Capitalizing on Healthcare Disruption

One of the best books on business strategy is “The Art of War” by Sun Tzu even though it was written sometime around 512 BC … and is actually about military strategy. I have read it several times over the years and many of the lessons it contains apply directly to business today.

In thinking about the current healthcare transformation, I am reminded of my favorite quote from Tzu’s book:

“In the midst of chaos, there is also opportunity”

Healthcare organizations are dealing with a nearly impossible amount of change. Most of it is not optional and the amount of disruption is difficult to comprehend for outsiders. The future health (pardon the pun) of today’s healthcare businesses depend on the actions they take in the next few years. Some organizations will take steps to acquire or be acquired … some will implement other strategies to sustain themselves and grow … and some (unfortunately) will go out of business.

Standing still is not an option. Survival in the healthcare industry of tomorrow requires participants (payers and providers) to change their priorities, develop and implement new skills and leverage new technologies.

As the shift to value based business models becomes mainstream, successful organizations will have …

  • A priority and focus on their customer (patient) relationships
  • A clear and differentiating value proposition from their competitors
  • A technology enabled approach that monetizes and maximizes their value

Doing things the same old way won’t work in tomorrow’s healthcare value based economy. What are you going to do when you are not paid for volume any more?

In other industries, businesses have a core strategy and operating principles that drive their value … and are the basis for customer loyalty …

  • Lowest Cost – Walmart, McDonalds, Holiday Inn, Southwest Airlines, etc.
  • Most Innovative – Apple, Tesla, Under Armour, Amazon, etc.
  • Best Quality – Ritz Carlton, Mercedes Benz, Bose, Harley Davidson, etc.

What steps are you taking to quantify and monetize your value?

We are at a unique point in time. The amount of transformation combined with current technological change presents a once-in-a-lifetime set of circumstances.

Many organizations are simply dealing with their transformation requirements as they come up one by one … just checking the boxes. A smaller group is looking at transformation more opportunistically and not as set of items that must be “dealt” with.

If this wasn’t daunting enough, there are major technology shifts and breakthroughs happening simultaneously.

Major shifts include Cloud Computing, Internet of Things (IoT), Mobile (Smartphones), Big Data, Analytics and Population Health. These all represent new or improved ways of doing things.

Cognitive Computing is more fundamental. It is a new computing paradigm that is changing computing as we know it. Today, computers do what they have been programmed to do. The cognitive computing model is based on learning from outcomes. It does not depend on upfront programming.

I recently blogged about taking advantage of cognitive computing innovation in several recent posts:

Cognitive Computing: A Once in a Generation Race

Since standing still is not an option. What are you doing to create advantages?

Healthcare data will continue to explode … so why not harness it and use it for your business advantage?

Today

  • Data flows from every device – replacing guessing with precise data
  • 8000 research papers / new clinical trial information published every day1
  • 88% of healthcare data is unstructured (Dark Data) and of little use2
  • Key outcome data (social determinants, behavioral) often not available

By 2020 …

  • 7MB of new data will created every second, for every person on the planet2
  • Average person’s medical data – equivalent to 300 million books of information2
  • The total amount of stored medical data will double every 73 days2

Established companies are being disrupted faster than ever before. The average company lifespan on S&P 500 Index (top performer) has shrunk from over 60 years in 1961 to under 20 years in 2012.3

75% of current S&P 500 Index companies will be replaced by 2027.4

According to Accenture, 93% of executives believe their long-term success is dependent on their ability to innovate.

I blogged about the five areas of general business that Cognitive Computing will impact the most in Innovation in the Cognitive Era.

I also discussed some of detailed implications in a recent interview with Open Minds entitled The Future Of Care Coordination? It’s Elementary, Watson

We are at the beginning of a perfect storm of disruption that could catapult your organization forward if taken advantage of. Why not use Cognitive Computing to create, capture and monetize value as you transform? Sun Tzu certainly would.

I will be speaking about all of this as part of my keynote address at the upcoming Open Minds Technology and Informatics Institute in Washington DC on November 10-11th.  I hope to see you there.

As always, leave me your thoughts and ideas here.

