Amputations or Analytics … a Call to Action for Entrepreneurs and Intrapreneurs Alike!

Doctor George Shearer practiced medicine in central Pennsylvania from 1825 to 1878 (in the Dillsburg area). He was a pillar of the community and is believed to have been an active surgeon during the Civil War. He was 61 at the time of the Gettysburg battle.

According to the National Library of Medicine, the exact number is not known, but approximately 60,000 surgeries, about three quarters of all of the operations performed during the Civil War, were amputations. Although seemingly drastic, the operation was intended to prevent deadly complications such as gangrene. There were no anti-biotics during this era.

Back then, amputation was the recommended treatment for major injuries, such as damage from gunshots or cannonballs. These amputations were performed with a handsaw, like the one Doctor Shearer used (shown below). During the war, surgeons prided themselves in the speed at which they could operate, some claiming to be able to remove a leg in under one minute. Ouch! Literally!

Image

(Photo: Doctor George Shearer’s Actual Surgical Kit)

Keep in mind that local anesthetics were not invented until the 1880s and many procedures were performed without ether or chloroform … the only real anesthetics during the era.

In 1861, this was the best standard of care for those injuries. I think we can reasonably conclude that better treatment options (and outcomes) exist today.

Recently, The Mayo Clinic published an eye-opening report entitled, A Decade of Reversal: An Analysis of 146 Contradicted Medical Practices. The report focuses on a published medical practices and how effective they are. Things must have improved since 1861 … right?

The report examines published articles in prominent medical journals of new and established medical practices (such as a treatment guidelines or therapies), over a recent 10 year period (2001-2010). 2044 medical practice articles were reviewed. The findings are fascinating but one section of the report jumped off the page at me. Of the 363 articles that tested an existing standard of care, 40.2% reversed the original standard of care … and only 38.0% reaffirmed the original standard of care. The rest were inconclusive.

In other words, (in this case study) the current published medical standards of care are wrong MORE then often then they are correct. Wow!

I do feel obligated to point out that this is a very limited slice of the overall published standards of care … but still. It is just me … or is this mind-blowing!

I am not talking about gulping down some Jack Daniels so I don’t feel my leg being sawed off. This is researched and tested medical standards of care within the last 13 years. And yet … over 40% of the time, it’s WRONG. In fairness I should point out that they were right 38% of the time. No wonder the US Healthcare system checks in as the 37th best worldwide despite outspending everyone else by a huge margin (per capita).

It’s 150 years later, has the standard of care improved enough? We may not be sawing legs off at the same rate these days, but maybe it’s time for a new approach. Why are other industries so much farther ahead in leveraging their data with analytics to improve quality, reduce costs and improve outcomes? What could be more important then saving life and limb?

Years of data have been piling up in electronic medical records systems. Genomics is not new anymore. Isn’t it about time we brought analytics to this set of opportunities?

Some leading organizations already are … innovative solutions and companies are popping up to meet this opportunity. Entrepreneurs like Scott Megill, co-founder and CEO of Coriell Life Sciences, is a great example. Coriell Life Sciences is an offshoot of the Coriell Institute for Medical Research, a 60-year-old non-profit research organization. In 2007, the Institute launched an effort to bring genomic information to bear on health management. Coriell Life Sciences was established to commercialize the results of that research. Vast amounts of genetic information about individual patients has been available for a number of years, but it has been difficult to get at and expensive. “This company bridges the gap,” said Dr. Michael Christman, the Institute’s CEO.

Coriell’s approach is so innovative, they recently walked away with the coveted “IBM Entrepreneur of the Year” award.

Intrapreneurs at IBM have been busy commercializing the breakthrough innovation, IBM Watson – that originally debuted on Jeopardy! in 2011. Watson is based on a cognitive computing model.

Grabbing a few less headlines is IBM Patient Similarity Analytics, which uses traditional data driven predictive analysis combined with new similarity algorithms and new visualization techniques to identify personalized patient intervention opportunities (that were not previously possible).

