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Is Amazon Ready To Protect Patient Data?

Posted on July 6, 2018 I Written By

Anne Zieger is a healthcare journalist who has written about the industry for 30 years. Her work has appeared in all of the leading healthcare industry publications, and she's served as editor in chief of several healthcare B2B sites.

Late last month, a Connecticut woman found out that a third-party Amazon vendor she had done business with had exposed her personal medical data to the world, including her medical conditions, along with her name, birthdate and emergency contact information.

The story suggests that Amazon engaged in a bit of bureaucratic foot shuffling when called on the privacy lapse. According to the woman, an Amazon call center rep told her it would investigate the issue, but a further email told her they would not be able to release the outcome of this investigation. It’s little wonder she wasn’t satisfied.

Ultimately, it appears that she was only able to get immediate action once she contacted the third-party seller, which took the photos containing the information down promptly upon her request.

Though no small matter for the woman involved, the episode means little for the future of Amazon, in and of itself. However, it does suggest that the marriage of Amazon technology and healthcare data may pose unexpected problems.

For those who have been sleeping under a rock, in late June Amazon announced that it had acquired online pharmacy PillPack for what reports say was just under $1 billion. PillPack, which competes with services delivered by giants like CVS, lets users buy their meds in pre-made doses. News stories suggest that Amazon beat out fellow retail giant Walmart in making the buy, which should close the second half of this year.

Without a doubt, this was a banner day in the history of Amazon, which has officially stamped into healthcare in 10-ton boots. The deal could not only mark the beginning of new era for the retailer, but also the healthcare industry, which hasn’t yet seen a tech company take a lead in any consumer-facing healthcare business.

That being said, perhaps a more important question for readers of this publication is how it will manage data generated by PillPack, a store likely to grow exponentially as Amazon integrates the online pharmacy into its ecosystem.

While there are obviously many good things its staggering fulfillment and logistics capabilities can bring to PillPack, Amazon’s otherwise amazing systems weren’t built to protect patient health information.

When it comes to most any other company, I’d imagine these problems could be addressed by layering HIPAA-compliant technologies and policies over its existing infrastructure. However, given the widely distributed nature of its retail network, it’s not just a matter of rethinking some architecture. Sealing off health data could require completely transforming its approach to doing business. Just about every retail transaction could prove a chink in its armor.

Since it wasn’t itself required to meet HIPAA standards in this instance, Amazon won’t get any flack from regulators over the recent PHI exposure. Still, issues like this could undercut the trust it needs to integrate PillPack into its core business successfully.

If nothing else, Amazon had better put a strong PHI protection policy in place on its retail side. Otherwise, it could undermine the business it just spent almost $1 billion to buy.

How Hospitals Can Drive Revenue in Value-Based Care Using 7 Key Cycles of Their Data

Posted on July 5, 2018 I Written By

The following is a guest blog post by Richard A. Royer, Chief Executive Officer of Primaris.

Back in the day – the late 1960s, when social norms and the face of America was rapidly changing – a familiar public service announcement began preceding the nightly news cast. “It’s 10 p.m. Do you know where your children are?”

Today, as the healthcare landscape changes rapidly with a seismic shift from the fee-for-service payment model to value-based care models, there’s a similar but new clarion call for quality healthcare: “It’s 2018. Do you know where your data is?”

Compliance with the increasingly complex alphabet soup of quality reporting and reimbursement rules – indeed, the fuel for the engine driving value-based car – is strongly dependent on data. The promising benefits of the age of digital health, from electronic health records (EHRs) to wearable technology and other bells and whistles, will occur only as the result of accurate, reliable, actionable data. Providers and healthcare systems that master the data and then use it to improve quality of care for better population health and at less cost will benefit from financial incentives. Those who do not connect their data to quality improvement will suffer the consequences.

As for the alphabet soup? For starters, we’re as familiar now with these acronyms as we are with our own birth dates: MACRA (the Medicare Access and CHIP Reauthorization Act of 2015), which created the QPP (Quality Payment Program), which birthed MIPS (Merit-based Incentive Payment System).

