In the Field with our Experts

A Plea to the Engineers

By Stephen Putman, Senior Consultant

I am a computer engineer. I have been a computer engineer for over twenty years. I know a lot of other computer engineers. I have seen many techniques for implementing Business Intelligence systems, with varying degrees of effectiveness.

I have a simple request of my fellow engineers:

Please stop designing functions into your analytical applications without input from your business users.

It is a simple request, one that should be easy to understand and implement. Unfortunately, I have seen many companies where this idea is central to the fabric of the IT organization. Non-business managers are confident that they know what the business needs, either through hubris or a soured relationship with their users. Functionality is designed and developed that bears little relationship to how data is consumed and used in the business organization, usually without regard for the cost of development and maintenance of the resultant structures.

It’s easy to determine that this is happening in your organization - you attend meetings where the following phrases are used with increasing frequency:

“I think this is done...”
“They will need this eventually...”
“We didn’t think of that...”

We live in an age where the Business Intelligence projects that are completed are frequently over budget, late, and do not serve the needs of business users (too many projects are cancelled or abandoned as well). Much of this failure can be attributed to communication issues between the business users and the system implementation team. The system team means well by trying to fill in the gaps, but this often leads to frustration, uncertainty, and design based on hearsay. They really should try to resist that temptation.

How much more effective will your system be if you ask your users what they want, instead of guessing or assuming you know better?

photo by Wonderlane via Flickr (Creative Commons License)

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Stephen Putman has over 20 years experience supporting client/server and internet-based operations from small offices to major corporations.  He has extensive experience in a variety of front-end development tools, as well as relational database design and administration, and is extremely effective in project management and leadership roles. He is the co-author of The Data Governance eBook, available at information-management.com.

Healthcare Data’s Baby Steps: Meaningful Use, Pay for Performance, Quality Improvement (Part 2)

By Carol Newcomb, Senior Consultant

In my last post, I bemoaned the carrot-or-stick approach that has resulted in immature, disintegrated reporting solutions.  In this post, I challenge the notion that business intelligence, as opposed to formal data governance, is a fix to healthcare organizations’ fragmented data. 

Collecting data is easy.  Most electronic health records (EHRs) and personal health records (PHRs) are front-end data acquisition and transfer tools, with sophisticated built-in rules and customized algorithms.  Storing data, metadata, and even “data exhaust” gets cheaper by the hour.  The trick is turning all this data into meaningful information, and using that information to make smarter decisions.  How hard is it to turn data into reports using your current data warehousing technology?  How frequently do you get reports that just don’t jibe?  Can someone confidently explain how a number on a report got there?

IBM is running a catchy ad that resonates deeply in our data-saturated world.  “If you can’t believe the data, how can you believe the analytics?”  (Healthcare CFO)  Right on!  Business intelligence is NOT the place to tackle healthcare integrity issues.  Let me pose some fundamental BI questions:

• What’s your headcount of 3rd year medical residents today?  In each department.
• How many RNs and LPNs do you have in any single department?  Last Sunday?
• What is the average patient wait time in your GI lab?  What’s the variability?
• How many repeat lab tests did you have for patients who are being diagnosed with AIDS?  How many are positive?
• What’s the dollar amount for lawsuits filed for events that happened on the pediatrics unit?  Were those lawsuits for ‘Never Events’?
• What is the failure rate of certain brands of hip prostheses used in hip replacement surgery?  How long before they failed?
• What’s the infection rate on each day of the week in the ICU?  Does it change at shift-change?

These are straightforward administrative and clinical quality questions that most healthcare organizations are hard-pressed to answer without scraping around in spreadsheets, disconnected data systems or standard reports.  Why?  There is a fundamental lack of recognition that data governance and data integration are critical infrastructure elements that continually get no attention in a strategic IT budget, and get zero dollars as a result.  Healthcare data management is stuck in its infancy, crawling on hands and feet.

It is important for healthcare organizations to use the government’s incentive dollars and retool for the 21st century.  However, while incentives are there to facilitate the technology transition from paper to electronic data, there is huge risk in installing yet another front-end system and further complicating your back-end IT infrastructure.   Organizations that have spent years consolidating on SAP, Oracle and other ERP systems have needed to return to fundamental governance practices to resolve the confusion that usually results from inconsistent definitions and non-existent data usage rules even after years of effort. 

