This chapter will explore the many aspects of health – related patient decision – making, with particular emphasis on examining how computer technology can enhance patient’s ability to gather, analyze, and understand information key to their empowerment as active participants in the management of both their health and interactions with the healthcare system.

Health – Related Decision – Making

Health – related decision – making is challenging for patients for several key reasons. First, decision – making itself is a complex, perceptual, cognitive, and social process. The talents and limits of humans as decision makers, particularly in the face of substantial uncertainty, are well known and well described in the work of the human information processing theorists (Tversky and Kahneman, 1972). Essentially, information processing is a complex challenge, and humans employ simplifying mental mechanisms as coping strategies to help them sort significant from insignificant facts, to organize and interpret complex observations, to facilitate recall and synthesis of known knowledge with new facts, and to apply judgments under conditions where consequences and outcomes are uncertain. Stressors, such as anxiety, time pressures, and lack of knowledge, lead these efficient processes to deteriorate in such a way as to lead to suboptimal or even incorrect decision processes. Lay persons facing health crisis simultaneously experience many stressors, thus, their information processing skills are taxed repeatedly.

Second, health – related decision – making is complicated because the substance of the problems and choices is itself complex and exceeds the knowledge and education of most laypersons. At the very point that patients face a threat to their integrity as persons they are challenged to manage and attempt to understand large amounts of new and complex information, much of which they may not have the knowledge base to effectively process. So, with diminished information processing skills, the individual must attempt to comprehend and interpret new and unfamiliar facts and relate them to their own system of values and beliefs. Even those persons who are not in a health crisis may have difficulty locating and evaluating the quality and relevance of health information. Facts from authoritative sources mix with hearsay from unreliable mass media or personal interactions, resulting in an unsound basis for making health – related decisions such as following primary prevention recommendations or carrying out healthy lifestyle choices.

Finally, health – related decision – making is complex because it generally involves more than a single person. Two key groups must be considered: the family members of the person facing a health crisis or in need of health information and the healthcare delivery team. Family members hold values, beliefs, and attitudes that implicitly or explicitly influence the health choices of an individual. The healthcare industry holds clinical care standards, values, and attitudes about patients’ responsibilities for their care and organizational or personal traditions that may interfere with the person’s right to self – determination and self – care.

Healthcare decision – making is challenging because it involves uncertainty, taxes human information processing capabilities, deals with subject matter that is unfamiliar to the involved person, and there are multiple constituencies that must be observed.

Shared Decision – Making and Informed Choice

The emphasis on involving patients in healthcare decision – making has greatly increased with the widespread acceptance of shared decision – making approaches, which advocates that patients are best able to determine which values should govern their care. Traditionally, clinicians have assumed responsibility for judging the appropriateness of the clinical treatments and their associated outcomes in the frame of reference of both their own values and those of the patient. Shared decision – making is also known as relationship or collaborative decision – making which empowers patients to choose among the options available to them in consultation with their clinician(s) using their personal values to frame the choice among alternatives. To accommodate this range of preferred involvement, clinical providers must adapt through flexible and effective communications mechanisms (Schwartz, McDowell, and Yueh, 2004).

Related to shared decision – making is the concept of evidence – informed choice. Informed consent has been an established practice that involves the patient acknowledging that they have received adequate information to assent to the care that is recommended by the clinician. Informed consent is a passive process that simply requires that a patient has knowledge of the treatments and the probable outcomes (O’Connor and Jacobsen, 2003). Evidence – informed choice sets a much more rigorous standard that requires that patients both receive and understand information that enables them to evaluate risks and benefits of alternative options, examine how they value the benefits versus the risks, and then use that information collaboratively with their clinicians to decide on the optimal course of action consistent with the joint values of clinicians and patients (Ledley and Lusted, 1999). There are strong legal and ethical motivations for driving the clinician/patient relationship as far in the direction of informed choice as possible. The following is an excerpt from a malpractice case decided by the New Jersey State Court: Accordingly, the doctor must discuss all medically reasonable courses of treatment, including non – treatment, and the probable risks and outcomes of each alternative. By not discussing these alternatives, the doctor breaches the patient’s right to make an informed choice and effectively makes the choice for the patient. The doctor has a duty to explain, in worlds the patient can understand, all material medical information and risks. (NJ State Court Malpractice ruling)

Decision support technology is a key tool to enable a higher level of understanding and evaluation of alternatives available to the patient and thus serves as a key to achieving informed choice by insuring that information regarding courses of treatment is comprehensively and uniformly communicated.

A major determinant of choice and decision – making is the context provided by the values held by an individual; therefore, in this discussion of health – related decision – making we now turn attention to the concept of patient preferences.

Patient Preferences

Attention to patient preferences as an input into healthcare decision – making is rooted in the application of decision theory to understand personal choice.

