Knowledge, Standards and the Healthcare Crisis: Part 6

In the previous posts [click here for first in series], I described many of the problems facing the healthcare industry as it attempts to deal with data and technology standards. These daunting problems include high cost, complexity, difficulty accommodating changes, loss of meaning and nuance, trouble defining quality, inadequate measures, political influence, etc. I now discuss how an innovative approach to the use of health information technology would help solve these problems.

Solving the Problems with Healthcare Standards

From a technological perspective, what's needed to solve the problem with standards is a simple, low-cost, reliable, secure, hassle-free way to exchange and view structured & unstructured health information, anywhere and anytime, in a way that:

• Maintains the full meaning and nuance of the information being exchanged in order to maximize understanding and the information's usefulness, regardless of the data standards being used.


• Supports fluid connectivity between all IT systems, regardless of their technology standards.


• Gives all authorized consumers/patients, providers, suppliers, payers (insurers), and purchasers (employers and self-insured) the information they need, in the way they need it, to support decisions and guide actions.


• Supplies researchers with the information they need to evaluate clinical outcomes and care processes, so they can create, continually evolve, and widely disseminate evidence-based guidelines.

I contend that the best way to do this is through a secure node-to-node network architecture using template-driven software applications having "publisher-subscriber," data translation, and personalized reporting capabilities. Let me explain.

Why a Node-to-Node Architecture

In a node-to-node architecture, each node is a software application in a computer that sends and receives information from other nodes. This architecture supports "peer-to-peer" (P2P) networks in which each node stores its data files locally and shares them with other nodes without being controlled by a centralized server.[1] The telephone system and e-mail are good examples of node-to-node. Every phone and every computer are nodes. By picking up the phone and dial a number, or by typing in an e-mail address, you can communicate with whomever you want, and do it anytime and anywhere. Your call or e-mail is routed automatically to where you want it to go through a series of simple switches. This open network is quite different than a centralized system in which you must first sign on to a central server that determines who you are authorized to contact before sending them your message, i.e., all information must pass through a central authority that controls all communications. In addition, such centralized systems typically require the costly development and ongoing maintenance of a centralized patient record locator to know where to find patient data.

The following make the case for a node-to-node/peer-to-peer architecture for exchanging healthcare data:

• "The United States' National Health Information Network, or NHIN, will differ from the UK's project in a number of ways. Rather than having a single, closed network with a central database overseen by one government agency, the U.S. system will be decentralized, operating more like a peer-to-peer network, with records distributed across the system. Think Napster on steroids. …the NHIN will allow a doctor to quickly call up a patient's digital records from whatever databases they may reside in-at a hospital, at the family doctor's or dentist's office, at a clinical lab, wherever."[2]


• "After initial testing using a centralized patient index, [Massachusetts' MA-Share HIE determined that] the maintenance for that looked like it would be more than users would want to pay. So the exchange uses distributed peer-to-peer networking. The MA-Share exchange provides an appliance to let members push financial transactions, e-prescriptions, and clinical summaries-so a doctor can send a file to another doctor or provide prescription data to a pharmacy." [3]


• "To make significant gains in patient safety through the adoption of health IT, providers will need to adopt IT systems that can 'speak the same language' to each other. In computer terms, they should be 'interoperable.' But interoperability isn't enough. To communicate, different health IT systems must also be linked in some way. This is 'connectivity.' One model of connectivity, in a national health IT context, would be a non-proprietary 'network of networks.' …Several issues must be addressed if different health information systems are to communicate. …Some suggest that there should be one uniform national system with one central repository. This approach presents challenges: the sheer volume of data that would need to be handled, significant concerns about privacy and security threats, and likely disputes about governing and paying for a centralized system. Another option is a series of regional networks, as advocated by ONC [the Office of the National Coordinator of Health Information Technology, formally ONCHIT]. ONC's strategic frame- work suggests that a national network should be structured around regional health information organizations (RHIOs). RHIOs would store, organize and exchange patient health information within a defined geographic region, under local rather than national governance. These regional organizations would form a "network of networks" across the nation." [4]

Publisher-Scriber Communications Model

The nodes in these P2P networks employ a publisher-subscriber communications model in which a publisher node uses its communications software application to publish (send) information to one or more authorized subscriber (receiver) nodes. Once transmitted, the subscriber nodes use their subscriber applications to retrieve that information and present it as reports. In other words, the publisher-subscriber model uses an "application to application" transfer process in which each participating node uses a particular software application for exchanging (sending and receiving) information.