The failure to design data governance at the start of an integration project is expensive, and wastes the best features of even the most sophisticated business intelligence tools.  

It is sad to compare healthcare to retail, manufacturing, banking, and just about any other industry in terms of customer-centric capabilities.  Healthcare organizations have failed to take a sustainable approach to data infrastructure because they have ignored the need for data governance.  This churn will only continue until healthcare leaders finally step back and insist that solid infrastructure--including a data governance plan, an MDM plan, a data integration roadmap--are budgeted, staffed and successful.  Otherwise, they had better hope that attestation will continue to work.

photo by Yoni Lerner via Flickr (Creative Commons License)

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Carol Newcomb is a Senior Consultant with Baseline Consulting. She specializes in developing BI and data governance programs to drive competitive advantage and fact-based decision making. Carol has consulted for a variety of health care organizations, including Rush Health Associates, Kaiser Permanente, OSF Healthcare, the Blue Cross Blue Shield Association and more. While working at the Joint Commission and Northwestern Memorial Hospital, she designed and conducted scientific research projects and contributed to statistical analyses.

Healthcare Data’s Baby Steps: Meaningful Use, Pay for Performance, Quality Improvement (Part 1)

By Carol Newcomb, Senior Consultant

When I think about all the time and energy that has been spent and dollars invested in collecting and analyzing healthcare data over the past 30 years, it makes me crazy!!  From fee-for-service to HMOs to PPOs to HDHPs, billions of patient records exist that could inform policy on quality, safety, outcomes, effectiveness and efficiency, but as with our little Christmas-day Bomber incident, “We had the data, we just couldn’t put it all together.”  In the time that it has taken banks, retailers, even government to retool their operations to provide real-time, web-enabled, customer-focused applications, healthcare has gone nowhere.  The government is literally bribing healthcare organizations to convert from paper-based systems (or from stand-alone off the shelf-applications) to integrated electronic systems.  “Meaningful Use” is being touted as the newest data reporting incentive to show that your EHR system is up and at least partially running.  The first phase of evidence will include attestations that providers are using certain features of the system.  Not data, attestations!! 

And this isn’t the first attempt to get healthcare organizations to willingly embrace their data.  For the past two decades, outfits like the Joint Commission, AHRQ, NCQA, NQF and many others, have developed reporting metrics, hoping to drive down not just the cost of care, but the inefficient, wasteful and sometimes harmful practices that exist in our provider organizations.  Tying these measures to accreditation, CMS reimbursements, Pay for Performance and other carrot-or-stick mechanisms hasn’t been enough for hospital CIOs to seriously undertake more than just one-off fixes to their data infrastructure.  What they need is a back-to-basics roadmap for enterprise data management to integrate and govern data as it is gathered and aggregated over time.  Data governance is virtually non-existent in healthcare as a formal program.  Without governance, an organization cannot stand on its own data.

Healthcare is swimming in data.  Some would argue that what healthcare needs is better business intelligence.  Sure enough, it’s pretty hard to analyze a lot of loosely-coupled data that resides under any one healthcare organization’s roof, much less tie it to any one patient or doctor.  What good are analytics if the data haven’t been through rigorous quality controls?  What if the data aren’t stable or even accurate?   How many people contribute diagnosis and procedure and billing codes and modifiers to any single patient record?  How many people change it before a claim is submitted to a payer?  How many different sets of rules are there and who enforces them?  A few months ago, I went for an elective outpatient surgical procedure, but due to complications they were unable to operate.  Two days later, Blue Cross notified me of a claim for the full procedure.  When I called to contest it, they told me they couldn’t second-guess the doctor’s office.  Huh??  They paid for the un-performed procedure in full.  Wither our scarce healthcare dollars? 

(To be continued……)

photo by marisag via Flickr (Creative Common License)

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Carol Newcomb is a Senior Consultant with Baseline Consulting. She specializes in developing BI and data governance programs to drive competitive advantage and fact-based decision making. Carol has consulted for a variety of health care organizations, including Rush Health Associates, Kaiser Permanente, OSF Healthcare, the Blue Cross Blue Shield Association and more. While working at the Joint Commission and Northwestern Memorial Hospital, she designed and conducted scientific research projects and contributed to statistical analyses.

