Tackling the Tough One: Master Data Management for the Healthcare Enterprise

One of the big struggles in healthcare is the difficulty of Master Data Management.  A typical regional hospital organization can have upwards of 200+ healthcare applications, multiple versions of systems and, of course, many, many “hidden” departmental applications.  In that situation, Master Data Management for the enterprise as a whole can seem like a daunting task.  Experience dictates that those who are successful in this effort start with one important weapon: data and application governance.

Data and application governance can often be compared to building police stations, but it is much more than that.  Governance in healthcare must begin with an understanding of data as an asset to the enterprise.  For example, developing an Enterprise Master Patient Index (EMPI) is creating a key asset for healthcare providers to verify the identity of a patient independent of how they enter the healthcare delivery system.  Patients are more than a surgical case, an outpatient visit or pharmacy visit.  Master data management in healthcare is the cornerstone of moving to treating patients across the entire continuum of care, independent of applications and location of care.  Bringing the ambulatory, acute care and home care settings into one view will provide assurance to patients that a healthcare organization is managing the entire enterprise.

Tracking healthcare providers and their credentials across multiple hospitals, clinics and offices is another master data management challenge.  While there are specialized applications for managing doctor’s credentials, there are not enterprise-level views that encompass all types of healthcare professionals in a large healthcare organization and their respective certifications.  In addition, this provider provisioning should be closely aligned with security and access to protected healthcare information.  A well designed governance program can supervise the creation of this key master data and the integration across the organization. 

An enterprise view of Master Data provides a core foundation for exploiting an organizations data to its full potential and offers dividends beyond the required investment.  Healthcare organizations are facing many upcoming challenges with reference data as a part of master data management, especially as the mandated change from ICD-9 to ICD-10 codes approaches.   Hierarchies are the magic behind business analytics – the ability to define roll-up and drill-downs of information.  Core business concepts should be implemented as master data – how does the organization view itself?  The benefits of a carefully defined and well governed master data management program are many: Consistent reporting of trusted information, a common enterprise understanding of information, cost efficiencies of reliable data, improved decision making from trusted authoritative sources, and most importantly in healthcare, improved quality of care.

Data and application governance is the key to success with master data management.  Just like an inventory, key data elements, tables and reference data must be cataloged and carefully managed.  Master data must be guarded by three types of key people: a data owner, a data steward and a data guardian.  The data owner must take responsibility for the creation and maintenance of the key asset.  The data steward will be the subject matter expert that determines the quality of the master data and its appropriate application and security.  Finally, the data guardian is the information technology professional that oversees the database, the proper back-up and recovery of the data assets and manages the delivery of the information.  In all three roles, accountability is important and overseen by an enterprise information management (EIM) group that is composed of key data owners and executive IT management.

In summary, master data management provides the thread that ties all other data in the enterprise together.  It is worth the challenge to create, maintain and govern properly.  For success, pick the right people, understand the process and use a reliable technology.

Clinical Alerts – Why Good Intentions Must Start as Good Ideas

As the heated debate continues about ways to decrease the costs of our healthcare system while simultaneously improving its quality, it is critical to consider the most appropriate place to start – which depends on who you are. Much has been made about the advantages of clinical alerts especially with their use in areas high on the national radar like quality of care, medication use and allergic reactions, and adverse events.   Common sense, though, says walk before you run; in this case its crawl before you run. 

Clinical alerts are most often electronic messages sent via email, text, page, and even automated voice to notify a clinician or group of clinicians to conduct a course of action related to their patient care based on data retrieved in a Clinical Decision Support System (CDSS) designed for optimal outcomes. The rules engine that generates alerts is created specifically for various areas of patient safety and quality like administering vaccines to children, core measure compliance, and preventing complications like venous thromboembolism (VTE) (also a core measure). The benefits of using clinical alerts in various care settings are obvious if the right people, processes, and systems are in place to consume and manage the alerts appropriately. Numerous studies have been done highlighting the right and wrong ways of implementing and utilizing alerts. The best criteria I’ve seen used consider 5 major themes when designing alerts: Efficiency, Usefulness, Information Content, User Interface, and Workflow (I’ve personally confirmed each of these from numerous discussions with clinicians ranging from ED nurses to Anesthesiologists in the OR to hospitalists on the floors). And don’t forget one huge piece of the alerting discussion that often gets overlooked…….the patient! While some of these may be obvious, all must be considered as the design and implementation phases of the alerts progress.