The publisher and subscriber applications support a particular operating system OS) and Internet connection using broadband or dial-up service. A node that uses an e-mail client (such as Microsoft Outlook on Windows OS) is one such example.

At one end of the connection, the publisher node must authorize the information transfer by authenticating that the subscriber node is allowed to receive the information. At the other end of the connection, each subscriber node must allow the publisher to deposit the information into a directory (i.e., a folder in computer's drive) as a file with a specific format (such as an MS Word, Excel, or "comma separated value" file).

Universal Translation

A node-to node architecture incorporating "universal translation" provides a means for modifying (transforming, translating) information as it passes between nodes, so that each subscriber node receives from a publisher node the right information, in the right format (structure), and with the right terminologies (semantics).

This is where data and technology standards are handled. That is, the universal translator makes the necessary transformations to the information sent by a publisher node, so different subscriber nodes can use that information to generate their particular reports and, if desired, to store the information received in the subscriber nodes' databases. It can accommodate any data standards and operate with systems using any technology standards.

Advantages and Benefits of the Node-to-Node Architecture

The advantages and benefits of this asynchronous, publisher-subscriber, node-to-node architecture are many, including the following:

• Is exceptionally flexible:
• Accommodates any data and technology standards, so everyone gets the information they need in the way they need it
• Allows anyone to communicate with anyone else in any way
• Can use multiple connectivity options, i.e., radio transmission, satellite transmission, wire transmission, wireless transmission.


• Has maximum reliability since it leverages the most reliable network in the world, i.e., the switched network (like the telephone system).


• Is inexpensive to deploy and operate because it doesn't require changes to existing I.T. infrastructures and keeps implementation costs low by eschewing additional equipment and system purchases.
• Is robust and resilient since there is no single point of failure; so, unlike centralized networks that are disrupted if a central server goes down, the node-to-node network is survivable in a disaster since it keeps going even if individual nodes are disabled.


• Makes scalability a non-issue, which means there's no need to purchase new equipment or redesign software as the network grows; this is unlike a centralized system in which there tends to be significant costs in time and money to meet the needs of a growing network.
• Is highly secure since there are no external database queries; firewalls are not crossed.

In my next post, I discuss other parts of the solution: Composite Reporting and Application Integration.

References:

[1] Wikipedia - Peer to Peer and WellnessWiki - Network Architectures (see Node Mesh Network)

[2] Charett, R.N. (2006). Dying for Data: A comprehensive system of electronic medical records promises to save lives and cut health care costs-but how do you build one? IEEE Spectrum Online (Oct 2006)

[3] Kolbasuk McGee, M. (May 28, 2007). Urgent Care. Informationweek.com

[4] Linking Providers Via Health Information Networks. Alliance for Health Reform. (Dec 2006).

Knowledge, Standards, and the Healthcare Crisis: Part 5

In the previous four posts [click here for first in series], I described the data and technology standards commonly used to enable the exchange of health information between disparate data sources. I also discussed why such information exchange is vital to the creation and use of knowledge leading to increased healthcare value. In addition, I mentioned several challenges to using standards effectively.

In this post, I delve into the problems faced by the healthcare industry when dealing with standards.

The Problems with Healthcare Standards

We confront one set of problems with data (terminology, care measurement and care process) standards, and another with technology (messaging) standards.

Problems with Terminology Data Standards

Problems associated with terminology standards are significant:

• According to William Hammond, professor emeritus of community and family medicine at Duke University, there's "been ongoing discussion about implementing health data standards harmonization and cooperation for 20 years, yet no one has defined all the standards needed to support a national health information network, and no one has identified what's missing." Just agreeing on medical terminology is a big issue. And, according to Michael Rozen, vice chairman of the IEEE-USA Medical Technology Policy Committee, "When you say 'gross profit,' everyone in finance knows what that means [but] in medicine, there are 126 ways to say 'high blood pressure.' "[1]


• While setting an arbitrary standard for health-related terms is a way to foster widespread communications between people from different regions, organizations and healthcare cultures/communities, there's also a downside to such standards, i.e., they lose information due to reduced "semantic precision and nuance." In other words, there's a good reason to have multiple ways of saying high blood pressure. For example, malignant hypertension refers to very high blood pressure with swelling of the optic nerve behind the eye, which is usually accompanied by other organ damage like heart failure, kidney failure, and hypertensive encephalopathy. Pregnancy-induced hypertension, on the other hand, is a pregnancy-induced form of high blood pressure (also called toxemia or preeclampsia). Referring to a patient's condition using the standard term "hypertension," while clearly conveying that the person has high blood pressure, looses these important details, which could very well affect treatment decisions and outcomes.


