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How e-Prescribing Features Improve Your Practice Life

Posted on July 9, 2018 I Written By

The following is a guest blog post by Dr. Tom Giannulli, CMIO at Kareo.

e-Prescribing, the process of electronically fulfilling a medication prescription directly from your practice, is far from new. In fact, this service has been around long enough that the majority of patients have come to expect the convenience that accompanies it.

Most private practices are using some type of medical software that aids in the e-Prescribing process. Some may have incorporated said software because they felt obligated, but others have realized that an integrated software solution can do more than help meet the requirements for the meaningful use electronic health record (EHR) initiative.

They recognize that it may also help to improve their practice.

As the clinical leader for an electronic health record (EHR) vendor serving independent practices, I can attest that Kareo’s cloud-based software is designed with the intent to improve the unique needs of the private practice. The changes in regulations and requirements might mean you should change the way you practice, but it doesn’t have to reduce the personal connection between patients and their providers.

Improve Upon Value-Based Care

Value-based care is driven by data and has required practices to become more efficient and effective in order to reduce overall healthcare costs.

Without the automated support that accompanies e-prescribing, compiling the number of required reports could become overwhelming and significantly reduce your efficiency. Our software can make compiling this data with accurate reports both simple and manageable, which saves you valuable time. It makes tracking the quality metrics related to drug compliance much easier, but it’s also tracking quality by:

  • Helping to reduce your liability with legible prescriptions
  • Improving upon prescription accuracy
  • Reducing medication errors
  • Improving upon patient compliance
  • Monitoring fraud and abuse from duplicate prescriptions

Having an automated perspective on drug interactions and prescription history at your fingertips allows you to focus on measures that improve preventative care. This global perspective on each patient’s individual treatment can potentially reduce abuse and readmissions.

Leverage a “Heads Up” Philosophy

You won’t hear many, if any, physicians state that they chose medicine for the abundance of paperwork.

The time EHR can save on administrative tasks provides the physician with more time to do what they enjoy—care for their patients. Patients often choose a practice because they want that personal connection with their physician. Someone who knows their story, and is aware of their health history. Most patients don’t enjoy waiting while the physician is writing notes, asking them to repeat their medical history, or trying to find the correct button on the computer. This won’t help to increase patient satisfaction, and gain patient loyalty. With the information right in front of you, you have more time to devote to quality communication, which gains your patient’s trust.

There are several secondary key benefits to practicing “Heads Up” Medicine with e-prescribing that help improve the patient experience by devoting your attention to your patient, not your computer. You’re still getting the essential information with an easy method of information collection by pointing and clicking.

  • Reviewing key points and a simple question and answer interview can help you build your narrative.
  • Your EHR is accessible on a mobile device, such an IPad, and not just on a website
  • You don’t have to spend the extra time typing the narrative in each time and starting from scratch.

Save Significant Time

Time is valuable to you, and your patients. The time saved with automated support does more than make your patients happy by getting them in and out of their visit quicker, it also shows that you respect their time.

Less time waiting and more time with their providers often results in better patient satisfaction. Word of mouth is often the most effective form of marketing and satisfied patients refer new patients to help you continue to grow your business.

Our software takes care of the bulk of your work with chart, bill and fill to reduce administrative tasks and improve your workflow. It helps you write the note, ensures that you get the billing codes correct and fills the prescription and orders lab work. This allows you to improve your workflow by:

  • Getting the billing done quickly and accurately to expedite payment
  • Allowing you to see more patients in the same amount of time
  • Helping you gain a better balance between your work and personal life to reduce the risk of burnout
  • Making sure your patients don’t leave because of extended wait times

Maintain a Personal Connection

Engaging more with your clients can foster patient satisfaction and loyalty to your practice. Your patients want compassionate care provided and human interaction, and you can leverage this “heads up” philosophy with the simple solutions offered in EHR software to manage the bulk of your administrative work.

