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What Do You Think Of Data Lakes?

Posted on October 4, 2016 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.

Being that I am not a high-end technologist, I’m not always up on the latest trends in database management – so the following may not be news to everyone who reads this. As for me, though, the notion of a “data lake” is a new one, and I think it a valuable idea which could hold a lot of promise for managing unruly healthcare data.

The following is a definition of the term appearing on a site called KDnuggets which focuses on data mining, analytics, big data and data science:

A data lake is a storage repository that holds a vast amount of raw data in its native format, including structured, semi-structured and unstructured data. The data structure and requirements are not defined until the data is needed.

According to article author Tamara Dull, while a data warehouse contains data which is structured and processed, expensive to store, relies on a fixed configuration and used by business professionals, a data link contains everything from raw to structured data, is designed for low-cost storage (made possible largely because it relies on open source software Hadoop which can be installed on cheaper commodity hardware), can be configured and reconfigured as needed and is typically used by data scientists. It’s no secret where she comes down as to which model is more exciting.

Perhaps the only downside she identifies as an issue with data lakes is that security may still be a concern, at least when compared to data warehouses. “Data warehouse technologies have been around for decades,” Dull notes. “Thus, the ability to secure data in a data warehouse is much more mature than securing data in a data lake.” But this issue is likely to receive in the near future, as the big data industry is focused tightly on security of late, and to her it’s not a question of if security will mature but when.

It doesn’t take much to envision how the data lake model might benefit healthcare organizations. After all, it may make sense to collect data for which we don’t yet have a well-developed idea of its use. Wearables data comes to mind, as does video from telemedicine consults, but there are probably many other examples you could supply.

On the other hand, one could always counter that there’s not much value in storing data for which you don’t have an immediate use, and which isn’t structured for handy analysis by business analysts on the fly. So even if data lake technology is less costly than data warehousing, it may or may not be worth the investment.

For what it’s worth, I’d come down on the side of the data-lake boosters. Given the growing volume of heterogenous data being generated by healthcare organizations, it’s worth asking whether deploying a healthcare data lake makes sense. With a data lake in place, healthcare leaders can at least catalog and store large volumes of un-normalized data, and that’s probably a good thing. After all, it seems inevitable that we will have to wring value out of such data at some point.

Healthcare Big Data Humor

Posted on April 22, 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.

It’s Friday and so it’s always a good time for a little Fun Friday to humor to kick off your weekend. This week’s edition is dedicated to all those working through the piles of healthcare data.

Healthcare Big Data Humor - Too Much Focus on Data

I’ve seen this at a few organizations. Although, I think the other problem is likely even more challenging in healthcare. We have all this data and all of this opportunity, where do we start?

Enjoy your weekend!

Dilbert DNA and Health Care Big Data Cartoon

Posted on December 11, 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.

It’s Friday! Not just any Friday, but a Friday leading up to the holiday season. How’s your Christmas shopping going? You know on Fridays I often like to have a Fun Friday post. This week it comes in the form of a great Dilbert cartoon that incorporates genetic DNA testing with health care big data. Enjoy!

Dilbert Health Care Big Data Cartoon

Healthcare Big Data Use, Real Patient Engagement, and Practice Marketing

Posted on May 5, 2015 I Written By

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

I use to do these a lot more and I think people enjoyed them. So, maybe I’ll start doing them again. It’s basically a short Twitter round up of some interesting tweets and often some pithy commentary about the tweets. Let me know what you think.


This seems about in line with my own personal experience talking to people. Although, some might argue that 100% are clueless. We’re all still trying to figure out all the data.


Great article by Michelle. I agree with her that I hate patient engagement. I love engaging patients, but I think that meaningful use requirements have forever corrupted the term patient engagement. We better move on to a new term, because I assure you that what’s happening with meaningful use is not engaging patients.


This is a little self serving, but Wednesday (5/6/15) I’ll be doing a webinar on the topic of practice marketing. I’m going to cover quite a bit of ground from a high quality practice website, to search engine optimization (SEO), reputation management, and meaningful patient engagement (sorry I had to use the term after my last comment). I hope many of you will attend and then let me know what you thought of it.

The Future Of…Healthcare Big Data

Posted on March 12, 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.

This post is part of the #HIMSS15 Blog Carnival which explores “The Future of…” across 5 different healthcare IT topics.

In yesterday’s post about The Future of…The Connected Healthcare System, I talked a lot about healthcare data and the importance of that data. So, I won’t rehash those topics in this post. However, that post will serve as background for why I believe healthcare has no clue about what big data really is and what it will mean for patients.

