Health care decision-support systems needed now - Informatics

Physician Executive, July-August, 2003 by Fidel Davila

The foundations of health care delivery systems involve the integration of participant, clinical and financial data to generate important information.

Based on this information, actions may be taken, new practices developed, insurance coverage and payments determined and policies produced. Later, data may be recollected, information regenerated and the process repeated.

But regrettably, in no other industry has the promise of information technology to supply this essential information been less broadly realized than in health care.

Health information management systems do not meet current health care information exigencies. This is because they cannot group the tangible features of the participants and their related clinical processes, delivery and outcomes into reliable decision-support information.

These inabilities result from incompletely collecting the vital data elements into decision-support databases. Consequently, health information management systems remain data systems or, at best, incomplete decision-support systems.

Previously, health information management suffered from a lack of the bitways, middleware, applications and hardware necessary to make the decision-support databases feasible and affordable. This is no longer true.

Currently lacking is a clear concept of the decision-support databases needed and a description of the health care industry's collaborations needed to produce them.

Let's consider the concepts involved in creating databases and the various aspects of information management and health care industry collaborations that must be successfully assembled in order to make comprehensive decision-support databases possible.

Differentiating data from information

Data is distinct from information in that information is data that has meaning to its end user.

For example, the number "10 is essentially meaningless. To give meaning to '10", the "what" needs to be denoted. Ten dollars, 10 fingers, 10 injections, 10 anything conveys information provided the end user has the knowledge to evaluate it in its context.

Ten rubles, a hemogoblin of 10, 10 penny nails are examples where the end user's knowledge and the data's context are needed to realize meaning or generate information.

The data's context is very consequential to information generation and should not be overlooked. For example, $10 in New York City may have a different meaning (and be different information) than $10 in a third-world country.

Without knowledge and context, the "10 what" may simply remain data. Incomplete data and/or context, which lead to incomplete information, are the current state of affairs in health care information.

Data's meaning--and its transformation to information--is contingent on its context and on the end user's knowledge. Since machines do not yet have more than rudimentary knowledge, people remain an integral part of information generation.

Identifying the end users' knowledge level is key to preparing data and generating information. If the end users are experts, little to no data preparation may be needed. These experts will generate their own information.

If the end users do not have the knowledge necessary to generate information from the data presented, then intermediate steps by knowledgeable experts to do these data conversions are required. Finally, the data and/or information must be presented in a way that is usable by the end users.

Ultimately, information is valuable only if a decision may be made with it. Without the possibility of a decision, the benefits of collecting data and generating information must be questioned.

It is the decision-making value of information that makes information the bedrock of health care and there is a critical need for health care decision-support databases.

End user groups

In health care, there are four major information end users:

* Clinicians who need the information for clinical evaluation and management of patients

* Financial personnel who need the information for evaluating and managing the economics of health care

* Researchers who need the information to discover, integrate and/or apply new knowledge and propose new practices and policies

* Educators who need the information to school new knowledge experts/participants

Each of these major end users requires different decision-support databases to fulfill their needs. Yet, there is considerable duplication between each end user's data elements.

At the same time, there are data elements exclusive to only one end user's database. This combination of duplication and exclusivity makes cooperation between end users to share data elements an imperative.

The sharing of data elements will markedly reduce the costs of redundancy and collection of exclusive data elements making the decision-support databases affordable and, more importantly, complete.

Note, however, that privacy, trade and competition considerations will restrict the sharing of some exclusive data elements.

For example, it would be highly unusual for educators to need data that identifies participants. They could use all the data except participant identities for their schooling.