• Diagnostic code standards-including all versions of the ICD and DSM-have several serious limitations. These problems include the fact that (a) these standards are not detailed enough to describe the nuances of all diseases and conditions and (b) some diagnoses are not useful in making treatment decisions.[2] Since treatment selection is based (or should be based) on a patient's diagnosis, we need a diagnostic standards that have greater precision. This requirement is amplified with personalized care is, in which each patient's unique makeup (including genetics) and the mind-body connection are taken into account (not to mentions ones abilities and preferences).

Problems with Care Measurement and Process Data Standards

As I discussed in a previous post, care measurement and process standards relate to evaluating care quality and provider performance, and to establishing practice guidelines. Some of the problems associated with these standards, include the following:

• Achieving wide-ranging and meaningful quality standards requires many more years of dedicated effort by many people and substantial financial resources.[3]


• Standards should evolve continuously, changing as necessary to accommodate new knowledge. Unfortunately, it typically takes 17 years before clinical evidence is implemented in practice guidelines.[4] [5]


• Simply maintaining nation-wide data standards is a slow and costly process.


• And, as I discussed in an earlier post, there are many problems with practice guideline and quality measurement standards:


• It's difficult to determine when there is enough evidence supporting a practice guideline and there is no longer any need to spend time or money on its continuous evaluation.


• It's difficult to determine when a definition of quality is too narrow, which can happen, for example, when measuring quality based on cost or symptom reduction, without giving adequate consideration to prevention or the continuity of care.


• It's difficult to determine how best to measure quality when resources are scarce and optimal care for the community may require less than "the best" care for its individual members.


• It's difficult to determine how best to measure quality if outcomes are more strongly affected by patient compliance than by physician orders.


• It's difficult to determine if care quality is of poor when a provider follows the recommended practice guideline, but the patient is atypical and responds poorly.


• Using claims (administrative) data to measure care quality, as in often done today, is grossly inadequate.


• Assessing care quality using process data may not be valid since they do not necessarily reflect care outcomes.


• It's difficult to determine how to avoid political and ideological biases when determining what evidence to use as the basis for establishing the guidelines.


• Many areas of healthcare lack care process standards and useful quality measures. Different healthcare disciplines and specialties require different types of data to evaluate quality.

Problems with Technology Messaging Standards

The problems with standards aren't limited to data standards; they also plague technology messaging standards:

• When multiple information systems use the messaging standard to communicate, changing the standard cost huge sums as all the systems using them must be overhauled. A good real world example is the Year 2000 problem, where computer systems were built using a messaging standard that required only the last two digits of the year to be used when transmitting data containing dates. So, when 2000 rolled around, this data standard made it impossible to differentiate between years beginning with 19 and those beginning with 20 (i.e., 4/5/05 could be Apr 5, 1905 or 2005). This problem easily cost hundreds of billions of dollars to fix.


• The Healthcare Information Technology Standards Panel, which is setting technical standards for a nationwide record system, identified an initial set of 90 medical and technology standards, out of an original list of about 600. These standards specify such things as how lab reports are to be exchanged electronically and entered into a patient's electronic record, as well as how past lab results are to be requested. More than 190 organizations-representing consumers, providers, government agencies, and standards development organizations-participating in the panel. It's no wonder, therefore, that a consensus on medical standards is so difficult and fraught with politics as standards-setting involve intense negotiations and delicate compromises. And once such IT standards are set, software systems and databases must be designed to conform with those standards.[6]

Summary

While data and technology standards offer a way to handle information exchange challenges, they come with issues posing serious problems in terms of cost, effort, time, hassle, complexity, inefficiency, usability, reliability, information loss, political influence, etc.

In my next post, I will discuss ways to solve the daunting problems plaguing the use of healthcare standards.