OK, Now Back to Reality

A discussion about how clinical alerting can improve the quality of care is one limited to the very few provider organizations that already have the infrastructure setup and resources to implement such an initiative. This means that if you are seriously considering such a task, you should already have:

• an Enterprise Data Strategy and Roadmap that tells you how alerts tie into the broader mission;
• Data Governance  to assign ownership and accountability for the quality of your data and implement standards (especially when it comes to clinical documentation and data entry);
• standardized process flows that identify points for consistent, discrete data collection;
• surgeon, physician, anesthesiology, nursing, researcher, and hospitalist champions to gather support from various constituencies and facilitate education and buy-in; and
•  oh yeah, the technology and skilled staff to support a multi-system, highly integrated, complex rules-based environment that will likely change over time and be more scrutinized………

◊◊Or a strong relationship with an experienced consulting partner capable of handling all of these requirements and transferring the necessary knowledge along the way.◊◊

I must emphasize the second bullet for just a moment; data governance is critical to ensure that the quality of the data being collected passes the highest level of scrutiny, from doctors to administrators. This is of the utmost importance because the data is what forms the basis of the information that decision makers act on. The quickest way to lose momentum and buy in to any project is by putting bad data in front of a group of doctors and clinicians; trust me when I say it is infinitely more difficult to win their trust back once you’ve made that mistake. On the other hand, if they trust the data and understand the value of it in near real time across their spectrum of care, you turn them quickly into leaders willing to champion your efforts. And now you have a solid foundation for any healthcare analytics program.    

If you are like the majority of healthcare organizations in this country, you may have some pieces to this puzzle in various stages of design, development, deployment or implementation. In all likelihood, though, you are at the early stages of the Clinical Alerts Maturity Model

 

and with all things considered, should have alerting functionality in the later years of your strategic roadmap. Though, there are many  projects with low cost, fast implementations, quick ROIs, and ample examples to glean lessons learned from like, Computerized Physician Order Entry (CPOE), electronic nursing and physician documentation, Picture Archiving System (PACS), and a clinical data repository (CDR) to use alerting as a prototype or proof of concept to demonstrate the broader value proposition. Clinical alerting, to start, should be incorporated alongside projects that have proven impact across the Clinical Alerts Maturity Model before they are rolled out as stand-alone initiatives.

The Microsoft Clinical Information Feedback Loop

Microsoft recently purchased Rosetta Biosoftware from Merck & Co. for its Amalga Life Science platform; with this move, Microsoft is starting to differentiate itself from its competition by offering its integrated information solutions, which include HealthVault, Amalga UIS and Amalga Life Sciences, to both providers and producers. In its crosshairs are huge budgets available from Pharma for infrastructure solutions for drug R&D and clinical trials. Microsoft is posed to attract a whole new audience of customers from Pharma to integrated health systems that have their own research entities. If done correctly, Microsoft’s new strategy could become a model for improving the efficiency of clinical research, by drastically reducing the most costly resource needed for clinical trials, time.

The current Amalga UIS is fundamentally what I like to call a PDA (no not Public Display of Affection, rather a Patient Data Aggregator). There are three core components that include:

1. Data Aggregation and Distribution Engine (DADE) – sits on top of healthcare provider sources and listens for HL7 messages; then puts them through transformation and parsing scripts in preparation to be stored in Amalga and sends them to a data store;
2. Data Store – receives the messages from DADE; is a basic core storage engine and is a database with a set of tables specific to segments within the HL7 messages; and
3. Front End – a web-based presentation layer that was originally designed for patient level data viewing and has plug in capability to provide more appropriate tools for analysis.

The current needs of data integration seem to be met by this solution, and the high degree of customization that can accommodate an implementation makes it even more attractive. Microsoft’s footprint in healthcare is getting bigger; they must understand, though, that this space has many stakeholders. While addressing all their needs is nearly impossible (just ask our hard working politicians’ trying to pass healthcare reform legislation), the last people they want to alienate are those they’ve already convinced that Amalga is the healthcare platform of the future, most notably some high profile integrated health systems across the country.

