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.
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. 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:
Read specifically about IBM Research and Similarity Analytics by visiting:
As always … look forward to reading your comments and questions.
 Projection of Chronic Illness Prevalence and Cost Inflation from RAND Health, October 2000.
 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> Health Affairs 25:5 (2006): 378–388.
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. 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.
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 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 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.
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,
 S.Y. Wu, A. Green, “Projection of chronic illness prevalence and cost inflation,” RAND Health, 2000.
 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.
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?
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.
In my last blog posting Healthcare and ECM – What’s Up Doc?, I wrote about using ECM based content analytics technology to help accelerate decision making in an industry in transition.
But why stop there … how powerful would it be to turn those new insights (from unstructured information) into action by combining content analytics with predictive analytics or other business analytics?
This is transformational … by unlocking the 80% of information not currently being leveraged (explained in part 1) we unlock new ways to use information. More compellingly, we unlock never seen before trends and patterns in both clinical and operational data.
Think about it … do we know everything we need to know about healthcare and how to identify and treat diseases? Or can we benefit from new insights? The answer is obvious.
Combining content and predictive analytics enables:
- Accurate extraction of medical facts and relationships from unstructured data in clinical and operational sources – not easy, cost effective, or even possible in the past.
- Never seen before trends, patterns and anomalies are revealed – connections or relationships between diseases, patients and outcomes (and even costs) are now able to be surfaced and acted upon. Think of the medical research possibilities!
- The ability to predict future outcomes based on past and present scenarios – optimizing resource allocation and patient outcomes. One organization reduced cardiac surgery patient morbidity from 2.9% to 1.3% by doing this.
- New insights can be surfaced to any clinical or operational knowledge based on their respective role – this could be through dashboards, case management/care coordination system, EMR, claims processing or any number of other ways – enabling better decision making and action across the organization.
- The ability to leverage these new insights with other systems such as data warehouses, master patient data – maximizing and befitting from the use of other systems.
In my last posting, I commented that it was now an imperative to leverage clinical information and operational data in new ways … and that are obvious things to do to improve quality of care, patient satisfaction and business efficiency.
There are at least nine areas where this opportunity exists. The clinical scenarios are:
- Diagnostic Assistance: Highly correlated symptom to health/disease analysis issues visualized with predictive guidance on diagnosis to improve treatment and outcomes … with predicted or forecasted costs.
- Clinical Treatment Effectiveness: Examine patient-specific factors against the effectiveness of a healthcare organizations specific treatment options and protocols … including comparisons to industry wide outcomes and best practices.
- Critical Care Intervention: Early detection of unmanageable or high risk cases in the hospital that leads to interventions to reduce costs and maintain or improve clinical conditions … including case based interventions.
- Research for Improved Disease Management: Perform analysis and predict outcomes by extracting discreet facts from text, such as: patient smoking status, patient diet and patient exercise regime to find new and better treatment options … use a mechanism for differentiation or to secure research grants.
Operational scenarios include:
- Claims Management: All claims involve unstructured data and manually intensive analysis. Analyze claims information documented in cases, forms and web content to understand new trends and patterns to identify areas … perfect for process improvement, cost reduction and optimal service delivery.
- Fraud Detection and Prevention: Uncover eligibility, false assertions and fraud patterns trapped in the unstructured data to reduce risk before payments are made … usually represented by a word or combination of words in text that can’t be detected with just structured data.
- Voice of the Patient: Include unstructured data and sentiment analysis from surveys and web forms in analysis of patient and member satisfaction … this will be key as the industry moves to a value based model.
- Prevention of Readmissions: Discover key indicators which are indicative of readmission to alert healthcare organizations to these so that protocols can be altered to avoid readmission … this is key as new Medicare payment penalties go into effect in 2012.
- Patient Discharge and Follow-up Care: Understand and monitor patient behavior to proactively inform preventative and follow-up care coordinators before situations get worse.
According to the New England Journal of Medicine, one in five patients suffer from preventable readmissions. This represents $17.4 billion of the current $102.6 billion Medicare budget. Beginning in 2012, hospitals will be penalized for high readmission rates with reductions in Medicare discharge payments. Seton Healthcare Family is already ahead of the game.
“IBM Content and Predictive Analytics for Healthcare uses the same type of natural language processing as IBM Watson, enabling us to leverage our unstructured information in new ways not possible before,” said Charles J. Barnett, FACHE, President/Chief Executive Officer, Seton Healthcare Family. “With this solution, we can access an integrated view of relevant clinical and operational information to drive more informed decision making. For example, by predicting readmission candidates, we can reduce costly and preventable readmissions, decrease mortality rates, and ultimately improve the quality of life for our patients.”
This week at IOD … IBM is launching a new solution specifically designed to reveal clinical and operational insights in the high impact overlap between clinical and operational use cases – enabling low cost accountable care.
IBM Content and Predictive Analytics for Healthcare, a synergistic solution to IBM Watson, helps transform healthcare clinical and operational decision making for improved outcomes by uniquely applying multiple analytics services to derive and act on new insights in ways not previously possible … which is exactly what Seton Healthcare Family is doing. Dr. David Ramirez, Medical Director at Seton shares his perspective here.