References:
[1] Dying for Data: A comprehensive system of electronic medical records promises to save lives and cut health care costs—but how do you build one? IEEE Spectrum Online (Oct 2006)
[2] Current Diagnostic Codes are Inadequate – WellnessWiki
[3] U.S. Health Care Sector Moves Rapidly To Provide Consumer Information on Value. HHS (May 9, 2007)
[4] Balas, E. A., & Boren, S. A. (2000). Managing clinical knowledge for health care improvement. In J. Bemmel & A. T. McCray (Eds.), Yearbook of Medical Informatics (pp. 65-70). Stuttgart: Schattauer Verlagsgesellschaft mbH.
[5] Clancy, C. M., & Cronin, K. (2005). Evidence-based decision making: Global evidence, local decisions. Health Affairs, 24(1), 151-162.
[6]  Dying for Data: A comprehensive system of electronic medical records promises to save lives and cut health care costs—but how do you build one? IEEE Spectrum Online (Oct 2006)

Knowledge, Standards, and the Healthcare Crisis: Part 4

In the previous post, I discussed "care measurement and process standards," which are data standards dealing with diagnosing health problems, determining treatments, and assessing care quality and provider performance. [Click here for first post in series] In this post, I turn to technology standards, and to "messaging format" standards in particular. Whereas data standards focus on making information understandable and useful to humans, messaging format standards focus on enabling the exchange, or interoperability, of data and information (i.e., "transactions") across healthcare systems.

HL7 Messaging Standard

The Health Level Seven (HL7) Messaging Standard is the most recognized. It specifies the technical aspects of sending messages so that one software program can exchange information with another, so the information is "understood" by the receiving machine. This standard handles information related to order entry, scheduling, medical record and image management, patient administration, observation reporting, financial management, and patient care transactions.

For example, an HL7 formatted message delivering data about a patient's EKG would be written like this: "OBX2ST93000.1^VENTRICULAR RATE(EKG)91/MIN60-100". Here's what it means:

• OBX = The message is a report of an observation/result
• 2ST = The data value is a two character string
• 93000.1^VENTRICULAR RATE(EKG) = The code number and name of the EKG test
• 91/MIN = The observation value and units (beats/minute)
• 60-100 = The reference or normal range for this test. 

Note that this HL7 message standard just provides a message structure (syntax), i.e., the use of pipe symbols ("") to separate message elements, and the order in which the elements appear; none of the content (codes, terminologies, values) is defined by this HL7 standard. Also note that the next version of HL7 (version 3) will be tied to specific terminologies, thereby adding semantic capabilities that enable different data systems to communicate with each other.

HL7 Version 3 messages are XML documents, which use a very complex and verbose structure of "markup tags" to identify the data values. These tags are strings of characters surrounded by angle brackets, which are depicted in the figure below. The figure is a small section of an HL7 clinical document in XML, which includes the use of the SNOMED CT terminology standard in which the terms "Osteoarthritis", "finding site" and "right knee" are used to define the medical history note that the patient is "complaining of disabling osteoarthritis of the right knee."

The beauty of XML is that anything can be defined using the markup tags. A down side is that XML is very inefficient. For example, in the XML document above, it takes about 700 characters to record an observation that's only about 100 characters in length. Furthermore, such XML documents can be complex to write and difficult for humans to read. This concludes my description of standards used in healthcare. In the next post, I'll delve into the problems with today's standards and will the offer innovative strategies for solving those problems.

Knowledge, Standards, and the Healthcare Crisis: Part 3

In the previous post, I discussed "terminology standards," which deal with the meaning and use of words (terms). In this post, I continue with the discussion of data standards, focusing this time on standards for diagnosing health problems, determining treatments, and assessing care quality and provider performance. This all relates to care measurement and process standards. [Click here for first post in series]

Care Measurement and Process Standards

Care measurement and process standards focus on:

• Diagnosing health problems
• Selecting and delivering treatments
• Evaluating care performance and value.

Diagnosing Health Problems

Physiological (bodily) and psychological (mental-emotional-behavioral) measures are used to diagnose a patient’s health problems.

Physiological Measurement Standards

Physiological measurement standards include vital signs and lab test "reference ranges." For example, the standard measures for hypertension is systolic pressure consistently greater than 140 mm Hg, or diastolic pressure consistently 90 mm Hg or more and a standard measure for diabetes is fasting blood glucose level of 126 mg/dL or higher on two occasions. Genetic markers associated with illnesses may also be considered a type of biologic measurement standard. These standards not only help diagnose a patient's condition, but may also determine one's risk of developing a disease.

