Security Requirements for Electronic Health Records Redux

Click here for a commercial white paper on the title subject from the vendor Symantec. Although much of its content appears in earlier posts to this blog, this white paper presents a good summary of today's conventional wisdom on the subject.

Counter views abound, however. For example, many believe that there is no such thing as cybersecurity. That’s because no system can be 100% secure. There is no uncrackable code.

Wikileaks and EHR Security

As far as the leaked US cables are concerned, the fury of the US administration and of certain US politicians was, for a time, positively comical. It stopped being funny when they began talking about prosecuting Julian Assange for "espionage", given the draconian penalties that a conviction would carry. But the State Department's indignation over the leaks of allegedly valuable secrets was, and remains, preposterous.

Why? Because there is absolutely no way that a huge database containing 250,000 "secret" documents that can be lawfully accessed by more than a million officials can ever be secure. Any security engineer will tell you that it cannot be done: if you want to keep things secret online then the only way to do it is by compartmentalizing the system. Huge, monolithic computer systems are intrinsically insecure.

So, I believe that what is true of Wikileaks is true of Electronic Health Records (EHR) in so far as security (confidentiality) is concerned. Actually, as any reader of his or her hometown newspaper or local TV news knows, all computer systems are potentially insecure. Caveat emptor.

The Promise of Information Technology in Electronic Health Records

This blog is written by and largely viewed by IT professionals, 5,217 visits from 106 countries according to Google Analytics at last count. While the [technical] subject matter herein applies to many fields, its application to electronic heath records (EHR) has been a large part of my focus.

So, to add balance to this blog, I've provided below several links to material written by medical professionals within the healthcare industry who are concerned with these same subjects.

The Promise of Information Technology in Electronic Health Records (EHR)

Information Technology Tools to Support Best Practices in Health Care

Microsoft HealthVault Platform

Electronic Medical Records (EMR) and The Prospect of Real-Time Evidence Development

Executive Summary

Evidence-Based Medicine and the Changing Nature of Healthcare

Decision Makers Are Not Always "Insiders"

Over the past year or so, this blog has bandied about terms like interoperability, open-source, disambiguation, security and databases. All of this has been from the points of view shared by most "insiders" concerned with the introduction of electronic health records (EHR) systems into their local, regional or even national computer networks. I'm talking about individuals (including me) who typically follow other blogs like http://i2b2-zak.blogspot.com and http://geekdoctor.blogspot.com.

However, there are many more individuals who follow (and whose thinking is influenced by) publications like The Wall Street Journal and The New York Times. What they read is reports like "In a paper published last year, Alessandro Acquisti and Ralph Gross (two researchers from Carnegie Mellon University) reported that they could accurately predict the full, nine-digit Social Security numbers for 8.5 percent of the people born in the United States between 1989 and 2003 — nearly five million individuals." that I believe are sometimes more likely to influence their thinking than are the reports that you and I read in the blogs (and other publications) written by "insiders." So, with this last thought in mind, I place the following links to a few recent articles read by many of the decision makers out there.

http://www.nytimes.com/2009/11/16/business/16records.html?_r=1&scp=4&sq=electronic%20health%20records&st=cse

http://www.nytimes.com/2010/03/17/technology/17privacy.html

http://www.nytimes.com/2010/04/13/opinion/l13privacy.html

http://online.wsj.com/article/SB10001424052748704259304575043572008622004.html

http://online.wsj.com/article/SB10001424052748703625304575116512173339800.html

http://online.wsj.com/article/SB10001424052748703580904575132111888664060.html?KEYWORDS=electronic+health+records

This is not meant to be a representative sample. Just a reminder that you and I may or may not be speaking the same language as the general public, which counts among its numbers many high-ranking decision makers. So, what else is new?

Costs and Benefits of a Unique Patient Identifier for The U.S. Health Care System

In the healthcare industry, misidentification errors are not restricted to diagnostics and therapeutics but also may affect documentation. So, my earlier posts on semantics, ontologies, interoperability and the like notwithstanding, all is for naught when a given document doesn't provide information about a given patient. A chain is only as strong as its weakest link and patient identification is usually the first link in the healthcare chain.

Complicating the issue, not everybody can participate to the same degree or in the same way in the process of identifying a patient uniquely. Neonatal and senile patients are two groups where health providers and technology are on their own, when it comes to identifying the patient. Naturally, readers of this post fall into neither of these groups.

See, for example, Patient Misidentification in the Neonatal Intensive Care Unit: Quantification of Risk at

http://pediatrics.aappublications.org/cgi/reprint/117/1/e43.pdf

which provides a rather thorough study of errors in the first of these three groups.

The information that is used routinely for patient identification is frequently similar but often not recognizably unique.

In my November 20, 2009 post, Biometric and Other Identification Technologies, I discuss some leading technologies.

Although widely touted as “great” in security circles, all biometric devices (i.e., fingerprint, palm outline, iris, retina, et al) used for unique identification produce false positives and false negatives.



For example, an episode of Fox's "24" last season showed a White House visitor placing her thumb on a fingerprint scanner, a type of screening that is not typically used at the White House.

Fingerprint: false positives or negatives with scars, calluses, cracks in the skin, dirt, household cleaners and other variables.



Retina scan: susceptible to diseases such as glaucoma.



At the same time, non-biometric technologies have their own sources of error.



For a widely discussed examination of the costs and benefits of a unique patient identifier for the U.S. health care system, see

http://www.rand.org/pubs/monographs/2008/RAND_MG753.pdf

This recent study says using unique patient identification numbers for U.S. citizens would reduce medical errors, make electronic health records simpler and protect privacy.

The study says that despite a potential cost of $11 billion to create unique patient ID numbers, the effort "would likely return even more in benefits to the nation's health care system."

Most health care systems use statistical matching to find EHRs, according to the study by RAND Health, a research division of the RAND Corp. Statistical matching looks for demographic information, including names, birth dates and all or part of Social Security numbers.

See my November 17, 2009 post, Unique Patient Identification Numbers, Electronic Heath Records (EHR), Electronic Medical Records (EMR), and Social Security Numbers (SSN).

RAND researchers, who reviewed past studies, said that method causes errors or incomplete results about 8% of the time and leaves patients more exposed to privacy breaches.

"Assuming every health care system would have these [ID] numbers, then you'd be more likely to pick up all of the person's information," said Richard Hillestad, PhD, the study's lead author. "It would certainly make a lot of things easier."Using demographic information to locate EHRs causes errors or incomplete results about 8% of the time.

But critics expressed concerns.

"It's an absolutely terrible idea," said Deborah Peel, MD, a psychiatrist and chair of the Patient Privacy Rights Foundation, a watchdog group based in Austin, Texas. "Any database that has these numbers is bound to be a treasure trove for identity thieves."

