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Lou Agosta

Greetings and welcome to my blog focusing on reengineering healthcare using information technology. The commitment is to provide an engaging mixture of brainstorming, blue sky speculation and business intelligence vision with real world experiences – including those reported by you, the reader-participant – about what works and what doesn't in using healthcare information technology (HIT) to optimize consumer, provider and payer processes in healthcare. Keeping in mind that sometimes a scalpel, not a hammer, is the tool of choice, the approach is to be a stand for new possibilities in the face of entrenched mediocrity, to do so without tilting windmills and to follow the line of least resistance to getting the job done – a healthcare system that works for us all. So let me invite you to HIT me with your best shot at

About the author >

Lou Agosta is an independent industry analyst, specializing in data warehousing, data mining and data quality. A former industry analyst at Giga Information Group, Agosta has published extensively on industry trends in data warehousing, business and information technology. He is currently focusing on the challenge of transforming America’s healthcare system using information technology (HIT). He can be reached at

Editor's Note: More articles, resources, and events are available in Lou's BeyeNETWORK Expert Channel. Be sure to visit today!

Recently in Healthcare Information Technology (HIT) Category

The answer is clinical data warehousing, decision support, and analytics. What's the question? Wellpoint (one of the leading Blue Cross branded health insurance companies) is reportedly contracting to use IBM's computing grand challenge system nicknamed "Watson" (after IBM's founder) to address a list of clinical issues in medical diagnosis, treatment, and (potentially) cost. In the spirit of Jeopardy!, the question is will it advance in the direction of enabling comparative effectiveness research (CER) and pay for performance (P4P) while enhancing the quality of medical outcomes? Healthcare consumers tend to get a tad nervous when they suspect that insurance companies are going to deploy a new computer system as part of the physician payment approval process, nor (let us be clear) has anyone actually said that will happen in this case.

The diagnosis of a disease is part science, part intuition and artistry. The medical model trains doctors and healthcare specialists using an apprentice system (in addition, of course, to long schooling and lab work). The hierarchical nature of disease diagnosis has long invited automation using computers and databases. Early expert medical systems such as MYCIN at Stanford or CADUCEUS at Carnegie-Mellon University were initially modest sized arrays of if-then rules or semantic networks that grew explosively in resource consumption, time-to-manage, and cost and complexity of usability. They were compared in terms of accuracy and speed with the results generated by real world physicians. The matter of accountability and error was left to be worked out later. Early results were such that automated diagnoses was as much work, slower, and not significantly better - though the automation would occasionally be surprisingly "out of the box" with something no one else had imagined. One lessons learned? Any computer system is better managed and deployed like an automated co-pilot rather than a primary locus of decision making or responsibility.

Work has been ongoing at universities and research labs over the decades and new results are starting to emerge based on orders of magnitude improvements in computing power, reduced storage costs, ease of administration, and usability enhancements. The case in point is IBM's Watson, which has been programmed to handle significant aspects of natural language processing, play jeopardy (it beat the humans), and, as they say in the corporate world, other duties as assigned.

Watson generates and prunes back hypotheses in a way that simulates what human beings do in formulating a differential diagnoses. However, the computer system does so in an explicit, verbose, and even clunky way using massive parallel processing whereas the human expert distills the result out of experience, years of training, and unconscious pattern matching. Watson requires about eight refrigerator size cabinets for its hardware. The human brain still occupies a space about the size of a shoe box.

Still, the accomplishment is substantial. An initial application being considered is having Watson scan the vast medical literature on treatments and procedures to match evidence-based outcomes to individual persons or cohorts with the disease in question. This is where Waton's strengths in natural language processing, formulating hypotheses, and pruning them back based on confidence level calculations - the same strengths that enabled it to win at Jeopardy - come into play. In addition, oncology is a key initial target area because of the complexity of the underlying disorder as well as the sheer number of individual variables. Be ready for some surprises as Watson percolates up innovative approaches to treatment that are expensive and do not necessarily satisfy anyone's cost containment algorithm. Meanwhile, there are literally a million new medical articles published each year, though only a tiny fraction of them are relevant to any particular case. M.D.s are human beings and have been unable to "know everything" there is to know about a specialty for at least thirty years. In short,  Watson just could be the optimal technology for finding that elusive needle in a haystack - and doing so cost effectively.