 

Footnotes:

1 – CBS Sixty Minutes – October 2016

2 – IBM

3 – Innosight

4 – Academy for Corporate Entrepreneurship

Trick or Treating for State Healthcare Innovation Treats

When I was a wee lad, I loved to go trick or treating each Halloween. Nothing was better then dressing up in a great costume and walking door-to-door to get my plastic orange pumpkin filled up with candy. My favorite was those little root beer barrel hard candies … YUMMY!

I think my best costume was the year I went out dressed as Elvis. Imagine a 12 year old dressed as Vegas Elvis, with the white jumpsuit, big lapels and the mutton chop sideburns. I got alot of root beer barrels that year. This year I went to the 27th Annual NASHP Conference in Atlanta dressed as a confused IBM Executive.

As part of my role in IBM Smarter Care, I have recently been focused on understanding the government healthcare transformation strategies of the US States in the wake of the Affordable Care Act.

What a better place to get the goodies then the NASHP Conference. The event attracts a “who’s who” of state healthcare policy people who also drive the content and focus of the conference. I may have gone confused but came back armed with answers (my treats).

My plastic pumpkin was filled with goodies by the end of the pre-conference on the first day. The best treat (for me) was the keynote delivered by Dr. Elizabeth H. Bradley from Yale University. Her keynote was based on her new book The American Health Care Paradox: Why Spending More is Getting Us Less. Her point of view asserts that when you combine social services spending with healthcare spending you can achieve more. Our archaic division of health and social services, and our allergy to government programs, is hurting us. The book offers a unique and fresh perspective on the problems the Affordable Care Act won’t solve.

There were other treats as well. The pre-conference on care coordination was led by NASHP Program Manager, Dr. Barbara Wirth. It featured an all-star line-up of state executives sharing how they were using CMS Innovation Funding to improve state healthcare outcomes on behavioral health, infant mortality, long-term care and supporting services using care models such as Patient Centered Medical Homes, Health Homes and more.

The one treat that I really wanted … I didn’t get (and it wasn’t root beer barrels).  It was an understanding of the technology being used to help achieve the outcomes being cited in the sessions. Software is essential to enabling care models where patients are crossing care settlings, caregivers, locations and even care programs. There is no way this can be done economically using the good old fashioned way of paper, folders, faxes and phone calls.

Realistically, it’s too early for many of these new programs to expect a lot of detail on this. On the other hand, the omission(s) makes me scared (get the pun) that this may not be on the radar screen of those making policy decisions … and those responsible for rolling out these innovative programs.

Healthcare reform is not just about innovative payment models, policy design and care delivery models. It must also include innovative technology to deliver on the promise of consistent quality, scalable delivery and affordable care. The use of big data (not just EMRs), analytics and care coordination software all help enable the benefits Dr. Bradley spoke about where social programs and healthcare come together to enable better outcomes at lower costs. Dynamically linking these technologies to health policy is where innovation can and will happen. Not linking them may cause your programs end-up like an old Haunted House where dust and cobwebs cover up ghoulish and ghastly looking programs (ok, really sorry for the pun).

Maybe next year I’ll pull out my Elvis costume when I go to NASHP in Dallas (October 19-21, 2015) even though I know it’s far too small for me.

In the mean time, I’ll urge NASHP to push this technology agenda, along with all of those implementing reform through government healthcare transformation. Start thinking and planning for the technology that will power your initiative now.

For me, Halloween comes twice this year. The IBM Health and Social Programs Summit is being held October 20-21st in Washington, DC. This event convenes a global network of thought leaders, industry experts and practitioners to discuss industry trends and directions, and compare best practices and leading technology innovations in the fields of Health and Social Programs. I will be speaking, as will Dr. Barbara Wirth from NASHP, along with The Honorable Patrick J. Kennedy, Dr. Paul Grundy, Dr. Stephen Morgan and many more. I hope to see you there … and bring some root beer barrels!  There will be plenty of treats for you too.

How Do Data Loopholes Slow Down the Treatment of Breast Cancer?

Considering it’s Breast Cancer Awareness Month, the timing of this post is hopefully helping a very important cause.  For reasons I won’t go into here, I’ve recently become more familiar with breast cancer then I would have otherwise.  When confronted with a new topic of interest, it’s my nature to dig in and learn everything I can about it.

The National Cancer Institute provides a wealth of information on breast cancer but being a “software guy” … the way a mammogram results combined with a clinical breast exam can detect early signs of cancer stood out to me as an important information issue.

