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What’s in a Chart? – Fun Friday

Posted on November 30, 2018 I Written By

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

I’ve heard people say “What’s the difference between stats and lies? Nothing!” While that’s a bit of an exaggeration, there is something to say about stats that don’t share the real story. A good person with the right data can tell whatever story they want to tell.

I guess this fact is why this cartoon resonated so much to me and many others who shared it on social media.

The tweet is said from a marketer’s standpoint, but the same is true in healthcare as well. A nurse or doctor looking at a dashboard might miss something if the dashboards aren’t measuring the right thing. The same is true for any healthcare leader that spends time looking at dashboards. Dashboards are great…if you’re measuring and presenting the right things.

Don’t be WOWed by the fancy charts and graphs until you understand what the data really means.

Value Based Care: We Need a Better Health IT System to Measure It

Posted on April 16, 2018 I Written By

Healthcare as a Human Right. Physician Suicide Loss Survivor. Janae writes about Artificial Intelligence, Virtual Reality, Data Analytics, Engagement and Investing in Healthcare. twitter: @coherencemed

At HIMSS this year in Las Vegas I looked at the nature of the EHR and if we have the current computing and data infrastructure to enable better value based care.  Our data capabilities are failing to allow providers to align reimbursement with great care delivery.

Under the premise of “what gets watched gets done”, we understand that improving care delivery will require us to align incentives with desired outcomes. The challenge is that, among the many ills plaguing our version of the truth mined from data found in electronic health records systems, reimbursement data presents the core issue for informatics departments across the country. To resolve this issue, we need documentation to reflect the care we are delivering, and we need care delivery to center around patient care. Health information management should be heavily involved in data capture. To truly improve care, we need better tools to measure it, and healthcare data is expanding to answer difficult questions about care delivery and cost.

Our first challenge is stemming the proliferation of extraneous documentation, and healthcare is still addressing this issue. What used to be written on a 3-by-5 index card (and sometimes via illegible doctor’s notes) is now a single point in a huge electronic record that is, surprisingly, not portable. Central to our issues around the cost of care, we have also seen that quantity is valued more than quality in care delivery.

Duplicated testing or unnecessary procedures are grimly accepted as standard practice within the business of medicine. Meaningless and siloed care delivery only helps this issue proliferate across the health of a population. To resolve these issues, our workflow and records need to capture the outcomes we are trying to obtain and must be customized for the incentives of every party.

Incentives for providers and hospital administrators should center around value: delivering the best outcomes, rather than doing more tests. Carefully mapping the processes of healthcare delivery and looking at the resource costs at the medical condition level, from the personnel costs of everyone involved to perform a medical procedure to the cost of the medical device itself, moves organizations closer to understanding total actual costs of care.  Maximizing value in healthcare–higher quality care at lower costs–involves a closer look and better understanding of costs at the medical condition level. Value and incentives alignment should provide the framework for health records infrastructure.

When you walk into Starbucks, your app will tell you what song is playing and offer options to get extra points based on what you usually order. Starbucks understands their value to the customer and the cost of their products to serve them. From the type of bean, to the seasonal paper cup, to the amount of time it takes to make the perfect pumpkin spice latte, Starbucks develops products with their audience in mind–and they know both how much this production costs and how much the user is willing to pay. The cost of each experience starts well before the purchase of the beverage. For Starbucks, they know their role is more than how many lattes they sell; it is to deliver a holistic experience; delight the customer each time.  

Healthcare has much to learn about careful cost analysis from the food and beverage retail industry, including how to use personalized medicine to deliver the best care. Value-Based Healthcare reporting will help the healthcare industry as a whole move beyond the catch-up game we currently play and be proactive in promoting health with a precise knowledge of individual needs and cost of care. The investment into quantifying healthcare delivery very precisely and defining personal treatment will have massive investments in the coming years and deliver better care at a lowered cost. Do current healthcare information systems and analytics have the capacity to record this type of cost analysis?

