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

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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.

December 1, 2013 I Written By

John Lynn is the Founder of the HealthcareScene.com blog network which currently consists of 15 blogs containing almost 5000 articles with John having written over 2000 of the articles himself. These EMR and Healthcare IT related articles have been viewed over 9.3 million times. John also recently launched two new companies: InfluentialNetworks.com and Physia.com, and is an advisor to docBeat. John is highly involved in social media, and in addition to his blogs can also be found on Twitter: @techguy and @ehrandhit and Google Plus. Healthcare Scene can be found on Google+ as well.

What’s Ahead After TEDMED 2013

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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.

May 15, 2013 I Written By

John Lynn is the Founder of the HealthcareScene.com blog network which currently consists of 15 blogs containing almost 5000 articles with John having written over 2000 of the articles himself. These EMR and Healthcare IT related articles have been viewed over 9.3 million times. John also recently launched two new companies: InfluentialNetworks.com and Physia.com, and is an advisor to docBeat. John is highly involved in social media, and in addition to his blogs can also be found on Twitter: @techguy and @ehrandhit and Google Plus. Healthcare Scene can be found on Google+ as well.

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

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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?

May 8, 2013 I Written By

John Lynn is the Founder of the HealthcareScene.com blog network which currently consists of 15 blogs containing almost 5000 articles with John having written over 2000 of the articles himself. These EMR and Healthcare IT related articles have been viewed over 9.3 million times. John also recently launched two new companies: InfluentialNetworks.com and Physia.com, and is an advisor to docBeat. John is highly involved in social media, and in addition to his blogs can also be found on Twitter: @techguy and @ehrandhit and Google Plus. Healthcare Scene can be found on Google+ as well.

Analytics-Driven Compassionate Healthcare at El Camino Hospital

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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!

March 25, 2013 I Written By

Mandi Bishop is a healthcare IT consultant and a hardcore data geek with a Master's in English and a passion for big data analytics, who 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.

Health Data: Little White Lie Detector

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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.

December 31, 2012 I Written By

Mandi Bishop is a healthcare IT consultant and a hardcore data geek with a Master's in English and a passion for big data analytics, who 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.

Is Healthcare Big Data Biased?

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Have you ever wondered whether YOUR healthcare data is included in the “big data” everyone’s talking about? After all, healthcare big data analytics are going to change the world; shouldn’t those changes be representative of the population they will impact?

To answer that question, we have to identify the sources of the healthcare big data being used to effect change, and consider the likelihood that your data may have been captured and consumed by one of the reporting organizations. So let’s start with the “capture” part of that equation.

Have you received some type of healthcare service this year? That includes, but is not limited to: hospital visit, physical therapy, doctor visit, chiropractor visit, urgent care visit, e-visit or phone consultation, health risk assessment or health fair.

Have you purchased or requested any regulated healthcare product this year, such as prescription drugs?

Do you have private health insurance?

Are you enrolled in Medicare or Medicaid?

If yes to any of the above, and the last question, in particular, YES, your data is included in the “big data” analytics currently shaping policy. It is likely that each billable product and service is attached to your Electronic Health Record, available for review and reporting by each involved party from your PCP (Primary Care Provider) to your friendly insurance call center agent. Your individual collection of data points are aggregated into a larger population, and sliced and diced to provide insights into groundbreaking research efforts. Congratulations! But does that inclusion mean that the conclusions driven by healthcare big data are representative?

By nature, the relevance of data-driven insights increases in proportion to the size of the population – and data points – included. But what if the outliers for the general population are the norm for your data set? Are your conclusions skewed?

What if you represent a population segment that is recognized as underserved? Consider the following, from the first Health Disparities and Inequalities Report, prepared in 2011 by the CDC (Centers for Disease Control): “Increasingly, the research, policy, and public health practice literature report substantial disparities in life expectancy, morbidity, risk factors, and quality of life, as well as persistence of these disparities among segments of the population…defined by race/ethnicity, sex, education, income, geographic location, and disability status.”

If your access to healthcare is limited by any of the factors indicated above, your data may not be captured unless/until there is an acute episode which requires medical intervention. In the report, the CDC acknowledges the challenge of capturing national data to support health initiatives for these populations; it is widely accepted as a barrier to healthcare equality that must be overcome.

What if you’re healthy? I’ll use myself as an example. I don’t go to the doctor unless it’s urgent, and I haven’t visited my PCP in over a year. I’ve injured my shoulder and my back over the past year, both of which required MRI and CAT scans to diagnose severity; however, I do not follow any medically supervised treatment plan for rehabilitation. I don’t take any routine prescription medication. I’m an exercise enthusiast who works out intensely 5-6 days/week, and I sleep 8-9 hours a night. Yes, I do sleep that much. And no, me putting all this information into a blog does not constitute the data being captured for use in healthcare big data analytics. Because I haven’t needed to go to my PCP lately, don’t take routine prescription medication, and am not of age for Medicare or income level for Medicaid, the only current healthcare data available for analysis for me is orthopedic in nature and revolves around imaging data, not traditional clinical measures. Someone like me who had NOT experienced an acute care episode would have no current data available for consumption and reporting as part of a larger population.