The study was funded by a group of health information technology and IT companies, but Hillestad said that didn't influence the outcome. Dr. Peel is skeptical. "The combination [of data] is really deadly," she said. "That's why I say this is a data miner's dream."

The American Medical Association advocates prohibiting the sale and exchange of personally identifiable health information for commercial purposes without a patient's consent. The AMA also advocated in 1999 in favor of legislative action to repeal the portion of the Health Insurance Portability and Accountability Act of 1996 that mandated use of a unique patient identifier.

Hillestad said privacy is a big issue, but touted the ID numbers as a security boost.

"You're not sending all of the name and demographic information through the line to get connected," he said. "[Privacy] would depend on how much you protect the numbers."

Watson - An efficient access point to online ontologies - A gateway to the Semantic Web

Next generation semantic applications will be characterized by a large number of sometimes widely-distributed ontologies, some of them constantly evolving. That is, many next-generation semantic applications will rely on ontologies embedded in a network of already existing ontologies. Other semantic applications – e.g. some electronic health records (EHR) – will maintain a single, globally consistent semantic model that serves the needs of application developers and fully integrates a number of pre-existing ontologies.

As the Semantic Web gains momentum, more and more semantic data is becoming available online. Semantic Web applications need an efficient access point to this Semantic Web data. Watson, the main focus of this post, provides such a gateway. Two limited demonstrations of Watson - one video, the other static - are given below.

Overview of Watson Functionalities

The role of a gateway to the Semantic Web is to provide an efficient access point to online ontologies and semantic data. Therefore, such a gateway plays three main roles:

(1) it collects the available semantic content on the Web
(2) analyzes it to extract useful metadata and indexes, and
(3) implements efficient query facilities to access the data.

Watson provides a variety of access mechanisms, both for human users and software programs. The combination of mechanisms for searching semantic documents (keyword search), retrieving metadata about these documents and querying their content (e.g., through SPARQL) provides all the necessary elements for applications to select and exploit online semantic resources in a lightweight fashion, without having to download the corresponding ontologies.

For a easy-to-follow video demonstration of The Watson plug-in for the NeOn toolkit, click on

http://videolectures.net/iswc07_daquin_watson/

and, better still, click one of the Media Player links at this destination.

Note: There is a Watson plug-in for the ontology editor Protégé in the works.

Protégé (see my August 24 post below) is probably the most popular ontology editor available. In addition, its well established plug-in system facilitates the development of a plug-in using the Waston Web Services and API. To date, however, the Protégé site provides only what it describes as, “more a proof of concept or an example than a real plug-in.”

NeOn Toolkit

The NeOn architechture for ontology management supports the next generation semantics-based applications. The NeOn architecture is designed in an open and modular way and includes infrastructure services such as a registry and a repository and supports distributed components for ontology development, reasoning and collaboration in networked environments.

The NeOn toolkit, the reference implementation of the NeOn architechture, is based on the Eclipse infrastructure.

Ontology Management
Semantic Web, Semantic Web Services, and Business Applications
Copyright 2008 Springer

A static demonstration of the Watson plug-in for the NeOn toolkit

The Watson plug-in allows the user to select entities of the currently edited ontology he/she would like to inspect, and to automatically trigger queries to Watson, as a remote Web service. Results of these queries, i.e. semantic descriptions of the selected entities in online ontologies, are displayed in an additional view allowing further interactions. The figure below provides an example, where the user has selected the concept “human” and triggered a Watson search. The view on the right provides the query results (a list of definitions of class human which have been found on the Semantic Web) and allows easy integration of the results by simply clicking on one of the different “add”-buttons.

Finally, the core of the plug-in is the component that interacts with the core of the NeOn toolkit: its datamodel. Statements retrieved by Watson from external ontologies can be integrated in the edited ontology, requiring for the plug-in to extend this ontology through the NeOn toolkit datamodel and data management component.

{click on the image above for a larger view}

An interesting exercise:

Follow the link http://watson.kmi.open.ac.uk/WatsonWUI/

And search on "snomed"

And, this dynamic view is what you get after clicking the (view as graph) link.

Semantic Interoperability, EHR, etc. are of little use to someone whose claim is denied by his or her insurance company

Founded in 1945, Kaiser Permanente is this nation’s largest not-for-profit health plan, serving more than 8.6 million members, with headquarters in Oakland, California.

This blog has carried a prominently-placed electronic health records (EHR) video outlining some of the excellent information technology work that's being introduced by Kaiser Permanente.

So, given the placement of this video, I feel a responsibility to add here the reality that Kaiser Permanente's technology is only one facet of a system that daily makes decisions about who can and who cannot get health care.

The California Nurses Association/National Nurses Organizing Committee has just released new data that reveals more than one of every five requests for medical claims for insured patients, even when recommended by a patient’s physician, are rejected by California’s largest private insurers. (The Kaiser Permanente Health Plan membership in California is greater than 6 million.)

This is data that the health insurance companies have wanted to hide, and it’s just now becoming available. It documents that these insurance companies have denied, in California alone, 45 million claims since 2002. Some of these rates ranged as high as 40 percent (for UnitedHealthcare’s PacifiCare). And other large, giant insurers like Blue Cross, Health Net, CIGNA, and Kaiser were all in the range of 30 percent (Kaiser Permanente's denial rates is 28 percent). This report shows a clear pattern of very high denials by the very insurance companies that people depend upon to assure that they get the care they need when they need it.

There are a variety of reasons insurance companies claim why they make these denials: in the end though, it’s a war that goes on between the insurance companies and the doctors and the hospitals. (Note: Attorney General of California Jerry Brown has announced he’s going to conduct an investigation into the business practices of these companies and why these denial rates are so high.)

A recent piece in the Los Angeles Times quotes a spokeswoman for the California Association of Health Plans, responding to the data that the California Nurses Association/National Nurses Organizing Committee has just released, saying, “It appears [that] a good deal of the so-called denials are merely paperwork issues.”

It seems to me that even if you put the best face on the California Association of Health Plans' response, what it demonstrates is how much waste (aka administrative overhead) there is in the health insurance industry. It's been suspected for some time now that one-third of every healthcare dollar goes to waste and to enforcing claims denials in the United States..

Ontologies, Ontology Languages, and Semantic Interoperability -- Segue to Electronic Health Records (EHR)

This post serves as a transition between my previous one on OWL and ontologies and my upcoming ones on electronic health records (EHR) - past, present and future.

OWL is the latest standard in ontology languages from the World Wide Web Consortium (W3C) - it is built on top of RDF (i.e., OWL semantically extends RDF).