“Doctors want to deliver the best outcomes for their patients. They’re highly trained professionals. Value Based Healthcare allows you to implement a framework so every member of the care team operates at the top of his or her license.”

-Mahek Shah, MD of Harvard Business School.

These outcomes should be based on the population a given hospital serves, the group of people being treated, or at the medical condition level. Measures of good outcomes are dynamic and personalized to a population. One of the difficulties in healthcare is that while providers are working hard for the patient, healthcare systems are also working to make a profit.

It is possible to do well while doing good, but these two goals are seemingly in conflict within the billion dollar healthcare field. Providing as many services as possible in a fee-for-service-based system can obfuscate the goal of providing great healthcare. Many patients have seen multiple tests and unnecessary procedures that seem to be aligned with the incentive of getting more codes recorded for billing as opposed to better health outcomes for the patients.  

The work of Value Based Time Data Activity Based Costing can improve personalized delivery for delivery in underserved populations as well as for affluent populations. The World Health Organization (WHO) published the work of improving care delivery in Haiti. This picture of the care delivery team is population-specific. A young person after an accident will have different standards for what constitutes “right care right time right place” than a veteran with PTSD. Veterans might need different coverage than members of the general public, so value based care for a specific group of veterans might incorporate more mental health and behavioral health treatment than value based care serving the frail elderly, which could incorporate more palliative care and social (SDoH) care. Measuring costs with TDABC for that specific population would include not just the cost of specialists specific to each segment of the population, but of the entire team (social worker, nursing, nutritionist, psychologists) that is needed to deliver the right care, achieve the best outcomes, and meet the needs of the patient segment.

Healthcare systems are bombing providers and decision makers with information and trying to ferret out what that information really means. Where is it meaningful? Actionable? Process improvement teams for healthcare should look carefully at data with a solid strategy. This can start with cost analysis specific to given target populations. Frequently, the total cost of care delivery is not well understood, from the time spent at the clinic to prescribe a hip replacement to the time in the OR, to recovery time; capturing a better view includes accounting for every stage of care. Surgeons with better outcomes also have a lower total long-term cost of care, which impacts long-term expenses involved when viewing it through the lens of an entire care cycle. If you are a great surgeon–meaning your outcomes are better than others–you should get paid for it. The best care should be facilitated and compensated, rather than the greatest number of billing codes recorded. Capturing information about outcomes and care across multiple delivery areas means data must be more usable and more fluid than before.

Healthcare informatics systems should streamline the processes that are necessary to patient care and provider compensation. The beginning of this streamlined delivery involves capturing a picture of best care and mapping the cost of processes of care. The initial investment of TDABC in researching these care costs at the patient level can be a huge barrier for healthcare systems with small margins and limited resources. This alignment is an investment in your long-term viability and success.

Once you understand your underlying costs to deliver care, health systems will be better prepared to negotiate value-based payment contracts with payers and direct-to-employers. Pair your measurement of costs with your outcomes. Integrating care delivery with outcomes standards has improved in recent times through ICHOM. Medical systems need to incentivize health if healthy patients are a priority.  The analysis of specific costs to a system needs a better reporting system than a charge master or traditional EHR which is strongly designed toward recording fee for service work. We must align or incentives and our health IT with our desired outcomes in healthcare. The more billing codes I can create in an electronic health record, the more I am reimbursed. Reimbursement alignment should match desired outcomes and physicians operating at top of their license.

Under value-based care, health and well-being become a priority whereby often in the fee-for-service model, sickness can be the priority because you get paid by doing more interventions, which may not lead to the best outcomes. The careful measurement of care (i.e. TDABC) paired with standards of best care will improve care delivery and reduce the cost of that care delivery. Insights about improved models and standards of care for outcomes and healthcare delivery allow patients, providers, and administrators to align with the shared goal of healthier patient populations. I am looking forward to the data infrastructure to catch up with these goals of better care delivery and a great patient experience.

 

Does Your EHR Sell Your EHR Data?