Healthcare Big Data History
If we take a quick look back in the history of big data in healthcare, most people will think about the massive enterprise data warehouses that hospitals invested in over the years. Sadly, I say they were massive because the cost of the project was massive and not because the amount of data was massive. In most cases it was a significant amount of data, but it wasn’t overwhelming. The other massive part was the massive amount of work that was required to acquire and store the data in a usable format.

This is what most people think about when they think of big data in healthcare. A massive store of a healthcare system’s data that’s been taken from a variety of disparate systems and normalized into one enterprise data warehouse. The next question we should be asking is, “what were the results of this effort?”

The results of this effort is a massive data store of health information. You might say, “Fantastic! Now we can leverage this massive data store to improve patient health, lower costs, improve revenue, and make our healthcare organization great.” That’s a lovely idea, but unfortunately it’s far from the reality of most enterprise data warehouses in healthcare.

The reality is that the only outcome was the enterprise data warehouse. Most project plans didn’t include any sort of guiding framework on how the enterprise data warehouse would be used once it was in place. Most didn’t include budget for someone (let alone a team of people) to mine the data for key organization and patient insights. Nope. Their funding was just to roll out the data warehouse. Organizations therefore got what they paid for.

So many organizations (and there might be a few exceptions out there) thought that by having this new resource at their fingertips, their staff would somehow magically do the work required to find meaning in all that data. It’s a wonderful thought, but we all know that it doesn’t work that way. If you don’t plan and pay for something, it rarely happens.

Focused Data Efforts
Back in 2013, I wrote about a new trend towards what one company called Skinny Data. No doubt that was a reaction to many people’s poor experiences spending massive amounts of money on an enterprise data warehouse without any significant results. Healthcare executives had no doubt grown weary of the “big data” pitch and were shifting to only want to know what results the data could produce.

I believe this was a really healthy shift in the use of data in a healthcare organization. By focusing on the end result, you can do a focused analysis and aggregation of the right data to be able to produce high quality results for an organization. Plus, if done right, that focused analysis and aggregation of data can serve as the basis for other future projects that will use some of the same data.

We’re still deep in the heart of this smart, focused healthcare data experience. The reality is that healthcare can still benefit so much from small slices of data that we don’t need to go after the big data analysis. Talk about low hanging fruit. It’s everywhere in healthcare data.

The Future of Big Data
In the future, big data will matter in healthcare. However, we’re still laying the foundation for that work. Many healthcare organizations are laying a great foundation for using their data. Brick by brick (data slice by data slice if you will), the data is being brought together and will build something amazingly beautiful.

This house analogy is a great one. There are very few people in the world that can build an entire house by themselves. Instead, you need some architects, framers, plumbers, electricians, carpenters, roofers, painters, designers, gardeners, etc. Each one contributes their expertise to build something that’s amazing. If any one of them is missing, the end result isn’t as great. Imagine a house without a plumber.

The same is true for big data. In most healthcare organizations they’ve only employed the architect and possibly bought some raw materials. However, the real value of leveraging big data in healthcare is going to require dozens of people across an organization to share their expertise and build something that’s amazing. That will require a serious commitment and visionary leadership to achieve.

Plus, we can’t be afraid to share our expertise with other healthcare organizations. Imagine if you had to invent cement every time you built a house. That’s what we’re still doing with big data in healthcare. Every organization that starts digging into their data is having to reinvent things that have already been solved in other organizations.

I believe we’ll solve this problem. Healthcare organizations I know are happy to share their findings. However, we need to make it easy for them to share, easy for other organizations to consume, and provide appropriate compensation (financial and non-financial). This is not an easy problem to solve, but most things worth doing aren’t easy.

The future of big data in healthcare is extraordinary. As of today, we’ve barely scraped the surface. While many may consider this a disappointment, I consider it an amazing opportunity.

Physician Focus, Data as King, and Real Time EHR Data

Posted on December 1, 2013 I Written By

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


I’m a little torn on this tweet. While I agree that there is too much administrative overhead in healthcare that distracts from patients and lifelong learning, I also think that things like EMR could contribute to both. A well implemented EMR software can help doctors focus on patients and help the doctor learn. This is certainly not the way most doctors look at EMR. Is this an EMR image problem or EMR software that’s not living up to its potential?


Of course, you have to take this tweet with a grain of salt since it comes from our very own Big Data Geek, Mandi Bishop. However, it’s an interesting topic of discussion. How important is the EMR data in healthcare today?