Psychological Measurement Standards

Probably the most common psychological measurement standard is the IQ test, which defines a score of 90-110 as being within the "normal" range of intelligence. There are also standardized tests that measure mental status (e.g., awareness, memory and other cognitive functions), as well as depression, anxiety, personality traits and other psychological factors.

Selecting and Delivering Treatments

The diagnostic measurement standards are useful if they help select a particular practice guideline identifying a particular treatment for a particular patient with a particular diagnosis. The guidelines provide recommendations for the prevention, treatment, and maintenance of many nontrivial illnesses, conditions, disorders and other healthcare problems. There are three thorny problems, however:

1. Today's diagnostic systems often fail to point to the best treatment options.[1]
2. Few guideline standards are specific enough to account for individual differences in patient with the same diagnosis. For example, a recent study found that a moderately high total cholesterol level is associated with higher survival in certain patients with heart failure.[2]
3. Constantly evaluating and revising guidelines based on new knowledge is very difficult. But if they do not continually evolve, the guidelines are just "a record of the past, and little more-they should have an expiration date."[3]

Evaluating Care Performance and Value

At least three standards are related to clinician performance and care value:

1. Process compliance standards
2. Clinical outcome standards
3. Care value standards.

Process Compliance Standards Process compliance standards measure provider's performance based on whether they followed prescribed guidelines reflecting preferred care processes. For example, typical Pay for Performance (P4P) programs reward providers who perform certain predefined procedures (processes), such as doing a Hemoglobin A1c test a certain number of times each year for patients with diabetes. These standards measure the degree of compliance to such established procedures.

Clinical Outcomes Standards

Outcomes standards define whether clinical goals are achieved for patients with particular conditions. For example, the Hemoglobin A1c test target goal for diabetic control of blood glucose is defined as less than 7.0%. Unlike process compliance standards, clinical outcomes do not focus on whether specific procedures were followed; instead, they measure the effectiveness of whatever treatments were delivered.

Care Value Standards

If our healthcare system was rational and guided by wisdom, a top priority of healthcare professionals and consumers would be:

• Gaining valid knowledge about healthy living, the causes and diagnosis of physical and mental health problems, and the highest value treatments.
• Understanding how to use this knowledge to maximize value by increasing the effectiveness and efficiency of care delivery and self-maintenance.
• Continuously evolving this knowledge and using it to improve care quality and lower costs continually.

So, what is care "value."

Care value can be measured by dividing the quality if that care by its cost, i.e., V = Q / C:

• Q (Quality) is defined as the degree to which care is delivered safely, effectively and equitably. The care may include conventional and alternative interventions for treating illness, as well as wellness intervention for prevention and health optimization. Quality can be measured based process compliance standards, clinical outcomes standards, or both.
• C (Cost) is defined as the degree to which the care is delivered efficiently and economically.
• V (Value), therefore, can be defined as cost-effectiveness ("bang for the buck").

If there is to be significant improvement in healthcare delivery, a useful and reliable quality standard must be established for every healthcare domain/discipline/field. Only then can care value be determined.

Potential Pitfalls of Care Quality Measurement

While costs can sometimes be tricky to calculate, measuring quality is the major challenge. The potential pitfalls of quality measurement are enormous! Consider the following:

• We have a long way to go. According to HHS Secretary Mike Leavitt, "Medical associations and others have begun the work of developing quality standards and cost measurement, but we have many years of work ahead of us to achieve the wide-ranging and meaningful quality standards we need."[4]
• No mater what quality measures are used, there are complex issues to be resolved, such as:
• At what point is there sufficient confidence in an evidence-based practice guideline that there is no longer any need to spend time or money on the continuous evaluation of its reliable and validity?
• When is a definition of quality too narrow, e.g., by focusing on cost or symptom reduction, but not considering prevention, recurrence, coordination and continuity of care, or the patient-physician relationship?
• How do you measure quality when resources are scarce and optimal care for the community may require less than "the best" care for its individual members (e.g., delegating office nurses to perform certain activities that physicians used to do)?
• What is the best way to measure quality if outcomes are more strongly affected by patient compliance than by physician orders? This may occur, for example, if certain providers have personalities that trigger greater patient compliance, and visa versa.
• Is it poor quality care if a provider follows the recommended practice guideline, but the patient is atypical and responds poorly? [5]
• Use of claims (administrative) data to measure care quality is grossly inadequate for many reasons.[6]
• Assessing care quality using process data may not be valid since they do not necessarily reflect care outcomes.[7]
• One thorny issue is how to avoid political and ideological biases when determining what evidence to use as the basis for establishing the guidelines. [8]
• Many areas of healthcare lack care process standards and/or quality measures. Different healthcare disciplines and specialties require different types of data to evaluate quality. For example, it's foolish to measure the quality of mental healthcare services with data appropriate for evaluating cardiologists' performance; and the same is true for a podiatrist, dentist, chiropractor, etc.-each need different measures for determining quality, but they are often lacking.[9]