Posted on May 12, 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.

I recently saw a tongue-in-cheek tweet from Howard Green, MD about how healthcare shares data:

There has always been a disconnect between providers and EHR vendors saying they can’t share data and then EHR vendors can easily sell and share EHR data to the healthcare industry. If you don’t think this happens at large scales in healthcare, then you need to look no further than IMS which last I checked was a multi billion dollar public company on the back of our health data.

The “sharing” or should we say selling of EHR data is big business and happening a lot more than we realize. I know the Patient Privacy Rights organization was trying to make a map of all the ways your health data was being shared. However, you can imagine that’s an almost impossible task to accomplish. I think most of us would be shocked to see how far and wide are health data is shared.

I wonder how many doctors know the answer to this question, “Does your EHR sell your EHR data?”

My guess is that most doctors assume that their EHR data is not being sold. For a number of EHR vendors, that’s probably true. However, my guess is that most doctors don’t know their EHR vendor’s policy on selling EHR data. If you don’t know, you should ask your EHR vendor and find out.

For those EHR vendors that are selling EHR data, you can be sure that they will happily reply that any EHR data they sell is de-identified. They’ll argue that it’s not a violation of HIPAA because it doesn’t have any PHI because they’ve de-identified the data and only sell the data in aggregate. No doubt there are many that would argue that there’s no perfect way to totally de-identify your EHR data and that when combined with other sources, they can often identify your patients.

This is big business and so it’s easy to see why an EHR vendor would give the go ahead to de-identify and sell the data stored in their EHR. Although, it is disappointing when they’re doing this and their users don’t know that’s the case.

If you’ve asked your vendor if they sell your EHR data, we’d love to hear what they say. How did they respond? Are you ok with your EHR selling your de-identified EHR data?

EHR Data Integration and Changing Health Behaviors

Posted on November 16, 2015 I Written By

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

Every so often I like to highlight interesting tweets from the Twittersphere and add some of my own commentary. Here’s a few of them worth mentioning today.


How many EHR integrate with Fitbit? I’ve seen a few partial integrations, but none of them that really make an impact on the patients life. At best I’ve seen them take the data in, but then they do nothing with it. I’d love to see some examples where the EHR is actually doing something with the Fitbit data. In fact, is there anyone taking Fitbit data and making it more useful than what it is in the Fitbit app?


Speaking of outside data (Fitbit data), I agree with IBM that we’re heading towards a lot more data than just what the EMR can provide. In fact, I think the real breakthroughs in health care are going to come from the mixing of multiple data sources into a pretty little package with a bow on top. We’re still Christopher Columbus looking for the new world though. However, unlike Columbus, I know the world isn’t flat (ie. there’s value in the data).


I love when things are timely. I’m extremely interested in this discussion about behavior change in health care. I’m glad that the #hcldr chat is about this topic. I’ll be watching with a keen eye on what people share. I hope everyone will take the time to share their thoughts on how to change people’s health behaviors.

90% of the World’s Data Was Created in the Last 12 Months

Posted on October 14, 2015 I Written By

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

Vala Afshar shared the following data insight from @salesforce.

Then, Jim Rawson, MD asked an important question for those of us in healthcare: How do you plan to manage it?

I’ve been having a discussion around healthcare data with a lot of people recently. One person I talked with at MGMA says we need more filters with that data. I thought it was interesting that he used the word filters. I’m not sure it’s quite the right word since filters means you only look at part of the data. In healthcare we need something that looks at all the data, but only boils up the data that matters to the healthcare provider at the time and place they need it.

Unfortunately, I’ve seen most healthcare analytics and healthcare data companies focused on the data. I haven’t seen many of them really work on the intelligence (filters if you prefer) that’s needed on top of that data. Healthcare organizations need solutions. They don’t need more tools.

We all know why companies are providing tools as opposed to solutions. It’s much easier to build the tools. It’s much harder to discover and share the solutions. However, the reward is going to be massive for those organizations that provide solutions.

