In the Field with our Experts

I’m All Ears

By Caryn Maresic, Senior Consultant

Most Data Warehouse designs include constructs for Address, Phone, and/or Email for Customers.  Len Silverston came up with what he calls a Universal Data Model that does a very good job of abstracting address, email and phone number data.  I have seen clients use the Contact Point portion of his model as-is and with a few simplifications with great success.  That being said, in the area of Marketing and Sales, the manner in which we reach out to our customers and prospects gets more diverse every day.  Disneyland has just partnered with Verizon so that park guests can get real time information about the park and play Disney games on their phones….and, of course, Disney gets access to more information about its customers!

How does this new and ever changing world of communication change the way we think about and model contact points?  What would my “address” look like if I were near the Haunted Mansion looking for a lunch spot?  Would it be different than if I were at Downtown Disney looking for a cup of coffee?  On Main Street looking for Winnie the Pooh?  In all instances I would be using the same phone, possibly the same IP address, but I would be in different locations which would be important to the marketeers at Disney.

As time goes by (and cell phone GPS systems become more accurate) I suspect that the way we run marketing campaigns to smart phones will be similar to the way in which we use billboards today.  Where the customer is physically located at any given time will be as important as the phone number and/or IP address, thus creating a two dimensional contact point.

Have you come across this issue in your organization?  Have you changed your data model to include two dimensional contact points?  If not, has the use of smart phones changed your data model in other ways?

photo by wrayckage via Flickr (Creative Commons license)

---

Caryn has over 20 years experience in providing high-quality data solutions to clients in the areas of Business Intelligence, Data Warehousing and System Integration.  Caryn has expertise in across industries with an emphasis in Pharmaceutical, Manufacturing, and Insurance.  Prior to joining to Baseline, she ran her own consulting company.

A Data Governance Primer (Part 1 of 3)

By Carol Newcomb, Senior Consultant

They say that Data Governance is about People, Process and Organization.  Much of the design work in planning for data governance is around people’s roles and responsibilities, then designing the organizational structure that will provide authority for decisions to be made and enforced.  The processes, however, are not new.  They are probably already being practiced within your organization, just in a decentralized, informal way.  In this blog series, I discuss the processes for 1) investigating and isolating the data quality issues—Root Cause Analysis—, 2) starting to collect complete Metadata Definitions, and 3) performing Data Quality Analysis.  Only when your governance group has worked through each step, in order, will you be more likely to design the appropriate solution.

Root Cause Analysis

The process of data governance is fundamentally very simple.

1. Identify the data quality issues to address
2. Prioritize the portfolio of issues to isolate/tackle the most important
3. Perform Root Cause Analysis to determine the true source of the data issue
4. Design the corrective action
5. Formalize the correction through consideration & approval by the Data Governance organization
6. Implement the fix
7. Monitor the results

It seems like when we start to map out the discrete steps involved in the data governance process, much of the work is already being done in informal ways throughout the organization.  What some folks don’t realize is that data governance is often nothing more than formalizing a whole bunch of informal processes that either don’t get communicated, or aren’t accepted as a data standard.

Root Cause Analysis is the process of identifying probable causes of a data issue, and isolating the contributing factors.  In order to resolve any particular issue, root cause analysis involves fact-finding, drilling into details of the problem, talking to the right people, and separating out other associated (but not contributing) factors.

A standard tool for supporting the detailed findings is the Ishikawa Diagram, below.  

To conduct a thorough Root Cause Analysis, use the following checklist:

• Diagnose the problem as if you are a physician or a detective. Consider all possible sources of the symptom. Don’t rule anything out yet!
• Boil the ocean—be exhaustive and creative.
• Don't practice problem solving before collecting all possible causes.
• Practice the “5 Why’s”—don’t stop asking “Why” until you have exhausted every conceivable potential reason.
• Rank the factors if possible.  Identify the Primary causes versus the Secondary or associated factors.
• Rule out each possible factor one at a time.  Justify why (you may need to come back to this later).
• Find all potential business process and data owners to involve them in your understanding of the possible sources of the problem.
• Share the findings with everyone involved in troubleshooting. They could rule out certain factors with their knowledge.
• Test your hypotheses with actual data.   
• Fix the problem and test again.
• Publish/share your findings and fixes.  Communicating your findings may reveal additional factors you hadn’t considered.

After a thorough Root Cause Analysis has been completed, Data Stewards should proceed to Metadata Analysis and Data Quality Analysis.  These two techniques will be discussed in my next blogs.

---

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.

