Friday, June 01, 2007

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]


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.

[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)

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