Going back to the original question: How do you plan to manage all the data? I think that most healthcare providers have no idea. I think they assume they’ll be able to purchase solutions that do the work for them. I’m not seeing many of those solutions yet, but I’m sure they’re coming.

Understanding Personal Health Data: Not All Bits Are the Same (Part 4 of 4, Personal Health Data)

Posted on October 1, 2015 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.

Previous segments of this article explained what makes data sharing difficult in four major areas of Internet data: money, personal data, media content, and government information. Now it’s time to draw some lessons for the health care field.

Personal Health Data

So let’s look now at our health data. It’s clearly sensitive, because disclosure can lead to discrimination by employees and insurers, as well as ostracism by the general public. For instance, an interesting article highlights the prejudice faced by recovering opiate addicts among friends and co-workers if they dare to reveal that they are successfully dealing with their addition.

The value of personal health data is caught up with our very lives. We cannot change our diagnoses or genetic predispositions to disease the way we can change our bank accounts or credit cards. At the same time, whatever information we can provide about ourselves is of immense value to researchers who are trying to solve the health conditions we suffer from.

So we can assume that health data has an enhanced value and requires more protection than other types of personal data.

Currently, we rarely control our data. Anything we tell doctors is owned by them. HIPAA strictly controls the sharing of such data (especially as it was clarified a couple years ago in the handling of third parties known as “business associates”). But doctors have many ways to deny us access to our own data. One of my family members goes to a doctor who committed the sin of changing practices. We had to pay the old practice to transfer records to the new practice. (I have written about problems with interoperability and data exchange in many other contexts, including blog posts about the Health Datapalooza and the HIT Standards Committee. Data exchange problems hinder research, big data inquiries, and clinical interventions.)

A doctor might well claim, “Why shouldn’t I own that data? Didn’t I do the exam? Didn’t I order the test whose data is now in the record?” Using that logic, the doctor should grant the lab ownership of the test. Now that patients can order their own medical tests (at least in Arizona), how does this dynamic around ownership change? And as more and more patients collect data on themselves using things such as the Apple Watch, network-connected scales, and fitness devices — data that may contain inaccuracies but is still useful for understanding people’s behavior and health status — how does this affect the power balance between a patient and the healthcare provider, or a researcher pursuing a clinical trial?

It’s also interesting to note that although HIPAA covers data collected by people who treat us and insurers who pay for the treatment, it has no impact on data in other settings. In particular, anything we choose to share online joins the enormous stream of public data without restrictions on use.

And it’s disturbing how freely data can be shared with marketers. For instance, when Vermont tried to restrain pharmacies from selling data about prescriptions to marketers, it was overruled by the U.S Supreme Court. The court took it for granted that pharmacies would adequately de-identify patients, but this is by no means assured.

What are the competing priorities, then, about protection of health data? On the research side — where data can really help patients by finding cures or palliative measures — pressures are increasing to loosen our personal control over data. Laws and regulations are being amended to override the usual restrictions placed on researchers for the reuse of patient data.

The argument for reform is that researchers often find new uses for old data, and that the effort of contacting patients and getting permission to reuse the data impose prohibitive expenses on researchers.

Certainly, I would get annoyed to be asked every week to approve the particular reuse of my personal data. But I’d rather be asked than have my preferences overridden. In the Internet age, I find it ridiculous to argue that researchers would be overly burdened to request access to data for new uses.

A number of efforts have been launched to give researchers a general, transferable consent to patient data. Supposedly, the patient would grant a general release of data for some class of research at the beginning of data collection. But these efforts have all come to naught. Remember that a patient is often asked for consent to release data at a very tense moment — just after being diagnosed with a serious disease or while on the verge of starting a difficult treatment regimen. Furthermore, the task of designing a general class of research is a major semantic issue. It would require formalizing in software what the patient does and does not allow — and no one has solved that problem.