To summarize this post, it is critical to have useful, reliable standards to assist with diagnosing patient problems, selecting and delivering the best treatment options, evaluating clinical performance and identifying care value. Unfortunately, we have a long way to do before such standards become a reality.

This concludes by review of data standards. In my next post, I'll examine "technology standards," which focus on enabling the exchange, or interoperability, of information across healthcare systems.

References:
[1] Current Diagnostic Codes are Inadequate - WellnessWiki
[2] Reuters (Sep 20, 2006). Elevated cholesterol may benefit failing hearts.
[3] Gawande, A (2004). The Bell Curve. The New Yorker.
[4] Bush's Value-Driven Health Care Plan Gains Steam as More Employers Step Up (May 10, 2007)
[5] Donabedian, A. (2005). Evaluating the Quality of Medical Care. The Milbank Quarterly 83, 691-729.
[6] Use of claims data is inadequate - WellnessWiki
[7] HealthDay (July 5, 2006). Hospital Ratings Don't Fully Reflect Patient Outcomes. [
8] Healy, B. (Sep. 2006).Who Says What's Best? U.S. News and World Report. [
9] Need for specialy measures - WellnessWiki

Knowledge, Standards, and the Healthcare Crisis: Part 2

In my previous post, I discussed how knowledge is the foundation of healthcare improvement, and how health information exchange is vital for creating and using knowledge. I then introduced the notion that standards are essential for sharing information and implementing knowledge in a way that improves patient care. I also mentioned that, while beneficial, there are substantial challenges to the effective implementation of standards.

Continuing on the topic of Knowledge, Standards and the Healthcare Crisis, I will now begin define what standards actually are.

What are standards?

Standards are models, principles, policies, or rules that provide an agreed-upon framework for doing and understanding things. There are many different types of standards. When it comes to health information exchange, both data and technology standards are important. These standards describe (a) how health data are to be categorized and defined and (b) how different software systems are to communicate with each other when exchanging data. I will now discuss each.

Data standards

Data standards can be divided into at least four categories: terminology, measurement, care process, and messaging format standards. In this post, I describe terminology standards.

Terminology Standards Defined

Health-related terminologies are sets of terms representing a system of concepts within a specified field (domain) of healthcare. In other words, a terminology standard refers to a "nomenclature," i.e., a systemic way of naming and categorizing things in a given category.

Terminology standards include classifications and vocabularies that group together related terms so they can be more easily and consistently understood. Classifications arrange related terms for easy retrieval. Vocabularies use sets of specialized terms to facilitate communication by reducing ambiguity.

Take, for example, the term "high blood pressure" -- the following terms are synonyms of high blood pressure or the names of conditions referring to it:

accelerated hypertension; arteriolar nephrosclerosis; benign hypertension; benign intracranial hypertension; chronic hypertension; essential hypertension; familial hypertension; familial primary pulmonary hypertension; genetic hypertension; hypertension-essential; hypertension-malignant; hypertension-renovascular; hypertensive crisis; idiopathic hypertension; idiopathic pulmonary hypertension; malignant hypertension; nephrosclerosis-arteriolar; pph; pregnancy-induced hypertension; primary obliterative pulmonary vascular disease; primary pulmonary hypertension; primary pulmonary hypertension (pph); primary pulmonary vascular disease; pulmonary arterial hypertension, secondary; pulmonary hypertension; renal hypertension; secondary pulmonary hypertension; severe hypertension; toxemia; toxemia of pregnancy[1], hyperpiesia, and hyperpiesis.

