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Decision-support software clarifies cost, revenue division.

The basic premise for decision support has not really changed over the years. It's much less expensive and less harmful to play with a model of the organization and test the impact of key variables than it is to stand back and watch things unfold.

Another decision-support standard is that the business model is quite simple: revenue minus expense equals margin. As simple as this might seem, in healthcare, there has been an increased separation between the way revenue is earned and the way expenses are incurred. In the old fee-for-service world, providers were paid individually for each service they delivered. Ever since diagnosis related groups (DRGs), payments have been determined prospectively, regardless of the intensity of services delivered. This division of cost and revenue becomes even greater under a capitated agreement, where revenue is earned per member and costs are incurred only when members receive services. Consequently, decision-support products are essential to sort out this mess. Trends that we see emerging from this are:

Member management--This is the ability to analyze the demographic composition of a member population to determine a provider's exposure to offer healthcare services. Simply put, one must assess the revenue that can be generated on a per member, per month basis (the premium payment), against the actuarial risk of those members requiring healthcare.

Continuum of care--The consolidation of the healthcare industry has forced more and more institutions to become concerned with the longitudinal management of patient care across a variety of different healthcare providers. This is important from both a financial as well as a clinical perspective. One example of this business problem deals with "package pricing," in which a complex series of procedures is given a single price. For an integrated oncology service provider to quote a package price for a bone marrow transplant is no easy feat.

Another business problem related to the management of patient care across services and providers is case management. Case management is more operational in nature and is designed to ensure that resources are used efficiently and effectively. It is no longer adequate to determine how long a patient has stayed within an institution or what resources were utilized. It has become equally important to know the sequence in which care has been rendered and how that care compares to an organizational benchmark. Providers need this information in a real-time setting so that action can be taken quickly once a variance has been identified. Case management and critical development are perfect examples of this type of decision-support application.

Distributed applications--One of the greatest opportunities in the decision-support arena is to greatly expand the community of information users by making the analysis of data and access to information more readily available. Traditionally, decision-support applications have been the responsibility of a core group of analysts who evaluated problems and distributed the output throughout the institution. Increased computer fluency and the evolution of graphical user interfaces have made it possible for a much larger constituency of users to query the systems, build their own models, and analyze ad-hoc problems themselves.

Benchmarking--The availability of both public domain and proprietary datasets has made it much easier for organizations to compare their performance to that of other institutions similar in size and service delivery. It always seems easier to do something if you know that it's been done before. Consequently, the availability of clinical benchmark data has made it much easier for institutions to develop and implement clinical standards. This approach is often referred to as "best demonstrated practice."

Critical paths--Slowly, clinicians have become accepting of the notion of a standard protocol for delivering care. This defines clinical interventions in terms of repeatable processes that can be practiced again and again for those patients with a similar medical condition. This acceptance has allowed for the definition of a production function for care delivery that greatly improves the ability to predict resource consumption, control and manage risk, as well as improve the accountability for service delivery.

Outcome assessment--As competition has intensified, providers have, to date, competed primarily in price to gain market share. Increasingly, purchasers will demand value (or high-quality, low price) as the desired healthcare outcome. To date, the healthcare industry has not defined a standard measure for patient outcomes. Nevertheless, there are a great deal of proxies that can be used, such as length of stay, health status, patient satisfaction and the occurrence of an adverse incident. Decision-support systems must be able to integrate clinical outcomes as part of their standard analysis.

John Nunnelly is the vice president and general manager of The Amherst Product Group with HBO & Company, Atlanta.
COPYRIGHT 1996 Nelson Publishing
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Title Annotation:Technology Information; includes products features table
Author:Nunnelly, John
Publication:Health Management Technology
Date:Jun 1, 1996
Words:767
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