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.

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.

Healthcare and ECM – What’s Up Doc? (part 1 of 2)

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.

TV Re-runs, Watson and My Blog

When I was a wee lad … back in the 60s … I used to rush home from elementary school to watch the re-runs on TV.  This was long before middle school and girls.  HOMEWORK, SCHMOMEWORK !!!  … I just had to see those re-runs before anything else.  My favorites were I Love Lucy, Batman, Leave It To Beaver and The Munsters.  I also watched The Patty Duke Show (big time school boy crush) but my male ego prevents me from admitting I liked it.  Did you know the invention of the re-run is credited to Desi Arnaz?  The man was a genius even though Batman was always my favorite.  Still is.  I had my priorities straight even back then.

I am reminded of this because I have that same Batman-like re-run giddiness as I think about the upcoming re-runs of Jeopardy! currently scheduled to air September 12th – 14th.

You’ve probably figured out why I am so excited, but in case you’ve been living in a cave, not reading this blog, or both … IBM Watson competed (and won) on Jeopardy! in February against the two most accomplished Grand Champions in the history of the game show (Ken Jennings and Brad Rutter).  Watson (DeepQA) is the world’s most advanced question answering machine that uncovers answers by understanding the meaning buried in the context of a natural language question.  By combining advanced Natural Language Processing (NLP) and DeepQA automatic question answering technology, IBM was able to demonstrate a major breakthrough in computing.

Unlike traditional structured data, human natural language is full of ambiguity … it is nuanced and filled with contextual references.  Subtle meaning, irony, riddles, acronyms, idioms, abbreviations and other language complexities all present unique computing challenges not found with structured data.  This is precisely why IBM chose Jeopardy! as a way to showcase the Watson breakthrough.

Appropriately, I’ve decided that this posting should be a re-run of my own Watson and content analysis related postings.  So in the sprit of Desi, Lucy, Batman and Patty Duke … here we go:

  1. This is my favorite post of the bunch.  It explains how the same technology used to play Jeopardy! can give you better business insight today.  “What is Content Analytics?, Alex”
  2. I originally wrote this a few weeks before the first match was aired to explain some of the more interesting aspects of Watson.  10 Things You Need to Know About the Technology Behind Watson
  3. I wrote this posting just before the three day match was aired live (in February) and updated it with comments each day.  Humans vs. Watson (Programmed by Humans): Who Has The Advantage?
  4. Watson will be a big part of the future of Enterprise Content Management and I wrote this one in support of a keynote I delivered at the AIIM Conference.   Watson and The Future of ECM  (my slides from the same keynote are posted here).
  5. This was my most recent posting.  It covers another major IBM Research advancement in the same content analysis technology space.  TAKMI and Watson were recognized as part of IBM’s Centennial as two of the top 100 innovations of the last 100 years.  IBM at 100: TAKMI, Bringing Order to Unstructured Data
  6. I wrote a similar IBM Centennial posting about IBM Research and Watson.  IBM at 100: A Computer Called Watson
  7. This was my first Watson related post.  It introduced Watson and was posted before the first match was aired.  Goodbye Search … It’s About Finding Answers … Enter Watson vs. Jeopardy!

Desi Arnaz may have been a genius when it came to TV re-runs but the gang at IBM Research have made a compelling statement about the future of computing.  Jeopardy! shows what is possible and my blog postings show how this can be applied already.  The comments from your peers on these postings are interesting to read as well.

Don’t miss either re-broadcast.  Find out where and when Jeopardy! will be aired in your area.  After the TV re-broadcast, I will be doing some events including customer and public presentations.

On the web …

  • I will presenting IBM Watson and the Future of Enterprise Content Management on September 21, 2011 (replay here).
  • I will be speaking on Content Analytics in a free upcoming AIIM UK webinar on September 30, 2011 (replay here).

Or in person …

You might also want to check out the new Smarter Planet interview with Manoj Saxena (IBM Watson Solutions General Manager)

As always, your comments and thoughts are welcome here.

It’s a Bird … It’s a Plane … It’s ACM! (Advanced Case Management)

ECM and BPM evil doers beware!  The days of creeping requirements … endless application rollout delays … one-size fits all user experiences … and blaming IT for all of it are over!

Advanced Case Management is here to save us.  Long before this superhero capability arrived from a smarter planet, we’ve had to use a bevy of workflow and BPM technologies to address the needs of case-centric processes.  In most cases, this has not worked well.  That’s because case-centric processes are different.

Traditional BPM processes tend to be straight-through and transactional with the objective of completing the process in the most efficient way and at the lowest possible cost and risk.

Case centric processes are not straight-through.  They are ad-hoc, collaborative and involve exceptions … sometimes, lots of exceptions.  In certain cases, these processes are so ad-hoc or collaborative that it is not realistic or possible to map them.  That’s because the objective is to make the best decision (within the context of the case) and the path to the right decision may not be known.  Speed and cost are always important but take backseat to achieving the best outcome … which usually involves customers, partners, employees or even citizens / patients.  You get the idea.