Seek out technology and service solutions to improve your practice, increase patient satisfaction and provide you with more time to focus on priorities to aid in the growth of your practice, rather than being burdened with administrative tasks. Because you chose to work in private practice for the patients, not the paperwork.

About Tom Giannulli, MD, MS
Tom Giannulli, MD, MS, is the chief medical information officer at Kareo, a proud sponsor of Healthcare Scene. He is a respected innovator in the medical technology arena with more than 15 years of deep experience in mobile technology and medical software development. 

5 Steps to Ensure Revenue Integrity After Implementing a New EHR

Posted on June 18, 2018 I Written By

The following is a guest blog post by Lisa Eramo, a regular contributor to Kareo’s Go Practice Blog.

In the rush to implement EHRs for Meaningful Use incentives, many practices lost sight of what matters most for continued success—revenue integrity, says Joette Derricks, healthcare compliance and revenue integrity consultant in Baltimore, MD. Revenue integrity—the idea that practices must take proactive steps to capture and retain revenue—isn’t a novel concept. However, it’s becoming increasingly important for physician practices operating in a regulatory-driven environment, she adds.

Revenue integrity is also an important part of ensuring smooth cashflow during and after the transition to a new EHR, says Derricks. This is a time when revenue opportunities are easily overlooked as practices adjust to new navigation, templates, and more, she adds.

Revenue integrity is all about compliance, says Derricks. “It’s about taking a holistic approach to operational efficiency, regulatory compliance, and maximizing reimbursement,” she adds. “It’s about doing things the right way.”

Maximizing reimbursement isn’t about ‘gaming’ the system to upcode. Rather, it’s about implementing processes and procedures to ensure that practices are paid for all of the services they perform without leaving money on the table or generating revenue that payers will later recoup, she explains.

Derricks provides five simple steps practices can take to ensure revenue integrity following an EHR implementation:

1. Review EHR templates. Do templates include the most specific CPT and ICD-10-CM codes? And do physicians understand the importance of avoiding unspecified codes, when possible?

2. Examine the interface between the EHR and practice management system. Do the codes that physicians assign in the EHR feed correctly into the practice management system? For example, when a physician performs an E/M service in addition to a procedure, does the EHR map both codes to the practice management system for billing purposes? Does the practice management system correctly bundle and unbundle services, when appropriate?

3. Run your numbers frequently. Ideally, practices will perform a monthly data analysis to help gauge performance and identify potential missed revenue opportunities, says Derricks. For example, she suggests running a report of the practice’s top 20 billing codes in a particular month. Then, compare those codes with the top 20 codes the practice billed that same month in the previous year. What has changed, and why? And have these changes benefited or hurt the practice? For example, practices may see new codes in that list because they added chronic care or transitional care management, both of which provide additional revenue. Or practices may discover a system glitch that incorrectly bundled services that are separately payable, thus causing a revenue loss.

“Everybody can play the ‘I’m too busy’ game, but this is too important to fall into that trap,” says Derricks. “I applaud the office manager or practice administrator who recognizes the value of constantly being on the lookout for system-wide improvements and analyzing their own numbers.”

Some practice management systems provide robust billing analytics that can help practices identify the root cause of billing errors and omissions. Working with a consultant is another option, says Derricks. Consultants provide unbiased input regarding inefficiencies and vulnerabilities and can provide a ‘fresh set of eyes’ necessary to effect change. They also often have access to benchmarking tools and other resources that can help practices identify revenue gaps and delays, she adds.

For example, Derricks suggests performing an assessment for revenue gaps and roadblocks to reduce the workflow process errors that delay revenue. Download the assessment.

4. Provide physician training. Physicians need thorough training on how to use the EHR properly so as to avoid data omissions, says Derricks. They also need annual training on new CPT and ICD-10-CM codes as well as new documentation requirements, she adds.

5. Create an environment that promotes compliance. This requires a top-down approach from physicians and practice managers, says Derricks. “Everyone should have their eyes open and feel comfortable being able to address concerns,” she says. “It should be an open-door policy in terms of looking at processes versus putting your head down.”

