Epic Clarity Is Not a Data Warehouse

It’s not even the reporting tool for which your clinicians have been asking!

I have attended between four and eight patient safety and quality healthcare conferences a year for the past five years. Personally, I enjoy the opportunities to learn from what others are doing in the space. My expertise lies at the intersection of quality and technology; therefore, it’s what I’m eager to discuss at these events. I am most interested in understanding how health systems are addressing the burgeoning financial burden of reporting more (both internal and external compliance and regulatory mandates) with less (from tightening budgets and, quite honestly, allocating resources to the wrong places for the wrong reasons).

Let me be frank: there is job security in health care analysts, “report writers,” and decision support staff. They continue to plug away at reports, churn out dated spreadsheets, and present static, stale data without context or much value to the decision makers they serve. In my opinion, patient safety and quality departments are the worst culprits of this waste and inefficiency.

When I walk around these conferences and ask people, “How are you reporting your quality measures across the litany of applications, vendors, and care settings at your institution?,” you want to know the most frequent answer I get? “Oh, we have Epic (Clarity)”, “Oh, we have McKesson (HBI),” or “Oh, we have a decision support staff that does that”. I literally have to hold back a combination of emotions – amusement (because I’m so frustrated) and frustration (because all I can do is laugh). I’ll poke holes in just one example: If you have Epic and use Clarity to report here is what you have to look forward to straight from the mouth of a former Epic technical consultant:

“It is impossible to use Epic “out of the box” because the tables in Clarity must be joined together to present meaningful data. That may mean (probably will mean) a significant runtime burden because of the processing required. Unless you defer this burden to an overnight process (ETL) the end users will experience significant wait times as their report proceeds to execute these joins. Further, they will wait every time the report runs. Bear in mind that this applies to all of the reports that Epic provides. All of them are based directly on Clarity. Clarity is not a data warehouse. It is merely a relational version of the Chronicles data structures, and as such, is tied closely to the Chronicles architecture rather than a reporting structure. Report customers require de-normalized data marts for simplicity, and you need star schema behind them for performance and code re-use.”

You can’t pretend something is what it isn’t.

Translation that healthcare people will understand: Clarity only reports data in Epic. Clarity is not the best solution for providing users with fast query and report responses. There are better solutions (data marts) that provide faster reporting and allow for integration across systems. Patient safety and quality people know that you need to get data out of more than just your EMR to report quality measures. So why do so many of you think an EMR reporting tool is your answer?

There is a growing sense of urgency at the highest levels in large health systems to start holding quality departments accountable for the operational dollars they continue to waste on non-value added data crunching, report creation, and spreadsheets. Don’t believe me? Ask yourself, “Does my quality team spend more time collecting data and creating reports/spreadsheets or interacting with the organization to improve quality and, consequently, the data?”

Be honest with yourself. The ratio, at best, is 70% of an FTE is collection, 30% is analysis and action. So – get your people out of the basement, out from behind their computer screens, and put them to work. And by work, I mean acting on data and improving quality, not just reporting it.

Why EMR’s Are Not Panacea’s for Healthcare’s Data Problems

So, you’ve decided to go with Epic or Centricity or Cerner for your organization’s EMR.

Think your EMR is Hamlin’s Wizard Oil?

Good, the first tough decision is out of the way. If you’re a medium to large size healthcare organization, you likely allocated a few million to a few hundred million dollars on your implementation over five to ten years. I will acknowledge that this is a significant investment, probably one of the largest in your organizations history (aside from a new expansion, but these implementations can easily surpass the cost of building a new hospital).  But I will argue: “Does that really mean the other initiatives you’ve been working should suddenly be put on hold, take a back seat, or even cease to exist?”Absolutely not. The significant majority of healthcare organizations (save a few top performers) are already years and almost a decade behind the rest of the world in adapting technology for improving the way the healthcare is delivered. How do I know this? Well, you tell me, “What other industry continues to publicly have 100,000 mistakes a year?” Okay, glad we now agree. So, are you really going to argue with me that being single-threaded, with a narrow focus on a new system implementation, is the only thing your organization can be committed to? If you’re answer is yes, I have some Cher cassette tapes, a transistor radio, a mullet, and some knee highs that should suit you well in your outdated mentality.

An EMR implementation is a game-changer. Every single one of your clinical workflows will be adjusted, electronic documentation will become the standard, and clinicians will be held accountable like never before for their interaction with the new system. Yes, it depends on what modules you buy – Surgery, IP, OP, scheduling, billing, and the list goes on. But for those of us in the data integration world, trying every day to convince healthcare leaders that turning data into information should be top of mind, this boils down to one basic principle – you have added yet another source of data to your already complex, disparate application landscape. Is it a larger data source than most? Yes. But does this mean you treat it any differently when considering its impact on the larger need for real time, accurate integrated enterprise data analysis? No. Very much no. Does it also mean that your people are suddenly ready to embrace this new technology and leverage all of its benefits? Probably not. Why? Because an EMR, contrary to popular belief, is not a panacea for the personal accountability and data problems in healthcare:

• If you want to analyze any of the data from your EMR you still need to pull it into an enterprise data model with a solid master data foundation and structure to accommodate a lot more data than will just come from the system (how about materials management, imaging, research, quality, risk?)
  • And please don’t tell me your EMR is also your data warehouse because then you’re in much worse shape than I thought…
  • You’re not all of a sudden reporting real time. It will still take you way too long to produce those quality reports, service line dashboards, or <insert report name here>. Yes there is a real time feed available from the EMR back end database, but that doesn’t change the fact that there are still manual processes required for transforming some of this information, so a sound data quality and data governance strategy is critical BEFORE deploying such a huge, new system.