Why should you care?  Most “C” level survey these days lists Reinventing Customer Relationships at a top priority.  The same goals are seen again and again:

  • Get closer to customers (top theme)
  • Better understand our what customers need
  • Deliver unprecedented customer service

From a technology perspective … this means we need new tools to build those solutions that enable us to get closer, better understand and deliver optimal service to our customers.  Most customer oriented processes are case centric involving human interactions.  They tend not to be straight-through.

The traditional BPM model which depends on (1) process modeling, (2) process automation and (3) process optimization works fine for the straight-through processes … not so much for case management.

As such, a big gap exists today to build solutions that drive better case outcomes.  To close this gap, new tools that bring people, process and information together in the context of a case are needed when:

  • Processes are collaborative and ad-hoc
  • Activities are event-driven
  • Work is knowledge intensive
  • Content is essential for decision making
  • Outcomes are goal-oriented
  • The judgment of people impact how the goal is achieved
  • Process is often not predetermined

The discipline of case management is deeply rooted in industries like healthcare, public sector and the legal profession.  Case management concepts are being applied across all industries – and though organizations describe case management differently – they consistently describe the lack of tools needed for their knowledge workers to get their jobs done.  Some organizations may describe their challenges as complaint / dispute management, investigations, interventions, claims processing or other forms of business functions that have a common pattern or problem but not a straight-through process.  Cases also typically involve invoices, contracts, employees, vendors, customers, projects, change requests, exceptions, incidents, audits, electronic discovery and more.

Faster then a speeding bullet!

Yesterday’s BPM development tools simply don’t work for case management applications.  By the time you build the application, too much time has past, requirements change and IT usually gets the blame.  Time-to-value suffers.  I have nothing against BPM application development tools.  I just wouldn’t use a screwdriver to hammer a nail … and neither should you.  Case management solutions require a new kind of development environment and tools.  We need tools that are easy to use and allow a business user (not just IT) to very quickly build a solution.  They should be able to address the comprehensive nature of all case assets and provide a 360 degree view of a case.  They should leverage templates for a fast-start and represent industry best practices.  In the end, they need to significantly shorten time-to-value relative to other approaches.

More powerful then a locomotive!

Since the objective is to empower case based decision making, we need user experiences that are more robust and flexible then those of the past.  We need those experiences to be role-based and personalized so the end-user gets exactly the information they need to progress the case.  The user experience needs to be flexible and extensible … not to mention configurable, to meet unique business, case or user requirements.  The user experience should provide deep contextual data for case work and eliminate disjointed jumping between applications.  It must bring people, process and information together to drive case progression and optimal outcomes.  That way, a single case worker has all the information they need to improve case outcomes.

Able to leap tall buildings in a single bound!

Proactively advising case workers of best practices, historical outcomes, fraud indicators and other relevant insight is also needed.  Leveraging analytics to detect and surface trends, patterns and deviations contributes to better and more consistent outcomes.  In other words, we need powerful analytics for better case outcomes.  Comprehensive reporting and analysis gives case managers visibility across all information types to assess and act quickly.  Real-time dashboards help understand issues before they become a problem.  Unique content analytics can discover deeper case insight.  Bottom line … case managers need insight in order to impact results.

Anatomy of a superhero

Before being rocketed to Earth as some new problem solving superhero technology … a combination of capabilities are needed to address the needs of case management solutions.  Under the cape and tights of any case management superhero technology, you will find six core capabilities in a seamlessly integrated environment:

1 – Content.  By placing the case model in the content repository, information and other artifacts associated with cases are not only selected and viewed but also managed in the context of the case over its lifecycle.  These include collaborations, processes steps, and the other associated case elements.

2 – Process.  Cases may follow static processes that are prescribed for certain business situations.  They may also follow more dynamic paths based on changes to information associated with a case.  Straight through, transactional processes can be called as can more collaborative processes.

3 – Analytics.  Analytics help case workers to make the right decisions in case of fraudulent claims for insurance, social benefit coverage, eligibility for welfare programs and more. Analytics help detect patterns within or across cases or simply optimize the overall case handling to optimize case outcomes.

4 – Rules.  Many decisions in a case depend on set values, e.g. interest rates for loans based on credit rating, approval authority for transaction amounts, etc. By separating rules from process the case handling becomes much more agile as rules can change in lockstep with market changes.

5 – Collaboration.  Finding the right subject matter expert is often critical to make an ad-hoc decision required to bring a case to an optimal closure. Collaboration in form of instant messaging, presence awareness, and team rooms enables an organization and its case workers to work together to drive outcomes.

6 – Social Software.  Dynamic To Do Lists that are role based help case workers establish conversations and actions that must take place to close cases and link to information about the people that can help.  Users can brainstorm on appropriate solutions and actions and create wikis linked to particular case types to assist colleagues in their case work.

If you can’t do those six things … seamlessly … you aren’t very super … or advanced … and you certainly can’t meet the demands of case management solutions.

Advanced Case Management is now saving the world one case and solution at a time.

So “up, up and away” to better case management solutions and outcomes.  As always leave me your thoughts and comments here.