A medical differential diagnosis in medicine is a set of hypotheses that subsequently have to be first exploded, pruned, and finally combined based on confidence and prior probability to yield an answer. This corresponds to the so-called Deep Question and Answering Architecture implemented in Watson. Within five years, similar technologies will have been licensed and migrated to clinical decision support systems from standard EMR/EHR vendors.

While your clinical data warehouse may not be running 3,000 Power 750 cores and terabytes of self-contained data in a physical footprint about the size of eight refrigerators, some key lessons learned are available even for a modest implementation of clinical data warehousing decision support:

  • Position the clinical data warehouse as a physician's assistant (think: co-pilot) to answer questions, provide a "sanity check," and fill in the gaps created by explosively growing treatments.
  • Plan on significant data preparation (and attention to data quality) to get data down to the level of granularity required to make a differential diagnoses. ICD-10 (currently mandated for 10/2013 but likely to slip), will help a lot, but may still have gaps.
  • Plan on significant data preparation (and more attention to data quality) to get data down to the level of granularity required to make a meaningful financial decision about the effectiveness of a given treatment or procedure. Pricing and cost data is dynamic, changing over time. New treatments start out expensive and become less costly. Time series pricing data will be critical path. ICD-10 (currently mandated for 10/2013 but likely to slip) will help but will need to be augmented significantly into new pricing data structures and even then but may still have gaps.
  • Often there is no one right answer in medicine - it is called a "differential diagnosis" - prefer systems that show the differential (few of them today do, though reportedly Watson can be so configured) and trace the logic at a high level for medical review.
  • Continue to lobby for tort and liability reform as computers are made part of the health care team, even in an assistant role. Legal issues may delay, but will not stop implementation in the service of better quality care.
  • Look to natural language interfaces to make the computing system a part of the health care team, but be prepared to work with a print out to a screen till then.
  • Advanced clinical decision support, rare in the market at this time, is like a resident in psychiatry, in that it learns from its right and wrong answers using machine learning technologies as well as "hard coded" answers from a database of semantic network.
  • This will take "before Google (BG)" and "after Google (AG)" in medical training to a new level. Watson-like systems will be available on a smart phone or tablet to residents and attendings at the bedside.

Finally, for the curious, the cost of the hardware and customized software for some 3,000 Power 750 cores (commercially available "off the shelf"), terabytes of data and including time and effort of a development team of some 25 people with Ph.D.s working for four years (the later being the real expense), my back of the envelope pricing (after all this is a blog post!) weighs in at least in the ball park of $100 million. This is probably low, but I am embarrassed to price it higher. This does not include the cost of preparing the videos and marketing. One final thought. The four year development time of this project is about the length of time to train a psychiatrist in a standard residency program.


  1. "Wellpoint's New Hire. What is Watson?" The Wall Street Journal. September 13, 2011.

  2. IBM: "The Science Behind and Answer":


Posted September 14, 2011 4:06 PM
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What happens when an irresistible force meets an immovable object? We are about to find out. The irresistible force of BI, eDiscovery, compliance, fraud detection, governance, risk management, and other analytic and regulatory mandates is heading straight toward the immovable rock of year-to-year 10% reductions in information technology budgets.


The convergence of the markets for structured and unstructured data has been heralded many times, but maybe the time has come. We think that the new generation of solutions with increasing overlap of structured and unstructured data and multi-functionality will emerge and that BMMsoft EDMT® Server is the pioneer in that space. Looking into the crystal ball, what will happen is that an increasing overlap already underway will disrupt incumbents across these diverse markets.

