In the Field with our Experts

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

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.

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

By Carol Newcomb, Senior Consultant

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

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

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

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

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

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

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

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

photo by Yoni Lerner via Flickr (Creative Commons License)

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

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

By Carol Newcomb, Senior Consultant

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

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

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

(To be continued……)

photo by marisag via Flickr (Creative Common License)

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

The Project Manager as News Reporter

By Stephen Putman, Senior Consultant

photo by rabbleradio

I recently read a post on the PM Blog relating to Data Integration Management Processes. In the post, the author details the seven steps in the Project Management Book of Knowledge (PMBOK) Guide relating to a Data Integration project for completion of a project. The steps are pretty much what you would expect-- develop a project charter and scope, develop the project plan, implement change control, etc.-- for any project, not just Data Integration. I found one of the steps particularly interesting for what it left out:

4. Direct and Manage Project Execution The Project Manager needs to make sure that everybody is doing what they should be doing...(emphasis mine)

The italicized statement is a huge assumption-- it supposes that the manager and team knows what tasks are required, how they are related, how long they should take, and why they are required. In other words, it supposes that the project manager can answer the famous "six questions" that all reporters ask about a news story. Unfortunately, many times this is a faulty assumption, since project managers do not necessarily have experience in data integration solutions, and may not have access to this expertise.

At Baseline, we answer these questions every day:

• Who - Who should be involved in each step of the implementation process
• What - What should be done to successfully accomplish the project, based on vast experience in developing and implementing industry best practices
• When - What sequence in which the tasks should be performed, and how they are related to each other
• How - The skills required to accomplish the tasks, and the ability to identify these skills in client staff
• Where - Assumed to be the client site (the least important question)
• Why - How detailed tasks relate to a proven project implementation methodology that has been developed by Baseline

The simple act of answering these six questions puts you well on the road to successful project completion. Of course, that?s not all there is to it, but it?s a good start?one that a surprising number of experienced project managers forget!

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

7 Ways to Cover Your Asset

By Rob Paller, Consultant

If you have attended a conference on data warehousing, business intelligence, data management, or data governance recently you ultimately have heard the saying, “Treat your data as a corporate asset”, countless times. Treating your data as a corporate asset means investing in data comparable to other corporate assets in terms of resources and technology. It also means that you understand the cost of poor quality, inaccurate, or missing data.

We have belabored the point that you should invest time and money into data integration technologies to assist you in mastering your master data elements. This is after you have invested the time, people and money to establish sound data governance policies. Nor does it discount the investment in the BI tools that can translate your data into information that can be used, understood and profitable.

What cannot be overstated is to the need to protect what is arguably your most important corporate asset from not only natural disasters and hardware failures but from both accidental deletion and intentional sabotage.  At the end of 2008, JournalSpace, an online company in business since 2002, was forced offline permanently because their entire database was overwritten as the result of a malicious act. What made the situation fatal was their reliance on the hardware redundancy of RAID alone to protect their database. In other words, they did not archive their data.

photo by wonderferret

Are you doing everything possible to cover your corporate asset?  Here are several questions to help you determine your own disaster readiness:

• Are you logging all queries submitted to your databases and archiving these logs daily?
• Have you established a disaster recovery process for your database systems?
• Have you performed periodic disaster recovery drills to make sure you are able to bring the affected system(s) back online?
• Are your archives maintained in separate location from your database systems?
• When an employee with administrator privileges leaves the company do you not only remove their user accounts but change the admin passwords on the systems administered by the employee?
• Do you maintain an audit log of users with the ability to purge production data and failed logon attempts for these users or admin users?
• Do you maintain a separation of duties and/or environments for testing ETL processes?

Are you able to sleep well tonight knowing how well your asset is covered? If not, follow the seven steps above and you’ll rest easy.

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

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.