The colorful acronyms are deeply rooted in data. As a result, understanding the data life cycle of quality reporting for MACRA and MIPS, along with myriad registries, core measures, and others, is crucial for both compliance and optimal reimbursement. There is a lot at stake. For example, the Hospital Readmissions Reduction Program (HRRP) is an example of a program that has changed how hospitals manage their patients. For the 2017 fiscal year, around half of the hospitals in the United States were dinged with readmission penalties. Those penalties resulted in hospitals losing an estimated $528 million for fiscal year 2017.

The key to achieving new financial incentives (with red-ink consequences increasingly in play) is data that is reliable, accurate and actionable. Now, more than ever, it is crucial to understand the data life cycle and how it affects healthcare organizations. The list below varies slightly in order and emphasis compared with other data life cycle charts.

  • Find the data.
  • Capture the data.
  • Normalize the data.
  • Aggregate the data.
  • Report the data.
  • Understand the data.
  • Act upon the data.


One additional stage, which is a combination of several, is secure, manage, and maintain the data.

  • Find the data. Where is it located? Paper charts? Electronic health records (EHRs)? Claims Systems? Revenue Cycle Systems? And how many different EHRs are used by providers – from radiology to labs to primary care or specialists’ offices to others providing care? This step is even more crucial now as providers locate the sources of data required for quality and other reporting.
  • Capture the data. Some data will be available electronically, some can be acquired electronically, but some will require manual abstraction. If a provider, health system or Accountable Care Organization (ACO) outsources that important work, it is imperative that the abstraction partner understand how to get into each EHR or paper-recording system.
     
    And there is structured and unstructured data. A structured item in the EHR like a check box or treatment/diagnosis code can be captured electronically, but a qualitative clinician note must be abstracted manually. A patient presenting with frequent headaches will have details noted on a chart that might be digitally extracted, but the clinician’s note, “Patient was tense due to job situation,” requires manual retrieval.
  • Normalize the data. Normalization ensures the data can be more than a number or a note but meaningful data that can form the basis for action. One simple example of normalizing data is reconciling formats of the data. For example, a reconciling a form that lists patients’ last names first with a chart that lists the patients’ first name first. Are we abstracting data for “Doe, John O.” or “John O. Doe?” Different EHR and other systems will have different ways of recording that information.
     
    Normalization ensures that information is used in the same way. The accuracy and reliability that results from normalization is of paramount importance. Normalization makes the information unambiguous.
  • Aggregate the data. This step is crucial for value-based care because it consolidates the data from individual patients to groups or pools of patients. For example, if there is a pool of 100,000 lives, we can list ages, diagnosis, tests, clinical protocols and outcomes for each patient. Aggregating the data is necessary before healthcare providers can analyze the overall impact and performance of the whole pool.
     
    If a healthcare organization has quality and cost responsibilities for a pool of patients, they must be able to closely identify the patients that will affect the patient pool’s risks. Aggregation and analyzing provides that opportunity.
  • Report the data. Reporting of healthcare data to registries and the Centers for Medicare and Medicaid Services (CMS) is not new, but it is a growing need. Required reporting will become even more integral to health care quality improvement as private payers follow the CMS lead towards value-base care.
  • Understand the data. What was effective? What is the clinical point of view versus a dollars/cost point of view? How are these two points of view reconciled to get the “right” results?
     
    When Drug B is half the price but equally as effective as Drug A, that is an example of evidence-based medicine, which was the result of the data life cycle. When healthcare organizations and providers have data they can understand, a root cause analysis is an ideal way to achieve sometimes conflicting goals of quality and cost– and move forward – on solving deficiencies or other problems flagged by the data.
  • Use the data. There are other crucial facets of the data life cycle that must be dealt with, including data maintenance and management and purging or destroying data in a way that is compliant with HIPAA. But the most important function of data is using it to improve clinical processes and outcomes, the patient experience, and the financial bottom line.
     