How, then, do I suggest resolving the question of how we should handle patient data? First, patients need to control all data about themselves. All clinicians, pharmacies, labs, and other institutions exist to serve patients and support their health. They can certainly validate data — for instance, by providing digital signatures indicating the diagnoses, test results, and other information are accurate — but they do not own the data.

A look at how we’re protecting money on the Internet may help us understand the urgency of protecting health data: storing it securely, encrypting it, and making outside organizations jump through hoops to access it.

Ownership of patient data is currently as murky as personal data of other types, HIPAA notwithstanding. We can use many of the same arguments and concepts for health data that we’ve seen for other personal data. As with government data, we can hold interesting discussions about how much difference anonymization makes to ownership — do you have no right to restrict the use of your health or government data once it is supposedly anonymized?

Dr. Adrian Gropper, CTO of Patient Privacy Rights, says that the concept of “ownership” is not helpful for patient data. It is better in terms of both law and computer science to speak of authorization: who can look at the data and who grants the right to look at it. Gropper works on the open source HEART WG project, which is creating an OAuth-based system to support patient control, and which he and I have written about on the Radar site.

The corollary of this principle is that patients need repositories for their data that are easy to manage. HEART WG can tie together data in different repositories — the patient’s, the clinicians’, and others — and control the flow from one repository to another.

Finally, researchers must contact patients to explain how their data will be used and to request permission. With Internet tools, this should not be onerous for the researcher or the patient. Hey, everybody in medicine nowadays touts “patient engagement.” One is likely to get better data if one engages. So, let’s do it. And that way we can avoid the uncertain protection of anonymization or de-identification, which degrades patient data in order to render it harder to track back to an individual.

Researchers worry about request fatigue if individuals have to respond to every request manually, although I see this as a great opportunity for research projects to explain their goals and drum up public support. A number of organizations are trying to design systems to let individuals approve use of their data in advance, and I wish them the best, but all such attempts have shipwrecked on two unforgiving shoals. First is the impossibility of anticipating new research and the radically different directions it can take. Second is the trap of ontologies: who can define a useful concept such as “non-profit research” in terms strict enough to be written into computer programs? And how will the health care world agree on representations of the ontologies and produce perfectly interoperable computer programs to automate consent?

Value, ownership, and protection are difficult questions on an Internet that was designed in the 1960s and 1970s as a loose, open platform. We can fill the gaps through policy measures and technical protections based on well-grounded principles. Patients care about their data and its privacy. We can give them the control they crave and deserve.

Understanding Personal Health Data: Not All Bits Are the Same (Part 3 of 4, Government Information)

Posted on September 30, 2015 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.

Previous segments of this article (parts 1 and 2) have explored the special characteristics of various types of data shared on the Internet. This one will look at one more type of data before we turn to the health care field.

Government Information

Governments generate data during their routine activities, often in wild and unstructured ways. They have exploited this data for a long time, as some friends of mine found out a good 35 years ago when they started receiving promotions for wedding registries from local companies. They decided that the only way those companies could know they were getting married is from the town where they obtained their marriage license.

Government data offers many less exploitative uses, however; it forms a whole discipline of its own explored by such groups as the Governance Lab and the Personal Democracy Forum. Governments open data on transportation, bills and regulations, and crime and enforcement, among other things, to promote civic engagement and new businesses.

The value of such data comes from its reliability. Therefore, data that is inconsistently collected, poorly coded, outdated, or inordinately redacted reduces public confidence. Such lapses are all too common, even on the U.S. government’s celebrated data.gov site.

Joel Gurin, president and founder of the Center for Open Data Enterprise, told me that some of the most advanced federal agencies in the open data area — the Departments of Health and Human Services, Energy, Transportation, and Commerce — provide better access to their records on their own sites than on data.gov. The latter is not set up as well for finding data or getting information about its provenance, meaning, and use.