About Lisa Eramo
Lisa Eramo is a regular contributor to Kareo’s Go Practice Blog, as well as other healthcare publications, websites and blogs, including the AHIMA Journal. Her focus areas are medical coding, clinical documentation improvement and healthcare quality/efficiency.  Kareo is a proud sponsor of Healthcare Scene.

Designing for the Whole Patient Journey: Lumeon Enters the US Health Provider Market

Posted on April 23, 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 ( 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.

Lots of companies strive to unshackle health IT’s potential to make the health care industry more engaging, more adaptable, and more efficient. Lumeon intrigues me in this space because they have a holistic approach that seems to be producing good results in the UK and Europe–and recently they have entered the US market.

Superficially, the elements of the Lumeon platform echo advances made by many other health IT applications. Alerts and reminders? Check. Workflow automation? Check. Integration with a variety of EHRs? Of course! But there is something more to Lumeon’s approach to design that makes it a significant player. I had the opportunity to talk to Andrew Wyatt, Chief Operating Officer, to hear what he felt were Lumeon’s unique strengths.

Before discussing the platform itself, we have to understand Lumeon’s devotion to understanding the patient’s end-to-end experience, also sometimes known as the patient journey. Lumeon is not so idealistic as to ask providers to consider a patient’s needs from womb to tomb–although that would certainly help. But they ask such questions as: can the patient physically get to appointments? Can she navigate her apartment building’s stairs and her apartment after discharge from surgery? Can she get her medication?

Lumeon workflow view

*Lumeon workflow view

Such questions are the beginning of good user experience design (UX), and are critical to successful treatment. This is why I covered the HxRefactored conference in Boston in 2016 and 2017. Such questions were central to the conference.

It’s also intriguing that criminal justice reformers focus attention on the whole sequence of punishment and rehabilitation, including reentry into mainstream society.

Thinking about every step of the patient experience, before and after treatments as well as when she enters the office, is called a longitudinal view. Even in countries with national health care systems, less than half the institutions take such a view, and adoption of the view is growing only slowly.

Another trait of longitudinal thinking Wyatt looks for is coordinated care with strong involvement from the family. The main problem he ascribed to current health IT systems is that they serve the clinician. (I think many doctors would dispute this, saying that the systems serve only administrators and payers–not the clinician or the patient.)

Here are a couple success stories from Wyatt. After summarizing them, I’ll look at the platform that made them possible.

Alliance Medical, a major provider of MRI scans and other imaging services, used Lumeon to streamline the entire patient journey, from initial referral to delivery of final image and report. For instance, an online form asks patients during the intake process whether the patient has metal in his body, which would indicate the use of an alternative test instead of an MRI. The next question then becomes what test would meet the current diagnostic needs and be reimbursed by the payer. Lumeon automates these logistical tasks. After the test, automation provided by the Lumeon platform can make sure that a clinician reviews the image within the required time and that the image gets to the people who need it.

Another large provider in ophthalmology looked for a way to improve efficiency and outcomes in the common disease of glaucoma, by putting images of the eye in a cloud and providing a preliminary, automated diagnosis that the doctor would check. None of the cloud and telemedicine solutions covered ophthalmology, so the practice used the Lumeon platform to create one. The design process functioned as a discipline allowing them to put a robust process for processing patients in place, leading to better outcomes. From the patient’s point of view, the change was even more dramatic: they could come in to the office just once instead of four times to get their diagnosis.

An imaging provider found that they wasted 5 to 10 minutes each time they moved a machine between an upper body position and a lower body position. They saved many hours–and therefore millions of dollars–simply by scheduling all the upper body scans for one part of the day and all lower body scans for another. Lumeon made this planning possible.