    Data that is accurate and reliable is not all that useful until it is actionable. How is the data being used to manage quality of care and cost of care? The final stage in the data life cycle is certainly the most important. The technology and human capital needed to accomplish the other aspects of the life cycle are extensive, and expensive. But data gathering is a lost cause and, really, an exercise in futility unless the flurry of data and reporting activity leads to action. In the age of value-based healthcare, data is the key that will allow providers to be financially successful in the future as payments become more heavily based on value, and patients seek providers that meet their growing expectations.

About Primaris
Richard A. Royer, Chief Executive Officer of Primaris, a healthcare consulting and services firm that works with hospitals, physicians and nursing homes to drive better health outcomes, improve patient experiences and reduce costs.

Healthcare AI Needs a Breadth and Depth of Data

Posted on May 17, 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.

Today I’m enjoying the New England HIMSS Spring Conference including an amazing keynote session by Dale Sanders from Health Catalyst. Next week I’ll be following up this blog post with some other insights that Dale shared at the New England HIMSS event, but today I just wanted to highlight one powerful concept that he shared:

Healthcare AI Needs a Breadth and Depth of Data

As part of this idea, Dale shared the following image to illustrate how much data is really needed for AI to effectively assess our health:

Dale pointed out that in healthcare today we really only have access to the data in the bottom right corner. That’s not enough data for AI to be able to properly assess someone’s health. Dale also suggested the following about EHR data:

Long story short, the EHR data is not going to be enough to truly assess someone’s health. As Google recently proved, a simple algorithm with more data is much more powerful than a sophisticated algorithm with less data. While we think we have a lot of data in healthcare, we really don’t have that much data. Dale Sanders made a great case for why we need more data if we want AI to be effective in healthcare.

What are you doing in your organization to collect data? What are you doing to get access to this data? Does collection of all of this data scare anyone? How far away are we from this data driven, AI future? Let us know your thoughts in the comments.

Consumer Data Liquidity – The Road So Far, The Road Ahead – #HITsm Chat Topic

Posted on August 23, 2017 I Written By

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

We’re excited to share the topic and questions for this week’s #HITsm chat happening Friday, 8/25 at Noon ET (9 AM PT). This week’s chat will be hosted by Greg Meyer (@Greg_Meyer93) on the topic of “Consumer Data Liquidity – The Road So Far, The Road Ahead.”

As my summer tour of interoperability forums, lectures, and webinars winds down, patient engagement/data liquidity is arguably the hottest talk in town.  This leads me to a time of reflection looking back to my own personal experience over the last 10-15 years (yes, I’m still a fairly young guy) starting with early attempts to access my own family’s records, moving on to witnessing the consumer revolution of Dave deBronkart and Regina Holiday, and finally tracking the progression of HealthIT and public health legislation.  We’ve come a long way from the ubiquity of paper and binders and Xerox (oh my) to CDs and PDFs to most recently CDAs, Direct, and FHIR with the latter paving the way for a new breed of apps and tools.

With the lightning speed of change in technology and disruption vis-à-vis consumer devices, one would expect a dramatic shift in the consumer experience over the past 10 years with nirvana in the not too distant future.  Contrary to intuitive thinking, we haven’t come as far as we would like to think.  Even with legislation and a progression of technology such as C-CDA, OpenNotes, Direct, BlueButton, FHIR, and the promise of apps to bring it all together, pragmatically a lot of same the core broken processes and frustrations still exist today.  In July, ONC released a study on the health records request process based on a small sampling of consumers and 50 large health organizations.  Although most of the stories include modern technical capabilities, the processes reek of variance and inefficiencies that have persisted since the long lost days of the house call.

Not to put the whole state of affairs in gloom, there is still a potentially bright future not too far ahead.  With the convergence of forces from contemporary technical standards and recent legislation like the 21st Century Cures Act, consumer data liquidity is staying in the forefront of public health.  And let’s not forget the consumer.  It is partly because of the consumer revolution and patients demanding portability of their records that is forcing providers and vendors to open their systems as platforms of accessibility instead of fostering silos and walled gardens.