Some government data requires protection because it contains sensitive personal information. Legal battles often arise regarding whether data should be released on elected officials and employees — for instance, on police officers who were arrested for drunk driving — because the privacy rights of the official clash with the public’s right to know. De-identification is not always done properly, or succumbs to later re-identification efforts. And data can be misleading in the cases where analysts and journalists don’t understand the constraints around data collection. In addition, protection is currently decided on a rather arbitrary basis, and varies wildly from jurisdiction to jurisdiction.

For a long-range perspective on government data quality, I talked to Stefaan G. Verhulst, co-founder and Chief Research and Development Officer of the Governance Laboratory at NYU. He said, “The question is whether a government should only share data that is of high value and high quality, or whether we can benefit from a hybrid approach where the market addresses some of the current weaknesses of data. A site such as data.gov represents a long tail: some data may be of value only to a tiny set of people, but they may be willing to invest money in extracting the data from formats and repositories that are less than optimal. And hopefully, weaknesses will be rectified at the source by governments over time.”

Gurin, in his book Open Data Now (which I reviewed), calls for government outreach and partnerships with stakeholders, such as businesses that can capitalize on open data. Such partnerships would help decide what data to release and where to put resources to improve data.

One gets interesting results when asking who owns government data. The obvious answer is that it belongs to the taxpayers who paid for its collection, and by extension (because restricting it to taxpayers is unfeasible) to the public as a whole.

Nonetheless, many foreign countries and local U.S. governments copyright data. Access to such data is prohibitively expensive. Even when information is supposedly in the public domain, obscure data formats make it hard to retrieve online, and government agencies throughout the U.S. often charge exorbitant fees to people who obtain data, even when requests are granted under the Freedom of Information Act. Recent low points include resistance in Massachusetts to reforming the worst public record policies in the country, and the bizarre persecution of open government advocates by Georgia and Oregon. Unfortunately, the idea that government data should be open to all is intuitive, but far from universally accepted.

Now we have looked at four types of data in a series of articles; the next one will bring the focus back to health care.

Understanding Personal Health Data: Not All Bits Are the Same (Part 2 of 4, Personal Data and Media Content)

Posted on September 29, 2015 I Written By

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

The previous segment of this article introduced the notion that many types of data on the Internet, including personal health data, come entangled with constraints on how we can store, share, and use it. I’ll examine two more types of data–personal data and media content–in this article, and government information in the next.

Personal Data

The photos, status updates, hotel reviews, and other personal postings we upload daily constitute a huge repository of data, along with a huge market. This section talks about the melange of information that determined seekers can find about us online: usually things we voluntarily offer through Facebook, Instagram, etc., but also things that others say about us and “data exhaust” generated by our purchases and other activity that companies and governments track. When we go online, we tend to present the sides of ourselves we would like others to know about–but we don’t always understand what we’re revealing about our predilections, prejudices, and drives.

A 2012 McKinsey report suggests that social technologies offer anywhere from $900 billion to $1.3 trillion in annual value — and that’s just counting four industries (page 9 of the report).

So our personal data clearly has value. However, there are qualifications to this value. The problem is that no one is tasked with making sure the information is correct. People enter lies and distorted versions of their life events to social networks all the time. Marketers and other data-slurping companies hope that the inaccuracies work themselves out during big-data processing. But that assumes that the truth lies in there somewhere (a dubious proposition) and that sophisticated data mining techniques can eliminate inaccurate outliers.

Ownership is a curious and fascinating question for personal data. Do you “own” the data item indicating that you just purchased a shirt from Everlane? Proponents of vendor relationship management would say yes. These Internet reformers would like consumers to be in charge of the data related to their transactions, and would like companies that want to use such data for marketing or planning to pay customers. Others would argue that Everlane has just as much a right to the data as you do — you are both parties to a transaction.

As I have indicated elsewhere, ownership is a slippery concept, even when you generate it yourself. When I take photos of friends, they often ask me not to post the pictures to Facebook. I respect this, treating them as owners of their digital images. It’s interesting, incidentally, that this question of intrusive photo-taking underlies the seminal work on privacy: the 1890 Harvard Law Review article by Warren and Brandeis.