In most of the US, value-based care is still in its infancy. The longitudinal view is not found widely in health care. But Wyatt says his service can help businesses stuck in the fee-for-service model too. For example, one surgical practice suffered lots of delays and cancellations because the necessary paperwork wasn’t complete the day before surgery. Lumeon helped them build a system that knew what tests were needed before each surgery and that prompted staff to get them done on time. The system required coordination of many physicians and labs.

Another example of a solution that is valuable in fee-for-service contexts is creating a reminder for calling colonoscopy patients when they need to repeat the procedure. Each patient has to be called at a different time interval, which can be years in the future.

Lumeon has been in business 12 years and serves about 60 providers in the UK and Europe, some very large. They provide the service on a SaaS basis, running on a HIPAA-compliant AWS cloud except in the UK, where they run their own data center in order to interact with legacy National Health Service systems.

The company has encountered along the way an enormous range of health care disciplines, with organizations ranging from small to huge in size, and some needing only a simple alerting service while others re-imagined the whole patient journey. Wyatt says that their design process helps the care provider articulate the care pathway they want to support and then automate it. Certainly, a powerful and flexible platform is needed to support so many services. As Wyatt said, “Health care is not linear.” He describes three key parts to the Lumeon system:

  1. Integration engine. This is what allows them to interact with the EHR, as well as with other IT systems such as Salesforce. Often, the unique workflow system developed by Lumeon for the site can pop up inside the EHR interface, which is important because doctors hate to exit a workflow and start up another.

    Any new system they encounter–for instance, some institutions have unique IT systems they created in-house–can be plugged in by developing a driver for it. Wyatt made this seem like a small job, which underscores that a lack of data exchange among hospitals is due to business and organizational factors, not technical EHR problems. Web services and a growing support for FHIR make integration easier

  2. Communications. Like the integration engine, this has a common substrate and a multiplicity of interfaces so doctors, patients, and all those involved in the health care journey can use text, email, web forms, and mobile apps as they choose.

  3. Workflow or content engine. Once they learn the system, clinicians can develop pathways without going back to Lumeon for support. The body scan solution mentioned earlier is an example of a solution designed and implemented entirely by the clinical service on its own.

  4. Transparency is another benefit of a good workflow design. In most environments, staff must remember complex sequences of events that vary from patient to patient (ordering labs, making referrals, etc.). The sequence is usually opaque to the patient herself. A typical Lumeon design will show the milestones in a visual form so everybody knows what steps took place and what remain to be done.

Wyatt describes Lumeon as a big step beyond most current workflow and messaging solutions. It will be interesting to watch the company’s growth, and to see which of its traits are adopted by other health IT firms.

Moving from “Reporting on” to “Leading” Healthcare – A Conversation with Dr. Halee Fischer-Wright, President & CEO of MGMA

Posted on October 11, 2017 I Written By

Colin Hung is the co-founder of the #hcldr (healthcare leadership) tweetchat one of the most popular and active healthcare social media communities on Twitter. Colin speaks, tweets and blogs regularly about healthcare, technology, marketing and leadership. He is currently an independent marketing consultant working with leading healthIT companies. Colin is a member of #TheWalkingGallery. His Twitter handle is: @Colin_Hung.

In Chapter 3 of Dr. Halee Fischer-Wright’s new book Back to Balance, she writes: “People are increasingly being treated as if they are the same. Science and data are being used to decrease variability in an attempt to get doctors to treat patients in predictable ways.” This statement is Fischer-Wright’s way of saying that the current focus on standardization of healthcare processes in the quest to reduce costs and increase quality may not be the brass ring we should be striving for. She believes that a balance is needed between healthcare standardization and the fact that each patient is a unique individual.

As president of the Medical Group Management Association (MGMA), a role Fischer-Wright has held since 2015, she is uniquely positioned to see first-hand the impact standardization (from both legislative and technological forces) has had on the medical profession. With over 40,000 members, MGMA represents many of America’s physician practices – a group particularly hard hit over the past few years by the technology compliance requirements of Meaningful Use and changes to reimbursements.