This week’s chat will explore the progression of health data access from the consumer’s perspective.

Here are the questions that will serve as the framework for this week’s #HITsm chat:
T1: Describe your perception/experiences of consumer data access 10-15 years ago. #HITsm

T2: Contrast your previous experience to today. Is your experience better, worse, or the same? #HITsm

T3: What gaps exist between what is available today (data, apps, networks, etc.) vs what you would like to have? #HITsm

T4: Would you prefer to manage/move your data yourself or expect HealthIT to do it for you. #HITsm

T5: Beyond FHIR, APIs, and apps, what is the future of consumer access and data liquidity? #HITsm

Bonus: Remember “Gimme My DaM Data?” What would be your slogan for consumer access? #HITsm

Upcoming #HITsm Chat Schedule
9/1 – Digital Strategies for Improving Consumer Experience
Hosted by Kyra Hagan (@HIT_Mktg_Maven from @InfluenceHlth)

9/8 – Digital Health Innovation in Pharma
Hosted by Naomi Fried (@naomifried

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

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

Healthcare Data Integration Cutting Room Floor: Cluttered with Valuable Unused and ‘Laundered’ Data – #HITsm Chat Topic

Posted on July 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.

We’re excited to share the topic and questions for this week’s #HITsm chat happening Friday, 7/14 at Noon ET (9 AM PT). This week’s chat will be hosted by Bill Fox (@FoxBigData) of @MarkLogic on the topic of “Healthcare Data Integration Cutting Room Floor: Cluttered with valuable unused and ‘laundered’ data.”

Improving healthcare data integration, flexibility, agility and time to market for development and implementation starts with ingesting data and ends with analytics and insights-an operationalize before you analyze best practice approach.

How healthcare data is captured, represented, secured and made available to the application services intended to support the value-based models of care everyone expects to improve patient outcomes, while addressing escalating costs, is a fundamental necessity for digitally transforming today’s healthcare organizations.

Thankfully, operational data integration technologies have rapidly emerged that address and support the critical functionality healthcare providers, health plans and ancillary organizations need to support the healthcare consumers and patients, and effect true health care outcome improvement and cost containment challenges.

The intention of this chat is to share ideas, facts, thoughts, and opinions on the theme of whether the legacy technology that still dominates most IT shops in healthcare supports reform and innovation initiatives or not. Quite simply, are we leaving too much valuable, unused and ‘laundered’ healthcare data” on the ‘Cutting Room Floor’ of the very healthcare organizations we’re all counting on to best leverage that data? Our hope is that this chat helps to surface how healthcare organizations – providers, payers, 3rd parties and vendors – can get the most from our respective investment in our healthcare data platforms.

Reference & Resources:

This Week’s Topics
T1: What’s your biggest, most expensive health data “hairball” or pain point in combining data across domains or multiple systems? #HITsm

T2: What is the most valuable data that’s not being used today in #healthcare due to cost / complexity of integration? #HITsm

T3: What data impacts #healthcare consumer / member / patient experience and service the most? #HITsm

T4: 80% of all data is unstructured. What types of unstructured data can help improve service, outcomes & lower costs the most? #HITsm

T5: Why should scarce resources be invested in analytics before combining, enriching, harmonizing and operationalizing data first? #HITsm

Bonus: Why do firms continue using legacy ETL & tools vs adopting a “next gen” data integration platform approach? #HITsm

Upcoming #HITsm Chat Schedule
7/21 – Meeting the Patient Where They Are
Hosted by Melody Smith Jones (@MelSmithJones) from HYP3R

7/28 – How Does Age Impact Patient Satisfaction & Provider Switching?
Hosted by Lea Chatham (@leachatham) from @SolutionReach

8/4 – TBD
Hosted by Alan Portela (@AlanWPortela) from Airstrip

8/11 – TBD
Hosted by TBD

8/18 – Diversity in HIT
Hosted by Jeanmarie Loria (@JeanmarieLoria) from @advizehealth

8/25 – TBD
Hosted by TBD

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

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

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 (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.