These two languages are explained in

http://www.co-ode.org/resources/tutorials/intro/slides/OWLFoundationsSlides.pdf

As an example of an ontology that's central to building an interoperable EHR system, I'll cite the Systematized Nomenclature of Medicine (SNOMED) ontology, which is used in more than 50 countries around the world:



{click to enlarge}

The Systematized Nomenclature of Medicine Clinical Terms (SNOMED CT) Ontology includes a Core terminology of over 364,000 health care concepts with unique meanings and formal logic—based definitions organized into hierarchies. As of January 2005, the fully populated table with unique descriptions for each concept contained more than 984,000 descriptions. Approximately 1.45 million semantic relationships exist to enable reliability and consistency of data retrieval. SNOMED CT is available in English, Spanish and German language editions.

What is its structure?

Core content includes the concepts table, descriptions table, relationships table, history table, an ICD-9-CM mapping, and the Technical Reference Guide. And, it can map to other medical terminologies and classification systems already in use.

SNOMED is meant to be complementary to LOINC (Logical Observations Identifiers, Names, Codes), another clinical terminology important for laboratory test orders and results.

See http://www.ihtsdo.org/ for a good deal more information on SNOMED.

For advanced IT readers

The DL that SNOMED uses is much less expressive than OWL. The result is, even though you can mechanically translate SNOMED into OWL, the resulting OWL ontology will be very unlike anything an OWL author would create starting from scratch, and might also be a challenge to classify successfully under an OWL reasoner without a lot of manual editing.

Furthermore, even at that point it would also be of limited value in supporting reasoning over OWL instances, as many kinds of assertions that would be routine in an OWL ontology (like disjoints, explicit domain/range constraints, etc.) do not exist in native SNOMED and would have to be created.

One opinion holds that creating your own OWL ontology and using SNOMED as a mapping target leverages SNOMED in a more useful way for most conceivable applications.

Finally, for a good deal more on the topics covered so far, consider

Clinical Decision Support Systems
Theory and Practice
Series: Health Informatics
Berner, Eta S. (Ed.)
2nd ed., 2007
ISBN: 978-0-387-33914-6

Semantic Interoperability -- Part III Ontologies -- Prelude to Electronic Health Records (EHR)

This post is a continuation of the introduction to ontologies that I posted on August 4 and July 20.

Readers new to this subject might also find the 2 ½ minute video What Is Web 3.0, Anyway? worthwhile.

An ontology is an explicit specification of a conceptualization (defined earlier), that is to say, a formal representation of a knowledge domain. Usually an ontology consists of: (i) classes, which represent the concepts of the domain (for example, in an ontology about the domain of Telecommunications, as in the listing below, a possible concept could be "Phone"); (ii) properties, to establish relationships between the concepts (for example, a "Phone" concept could have as property the "Company"; (iii) instances, with concrete examples associated with every concept (for example, "Siemens" could be an instance of the "Company” concept); and (iv) axioms, which are restrictions applicable to certain elements of the ontology, necessary to specify completely the knowledge domain (for example, in the ontology about telecommunications, it could define a restriction to indicate that in this domain a "Phone" must have always, at least, a "Company").

Ontologies can be stored using XML-based markup languages such as OWL (Ontology Web Language), which facilitates their reuse in different semantic platforms to annotate and search resources. These languages allow us to define tags in order to represent the different ontology elements. The listing below shows an extract of a OWL file containing the Telecommunications example ontology that has been created using the Protegé tool. As you can observe, in this language, the concepts are delimited by the Class tag, the properties by the ObjectProperty tag, the instances by the tag corresponding to the associate class (in the example, the class Company has as instance "Siemens"), and the axioms with tags like Restriction or subClassOf (this one is used in the example for representing that "Cellphone" is a type of "Phone").



Content of a OWL file - a fragment of an
Ontology about Telecommunications
{click to enlarge}

Today one of the main uses of ontologies is to support the Semantic Web (aka Web 3.0), especially for annotating Web resources and facilitating the localization of these annotated resources when users formulate queries to semantic search engines. For this purpose, in the previous example of Telecommunications, an ontology has included two annotations as instances of the “Phone” and “Cellphone” classes which correspond to two documents (“Gigaset3015Classic.pdf” and “MobileC55.pdf”, respectively) located in a hypothetical Web server (“http://www.telecosiemens.com”).

The Reality

Researchers have written much about the potential benefits of using ontologies, and most of us regard them as central building blocks of the Semantic Web and other semantic systems. Unfortunately, the number and quality of actual, “non-toy” ontologies available on the Web today is remarkably low. This implies that the Semantic Web community has yet to build practically useful ontologies for a lot of relevant domains in order to make the Semantic Web a reality.

In striking contrast to the data within a stand-alone document, publications have yet to benefit from the opportunities offered by cyber infrastructure. While the means of distributing publications has vastly improved, publishers have done little else to capitalize on the electronic medium. In particular, semantic information describing the content of these publications is generally sorely lacking, as is the integration of this information with data in public repositories.

The Reasoner

One of the key features of ontologies is that they can be processed by a reasoner. One of the main services offered by a reasoner is to test whether or not one class is a subclass of another class. By performing such tests on all of the classes in an ontology it is possible for a reasoner to compute the inferred ontology class hierarchy. Another standard service that is offered by reasoners is consistency checking. Based on the description (conditions) of a class, the reasoner can check whether or not it is possible for the class to have any instances. A class is deemed to be inconsistent if it cannot possibly have any instances.

Reasoning with Protégé 4.0

Reasoning with your ontology is one of the most commonly performed activities and the ontology editor Protege 4.0 comes with 2 built-in reasoners, FaCT++ and Pellet. To classify your ontology, open the Reasoner menu and select one of the available reasoners. FaCT++ will automatically classify your ontology. Pellet requires that you select classify. Once you have done this, the class hierarchy on the Entites tab changes to show the inferred class hierarchy. Unsatisfiable classes appear in red under Nothing, and everything else appears in the hierarchy under their inferred superclasses. The asserted class hierarchy is still available, stacked under the asserted one, as shown in the next screenshot.



Screenshot of an inferred class hierarchy
{click to enlarge}

Instructions for getting started with the OWL editor in Protege 4:
http://protegewiki.stanford.edu/index.php/Protege4GettingStarted

A Practical Guide To Building OWL Ontologies:
http://www.co-ode.org/resources/tutorials/ProtegeOWLTutorial-p4.0.pdf

The Microsoft Word Add-in For Ontology Recognition – An Introduction

There are other tools (e.g., The Microsoft Word Add-in For Ontology Recognition to name one) that might be suitable for your needs. It’s a MS Word 2007 add-in that enables the annotation of Word documents based on terms that appear in ontologies.