For many physician practices Meaningful Use has turned out to be more of a compliance program rather than an incentive program. To meet the program’s requirements, physicians have had to alter their workflows and documentation approaches. Complying with the program and satisfying the reporting requirements became the focus, which Fischer-Wright believes is a terrible unintended consequence.

“We have been so focused on standardizing the way doctors work that we have taken our eyes off the real goal,” said Fischer-Wright in and interview with HealthcareScene. “As physicians our focus needs to be on patient outcomes not whether we documented the encounter in a certain way. In our drive to mass standardization, we are in danger of ingraining the false belief that populations of patients behave in the same way and can be treated through a single standardized treatment regimen. That’s simply not the case. Patients are unique.”

Achieving a balance in healthcare will not be easy – a sentiment that permeates Back to Balance, but Fischer-Wright is certain that healthcare technology will play a key role: “We need HealthIT companies to stop focusing just on what can be done and start working on enabling what needs to be done. Physicians want to leverage technology to deliver better care to patient at a lower cost, but not at the expense of the patient/physician relationship. Let’s stop building tools that force doctors to stare at the computer screen instead of making eye contact with their patients.”

To that end, Fischer-Wright issued a friendly challenge to the vendors in the MGMA17 exhibit hall: “Create products and services that physicians actually enjoy using. Help reduce barriers between physician, patients and between healthcare organizations. Empower care don’t detract from it.”

She went on to say that MGMA itself will be stepping up to help champion the cause of better HealthIT for patients AND physicians. In fact, Fischer-Wright was excited to talk about the new direction for MGMA as an organization. For most of its history, MGMA has reported on the healthcare industry from a physician practice perspective. Over the past year with the help of a supportive Board of Directors and active members, the MGMA leadership team has begun to shift the organization to a more prominent leadership role.

“We are going to take a much more active role in healthcare. We are going to focus on fixing healthcare from the ground up –  from providers & patients upwards. In the next few years MGMA will be much bigger, much strong and even more relevant to physician practices. We are forging partnerships with other key players in healthcare, federal/state/local governments and other associations/societies.“

Members should expect more conferences, more educational opportunities and more publications on a more frequent basis from MGMA going forward. Fischer-Wright also hinted at several new technology-related offerings but opted not to provide details. Looking at the latest news from MGMA on their revamped data-gathering/analytics, however, it would not be surprising if their new offerings were data related. MGMA is one of the few organizations that regularly collects information on and provides context on the state of physician practices in the US.

It will be exciting to watch MGMA evolve in the years ahead.

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

Posted on January 27, 2017 I Written By

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

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

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

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

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

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

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

Data for sale, but not for treatment

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

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

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

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

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

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

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

Posted on January 26, 2017 I Written By

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

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

The identified patient

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

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

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

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

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

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

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

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

Almighty capitalism

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

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

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

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

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

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

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

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

Posted on January 25, 2017 I Written By

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

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

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

How we got to our current data collection practices

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

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

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

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

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

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

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

The Pain of Recording Patient Risk Factors as Illuminated by Apixio (Part 2 of 2)

Posted on October 28, 2016 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 ( 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 section of this article introduced Apixio’s analytics for payers in the Medicare Advantage program. Now we’ll step through how Apixio extracts relevant diagnostic data.

The technology of PDF scraping
Providers usually submit SOAP notes to the Apixio web site in the form of PDFs. This comes to me as a surprise, after hearing about the extravagant efforts that have gone into new CCDs and other formats such as the Blue Button project launched by the VA. Normally provided in an XML format, these documents claim to adhere to standards and offer a relatively gentle face to a computer program. In contrast, a PDF is one of the most challenging formats to parse: words and other characters are reduced to graphical symbols, while layout bears little relation to the human meaning of the data.