Integrated health systems (IHS) often provide a combination of services including care delivery, research, education, and even  their own health plan (think KP, John Hopkins, Geisinger, and Sentara). These entities have a unique opportunity to leverage the MS offerings by creating a continuous feedback loop of information from patient to provider to researcher that improves the quality and accuracy of the data throughout the process. Let’s start with the patient:

• Patient information in HealthVault – As patient’s progress from being baby boomers (less tech-savvy) to Generation X & Yer’s (tech-hungry), clinical information will no longer be in the sole possession of the doctors. Rather, the demand will be for online, mobile, 24×7 access that is shared and can be updated real-time as health data is gathered by both patients and their doctors. Patients, thus, become a stand-alone data quality tool as they become more comfortable verifying, updating, and changing the information in their medical records.
• Research information in Amalga Life Sciences – Researchers are all too familiar with the tedious, error-prone process of identifying patients with the correct diagnosis and conditions as candidates for clinical trials. As patients become more empowered with their medical records, they make the segmentation of populations a much simpler process.
• Clinical information in Amalga UIS – Amalga UIS is a mechanism for driving continuous improvement in clinical care by integrating data across the enterprise. One way to improve care is by incorporating best practices identified through clinical research. The information learned from improved research methods are then implemented directly into the standard delivery of patient care offered by provider institutions.

The Amalga UIS is currently operational in 12 domestic organizations. Because most of these clients are IHS’ and have research entities, they are in the best position to capitalize on the Amalga Life Sciences offering. These will also be the locations where the ROI MS is hoping will be formulated for less prestigious organizations to eventually imitate. It begs the following question, though, that some of the current customers will ask, “How can the existing components of Amalga Unified Intelligence System (UIS) be leveraged in this new offering to make it attractive to the widest audience possible and more importantly, be affordable?” Well, if you can articulate the argument above, and identify the huge benefits that can come from the Microsoft Feedback Loop, your argument might be easier to make than you think. And don’t forget, this feedback mechanism is built on the fundamental principle that all stakeholders must have the collective groups’ best interest in mind; so don’t forget to share what you find with your neighbor.

The Patient Centered Medical Home (PCMH)

In a recent article released by IBM, an argument is made for a transition in the U.S. healthcare system to a team-based approach based on the Patient Centered Medical Home (PCMH) model. A strong case is made from a description of the model, its’ players, technology, and benefits. The critical change that must be established first, though, is the healthcare systems’ evolution to a data-driven system. The access to, higher quality and integration of data, across disparate silos of information, will provide the foundation for this change. Only then can the position of Dr. Douglas Henley, EVP and CEO of the American Academy of Family Physicians, “ A smarter health system is one based in comprehensive patient centered primary care which improves patient/physician communication, the coordination and integration of care, and the quality and cost efficiency of care” be achieved.

The quality and cost of care is what we hear the most about in news headlines. However, the success of each piece of Dr. Henley’s statement is based on the ability of a team of providers to access accurate and updated patient data across care settings and over time in order to proactively suggest lifestyle improvements and reactively diagnose and recommend appropriate treatments.  Fundamentally, each decision maker and operating entity needs a data strategy for how it will achieve the ambitious and often ambiguous goals it likes to claim.

I’ll recite a popular management mantra I’ve heard numerous times, “you can’t manage what you can’t measure.” The healthcare system is a data rich environment. Cleaning, manipulating, and leveraging  the huge volume of data available will become the critical success factors that will enable the linkage between education, research, the delivery of care and its outcomes, to benchmark and monitor the performance of the continuous improvements necessary to bring costs down and quality up.  

Players in the healthcare world will soon find out (if they haven’t already) a principle all those in the data world already know:

• Good data, appropriately aggregated and manipulated, drives accurate information;
• Accurate information is not a luxury that most decision makers have;
• The executives, managers, physicians, nurses, nurse practitioners, educators, pharmacists, researchers, and other stakeholders that do have access to accurate information are in a position to leverage and evolve this data and information from satisfying compliance and regulatory requirements to enabling an organizational knowledge-based asset.

Actionable data will drive the improvements that you see scattered across headlines and mentioned in political speeches in the past and no doubt, in the future.

Image courtesy of Texas Family Physician

Knowledge-based computing: next generation of business intelligence?