This Word Add-in For Ontology Recognition is a free Microsoft download. With it, as shown in the figures below, you select and then download one or more ontologies which are thereafter available automatically from within your Word document.

The Microsoft Word Add-in For Ontology Recognition – An Overview

This add-in enables authors who use Microsoft Word for content creation to incorporate semantic knowledge into the content. This add-in should simplify the development and validation of ontologies, by making ontologies more accessible to a wide audience of authors and by enabling semantic content to be integrated in the authoring experience, capturing the author’s intent and knowledge at the source, and facilitating downstream discoverability.

The goal of the add-in is to assist authors in writing a manuscript that is easily integrated with existing and pending electronic resources. The major aims of this project are to add semantic information as XML mark-up to the manuscript using ontologies and controlled vocabularies (from the National Center for Biomedical Ontology) and identifiers from major biological databases, and to integrate manuscript content with existing public data repositories.

As part of the publishing workflow and archiving process, the terms added by the add-in, providing the semantic information, can be extracted from Word files, as they are stored as custom XML tags as part of the content. The semantic knowledge can then be preserved as the documented is converted to other formats, such as HTML or the XML format from the National Library of Medicine, which is commonly used for archiving.

The full benefit of semantic-rich content will result from an end-to-end approach to the preservation of semantics and metadata through the publishing pipeline, starting with capturing knowledge from the subject experts, the authors, and enabling this knowledge to be preserved when published, as well as made available to search engines and presented to people consuming the content.

The Microsoft Word Add-in For Ontology Recognition – Screen Shots

{click to enlarge}

The Word Add-in For Ontology Recognition User’s Guide for the Semantic Mark-up and XML Formatting of Scholarly Articles is a good place to start for further information on this tool.

Semantic Tagging

When a word or set of words is tagged by the add-in, the word is wrapped with some tags that associate it with the ontology term. The example below shows the word "disease" being tagged with Human Disease ontology.

{click to enlarge}

If the Word file (docx) is to be transformed to other formats, this set of tags would need to be processed using xslt or other technologies. Note that there are other CodePlex projects available which implement transformations of docx files to other formats, which one can start from.

Ontology Add-in for Microsoft Office Word 2007 Video

Electronic Health Records (EHR) – Semantic Interoperability – Part 2

Before discussing the connection(s) between electronic health records (EHR) and semantic interoperability directly, I’d like to spend a little time talking about Semantic Web interoperability (not necessarily the same thing as the interoperability of present and future EHS systems).

In general, semantics is the study of meaning. Semantic Web (also called Web 3.0) technologies help separate meanings from data, document content, or application code, using technologies based on open standards. If a computer understands the semantics of a document, it doesn't just interpret the series of characters that make up that document: it understands the document's meaning. See my July 20 post for a brief introduction to this material.



Benefits of the Semantic Web to the World Wide Web

The World Wide Web is the biggest repository of information ever created, with growing contents in various languages and fields of knowledge. Search engines might help you find content containing specific words, but that content might not be exactly what you want. What is lacking? The search is based on the contents of pages and not the semantic meaning of the page's contents or information about the page.

Once the Semantic Web exists, it can provide the ability to tag all content on the Web, describe what each piece of information is about and give semantic meaning to the content item. Thus, search engines become more effective than they are now, and users can find the precise information they are hunting. Organizations that provide various services can tag those services with meaning (service-oriented architectures -- SOA -- are discussed in my June 20 post and mentioned again below, this time in the context of semantics); using Web-based software agents, you can dynamically find these services on the fly and use them to your benefit or in collaboration with other services (See http://www.oracle.com/technology/pub/articles/matjaz_bpel1.html for a discussion of the orchestration and choreography of Web services).

Ontologies

The use of words to refer to concepts (the meanings of the words used) is very sensitive to the context and the purpose of these words.

An ontology is a formal representation of a set of concepts within a domain and the relationships between those concepts. It may be used to reason about the properties of that domain, and may be used to define the domain. The word “reason” is an important part of this story and will come up again in a later post, when I talk about Protégé, a free, open source ontology editor and knowledge-base framework.

A domain ontology (or domain-specific ontology) models a specific domain, or part of the world (e.g., healthcare, banking or politics). It represents the particular meanings of terms as they apply to that domain. For example the word card has many different meanings. An ontology about the domain of poker would model the "playing card" meaning of the word, while an ontology about the domain of computer hardware would model the "video card" meaning.

For each domain of human knowledge, an ontology must be constructed, partly by hand and partly with the aid of dialog-driven ontology construction tools (to be discussed in an upcoming post).

Ontologies are not knowledge nor are they information. They are meta-information. In other words, ontologies are information about information. In the context of the Semantic Web, they encode, using an ontology language (to be discussed in an upcoming post), the relationships between the various terms within the information. Those relationships, which may be thought of as the axioms (basic assumptions), together with the rules governing the inference process, both enable as well as constrain the interpretation (and well-formed use) of those terms by the Info Agents (to be discussed in an upcoming post) to reason new conclusions based on existing information, i.e. to think. In other words, theorems (formal deductive propositions that are provable based on the axioms and the rules of inference) may be generated by the software, thus allowing formal deductive reasoning at the machine level. And given that an ontology, as described here, is a statement of Logic Theory, two or more independent Info Agents processing the same domain-specific ontology will be able to collaborate and deduce an answer to a query, without being driven by the same software.

When an organization adapts an ontology-driven approach, it can capture and represent its total knowledge in a language-neutral form and deploy the knowledge in a central repository that provides the same semantic meaning across applications.

Semantics are the future of service-oriented integration

To properly model and manage a service-oriented architecture (SOA), enterprise architects must maintain active representations of the services available to the enterprise. Specifically, to discover and organize their services, the architects must use best practices that model and assemble services using metadata, encapsulate business logic in metadata for dynamic binding, and manage with metadata. Ontologies provide a very powerful and flexible way to aggregate, visualize, and normalize this service metadata layer.

Note: When delivering services as part of a service inventory, there is a constant risk that services will be created with overlapping functional boundaries, making it difficult to enable wide-spread reuse. Normalization addresses this problem. When services are delivered with complementary and well-aligned boundaries, normalization across the inventory is attained. Note also how the quantity of required services is reduced.

Semantic technologies provide an abstraction layer above existing IT technologies, one that enables the bridging and interconnection of data, content, and processes across business and IT silos.

For advanced IT readers

For a rather technical discussions of this topic, see the video Artificial Neural Network based Techniques for Semantic Data Interoperability below and parts of my article Using Neural Networks and OLAP Tools to Make Business Decisions to which there is a link in the bibliography at the very bottom of this blog.



