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Biomedical subjects

Ariel Linden

Publications and source records attributed to Ariel Linden.

At least 19 recordsLinked to original sources
PubMed2006

Effect of motivational interviewing-based health coaching on employees' physical and mental health status.

Motivational Interviewing (MI) based health coaching is a relatively new behavioral intervention that has gained popularity in public health because of its ability to address multiple behaviors, health risks, and illness self-management. In this study, 276 employees at a medical center self-selected to participate in either a 3-month health coaching intervention or control group. The treatment group showed significant improvement in both SF-12 physical (p = .035) and mental (p = .0001) health status compared to controls. Because of concerns of selection bias, a matched case-control analysis was also performed, eliciting similar results. These findings suggest that MI-based health coaching is effective in improving both physical and mental health status in an occupational setting.

Adult↗
PubMed2006

Disease management interventions II: What else is in the black box?

The success of any disease management (DM) program ultimately depends upon the ability and willingness of participants to change and maintain desired health behaviors. To achieve those results, DM program administrators have several issues to consider, including the type of behavioral change desired, the scope of intervention that the organization is willing and capable of implementing, and whether the appropriate support structures are available to ensure successful achievement of program goals. An understanding of these issues will assist program designers in selecting the appropriate change models. This paper serves as an extension of our prior paper in which eight core psychosocial behavioral change models were described. Here, five more recently developed theory-based approaches are introduced, providing readers with up-to-date information in this area.

Community Networks↗
PubMed2006

Evaluating disease management programme effectiveness: an introduction to the regression discontinuity design.

Although disease management (DM) has been in existence for over a decade, there is still much uncertainty as to its effectiveness in improving health status and reducing medical cost. The main reason is that most programme evaluations typically follow weak observational study designs that are subject to bias, most notably selection bias and regression to the mean. The regression discontinuity (RD) design may be the best alternative to randomized studies for evaluating DM programme effectiveness. The most crucial element of the RD design is its use of a 'cut-off' score on a pre-test measure to determine assignment to intervention or control. A valuable feature of this technique is that the pre-test measure does not have to be the same as the outcome measure, thus maximizing the programme's ability to use research-based practice guidelines, survey instruments and other tools to identify those individuals in greatest need of the programme intervention. Similarly, the cut-off score can be based on clinical understanding of the disease process, empirically derived, or resource-based. In the RD design, programme effectiveness is determined by a change in the pre-post relationship at the cut-off point. While the RD design is uniquely suitable for DM programme evaluation, its success will depend, in large part, on fundamental changes being made in the way DM programmes identify and assign individuals to the programme intervention.

Disease Management↗
PubMed2006

Measuring diagnostic and predictive accuracy in disease management: an introduction to receiver operating characteristic (ROC) analysis.

Diagnostic or predictive accuracy concerns are common in all phases of a disease management (DM) programme, and ultimately play an influential role in the assessment of programme effectiveness. Areas, such as the identification of diseased patients, predictive modelling of future health status and costs and risk stratification, are just a few of the domains in which assessment of accuracy is beneficial, if not critical. The most commonly used analytical model for this purpose is the standard 2 x 2 table method in which sensitivity and specificity are calculated. However, there are several limitations to this approach, including the reliance on a single defined criterion or cut-off for determining a true-positive result, use of non-standardized measurement instruments and sensitivity to outcome prevalence. This paper introduces the receiver operator characteristic (ROC) analysis as a more appropriate and useful technique for assessing diagnostic and predictive accuracy in DM. Its advantages include; testing accuracy across the entire range of scores and thereby not requiring a predetermined cut-off point, easily examined visual and statistical comparisons across tests or scores, and independence from outcome prevalence. Therefore the implementation of ROC as an evaluation tool should be strongly considered in the various phases of a DM programme.

Data Interpretation, Statistical↗
PubMed2006

Strengthening the case for disease management effectiveness: un-hiding the hidden bias.

As is the case with most health care program evaluations, disease management (DM) programs typically follow an observational study design, indicating that randomization to treatment or control was not performed. The foremost limitation of observational studies, compared to randomized studies, is that the only biases that can be controlled for are those associated with observed variables. Hidden bias refers to all those unobserved covariates that may distort the conclusions of the study. This paper introduces a sensitivity analysis that is used to determine the magnitude of hidden bias necessary to alter the conclusion that a DM program intervention was indeed effective.

Bias↗
PubMed2006

Evaluating disease management programme effectiveness: an introduction to instrumental variables.

This paper introduces the concept of instrumental variables (IVs) as a means of providing an unbiased estimate of treatment effects in evaluating disease management (DM) programme effectiveness. Model development is described using zip codes as the IV. Three diabetes DM outcomes were evaluated: annual diabetes costs, emergency department (ED) visits and hospital days. Both ordinary least squares (OLS) and IV estimates showed a significant treatment effect for diabetes costs (P = 0.011) but neither model produced a significant treatment effect for ED visits. However, the IV estimate showed a significant treatment effect for hospital days (P = 0.006) whereas the OLS model did not. These results illustrate the utility of IV estimation when the OLS model is sensitive to the confounding effect of hidden bias.

Data Interpretation, Statistical↗
PubMed2006

Evaluating program effectiveness using the regression point displacement design.