The Change Paradox

By Carol Newcomb, Senior Consultant

Change is hard.  How does your company manage change?  How do you personally respond to change?  Why does it seem like everything changes at the same time, or that it’s never-ending?  How can you possibly do your daily job while incorporating all the NEW ideas and NEW technologies and NEW demands on your time?  When are you supposed to get a chance to actually think about the purpose of changes, and figure out how to put them all together into a manageable pattern?  Sometimes it just seems like all too much!

I recently worked for a few years studying patient safety in hospitals, and collecting a lot of data from across the country on types of errors.  Humans, it turns out, are very adaptable, but also very entrenched in our learned patterns of behavior and physical agility.  By this I mean that things like driving a car, while initially quite challenging (remember how to slip the clutch while starting from a dead stop on a snowy uphill slope?) become simple with repetition.  After a while, you don’t even think about all the little steps involved, and you just quickly let out the clutch, gage the slipperiness of the slope, and adjust the gas.  No problem.  And when some smarty-pants (like your husband) tells you you’re doing it all wrong, the response is undoubtedly scornful.  Such is most people’s response to change.  “I can handle this myself, thank you very much.”  Remember your first response to having to use a mouse or a keyboard?  Several doctors I know were not too tickled.

And yet humans are also very adaptable.   The literature on patient safety, and use of other technologies for that matter, document how once the brain stops consciously THINKING about how to do something, the subconscious brain kicks in and takes over, instinctively executing each step of a process without needing to affirm the need for each particular step.  It becomes part of a repeatable pattern—like lifting a beer mug without spilling a drop—or speaking a different language—that you can do without conscious or deliberate parsing of each micro-step.  With enough time and practice, humans can adapt pretty well.

As consultants, what we work on with clients ALWAYS entails change.   And here lies the paradox.  While most clients look for consultants to make recommendations—to help them  with things they are challenged with, to provide expert advice, to design a new way of managing their business, to recommend new technology, to train staff on new ways of collecting information, etc.—the ability to consume that advice or to incorporate new practices is tempered by the organization’s sensitivity to the impact such changes will have on their environment.

How many times have you heard, “That’ll never work here; our culture just isn’t like that” or “That may be a ‘best practice’ but who has time for all the extra work?”  New ways of doing things may be good ideas, but deliberate attention needs to be spent on incorporating them, digesting them—so to speak—so that they are not only accepted, but willingly practiced and even improved on over time.  The biggest risk, when we leave a consulting engagement, is that the organization will simply ignore or reject our suggestions, and go back to business as usual.

This is the Change Paradox: Change is good, but change is bad. Change is hard when there is too much change at the same time.  Change is necessary, but it can be overwhelming and destabilizing, especially if it compromises the work at hand.   Change can be refreshing if the purpose and the steps are broadly understood and accepted, otherwise it can be a needless waste of energy that ultimately accomplishes nothing.  Change can introduce new ways of thinking about the business and smarter ways to achieve business goals, but if can feel like chaos if not managed in digestible increments.

As you embark on a new project or a new set of practices, take the time to enlist the understanding and acceptance of those who will be most impacted by the proposed changes.  Provide the time, the training, the leadership vision and purpose, and the support resources to ensure that all the ‘newness’ can be incorporated, repeated and rewarded.  Change is simple, but it’s also complex.

Image by Rafa Garcés via Flickr (Creative Commons License)

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Carol Newcomb is a Senior Consultant with Baseline Consulting. She specializes in developing BI and data governance programs to drive competitive advantage and fact-based decision making. Carol has consulted for a variety of health care organizations, including Rush Health Associates, Kaiser Permanente, OSF Healthcare, the Blue Cross Blue Shield Association and more. While working at the Joint Commission and Northwestern Memorial Hospital, she designed and conducted scientific research projects and contributed to statistical analyses.

Selling Enterprise Information Management

By Andy Field, Senior Consultant

I find it interesting how companies tend to focus on the hot issue of the day—and then fire up ad hoc teams of available resources to attack the immediate problem. For those of us who have had a long-term career focus on enterprise information management, this behavior has been a recurring source of frustration. This is because many of the problems being addressed in this manner are the result of not having good data management practices in place. Not only that, but each one of these ad hoc projects typically compounds the data management problems in a never-ending cycle. Let me explain.

The “best” example I have seen of this was working for a company that had embraced agile development. Every time there was a request from the business for new systems capability, the agile team would take great pride in building something quickly in response to the business demand. The business was initially happy that IT was responsive to their needs, and IT was proud of the statistics that showed they had delivered 240 new applications in the prior year. This was a very large company, so the number of systems is not an exaggeration.

