In the Field with our Experts

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.

Who’s on first when it comes to integrating customer data into a single version of the truth…

By Steve Bebout, Senior Consultant

 

We’ve all had situations when we think, Does this company even know who I am? Why do I get contacted about products or services I have ‘opted’ out or for which I am not a viable candidate?
Additionally, when I specifically request information, why can’t they provide me with that information?  Addressing these issues is complex but at the crux of why a single version of truth is worth investing in.

I was recently involved in a project where each line of business (LOB) was positioned against the other from a revenue standpoint.  Each LOB was rewarded by creating the largest amount of gross revenue for the company which made information sharing not a top priority.  It wasn’t a situation where they didn’t see that customer data was an asset; however the value of that asset was directly tied to the LOB’s profitability.  Senior management saw this as a problem and realized the opportunity cost associated with multiple versions of the truth.

Without coming to an agreement on a single version of the truth for customer, they would never know "Who’s on First" and how to maximize "who's" lifetime value.  Why am I using "Who’s on First?" as an analogy?  Well, it’s an Abbot and Costello routine that portrays confusion around a person’s identity.  In this parody, even with the best of intentions, they are talking past each other.  Their disagreements reveal their lack of understanding of "who" really is, what he does, and what his value is…

After creating agreement on the enterprise data definitions, senior management provided us with future business goals that we used to create key business drivers for the company.  The key business drivers were:

• Create efficient data accessibility and ensure data quality across the enterprise
• Measure the effectiveness of marketing activity
• Understand behavior for more targeted cross-sell and up-sell
• Share integrated, cleansed, and enriched customer data

Alignment was key to developing support and creating a new cultural view of data sharing.  Aligning analytical processes, key functional capabilities, and subject areas supporting customer behavior to business drivers created prioritization and a road map for creating a single version of truth.  How do you support your assumptions to the client?  We created traceability examples for the key business drivers mentioned above.

By providing the company with basic examples of how bringing together the customer data from the LOBs disparate sources into a single centralized data repository can provide value opened their eyes and was a key way to get the enterprise ‘onboard’ to create a single version of the truth.

I found traceability matrices to be a valuable tool for resolving the multiple versions of the truth in identifying ‘who’, has anyone else used this approach or have other techniques that they have found been successful?

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Steve Bebout is a consultant with over 15 years of experience in information technology in the areas of application development, business requirements, data analysis, and project management.  He has experience in the Communications Media and Entertainment, Financial Services, Life Sciences, and Insurance vertical markets; spending the majority of his efforts on customer data integration initiatives. 

Welcome to the MDM Show, “What’s My Role?”

By Bob Wall, Senior Consultant

Years ago, I regularly watched a television show called “What’s My Line”, moderated by John Charles Daly. The show featured a panel of four celebrities (e.g. Bennet Cerf, Steve Allen, Dorothy Kilgallen and Arlene Francis) who questioned the contestants and tried to discern their occupation. The panelist would begin asking the contestant yes-or-no questions. If they received a “yes” answer they continued, but if they received a “no” questioning passed to the next panelist and $5 was added to the prize tallied on cards Daly would flip over. A contestant won by receiving ten "no" answers (as Daly occasionally noted, "10 flips and they are a flop!") or if time ran out.

If you’ve worked on a Master Data Management (MDM) project, specifically a Customer Data Integration (CDI) effort,  you may think you’re in a remake of “What’s My Line”, but now it is called “What’s My Role.” Because we are trying to integrate customer data from possibly dozens of systems, we need to understand what role the customer played in that system.

For instance, we may designate Jane Doe as a customer. For argument’s sake, a customer defined as a person or organization who has purchased one of our products, but in the shipping system she shows up as ship-to-customer, in the accounts payable system as a bill-to-customer, and in the HR system as an employee.

One way to get around this is to use a generic Party data model (with subtypes of Person or Organization) and to use 2 types of roles for that Party; Declarative and Contextual. For a Party, we might define a “role” for the party, and declare that role as “customer,” the part that an organization or person plays within the enterprise. We then might look at the associative relationship between a source application system and Party and define a contextual role, how a party is or was involved within the context of another entity. Here the roles might be ship-to, billed-to, or employee, but we always establish one instance of the specific party, in this case Jane Doe. A good discussion of using roles can be found in Len Silverston’s “The Data Model Resource Book, Volume 3, Universal Patterns for Data Modeling.”

Using a generic approach to roles for your MDM/CDI project may help you to integrate these roles and not wind up like panelists on “What’s My Role,” where you could face 10 flips and you’re out!

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Bob Wall is a senior consultant with Baseline Consulting. He is an information technology specialist with 30 years experience in all areas of data warehouse administration, data architecture, data resource management, training, and applications systems development, as well as in corporate management.