Agile Analytics: Put Your SaaS in the Sand

By Rob Paller, Consultant

Recently at a client, the data warehouse administrator was asked to define a sandbox environment in the production data warehouse for  analysts and developers working on a small project. The idea behind this sandbox was to allow the team a working area for collaboration and intermediate storage of results while working with the data in a purely ad hoc capacity. Instantly it was recognized this could be the start of something bigger within the organization—something that could not currently be provided by the incumbent business intelligence tools. The response had to be formulated quickly in order to avoid stifling the creativity of the analysts—or worse, the progress of the project—but care had to be taken as well; if managed incorrectly it could get out of hand and become a waste of system resources and a drain on human resources that had already been spread thin.  The business unit in question is looking to move from the confines the current business intelligence environment and push the edges.

This was a group of analysts that wanted to get their hands dirty and weren’t afraid to fail. They wanted to mash data together that previously could not be done by the business intelligence tools in their controlled ad hoc environments. This was data mining for the next set of KPIs that would shape the way business moves forward.

The concept of agile analytics is not new, eBay presented on and blogged about this concept in 2008. The idea at this client was simple. By leveraging the existing enterprise data warehouse system to house their sandbox environment the duplication of data is all but eliminated. Groups interested in sharing data between their sandbox environments are strongly discouraged until the data has been properly integrated into the production environment. The sandbox environments would also be given a short life expectancy at their inception to prevent the prototypes from becoming production and data ending up in a wasteland. This all sounded great on paper.

In the midst of a development architecture overview, a brief conversation among a few enterprise architects uncovered the potential Screw-Me Scenario that could bring the concept of agile analytics to an untimely demise. “The users of the data warehouse are not permitted to write ad hoc queries outside of a controlled business intelligence tool. They might write a bad query.” Thanks for the warning, we’ll be sure to refine our pitch to the enterprise architects to diffuse this scenario before it turns ugly.

In Oliver Ratzesberger’s presentation for eBay’s Analytics as a Service, he acknowledges that the metrics we already know are cheap and the unknown metrics are expensive. But the known metrics are not pushing the edges. Known metrics are found in the middle of the box. Agile analytics is about pushing the edges about how your enterprise data warehouse is used to improve response to the needs of the business. It is about the evolution of the user community from one who plays in controlled ad hoc environments to encouraging them to experiment with new ideas and not to fear failing along the way. Agile analytics is about encouraging your users reach out for the edges and P U S H. Only once the edges are stretched can the middle of the box redefined.

photo by edenpictures via Flickr (Creative Commons License)

---

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.

The Role of Logical Data Models

By Caryn Maresic, Senior Consultant

Logical Data Models (LDMs) were the standard means of recording business rules and data definitions back in the ‘80s and ‘90s.  Business and IT partnered to learn the art of data modeling and 3rd normal form in hopes of finding a common ground to record requirements.  Over time, it became evident that in today’s fast-paced development environment the business doesn’t have the time to digest all the nuances in a data model.  It is IT’s job to gather information via interviews.  Many times, we skip the LDM task because we don’t have time and LDMs are too academic and we find ourselves asking…

Are Logical Data Models really necessary?  Yes!

LDMs drive BI design by defining business rules independent of physical implementation.  There are many tools (CA ERwin Data Modeling, Embarcadero Technologies), and techniques (UML, IDEF, Bachman) in use today. To illustrate the difference between the benefits of a PDM and an LDM, let’s look at an example:  Customer/Address relationships.

The PDM above (created using) supports many Customer/Address business rules, but it does not tell me the Customer/Address relationships as defined by the business.

In contrast, the following LDM is more rigorous and definitive:

What does the LDM above tell me that the PDM did not? 

• There are two types of Addresses – Physical and Mailing.
• The Customer Bill To Address can be either a Physical or Mailing Address
• Customer Ship To Addresses must be Physical Addresses.
• Every customer must have a Bill To Address.
• A Customer has zero, one or many Ship To Addresses.

All of these relationships can be implemented using the PDM, but they aren’t dictated by the PDM.  The LDM explicitly defines each relationship and prompts us to ask better questions of the business, such as:

• Are there any Customers who do not have a Bill To Address?
• Are there any Customers who do not have a Ship To Address?
• When there are multiple Ship To Addresses, how do you know which one to use?
• Are there any Customers who have more than one Bill To Address?
• Do you know of any upcoming business initiatives that might change these rules?
• Was there ever a time when the business rules were different?

The answers to these questions may require changes to the model, but without the LDM, we may not have asked the right questions!  The best part about the LDM is how valuable it is to the rest of the development process.  It will be used in production of training materials, test plans and test cases, data profiling and migration efforts and, finally, physical database design.