The problem was that to sustain this pace of delivery, there was no attempt to engage enterprise architecture—let alone analyze information requirements against existing data sources. The result was that in the year mentioned, nearly 240 new SQL Server databases got built to support the 240 new application systems. After some time, the business realized there was some redundancy in these applications when they began to use them to report operational statistics and corporate performance indicators. They began to realize there were irreconcilable differences between these databases due to semantic inconsistencies. The application owners began to bicker over the differences and ended up divorcing themselves of each other. This resulted in fire drills every time a VP demanded that someone “just give me the numbers.” This wasn’t easy since they weren’t talking to each other anymore. Ad hoc queries, Access databases and spreadsheets began to spread like wildfire throughout the organization.

Over time, nobody knew what was where, as the ad hoc teams that had been put together for each of these fire drills had moved onto other things once the immediate problem was resolved.

Businesses are no different than individuals in their behavior.  If someone tells us we should plan for medical emergencies or, heaven forbid, our own demise by saving or acquiring adequate insurance, we may put this off because we have more immediate pressures from our family to purchase a new car or buy a bigger home. These are things we can realize immediate gratification from—not something that may happen in the foggy distant future. The problem becomes exacerbated when we not only put off investments to provide for our financial security, but we also mortgage our future by acquiring debt to satisfy our immediate personal and family desires.

In the company I talked about above, that’s what was happening. They were not only not investing in data management practices that would have curtailed the proliferation of standalone application databases, but they were also incurring a growing debt in terms of the cost and effort that, at some point in the future, would have to be accounted for. The ad hoc reporting currently taking place was becoming increasingly unsustainable. This was recognized and an enterprise data warehouse was seen as a more strategic solution to consolidated reporting in the long-term. However, they kept digging a bigger hole in terms of the number of source systems and mapping complexity that would need to be dealt with to build an EDW. Also, with every ad hoc solution came more Access databases and spreadsheets that took on a life of their own and further compounded the data management issues.

There is one significant difference, however, between our personal lives and business. Most of us are taken care of to a certain extent by our employer’s benefits plans and government programs—such as social security that automates these savings and would otherwise require us to make a conscious effort to not participate in these programs. This is not the case in business. In business, there is stiff competition for every cent to be spent that mandates the development of a strong business case if you hope to acquire the funding you need for your purposes. So rather than the relatively passive approach we take in our personal lives to ensure our long-term financial security, there is a need to aggressively secure funding to reduce our businesses data management risks.

So if you’d like to improve data management at your company, but have been frustrated by the lack of authority, resources, or company politics, there are a couple of things you can do:

• Make a business case—so they’ll give you the money. 
• Institutionalize data management—so it becomes like a benefits program that just keeps getting contributed to.

The most success I have had in addressing these two recommendations centers on cost justifying an Enterprise Information Management Competency Center (EIMCC) and embedding its roles into the company’s management, systems development, maintenance and compliance processes.  The following is the approach I successfully used at one company to do this:

• First, I analyzed the costs of the data management work (data architecture, data analysis, data modeling, DB design & support, ETL, data integration, information delivery, data policies and access, etc.) that had been done for all significant projects over the last couple of years. This provided heuristics to use for projecting the costs of this work against all future projects.
• I then analyzed the redundant data infrastructure and databases that had been created and were continuing to grow.

Using this information, I was able to develop a business case to demonstrate that had the company implemented an EIMCC, the costs for these activities and infrastructure would have been approximately 50% less than had been incurred. The main reason for the reduction in costs would be that data infrastructure and data content would have been leveraged across multiple projects instead of duplicated.  By using EIMCC resources on each of these projects, they would have also executed more efficiently because the specialized resources would already have the core data knowledge, designs and metadata available to them to support the new initiatives. The business case also addressed compliance by identifying data governance and compliance accountability and embedding the processes for these into the SDLC.

After implementing the EIMCC and institutionalizing the appropriate management disciplines and development processes, the cost of these activities after two years had dropped to 30% of what they were prior to having the EIMCC in place.  Some new resources had to be hired with the appropriate skills; however, these were more than offset by reductions of staff, primarily in the application development and maintenance groups.

