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Ariel Linden

Publications and source records attributed to Ariel Linden.

24 records · Page 2Linked to original sources
PubMed2004

Generalizing disease management program results: how to get from here to there.

For a disease management (DM) program, the ability to generalize results from the intervention group to the population, to other populations, or to other diseases is as important as demonstrating internal validity. This article provides an overview of the threats to external validity of DM programs, and offers methods to improve the capability for generalizing results obtained through the program. The external validity of DM programs must be evaluated even before program selection and implementation are begun with a prospective new client. Any fundamental differences in characteristics between individuals in an established DM program and in a new population/environment may limit the ability to generalize.

Disease Management↗
PubMed2003

The complete "how to" guide for selecting a disease management vendor.

Decision-makers in health plans, large medical groups, and self-insured employers face many challenges in selecting and implementing disease management programs. One strategy is the "buy" approach, utilizing one or more of the many vendors to provide disease management services for the purchasing organization. As a relatively new field, the disease management vendor landscape is continually changing, uncovering the many uncertainties about demonstrating outcomes, corporate stability, or successful business models. Given the large investment an organization may make in each disease management program (many cost 1 million dollars or more in annual fees for a moderately sized population), careful consideration must be given in selecting a disease management partner. This paper describes, in detail, the specific steps necessary and the issues to consider in achieving a successful contract with a vendor for full-service disease management.

Contract Services↗
PubMed2003

An assessment of the total population approach for evaluating disease management program effectiveness.

A key challenge currently facing the disease management industry is accurately demonstrating program effectiveness at controlling utilization of services and medical costs of populations with chronic disease. The most common method used in the disease management industry to date for determining financial outcomes is referred to as the "total population approach." This model is a pretest-posttest design, which is a relatively weak research and evaluation technique. This paper describes the "total population approach," details many of the biases and confounding factors that may influence outcomes using this method, and illustrates the potential consequences of these factors.

Bias↗
PubMed2003

Evaluating disease management program effectiveness: an introduction to time-series analysis.

Currently, the most widely used method in the disease management (DM) industry for evaluating program effectiveness is referred to as the "total population approach." This model is a pretest-posttest design, with the most basic limitation being that without a control group, there may be sources of bias and/or competing extraneous confounding factors that offer a plausible rationale explaining the change from baseline. Furthermore, with the current inclination of DM programs to use financial indicators rather than program-specific utilization indicators as the principal measure of program success, additional biases are introduced that may cloud evaluation results. This paper presents a non-technical introduction to time-series analysis (using disease-specific utilization measures) as an alternative, and more appropriate, approach to evaluating DM program effectiveness than the current total population approach.

Disease Management↗