The world wide BI Market as defined by Gartner is sized at $8.8B.[1] Realistically that includes a lot of Business Objects (SAP), SAS applications, and IBM solutions so the database part of that is probably closer to $6 billion.[2] The document management software market is estimated at nearly $3 billion.[3] While email archiving is relatively new and growing rapidly due to near federal regulations, it has now reached the $1 billion "take off" point. In short, at nearly $10 billion total, a product that addressed requirements across all three of these markets with a reasonable prospect of response from even one third of the enterprises, would have an outside boundary of over $3 billion. This is a substantial market under any interpretation.


In the meantime, the exiting markets for these three classes of products is fragmented into silos of the traditional data warehousing vendors, email archiving, and document management, the latter sometimes including compliance and governance software. The first are well known in the market - extending from such stalwarts as HP, IBM, Oracle, Microsoft, SAP, to data warehousing appliances and column-oriented databases - and will not be repeated here (though one new developments will be noted below). Document management systems include IBM FileNet Business Process Manager (, EMC Documentum (, OpenText LiveLink ECM (, Autonomy Cardiff Liquid Office ( Strictly speaking, risk management is considered a separate market from document management. Risk management and compliance offerings include Aventis (, BWise (, Cura (, Protiviti (, Compliance 360 ( and IBM, which has at least two offerings one based on Lotus Notes and one based on FileNet. This list is partial and could easily be expanded with many best of breed offerings. The result? Fragmentation. Diversity, though not in a positive sense. Many offerings instead of a comprehensive approach to unified access and unified analysis.


Five years from now data will be as heterogeneous as ever and the uses of data even more so, but individual products - single instance products, not solutions - will characterize a transformed market for database management that traverses the boundaries between email archiving, document management, and data warehousing with agility that is only dreamt about in today's world. Video clips are now common on social networking sites such as Facebook and YouTube. Corporate sponsorship of such opportunities for viral marketing is becoming more common. The requirement to track and manage product brands and images will necessitate the archiving of such material, so multi-media (image/video/audio) are being added to the mix.


This future is being driven and realized by the imperative for business transparency, risk management and compliance, and growing regulatory requirements layered on top of existing business intelligence and document management requirements. Still, document management is distinct from workflow. If an enterprise needs workflow, then it will continue to require a special purpose document management system. Workflow was invented by FileNet in 1985, acquired by IBM in 2006, and continues to lead the pack where detailed step-by-step process engineering is required. Elaborate rules-engines for enterprise decision management are different than compliance. If an enterprise requires a rule-engine for compliance and governance, then it will need a special purpose compliance, risk management, and governance system. Such solutions would be over-kill for those enterprises that require email archiving for eDiscovery, document management for first order compliance, and cross references to transactional data in the data warehouse. While the future is uncertain, one of the vendors to watch is BMMsoft.


Innovation Happens

BMMsoft has put together a product delivering functionality across these three previously unrelated silos - data warehousing, eDiscovery (e-mail), and document management - and able to be purchased as a EDMT®Server - a single part number from BMMsoft (EDMT stands for "E-Mail, Documents, Media, Transactions"). The database "under the hood" is Sybase IQ, a column-oriented data store with a proven track record and several large objectively audited benchmarks. The latest of these weighs in at 1000 terabytes - a petabyte - and was audited by Francois Raab, the same professional who audits the benchmarks.[4]  The business need is real and based on customer acceptance. So is the product.


The three keys to connect and make intelligible the data from the three different sources are:


1) extreme scalability to handle the data volumes - this is where a column-oriented database would come in handy since the storage compaction is intrinsic and prior to the additional compression that could be applied;

2) parallel, real-time high performance ETL functionality to load all the data; and finally

3) search capabilities that enable high performance inquiries against the data.

Such unified access to diverse data types, intelligently connected by metadata, is also sometimes described as a "data mashup."