Could it be that much, if not most, healthcare big data cited for research purposes is comprised primarily of a triangle of outlier population segments: 1) oldest, 2) poorest, and 3) sickest?

Perhaps. So, when reading on the advances in healthcare big data analytics, ask yourself whether that “big data” means “YOUR data”.

PS – For those of you curious about defining “big data” in healthcare, read Dr. Graham Hughes blog post for SAS, “How Big Is Big Data In Healthcare?”, detailing the nuances of the term as it relates to data size, complexity, and usage. Also, I’d like to thank the good folks at Vanderbilt University for compiling a fairly comprehensive list of healthcare data resources; it has been highly educational. Finally, if you’d like to read the complete CDC report, you can find it here.

November 30, 2012 I Written By

Mandi Bishop is a healthcare IT consultant and a hardcore data geek with a Master's in English and a passion for big data analytics, who 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.

Major Healthcare Issues I Think IT Could Help Solve

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Yesterday and today I spent my time at the Accountable Care Expo in Las Vegas. It was a small intimate event, but those that were there were some really smart people who knew a lot about healthcare and about accountable care organizations. It was quite the education for me. Plus, as with most learning, as I learned more about ACOs I realized how much more I still don’t know.

During the conference I started to think about something I’d heard quoted quite a few times. At this conference they said, “3% of patients are consuming 60% of healthcare dollars.” I’ve heard a lot of different numbers on this. I remember hearing that 10% of patients have 80% of healthcare costs. Regardless of the exact numbers, I’ve heard this enough to believe that a small number of patients drive a abnormally large portion of the healthcare costs in this country.

When you think about this, it becomes quite clear that these “expensive patients” are likely those with chronic conditions. That’s the easy part. The harder part is that I’ve never seen anyone analyze the makeup of the 3-10% that are driving up healthcare costs. For example, what if 90% of those “expensive patients” are chronic patients over the age of 65. Solving this problem would be very different than if we found that 50% of expensive patients are diabetics under the age of 20.

How does this apply to health IT? First, health IT should be able to sort through all the big data in healthcare and answer the above questions. How is anyone going to solve the problems of these “expensive patients” if we don’t really know the makeup of why they’re so expensive?

Second, I believe that some health IT solutions can be implemented to help lower the costs of these chronic patients. I’ve seen a number of mHealth programs focused on diabetes that have done tremendous things to help diabetic patients live healthier lives. That’s a big win for the patients and healthcare. We need more big wins like this and I think IT can facilitate these benefits.

Since this post has taken a slight diversion away from my regular topics, I wanted to look at another thought I had today about healthcare. This tweet I sent today summarizes the idea:

All of the numbers I’ve seen indicate that hospitals are the most expensive part of healthcare today. Hospitals are just expensive to run. They have a lot of overhead. They work miracles regularly, but they come at a cost. While more could always be done, I feel safe saying that many hospitals have squeezed out as much cost savings they can out of the hospital. This means that in order to save money in healthcare we can’t strip more cost savings out of hospitals. Instead, we need to work to keep patients from going to the hospital.

There are a lot of ways to solve this problem (I heard of one payer putting instacare clinics next to ERs to save money), but the one I hear most common is the need for primary care doctors to have a more active role in the patient care. If they had a more active role once a patient is discharged from the hospital, then fewer patients would be readmitted to the hospital.

How then can we structure a program for primary care doctors to be paid to keep their patients from being readmitted to the hospital? That’s the million dollar question (literally). Everyone I know would happily pay a primary care doctor a half a million dollars in order to save millions of dollars in hospital bills. That extra money might also help us solve the primary care doctor shortage that I hear so many talk about.

I can’t say I have all the solutions here, and I don’t expect these things to change over night. Although, I think these will be important changes that will need to happen in healthcare to lower costs. Plus, I think IT will facilitate an important role in making these changes happen. Imagine something as simple as an HIE notifying a primary care doctor that their patient was admitted or discharged from the hospital. This would mean the doctor could go to work. Now we just need to find the right financial mechanism to be sure they act on that notification.

I’ll be chewing on these ideas this weekend. I look forward to hearing other people’s thoughts on these issues.

November 16, 2012 I Written By

John Lynn is the Founder of the HealthcareScene.com blog network which currently consists of 15 blogs containing almost 5000 articles with John having written over 2000 of the articles himself. These EMR and Healthcare IT related articles have been viewed over 9.3 million times. John also recently launched two new companies: InfluentialNetworks.com and Physia.com, and is an advisor to docBeat. John is highly involved in social media, and in addition to his blogs can also be found on Twitter: @techguy and @ehrandhit and Google Plus. Healthcare Scene can be found on Google+ as well.