Hopefully, you can use what’s been said here to help you come up with a successful strategy for your business and get beyond your data management frustration.  I’d love to hear from you about what approaches you have tried and what has and hasn’t worked.

photo by Daniel Voyager via Flickr (Creative Commons License)

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Andy Field has been involved in information management activities for over thirty years in both the private and public sectors internationally and domestically. He has held senior leadership positions accountable for establishing Enterprise Information Management practices in several organizations, including Fortune 500 companies. Andy has also consulted with clients from many industries and government sectors over the years and established and ran as president a consulting firm specializing in strategic information systems planning. He has broad experience in both operational and data warehouse projects from both a hands on and leadership perspective. Andy is currently a consultant specializing in Enterprise Information Management.

The “And” Syndrome: How to Keep BI Development Proactive

By Mary Anne Hopper, Senior Consultant

Remember way back when your Business Intelligence organization was a small group of one or two people?  And then business found value in what you were producing so your group grew?   And then you created clear roles and responsibilities for the team?  And you started creating project backlogs?  And your group continued to deliver great results for the business?  And your team grew more?  And then things seemed to slow down in your ability to deliver and it was hard to figure out why?

Could it have been that each time you delivered new functionality, someone had to stop what they were doing to support it?   The impact of this can mean missed delivery dates and ticked off end users.

Each time the business needs something they have the ability to talk directly to the person who built the functionality for troubleshooting everything from data loads to data anomalies to report issues.  So, every time functionality is introduced into the environment, somebody is in charge of supporting it in addition to their ongoing project delivery responsibilities.

I call this the “and” syndrome.  Nobody can support “insert system/data/report functionality” and “insert system/data/report functionality” and “insert system/data/report functionality” and “insert system/data/report functionality” [I’ll stop here because you get the idea] without impacting the ability to deliver new functionality.

Here are some tricks and standards I’ve seen in different organizations along with their results.

Across the board reduction in project allocation (eg 10% of developer’s time is dedicated to support issues)	This can wreak havoc with project planning as the project team never knows when that “10%” will occur
The development takes turn with support on-call duties	This causes a lack of continuity with development efforts, especially when developers are moving in and out of support issues
The person with the least development impact supports the issue	This results in prioritization discussion each time there is an issue that needs to be addressed
The person who developed it will support it	This creates the “ands” scenario and does not accommodate staff or role changes as the organization grows

Does any of this sound familiar to your organization? If so, it’s time to step back and review your project plans. One of the biggest mistakes BI teams make is not acknowledging the fact that BI projects will likely have multiple releases.  Each new release will likely be a new project with its own plan.  Moreover IT management should acknowledge that BI needs support resources—including problem resolution experts, training, and documentation specialists—who will support new releases and enhancements to BI applications and the data they use.  Factoring in these issues will allow your group to get back to delivery, and stop supporting all the “ands.”

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Mary Anne has 15 years of experience as a data management professional in all aspects of successful delivery of data solutions to support business needs.  She has worked in the capacity of both project manager and business analyst to lead business and technical project teams through data warehouse/data mart implementation, data integration, tool selection and implementation, and process automation projects.

MDM and All That Jazz

By Rob Paller, Consultant

Recently, senior consultant Bob Wall mentioned building the team as one of four proven starting points for getting your MDM program rolling. By now, you have probably identified the C-level executive experiencing the most pain as a result of working with unmanageable data. You have even gone so far as to identify a small project that allows you to demonstrate the value that MDM will provide the company.

However, the success of your MDM pilot project and overall MDM strategy will come down to the team you assemble to support it. Interestingly enough, there are a few parallels that can be drawn from what makes up a good jazz ensemble that can be applied to building your MDM team. Every musician must possess a core set of competencies—just like employees possess in order to do their jobs. However, in order for a jazz musician to excel, he must possess an additional set of skills that is, for the most part, unteachable, but enables him to reach a level where his inspiration and creativity matches his ability to execute. 

• A jazz musician must be a virtuoso of his instrument in order to communicate what he visualizes in his mind instantly through his instrument. An MDM team member must be a subject matter expert with an innate technical understanding of the source system or a similar understanding of the business processes surrounding the subject matter.

• A jazz musician must possess a trained ear that is not only able to identify something being "in tune" or "out of tune," but hear the melodic structures and chord progressions. A soloist must be able to adapt to the piano player making chord substitutions underneath the soloist's performance. Any member of your MDM team needs to be able to find the nuances in the business when it comes to defining the core subject areas of your business. For example, defining what a "student" vs "alumni" vs "donor" means to your university.