IBM Content and Predictive Analytics for Healthcare (ICPA) is Watson Ready and is designed to complement and leverage IBM Watson for Healthcare through the ability to analyze and visualize the past, understand the present, and predict future outcomes.
ICPA, as the first Watson Ready offering, not only provides assurance of Watson solution interoperability but extends the value ultimately delivered to clients. For example, using input from ICPA outcomes, IBM Watson will be able to provide better diagnostic recommendation and treatment protocols as well as learn from the confidence based responses.
The press release is available here for those seeking more information. I will be doing a high level main stage demo of ICPA on Wednesday which will be streamed live. I will post the replay when available.
But it’s not just healthcare … every industry is impacted by the explosion of information and has the same opportunity to leverage the 80+ percent that is unstructured to turn insights into action.
As always, leave me your thoughts and comments here.
This is one of those industry centric topics everyone can relate to … we all need healthcare and we’ll all use it at some point in our lives. I plan to do a couple of postings on Healthcare and ECM … here is the first.
The healthcare industry is undergoing a major transformation. We have a legacy health system that is fee for service based resulting in a care system that is high cost with inconsistent quality. Healthcare provider consolidation is accelerating; competitors as well as payers/providers are merging. Clinical transformation is already occurring … disease management, health and wellness management, and behavioral health are integrating. The industry is moving to a more patient centric, evidence based and competitive care system where the players are held accountable and will have to compete on the value they deliver and not rely solely on quantity based reimbursements.
This transformation is driving new thinking, new business models and a restructuring of clinical and operational care models. The expectation of value is changing and healthcare organizations have to adjust their business models to deliver value, not just volume. This type of transformation requires innovation … the kind of innovation that improves productivity and competitive advantage … and not just advancing medical technology for technology sake. The main consideration must be for total well being and cost, and not one for the sake of the other.
As the backbone for a transformed healthcare system, leveraging clinical information and operational data in new ways are obvious things to do to improve quality of care, patient satisfaction and business efficiency. This places a premium on making this information accessible and actionable to optimize outcomes! … and where ECM comes in!
There are many ways ECM technologies are being applied to solve problems in healthcare. Obvious ones are document capture conversion of paper based patient records and advanced case management for care coordination. I am going to focus on content analytics and leveraging unstructured information to reveal insights currently trapped in documents, records and other content. I believe this has significant transformative potential as an ECM based information technology.
Studies show that healthcare information is growing at 35% per year and that over 80% of information is unstructured data (or content). The explosion of information makes accessing and leveraging it a harder task, but this is now an imperative.
Unstructured data resides in many sources: physician notes, registration forms, discharge summaries, text messages, documents and more. Because this content lacks structure, it is arduous for healthcare enterprises to include it in business analysis and therefore it is routinely left out.
The impact of this is staggering. If you had a choice – would you choose to leverage all of your available information or just the 20% that is structured data and found in databases? This is exactly the type of thing that can accelerate transformation. We need to leverage the remaining 80% of available information. After all … would you want your Doctor making decisions about your health on 1/5th of the available information?
It’s such a simple premise but the reality is that until recently, the technology wasn’t available to easily and accurately analyze and unlock insights contained in the unstructured information. This is where natural language processing (NLP) and breakthrough technologies like IBM Watson and IBM Content Analytics come in. So let’s apply this to the real world.
Smoking has long been known as a habit that contributes to poor health and diseases like Congestive Heart Failure but how accurately do the healthcare systems of today reflect the patient’s current smoking status? To understand a patient’s smoking status … it cannot only be a yes/no checklist question found in structured data. How can a check box know you if you quit 3 years ago … or started again last year and just recently quit again … or that you recently took up casual cigar smoking … or that you cut down from 2 packs to 1 pack a day? A structured data field can’t understand these nuances. This is natural language based information found only in text. These text based descriptions are often captured in registration forms, history and physical reports, progress reports and other update reports. Most systems have not factored in this kind of information due to the cost and time taken to manually extract it. It’s often too costly and too late. Yet it is exactly this kind of information that could be most critical in improving care.
In a recent private IBM customer data study, we found 40% of the total population of smoking patients were identified in the text of unstructured physician notes, and not the structured data. This is huge! Can you imagine doing research on smoking without including this kind of information? … or not including 40% of the total smoking population?
BJC Healthcare has figured out the value of leveraging unstructured data. They found that structured data alone was not enough when doing research often resulting in the reading of documents … many many documents … one by one. You can imagine how fun and helpful that was. They are now using IBM Content Analytics to extract key medical facts and relationships from more than 50 million documents in medical records, speeding up research to ultimately provide better care for patients worldwide… See the recent case study.
I feel strongly that ECM technologies, and especially Content Analytics, can make a huge impact in both the clinical and operational healthcare transformation underway. I’ll be back in two weeks with more on this topic … which is now published as Healthcare and ECM – What’s Next Doc?
As always, leave me your thoughts and comments here.