This tweet is related to the healthcare data tweet above. We all know that the EHR data isn’t perfect. Although, it’s worth noting that the paper chart wasn’t perfect either. However, I was more interested in the idea of real-time EHR data. I don’t think we’re there yet, but I’m interested to see how we could get there.

What’s Ahead After TEDMED 2013

Posted on May 15, 2013 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.

Last week, a number of TEDMED attendees and myself participated in a Google+ Hangout sponsored by Xerox to take a look back at our unique experiences at TEDMED 2013. The discussion included the following people:

  • Markus Fromherz, chief innovation officer of Xerox Healthcare
  • Benjamin Miller, assistant professor at the University of Colorado Denver School of Medicine
  • Nick Dawson, chief experience officer at Frontier Health Consulting
  • John Lynn, editor and founder of the Healthcare Scene blog network

We made it a really focused 15 minute discussion of the key takeaways from TEDMED. Some of the topics we discussed included: healthcare big data, multidisciplinary collaboration, citizen science, patient centered care, and a look at TEDMED topics 5-10 years from now. It was a really great discussion, and I encourage you to watch the TEDMED recap video embedded below.

Read more coverage from TEDMED from Xerox on the Real Business at Xerox Blog and follow @XeroxHealthcare.

How Do You Improve the Quality of EHR Data for Healthcare Analytics?

Posted on May 8, 2013 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.

A month or so ago I wrote a post comparing healthcare big data with skinny data. I was introduced to the concept of skinny data by Encore Health Resources at HIMSS. I absolutely love the idea of skinny data that provides meaningful results. I wish we could see more of it in healthcare.

However, I was also intrigued by something else that James Kouba, HIT Strategist at Encore Health Resources, told me during our discussion at HIMSS. James has a long background in doing big data in healthcare. He told me about a number of projects he’d worked on including full enterprise data warehouses for hospitals. Then, he described the challenge he’d faced on his previous healthcare data warehouse projects: quality data.

Anyone that’s participated in a healthcare data project won’t find the concept of quality data that intriguing. However, James then proceeded to tell me that he loved doing healthcare data projects with Encore Health Resources (largely a consulting company) because they could help improve the quality of the data.

When you think about the consulting services that Encore Health Resources and other consulting companies provide, they are well positioned to improve data quality. First, they know the data because they usually helped implement the EHR or other system that’s collecting the data. Second, they know how to change the systems that are collecting the data so that they’re collecting the right data. Third, these consultants are often much better at working with the end users to ensure they’re entering the data accurately. Most of the consultants have been end users before and so they know and often have a relationship with the end users. An EHR consultant’s discussion with an end user about data is very different than a big data analyst trying to convince the end user why data matters.

I found this to be a really unique opportunity for companies like Encore Health Resources. They can bridge the gap between medical workflows and data. Plus, if you’re focused on skinny data versus big data, then you know that all of the data you’re collecting is for a meaningful purpose.

I’d love to hear other methods you use to improve the quality of the EHR data. What have you seen work? Is the garbage in leads to garbage out the key to quality data? Many of the future healthcare IT innovations are going to come from the use of healthcare data. What can we do to make sure the healthcare data is worth using?

Analytics-Driven Compassionate Healthcare at El Camino Hospital

Posted on March 25, 2013 I Written By

Mandi Bishop is a hardcore health data geek with a Master's in English and a passion for big data analytics, which she brings to her role as Dell Health’s Analytics Solutions Lead. She fell in love with her PCjr at 9 when she learned to program in BASIC. Individual accountability zealot, patient engagement advocate, innovation lover and ceaseless dreamer. Relentless in pursuit of answers to the question: "How do we GET there from here?" More byte-sized commentary on Twitter: @MandiBPro.

Given its location in the heart of Silicon Valley, it may not be remarkable that El Camino Hospital was the first hospital in the US to implement EMR. What IS remarkable is that El Camino implemented EMR 51 years ago, leveraging an IBM mainframe system that Lockheed Martin refactored for healthcare from its original intended use for the space program.

Take a moment to process that. El Camino didn’t need PPACA, Meaningful Use, HITECH, or HIPAA to tell them health data is critical. El Camino saw the value in investing in healthcare IT for electronic data capture and communication without federal incentive programs or lobbyists. With that kind of track record of visionary leadership, it’s no wonder they became early analytics program adopters, and recently turned to Health Care DataWorks (HCD) as a trusted partner.