Structured documents such as CCDs contain only about 20% of what CMS requires, and often are formatted in idiosyncratic ways so that even the best CCDs would be no more informative than a Word document or PDF. But the main barrier to getting information, according to Schneider, is that Medicare Advantage works through the payers, and providers can be reluctant to give payers direct access to their EHR data. This reluctance springs from a variety of reasons, including worries about security, the feeling of being deluged by requests from payers, and a belief that the providers’ IT infrastructure cannot handle the burden of data extraction. Their stance has nothing to do with protecting patient privacy, because HIPAA explicitly allows providers to share patient data for treatment, payment, and operations, and that is what they are doing giving sensitive data to Apixio in PDF form. Thus, Apixio had to master OCR and text processing to serve that market.

Processing a PDF requires several steps, integrated within Apixio’s platform:

  1. Optical character recognition to re-create the text from a photo of the PDF.

  2. Further structuring to recognize, for instance, when the PDF contains a table that needs to be broken up horizontally into columns, or constructs such the field name “Diagnosis” followed by the desired data.

  3. Natural language processing to find the grammatical patterns in the text. This processing naturally must understand medical terminology, common abbreviations such as CHF, and codings.

  4. Analytics that pull out the data relevant to risk and presents it in a usable format to a human coder.

Apixio can accept dozens of notes covering the patient’s history. It often turns up diagnoses that “fell through the cracks,” as Schneider puts it. The diagnostic information Apixio returns can be used by medical professionals to generate reports for Medicare, but it has other uses as well. Apixio tells providers when they are treating a patient for an illness that does not appear in their master database. Providers can use that information to deduce when patients are left out of key care programs that can help them. In this way, the information can improve patient care. One coder they followed could triple her rate of reviewing patient charts with Apixio’s service.

Caught between past and future
If the Apixio approach to culling risk factors appears round-about and overwrought, like bringing in a bulldozer to plant a rosebush, think back to the role of historical factors in health care. Given the ways doctors have been taught to record medical conditions, and available tools, Apixio does a small part in promoting the progressive role of accountable care.

Hopefully, changes to the health care field will permit more direct ways to deliver accountable care in the future. Medical schools will convey the requirements of accountable care to their students and teach them how to record data that satisfies these requirements. Technologies will make it easier to record risk factors the first time around. Quality measures and the data needed by policy-makers will be clarified. And most of all, the advantages of collaboration will lead providers and payers to form business agreements or even merge, at which point the EHR data will be opened to the payer. The contortions providers currently need to go through, in trying to achieve 21st-century quality, reminds us of where the field needs to go.

The Pain of Recording Patient Risk Factors as Illuminated by Apixio (Part 1 of 2)

Posted on October 27, 2016 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 ( 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.

Many of us strain against the bonds of tradition in our workplace, harboring a secret dream that the industry could start afresh, streamlined and free of hampering traditions. But history weighs on nearly every field, including my own (publishing) and the one I cover in this blog (health care). Applying technology in such a field often involves the legerdemain of extracting new value from the imperfect records and processes with deep roots.

Along these lines, when Apixio aimed machine learning and data analytics at health care, they unveiled a business model based on measuring risk more accurately so that Medicare Advantage payments to health care payers and providers reflect their patient populations more appropriately. Apixio’s tools permit improvements to patient care, as we shall see. But the core of the platform they offer involves uploading SOAP notes, usually in PDF form, and extracting diagnostic codes that coders may have missed or that may not be supportable. Machine learning techniques extract the diagnostic codes for each patient over the entire history provided.

Many questions jostled in my mind as I talked to Apixio CTO John Schneider. Why are these particular notes so important to the Centers for Medicare & Medicaid Services (CMS)? Why don’t doctors keep track of relevant diagnoses as they go along in an easy-to-retrieve manner that could be pipelined straight to Medicare? Can’t modern EHRs, after seven years of Meaningful Use, provide better formats than PDFs? I asked him these things.

A mini-seminar ensued on the evolution of health care and its documentation. A combination of policy changes and persistent cultural habits have tangled up the various sources of information over many years. In the following sections, I’ll look at each aspect of the documentation bouillabaisse.