• Many jazz musicians are able to recall hundreds of songs at a moment’s notice—not only the melody, but the chords as well. Your MDM team must understand how and be able to explain the countless business processes and policies surrounding your master data.


• A good jazz musician must be able to listen and interact with the group in the present moment and contribute to improving the overall quality of the performance. This is one of the most important skills that an MDM team member needs to possess. The MDM team must be willing to listen to the rest of the business and communicate effectively. There are no ivory towers in MDM or jazz. 


Keep in mind when assembling your MDM team that it must be a balance of members from both the business and IT—much like a jazz ensemble consists of a balance among percussion, brass, and/or woodwind instruments. If you are unable to find a balance, you risk discord in what you produce.


photo by Fillmore Photography via Flickr (Creative Commons License)

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Rob Paller is an expert at business analytics and database administration. Since joining Baseline, Rob has been responsible for developing a case analysis system to streamline the oversight of food assistance benefits, implementing a common citizen data model, and assisting in the rollout of a new public assistance data model integrating data from over 10 years of legacy with a new benefit eligibility determination system.

Why Data Governance Matters in 2010

By Andy Field, Senior Consultant

Imagine a large family with many teenagers and one car. It’s Friday night, the parents are out of town, and the teenagers have plans in different parts of the city.  On top of this, everyone is broke and the car is out of gas.  This situation has been coming up more and more over the last few years.  Over time some of the kids get jobs and buy their own cars, trucks, motorcycles to get around.  They don’t figure in the cost of insurance, repairs, maintenance, or parking.   In retrospect they realize they can’t afford to keep this up and some of the vehicles, especially the motorcycles, just don’t meet their needs when, say, the weather gets bad.

If you can envision this  then you have a pretty good idea of what occurs in many companies that have not implemented a data governance program.  There is no common data warehouse platform, let alone other shared data assets, costs are out of control and there is no room to grow. And there are no formal ways of making or delegating decisions. Things go from bad to worse.

What if the kids got together and met with their parents to lay out their transportation needs in advance?  They may figure out that if they had two cars and a truck, instead of one reliable car, an old clunker, a beat up pickup and two motorcycles that 90 percent of their needs would be met over the foreseeable future at half the cost.  The parents and kids could also lay out who is in charge in their absence and how conflicting needs are to be resolved and gas costs shared.  With the savings over the current situation some money could be put in a pot for a taxi or rental fund when there is an extraordinary need for transportation.

Hopefully you can appreciate what a sound data governance program can do for your company.  It may substantively end years of organizational friction, bringing with it the inherent benefits of a collaborative work environment while reducing costs and improving information accessibility and delivery for everyone to help address business opportunities. The question is: are you ready?

Data governance is an essential component of an overall Enterprise Information Management program.  It provides the forum and processes for making all significant organizational and policy decisions with respect to how data is managed in the enterprise.  It includes formalizing decision processes around privacy, security, and accessibility of corporate information. It enforces accountability at the business level, specifically among subject matter experts, key business users, and data stewards. And it sanctions standards around products, customers, and other master data.  

Data governance is difficult, often broad in scope, and requires a structured process to get right. Data governance is typically deployed incrementally to address issues that span functional, project and geographic boundaries.  For local decisions where the scope of the data decision will only impact one project the data decisions may be limited to a subset of data constituents (though sanctioned by the data governance council).  At the other end of the spectrum when decisions need to be made that may have serious consequences to the brand perception of the company (such as privacy policies)  then executive input is required to balance the perspectives of individuals directly representing this leadership such as legal (General Counsel), marketing (Vice President), operations (COO) and IT (CIO).

So if your company reminds you of the family described in this blog post there may be a better path to family harmony through the implementation of data governance.  Understanding that data governance can involve the whole “family” with benefits for all who use information in their jobs is key to ensuring that the right constituents and scope are considered when planning its long-term deployment. Maybe 2010 is the year you’ll get started. Good luck on your journey!

photo by brightroyalty via Flickr (Creative Commons License)

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Andy Field has been involved in information management activities for over thirty years in both the private and public sectors internationally and domestically. He has held senior leadership positions accountable for establishing Enterprise Information Management practices in several organizations, including Fortune 500 companies. Andy has also consulted with clients from many industries and government sectors over the years and established and ran as president a consulting firm specializing in strategic information systems planning. He has broad experience in both operational and data warehouse projects from both a hands on and leadership perspective. Andy is currently a consultant specializing in Enterprise Information Management.