When I sat down with executive leadership from El Camino and HCD to discuss the journey up Tom Davenport‘s analytics maturity scale from rudimentary operational reporting to advanced analytics, I expected a familiar story of cost pressure, clinical informatics, quality measure incentives or alternative payment models as the business drivers for new insights development. Instead, I heard the burgeoning plan for a visionary approach to patient engagement and “analytics-driven compassionate care”.

Greg Walton, CIO of El Camino Hospital, admitted that initial efforts to implement an analytics program had resulted in “textbook errors”: “’Competing on Analytics’ was easier to write than execute,” he said. Their early efforts to adopt and conform to a commercially-available data model were hindered by the complexity of the solution and the philosophy of the vendor. “One of the messages I would give to anybody is: do NOT attempt this at home,” Greg laughed, and El Camino decided to change their approach. They sought a “different type of company…a real-life company with applicable lessons learned in this space.”

“The most important thing to remember in this sector: you’re investing in PEOPLE. This is a PEOPLE business,” Greg said. “And that if there’s any aspect of IT that’s the most people-oriented, it’s analytics. You have to triangulate between how much can the organization absorb, and how fast they can absorb it.” In HCD, El Camino found an analytics organization partner whose leadership and resources understand healthcare challenges first, and technology second.

To address El Camino’s need for aggregated data access across multiple operational systems, HCD is implementing their pioneering KnowledgeEdge Enterprise Data Warehouse solution,including its enterprise data model, analytic dashboards, applications and reports. HCD’s technology, implementation process, and culture is rooted in their deep clinical and provider industry expertise.

“The people (at HCD) have all worked in hospitals, and many still work there occasionally. Laypersons do not have the same understanding; HCD’s exposure to the healthcare provider environment and their level of experience provides a differentiator,” Greg explained. HCD impressed with their willingness to roll up their sleeves and work with the hospital stakeholders to address macro and micro program issues, from driving the evaluation and prioritization of analytics projects to identifying the business rules defining discharge destination. And both the programmers and staff are “thrilled,” Greg says: “My programmers are so happy, they think they’ve died and gone to heaven!”

This collaborative approach to adopting analytics as a catalyst for organizational and cultural change has lit a fire to address the plight of the patient using data as a critical tool. Greg expounded upon his vision to achieve what Aggie Haslup, Vice President of Marketing for HCD, termed “analytics-driven compassionate care”:

We need to change the culture about data without losing, and in fact enhancing, our culture around compassion. People get into healthcare because they’re passionate about compassion. Data can help us be more compassionate. US Healthcare Satisfaction scores have been basically flat over the last 10 years. Lots of organizations have tried to adopt other service industry tools: LEAN,6S; none of those address the plight of the patient. We’ve got to learn that we have to go back to our roots of compassion. We need to get back to the patient, which means “one who suffers in pain.” We want (to use data) to help understand more about person who’s suffering. My (recent) revelation: what do you do w/ guests in your house? Clean the house, put away the pets, get food, do everything you can to make guests comfortable. We want to know more about patients’ ethnicity, cultural heritage, the CONTEXT of their lives because when you’re in pain, what do you fall back on? Cultural values. We want a holistic view of the patient, because we can provide better, compassionate care through knowing more about patients. We want to deploy a contextual longitudinal view of the patient…and detect trends in satisfaction with demographics, clinical, medical data.

What a concept. Imagine the possibilities when a progressive healthcare provider teams with an innovative analytics provider to harness the power of data to better serve the patient population. I will definitely keep my eye on this pairing!

Health Data: Little White Lie Detector

Posted on December 31, 2012 I Written By

Mandi Bishop is a hardcore health data geek with a Master's in English and a passion for big data analytics, which she brings to her role as Dell Health’s Analytics Solutions Lead. She fell in love with her PCjr at 9 when she learned to program in BASIC. Individual accountability zealot, patient engagement advocate, innovation lover and ceaseless dreamer. Relentless in pursuit of answers to the question: "How do we GET there from here?" More byte-sized commentary on Twitter: @MandiBPro.

As we bring 2012 to a close and ponder the new year ahead, many of us make resolutions to change something in our lives, and frequently, that something is our health. According to the University of Scranton Journal of Psychology, 47% of Americans make New Years Resolutions. Of those, the #1 New Years Resolution for 2012 is to lose weight. Staying fit and healthy and quitting smoking also appear in the top 10. Each of these health-related resolutions translates into quantifiable healthcare data that is, or can be, captured and measured to assist the resolution-makers in achieving their goals. Our calorie consumption and burn can be calculated, our blood oxygen level monitored, our ratio of fat:lean muscle mass tracked over time. If only we were all a bit more like George Washington, and couldn’t tell a lie, the success rate for annual resolutions would be higher than 8%.