A part of the challenge that a start up - and up start - such as BMMsoft will face is building credibility, which BMMsoft has already solved with numerous client installations in production and success stories. In the case of BMMsoft EDMT® Server there is another consideration:  metadata is an underestimated and underdeveloped opportunity. Innovations in metadata that make possible many applications that require cross referencing emails, documents, and the transactional data. For example, fraud detection, threat identification, enhanced customer relations - all require navigating across the different data types. Metadata makes that possible. That is not an easy problem to solve; and BMMsoft has demonstrated significant progress with it. Second, the column-oriented database is intrinsically skinny in terms of data storage in comparison with the standard relational database, which continues to be challenged by database obesity. As data warehouses scale up, the cost of storage technology becomes a disproportionably large part of the price of the entire system. Note that for the column-oriented approach proportional cost savings come into view and are realized. Third, this also has significant performance implications, since if there is less data - in terms of volume points - to manage, then it is faster to do so. So when all the reasons are considered, the claims are quite modest, or at least in line with common sense. The wonder is that no one thought of it sooner.


When you think about it for a minute, there is every reason that an underlying database should be capable of storing a variety of different data types and doing so intelligently. The latter intelligence is the "secret sauce" that differentiates BMMsoft. The relationships between the different types of data are built as the data is being loaded by BMMsoft using multiple software technology patents.  The column-orientation of the underlying data store - Sybase IQ - intrinsically condenses the amount of space required to persist the information, yielding up to an order of magnitude - more typically a factor of two or three - in storage savings, even prior to the application of formal compression algorithms. This fights database obesity across all segments - email, document, media, transactional (structured) data warehousing information. This means that the application that lives off of the underlying data is able to take advantage of performance improvements since less data is being stored and more being fetched with every data retrieval. For those enterprises with a commitment to installed Oracle or MySQL infrastructure, BMMsoft provides investment protection. The EDMT® Server runs also on Oracle, Netezza and MySQL and can be easily ported to any other relational Database. 


Thus, BMMsoft is a triple threat and is able to function as a standalone product addressing data warehousing, email archiving, and document management requirements as separate silos. But just as importantly, for those enterprises that need or want to compete with advanced applications in fraud detection, security threat assessment, customer data mining beyond structured data, BMMsoft offers the infrastructure and application to do so. For example, the ability to perform cross-analysis between securities traded on the stock market and those companies named in email and voice mail (remember multimedia handling) will immediately provide a short list for follow up detection on on-going insider trading or other fraudulent scheme. While hindsight is 20-20, a similar method of identifying emerging patterns through cross-analysis would have been be useful in surfacing the 8 billion dollar Societe General fraud, Madoff's nonexistent options plays at the basis of the pyramid, the Georgia Tech shooter, and relevant chatter that shows up prior to terrorist attacks. Going forward, this technology is distinct in that it can be deployed on a small, medium, or large scale to highlight emerging hot spots that require attention.


One may object - but won't the competition be able to reverse engineer the functionality and provide something similar using different methods? Of course, eventually every innovation will be competitively attacked by some more-or-less effective "work around." Read the prospectus - new start ups and existing software laboratories at HP, IBM, etc. will eventually produce innovations that challenge the contender. However, that could require three to five years. Then there is the matter of bringing it to market. IBM provides an example, based on publicly available news reports. IBM went out and purchased FileNet for about $5 billion dollars. FileNet is a great company, which virtually invented workflow, and if one requires advanced workflow capabilities, it is hands down a good choice. However, it does not do data warehousing or email archiving. As a subsidiary of IBM which delivers substantial revenue to the "mother ship," the executives in charge will set a high bar on any IBM innovations which combine email archiving and structured data warehousing with document management. In short, IBM is faced with the classic innovator's dilemma.[5] The price points that interest it - both internally and externally - are further up on the curve than the deals that BMMsoft will be able to complete. Given that BMMsoft has established presence in the market, it has a good chance of marching up market, displacing the installed, legacy solutions as it goes. This happened before in the client server revolution when IBM mainframe deals at the several million dollar price point were undercut by a copy of PowerBuilder and a copy of Sybase, albeit a different version of the database. Given that BMMsoft has a head-start, it is exploiting first mover advantages and building an installed base that will be challenged only with great difficulty. The relevance of such technology in the context of healthcare information technology (HIT) will be explored in a pending post. Please stand by for update - and keep in touch!