Currently, ownership is something of a Wild West where anyone who gets your personal data can use it, unless you have explicitly put it under license. So protection — the third trait of Internet data I address throughout this article — is weak and oft trampled on in personal data. I think we all want to protect personal health data from this situation, a theme I’ll return to when we get to that section of the article.

Media Content

Because I work for a publisher — and one particularly prescient in its adaptation to the wired world — I have participated in many discussions of media content. I’m talking here of things that aren’t just thrown on the open Internet, like articles on this Radar blog, but are hidden behind walls that you can enter only after paying, or at least by entering an email address and some personal information such as the size and industry of your company. Your email address is tremendously useful to the company providing the content, whether they use it to shove ads at you, sell information to vendors, or determine what future content to produce.

Is media content valuable? Certainly it is, thanks to the years of expertise and hours of effort invested by those who created and curated it. Note that in the previous section, I cited a McKinsey report. I didn’t spend hours vetting the report or checking McKinsey’s credentials. I relied on their reputation as a key source of information in the tech industry — an example of the value created by trusted content sites.

This confirms the dictum that information on the Internet wants to be expensive, as famously said by Stewart Brand. That’s why many people spend good money to access news sites and online books, and other people go to great efforts to get it for free.

The question of ownership is resolved by copyright law, but in ways that are not entirely compatible with the Internet. For instance, many researchers would often love to share their papers with all who want them, but the publishers usually own the content and place restrictions on such sharing. Luckily, many academic publishers now allow authors to place early pre-publication drafts online for free download. I can locate a free copy of most research articles by entering the title and author names into a search engine.

Indeed, when we talk about “owning” data, we fall into a trap prepared by large corporate interests who depend upon notions of Intellectual Property to maintain their income flows. I am not opposed to the exercise of copyrights, patents, and trademarks, but I worry about the extension of these carefully defined concepts to a larger context where casual references to property and (as a consequence) ownership in are at best unhelpful and at worst meaningless.

Protection is also a controversial topic hre. Many publishers (but definitely not my company, O’Reilly Media) take extraordinary efforts to protect data, notably digital rights management, which I cover in other articles. It’s notable that no laws restrict you from downloading software from the Internet to make a gun, but severe laws punish not just downloading copyrighted content, but offering tools that let people break the digital rights management on that content.

Further segments of this article will continue to explore Internet information and its meaning for the health care field.

Understanding Personal Health Data: Not All Bits Are the Same (Part 1 of 4)

Posted on September 28, 2015 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.

When people run out of new things to say in the field of health IT, they utter the canard, “Why can’t exchanging patient data be as easy as downloading a file on the Internet?” For a long time, I was equally smitten by the notion of seamless exchange, which underlies the goals of accountable care, patient-centered medical homes, big data research, and the Precision Medicine Initiative so dear to the White House. Then I began to notice that patient information differs in deep ways from arbitrary data on the Internet.

Personal health data isn’t alone in having special characteristics that make handling it fraught with dangers and complications. In this article, I’ll look at several other types of online data laden with complexity — money, personal data, media content, and government information — and draw some conclusions for how we might handle health data.

Money

I am not an early adopter by habit, even though I work in high tech. When someone announces, “Now you can pay your bills using your phone!” it sounds to me like “Now you can mow your lawn using your violin!” Certain things just don’t go together naturally. Money is not like other bits; you can’t copy it the way people casually share their photos or email messages.

Of course I endorse the idea of online payment systems. They have transformed the economies of rural communities in underdeveloped parts of the world like sub-Saharan Africa. They can be useful in the U.S. for people who can’t get credit cards or even checking accounts.

Perhaps that’s why there are at least 235 (as of the time of publication) online payment systems. But money isn’t a casual commodity. It requires coordination and control. Even the ballyhooed Bitcoin system needs checks and balances. Famously described as decentralized because many uncoordinated systems create the coins and individuals store their own, Bitcoin-like systems are actually heavily centralized around the blockchain they hold in common.