These are a couple of obvious examples for me, but in reality we are just at the beginning of leveraging big data. New analytics and visualization tools must become the “handsaw” of today. We need these tools to be at the root of today’s modern standards of care.   If Dr. Shearer were alive today, you can bet his old surgical kit would be on the shelf, having been replaced by analytics that he could bring to the point of care.

For many Entrepreneurs and Intrapreneurs, the journey is just beginning, but there is a long way to go. A 2011 McKinsey report estimated that the healthcare industry can realize as much as $300 billion in annual value through analytics. Yowza!

What are you waiting for?

As always, leave my your thoughts below.

Best Business Decision Making Tool Ever?

Those of you who know me, know that I have spent my career as both an Entrepreneur and more recently … an Intrapreneur.   I have also been on a slightly quirky quest to find useful tools and models that will enable me to make better, more objective decisions that increase the chance of success and minimize risks.

Whether you call yourself an Intrapreneur or not, you probably have a need to make decisions about your own projects. Which brings me to today’s posting. I have come to rely on a handful of basic but indispensible tools and am curious about whether these are same tools and methods that everyone else relies on.

My favorite tool by far is the SWOT Analysis. I have used it hundreds of times over the years for everything from personal career planning to many different business situational assessments. I even used it to start the project that eventually became IBM’s Patient Care and Insights solution. At the time, we were trying to decide how to approach bringing a major breakthrough in healthcare analytics innovation to market that has been invented by IBM Research named Patient Similarity Analytics … and it all started with a SWOT Analysis.

A SWOT Analysis is an easy to understand and versatile way to assess your position at any point in time. Think of it a tool to assess any situation from 4 different positions or perspectives (Strengths, Weaknesses, Opportunities, Threats).  It can be as simple as making a list for each of the SWOT categories. There are many different ways to apply and use SWOT Analysis. It’s power is in it’s versatility.  Once the items are listed, decision making becomes easier bordering on obvious. The listed items can also be scored or weighted with other relevant information such as likelihood, impact, etc.

Strengths

  • List strength 1
  • List strength 2
  • List strength 3

Weaknesses

  • List weakness 1
  • List weakness 2
  • List weakness 3 
Opportunities

  • List opportunity 1
  • List opportunity 2
  • List opportunity 3
Threats

  • List threat 1
  • List threat 2
  • List threat 3

Catherine Kaputa has a wonderful way to use SWOT as a career / personal brand assessment in her book on personal branding titled “You Are A Brand”

Eisenhower Analysis

Credited to Dwight D. Eisenhower, an Eisenhower Analysis (also known as the Eisenhower Box, or Decision Matrix) is a time management or prioritization tool. It is best used when trying prioritize, organize or otherwise get control of a situation or workload. The goal is to easily recognize the highest priority items from the noise. As an example, many people confuse “urgent” work with “important” work and this helps bring clarity to those kinds of issues. Once the items are listed, prioritizing is clear. Like SWOT, the listed items can also be scored or weighted with other relevant information such as likelihood, impact, etc.

Urgent and Important (1)

  • List urgent important item 1
  • List urgent important item 2
  • List urgent important item 3
Important (2)

  • List important item 1
  • List important item 2
  • List important item 3
Urgent (3)

  • List urgent item 1
  • List urgent item 2
  • List urgent item 3
Neither Urgent nor Important (4)

  • List non-urgent non-important item 1
  • List non-urgent non-important item 2
  • List non-urgent non-important item 3

Franklin Analysis (Pros / Cons)

Credited to Benjamin Franklin, the Franklin Analysis (also known as the pros and cons analysis) is a comparison tool for decision making … typically in a list format. It is used when comparing the pros (positives, pluses or benefits) to the cons (negatives, minuses or costs). This tool is most effective when there are many items to consider and the decision is not obvious. Many people bring emotion into decision making and this approach helps keep an objective perspective. Like the SWOT and Eisenhower approaches, the listed items can also be scored or weighted with other relevant information such as degree of importance, etc.   Totals can even be calculated from the weighted scores.

