In the Field with our Experts

Data Architect – Who is right for the job?

by Caryn Maresic, Senior Consultant

The Data Architect is the core of any BI team.  It is important to choose a person with the right skill set.  As I tried to put together a list of skills I looked to IT Toolbox and Database Answers for help, but my mind wandered a bit.  System Construction. Data Architect. Data Warehouse. Software Factory.  We like to portray what we do in terms of construction and/or manufacturing.  A recent client bemoaned her departments inability to move from “building custom cars” to “an assembly line”.  Comparing ourselves to these burly industries might make us feel strong, but it does it accurately represent what we aspire to be?

What is a Data Architect?  What should they know how to do?   I borrowed the following description from this article. Before you click, read on and see if you can guess what this is really describing.  I think it is a great description for a Data Architect:

A Data Architect is qualified by education, experience, and imagination to enhance the function and quality of systems. The purpose of this pursuit is to improve the quality of life, increase productivity, and protect the health, security, and welfare of the business.

The best Data Architects are capable of analyzing a client's needs, goals, safety and business requirements and integrating this information into a design that is both pleasing to the eye and functional. They will work with the client closely to develop preliminary design concepts that meet their aesthetic, functional, and economic needs while maintaining adherence to standards.

In essence, the best Data Architects are part detective, part artist, and part psychologist and they use these skill sets to create systems that fit a client's tastes and needs with their budget in mind.

Doesn’t that sound like a great job?  Sign me up!  What this is actually describing is an interior designer.  While I doubt that HGTV has any plans to showcase the next dashboard you build, we are indeed closer to Designing Women than Rosie the Riveter!  Stay tuned for future posts on the talents of a good Data Design Star.

photo by Annahape Gallery via Flickr (Creative Commons License)

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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 3 of 3)

By Carol Newcomb, Senior Consultant

Diamond in the Rough: Data Quality

The third part of my summertime primer addresses Data Quality Analysis.  Don’t even start a data quality analysis until you have completed the first two steps of your Root Cause Analysis--investigate & prioritize any potential causative factors, and start your metadata assessment.  Otherwise, you may be misled by your findings.

Data quality is defined as complete and accurate data that is ready for business consumption.  Sources of poor data quality may include lack of data entry rules, unclear data element definitions, inconsistent metadata definitions for field type, format or intent, or breakdowns in data transformation processes as data flow between systems or applications.  Poor data quality results in bad business decisions; it contributes to major problems in using data effectively, and costs companies millions of dollars/year in terms of rework and inefficiency.  Data quality, in combination with robust metadata definitions, is part of the foundation of good data governance.

Data Quality Analysis

A Data Quality Management process should be designed to enable an area to start with a simple approach and over time to mature to one that is more proactive and comprehensive.  Initially, investigation may be focused on single data elements or events.  As patterns, data commonalities and other relationships appear, the data quality management process will grow to support complete business processes.   A mature data quality management process will not just resolve individual issues; it will also track relationships between data elements, ensure that business rules are consistent and generate statistical analyses to monitor previously addressed issues to ensure that data quality is stable and that an early warning system is in place as part of the data governance program.  The goal is to design a data quality management lifecycle, as shown in this diagram:

Initial Data Quality Analysis Process

I. Define data scope

• Determine data elements that are associated with or are direct results of the reported issue
• Check that all metadata definitions are present and current
• Enlist the involvement of the Data SME or Data Stewards
• Identify all source systems where the data originates, is  entered or derived

II. Extract and profile the data

• Extract the relevant data from all key source systems.
• Design the profile.  A profile will consist, at a minimum, of total record counts, min/max values, frequency of unique values, and frequency of invalid values (if defined) for each data element profiled. 
• Profile the data to determine key characteristics that are contributing to the issue, such as:
• 1. Wrong values
  2. Missing values
  3. Corrupt transformation processes
  4. Incorrect business rules
  5. Incorrect usage rules

III. Analyze Data Profile Results

• Summarize the key findings from the profile detail
• Determine what key drivers are contributing to the impact
• Determine accountability for the data quality issue
• Involve other Data Stewards in troubleshooting and designing the data quality solution

IV. Design the Corrective Action Plan

Two types of plans should be developed to address known data quality issues: a corrective action plan to fix the immediate source of the problem identified, and an ongoing monitoring plan, where thresholds have been determined and metrics are routinely collected and reported to data stakeholders.  This monitoring process should be scalable based on the number of data elements being tracked.

1. Corrective Action Plan
2. • Does scope of problem warrant change in metadata definitions, business practices or data entry rules?
   • Does scope of problem warrant a data governance standard?
   • Does the corrective action plan include details on how to fix the source of the problem as well as ways to correct historical data in the system?
3. Preventive Action Plan
4. • This plan will be designed to minimize the probability of data quality issues from recurring
   • Determine ‘early warning triggers’ based on designated thresholds.  These thresholds should reflect the business tolerance for inaccurate data (is 95% acceptable?)
   • If data latency is the source of a data quality issue, then latency thresholds should be included in the monitoring plan
   • Determine how frequently results of the monitoring plan will be reported to data stakeholders or governance oversight committees

So, now that summer is officially here, this wraps up my Data Governance Primer series.  Time for some iced tea and my favorite beach towel.  Come August, these little refreshers might be just the thing!

photo by Swamibu 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.