---

Caryn has over 20 years experience in providing high-quality data solutions to clients in the areas of Business Intelligence, Data Warehousing and System Integration.  Caryn has expertise in across industries with an emphasis in Pharmaceutical, Manufacturing, and Insurance.  Prior to joining to Baseline, she ran her own consulting company.

To Infinity and Beyond! — Healthcare in the Cloud (Part 3)

By Carol Newcomb, Senior Consultant

This is the final installment in a 3-part blog series, discussing the opportunities that cloud computing offers in healthcare. I present futuristic scenarios from each healthcare contingent’s vantage point:  patients, providers and payers.   A myriad of technologies exist today.  It will be up to healthcare organizations of all types to get their data ready to meet the demands for data integration, security, portability, transparency and accountability in this brave new world.  Mature data governance systems and enterprise-wide data integration will be critical in this endeavor.

III: Payers

I’ve worked in the insurance industry for 30 years now, and I’ve never seen anything like this!  For years, we had consultants running around here, designing portals, databases, dashboards and training us on new software to use in running analyses.  But half the time, we just went back to using the antiquated systems that most of our data servers were designed to use.  In the last couple of years, we have completely stopped talking about those different hardware platforms.  Now, the Risk Management division, Actuarial, Medical, Quality, Marketing and Claims Processing all use one software package; all the data management (since we get millions of transactions and claims each day) is handled offsite.  It’s never been this simple!  We still have our Data Governance Committee that works with each department to flush out any issues, but things have really improved.Since all the data is now integrated on what they call “The Cloud”, we get daily dashboard updates on our desktops, showing use trends, costs, disease prevalence around our region, geographic mappings of where the large employers have families, results of marketing activities, and new health and wellness information that is also shared with our subscribers.  We had a period of some pretty large layoffs, since our entire IT department is now outsourced, and all those consultants have disappeared.

Our Fraud and Risk Management analysts used to have hundreds of spreadsheets on their desktop computers, which they would constantly be updating and piecing together to look for patterns or gaps.  Now they can use more sophisticated statistical detection algorithms because the data is updated and fed to them each night.  The simplest algorithms are actually run offsite, and those results are delivered to them daily.  From those, they can then drill into services or providers that look suspect.  They can now turn around reports in about 2 days, where it used to take them 2 months!

Our business model has shifted from using claims history to cut anticipated high-cost cases, to using the data from those same claims to design health and wellness programs stressing prevention and care management.  If we see pockets of infectious diseases in one region, for example, we do further analysis on the saturation and specialty mix of our network providers, the employer group mix, and the educational factors that may be contributing to higher medical costs.  We then work with the schools, employers and practitioners to combat the spread of that particular disease.  We have been able to avert hundreds of hospital admissions through lower cost prevention measures, which we track through our “Population Health” Program.  Across our company, there is a competition to target and drive down claims rates, and each group gets an annual part of their bonus based on their results.  It’s pretty interesting.

Even though we don’t get as much data through claims-processing as the local hospitals get in their clinical systems, we participate in national research studies of medical effectiveness.  Some procedures that we used to consider experimental, we now collect data on and enlist our Actuarial Department to help with statistical analysis and cost-accounting.   Our Medical Quality Department works with other government-sponsored research groups to compare the results of our findings, and often they lead to some pretty surprising conclusions.  Where we thought we were saving money in the past, we were actually driving up utilization in higher cost medical facilities, but now we’re helping encourage subscribers to get medical attention sooner, which prevents some pretty catastrophic claims.  We couldn’t do this before because our Utilization Review desk would deny those services that we now know help reduce lifetime costs.

Probably the best benefit we’ve gained from using Cloud computing services is that our claims processing is now practically effortless.  Twenty-five percent of our cost of business was dedicated to claims handling, denials, reviews, exceptions, and remediation.  Not only have we saved money, we have reduced premiums to our subscribers, and the healthcare providers have reduced the cost of their back-office operations, which used to handle all the churn of rejected and resubmitted claims.  They now get paid faster and we save money.  How’s that for amazing! We’ve also noticed another side benefit: our subscriber turnover rate has dropped by 10%!  Customer satisfaction rates have never been higher!

---

Integration of claims data with population-based data and actuarial model results has significant business impact.  Clearly, departmental governance representation was required to mesh all the different data types.  Several large data warehousing systems can be housed and integrated using Cloud technology, but the rules in how to match and align different data collected for entirely different purposes is the key to analytic power in this example.  As companies offload daily transaction processing, which can be automated and scaled, business dollars can be better deployed to more strategic purposes, and those resources then achieve more with the data at hand.

photo by Simone Ravella via Flickr (Creative Commons license)

---

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.