Most health care initiatives are evaluated using observational study designs in lieu of randomized controlled trials (RCT) due primarily to resource limitations. However, although observational studies are less expensive to implement and evaluate, they are also more problematic in determining causality than the RCT. This trade off is most apparent in the initial planning stage of program development. An RCT is generally preferred though the cost of implementing a pilot program using the RCT might outstrip the potential benefit if the desired results are not obtained. This article describes a simple quasi-experimental model called the regression point displacement (RPD) design, which compares the prepost results of a single or multiple treatment groups to that of a control population. This design has shown great potential in evaluating health care pilot programs or demonstration projects-especially those that are community based-due to its relative ease of implementation and low cost of analysis.

Humans↗
PubMed2006

Is Israel ready for disease management?

Approximately 60% of all worldwide deaths are caused by chronic disease resulting from modifiable health behaviors. In the United States, structured programs tailored to identify and modify health behaviors of patients with chronic illness have grown into a robust industry called disease management. DM is premised upon the basic assumption that health services utilization and morbidity can be reduced for those with chronic illness by augmenting traditional episodic medical care services and support between physician visits. Given that Israel and the U.S. have similar demographics in their chronically ill populations, it would make intuitive sense for Israel to replicate efforts made in the U.S. to incorporate DM strategies. This paper provides a conceptual framework of how DM could be integrated within the current organizational structure of the Israeli healthcare system, which is uniquely conducive to the implementation of DM on a population-wide basis. While ultimately the decision to invest in DM lies with stakeholders at various institutional levels in Israel, this paper is intended to provide direction and support for that decision-making process.

Adult↗
PubMed2005

Using visual displays as a tool to demonstrate disease management program effectiveness.

With the rapid introduction of new medical and information technologies, there are much more data available today than ever before. Translating data into useful information for a wide variety of audiences is a challenge for health care in general and disease management programs in particular. This paper addresses these issues by introducing several visual displays that illustrate important data elements in an unencumbered fashion. Examples are provided using the various stages of a hypothetical congestive heart failure (CHF) disease management program (ie, patient identification, program enrollment, intervention process, and outcomes evaluation).

Coronary Disease↗
PubMed2005

A user's guide to the disease management literature: recommendations for reporting and assessing program outcomes.

Recently there has been tremendous growth in the number of lay-press articles and peer-reviewed journal articles reporting extraordinary improvements in health status and financial outcomes due to disease management (DM) interventions. However, closer scrutiny of these reports reveals serious flaws in research design and/or analysis, leaving many to question the veracity of the claims. In recent years, there have been numerous contributions to the literature on how to assess the quality of medical research papers. However, these guidelines focus primarily on randomized controlled trials, with little attention given to the observational study designs typically used in DM outcome studies. As such, general guides to evaluating the medical literature are inadequate in their utility to assist authors and readers of DM outcomes research. The purpose of this paper is to provide authors with a clear and comprehensive guide to the reporting of DM outcomes, as well as to educate readers of the DM literature (both lay and peer reviewed) in how to assess the quality of the findings presented.

Disease Management↗
PubMed2004

Using an empirical method for establishing clinical outcome targets in disease management programs.

The disease management (DM) industry is being scrutinized now more than ever before, with programs being asked to demonstrate improvement in clinical quality in addition to the expected reduction in medical costs. In healthcare, clinical improvement targets are often set at levels considered to be clinically meaningful. This difference may or may not be statistically significant. The term "effect size" refers to the smallest difference that could be detected statistically. This paper proposes a simple empirical method for determining the minimum expected improvement level for DM clinical outcome measures in which two proportions are being compared. This method is useful in situations where the outcome measure does not lend itself to be determined by the subjective judgment of medical expertise. Graphical displays are provided for the reader to use to help determine appropriate effect sizes for studies in lieu of, or in addition to, the statistical calculations.

Cost Control↗
PubMed2004

Evaluating disease management program effectiveness: an introduction to survival analysis.

Currently, the most widely used method in the disease management industry for evaluating program effectiveness is 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 plausible rationale explaining the change from baseline. Survival analysis allows for the inclusion of data from censored cases, those subjects who either "survived" the program without experiencing the event (e.g., achievement of target clinical levels, hospitalization) or left the program prematurely, due to disenrollement from the health plan or program, or were lost to follow-up. Additionally, independent variables may be included in the model to help explain the variability in the outcome measure. In order to maximize the potential of this statistical method, validity of the model and research design must be assured. This paper reviews survival analysis as an alternative, and more appropriate, approach to evaluating DM program effectiveness than the current total population approach.

Disease Management↗
PubMed2004

Disease management interventions: what's in the black box?

In discussing evaluation techniques to assess disease management (DM) program outcomes, it is often assumed that DM program interventions are premised on sound clinical judgment, an understanding of the disease process, and knowledge of the psychosocial models of behavioral change that must be used to effect those processes and ultimately improve the health outcomes that are being evaluated. This paper describes eight commonly used behavioral change models applied in the healthcare industry today. They represent programs designed to address individual, interpersonal, and community level factors as well as "packaged" comprehensive approaches. These models illustrate the breadth of approaches to consider when designing or assessing DM program interventions. Careful consideration of the type of behavioral change desired and the theories of how to effect such change should be an integral part of designing disease management program interventions.

Attitude to Health↗