[2] For an alternative point of view see an IDC forecast (published 2007) that pegs the Data Warehouse management/platforms market as approx $8.97B in 2010

[3] Cited in "Document Management Systems Market: 2007 - 2010,":



[5] Clayton Christensen, The Innovator's Dilemma. Cambridge, MA: Harvard Business School Press, 1998.

Posted August 9, 2010 8:31 AM
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Datawatch provides an ingenious solution to information management, integration, and synthesis by working from the outside inwards. Datawatch's Monarch technology reverse engineers the information in the text files that would otherwise be sent to be printed as a hardcopy, using the text file as input to drive further processing, aggregation, calculation, and transformation of data into usable information. The text files, PDFs, spreadsheets, and related printer input become new data sources. With no rekeying of data and no programming, business analysts have a new data source to build bridges between silos of data in previously disparate systems and attain new levels of data integration and cohesion.


datawatch (5).JPG

For those enterprises running an ERP system for back office billing such as SAP or a hospital information system (HIS) such as Meditech, the task of getting the data out of the system using proprietary SAP coding or native MUMPS data store can be a high bar, requiring custom coding. Datawatch intelligently zooms through the existing externalization of the data in the reports, making short work of opening up otherwise proprietary systems.


Note that a trade-off is implied here. If your reporting is a strong point, Datawatch can take an installation to the next level, enabling coordination and collaboration, breaking down barriers between reporting silos that were previously impossible to bridge and doing so with velocity. Programming is not needed, and the level of difficulty is comparable to that of managing an excel spreadsheet targeting a smart business analyst. However, if the reports are inaccurate or even junk, even Datawatch cannot spin the straw into gold. You will still have to fix the data at its source.


Naturally, cross functional report mining works well in most verticals extending from finance to retail, from manufacturing to media, from the public sector to not for profit organizations. However, what makes healthcare a particularly inviting target is the relatively late and still on-going adoption of data warehousing combined with the immediate need to report on numerous clinical, quality and financial metrics such as the pending "Meaningful Use" metrics created via the HITECH Act. This is not a tutorial on meaningful use; however, further details can be found in a related article entitled "Game on! Healthcare IT Proposed Criteria on 'Meaningful Use' Weigh in at 556 Pages" click here. One of the goals of "meaningful use" in HIT is to combine clinical information with financial data in order to drive improvements in quality care, patient safety and operational efficiency while simultaneously optimizing cost control and reduction. The use of report mining and integration of disparate sources also allow the healthcare industry to migrate towards a pay-for-performance model, whereby providers will be reimbursed based on the quality and efficiency of care provided. However, financial, quality, clinical metrics and the evolving P4P models all require cross functional reporting from multiple systems. Even for many modern hospital information systems (HIS) that is a high bar. For those enterprises without an enterprise-wide data warehousing solution, no one is proposing to wait three to five years for a multi-step installation prior to learning the needed data still requires customization. In the interim, Datawatch has a feasible approach worth investigating.


In conversations with Datawatch executives John Kitchen (SVP Marketing) and Tom Callahan (Healthcare Product Manager), I learned that Datawatch has more than 1,000 organizations in the healthcare sector using Datawatch technology. Datawatch is surely a well kept secret, at least up until now. This is a substantial resource for best practices, methods and models, and lessons learned in the healthcare area. Datawatch can leverage these resources to its advantage and the benefit of its clients. While this is not a recommendation to buy or sell any security (or product), as a publicly traded firm, Datawatch is well positioned to benefit as the healthcare market continues its expansion. Datawatch provides a compelling business case with favorable ROI from the time of installation to the delivery of problem-solving value for the end user client. The level of IT support required by Datawatch is minimal, and sophisticated client departments have sometimes gone directly to Datawatch to get the job done.