The list goes on. If you want to hear more, I’m armed to the teeth with examples of why an EMR implementation should be just that, a focused implementation. Yes it will require more resources, time and commitment, but don’t lose sight of the fact that there are plenty more things you needed to do with your data before the EMR came, and the same will be the case once your frenzied EMR-centric mentality is gone.

Healthcare IT Gets Snubbed in “State of the Union”……So What!

I would not want to be the President right now. No matter what he said on Wednesday night, he undoubtedly would leave someone out; some initiative, some special interest, some high priority agenda item. Then how, with tackling the exorbitantly high cost of healthcare as the single highest profile item on his desk, did he forget to mention Healthcare IT (HIT)? Seriously, how?

There was no mention of the ARRA and HITECH money allocated to demonstrating “meaningful use” of healthcare IT that hospitals, doctors offices, healthcare clinics and every other possible recipient has been scrambling like chickens with their heads cut off to understand for the past 6 months. There have literally been new businesses created to analyze and make sense of this information; new government committees established to oversee the process; experts and pundits claiming this and that on national stages, radio shows, conferences; with all the press HIT has gotten from the day the President was sworn in, you’d think he would’ve give us a progress report, at least from his point view.

There was no mention of the EMRs, CPOE, Clinical Data Repositories, PACS, and Electronic Documentation that are all suitable candidates for the initial projects providers can tackle because of the availability of research and best practices available for these initiatives. No mention of the increased regulations from JCAHO, HIPAA, and CMS. No mention of the accessibility issue so closely related to the President’s broadband initiative that will determine patient accessibility beyond the hospital walls.

There was no mention of the strategies that CIOs, CMIOs, CEOs, and CFOs are utilizing such as data warehousing, clinical data marts, electronic capture of patient information through kiosks (just like when you check in at the airport); clinical alerting to increase compliance with Core Measures and other regulations; and using evidence-based decision making from strong data quality, discrete, standard, timely data collection, and last but not least, enterprise-wide data governance strategies.

Ok, so we were all left out, but as Tom Hanks would say “there’s no crying in baseball”.  Good thing for us, we don’t have time to sit and sulk.  First things first, get your act together.  You will never understand where your weaknesses lie and your opportunities for improvement sit without understanding the information you’re collecting, on a day-to-day basis, across the entire spectrum of your healthcare organization.  The average hospital has 120 different software applications, mostly transactional, that all have their own subset of data. Understanding this vast landscape, and integrating the data and transforming it, in a timely manner, into actionable information, is critical for any executive; the providers able to balance government demanding reform, patients begging for lower costs of care, researchers advancing the standards for higher quality, and the constant advancements in technology will be the ones who not only survive, but emerge from this recession stronger than when they entered. You will be looking for a roadmap!

From Free Text Clinical Documentation to Data-rich Actionable Information

Hey healthcare providers! Yeah you the “little guy”, the rural community hospital; or you the “average Joe”, the few-hundred bed hub hospital with outpatient clinics, an ED, and some sub-specialties; or you the “behemoth”, the one with the health plan, physician group, outpatient, inpatient, and multi-discipline, multi-care setting institution. Is your EMR really just an electronic filing cabinet? Do nursing and physician notes, standard lab and imaging orders, registration and other critical documents just get scanned into a central system that can’t be referenced later on to meet your analytic needs? Don’t worry, you’re not alone…

Recently, I blogged about some of the advantages of Microsoft’s new Amalga platform; I want to emphasize a capability of Amalga Life Sciences that I hope finds its way into the range of healthcare provider organizations mentioned above, and quick! That is, the ability to create a standard ontology for displaying and navigating the unstructured information collected by providers across care settings and patient visits (see my response to a comment about Amalga Life Science utilization of UMLS for a model of standardized terminology). I don’t have to make this case to the huge group of clinicians already too familiar with this process in hospitals across the country; but the argument (and likely ROI) clearly needs to be articulated for those individuals responsible for transitioning from paper to digital records at the organizations who are dragging their feet (>90%). The question I have for these individuals is, “why is this taking so long? Why haven’t you been able to identify the clear cut benefits from moving from paper-laden manual processes to automated, digital interfaces and streamlined workflows?” These folks should ask the Corporate Executives at hospitals in New Orleans after Hurricane Katrina whether they had hoped to have this debate long before their entire patient population medical records’ drowned; just one reason why “all paper” is a strategy of the past.   