The financial role of diagnosis and risk
Accountable care, in varying degrees of sophistication, calculates the risk of patient populations in order to gradually replace fee-for-service with payments that reflect how adeptly the health care provider has treated the patient. Accountable care lay behind the Affordable Care Act and got an extra boost at the beginning of 2016 when CMS took on the “goal of tying 30 percent of traditional, or fee-for-service, Medicare payments to alternative payment models, such as ACOs, by the end of 2016 — and 50 percent by the end of 2018.

Although many accountable care contracts–like those of the much-maligned 1970s Managed Care era–ignore differences between patients, more thoughtful programs recognize that accurate and fair payments require measurement of how much risk the health care provider is taking on–that is, how sick their patients are. Thus, providers benefit from scrupulously complete documentation (having learned that upcoding and sloppiness will no longer be tolerated and will lead to significant fines, according to Schneider). And this would seem to provide an incentive for the provider to capture every nuance of a patient’s condition in a clearly code, structured way.

But this is not how doctors operate, according to Schneider. They rebel when presented with dozens of boxes to check off, as crude EHRs tend to present things. They stick to the free-text SOAP note (fields for subjective observations, objective observations, assessment, and plan) that has been taught for decades. It’s often up to post-processing tools to code exactly what’s wrong with the patient. Sometimes the SOAP notes don’t even distinguish the four parts in electronic form, but exist as free-flowing Word documents.

A number of key diagnoses come from doctors who have privileges at the hospital but come in only sporadically to do consultations, and who therefore don’t understand the layout of the EHR or make attempts to use what little structure it provides. Another reason codes get missed or don’t easily surface is that doctors are overwhelmed, so that accurately recording diagnostic information in a structured way is a significant extra burden, an essentially clerical function loaded onto these highly skilled healthcare professionals. Thus, extracting diagnostic information many times involves “reading between the lines,” as Schneider puts it.

For Medicare Advantage payments, CMS wants a precise delineation of properly coded diagnoses in order to discern the risk presented by each patient. This is where Apixio come in: by mining the free-text SOAP notes for information that can enhance such coding. We’ll see what they do in the next section of this article.

Has Electronic Health Record Replacement Failed?

Posted on June 23, 2016 I Written By

The following is a guest blog post by Justin Campbell, Vice President, Galen Healthcare.
Justin Campbell
A recent Black Book survey of hospital executives and IT employees who have replaced their Electronic Health Record system in the past three years paints a grim picture. Respondents report higher than expected costs, layoffs, declining revenues, disenfranchised clinicians and serious misgivings about the benefits of switching systems. Specifically:

  • 14% of all hospitals that replaced their original EHR since 2011 were losing inpatient revenue at a pace that wouldn’t support the total cost of their replacement EHR
  • 87% of hospitals facing financial challenges now regret the decision to change systems
  • 63% of executive level respondents admitted they feared losing their jobs as a result of the EHR replacement process
  • 66% of system users believe that interoperability and patient data exchange functionality have declined

Surely, this was not the outcome expected when hospitals rushed to replace paper records in response to Congressional incentives (and penalties) included in the 2009 American Recovery and Reinvestment Act.

But the disappointment reflected in this survey only sheds light on part of the story. The majority of hospitals depicted here were already in financial difficulty. It is understandable that they felt impelled to make a significant change and to do so as quickly as possible. But installing an electronic record system, or replacing one that is antiquated, requires much more than a decision to do so. We should not be surprised that a complex undertaking like this would be burdened by complicated and confusing challenges, chief among which turned out to be “usability” and acceptance.