The inclination to tell little white lies to protect ourselves from inconvenient, uncomfortable truths exists in all of us. “Do these jeans make my butt look fat,” meets, “Of course not,” rather than, “Yes, your butt DOES look fat in those jeans – but it’s not the jeans’ fault.” “Can Timmy come play,” warrants, “We already have plans – let’s rain check,” in lieu of, “Your child is a brat who cannot enter my home because I prefer to keep all my hair rooted in my scalp.”

Many, if not most, of us extend these white lies to ourselves. The dress that fit last month but doesn’t today “shrunk at the dry cleaner”. Cigarettes only smoked during cocktail hour don’t really count as “smoking”. You count the time you spend standing to give office presentations as “exercise”. You “usually” eat healthy, except for the tell-tale McDonald’s bags in your garbage showing a once-a-day burger and fries habit.

What if there were a way to identify and hold you accountable for these self-delusions – a health data lie detector? Would you change your behavior? Could you achieve your healthy resolution? And might it have a quantifiable impact on healthcare cost if you did?

I had a partial thyroidectomy a few years ago. A year after my surgery, I found I had gained 7 pounds in 11 days, was feeling lethargic and was having difficulty sleeping. As a very active adult who meticulously maintained body weight for a decade, I was disturbed, and convinced that my symptoms were a result of my remaining thyroid tissue failing. I went to my primary care physician to request a hormone test.

The nurse and doctor both agreed that, in 90% of cases, the root cause of weight gain is diet, and they asked myriad questions, capturing all my answers in the clinical notes of their EMR: had I been eating differently, had I altered my exercise routine, had I been traveling. I was adamant that nothing had drastically changed. Given my fitness and history, they agreed to order the hormone test, and a blood vitamin test, as well.

All lab work came back normal. BETTER than normal. So I retraced every detail of my routine over those 11 days. And I discovered the culprit: office candy.

A bad meeting one day led to grabbing a handful of chocolates from one co-workers bowl, which became grabbing a handful of chocolates from each bowl I encountered on my department’s floor…several times a day. Did you know there are 35 calories in a single Hershey’s kiss? 220 calories in a handful of peanut M&Ms? 96 calories in a mini-Butterfinger bar? Turns out, I was eating between 500-700 calories a day in office candy. And that wasn’t all.

Along with the chocolate snacks, I’d fallen into some poor nutrition habits at meals. I started to consume other starchy carbs regularly: the pre-dinner bread basket at restaurants, pizza, pasta, sandwich bread. I didn’t feel I ate to excess, but I also didn’t take into account the difference in nutrient density between the mass quantities of fruits and vegetables I had been eating for years, and the smaller (yet still plentiful) quantities of processed starches I was currently eating.

The changes in diet likely disturbed my sleeping pattern and led to my lethargy, which in turn made my daily workouts less intense and effective at calorie-burning.

In short, my weight gain was legit, and the two doctor visits and the lab tests could have been avoided had I been completely honest with myself. I cost each actor in the healthcare system money with my self-deluding little white lie: the office administrative staff, the LRNP, the doctor, the medical coder, the lab, the insurance company, myself. There is also a per-transaction cost associated with each HIPAA-covered request that the doctors’ office EMR and lab information system generated. Given that I have only been to the doctor three times this year, and twice was for this weight gain concern, one could accurately conclude that 66% of my annual medical costs could have been avoided in 2012.

The health data exists within Meaningful Use-certified EMR systems to capture and communicate both the absolute data (height, weight, lab results, etc.) and the unstructured notes data (patient comments, doctor notes, responses to questionnaires, etc.). The capability to automatically compare the absolute with the unstructured data already exists. It wouldn’t take an inordinate amount of effort to program a lie detector to call out many of the most common little white lies.

What would happen to medical cost if we stopped lying to ourselves, and to our healthcare providers? And how high a percentage of the nation’s total healthcare bill could be avoided by this type of analysis? Better still, how much would the healthcare industry change if patients not only took responsibility for their own action/inaction, but modified their behaviors accordingly?

I’ll tell you what happened to me. I dropped the candy and starchy carbs, and I lost those 7 pounds. Keeping them off will be 2013’s New Years Resolution.