Pablo Picasso once said “Computers are useless.  They only give you answers.”  The truth is that computers have to work very hard to provide answers to what appear to be simple questions.  While we are buried in terabytes, petabytes and exobytes of data – answers and information can be very hard to come by, especially information necessary for serious business decisions.   Data must be viewed in context of a subject area to become information, and analytic techniques must be applied to information in order to create knowledge worthy of taking action.  The challenge is getting data into context within a subject area and applying the right analytic techniques to get “real” answers.

Enter Wolfram Alpha, as an “answer” engine.  Once touted as the next generation of search engine, this web application combines free form natural language input, i.e. simple questions, and dynamically computed results.  Behind the scenes, a series of supercomputers provide linguistic analysis (context for both the question and the answer), ten terabytes of curated data that is constantly being updated, dynamic computation using 50,000 types of algorithms and equations, and computed presentation with 5,000+ types of visual and tabular output.  Sound impressive?  It could easily be a glimpse of the next generation of business intelligence and decision-support systems.

Wolfram Alpha lets you input a query that requires data analysis or computation, and it delivers the results for you. It’s “curated” data is specially prepared for computation— data that’s been hand-selected by experts working with Wolfram, who go through steps to make sure the raw data is tagged semantically and is presented unambiguously and precisely enough that it can be used for accurate computation.  Alpha demonstrates the real power of metadata – data about data, and the importance of semantic tags for categorizing data into a context necessary for providing knowledge and, thus, answers.

Wolfram Alpha is not a search engine according to Wolfram Research co-founder Theodore Grey.  It is not a replacement for Google.  He says that Alpha is very, very different from a search engine. “Search engines are like reference librarians,” Grey explained. “Reference librarians are good at finding the book you might need, but they’re useless at interpreting the information for you.”  Alpha takes reams of raw information and performs computations using those data.  It produces pages of new information that have never existed on the Internet. “Search engines can’t find an answer for you that a Web page doesn’t have,” Grey explained.

“It’s been a dream of many people for a long time to have a computer that can answer questions,” said Grey. “A lot of people may think of a search engine as that, but if you think about it, what search engines do is an extreme limited subset of that sort of thing.”  Examples of how Alpha can be used today range from solving difficult math equations to doing genetic analysis, examining the historic earnings of public companies, comparing the gross domestic products of different countries, even measuring the caloric content of a meal you plan to make. You can find out what day of the week it was on your birthday, or show the average temperature in your area going back days, months or years.

Wolfram Alpha would make an “ultimate” business intelligence application by computing over an enterprise data warehouse once the data was properly “curated.”  The ability to create knowledge from data, particularly to create actionable answers is what business executives really expect – not prettier presentations.  The only questions left for Alpha are:

1. who can curate your data for you, and
2. how quick can you see Alpha running over your data?

Are your KPIs creating a no-win situation?

Businesses that start implementing KPIs at a departmental level, without an enterprise wide effort to define a balanced set of key performance indicators, can unwittingly push their businesses into a no-win situation, as in these real-world scenarios:

• Customer Call Centers (often ahead of the curve as far as setting metrics) are tracking and incentivizing their call center agents to keep their call times short. Call center agents, in an effort to shave seconds off of each call, omit the crucial step of searching for a customer before entering a new one while logging interactions. Result: Duplicate customer records, which  may even be pushed to other systems, creating pain throughout multiple departments.
• In the push to meet monthly sales quotas, hyper-discounting  behavior becomes the norm among the sales team.  If the pricebook is complex and no one can get a true read on profitability, inappropriate discounting may be approved when management doesn’t have access to the right information to make an informed approval decision.
• Some businesses steer only by financial performance measures, but these are lagging indicators, and can seldom, in and of themselves, provide the required agility to succeed in rapidly changing situations.

The key, of course, is to strive for balance when implementing KPIs:

• Balance between leading (forward-looking) and lagging (backward-looking) indicators.
• Balance across stakeholder perspectives. The Balanced Scorecard as a starting point works well to achieve balance across core stakeholder viewpoints of financial, customer, process, and learning/growth.
• Balance across levels in your business hierarchy. Kaplan and Norton expanded on the balanced scorecard approach to help businesses drive metrics down through their organizations via strategy maps.
• Balancing speed metrics with quality metrics

Image courtesy of memory-alpha.org

The alternative to a balanced approach at the outset is usually a technology desparation move, such as manually cobbling together some key reports, manually trying to scrub out duplicate data, implementing undesirable or even temporary customizations to packaged programs. There’s usually at least one person in the IT department who’s enough of a Star Trek fan to want to reprogram that no-win scenario, just like the young James Kirk did with the Kobayashi Maru.