      Von Neumann and Morgenstern (1964)
First proposed that the personal values and attitudes that drive individual choice could be understood through mathematical formulations. These formulations are based on an economic analysis model where numerical rating of a health state corresponds to its relative desirability. Following on their work:

    Ledley and Lusted (1999)
Introduced the concepts of mathematical reasoning to medical decision – making, with particular attention to decision – making under uncertainty.

       Raiffa (1968)
Explicated decision analytic strategies that brought the treatment of personal preference and uncertainty into a form accessible in an interpersonal interview.

Recently, the work of Pauker and McNeil (1981) and associates (Sonnenberg and Pauker, 1986; Pauker, Pauker,  and    MacNeil, 1981)
Demonstrated the feasibility of using decision analysis to better understand treatment choices that are complicated by multiple uncertainties and personal values.

            These works offer a theoretical foundation for building health informatics tools that aid in the assessment of patient preferences.

The two main branches of decision theory: decision analysis and normative decision theory, both help make patient preferences     accessible for clinical decision – making. Decision analysis helps in choosing one course of action from several when the most desired strategy depends, in part, on the knowledge of the costs, benefits, and probabilities of the resolution of the outcomes of that strategy.

Multiattribute utility theory (MAUT) provides the mechanisms for quantifying the subjective value of health states and therefore can be very useful to patients who must make healthcare decisions (Ledley and Lusted, 1999; Keeney and Raiffa, 1976). In MAUT, a utility is a numerical representation of the value, desirability, or preference for a health state. MAUT defines preferences as the ordering of entities over a value space where the ordering corresponds to the relative preference for the entity (Keeney and Raiffa, 1976). The entities about which one developed preferences are discrete objects, such as cars or job candidates or in healthcare parlance, specific health outcomes and the order of the preference value of the entity is a surrogate for the relative preference of the entity itself. Entities were viewed as multidimensional with the value space describing the n – dimensional intersection of a specific set of entities specified simultaneously on all dimensions. The set of possible values in each of the dimensions and the relative weights of each defined the ordering of the preference structure. MAUT provides a way to establish a quantitative expression of an individual’s values with respects to a given set of alternatives, with preference for a given health outcome being expressed as a score on the weighted sum of the entities and their relative weights. A utility function computes a score for each treatment alternative that explicitly incorporates the probability of the outcome of each treatment and a quantitative estimate of the desirability of the outcomes following each treatment.

MAUT is based on compensatory rules that allow for assessing tradeoffs among entities in such a way that a high value for one entity is compensated for by a low value in another entity. The method of incorporating both desirability and probability results in a value that reflects both risk and cost/reward. The highest weighted score thus calculated would be the choice preferred by a rational decision maker under this decision model.

Most informatics tools designed to elicit patient preferences are grounded in these decision theoretic conceptualizations (Lenert and Soetikno, 1997). In this context, the preference statement denotes the extent to which given health states are desirable according to some implicit or explicit valuing scheme. Other uses of the term patient preferences also exist.

       Alternate Meanings of the Term “Preferences”

The distinction between preferences as a formalization of values, vis – à –vis a set of healthcare entities and preferences as the identified option chosen from the set of healthcare – related entities becomes important when one examine how computers could be of assistance in eliciting patient preferences. Different kinds of computer programs and utilities support decision – making; some consider preference as an input to a decision while others view preference as the final choice resulting from a decision. Clarifying the exact referent of preferences is a necessary precursor to the design of computer systems to support the use of patient preferences in healthcare. Donabedian’s three – part quality model (Donabedian, 1968) provides a useful heuristic for sorting out the various referents about which individuals may develop preferences. Individuals may establish preferences about structural aspects of healthcare, such as belonging to a health maintenance organization (Saintfort and Booske, 1996) or their preferences for information or decision –making (Deber, Kraetschmer, and Irvine, 1996). Preferences for treatment options, such as surgical versus medical interventions, represent the individual’s appraisal about process aspects of healthcare. A third referent for preference is the outcomes of health actions.

Some use the term “preference” to represent an individual’s final choice of one option from many possible treatment options.

Moore and Kramer (1998) used the term “preferences” to identify those features of cardiac rehabilitation programs deemed most desirable by patients. In this case, the preferences expresses the desirability of the features of a program, not the program (entity) itself. Henry and Holzemer (1995) identified preferences as “patient – specific inputs to the care process.” Under this definition, preferences are automatic judgments that can be integrated with other components of the patient assessment and subsequently used to select treatment strategies.