Another Black Book report, this one from 2013, revealed:

  • 66% of doctors using EHR systems did not do so willingly
  • 87% of those unwilling to use the system claimed usability as their primary complaint
  • 84% of physician groups chose their EHR to reach meaningful use incentives
  • 92% of practices described their EHR as “clunky” and/or difficult to use

None of this should surprise us but we need to ask: was usability really the key driver for EHR replacement? Is usability alone accountable for lost revenue, employment anxiety and buyers’ remorse? Surely organizations would not have dumped millions into failed EHR implementations only to rip-and-replace them due to usability problems and provider dissatisfaction. Indeed, despite the persistence of functional obstacles such as outdated technology, hospitals continue to make new EMR purchases. Maybe the “reason for the rip-and-replace approach by some hospitals is to reach interoperability between inpatient and outpatient data,” wrote Dr. Donald Voltz, MD in EMR and EHR.

Interoperability is linked to another one of the main drivers of EHR replacement: the mission to support value-based care, that is, to improve the delivery of care by streamlining operations and facilitating the exchange of health information between a hospital’s own providers and the caregivers at other hospitals or health facilities. This can be almost impossible to achieve if hospitals have legacy systems that include multiple and non-communicative EHRs.

As explained by Chief Nurse Executive Gail Carlson, in an article for Modern Healthcare, “Interoperability between EHRs has become crucial for their successful integration of operations – and sometimes requires dumping legacy systems that can’t talk to each other.

Many hospitals have numerous ancillary services, each with their own programs. The EHRs are often “best of breed.” That means they employ highly specialized software that provides excellent service in specific areas such as emergency departments, obstetrics or lab work. But communication between these departments is compromised because they display data differently.

In order to judge EHR replacement outcomes objectively, one needs to not just examine the near-term financials and sentiment (admittedly, replacement causes disruption and is not easy), but to also take a holistic view of the impact to the system’s portfolio by way of simplification and future positioning for value-based care. The majority of the negative sentiment and disappointing outcomes may actually stem from the migration and new system implementation process in and of itself. Many groups likely underestimated the scope of the undertaking and compromised new system adoption through a lackluster migration.

Not everyone plunged into the replacement frenzy. Some pursued a solution such as dBMotion to foster care for patients via intercommunications across all care venues. In fact, Allscripts acquired dBMotion to solve for interoperability between its inpatient solution (Eclipsys SCM) and its outpatient EMR offering (Touchworks). dBMotion provides a solution for those organizations with different inpatient and outpatient vendors, offering semantic interoperability, vocabulary management, EMPI and ultimately facilitating a true community-based record.

Yet others chose to optimize what they had, driven by financial constraints. There is a thin line separating EHR replacement from EHR optimization. This is especially true for those HCOs that are neither large enough nor sufficiently funded to be able to afford a replacement; they are instead forced to squeeze out the most value they can from their current investment.

The optimization path is much more pronounced with MEDITECH clients, where a large percentage of their base remains on the legacy MAGIC and C/S platforms.

Denni McColm, a hospital CIO, told healthsystemCIO why many MEDITECH clients are watching and waiting before they commit to a more advanced platform:

“We’re on MEDITECH’s Client/Server version, which is not their older version and not their newest version, and we have it implemented really everywhere that MEDITECH serves. So we have the hospital systems, home care, long-term care, emergency services, surgical center — all the way across the continuum. We plan to go to their latest version sometime in the next few years to get the ambulatory interface for the providers. It should be very efficient — reduced clicks, it’s mobile friendly, and our docs are anxious to move to it,” but we’ll decide when the time is right, she says.

What can we discern from these different approaches and studies?  It’s too early to be sure of the final score. One thing is certain though: the migrations and archival underpinnings of system replacement are essential. They allow the replacement to deliver on the promise of improved usability, enhanced interoperability and take us closer to the goal of value-based care.

About Justin Campbell
Justin is Vice President, Strategy, at Galen Healthcare Solutions. He is responsible for market intelligence, segmentation, business and market development and competitive strategy. Justin has been consulting in Health IT for over 10 years, guiding clients in the implementation, integration and optimization of clinical systems. He has been on the front lines of system replacement and data migration and is passionate about advancing interoperability in healthcare and harnessing analytical insights to realize improvements in patient care. Justin can be found on Twitter at @TJustinCampbell