To be continued …

Electronic Health Records (EHR) - Semantic Interoperability - Part 1

To press a suit means one thing to a tailor and another thing to a lawyer.

A free radical means one thing to a chemist but meant another thing to members of the House Un-American Activities Committee (HUAC) during the 1950’s.

And, medication for pain and pain medication don’t always mean the same thing. The controversy surrounding the recent death of Michael Jackson illustrates this last point.

In the examples above, as in clinical terminology, words can take on different meanings depending on factors like time or place (i.e., context).

Furthermore, clinicians and organizations use different clinical terms that mean the same thing. For example, the terms heart attack, myocardial infarction, and MI may mean the same thing to a cardiologist, but, to a computer, they are all different. There is a need to exchange clinical information consistently between different health care providers, care settings, researchers and others (semantic interoperability), and because medical information is recorded differently from place to place (on paper or electronically), a comprehensive, unified medical terminology system is needed as part of the information infrastructure.

Interoperability

Interoperability is the ability of two parties, either human or machine, to exchange data or information.

First, syntactic interoperability guarantees the exchange of the structure of the data, but carries no assurance that the meaning will be interpreted identically by all parties. Web pages built with HTML or XML are good examples of machine-to-machine syntactic interoperability because a properly structured page can be read by any machine with a Web browser. The meaning of the page to a particular machine may vary substantially; however, this is not usually considered a problem because the semantics of a page are meant to be interpreted by human viewers.

Next, human or semantic interoperability guarantees that the meaning of a structure is unambiguously exchanged between humans. Documents such as progress notes, referrals, consults, and others achieve semantic interoperability at a clinician-to-clinician level by relying on common medical vocabularies.

Finally, computable semantic interoperability requires that the meaning of data be unambiguously exchanged from machine to machine (as shown in the figure below). This does not necessarily mean that all machines need to process the received data the same way, but rather that each machine will make its processing decisions based on the same meaning.



Words and Meanings

The meanings of words change, sometimes rapidly. But a formal language such as used in an ontology -- a rigorous and exhaustive organization of some knowledge domain that is usually hierarchical and contains all the relevant entities and their relations -- can encode the meanings (semantics) of concepts in a form that does not change. In order to determine what is the meaning of a particular word (or term in a database, for example), it is necessary to label each fixed concept representation in an ontology with the word(s) or term(s) that may refer to that concept.

When multiple words refer to the same (fixed) concept, in language this is called synonymy; when one word is used to refer to more than one concept, that is called ambiguity. Ambiguity and synonymy are among the factors that make computer understanding of language very difficult. The use of words to refer to concepts (the meanings of the words used) is very sensitive to the context and the purpose of any use for many human-readable terms.

The use of ontologies in supporting semantic interoperability is to provide a fixed set of concepts whose meanings and relations are stable and can be agreed to by users. When a word used in some interoperability context changes its meaning, then to preserve interoperability it is necessary to change the pointer to the ontology element(s) that specifies the meaning of that word.

There are a number of tools for the programmatic handling (i.e., creating, querying, etc.) of ontologies. The visual representation of ontologies is an important contribution of these tools.



IBM Integrated ontology Development Toolkit (formerly named IBM Semantics Toolkit) is one of many toolkits designed for storage, manipulation, query, and inference of ontologies and corresponding instances.

An upcoming post will discuss the role and value of semantic technology in service-oriented architectures (SOA).

Cyberattacks Can Harm And Website Monitoring Can Benefit Electronic Health Records (EHR)

Cyberattacks have crippled the Web sites of several major American and South Korean government agencies since the July 4th holiday (U.S.) weekend.

The Washington Post , which also came under attack, reported on its Web site today that a total of 26 Web sites were targeted. In addition to sites run by government agencies, several commercial Web sites were also attacked, including those operated by Nasdaq, it reported, citing researchers involved in the investigation.

Authorities suspected that the hackers used a new variant of the denial-of-service (DoS) program to attack the Web sites. A denial-of-service attack is one in which an attack, sometimes from a single source, overwhelms a target computer with messages, denying access to legitimate users without actually having to compromise the targeted computer. Although frequently intentional, a DoS can also occur unintentionally through a misconfigured system.

In several of my prior posts, I discussed the [national and international] push toward a system of interconnected Electronic Health Records (EHR) networks. This system, like those cited in today's media reports, depends on the availability of well performing Web sites.

An Associated Press article in today's newspapers refers to Keynote Systems, a company that monitors such events, so I decided to look into their services. I had written on other aspects of this subject in the article Capacity and Disaster Recovery Planning for an Internet Connection to which I link in my bibliography at the very bottom of this blog.

Keynote Systems is a mobile and Web site monitoring company based in San Mateo, Calif. The company publishes data detailing outages on Web sites, including 40 government sites it watches.

Managing a single Web application with thousands of users typically requires a system administrator and a few support personnel, at a cost of up to $30,000 per month. About 20% of this cost, or about $6,000 per month or $72,000 per year, is spent on monitoring the reliability and availability of these services. Keynote Systems’ services to monitor URLs start at $100 per month or $1,200 per year -- the cost savings to an operations team can be significant.

Keynote Systems’ test and measurement products and services are driven by a global network of more than 2,600 measurement computers and mobile devices in more than 240 locations in 160 metropolitan areas around the world -- the largest on-demand test and measurement network in the world. Users know precisely how Web sites, content, applications, and services will perform on mobile networks and devices -- all with hard metrics that test precise behavior patterns and more accurately predict performance problems.

Test and measurement products and services deliver in-depth, relevant KPIs (a subject which I discuss in my article cited above) that are easily understood and accurately represent what happens in the real world -- using real browsers and real devices. The economic gains provided by using Keynote Systems’ network are upfront -- using Keynote Systems does not require an increase in capital expenses.

Their Web site offers a free evaluation of their software-as-a-service (SaaS) and downloadable software products. While I don't recommend that you immediately send them or any other like organization a check, I do think you should know about what they have to offer.

Electronic Health Records (EHR) – Interoperability of Disparate Systems – Political

The top box in the figure below (adapted from the first figure in my June 16 post) contains the label "Political," and the video in my May 13th post (repeated below) shows excerpts from a recently held hearing in the United States Senate Committee On Finance, chaired by Senator Max Baucus. The latter motivated the former. But, I've had additional reasons to think about the influence of national politics upon the architecture of the upcoming IT-based EHR system.








Montana Senator Baucus is the Senate’s point man on healthcare reform. A new article in the Montana Standard finds that Senator Baucus has received more campaign money from health and insurance industry interests than any other member of Congress. The article says, “In the past six years, nearly one-fourth of every dime raised by Baucus and his political-action committee has come from groups and individuals associated with drug companies, insurers, hospitals, medical-supply firms, health-service companies and other health professionals.”