I began to wonder where that information was captured and stored (after the test and examination) … and how it was ultimately used in follow-up care with the patient.  I didn’t expect to learn what I did.

The American College of Radiology (ACR) has established a uniform way for radiologists to describe mammogram findings.  The system is called BI-RADS and includes standardized structured codes or values.  Each BI-RADS code has a follow-up plan associated with it to help radiologists and other physicians manage a patient’s care.  These values are often used to trigger notifications of the findings or other follow-up steps.  This makes perfect sense to me except there is a (big data loophole) problem.

The BI-RAD findings (or values) are typically found on a text based report … or determined by the examining physician.  They are then captured or manually transcribed in the EMR as free text notes that are added to the medical record as text … unstructured data living in a structured data environment.  This is the loophole!  It’s technically there but not able to be used.

Sometimes this step can be missed completely and the results are not put into the EMR system at all (human error) … or, more likely, the BI-RAD value is not transcribed in the right place as a structured data field.  There are just two of the reasons reasons this loophole can be caused.

You may not be aware, but an Electronic Medical Records (EMR) system is generally optimized for structured data.  Most EMRs don’t leverage text based unstructured data (test results, physician notes, observations, findings, etc.) in ways that they could.  It’s a known weakness of many of today’s EMR systems.

To net this out … it’s entirely possible that cancer is detected using the BI-RADS value but the information does not find it’s way into the right place in the EMR system because it’s text based and the EMR cannot recognize it.  This EMR system limitation has no way of determining what the text based information is, or how to use it.

The impact of this is staggering.  Let’s think about this in terms of timely follow-up on cancer detection.  A system that is not able to use the BI-RAD value could mean patients are not being followed-up on properly (or at all) – even though they are diagnosed with breast cancer.  Yes, this  can actually happen if the value is buried in the text and not being used by the EMR.  The unstructured data loophole is a big deal!

Don’t take my word for it.  University of North Carolina Health Care (UNCH) has announced new findings from mining clinical data to improve the accuracy of its 2012 Physician Quality Reporting System (PQRS) measures, achieving double digit quality improvements in the areas of mammogram, colon cancer and pneumonia screening.  They are taking steps to close data loopholes.

The new findings indicate mammogram values are present in structured data 52% of the time … and present in unstructured data 48% of the time.  Almost half the time the unstructured data is not presented with the rest of the structured data.  Ouch, that’s a big data loophole.

The new findings also indicate CRC screening (colon cancer) values are present in structured data just 17% of the time … and present in unstructured data 83% of the time.  As a man of a certain age, this scares me in words that can’t be published.  Another big data loophole.

Thankfully leading organizations like UNCH are closing these data loopholes today with solutions that understand unstructured data and can “structure it” for use in EMR systems … pasted from an IBM press release dated today:

Timely Follow-up of Abnormal Cancer Screening Results:  Follow-up care for patients with abnormal tests is often delayed because the results are buried in electronic medical records.  Using IBM Content Analytics, UNCHC can extract abnormal results from cancer screening reports such as mammograms and colonoscopies and store the results as structured data.  The structured results are used to generate alerts immediately for physicians to proactively follow-up with patients that have abnormal cancer screening results.

This is an example of what IBM calls Smarter Care … where advanced analytics and cognitive computing can enable more holistic approach to individuals’ care, and can lead to an evolution in care delivery, with the potential for more effective outcomes and lower costs.  If an ounce of prevention is worth a pound of cure, an ounce of perspective extracted from a ton of data is priceless in potential savings.  IBM Content Analytics is part of the IBM Patient Care and Insights solution suite.

I’ve written several previous blogs on related topics that you might find interesting:

I am also speaking at the PCPCC Annual Fall Conference next Monday October 14th at 10am and will be discussing Smarter Care, UNCH’s findings and more.  Hope to see you there.

As always, leave me your feedback, questions and suggestions.

Moving Beyond One-Size-Fits-All Medicine to Data-Driven Insights with Similarity Analytics

Traditionally, Doctors have been oriented toward diagnosing and treating individual organ systems.  Clinical trials and medical research has typically focused on one disease at a time.  And today’s treatment guidelines are geared toward treating a “standard” patient with a single illness.

