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.

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?

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.

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?

Why creating actionable information from your existing systems is so difficult

With all the easy to use business intelligence tools and technology we have today, why is it so difficult to create actionable information from the wealth of data in our organizations?

One needs to understand, at a high level, the systems we have built and how they got that way. Your core business systems have evolved over time, budget cycle by budget cycle with no eye towards the overall enterprise. Systems were built to support core business functions – Payroll/HR, General Ledger, Inventory, etc. They were transactional in nature; designed to meet the immediate requirements (e.g. cut payroll checks, track inventory, manage an assembly line, etc.) which did not include getting business intelligence out. Over time these systems became islands of data, popularly known as silos.

Add the fact that silos are structured differently and common data like product and customer is typically not standardized, answering questions across silos is difficult and labor intensive.

As these systems matured, the owners of each silo had departmental Business Intelligence needs. So as budget became available they added a data warehouse or data mart on top of their silo and created something like this.

The result is larger silos with larger sunk investment and still no ability to provide enterprise answers or actionable information. This approach worked for immediate departmental BI needs but if the business asks a question from data that resides in two or more of the silos, getting the answer usually involves a significant IT effort. By the time IT responds the business has gone onto a different question. The business analyst starts gluing spreadsheets together to provide some insight kicking off the next activity in the BI food chain – manual analytics.