Pros

  • List positive item 1
  • List positive item 2
  • List positive item 3
  • List positive item 4
  • List positive item 5
Cons

  • List negative item 1
  • List negative item 2
  • List negative item 3
  • List negative item 4
  • List negative item 5

There are literally hundreds of decision making models and approaches but I kept this list to the most well known. What is your favorite tool? As always, leave me your thoughts and ideas here.

Entrepreneurship Alive and Well in Local DC Area

I had lunch recently with my good friend and local entrepreneurial legend Ching-Ho Fung.  Ching-Ho was the Chairman of Parature until the recent sale to Microsoft in January for $100 million.  We were both in a reflective mood and discussed the state of entrepreneurship in the DC region.  I was struck how much is happening in support of entrepreneurship.

Along those lines, I attended a DC I-Corps event on 3/28 on the recommendation of Jim Chung, the Executive Director of Entrepreneurship and Technology Transfer for George Washington University.  DC I-Corps is a regional program designed to foster, grow and nurture an innovation ecosystem in the nation’s capital, the nearby states of Maryland and Virginia, and the mid-Atlantic region.  It is sponsored by the National Science Foundation and jointly run by the University of Maryland College Park, George Washington University, and Virginia Tech.

The event featured presentations by four “teams” who are graduating to the next phase of the program and will/may become ventures one day soon.  Keep an eye out for ToxFix, ReadAhead, SmartPupilometer and RedShred.  All four look promising because they address real-world problems.  It’s part of the “Evidence Based Entrepreneurship” concept taught within the program.  I was particularly fascinated with RedShred as their RFP response solution is based on natural language processing techniques … something I have blogged about many times.  It’s a great example of a burgeoning Cognitive Computing solution.

Interestingly … the definition of a start-up by the I-Corps program is “A temporary organization designed to search for a scalable repeatable business model”.  I love this concept since it teaches entrepreneurs to find problems first that they can then address with a solution (and it’s value) before anything else.  It’s the right model.

I can’t tell you how many meetings I’ve been in where some product manager is trying to apply the “every problem looks like a nail if you are a hammer” mentality.  That’s the wrong model.  Additionally, many entrepreneurs think the first thing they need to do to find venture capital.  There is a place for that but it’s not first.

I-Corps has gotten it right.  From what I can tell, this looks like a good program for anyone with an idea, or invention, to get started on the right foot.  In recent years, all of the local universities seem to be been expanding their focus on entrepreneurship.

The Dingman Center for Entrepreneurship at the University of Maryland have certainly expanded their offerings.  Next for them is the 2014 Cupid’s Cup Business Competition.

I plan to attend that one as well … and hope to see you there.  I also plan to blog more on both entrepreneurship (as well as intrapreneurship) going forward … so stop back soon.

As always, leave me your thoughts and comments below.

 

 

 

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.

Healthcare Data is the New Oil: Delivering Smarter Care with Advanced Analytics

It has been said that “data” is the new “oil” of the 21st century.  That is certainly true in healthcare where a unique opportunity exists to leverage data – as fuel for better health outcomes.  Everything that happens with our health is documented … initially this was on paper … and more recently, in the form of electronic medical records.

Despite billions of incentive dollars being dolled out by the federal government to purchase Electronic Medical Record (EMR) systems and use in meaningful ways, there continues to be significant dissatisfaction with these systems.

In a recent Black Book Rankings survey, 80% surveyed claim their EMR solution does not meet the practice’s individual needs.  This is consistent with my own observations, where many express frustration that “the information goes in … but rarely, if ever, comes out”.

If the information never comes out, or it’s too hard to access, are we really maximizing its value?

It all boils down to our ability to leverage years and years of longitudinal patient population data to surface currently hidden insights … and put those insights to work to improve care.

