Harvard Medical School and mostly funded by NIH.

  • The “trans” in tranSMART stands for translational research, the scientific effort that turns chemistry and biology into useful cures. It was a visionary impulse among several pharma companies that led them to create the tranSMART Foundation in 2013 from a Johnson & Johnson project, as I have documented elsewhere, and then to keep it open source and turn it into a model of successful collaboration. Their software helps researchers represent clinical and research data in ways that facilitate analytics and visualizations. In an inspired moment, the founders of the tranSMART project chose the i2b2 data format as the basis for their project. So the tranSMART and i2b2 foundations have always worked on joint projects and coordinated their progress, working also with the SMART open source API.

Why, then, have tranSMART and i2b2 remained separate organizations for the past three or four years? I talked recently with Keith Elliston, CEO of the tranSMART, who pointed to cultural differences as the factor that kept them apart. A physician culture drove i2b2, whereas a pharma and biochemistry research culture drove tranSMART. In addition, as development shops, they evolved in very different ways from the start.

tranSMART, as I said, adopted a robust open source strategy early on. They recognized the importance of developing a community, and the whole point of developing a foundation–just like other stalwarts of the free software community, such as the Apache Foundation, OpenStack Foundation, and Linux Foundation–was to provide a nurturing but neutral watering hole from which many different companies and contributors could draw what they need. Now the tranSMART code base benefits from 125 different individual contributors.

In contrast, i2b2 started and remained a small, closely-knit team. Although the software was under an open source license, the project operated in a more conservative model, although accepting external contributions.

Elliston says the two projects have been talking for the last two and a half years about improving integration and more recently merging, and that each has learned the best of what the other has to offer in order to meet in the middle. tranSMART is adopting some of i2b2’s planning, while i2b2 is learning how to organize a community around its work.

Together they believe their projects can improve more quickly. Ultimately, they’ll contribute to the movement to target cures to patients, proceeding now under the name Precision Medicine. Fund-raising and partnerships will be easier.

I have written repeatedly about these organizations to show the power that free and open source software brings to medicine. Their timely merger shows that open source overcomes cultural and institutional barriers. What it did for these two organizations it can do for the fractured landscape of hospitals, clinics, long-term care facilities, behavioral health centers, and other medical institutions struggling to work together. My hope is that the new foundation’s model for collaboration, as well as the results of its research, can slay the growing monster of health care costs and make us all healthier.

#TransformHIT Think Tank Hosted by DellEMC

Posted on April 5, 2017 I Written By

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


DellEMC has once again invited me back to participate at the 6th annual #TransformHIT Healthcare Think Tank event happening Tuesday, April 18, 2017 from Noon ET (9 AM PT) – 3 PM ET (Noon PT). I think I’ve been lucky enough to participate 5 of the 6 years and I’ve really enjoyed every one of them. DellEMC does a great job bringing together really smart, interesting people and encourages a sincere, open discussion of major healthcare IT topics. Plus, they do a great job making it so everyone can participate, watch, and share virtually as well.

This year they asked me to moderate the Think Tank which will be a fun new adventure for me, but my job will be made easy by this exceptional list of people that will be participating:

  • John Lynn (@techguy)
  • Paul Sonnier (@Paul_Sonnier)
  • Linda Stotsky (@EMRAnswers)
  • Joe Babaian (@JoeBabaian)
  • Dr. Joe Kim (@DrJosephKim)
  • Andy DeLaO (@cancergeek)
  • Dan Munro (@danmunro)
  • Dr. Jeff Trent (@TGen)
  • Shahid Shah (@ShahidNShah)
  • Dave Dimond(@NextGenHIT)
  • Mike Feibus (@MikeFeibus)

This panel is going to take on three hot topics in the healthcare industry today:

  • Consumerism in Healthcare
  • Precision Medicine
  • Big Data and AI in Healthcare

The great thing is that you can watch the whole #TransformHIT Think Tank event remotely on Livestream (recording will be available after as well). We’ll be watching the #TransformHIT tweet stream and messages to @DellEMCHealth during the event as well if you want to ask any questions or share any insights. We’ll do our best to add outside people’s comments and questions into the discussion. The Think Tank is being held in Phoenix, AZ, so if you’re local there are a few audience seats available if you’d like to come watch live and meet any of the panelists in person. Just let me know in the comments or on our contact us page and I can give you more details.

If you have an interest in healthcare consumerism, precision medicine, or big data and AI in healthcare, then please join us on Tuesday, April 18, 2017 from Noon ET (9 AM PT) – 3 PM ET (Noon PT) for the live stream. It’s sure to be a lively and interesting discussion.
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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.