Let's end with a client success story in HIT. Michele Clark, Hospital Revenue Business Analyst, Los Angles based Good Samaritan Hospital, comments on the application of Datawatch's Monarch Pro: "We simply run certain reports from MEDITECH's scheduling module, containing data for surgeries already scheduled, by location, by surgeon. We then bring those reports into Monarch Pro. Then, in conjunction with its powerful calculated fields, Monarch allows us to report on room utilization, block time usage and estimated times for various surgical procedures. The flexibility of Monarch to integrate data from other sources results in a customized, consolidated dataset in Monarch. We can then analyze, filter and summarize the data in a variety of ways to maximize the efficiency of our operating room resources. Thanks to Monarch, we have dramatically improved the utilization of our operating rooms, can more easily match available surgeons with required upcoming procedures, and better manage surgeon time and resources. Our patients are receiving the outstanding standard of care they expect, while we make the most of our surgical resources. This kind of resource efficiency is talked about a lot in the healthcare community. With Monarch, we are achieving it."  This makes Datawatch one to watch.

Posted July 1, 2010 9:59 AM
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There are so many challenges that it is hard to know where to begin. For those providers (hospitals and large physician practices) that have already attained a basic degree of automation there is an obvious next step - performance improvement. For example, if an enterprise is operating eClinic Works (ECW) or similar run-your-provider EHR system, then it makes sense to take the next step and get one's hand on the actual levers and dials
that drive revenues and costs.

Hospitals (and physician practices) often do not understand their actual costs, so they are struggling to control and reduce the costs of providing care. They are unable to say with assurance what services are the most profitable, so they are unable to concentrate on increasing market share in those services. Often times when the billing system drives provider performance management, the data, which is adequate for collecting payments, is totally unsatisfactory for improving the cost-effective delivery of clinical services. If the billing system codes the admitting doctor as responsible for the revenue, and it is the attending physician or some other doctor who performs the surgery, then accurately tracking costs will be a hopeless data mess. The amount of revenue collected by the hospital may indeed be accurate overall; but the medical, clinical side of the house will have no idea how to manage the process or improve the actual delivery of medical procedures.

Thumbnail image for Thumbnail image for riverlogicjpg.JPG

Into this dynamic, enters River Logic's Integrated Delivery System (IDS) Planner ( The really innovative thing about the offering is that it models the causal relationship between activities,
resources, costs, revenues, and profits in the healthcare context. It takes what-if analyses to new levels, using its custom algorithms in the theory of constraints, delivering forecasts and analyses that show how to improve performance (i.e., revenue, as well as other key outcomes such as quality) based on the trade-offs between relevant system constraints. For example, at one hospital, the operating room was showing up as a constraint, limiting procedures and related revenues; however, careful examination of the data showed that the operating room was not being utilized between 1 PM and 3 PM. The  way to bust through this constraint was to charge less for the facility, thereby incenting physicians to use it at what was for them not an optimal time in comparison with golf or late lunches or siesta time. Of course, this is just an over-simplified tip of the iceberg.


IDS Planner enables physician-centric coordination, where costs, resources, and activities are tracked and assessed in terms of the workflow of the entire, integrated system. This creates a context of physician decision-making and its relationship to costs and revenues. Doctors appreciate the requirement to control costs, consistent with sustaining and improving quality, and they are eager to do so when shown the facts. When properly configured and implemented, IDS Planner delivers the facts. According to River Logic, this enabled the Institute for Musculosketal Health and Wellness at the Greenville Hospital System to improve profit  by more than $10M a year by identifying operational discrepancies, increase physician-generated revenue over $1,700 a month, and reduce accounts receivable by 62 down to 44 days (and still falling), which represents the top 1% of the industry.  Full disclosure: this success was made possible through a template approach with some upfront services that integrated the software with the upstream EHR system, solved rampant data quality issues, and obtained physician "buy in" by showing this constituency that the effort was win-win.

The underlying technology for IDS Planner is based on the Microsoft SQL Server (2008) database and Share Point for web-enabled information delivery.