       Challenges to Using Patient Preferences for Health – Related Decision – Making

Although the value of understanding and using patient preferences in healthcare decision – making is well recognized (O’Connor et al., 1999; Eraker, Kirschtk, and Becker, 1984), actually dong so can present a daunting challenge to patients (Gerteis et al., 1993). Imagining what a future health state could be like and determining the desirability of that future state is a complex cognitive task. Additionally, many patients lack experience with thinking about abstract concepts such as values, preferences, and risks. When patients are asked to evaluate complex situations with potentially adverse consequences under the stressful circumstance of the clinical encounter their cognitive ability is taxed to an even greater degree. Skilled interpersonal interaction can lead to an accurate assessment of an individual’s preferences but the fragmented, time – limited nature of contemporary health encounters leaves little opportunity to conduct the intense, interpersonal exploration needed to elicit and use patient preferences (Sonnenberg and Pauker, 1986; Pauker and McNeil, 1981).

Computer Technology and Patient Decision – Making

       Assessing Utilities of Health Outcomes

The Stanford Center for the Study of Patient Preference (the Center) was a pioneer in the use of computers and the Internet for low – cost elicitation of patient preferences for health states. Initially, computerized surveys and instructional programs available on the WWW walked the patient through classic decision analytic methods to help them clarify their preferences. Subsequently, patients approached a rating task through programs that elicit preferences for specific health states (Lenert and Soetikno, 1997; Goldstein et al., 1994). These preference assessments used visual analog scales (VAS), pair – wise comparisons (PWC), standard gamble (SG), and time trade-off (TT) methods to measure patient utilities (Keating et al.,2003).

The SG method asks the patient to determine the indifference point where living in specified health state is perceived to be equivalently preferable to a specific probability of death, which provides a preference ordering where alternatives that are equivalent to a higher probability of death are preferred. The VAS method uses a visual representation of a linear scale with one end representing the best possible health state being evaluated in the position that best describes their preferences relative to the extremes, yielding an ordering of alternatives based on the visual analog scale. PWC asks patient to evaluate their preferences for each possible health state or treatment in a pair-wise fashion, which then determines a preference ordering for all alternatives. The TT method asks the patient to determine the number of years that life in perfect health would be equally preferable to a longer period in the health state in question. The resulting ratio represents the preference for the poorer health state.

       Envisioning Treatment Options

The technology – based Shared Decision – Making Program (SDP) was developed within a framework grounded in the idea that rational treatment decision – making considers both what the patient wants and what the clinician views as appropriate. The SDP was assigned for use in the clinic setting to aid patients facing complex treatment choices (Liao et al.,1996; Kasper, Mulley, and Wennberg, 1992).

       Facilitating Data Management

At Dana – Farber Cancer Institute, reports of health – related quality of life (HRQOL) are obtained from cancer patients each time they go to the breast cancer outpatient clinic. Patrick and Erikson (1993) define HRQOL as “the value assigned to the duration of life as modified by the social opportunities, perceptions, functional states, and impairments that are influenced by disease, injuries, treatments, or policy”. Patients’ assignation of a value to their current QOL,  vis – à –vis their preference for a health state, can be quantified on a continuum from 0 to 1. The longitudinal elicitation of patients’ perceptions of the effects of both the cancer and the treatment on their QOL presents the clinician with multiple opportunities to improve patient care. The clinician receives self – reported information that can instigate further discussion with patients about their preferences during the visit. These elicited data also act as feedback to the clinician about the outcomes of care since the last visit.

       Linking Preferences with Treatment Decisions

The Department of Family Practice at the Medical College of Virginia, Virginia Commonwealth University, designed HealthTouch, a computerized health information system for health promotion and disease prevention for use in primary care (Williams, Boles, and Johnson, 1995). Evaluated in a randomized clinical trial involving 29 primary care practices, HealthTouch was intended to supplement clinician involvement in patient – focused preventive services. As factors that contribute to variation in health and prevention outcomes, patient preferences regarding diet management, exercise routines, weight control strategies, and other practices served as the basis for the customized computer recommendations for prevention. The preference assessment in HealthTouch is semantic in nature and does not rely on an explicit decision theoretic model.

Efficacy of Decision Aids

Decision aids for providing information regarding treatment options and health states leading to the elicitation of patient preferences have been developed to provide assistance to patients who are facing complex healthcare decisions. As described earlier, patients may have preferences for the structural, process, or outcomes dimensions of healthcare. Clinicians and health providers generally place a high value on achieving optimal clinical outcomes. While the value systems of the patients and clinicians may sometimes conflict, the goal is for decision aids to support and enhance patients’ ability to choose a course of treatment that is consistent with their values along each of these three dimensions while simultaneously yielding optimal clinical outcomes. Measures of efficacy should therefore evaluate the degree to which the decision aids positively impact all of these outcomes. The Ottawa Health Research Institute (OHRI) has developed a set of evaluation measures and instruments that can be used by implementation of Decision Support System (DSS) to assess their systems performance along the dimensions of choice predisposition, decisional conflict, regret, acceptability, knowledge, realistic expectations, values, preparation for decision – making, and decision self – efficacy (http://decionaid.ohri.ca/eval.html).