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.

New Data Driven Perspectives in Healthcare w/ @MandiBPro and @Ashish_P

Posted on April 13, 2016 I Written By

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

I was going through the Healthcare Scene archive of videos and realized I’d never shared my discussion with Mandi Bishop, Health Plan Analytics Innovation Practice Lead at Dell and Ashish Patel, Co-Founder of CareSet.com and DocGraph.com, about healthcare data. This was a really interesting discussion about various health data sources and what those sources of data could mean to healthcare. If you’re into healthcare data, you’ll really enjoy this discussion with two health data geeks (said with much affection).

2 Great Healthcare IT Data Images

Posted on September 10, 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.


We’re doing this in every industry, but no where is it more important than in healthcare. No where is it more challenging either.


Can we move this raw data on “food deserts” into knowledge that can be used by healthcare?

How Do We Balance Improved Outcomes with Protecting Personal Information?

Posted on July 29, 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.

There’s an interesting article by the Pacific Standard (never heard of them before now) about the “hidden market” of medical data that exists. The final paragraph provides a great summary of the challenges we face when it comes to health data:

There is no perfect way to balance the competing priorities of using big data for improved health outcomes and protecting our personal information. Opinions on which interests should come first will differ—and should. But the debate cannot be open, honest, or effective if major companies like Walgreens or Safeway are secretive about what they do. People are often generous when it comes to volunteering personal data for the purpose of advancing medicine. They are less so when it comes to enriching sellers of information. Either way, the proper course of action is disclosure. Simply put, if our medical data is being bought and sold, we deserve to know it—and have a say. Perhaps making our data available to others is as helpful to medicine as IMS claims. But shouldn’t that be up to us?

That’s the best summary of balancing improved outcomes and personal information that I’ve ever read. We all want better outcomes and I think that most of us believe that the right healthcare data will get us to better outcomes. We also all want our data to be protected from people who will use it inappropriately. The balance between the two competing priorities will never be perfect.

The reality is that there’s going to be more and more healthcare data available about all of us. Much of that data is going to be shared with a large number of organizations. Most people are just fine with that sharing assuming they believe the sharing will help them receive better care. However, there does need to be some mechanism of transparency and disclosure about when and how data is used. That doesn’t happen today, but it should happen.

The challenge is that pandora’s already out of the box. The data is already flowing a lot of places and putting in accountability now will be a real challenge. Not that I’m against challenging things, but we’re kidding ourselves if we think that accountability and transparency around where and when are data is shared is going to be easy to accomplish. First, companies are going to be dragged kicking and screaming to make it happen. Some because they know they’re doing some things that are at least in the grey area and some are totally shady. Others aren’t doing anything inappropriate, but they realize the costs to implement transparency and accountability for the health data they share is going to be very high. A high cost project that doesn’t add any more revenue is a hard business proposition.

While I’m not hopeful that we’ll see a widespread transparency about what health data’s being shared where, I do think that some forward thinking healthcare companies could push this agenda forward. It will likely happen with some of the companies who have avoided the grey and shady areas of health data sharing that want to create a competitive advantage over their competitors and build trust with their users. Then, some others will follow along.

What do you think that could be done to make health data sharing that’s happening today more transparent?

The Importance of Information Governance in Healthcare – Where Should We Start?

Posted on July 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.

As more and more health data is being captured be health organizations, health information governance is becoming an extremely important topic. In order to better understand what’s happening with health Information Governance, I sat down with Rita Bowen, Senior Vice President of HIM and Privacy Officer at HealthPort, to talk about the topic. We shot these videos as one long video, but then chopped them up into shorter versions so you could more easily watch the ones that interest you most. You can find 3 of the videos below and 2 more over on Hospital EMR and EHR.

The State of Information Governance

What’s HIM’s Role in Health Information Governance?

Where Should We Start with Information Governance?