In my opinion, there is no tool on the market today that does exactly what IDS Planner does in the areas of optimizing provider performance.River Logic's IDS Planner has marched ahead of the competition, including successfully getting the word out about its capabilities. The obvious question is for how long? The evidence is that this is a growth area based on the real and urgent needs of hospitals and large provider practices. There is no market unless there is competition; and an overview of the market indicates offerings
such as Mediware's InSight (, Dimensional Insight ( with a suite of the same name, Vantage Point HIS  ( once again with a product of the same name. It is easy to predict that sleeping giants such as Cognos (IBM) and Business Objects (SAP) and Hyperion (Oracle) are about to reposition the existing performance management capabilities of these products in the direction of healthcare providers. Microsoft is participating, though mostly from a data integration perspective (but that is another story), with its Amalga Life Science offering with a ProClarity frontend. It is a buyer talking point whether and how these offerings are able to furnish useable software algorithms that implement a robust approach to identifying and busting through performance constraints. In every case, all the usual disclaimers apply. Software is a proven method of improving productivity, but only if properly deployed and integrated into the enterprise so that professionals can work smarter. Finally, given market dynamics in this anemic economic recovery, for those end-user enterprises with budget, it is a buyer's market. Drive a hard bargain. Many sellers are hungry for it and are willing to go the extra mile in terms of extra training, services, or payment terms.

Posted April 5, 2010 11:33 AM
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Just as it is often true that some generals prepare to fight the last war, so too it is also the case that certification is getting ready to validate last generation's client-server technology. The famous example is the construction after World War I of the Maginot Line by France, a series of trench like fortifications in anticipation of trench warfare. Equally famous is (German) General Heinz Guderian's World War II blitzkrieg that went around (and over) the wall using tanks and aircraft, rendering the Maginot line obsolete. Innovations in cloud computing, the provisioning of web based EHRs (EMRs) and database appliances are the equivalent of an emerging technology blitzkrieg in HIT. While we should not represent different players in the market as enemies, large and established HIT vendors (see separately published research here for an overview) are likely to confront a looming innovator's dilemma.[1] In the 1980s IBM would not look at any sales under $1 million dollars and was nearly driven out of business by upstarts building systems with a copy of SQL Server and a copy of PowerBuilder priced one tenth the cost. The dominant HIS and PPMS vendors are positioning themselves to be the next candidates for the unwelcome category of "too big to fail," though this time in HIT, not banking. The recommendation? Stop them before they hurt themselves. Stop them by deleting the word 'certified'. 


The prospective buyers of EHR technology are physician practices (eligible professionals (EP)) and hospitals. They are best protected from the unprofessional practices of a small - very small - minority of system sales people through their own professional procurement practices, diligently reviewing written proposals, specifications, and contracts. It is unlikely to be the basis of a legal claim against a software vendor installed at a given site that failed to satisfy meaningful use criteria for provider that the software system or modules were certified. The provider receives the reimbursement (if any is forthcoming) and is responsible for attaining and demonstrating a use of the system that delivers healthcare services in a more effective way according to the criteria of meaningful use. From such a perspective, certification may be of value to the vendor but it furnishes a false sense of security to the prospective buyers (such as hospitals and eligible physicians).


On the one hand, some of the meaningful use criteria, including those that capture basic clinical transactions, are best delivered by an underlying system consisting of a standard relational database, a frontend query and reporting tool, and a data model that represents the patient demographics, diagnoses and procedures, and related clinical nomenclature. The user interface at which the meaningful use of the technology, including the capturing of data electronically, reporting of data electronically, and the manipulation of quality measures is the contract at which the meaningful use is delivered. Multimillion dollar HIS systems are candidates for "over kill" from a transactional perspective in addressing such use yet at the same time lack the necessary infrastructure to support quality metrics through aggregation and inquiry via a data warehouse function. 