To Infinity and Beyond! — Healthcare in the Cloud (Part 2)

By Carol Newcomb, Senior Consultant

This is the second installment in a 3-part blog series, discussing the opportunities that cloud computing offers in healthcare. I present futuristic scenarios from each healthcare contingent’s vantage point:  patients, providers and payers.   A myriad of technologies exist today.  It will be up to healthcare organizations of all types to get their data ready to meet the demands for data integration, security, portability, transparency and accountability in this brave new world.  Mature data governance systems and enterprise-wide data integration will be critical in this endeavor.

II:  Providers

It’s 11:30pm on a Sunday night, and I’m working a double shift in the Emergency Room.  It’s been a little slow tonight, so I’ve been online researching some reasons why we’ve been seeing more Somali children here recently.  The hospital keeps the latest demographic figures on the intranet.  Turns out that at first just a few families settled here, but more relatives are joining them and many of the children are orphaned.  When they came through immigration, though, they were given medical checkups and those records are available for each of the kids I have treated recently, since they were each given a universal medical record number.  I can see the results of any tests they had, and whether they were given any medications or treatment.  There’s also a public health database through CDCP that tracks the incidence of infectious diseases, and some primary prevention steps we should prescribe at the time we treat these kids.  It’s very helpful stuff.  I’m going to share what I learn through the local Health Information Exchange (HIE) with the social workers in the neighborhood clinics.

Whoa!  Suddenly things are picking up!  There’s a little old lady with severe dementia who fell and knocked out her tooth, and a teenager who got into a brawl with a black eye.  Where are his parents!?  First I try talking to the lady, but she’s confused.  At least she is wearing an ID tag, so I look up her name (Dorothy Xu) in the HIE portal.  She’s never been to this hospital, but I see that she has spent some time in a local nursing home, and was recently discharged to the care of Dr. Jones.  We start a new hospital record for her, instantly downloading the data from the nursing home and Dr. Jones’ office.  I also see that her daughter lives nearby and that her husband died less than a year ago, so I get her daughter’s telephone number and give her a call.  I’m surprised that the ambulance techs didn’t do this already, but they were making sure she didn’t have any other injuries.  When her daughter arrives, she has brought a list of all of Dorothy’s medications, and one of her favorite nightgowns.  I send an email requesting an urgent appointment with one of the dentists in her insurance network first thing in the morning, and then make sure she has a prescription for some pain medications to get her through the night.  Poor thing.  Also, I talk to her daughter about whether Dorothy has seemed depressed since her husband’s death.  I give her daughter a printout of some good psychiatrists in in the local area, and then she takes her Mom home.

So what about this black eye?  Jason is 15 and it looks like he already has a police record.  He refuses to talk to me, so we get his fingerprint and match it to his medical record.  He doesn’t want the police to know he was in another fight, but I’m going to have to call his parents to pick him up.  He seems concerned, but refuses to say anything more.  I scan through his chart and his Mom’s, and I see that she has also been treated for a fractured wrist and a black eye about 4 weeks ago.  Either this family is really clumsy, or something is going on here!  Apparently, a social worker at school also noted a few things in his record, and the pieces are beginning to suggest some family abuse problems.

When his Mom arrives, we talk privately for a few minutes.  I tell her I am concerned, but don’t really have very much information.   As we talk, she becomes visibly upset.  Eventually, she admits that her husband, who was recently laid off, has been taking his anger out first at her son, and also at her.  I ask if she needs any other assistance or resources, but she refuses.  So I treat her son’s eye and send them on their way.  I also make note in the husband’s record that he may have some history of abusive behavior, and I notice that he was recently in the hospital as a result of a car wreck.  At least this information will be available to the nurses in case they are treated here again.

---

Offloading disparate datasets to integrated Cloud servicers that will also perform basic analytics can greatly empower healthcare providers with ready access to data.  Remotely hosted Health Information Exchanges will empower social workers, schools and hospitals, even law enforcement authorities, to work together in assisting their local communities.   This is the profile of the new healthcare provider as a result of Cloud technology.

photo by dawvon via Flickr (Creative Commons license)

---

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.

To Infinity and Beyond! — Healthcare in the Cloud

By Carol Newcomb, Senior Consultant

This is the first in a 3-part blog series, discussing the opportunities that cloud computing offers in healthcare. I present futuristic scenarios from each healthcare contingent’s vantage point:  patients, providers and payers.   A myriad of technologies exist today.  It will be up to healthcare organizations of all types to get their data ready to meet the demands for data integration, security, portability, transparency and accountability in this brave new world.  Mature data governance systems and enterprise-wide data integration will be critical in this endeavor.