Feel Like Your Pants Are Down – Check Your Data

Few things are as embarassing (cool double entendre) as having your pants pulled down in public.  While it is great fun to see it done figuratively to someone else (real world on the grade school playground), it can really cramp both careers and management practice in the corporate world.

Just when you have the fought for budget money to put into some cool analytics or reporting…the project is up and running, you’re day dreaming about keeping your job (maybe a raise!). Then, the project status rolls across your desk: bad data, instant project over-run.  The problem is worst-case scenario; bad data tends to be discovered just as the project goes live with no quiet opportunity to fix it.  The money is gone and everybody is looking (yep, just like second grade, lunch money is gone and…).

Don’t you just love going back to the C-Suite for more  project budget (feel like you’ve just been pantsed?)  Ouch!  This one needs to be avoided at all costs.  How to do it is the tough part.  Does anybody really want to go into the C-Suite and say, “Our corporate data may not be, uh, kosher?”  There is a real career enhancer for any CIO.  So you need to do what any red-blooded CIO would do, bury the cost in another part of the project.  No one wants to champion a project entitled “Fixing Corporate Data Assets”.  How did they get messed-up in the first place?  Guess who gets blamed?

No one wants to hear about legacy code and budget reductions.  There is no wide-eyed fascination regarding aging databases suffering from too many corporate mergers and grafted on must-have emergency applications.  Well, if they don’t want to hear about that, why go through it.  Don’t tell me problems, get me solutions!  Well, here you go, as requested.

Instead, early in an analytics or reporting initative, put in a sub-task to test existing data for proper compliance to the stated information delivery  goals (see that doesn’t feel scary, just numbing).  Overall match to corporate data governance and meta data management standards can be tested at the same time, who could argue with that?  Just a little bit of testing, call it project (career)  insurance.

The next step should be obvious, even to a CIO.  Slide one of these sub-tasks in each project initative (they go togther like “sliders, beer, and baseball”!).  Before you know it the Corporate Data Assets are in tip top shape, without the nasty embarassment of a major budget line item.

Budgeting from the trenches

Have you ever noticed how text books understate the budgeting process? They tend to gloss over the topic as four steps:

1. Determine revenues
2. Forecast expenses
3. Adjust
4. Communicate

Some text books suggest that that the process has iterations. This general outline of the process rings true, but its oversimplification makes the budgeting process sections meaningless when it comes time to map one out. I have found that undertaking the budgeting challenge is different between organizations. The process design is similar to perhaps how Generals draw up battle plans. The available personnel, supplies and equipment are assessed and the desired outcome is clear. However, the details of the approach are dependent on the specific terrain and rely on the latest tools and information. For this reason, organizations tend to see its budgeting strategy as unique.

Strategy is a fair term to use in budgeting as its outcome has a great deal at stake. Every staff member submitting input for calculations or making a request for funds has credibility on the line. Without complete information the profitability of a product, service, region or division is at jeopardy. And, day-to-day performance of the organization can be besieged from the pressure and time consumption when gathering intelligence from the field.

There is a point where this analogy between a battle plan and a budgeting process falls apart: That is, a battle will end and budgeting does not. A budget plan will play itself over and over. This exposes a point of vulnerability in the budgeting process as it was designed for a set of conditions that most likely has changed. It may no longer be sufficient to budget annually. Reporting requirements may change. Consolidations in the industry confuse the financial results. Or, new competitors, products, clients, regions and staff render the plan obsolete. When there is such a difference between the framework and reality, the budgeting framework cannot be trusted for strategic forecasting.

In the wake of the global financial crisis as organizations seek to maximize cash reserves, evaluate expenses and eliminate risk; the budget process surfaces as a key strategy. Those giving strategic input and making decisions have unprecedented pressures to assure accuracy and agility in cost cutting. Those who need to find opportunities for revenue are at a loss for validating an option’s viability. An organization is likely to forgo an opportunity without the ability to articulate its profitability, avoiding the risk of catastrophe.