A Data Governance Primer (Part 2 of 3)

By Carol Newcomb, Senior Consultant

Minding Your Metadata

The second part of my summertime primer addresses ‘Minding your Metadata’.  I can just hear the collective groans and yawns now.  Sorry, but metadata collection is one of those necessary evils that may not be fun in the doing, but having it available as a resource to understand your data and use it appropriately is invaluable.  And you just might find some interesting surprises along the way!

Metadata: What Is It & Why Do I Need It?

As you start your Root Cause Analysis (see last week’s primer), you first need to examine existing data definitions (or lack thereof).  Metadata is the foundation of good data management and forms the basis for Data Governance.   Pardon me for stating the obvious, but metadata is fundamental to investigating and resolving data issues and it is the first place to start when investigating data quality issues.

Metadata is “data about data”.  Plain and simple.  It includes descriptive information about electronic data used in common daily business practice.  Metadata includes items usually found in a data dictionary: field name, field length, retention rules, and security access, as well as additional descriptive information that may include data origin (source or system), creation/entry date, method of creation (key-entry or the result of a calculation), purpose of the data (its intended use), how frequently it gets updated or refreshed, and current location in a database (table, view, schema).  If a data element is the result of calculation logic or groupings (such as age categories), those business rules used to generate the resulting data values should be collected as part of the metadata.

A good example of metadata that you may use every day would be ‘document properties’ in a Word document.  This feature captures data on the original document creation date, most recent access and update times, document creator, count of characters, words and pages.  If the document should be private, this will be indicated in its properties.  You may also tag the document by indicating key words in order to make it easier to find by you or others.

A few of the benefits of Metadata Management include:

• Clarify rules for data entry
• Reduce ambiguity around appropriate use of data elements
• Eliminate problems associated with not having data definitions, business rules or transformation logic available
• Validate legitimate values at the data element level
• Provide evidence to regulators that security and confidentiality are protected
• Centralize the storage and accessibility of metadata for end-users
• Reduce the amount of effort required to research data results.

A Metadata Management Repository is a central location or system to collect and store metadata that may exist in disparate parts of the organization (data dictionaries, systems, spreadsheets, or people’s brains). The metadata repository will store detailed definitions centrally on a network where other users can find it.

There are three general sources of metadata that should be included in this repository:

Business Metadata – Business metadata attributes facilitate identification, understanding, and appropriate use of existing data elements.  These include clear business names and descriptions, relevant business rules, descriptions of the data sources, security and privacy rules, etc. 

Technical Metadata – Describes the technical attributes of data such as physical location (host server, database server, schema, etc.), data types, any transformations applied and domain of valid values, relationships to other data elements, precision, and lineage.  Technical metadata is used by business users and by IT staff to design efficient databases, queries, and applications, and to reduce duplication of data. 

Operational Metadata – Describes the attributes of routine operations on data and related statistics.  These include job schedules and descriptions, data movement and transformation processes, data read, update and performance statistics, volume statistics, backup and archival information.  Operational metadata is used by operations staff, and DBA’s to tune the system and ensure its continued efficient operations.  It is also used by business users to track such events as “last use” of a field, and “last load” of a data element.

Exciting stuff, huh?  Well, the whole point of metadata is to have the information about data available to a multitude of users when they need it, to keep it current, and to avoid confusion around usage.  So if you appreciate having a clean bathroom, and knowing where you keep your antiperspirant, you will also appreciate having good metadata!  The time for spring cleaning is well overdue.

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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.

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.

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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.

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!

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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)

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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.

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.

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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)

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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.

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!

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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)

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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.

Data Profiling with SQL is Hazardous to Your Company’s Health

By Stephen Putman, Senior Consultant

Data Profiling is the act of extracting descriptive  and statistical data about what resides in a database in order to facilitate data quality and design efforts, among other things. Tools for performing profiling range from open-source software (DataCleaner, Talend) to database system tools (Microsoft SQL Server, Oracle) to specific and purpose-built products (vendors Trillium and DataFlux both offer data profiling solutions). All of these tools automate and standardize the data profiling task, and integrate with other areas of the design effort to varying degrees. However, most organizations do not use them, relying instead on SQL queries, run manually. Why does this happen?

The main reason is that data profiling is not seen as a high-priority activity. Therefore, the investment in licenses, hardware resources, and training for a profiling system is seen to be an unnecessary expense. Consequently, data and business analysts are forced to use existing tools such as SQL query clients to perform basic profiling tasks.

Many times, this is due to a basic misunderstanding of the power of a data profiling tool, and the data it analyzes. Profiling data is useful in logical and physical data modeling, functional system requirement production, and test script generation. Profiling gives the overall system scope and context, and ensures that the system produced satisfies the requirements of the business by characterizing the values present in source systems.

Manual SQL data profiling is fraught with risk. It relies on the skill and knowledge of the analysts producing the queries for accuracy and effectiveness, and there is no guarantee of consistency of results or standardization.. Modern profiling tools have optimized data gathering technology that is faster than manual SQL and scenario repositories that can be shared between analysts and projects to promote reuse.

Implementing a robust data profiling system is an essential part of an effective data management environment. The cost of putting such a system into place is more than offset by the benefits of deep analysis of data sources in both the transactional and analytic spaces. The essential activity is to discover just how powerful such a system can be in your organization.

Photo by mandolux via Flickr (Creative Commons license)

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.