That’s nice… But the real world doesn’t work that way.

Most of us patients do not fit these narrow profiles … especially as we grow older and things get complicated.  We (patients) might display symptoms common to a variety of illnesses, or might already be suffering from multiple diseases.  Almost 25% of Medicaid patients have at least five comorbidities.[1]

This might explain why it’s estimated that physicians deviate from the recommended guidelines 40% of the time.  It might also explain why there is a real thirst in healthcare for evidence-based insights derived from patient population data.

In other industries, data-driven insights are often the only way organizations work with their customers.  Think of retailing and Amazon.com.  Amazon analyzes your past purchases, your past clicks and other data to anticipate what you might need and present you with a variety of options all based on data driven insights.  You might think that by now, every industry would analyze data from the past to predict the future.

That’s not true in healthcare where treating complex patients can be challenging and technology to handle this level of complexity really hasn’t existed.  Treatment guidelines are sometimes vague and may not exist at all when a patient has multiple diseases or is at risk for developing them.  In other words, one-size-fits-all approaches tend to be self limiting.

Treating patients with multiple conditions is also costly. In fact, 76% of all Medicare expenditures apply to patients with five or more chronic conditions.[2]  To reduce costs, doctors need ways to identify early intervention opportunities that address not only the primary disease but also any additional conditions that a patient might develop.

Consequently, Doctors are forced to adopt ad hoc strategies that include relying on their own personal experiences (and knowledge) among other approaches.  Straying from those guidelines (where available) might not deliver the best outcomes but it’s been the only option they have … until now

Similarity analytics offers a way to augment traditional treatment guidelines, enabling healthcare providers to use individual patient data (including both structured and unstructured data) as well as insights from a similar patient population to enhance clinical decision-making.  With similarity analytics, healthcare providers and payers can move beyond a one-size-fits-all approach to deliver data-driven, personalized care that helps improve outcomes, increase the quality of care and reduce costs.

IBM similarity analytics capabilities, developed by IBM Research, play an essential role in IBM Patient Care and Insights … a comprehensive healthcare solution that provides a range of advanced analytics capabilities to support patient-centered care processes.  Here is a link to a video (with yours truly) from the recent launch in Las Vegas (my part starts at 8:45 mins).

How do similarity analytics capabilities work?

Let’s take an elderly patient with diabetes (a chronic disease) who presents with ankle swelling, dyspnea (difficulty breathing) and rales (a rattling sound heard during examination with a stethoscope).   Diabetes by itself is bad enough … but the care process gets more complicated (and more costly) when other comorbid conditions are present.

With these reported symptoms and observed signs, the patient might be at risk for other chronic diseases such as congestive heart failure.  But exactly how much at risk and when?

In the past, Doctors have had no way of knowing this.  There are tens of thousands of possible dimensions that need to be understood, analyzed and compared to get an answer to this question.  Think of a spreadsheet where the patient is a single row … and in that spreadsheet and there are 30,000 columns of data that need to be analyzed in an instant … and someone’s life could be at stake based on the outcome of the analysis.  In other words, Doctors have been handicapped in their ability to deliver quality care because of the absence of this type of analysis.

With IBM Patient Care and Insights (IPCI), a healthcare organization can collect and integrate a broad range of patient data from electronic medical records systems and other data sources (such as claims, socioeconomic and operational) … from past test results to clinical notes … into a single, longitudinal record.  Similarity analytics then enables the provider to draw on this comprehensive collection of data to compare the patient with other patients in a larger population.  With IBM Similarity Analytics (part of IPCI), the provider can analyze tens of thousands of possible comparison points to find similar patients … those patients with the most similar clinical traits at the same point in their disease progression as the patient in question.

Why is finding similar patients helpful?  First, providers can see what primary diagnoses and treatments have been applied to similar patients … some diagnoses and treatments might have otherwise eluded Doctors.  Second, providers (and payers) can identify hidden intervention opportunities … such as an illness that the patient is at risk of developing or the risk of the patient’s current condition deteriorating.  Surfacing hidden intervention opportunities is critical in addressing the costs and complexity of healthcare … especially when treating patients with multiple diseases.

Importantly, providers can also predict potential outcomes for an individual patient based on the outcomes of similar patients. Knowing what has happened to a patient’s peer group given certain treatments can help doctors hone in on the right intervention for this particular patient … before things take a turn for the worse.