Moreover, it’s hard for even the most casual follower of the daily news to avoid finding his or her own reason to believe that national politics will play a major role in the rollout of our EHR system.

The vast majority of the funds within the HITECH Act (the health IT component of the American Recovery & Reinvestment Act) are assigned to payments that will reward physicians and hospitals for effectively using a robust, connected EHR system. Few should doubt that how these billions of dollars are distributed will have a profound impact on the shaping of our national EHR system.

In addition to funding EHRs (also called electronic medical records, or EMRs), HITECH adds some privacy-enforcement teeth to HIPAA, which has long been criticized for loopholes’ allowing the release of medical record information to health care vendors for marketing purposes. Pharmaceutical companies, for instance, have frequently used prescription information from these records to target their mailings for new or alternative drugs and treatments. HITECH now mandates that individual patients’ consent be obtained before releasing any information to vendors -- or to anyone not in the immediate health care loop that includes physicians and hospitals as well as insuring and billing entities (for a scary look at how easily this loop can expand, read “Health Privacy—The Way We Live Now”). The new provisions also require voluntary and affirmative disclosure of any breaches or violations of private records. However, as the final stimulus package wended its way through Congress, so many loopholes (five pages’ worth) were added that just about any group with political connections, or with loose medical affiliations, could gain access to everyone’s personal EHR just by asking for it or by paying for it.

While there are workgroups and committees of experts working diligently to shape an EHR that helps bring about a better healthcare system for the nation, the members of these groups don’t control the purse strings. Politicians do. Stay tuned.

Electronic Health Records (EHR) & “Meaningful Use” survey results & Speech Recognition Technology

Electronic health records (EHR) have been recognized as a core component to national healthcare reform in the United States. Beginning in 2011, physicians and hospitals can receive bonus payments under Medicare and Medicaid, but only if they are found to be “meaningful EHR users.” Nuance Communications, Inc., a leading supplier of speech solutions that are expected to help with the transition to and utilization of EHR, surveyed physicians to better understand how “meaningful use” should, from their point of view, ultimately be defined.

A couple of my prior posts were about speech recognition and another couple of my prior posts were about electronic health records. So, I was interested in the results of this survey.

The EHR Meaningful Use Physician Study shows —

93 percent of doctors “disagree” or “strongly disagree” that using an EHR has reduced time spent documenting care.

When asked what doctors consider an “incentive to drive national EHR adoption,” 75 percent of the physicians surveyed said they consider “access to tools that would help doctors to better document within an EHR (beyond the keyboard), such as speech recognition” an incentive; whereas 69 percent cited “stimulus money.”

When asked about qualifications that the federal government should measure as part of pay-outs associated with EHR meaningful use, physicians cited the following:

• 90 percent said “access to medical records faster without waiting for records to come out of transcription,” was “important” or “very important.”
• 83 percent said “more complete patient reports, with higher levels or detail on the patient’s condition and visit,” was “important” or “very important.”
• 83 percent said “better caregiver-to-caregiver communication based on improved reporting that is more accessible and easily shareable,” was “important” or “very important.”
• 79 percent said, “improved documentation by pairing the EHR point-and-click template with physician narrative,” was “important” or “very important.”

When asked about the importance of various EHR components, physicians identified the following as the five most important:

• Lab test results reporting and review
• Documentation tools that allow doctors to speak the physician narrative into the EHR
• E-Prescribing
• Secure health messaging between caregivers
• Keyboard support via speech recognition for data entry into the EHR

74 percent of the doctors surveyed said “EHR cookie cutter templates” and “patient notes with no uniqueness” are challenges to realizing the full value of EHRs.

93 percent of doctors surveyed either “agree” or “strongly agree” with the following statement, “I think capturing physician narrative as part of the documentation process is necessary for complete and quality patient notes.”

67 percent of the doctors surveyed cited “time associated with reliance on keyboard and mouse to document within an EHR” as a major hurdle.

My thoughts, after perusing these findings: On the one hand, this is a self-serving report created by a vendor of speech recognition products; On the other hand, this report suggests that there could be a good deal of resistance to EHR by physicians and hospitals if speech recognition technology is not successfully integrated into the coming EHR system(s). Stay tuned.

Electronic Health Records (EHR) - Interoperability of Disparate Systems

As suggested in the figure below, there are technical, semantic, organizational, political and economic matters to consider when deciding how to move data and/or exert operations from one place to another. These challenges are present in the recently reinvigorated campaign to adopt and exchange electronic health records.



On February 17, 2009, President Barack Obama signed into law the American Recovery & Reinvestment Act (ARRA). The health IT component of the Bill is the HITECH Act, which appropriates a net $19.5 billion dollars to encourage healthcare organizations to adopt and effectively utilize Electronic Health Records (EHR) and establish health information exchange networks at a regional level, all while ensuring that the systems deployed protect and safeguard the critical patient data at the core of the system.

There are two portions of the HITECH Act -- one providing $2 billion immediately to the Department of Health & Human Services (HHS) and its sub-agency, the Office of the National Coordinator for Health IT (ONC), and directs creation of standards and policy committees; a second that allocates $36 billion that will be paid to healthcare providers who demonstrate use of Electronic Health Records.

The government is focused on two primary goals in this legislation: moving physicians who have been slow to adopt Electronic Health Records to a computerized environment, and ensuring that patient data no longer sits in silos within individual provider organizations but instead is actively and securely exchanged between healthcare professionals. Therefore, the vast majority of the funds within the HITECH Act are assigned to payments that will reward physicians and hospitals for effectively using a robust, connected EHR system.

In short, a great deal of largely-Government-funded IT work is about to be undertaken to enable often-disparate healthcare recordkeeping systems to interoperate. In the next few posts, I will address a number of the issues that need to be considered when planning such projects.

Data Envelopment Analysis and Electronic Health Records

Evaluating the efficiency of an interconnected electronic health information infrastructure

We are moving toward the creation of a nation-wide interconnected electronic health information infrastructure whose primary goal is to provide better healthcare. At the same time, many regional health care organizations are only now adopting electronic health record systems. And, close by, vendors and other entities of all sorts are vying for influence over these advances.

Throughout this many-player process, it is imperative that healthcare organizations give value in return for the money they receive from the government and others and that businesses remain competitive. To understand the results of the enormous investments that are being made in the new electronic health information infrastructure, continuous measurement of the key variables is essential.

Every organization has a lot of information about its operation or has the ability to gather such information should it choose to. The problem is making good use of this information. Purely financial measures of performance are insufficient to ensure long-term improvement in healthcare. This is where Data Envelopment Analysis (DEA) can help. It's a mathematical technique that combines traditional performance ratios into a single efficiency score. That is, DEA, unlike many other quantitative methods, does not rely on a single criterion for measuring performance.