Let’s take one example most provider organizations can conceptualize: a pneumonia patient flow through the Emergency Department. There are numerous points throughout this process that could be considered “data collection points”. These, collectively and over time, paint a vivid picture of the patient experience from registration to triage to physical exam and diagnostic testing to possible admission or discharge. With this data you can do things like real or near-real time clinical alerting that would improve patient outcomes and compliance with regulations like CMS Core Measures; you can identify weak points or bottlenecks in the process to allocate additional resources; you can model best practices identified over time to improve clinical and operational efficiencies. Individually, though, with this data written on a piece of paper (and remember 1 piece of paper for registration, a separate piece for the “Core Measure Checklist”, another for the physician exam, another for the lab/X-ray report, etc.) and maybe scanned into a central system, this information tells you very little. You are also, then, at the mercy of the ability to actually read a physicians handwriting and analyze scanned documents of information vs. delineated data fields that can be trended over time, summarized, visualized, drilled down to, and so on.

Vulnerabilities and Liabilities from Poor Documentation

Relying on poor documentation like illegible penmanship, incomplete charting and unapproved abbreviations burdens nurses and creates a huge liability. With all of the requirements and suggestions for the proper way to document, it’s no wonder why this area is so prone to errors. There are a variety of consequences from performing patient care based on “best guesses” when reading clinical documentation. Fortunately, improving documentation directly correlates with reduced medical errors. The value proposition for improved data collection and standardized terminology for that data makes sense operationally, financially, and clinically.   

So Let’s Get On With It, Shall We?

Advancing clinical care through the use of technology is seemingly one component of the larger healthcare debate in this country centered on “how do we improve the system?” Unfortunately, too many providers want to sprint before they can crawl. Moving off of paper helps you crawl first; it is a valuable, achievable goal across that the majority of organizations burdened with manual processes and their costs and if done properly, the ROI can be realized in a short amount of time with manageable effort. Having said this, the question quickly then becomes, “are we prepared to do what it takes to actually make the system improve?” Are you?

Physicians Insist, Leave No Data Behind

“I want it all.” This sentiment is shared by nearly all of the clinicians we’ve met with, from the largest integrated health systems (IHS) to the smallest physician practices, in reference to what data they want access to once an aggregation solution like a data warehouse is implemented.  From discussions with organizations throughout the country and across care settings, we understand a problem that plagues many of these solutions: the disparity between what clinical users would like and what technical support staff can provide.

For instance, when building a Surgical Data Mart, an IHS can collect standard patient demographics from a number of its transactional systems.  When asked, “which ‘patient weight’ would you like to keep, the one from your OR system (Picis), your registration system (HBOC) or your EMR (Epic)?” and sure enough, the doctors will respond, “all 3”. Unfortunately, the doctors often do not consider the cost and effort associated with providing three versions of the same data element to end consumers before answering, “I want it all”.  And therein lies our theory for accommodating this request: Leave No Data Behind. In support of this principle, we are not alone.

By now you’ve all heard that Microsoft is making a play in healthcare with its Amalga platform. MS will continue its strategy of integrating expertise through acquisition and so far, it seems to be working. MS claims an advantage of Amalga is its ability to store and manage an infinite amount of data associated with a patient encounter, across care settings and over time, for a truly horizontal and vertical view of the patient experience. Simply put, No Data Left Behind.  The other major players (GE, Siemens, Google) are shoring up their offerings through partnerships that highlight the importance of access to and management of huge volumes of clinical and patient data.

Why is the concept of No Data Left Behind important? Clinicians have stated emphatically, “we do not know what questions we’ll be expected to answer in 3-5 years, either based on new quality initiatives or regulatory compliance, and therefore we’d like all the raw and unfiltered data we can get.” Additionally, the recent popularity of using clinical dashboards and alerts (or “interventional informatics”) in clinical settings further supports this claim. While alerts can be useful and help prevent errors, decrease cost and improve quality, studies suggest that the accuracy of alerts is critical for clinician acceptance; the type of alert and its placement and integration in the clinical workflow is also very important in determining its usefulness. As mentioned above, many organizations understand the need to accommodate the “I want it all” claim, but few combine this with expertise of the aggregation, presentation, and appropriate distribution of this information for improved decision making and tangible quality, compliance, and bottom-line impacts. Fortunately, there are a few of us who’ve witnessed and collaborated with institutions to help evolve from theory to strategy to solution.

Providers must formulate a strategy to capitalize on the mountains of data that will come once the healthcare industry figures out how to integrate technology across its outdated, paper-laden landscape.  Producers and payers must implement the proper technology and processes to consume this data via enterprise performance management front-ends so that the entire value chain becomes more seamless. The emphasis on data presentation (think BI, alerting, and predictive analytics) continues to dominate the headlines and budget requests. Healthcare institutions, though, understand these kinds of advanced analytics require the appropriate clinical and technical expertise for implementation. Organizations, now more than ever, are embarking on this journey. We’ve had the opportunity to help overcome the challenges of siloed systems, latent data, and an incomplete view of the patient experience to help institutions realize the promise of an EMR, the benefits of integrated data sets, and the decision making power of consolidated, timely reporting. None of these initiatives will be successful, though, with incomplete data sets; a successful enterprise data strategy, therefore, always embraces the principle of “No Data Left Behind”.