Now imagine two electronic health record systems attempting to exchange patient data. One system is able to recognize the term "high blood pressure" and the other the term "hypertension," but neither can recognize both terms. These two computers would be unable to share the data because they don't "understand" what each other is "saying." This is because computers cannot deal with synonyms (using different words to say the same thing) or homonyms (when the same terms or phrase means different things in different contexts). So, when multiple healthcare providers treat the same patient (who may have multiple health problems), exchanging patient data can be difficult, which is due to the issues of semantics and syntax.

Semantics and Syntax

Semantics and syntax are standards of language. Semantics refers to the meaning of words, expressions and sentences, i.e., how they are defined. Syntax, on the other hand, is the structural or grammatical rules that define how symbols in a language may be combined to form words, phrases, expressions, etc., which includes spelling and word order. For example, in the U.S., this pattern of numbers "###-##-####" could be the syntax for coding a Social Security Number and mmm/dd/yyyy the syntax for a date.

In the situation above, there is semantic confusion since the two software systems define excessive blood pressure using different terms (high blood pressure vs. hypertension). Syntax would be a problem if, for example, both systems used the term high blood pressure, but only one required that the three words be connected, i.e., "high_blood_pressure".

Classifications and vocabulary terminology standards attempt to address these issues.

Classifications

Terminology classification standards in healthcare use a hierarchical index. The ICD-9 diagnostic standards, for example, classifies high blood pressure using this hierarchical index: Diseases of the circulatory system > Hypertensive disease > Essential hypertension (which includes high blood pressure; hyperpiesia; hyperpiesis; arterial, essential, primary and systemic hypertension; and hypertensive vascular); and it gives it a classification code number of 401.[Update; The ICD-10 is not being used]

Vocabularies

Terminology vocabularies standards, on the other hand, often consist of "controlled vocabularies," which are similar to the Library of Congress Subject Headings used by most libraries cataloguing books. Another example is the Yellow Pages in the phone book where, for example, car dealerships are listed under "Automobiles" instead of "Cars" or "Dealerships." Automobiles is, therefore, the "controlled vocabulary" used by the yellow pages.

In healthcare, the MeSH thesaurus is a controlled vocabulary catalog for searching biomedical and health-related information and documents. Searching for "high blood pressure" in the MeSH database returns the heading "Hypertension" and defines it as "Persistently high systemic arterial BLOOD PRESSURE. Based on multiple readings (BLOOD PRESSURE DETERMINATION), hypertension is currently defined as when SYSTOLIC PRESSURE is consistently greater than 140 mm Hg or when DIASTOLIC PRESSURE is consistently 90 mm Hg or more." So, MeSH says "hypertension" should be the term everyone uses to define blood pressure readings within this range; and if they use a different term, it should be translated to "hypertension."

Examples of Existing Terminology Standards

Following are some of the healthcare terminology standards system in use today:

• International Classification of Diseases (ICD) codes for diagnosis disorders
• International Classification of Impairments, Disabilities and Handicaps (ICIDH) codes for diagnosis handicaps
• International Classification of Nursing Practice (ICNP) for class nursing vocabularies
• Diagnostic and Statistical Manual (DSM) codes for classification of mental disorders
• Logical Observations: Identifiers, Names, and Codes (LOINC) codes for representing laboratory tests and procedures
• Current Procedural Terminology (CPT) codes for identifying conventional treatment procedures
• Advanced Billing Concept (ABC) codes for identifying integrative medicine procedures
• Digital Imaging and Communications in Medicine (DICOM) for distributing and viewing any kind of medical image
• Health Care Financing Association (HCFA) that controls Medicare and Medicaid and supports standards for reimbursement
• Unified Medical Language System (UMLS), a system linking together various medical vocabularies
• Systematized Nomenclature of Medicine-Clinical Terms (SNOMED-CT), a system of standardized medical terminology
• The Medical Subject Headings (MeSH) thesaurus, a controlled vocabulary produced by the National Library of Medicine and used for indexing, cataloging, and searching for biomedical and health-related information and documents
• Health Plan Employer Data and Information Set (HEDIS) is a standardized set of 60 performance measures for managed care plans.

In my next post, I define measurement, care process and messaging standards.

Reference:

[1] ICON Health Publications Official Health Sourcebooks