Finding the Gold in Enterprise Data

By Adam White, Senior Consultant

Without control of their data corporations can no longer compete. The emphasis on globalization, new competitive pressures, and the widespread adoption of the Internet have introduced a new sense of urgency around data integration and management.  These days a company must not only have control of its physical data, the actual information that it has captured, it must have the ability to derive information from that data, interpreting it accurately and leveraging it for operational efficiencies and strategic advantage. And it must be able to deploy enterprise data across business users, including call center agents, point of sale staff, sales representatives, managers, directors, and executives. The data may also be required for direct use by customers and/or prospects.

Corporate control of data extends beyond just using data. Data management is also an emerging discipline in most companies. Data must be protected. There are regulatory requirements, customer privacy expectations, and the need to securethe customer base from competitors.  The unforeseen forces of a global economy mean that companies must be able to work with data that is not English. And, of course, the need to integrate the data across various external and internal silos means that point-to-point interfaces must be phased out in favor of a single version of the truth.

Indeed, many of your company’s databases are replicated to provide access to various constituencies. But the point-to-point interfaces they use to exchange data are costly both development and maintenance-wise. Control over heterogeneous data—not to mention sustained management and integration of that data—is next to impossible.  Application upgrades and data enhancements mean large, expensive projects that come with a high risk of failure. A deliberate approach to enterprise data integration can help IT save time, headcount, and rework, as well as launching efficiencies that can not only drive down costs—ensuring responsiveness, repeatable development practices, and data reuse—but, more importantly, delivering value to the business.

So what are the approaches that will give you control over your data and provide the functionality that you need to support your business?  There are different options for integration (the sharing of data and services), including: Extract, Transformation, and Loading (ETL); Enterprise Application Integration (EAI); Enterprise Information Integration (EII); and Customer Data Integration (CDI). The optimal solution at your company may be one of these, or a combination of all four. The point is that decisions about data integration remedies should involve a complete understanding of the differences of data integration solutions, and a deliberate technology selection process.  Also, the implementation of the solution means that you will have to have a robust data governance and data management framework in place so that data integration supports your needs going forward, and doesn’t over time revert back into the original legacy architecture, losing the capabilities that are instrumental in implementing the solution in the first place.

Note: Baseline Partner Evan Levy will be teaching a class at the TDWI World Conference in Las Vegas next month, titled Architectural Options for Data Integration, in which he will outline the differences in these and other integration approaches, compare the strengths and weaknesses of each, and offer lists of vendors in each category. I strongly recommend attending!

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Adam White is an expert in Customer Data Integration (CDI), Services Oriented Architecture (SOA), Systems Development Life Cycle Methodology, Project Management, IT Governance, and establishing IT Process and Organization. Since joining Baseline, Adam has been responsible for conducting enterprise CDI assessments, implementing CDI, implementing SOA architecture, and implementing development methodologies.

One Problem, Three Parts: Making MDM Work at a Bank

By Frank Dravis, Senior Consultant

We have a banking client that is grappling with how to get a single view of their customers from the nine major types of systems they have and the myriad of subsystems. You’ll recognize those systems as you probably have them yourself: Billing, Product, Agent, G/L, Call Center, etc. When you first approach the problem there is a tendency to think, “Oh, this is a data warehouse problem. The bank wants to get a 360 degree view of their customers.”

But the problem is that our client’s challenges and issues go deeper. Yes, a data warehouse would achieve one objective of their goals, getting customer data from heterogeneous systems loaded onto a common platform, but the problem is more multidimensional. 

A main component of the solution is customer master data management, otherwise known as Customer Data Integration (CDI). CDI solutions automate the harmonization and integration of customer data from operational systems. Unlike a data warehouse, CDI guarantees that the data is cleansed and that business rules are applied before the customer data is stored on the CDI hub.

However, for the CDI system to function effectively it cannot do so in isolation. The rules it needs to apply should be defined. Enter data management. Data management is the practice of implementing rules and policies on data.  It is all about building systems and processes  to ensure consistent application of data quality, data integration including ETL and federation, metadata management, security, access, etc. Moreover, data management establishes the framework and culture for the original source systems to turn into target systems when the CDI solution serves as the system of record or reference and sends data back. Yes, for those of you who have implemented a successful MDM system, you have some form of functioning data management effort.

But unless senior management at the bank is willing to engage in sanctioning the right business rules, security and access policies, and corporate definitions, neither CDI nor data management will work. Thus data governance is a critical component of the bank’s long term data integration success. Understand, that data governance comes in big and little forms, such as the more operational policy of “the parts data mart shall attain a quality score of 82% in within the next six months.” At the bank, data governance policies serve as crucial guidance to business, IT, and their data stewards. 

So the next time you see one of those star-like diagrams with disparate data sources all feeding towards the customer center, consider there is a multi-dimensional view of that diagram: MDM, data management, and data governance.

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Frank Dravis is a senior consultant with 21 years of experience in enterprise information management (EIM) and data quality solutions design, implementation, and consulting. He specializes in data integration, data quality, and data governance solutions, advising key clients and industry vendors.