Furthermore, DEA tells you where an organizational unit can improve, based on the performance of its peers (see "A composite hospital - dual prices" below). Since it is a peer based comparison, the targets set for improvement are realistic and, therefore, more likely to be achieved. So, using DEA to compare the efficiency of a large urban teaching hospital with a small rural private practice -- apples with oranges -- would be an inappropriate use of this method.

DEA can be applied either spatially or temporally: i.e., at a single instance of time, it can be applied to compare the efficiency of distinct organizations (or systems) or, at different points of time, it can be applied to a single organization (or system). In the case of a system that’s still largely on the drawing boards, e.g., the nation-wide electronic health information infrastructure, initial comparisons need to be made among computer simulations of the different proposed solutions.

As the simple example that follows will illustrate, DEA can help you get a good overall picture of your organization's performance and where potential improvements might be made. However, as anyone reading this post already knows, the terrain in which DEA operates is very complex.

Linking Patient Records is currently being handled by different organizations in different ways. For example, in Massachusetts, MA-Share is using a federated architecture, with a shared record locator service (RLS). In California, the Mendocino HRE uses a brokered architecture with mirrored data at a central HRE. And in Indiana, IHIE is using a central data repository with standardized data.

The figure below serves to illustrate the three models. Furthermore, each of these groups employs different standards, software preferences, etc., which adds complexity when it comes to one of these groups interoperating with another.



Achieving an efficient interconnected electronic health information infrastructure requires the collaboration of individuals from many disciplines. My reference section below reflects this by citing the application of DEA to the optimization of computer networks in addition to the outputs of hospitals and physicians.

The nation-wide electronic health information system will be established by interconnecting a large number of preexisting regional systems, including many of the kind shown in the figure above, plus another large number of heretofore all-paper-record-keeping organizations. DEA methods may be applied to any and all of these, as long as you avoid comparing apples with oranges .

As a standalone system evolves, you should compare its sole performance before and after changes are made. Similarly, as individual systems join a national (and, perhaps, eventually, an international) grid, you need to compare their performances before and after they do so. The obvious question is which metrics should be tracked and included in the analyses. This is a very big question and far beyond the scope of this post. To illustrate how DEA works, however, I'll proceed with a simple example.

Consider a group of three hospitals. To simplify matters, assume that each hospital "converts" two inputs into three different outputs. The two inputs used by each hospital are

Input 1 = capital (measured by the number of hospital beds)
Input 2 = labor (measured in thousands of labor hours used during a month)

The outputs produced by each hospital are

Output 1 = hundreds of patient-days during months for patients under age 14
Output 2 = hundreds of patient-days during months for patients between 14 and 65
Output 3 = hundreds of patient-days during months for patients over 65

Illustrative inputs and outputs for these three hospitals are given in the table below.



The efficiency of hospital x = value of hospital x’s outputs / cost of hospital x’s inputs

From here, the math and theory get rather complicated.

Software solutions

Fortunately, there are computer programs -- some free, others not -- that can help you with all of this. Most of the general-purpose mathematical optimization software can be adapted to solve Data Envelopment Analysis problems. In addition, there are several DEA-specific programs that provide a variety of interesting facilities.

For a technical introduction to DEA, the following two videos

Data Envelopment Analysis 1
http://www.youtube.com/watch?v=xjECr9dveKk

Note: this video starts off referring to "the efficient frontier." For a review of this concept, you might take a look at my article “Capital Budgeting: Managing Efficient IT Project Portfolios,” which I cite in the bibliography at the bottom of this blog.

and

Data Envelopment Analysis 2
http://www.youtube.com/watch?v=eBx9iZPc4i8&feature=related

plus

a brief white paper that explains how DEA can help you get a good overall picture of your organization's performance and where potential improvements might be made http://www.banxia.com/frontier/pdf/FA_InUse.pdf

may be helpful.

Breakups or mergers

The Options For Clinical Data figure above shows three of the many different ways in which individual silos of clinical data can be distributed; i.e., broken up or merged. The decision on which architecture to adopt is based on considerations of security, privacy and many other factors.

With DEA, you can evaluate the performance of a silo and one or a combination of a few other silos. Such a comparison can indicate whether or not a breakup or merger of units needs to be considered. The scope of this kind of analysis may be limited by many factors, not the least of which is the degree of cooperation forthcoming from those who control the individual components of the overall system. Thus, sometimes breakups and mergers are not an option, even though DEA indicates there might be benefits from a breakup or merger.

If the output bundles produced individually by two hospitals (or other units) can be produced more efficiently together by a single hospital (or other unit), there is an efficiency argument in favor of merging these two units. Similarly, in some cases, breaking up an existing hospital (or other unit) into a number of smaller units would improve efficiency.

Attaining technical efficiency ensures that a unit produces the maximum output possible from a given input bundle or uses a minimum input quantity to produce a specified output level. Full economic efficiency lies in selecting the cost-minimizing input bundle when the output is exogenously determined (e.g., the number of patients treated in a unit) and in selecting the profit-maximizing input and output bundles when both are choice variables, as in the case of a business firm.

A composite hospital - dual prices

DEA analysis reports typically have a column entitled "Dual prices" that can give you great insight into Hospital 2's (or any organization's found inefficient by DEA) inefficiency.

After running a DEA analysis on the data in the table shown above, you would be inclined to create a composite hospital derived from the model that's made out of, say, 26.1 percent of the input level used by hospital number 1 and 66.1 percent of the input used by hospital number 3. If it were then found that the composite hospital uses "a" capital and "b" labor (with a or b lower than the corresponding amount required by Hospital number 2 and the other of these two variables no larger than its corresponding amount) to achieve the same level of outputs achieved by hospital 2, you could use these numbers as performance targets for hospital 2.

Bottom line: Dual prices can sometimes find a composite hospital that is superior to an inefficient hospital and where the origen of this inefficiency arises. Breakups and mergers aren't the only options.

In the news: The Johns Hopkins Health System Corporation has recently aquired Suburban Hospital in nearby Bethesda, MD, converting it into a Hopkins subsidiary. With its new association with an information technology-, medical- and management-savy institution like Johns Hopkins University, Suburban Hospital is now well positioned to participate in the development of the coming nation-wide interconnected electronic health information infrastructure.