Furthermore, most people don’t feel safe storing large quantities of bitcoins on personal servers, so they end up using centralized exchanges, which in turn suffer serious security breaches, as happened to Mt. Gox and Bitstamp.

So let’s look at some special aspects of money as data.

First, money has value. Ultimately — as we have seen in the crisis of the Euro and the narrowly averted default by Greece — money’s value comes from guarantees by banks, including countries’ central banks. Money’s value is increased by the importance placed on it by the people that want to steal it from us or cheat us out of it.

Second, money has an owner. In fact, I can’t imagine money without an owner. It would be like gold bullion buried on a desert island, contributing nothing to the world economy. So, the Internet culture of sharing has no meaning for money.

Third, money must be protected. Most of us — who can — use credit cards, because they are backed by complex systems for detecting theft and fraud run by multinational corporations who can indemnify us and handle our mishaps. If we store our money outside the banking system, we lack these protections.

These three traits — value, ownership, and protection — will turn up again in each of the types of Internet content I’ll look at in upcoming installments of this article. Does a review of money on the Internet help us assess health data? Comparisons are shaky, because they are very different. But because health data is so sensitive, we might learn a lot about its protection by paying attention to how money is handled.

Capturing Unstructured Data for Better Patient Care

Posted on October 9, 2014 I Written By

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

The following is a guest blog post by Dr. Chris Tackaberry, CEO of Clinithink.
Clinithink_Chris Tackaberry_CEO and Founder
There is a veritable gold mine of high value data locked inside the free text fields of all EHR systems, as well as in the free text of other sources of clinical documentation such as progress notes, discharge summaries, consult requests and diagnostic reports. In all of these sources, rich, actionable patient data is trapped in unstructured text—stored side by side with more easily accessible structured data.

Take echocardiography reports, for example. The data contained within them—specifically ejection fraction, for instance—are crucial to the management of heart failure as outlined within NQMC core measures for this serious chronic condition. Yet seldom is an ejection fraction captured as structured data. Instead, it is usually documented as free text.

Since narrative has been an inherent part of clinical workflow for many years, HIT software vendors have reasonably added free text fields to their applications. While there is clearly value in driving insights from structured data captured in such systems, the unstructured piece in free text fields remains untapped. This represents a source of potentially significant additional value that can be gleaned from EHR and other clinical documentation sources. However, conventional structured data tools do not support the ability to exploit it for use in clinical decision making.

Unlocking the clinical value in unstructured data

In the days of paper charts, highly experienced physicians were able to quickly scan large charts to find information such as allergies, medications, family history, past and current symptoms, social history, and other background detail that provided the context so critically important to any clinical encounter. This information was usually summarized in documents (discharge summaries, referrals, etc.). Ironically, such information is now more difficult to find when stored electronically.

If the existence of unstructured narrative data were known, discoverable, searchable and actionable for every patient—across any EMR or other health IT systems—the currently hidden additional diagnostic and clinical data could further increase the efficiency and quality of care. Clinical Natural Language Processing (CNLP) is a technology that enables access to unstructured narrative data which can be used to unlock this additional value. Using narrative data found in reports, web pages, transcribed output, EMRs, and other electronic sources of free text at the point of care can expand our knowledge of the patient beyond the information obtained from structured data.

Recently, the AMA issued a report in conjunction with the RAND Corporation on the need for EHR vendors to improve the software solutions they are delivering to better meet the needs of physicians. Utilizing CNLP technology to access the clinical value inherent in unstructured EHR data would allow vendors to begin addressing some of the potential improvements.

As we move from a world in which healthcare is delivered on an episodic basis retrospectively to one where care is delivered almost continuously and prospectively, CNLP increases the opportunity to deliver rich, actionable and meaningful clinical content to help improve decision-making for more accurate, evidence-based and effective care.

Dr. Chris Tackaberry is the CEO of Clinithink.