There are many areas where similarity analytics are helpful.  Disease onset prediction, readmissions prevention, physician matching, resource utilization and management and drug treatment efficacy are just a few of the use cases.  My colleagues in IBM Research have been working on this technology for years.

By finding similar patients, pinpointing risks and helping to predict results, similarity analytics can ultimately help healthcare providers and payers improve the quality of care and deliver better outcomes, even for patients with multiple illnesses.  By working with other analytics capabilities to enable providers to apply the right interventions earlier, similarity analytics can also help pinpoint the specific risk factors for a given patient.  Those risk factors can become the basis for an individualized care plan.

In a future blog post, I’ll focus on the care management capabilities of IBM Patient Care and Insights so you can see how this solution helps put analytics insights into action.

Until then, learn more about IBM Patient Care and Insights by visiting:

http://www-01.ibm.com/software/ecm/patient-care/

Read specifically about IBM Research and Similarity Analytics by visiting:

http://ibmresearchnews.blogspot.com/2012/10/data-driven-healthcare-analytics-from.html

As always …  look forward to reading your comments and questions.


[1] Projection of Chronic Illness Prevalence and Cost Inflation from RAND Health, October 2000.

[2] KE Thorpe and DH Howard, “The rise in spending among Medicare beneficiaries: the role of chronic disease prevalence and changes in treatment intensity,” <link: http://content.healthaffairs.org/content/25/5/w378.full&gt; Health Affairs 25:5 (2006): 378–388.

Advanced Analytics … The Next Big Thing in Healthcare

If you are in the healthcare industry, you know you’ are facing a number of significant challenges. First and foremost, you are being asked to meet rising expectations for higher-quality care, better outcomes and lower costs. But at the same time, you face a critical shortage of resources and an aging population that will require a greater portion of those limited resources every day.

Chronic diseases present some of the toughest challenges. Approximately 45 percent of adults in the United States have at least one chronic illness.[1] Those chronic illnesses not only make life difficult for patients, they also stretch healthcare resources thin and cost the U.S. economy more than $1 trillion annually.[2]

Advanced analytics can give you an edge in balancing all of these demands, and in figuring out how to continue the balancing act as the industry evolves. With advanced analytics, you can leverage a broader range of patient information and surface early, targeted intervention opportunities that ultimately help you enhance the quality of care, improve outcomes and reduce costs.

Content Analytics

Content Analytics capabilities, such as those offered through IBM Content and Predictive Analytics for Healthcare, can help you analyze a wider range of patient information than you could before. In the past, analytics solutions were frequently limited to structured data—such as the data found in electronic medical record (EMR) and claims systems. But content analytics lets you incorporate unstructured sources as well, including doctors’ dictated notes, discharge orders, radiology reports, faxes and more.  Powerful natural language processing is at work to enable this.

To see how valuable that unstructured information can be in uncovering insights, read my previous blog post, “Playing the Healthcare Analytics Shell Game.”

Predictive Analytics

Predictive analysis capabilities can help you identify patients at risk for developing additional illnesses or requiring further interventions. You can use predictive modeling, trending and scoring to anticipate patient outcomes and evaluate the potential effects of new interventions. 

Similarity Analytics

Using patient similarity analytics capabilities, such as those developed by IBM Research, a provider could examine thousands of patient attributes at once. That includes not only clinical attributes but also demographic, social and financial ones. By assessing similarities of attributes in broad patient population, providers can better anticipate disease onset, compare treatment effectiveness and develop more targeted healthcare plans.

Surface new intervention opportunities

The insights you gain from these analytics capabilities are the keys to  discovering opportunities for new, individualized and highly targeted patient interventions—interventions that can reduce expensive hospital readmissions for chronic patients, avoid the onset of other illnesses, prevent postoperative infections, slow the deterioration of conditions and more. That all adds up to better care and better outcomes at a lower cost.

In future posts, I’ll present a more in-depth discussion of patient similarity analytics and examine how advanced analytics can be integrated with care management.  In the meantime, I’d be eager to read your comments and questions.  In the mean time, check out some of the analytics research currently underway at IBM Research,


[1] S.Y. Wu, A. Green, “Projection of chronic illness prevalence and cost inflation,” RAND Health, 2000.