On the other hand, some of the meaningful use criteria - in particular the requirement to report about 100 quality measures - are poorly accommodated by the vast majority of HIS system on the market today. I do not know of a single exception where the HIS system provides for the aggregation and analysis of metrics in a way similar to what business intelligence (now 'clinical intelligence') does in business verticals such as retail, finance, telecommunications. Once again, the user interface at which the quality measures are surfaced is (in effect) the contract, but it will prove impractical to generate such a result. If these systems propose to perform the aggregations of a year's worth of data based on transactional detail, the predictable result will be a long, slow process. Reinventing the wheel is hard work, but that is the path on which existing HIS and PPMS (EHR) systems have embarked. I hasten to add that the quality metrics are critical path and required. However, the path to the efficient and effective production of them does not lie through certification.


Those providers that are currently reporting quality metrics from a data warehouse that gets its data from the transactional EHR are concerned that they are out of compliance. It is widely reported that Intermountain Healthcare is one such enterprise.[2] Are they now supposed to certify their data warehouse? Where is the value-added in that?


A long list of clinical quality measure requires reporting a percentage of a total aggregate of a given diagnostic condition; for example, percentage of patients aged 50 years and older who received an influenza immunization during the flu season (PQR1 110 / NQF 0041). These are what other business operations such as retail or finance call "business intelligence" questions. In the context of healthcare, we might call them "clinical intelligence" - or just plain quality measures - but the function is similar - to make possible the tracking of improvements in the delivery of care. To manage enhancements by measuring outcomes in aggregate. The periodic calculation of some 94 percentages requires scanning the entire database and performing an analysis, review, and aggregation of a totality of the diagnostic data. Even though most records will not satisfy a given diagnosis - whether coronary artery disease of diabetes mellitus - they will have to be examined. The lessons of some two decades of computing in finance, telecommunications, retail, manufacturing, and fast food are clear, but not always  obvious to those medical professional who have spent their time in healthcare IT. When you attempt to perform business intelligence (BI) against the operational, transactional system, then something has got to give. Either the performance of the transactional system is brought to its knees or the BI process has to wait. In fact, system design for high performance transactions processing are poorly adapted to generate the results required to perform business intelligence. The difference is between update intensive operations and query intensive ones, between updating and scanning. That is why the data warehouse was invented - in order to collect and aggregate the data required for reporting in optimal format, allowing the transactional system to do its job supporting clinical process whereas clinical intelligence is used to guide process improvements.


The counter-argument that certification of the functionality around clinical data warehousing ought to be rolled up into the certification process is the reduction to absurdity of the process itself. There will always be some such functionality - if not data warehousing, then cloud computing, database appliances, artificial intelligence in clinical decision support, and so on. The list is limited only by our imagination and that of our fellow innovators. The Meaningful Use Proposal has got it basically right (though one can (and should) argue about details). The reporting of quality measures, based on aggregated clinical detail is a proven method in other disciplines of driving improvements in outcomes by surfacing, managing, and reducing variability from the quality norm. ('If you can't measure it, you can't manage it') However, this value is provided by implementing systems that directly address the superset of meaningful use criteria captured in the proposal, not by driving the process up stream into one particular system architecture that happens to have emerged in the early 1990s and gotten some significant traction.

To see the full Comment submitted to on the rule-making for reimburseable EHR investments go here: Certification _Lou Agosta's Feedback to Centers for Medicare and Medicaid Services, HHS.pdf

[1] Clayton Christensen, The Innovator's Dilemma. Boston: HBS press, 1999. Professor Christensen tells the story of how successful companies - across incredibly diverse and different industries - are single-mindedly motivated to listen only to their biggest and best customers (in general, not a bad thing to do), and are, thereby, blind to innovations that get a foothold at a price point down market and subsequently disrupt the standard market dynamics, leading to the demise of the 'big guys.' Note that IBM, mentioned in the next sentence, is one of the few companies to have succeeded in turning itself around, admittedly in a painful and costly process led by the now legendary Lou Gerstner, and to have brought itself back from the brink. Watch for this dynamic to continue in HIT, albeit at a slower pace due to the friction caused by large government participation and with results such as that at IBM being the rare exception.

[2] For example -

Posted February 18, 2010 7:07 AM
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