Introduction: Welcome to the Brave New World

Now that the Healthcare Reform bill has passed (phew!), it is time to get back to the basics of Healthcare Data Infrastructure.   It is such an interesting time to be involved in Healthcare IT!  The technology is exciting.  The resources are available.  Healthcare providers are gaining some traction in the use of electronic health records.  Consumers are developing interest in eHealth, mHealth and other web-enabled resources.  RHIOs faltered but HIEs are picking up steam, and will also use new web technologies.  Could it be that healthcare data systems will deliver on the promise of accessible, portable, transparent data?  

But hold on a second!  If all that technology exists today, why aren’t we already using it?  It’s complicated, but the main reason is: DATA INFRASTRUCTURE.  There is a mad dash among providers (hospitals, physician’s offices) to transfer from paper-based systems to integrated EHRs.  But, systems that traditionally haven’t been integrated (Finance, HR, Education, Credentialing, Inventory, Purchasing) still won’t be integrated.  Insurance companies receive claims from a host of different feeds, and different departments use data in completely different ways.  Coordination of Benefits isn’t (technically) well coordinated.  My personal ability to assemble a complete PHR might require information from many different doctors’ offices.  Mergers and acquisitions challenge even the most sophisticated health systems.  The list goes on.  Data integration and the need for better data governance everywhere is critical.

There are many kinds of cloud computing, ranging from platform, software and application services to storage and raw computing power.  They will offer opportunities for efficiencies of scale and analytic capabilities, housing massive amounts of raw data.  The technologies and capabilities at hand are truly exciting, but they won’t succeed without sustained infrastructure design and mature governance standards to ensure common usage, security and data integrity. 

I: Patients

It’s so different now when I go to the doctor’s office.  When I show up at the front desk, the receptionist, instead of shoving a clipboard and a pen at me, greets me by name.  “How are you today, Carol?  Please take a seat and use this e-tablet to write down any changes in your insurance, and what you want to see the doctor about today.  Your wait will be about 10 minutes, but in the meantime, have a cup of coffee, and use the e-tablet to write down anything else that you would like to discuss.  You can also look up some health education materials you may want to find.  Our online library is more trustworthy than some popular websites.  If you like, we can include this information with your medical record, which you will get an electronic copy of when you check out.”

Even though I’m feeling crummy, when I get escorted to the exam room, I’m surprised to see that the setup is completely different.  Instead of the tiny stool the doctor used to sit on, hunched over a computer terminal tucked into the farthest corner of the room (so they can type without prying eyes?), there is a full-sized desk with TWO chairs.  The doctor comes in, shakes my hand, and says “I see your blood pressure has been under control.  But your blood glucose is a little high.  Let’s look at your readings.”  She sits next to me as she pulls up my chart on the computer, and shows me a graph of the data I’ve been sending through my monitor at home.  Twice a day, the monitor reminds me to check my vitals and transmit them to the office, where they are downloaded to my chart.  “This could be why you’re feeling light-headed.”

As we look at the graph, we discuss my recent surgery and other medications I am taking, and we come up with a plan to make some modifications.  “I talked to your surgeon and he says things went well.  But I see from your e-tablet that you’ve also been researching Alzheimer’s.  Is there someone in your family who is having symptoms?”  We discuss my Dad for a while; then she adds the note to my family history, and makes some changes to my prescription.  She then offers to review everything we’ve discussed today on the computer, to make sure she has noted everything correctly.   “When can I see you again?”  I’m startled because usually the front-desk clerk makes all the appointments, but she finds an opening in her calendar, and automatically sends me an email reminder.

When I’m ready to leave, the front desk clerk hands me a printed copy of the summary from today’s visit, and she swipes my health card to update my medical record and file the bill at the same time.  I now carry this card in my wallet, along with my insurance cards and credit cards because I want to have it with me.  I can also access this information through the web at home, or even at work, through my personal login.  When I go next week to see the dentist, he will be able to access all of my latest drug and medical history information, in case he also has concerns about my blood glucose or blood pressure. 
That coffee was really good.  Maybe I’ll have another cup on the way out!

---

This benefit of having integrated data available through remote exchanges to this patient will enable her to have a data-informed discussion with her practitioner and to be a better informed patient.  Her office visit is more personalized and streamlined as a result of integrated scheduling and billing.  The time spent is more educational because she can get information for her condition as well as her relatives, from a trusted source.  And through sharing this information, important updates to her family history become part of her own health record.  This is the profile of the new healthcare consumer as a result of Cloud technology.

photo by kevinpoh via Flickr (Creative Commons license)

---

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.

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)

---

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)

---

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)

---

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.