[2] Milken Institute, “An Unhealthy America: The Economic Burden of Chronic Disease Charting a New Course to Save Lives and Increase Productivity and Economic Growth,” October 2007, http://www.milkeninstitute.org/healthreform/pdf/AnUnhealthyAmericaExecSumm.pdf.

Playing The Healthcare Analytics Shell Game

When I think of how most healthcare organizations are analyzing their clinical data today … I get a mental picture of the old depression era shell game – one that takes place in the shadows and back alleys. For many who were down and out, those games were their only means of survival.

The shell game (also known as Thimblerig) is a game of chance. It requires three walnut shells (or thimbles, plastic cups, whatever) and a small round ball, about the size of a pea, or even an actual pea. It is played on almost any flat surface. This conjures images of depression era men huddled together … each hoping to win some money to buy food … or support their vices. Can you imagine playing a shell game just to win some money so you could afford to eat? A bit dramatic I know – but not too far off the mark.

The person perpetrating the game (called the thimblerigger, operator, or shell man) started the game by putting the pea under one of the shells. The shells were quickly shuffled or slid around to confuse and mislead the players as to which of the shells the pea is actually under … and the betting ensued. We now know, that the games were usually rigged. Many people were conned and never had a chance to win at all. The pea was often palmed or hidden, and not under any of the shells … in other words, there were no winners.

Many healthcare analytics systems and projects are exactly like that today – lots of players and no pea. The main component needed to win (or gain the key insight) is missing.  The “pea” … in this case, is unstructured data. And while it’s not a con game, finding the pea is the key to success … and can literally be the difference between life and death. Making medical decisions about a patient’s health is pretty important stuff. I want my care givers using all of the available and relevant information (medical evidence) as part of my care.

In healthcare today, most analytics initiatives and research efforts are done by using structured data only (which only represents 20% of the available data). I am not kidding.

This is like betting on a shell game without playing with the pea – it’s not possible to win and you are just wasting your money. In healthcare, critical clinical information (or the pea) is trapped in the unstructured data, free text, images, recordings and other forms of content. Nurse’s notes, lab results and discharge summaries are just a few examples of unstructured information that should be analyzed but in most cases … are not.

The reason used to be (for not doing this) … it’s too hard, too complicated, too costly, not good enough or some combination of the above. This was a show stopper for many.

Well guess what … those days are over. The technology needed to do this is available today and the reasons for inaction no longer apply.

In fact – this is now a healthcare imperative! Consider that over 80% of information is unstructured. Why would you even want to do analysis on only 1/5th of your available information?

I’ve written about the value of analyzing unstructured data in the past with Healthcare and ECM – What’s Up Doc? (part 1) and Healthcare and ECM – What’s Up Doc? (part 2).

Let’s look at the results from an actual project involving the analysis of both structured and unstructured data to see what is now possible (when you play “with the pea”).

Seton Family Healthcare is analyzing both structured and unstructured clinical (and operational) data today. Not surprisingly, they are ranked as the top health care system in Texas and among the top 100 integrated health care systems in the country. They are currently featured in a Forbes article describing how they are transforming healthcare delivery with the use of IBM Content and Predictive Analytics for Healthcare. This is a new “smarter analytics” solution that leverages unstructured data with the same natural language processing technology found in IBM Watson.

Seton’s efforts are focused on preventing hospital readmissions of Congestive Heart Failure (CHF) patients through analysis and visualization of newly created evidence based information. Why CHF?  (see the video overview)

Heart disease has long been the leading cause of death in the United States. The most recent data from the CDC shows that heart disease accounted for over 27% of overall mortality in the U.S. The overall costs of treating heart disease are also on the rise – estimated to have been $183 billion in 2009. This is expected to increase to $186 billion in 2023. In 2006 alone, Medicare spent $24 billion on heart disease. Yikes!

Combine those staggering numbers with the fact that CHF patients are the leading cause of readmissions in the United States. One in five patients suffer from preventable readmissions, according to the New England Journal of Medicine. Preventable readmissions also represent a whopping $17.4 billion in expenditures from the current $102.6 billion Medicare budget. Wow! How can they afford to pay for everything else?