BI in Healthcare: Imagine the Possibilities (a Parting 2009 Holiday Wish)

By Carol Newcomb, Senior Consultant

Imagine, for a moment, that we have moved on from the ugly, tedious healthcare debate of 2009 and the year is 2014. Pretend that Lieberman has joined the Republican Party and that the 2010 ‘Kennedy Healthcare Reform Bill’ passed with a sweeping majority of both Democrats and Republicans, and healthcare providers and insurers are cooperating to provide decent, affordable, cost-justified healthcare. No death panels for grandma and even people without jobs can see a doctor. Now, what have those healthcare organizations been able to achieve in the last four years with their eye on BI? 

The Healthcare Providers

When the bill first passed, healthcare providers received Federal grant money to shore up their internal IT infrastructure. But it wasn’t just for technology; it was expected that CIOs would work with the Medical Directors, Nursing Directors, Lab Directors and Admitting Department to inventory the myriad of ‘systems’ and ‘packages’ that struggle to support daily operations.

They had to talk to Finance, Engineering, Purchasing and Human resource users. They had to reach out to the Physician Contracting Group, specialty physician networks that lease space in the hospital facilities, and even the Community Outreach Office to understand how they were handling transactions, contracting, marketing and recruiting.

They were responsible for taking the time to build a comprehensive logical data model of the entire organization, from Building & Grounds to Legal that every system user could understand and that reflected the operational AND analytical objectives their data was intended to support. Even a comprehensive data dictionary, two years in the making and still a work in progress, is available in 2014. That dictionary has information about source systems, data definitions, transformation logic and business rules for appropriate usage.

Customized dashboards for supervisors, managers and directors are available 24/7 that contain a myriad of types of summaries, from staffing and productivity to patient safety alerts or orthopedic device failure rates. End users can customize their dashboard and drill into multiple levels of detail, depending on their question of the hour.

ER physicians can look up the history of a patient that appears on their doorstep and get a comprehensive longitudinal history with shared data from other regional hospitals, since data can now be shared for patient care purposes. If an ER physician needs to speak with a patient’s regular physicians, that information is available at the touch of a button. The price of treating patients in the ER has dropped 40% due to the availability of data that was never there before, a reduction in unnecessary tests and more appropriate treatments.

The Insurance Companies

Insurance companies have NOT been given any Federal funding, but they HAVE been saddled with the responsibility to revisit their actuarial models and include beneficiaries with chronic diseases and pre-existing conditions. As it turns out, they are not that much more expensive to insure, when the cradle-to-grave concept of preventive services, coordinated care, counseling and therapy are included in an individual’s benefits.Instead of selling to employers and unions, insurance companies are responsible for covering populations in an entire geographic region, which prevents them from cherry-picking on the healthiest beneficiaries. If an insurance company can prove, with sufficient data, that the population they provide coverage for is indeed sicker, costlier and in need of more intensive services, then they need to work with both the State government and local physician networks to ensure that physicians are providing cost-effective care.

As a result, the variability across regions, previously attributed to doctors driving up the ratio of services-to-patient, reflects the true healthcare needs of the population, not the doctor’s income expectations. And insurance companies are held accountable by the Federal government to work hand-in-hand with physician advisors to determine which are the cost-effective treatments versus which are unnecessary. These studies are grounded in broad, population-based studies and are backed up with solid statistics.

Insurers are also expected to help medical and nursing students bear the cost of their education, and will place students in medically underserved parts of the country where skilled labor is scarce, so that students don’t incur massive debt to become medically qualified in their profession. Since insurers have been collecting claims data for decades, they are best equipped to use that data in forecasting the need for services, and underwriting studies to investigate emerging technology and preventive services.

The Professional Network

Finally, the professional network of doctors, nurses, social workers, mental health workers and long-term care providers have started on an ambitious, but not impossible, effort to share patient information. This is possible because the States sanction transfer of data based on security and visibility rules as determined through statewide data governance panels. Since platforms for data storage have been standardized based on the ICD-10 and SNO-Med codification systems, it is expected that all qualified professionals have ready access to relevant patient detail at the point of care. This is still a work in progress, but the goal is to have data that support the provision of services to ensure they are appropriate for the individual, and coordinated with other services.