References

This is a big topic and can only be handled by teams of experts who understand the complexities of the medical, financial, management and political issues involved! However, for anyone interested in more information on DEA (in addition to the two videos and the white paper cited above), consider these additional, albeit much more sophisticated, sources:

For examples using DEA in hospital and physician evaluation, see Chilingerian, J.A. (1994). Exploring why some physicians' hospital practices are more efficient: Taking DEA inside the hospital, in Charnes, A., Cooper, W.W., Lewin, A.Y., and Seiford, L.M. (Eds.), "Data Analysis: Theory, Methodology, and Applications, Boston: Kluwer Academic Publishers; Sherman, H.D. (1988). "Survive Organization Productivity." The Society of Management Accountants of Canada: Hamilton, Ontario.

and

Since Information Technology has a good deal to do with determining the overall efficiency of an interconnected, electronic health information infrastructure, some readers might also be interested in Medhi, D. and Ramasamy, K. (2007). "Network routing: algorithms, protocols, and architectures." San Fransisco: Morgan Kaufmann. In section 7.7, DEA is applied to the problem of finding the best topology for a computer network.

and

Comparing Performance with Data Envelopment Analysis
http://www.lindo.com/downloads/LINGO_text/Chapter14.pdf

and

A DEA tutorial
http://www.deazone.com/tutorial/index.htm

Electronic Health Records (EHR)

The health information technology provisions in the Obama administration's stimulus bill are a step toward the goal of nearly universal electronic health record adoption in the U.S. over the next 10 years (compared with 17 percent today). The central feature of the plan is incentive payments for using electronic records for improvements in health quality, efficiency, prevention and safety.

This kind of spending, if done wrong, can have the negative market consequence of interfering with rapid innovation by locking in today’s processes and technologies, which although well-intended, came about without a systemic view. And we risk locking out the very innovations we need for meaningful health information sharing to support better decisions.

The goal for health IT should not be primarily the creation of standards or the certification of software. Rather, standards and certification should support measurable health improvements. Health improvements are not achieved by the mere installation of software; they are achieved through the effective use of information for better decision-making.

At the same time, individual states -- for example Massachusetts, which has a newly passed law that requires hospitals and community health centers in the state to implement an electronic health record systems by Oct. 1, 2015 -- have also been moving to improve the delivery of better health care through the use of EHR.

To implement these programs, the healthcare industry seems poised to increase the adoption of EHR and electronic transmission standards to promote accuracy, transparency, and processing speed across disparate information systems. Today, they have a smorgasbord of health information technologies available to help them build a far better health system.

There are, in fact, too many standards and too many organizations writing them. There are the standards that support the systems we have in place today as well as the XML/Web-based standards that support newer web-centric systems and healthcare information exchanges.

While creating EHR data is an important first goal, an EHR is much more valuable if it can be summarized, moved, and shared. This and future posts will address EHR in that broader context. These discussions will address many technical, clinical, economic, political and managerial issues of electronic health record systems.

Perhaps the most difficult challenge is to bind the standards to structured vocabularies to ensure that there is the transfer of unambiguous knowledge of the meaning of the data among cooperating systems.

Before I get specific (sometimes talking about tools to facilitate implementation of these systems), I want to point out that building technology on U.S. standards alone would leave us with essentially a non-standard EHR platform. Remember that other countries, for example Canada and England, have single-payer health systems, while the U.S. does not.

A look back to before the PC, the Internet and all that

And, before looking ahead to how Electronic Health Records of the future will likely work, I thought I'd also display a pulmonary function test report produced at Yale New Haven Hospital (YNHH) several decades ago. While its underlying data (held in a PDP-8 computer) could be transmitted electronically -- analog modem to analog modem -- over a 128 bits/sec telephone line connection, these paper reports were generally sent from the laboratory where they were generated to the office of a YNHH physician via inter-office mail or to an outside physician via the U.S. Postal Service.



Computer-generated paper report: This photograph was produced by a Poloroid instant camera that was suspended over the front of a cathode ray tube (CRT). The CRT, along with a teletypewriter, provided human readable output for the PDP-8.

In subsequent posts, I'll include talk about safe wireless practices, Web services and other seemingly off-topic subjects, because they too are important parts of the EHR story.

Interoperability will be among these topics. The linking of vital information as patients receive care from a fragmented healthcare system is a problem that has consistently plagued interoperability efforts in healthcare. The privacy, technical, and policy issues involved need be addressed in order to effectively share information across multiple organizations. Making the information available will help to prevent drug interactions and adverse events, avoid medical errors, and help inform decision making for the patient and clinician. It will also enable the support of public health efforts, improvements in research, better physician and organizational performance and benchmarking, and greater empowerment of patients and families as active participants in their own healthcare, among other benefits.

In discussing these issues, I will sometimes cite my earlier writing on financial, legal and organizational issues that appears in articles available through links provided in my bibliography at the bottom of this page.

Finally, this might be a good time to introduce a few technical terms: HL7, HL7 mapping, HIPPA etc. They're central to the discussion of IT health records management.

Health Level 7 (HL7)

HL7 refers to both a standards organization and the set of healthcare messaging standards that it creates. Founded in 1987 to create a set of standards for hospital information systems (HIS), HL7 has expanded its reach to the creation of international standards that transgress hospitals to address clinical and administrative data in healthcare domains such as pharmaceutical, medical device, and insurance transactions. There are already a large number of countries that have mandated the use of HL7 for the transmission of healthcare data and there is an expectation that HL7 will become a part of the United State's Health Insurance Portability and Accountability Act (HIPAA) in the future.

For the large number of international healthcare organizations that are embracing the electronic transmission of healthcare data, there remain some formidable challenges. Though some compliance regulations specify the newer, XML-based HL7 v3.x, there are many jurisdictions that still need to update their legacy systems to handle this format, and many that even have multiple disparate data formats in the same system.

In the US, for example, many legacy HISs employ HL7 EDI messages alongside HIPAA X12N messages. Though these formats have quite a lot in common, syntactically speaking, they are by no means interoperable and must be mapped on-the-fly to create a dynamic workflow for managing healthcare transactions. Of course, the introduction of the XML-based HL7 v3.x adds EDI/XML mapping to the complication of mapping data from EDI to EDI.

HL7 mapping

There are off-the-shelf, any-to-any graphical data mapping tool (e.g., Altova MapForce) that supports mapping HL7 data, in its legacy EDI or newer XML-based format, to and from XML, databases, flat files, other EDI formats, and Web services. Mappings are implemented by simply importing the necessary data structures (MapForce ships with configuration files for the latest EDI standards and offers the full set of past and present HL7 standards as a free download on its Web site) and dragging lines to connect nodes. A built-in function library lets you add advanced data filters and functions to further manipulate the output data. MapForce can also facilitate the automation of your HL7 transaction workflow through code generation in Java, C#, or C++ and an accessible command line interface. Additional support for mapping HL7 data to and from Web services gives healthcare organizations the ability to meet new technology challenges and changing enterprise infrastructures as they unfold within internal and external provider domains.