They can’t … beginning in 2012, those hospitals with high readmission rates will be penalized. Given the above numbers, it shouldn’t be a shock that the new Medicare penalties will start with CHF readmissions. I imagine every hospital is paying attention to this right now.

Back to Seton … the work at Seton really underscores the value of analyzing your unstructured data. Here is a snapshot of some of the findings:

The Data We Thought Would Be Useful … Wasn’t

In some cases, the unstructured data is more valuable and more trustworthy then the structured data:

  • Left Ventricle Ejection Fraction (LVEF) values are found in both places but originate in text based lab results/reports. This is a test measurement of how much blood your left ventricle is pumping. Values of less than 50% can be an indicator of CHF. These values were found in just 2% of the structured data from patient encounters and 74% of the unstructured data from the same encounters.
  • Smoking Status indicators are also found in both places. I’ve written about this exact issue before in Healthcare and ECM – What’s Up Doc? (part 2). Indicators that a patient was smoking were found in 35% of the structured data from encounters and 81% of the unstructured data from the same encounters. But here’s the kicker … the structured data values were only 65% accurate and the unstructured data values were 95% accurate.

You tell me which is more valuable and trustworthy.

In other cases, the key insights could only be found from the unstructured data … as was no structured data at all or enough to be meaningful. This is equally as powerful.

  • Living Arrangement indicators were found in <1% of the structured data from the patient encounters. It was the unstructured data that revealed these insights (in 81% of the patient encounters). These unstructured values were also 100% accurate.
  • Drug and Alcohol Abuse indicators … same thing … 16% and 81% respectively.
  • Assisted Living indicators … same thing … 0% and 13% respectively. Even though only 13% of the encounters had a value, it was significant enough to rank in the top 18 of all predictors for CHF readmissions.

What this means … is that without including the unstructured data in the analysis, the ability to make accurate predictions about readmissions is highly compromised. In other words, it significantly undermines (or even prevents) the identification of the patients who are most at risk of readmission … and the most in need of care. HINT – Don’t play the game without the pea.

New Unexpected Indicators Emerged … CHF is a Highly Predictive Model

We started with 113 candidate predictors from structured and unstructured data sources. This list was expanded when new insights were surfaced like those mentioned above (and others). With the “right” information being analyzed the accuracy is compelling … the predictive accuracy was 49% at the 20th percentile and 97% at the 80th percentile. This means predictions about CHF readmissions should be pretty darn accurate.

18 Top CHF Readmission Predictors and Some Key Insights

The goal was not to find the top 18 predictors of readmissions … but to find the ones where taking a coordinated care approach makes sense and can change an outcome. Even though these predictors are specific to Seton’s patient population, they can serve as a baseline for others to start from.

  • Many of the highest indicators of CHF are not high predictors of 30-day readmissions. One might think LVEF values and Smoking Status are also high indicators of the probability of readmission … they are not. This could  only be determined through the analysis of both structured and unstructured data.
  • Some of the 18 predictors cannot impact the ability to reduce 30-day admissions. At least six fall into this category and examples include … Heart Disease History, Heart Attack History and Paid by Medicaid Indicator.
  • Many of the 18 predictors can impact the ability to reduce 30-day admissions and represent an opportunity to improve care through coordinated patient care. At least six fall into this category and examples include … Self Alcohol / Drug Use Indicator, Assisted Living Indicator, Lack of Emotion Support Indicator and Low Sodium Level Indicator. Social factors weigh heavily in determining those at risk of readmission and represent the best opportunity for coordinated/transitional care or ongoing case management.
  • The number one indicator came out of left field … Jugular Venous Distention Indicator. This was not one of the original 113 candidate indicators and only surfaced through the analysis of both structured and unstructured data (or finding the pea). For the non-cardiologists out there … this is when the jugular vein protrudes due to the associated pressure. It can be caused by a fluids imbalance or being “dried out”. This is a condition that would be observed by a clinician and would now be a key consideration of when to discharge a patient. It could also factor into any follow-up transitional care/case management programs.

But Wait … There’s More

Seton also examined other scenarios including resource utilization and identifying key waste areas (or unnecessary costs). We also studied Patient X – a random patient with 6 readmission encounters over an eight-month period. I’ll save Patient X for my next posting.