When, in 2014, I look back on 2009, I hope this year represents the year that healthcare finally emerged from the dark ages. That is my holiday wish for BI in healthcare. Imagine the possibilities……

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Carol Newcomb is a Senior Consultant with Baseline Consulting. She specializes in developing BI and data governance programs to drive competitive advantage and fact-based decision making. Carol has consulted for a variety of health care organizations, including Rush Health Associates, Kaiser Permanente, OSF Healthcare, the Blue Cross Blue Shield Association and more. While working at the Joint Commission and Northwestern Memorial Hospital, she designed and conducted scientific research projects and contributed to statistical analyses.

’Tis the Season for a Data Governance Carol (Part Two)

By Rob Paller, Consultant

 

The writing was on the wall, and Ebenezer was beginning to comprehend that while the mergers and acquisitions looked good on Wall Street, the company was imperiled by the aftermath of poorly integrated systems, an absence of proper data management, and a gaping void in the governance of the company’s data. Furthermore, Ebenezer knew that one more ghost had yet to call upon him. Never before had the fear of uncertainty enveloped Ebenezer, but tonight was different.

The Ghost of Data Governance Future

No sooner did Ebenezer begin to calm his nerves, when from the shadows of Ebenezer’s office, the Ghost of Data Governance Future appeared. Ebenezer’s pulse quickened as he did not want to acknowledge the ghost’s presence or what the ghost would ultimately share with him. It was with great reluctance that Ebenezer agreed to go with the final ghost.

Much to Ebenezer’s surprise, their travels did not take them very far into the upcoming decade before trouble surfaced. Poor Bob had suffered through another year and a half of cobbling reports together before reaching his saturation point and submitting his resignation. The remaining analysts scrambled to glean what they could from the frazzled, empty shell that was once the brightest among them. Surely a lone analyst would not beckon the fall of a Fortune 500 company, but it would begin the unraveling that followed in the months to come.

After Bob left the company, the remaining analysts fought to produce the fiscal reports on time. The data fixes that Bob orchestrated in the various systems to reconcile the reports were not being done. Some analysts were entertaining the idea of bringing in an outside firm to try and help address the problems, but IT was adamant they could shore up the problems on their end. Slowly the company’s competitive advantage began to erode and confidence on Wall Street went with it. It wasn’t long before rumors of bankruptcy began circulating. By then, Ebenezer had seen enough.

He couldn’t stand to see the company he so proudly guided through the first decade of the new century crumble before his eyes. He asked the Ghost of Data Governance Future if it was too late to turn things around. And if he promised to acknowledge all the data management, quality, integration, and governance issues that had been brought to light, would the company be able to avoid an inevitable collapse? But the ghost made no promises and offered no reassurances that the company could be saved. He simply returned Ebenezer to his office and quietly retreated back into the shadows of the office.

Ebenezer Wakes Up

Ebenezer, startled awake by the sensation of falling, quickly realized that he was still seated in his office chair. However, the relief was short-lived as he wiped the sleep from his eyes, only to see that the once-organized stacks of paper on his desk were strewn across the floor. Dream or no dream, the gravity of the situation was beginning to weigh on him:  The single most vital asset neglected for the past decade would be responsible for saving or crippling Ebenezer’s company.

There would be no quick solution. In order to build the momentum necessary to stay the course, Ebenezer needed to identify projects that would make an impact with the right sponsors who would, in turn, spread the word.

Slowly, over the course of several quarters, Ebenezer began to see the results of his mission to properly manage his company’s data as an asset. There were policies in place that supported the data governance initiative, and there were regular meetings to review the company’s progress with regards to an industry-approved maturity model. Data quality was being driven back to the source systems whenever possible.

There were formalized processes in place to deal with exceptions. There were master data hubs that dealt with integrating the various sources of master data across the company.  Data was beginning to move with the precision of a fine time piece.

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Rob Paller is an expert at business analytics and database administration. Since joining Baseline, Rob has been responsible for developing a case analysis system to streamline the oversight of food assistance benefits, implementing a common citizen data model, and assisting in the rollout of a new public assistance data model integrating data from over 10 years of legacy with a new benefit eligibility determination system. He also believes in Christmas, and he'll gladly give you a shilling to buy the prize turkey for Christmas  day.