It’s incredibly powerful to combine years of clinical patient population data (longitudinal patient histories) with other types of data such as social and lifestyle factors to surface new trends, patterns, anomalies and deviations.  These complex medical relationships (or context) trapped in the data are the key to identifying new ways to achieve better health outcomes.  Some organizations are already empowering physicians with these new insights.

Context can be critical in a lot of situations—but in healthcare, especially, it can be the difference between preventing a hospital readmission or not. It’s not enough, for example, to know that a patient has diabetes and smokes a pack of cigarettes each week. These factors are only part of the whole picture. Does she live on her own, with family or in a care facility? Does she have a knee injury that prevents her from an active exercise program? Has she been treated for any other illnesses recently? Did she experience a recent life-changing event, such as moving homes, getting a new job or having a baby? Is she able to cook meals for herself, does she rely on someone else to cook, or does she frequent cafeterias, restaurants or take-out windows?

All of these things and more can—and should—influence a patient’s care plan, because these are the factors that help determine which treatments will be most successful for each individual. And as our population grows and ages, a greater focus on individual wellness and increasing economic pressures are forcing providers, insurers, individuals and government agencies to find new ways to optimize healthcare outcomes while controlling costs.
Today’s data-driven healthcare environment provides the raw materials (or “oil”) to fuel this kind of personalized care, and make it cost-effective as well. But it takes savvy analysis to turn that data into the kind of reports and recommendations providers, patients and communities need to make informed decisions.

The good news: IBM is uniquely positioned to help organizations and individuals achieve these goals. The IBM® Smarter Care initiative draws on a comprehensive portfolio of advanced IBM technologies and services to help generate new patient insights that can improve the quality of care; facilitate collaboration among organizations, patients, government agencies and other groups; and promote wellness through a range of public health and social programs.

IBM Patient Care and Insights is a key component of the Smarter Care initiative. By incorporating advanced analytics with care management capabilities, Patient Care and Insights can produce valuable insights and enable holistic, individualized care.

Advanced analytics: Leading the way to Smarter Care

Several leading healthcare organizations are already on the path to Smarter Care and demonstrating the real-world benefits of advanced analytics from IBM. For example, in St. Louis, Missouri, BJC HealthCare—one of the largest nonprofit healthcare systems in the United States—is using natural language processing (NLP) and content analytics capabilities from IBM to extract information from patient records that are valuable for clinical research. By tapping into unstructured data, such as text-based doctors notes, BJC HealthCare is surfacing important social factors, demographic information and behavioral patterns that would otherwise be hidden from researchers.

BJC HealthCare is also using IBM technologies to reduce hospital readmissions for chronic heart failure (CHF). The organization is analyzing clinical data such as ejection fraction metrics (which represent the volume of blood pumped out of the heart with each beat) to better predict which patients are most likely to be readmitted. These insights enable providers to implement tailored interventions that can avoid some readmissions.

The University of North Carolina (UNC) Health Care is using Patient Care and Insights for three new pilot projects. First, UNC is employing NLP and content analytics on free-text clinical notes to discover predictors of hospital readmission, identifying patients at risk and improving pre-admission prediction models.

UNC is also using IBM technology to empower patients. IBM NLP technology is helping to transform clinical data contained electronic medical records (EMRs) into a format that can be presented to patients through an easy-to-use portal. Streamlined access to information will help patients make more informed decisions and encourage deeper participation in their own care.

Finally, UNC is using NLP to help generate alerts and reminders for physicians. With NLP, the organization is extracting key unstructured data from EMRs, such as abnormal cancer test results, and then storing this data in a structured form within a data warehouse. The structured data can then be used to produce alerts for prompt follow-up care.

This is just the beginning. As organizations continue to launch new projects that capitalize on advanced analytics, case management and other technologies from IBM, we expect to see some very innovative approaches to delivering Smarter Care.

Learn more about IBM Smarter Care by visiting:

ibm.com/smarterplanet/us/en/smarter_care/overview/

For more about IBM Patient Care and Insights, visit:

ibm.com/software/ecm/patient-care/

As always, share your comments or questions below.

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.