Smarter Analytics and Smarter Healthcare

It’s easy to see why Seton is ranked as the top health care system in Texas and among the top 100 integrated health care systems in the country. They are a shining example of an organization on the forefront of the healthcare transformation. The way they have put their content in motion with analytics to improve patient care, reduce unnecessary costs and avoid the Medicare penalties is something all healthcare organizations should strive for.

Perhaps most impressively, they’ve figured out how to play the healthcare analytics shell game and find the pea every time.  In doing so … everyone wins!

As always, leave me your comments and thoughts.

ECM Systems: Is Yours A Five Tool Player?

I grew up in Baltimore and baseball was my sport. I played Wiffle Ball in my backyard and Little League with my friends. It was all we ever talked and thought about. I played on all-star teams, destroyed my knees catching and worshipped the Orioles. And while I think Billy Beane’s use of analytics in “Moneyball” was absolute genius (read the book) … every good Orioles fan knows that starting pitching and three run homers wins baseball games … at least according to the Earl of Baltimore (sorry for the obscure Earl Weaver reference).

Brooks Robinson (Mr. Hoover) was my favorite player (only the greatest 3rd baseman of all time). I still have an autographed baseball he signed for me, as a kid, on prominent display in my office. I stood in line at the local Crown gas station for several hours with my Dad to get that ball.

But alas, baseball has fallen on hard times in Baltimore and even I had drifted away from the game. Good ole Brooksie was a fond nostalgic memory for me until the other day. This posting is not about baseball … it’s about ECM … really it is.

The recently concluded World Series is one of the most remarkable ever played. The late inning heroics in game six were amazing. Though neither team would give up, one had to prevail. Watching the end of that game got me thinking about ECM … no, really!

Baseball is a game that transfixes you when the ball is put into play … or in motion. And quite frankly, the game is pretty boring in between the action … or when things are at rest. So much so that the game is almost unwatchable unless things are in motion. The game comes alive with the tag-up on a sacrifice fly … or the stolen base … or a runner stretching a single into a double … or best of all, the inside-the-park homer. What do they all have in common? Action! Excitement! Motion!

No one care really cares what happens between the pitches. Everyone wants the action. That’s why you pay the ticket price … to sit on the edge of your seat and wait for ball to be put into play. The same is true for your enterprise content. It’s much more valuable when you put it into play … or in action. Letting your content sit idle is just driving up your costs (and risks too). Your goal should be to put it in motion. I recently wrote about this with Content at Rest or Content in Motion? Which is Better?.

However … putting your content in motion requires having the right tools. In baseball, the most coveted players are five tool players. They hit for average, hit for power, have base running skills (with speed), throwing ability, and fielding abilities.

The best ECM systems are also five tool players. They have five key capabilities. If you want the maximum value from your content, your ECM system must be able to:

1) Capture and manage content

2) Socialize content with communities of interest

3) Govern the lifecycle of content

4) Activate content through case centric processes

5) Analyze and understand content

I was lucky enough to have recently been interviewed by Wes Simonds who wrote a nice piece on these same five areas of value for ECM. These five tools are coveted, just like baseball. Why? Think about it … no one buys an ECM system unless they want to put their content in motion in one way or another.

Here’s the rub … far too often I see ECM practitioners who are only using one, or two, or maybe three, of their ECM capabilities even though they could be doing more. Why is this? It’s like being happy with being a .220 average hitter in baseball (or a one or two tool player). No one is getting a fat contract or going to the Hall of Fame by hitting .220 and just keeping your head above the Mendoza line (another obscure baseball reference). Like in baseball, you need to use all five skills to get to the big contracts … or get the maximum value from your ECM based information.

Brooks Robinson didn’t win a record 16 straight Gold Gloves, the Most Valuable Player Award or play in 18 consecutive All Star games because he had one or two skills. He was named to the All Century team and elected to the Hall of Fame on the first ballot with a landslide 92% of the votes because he put the ball in motion and made the most of the skills and tools he had.

It’s simple … those new to ECM should only consider systems with all five capabilities.

And today’s existing ECM practitioners should be promoting, using and benefiting from all five tools, not just a few. Putting content in motion with all five tools benefits your career and maximizes your ECM program. It enables your organization get the maximum value from the 80% of your data that is unstructured content.

As always, leave your thoughts and comments here.