Today’s battlefield is dynamic and most participants are deep in the trenches. We know that this gloomy economy will end and we intend to abandon the trench to take new ground. Our challenge is timing and selecting the method to move forward. While we are trenched, let’s review the budgeting tools and design a system giving us the agility to adapt to the changing markets, locate opportunities and operate effectively.

Cutting costs should not mean cutting revenue.

Image courtesy of BusinessWeek 9/25/08: "AmerisourceBergen's Scrimp-and-Save Dave"

The financial panic of late has caused a lot of attention on cutting costs – from frivolities like pens at customer service counters to headcount – organizations are slowing spending. Bad times force management to review every expense, and in these times obsess with them. Financial peace however has two sides – expense and revenue.

A side effect of cost cutting can be stunted revenue, over both the short and long terms. It is easier to evaluate costs than to uncover revenue opportunities, such as determining  truly profitable offerings and adapting your strategies to maximize sales. Also as difficult to quantify are the true loses in unprofitable transactions, and competitive strategies that can negatively impact your competition.

The answers to many of these questions can be  unearthed from data scattered around an organization, groking customers and instantly shared knowledge between disciplines. For example, by combining:

• customer survey data;
• external observations;
• clues left on web visits;
• and other correspondence within the corporation;

…an organization can uncover unmet needs to satisfy before the competition, and at reduced investment cost.

When external factors, like a gloomy job outlook, cause customers to change behavior, it is time to use all information at your disposal. Those prospects changing preferences for your offerings can provide golden intelligence about the competition or unmet needs.

Pumping information like this is the heart of business intelligence. Marketing and Sales can uncover the opportunity; however, it is up to the enterprise to determine how to execute a timely offering. Financials, human capital planning, and operations, work in concert to develop the strategy which requires forecasting data, operational statistics and capacity planning data to line up.

A good strategist views all angles, not just reducing cost.

Enterprise Information Strategy in 2009

If you’ve been in an IT-related role for more than 10 years, you’ve likely enjoyed the boom and bust the economy has provided. Healthier times enhance business capabilities in the form of multi-million dollar, cross organization implementations, while leaner times like these afford only the most critical needs to be fulfilled. So while the volatility wreaks havoc on your organization, one IT spend continues to stay strong. Strategy.

Strategy is a sound investment in prosperous times since the confirmation it provides protects the investment of larger scale initiatives. For example, a company committed to providing better customer service and market additional products into its customer base will undertake a 2 to 3 year set of tactical CRM initiatives. Success factors include the three usual suspects, ‘People, Process and Technology’, and aligning each with an ideal future state vision is critical.

A well executed strategy provides an education for stakeholders and builds consensus among individuals who may have never sat around the same conference room table before. It coalesces and prioritizes goals and objectives, drafts a future state architectural blueprint and describes business processes that will endure, and establishes a long term Road Map that orchestrates incremental efforts and illustrates progress.

So if strategy is a safe bet in better times, why invest in one now?  For executives I’ve met with most recently (Q3 and Q4 2008), a popular form of strategy is analogous to grandma’s attic. At some point, it may have occurred to you to look in grandma’s attic for something that may be useful, and if you’re truly fortunate, there may be something extremely valuable you hadn’t counted on. For C-level executives looking for ways to improve their bottom lines, the same treasure hunt exists in the corporate information they already possess.

To understand whether your enterprise information holds hidden treasures, explore these 10 questions with your organization. Answering ‘No’ or ‘Not sure’ to any questions that have exceptional relevance within your organization may suggest looking into an Enterprise Information Strategy enagement.

1. Do visionaries within my company have visibility to key performance indicators that drive revenue or lower costs?
2. Do I understand who my customers are and which products they own?
3. Am I able to confidently market additional products into my existing customer base?
4. Do I possess data today that would provide value to complimentary industries through new information based offerings?
5. Will my information platforms readily scale and integrate to meet the demands of company growth through acquisition?
6. Am I leveraging the most cost effective RDBMS software and warehouse appliance technologies?
7. Do I understand the systemic data quality issues that exist in the information I disseminate?
8. Do the organizations I support understand the reporting limitations of my current state architecture?
9. Is there an architectural blueprint that illustrates an ideal 2 to 3 year business intelligence future state?
10. Does my company have visibility to a Road Map that timelines planned projects and the incremental delivery of new business insights?