Ethical Issues of Health Management Predictive Modeling

Ethical Issues of Health Management Predictive Modeling

Elizabeth McGrady (University of Dallas, USA) and Linda W. Nelms (Tennessee Department of Health, USA)
DOI: 10.4018/978-1-60566-266-4.ch007
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Abstract

In the wake of continuously escalating healthcare costs, health management in the workplace has gained new momentum as employers strategize to optimize the health of their workforce while containing healthcare costs. Gaining acceptance as a viable tool to aid employers is a process called Predictive Modeling. On the surface, Predictive Modeling may contribute significantly to delivering the right interventions to the right person at the right time by identifying high risk individuals, and underusers and overusers of health services. This chapter discusses the ethical principles of nonmaleficence, beneficence, justice and autonomy, as well as value judgments and human rights as applied to Predictive Modeling to guide professionals and employers in health management decisions.
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Background

First the chapter will define Predictive Modeling, Health Management and Ethics and then discuss a framework for application of ethics to Health Management Predictive Modeling.

Predictive Modeling

Predictive Modeling (PM) is defined by Jonathan Weiner, PhD of Johns Hopkins Bloomberg School of Public Health as ‘a process that applies available data to identify persons who have high medical need and are at risk for above-average future medical service utilization’ (Carlson, 2003). Predictive Modeling is currently a diverse field and generally utilizes algorithms derived from regression analysis, decision trees, rule-based systems or neural networks to identify high risk individuals. The overall goal is to improve health status while reducing cost (Axelrod & Vogel, 2003). Uses include (Tremblay, 2005):

  • 1.

    Identifying healthy persons whose health is likely to decline;

  • 2.

    Reducing inconsistency in care;

  • 3.

    Predicting who is likely to be non-compliant with prescribed treatments and medications;

  • 4.

    Identifying patients with high risk for actionable chronic diseases such as diabetes, congestive heart failure (CHF), asthma, chronic obstructive pulmonary disease (COPD), and depression.

Factors such as age, gender, total co-payments at drug initiation, total medication burden, and initial compliance with prescribed care are predictive of long term compliance (Carlson, 2003). Limitations of PM are that it must use readily available data and generally does not include affective constructs such as attitudes and beliefs. As adoption of PM applications becomes more widespread it is incumbent upon health management professionals to use ethical guidelines as a framework in deciding when to use it and how to use it. A review of the basic tenets of ethics in relation to PM provides a guideline for recommending and designing programs.

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