A range of studies of decision aids for specific diseases has shown results that are similar to the systematic review (Janz et al., 2004; Sculpher et al., 2004; Whelan et al., 2003) and one showed an increase in social support for the patient (Lenert  and Kaplan, 2000) with another showing decreased costs of treatment (Kennedy, et al., 2002). At this point, there are no studies that show conclusive improvements in clinical outcomes for those patients who used decision aids when compared to those who did not, which is consistent with their focus on improving the congruence with the values and preferences of patients, which are distinct from the health providers’ values on clinical outcomes.

Points of Decision Support System Intervention

DSSs have been introduced at several types of intervention points. The most commonly deployed systems are those that are used when a patient has entered an acute phase of a disease. In these cases, DSS are narrowly targeted to providing the patient with a level of information adequate to allow them to make informed choices and participate in the shared decision process.

An emerging application of computer – based DSS is in the area of chronic disease management. The success of the management of chronic disease is characterized by the need to timely monitor patients’ status and their compliance with the treatment protocols over an extended period of time. Patients may require access to information and their clinician throughout the course of their illness so that they can understand their health states and move toward self – management of their disease. The computer – based DSS can function as an intelligent disease management agent, which can be used to remotely acquire and transmit health indicators such as heart rate and weight and can be used to prompt patients when it is time to take their medicine or perform physical therapy activities.

Effective behavior change is needed in diverse areas such as weight control, good nutrition, smoking cessation, or substance abuse treatment. In addition, behavior modification methods are important in increasing compliance with medication in conditions such as hypertension, asthma, diabetes, and, human immunodeficiency virus – acquired immunodeficiency syndrome (HIV - AIDS). Computer based DSSs have great potential to provide the information and reinforcement that is required to achieve changes in the chain of decisions that define an undesirable behavior.

       Acute Disease Decision Support Systems

DSSs that are employed in the support of acute disease states typically are narrowly focused on supporting the patient by providing for their informational and preference determination needs regarding a single episode of treatment choices. The degree of comprehensiveness of the DSS is tailored to meet the specific characteristics of the disease with regard to the level of decision that the patient is going to be called on to participate in. Generally these diseases states with these types of characteristics require increasing levels of information presentation and decision support (Kassirer, 1994).

·  Alternatives differ greatly in their outcomes, complications, or side effects.

·  Alternatives require trading off long – term and short – term outcomes.

·  A choice or choices may result in a small chance of a grave outcome.

·  There are only small differences in the outcomes of treatment alternatives.

Commonly, the disease has been diagnosed by a specialist with whom the patient may only have a newly formed relationship. In this case, the clinician – patient relationship that is a prerequisite for the shared decision process does not exist. It is particularly important, in this case, for the DSS to provide a structured method of the joint examination of the patient’s preferences that result and that the DSS include mechanisms for determining and conveying to the clinician the level of participation and control in the decision process that the patient prefers.

The Comprehensive Health Enhancement Support System (CHESS) is a health promotion and support network application that operates as a module – based computer system for in-home or healthcare setting use (Gustafson et al., 1994). People with major illnesses or health concerns can access information, decision support, social support, skill training, and a referral resource. Several of the CHESS services help patients to clarify their values in preparation to make decisions that are consistent with their preferences.

       Chronic Disease Management Decision Support Systems

Chronic diseases such as multiple sclerosis (MS), HIV/AIDS, amyotrophic lateral sclerosis (ALS), asthma, cancer, and hypertension are present at significant and growing rates in many of the countries around the world. Computer-based DSSs have the capability to enable the patient to better monitor and treat these diseases resulting in increased lifespan and QOL. There have been a relatively large number of clinical trials that employ computers as key mechanisms for management of chronic diseases and they have been shown to improve outcomes (Montgomery and Fahey, 1998), enhance treatment compliance, and increase shared decision – making. The primary difference between DSS that support chronic diseases from those that support singular treatment acute diseases is their extension to handle symptom management (Ruland et al., 2003).

The primary components of a chronic disease DSS are assessment, information, and communication.

The assessment component is used to measure the patient’s health state along the key dimensions of physical conditions, functional status, and behavioral tendencies. These systems allow the patient to determine these levels through self – assessment exercises in the privacy of their own residence. These assessments are then available for use by the patient or when transmitted via the Internet by the clinician. The availability of these periodic, timely self – assessments is the basis for monitoring the health state and alerting the patient or clinician when a significant change has occurred. These assessments are also used as the basis for evaluating the health state of the patient relative to their preferences for health states, thereby allowing for the adjustment of their treatment to better align their preferences and outcomes. The assessments also can be used to drive the accessing or delivery of information that is specific to their current health state and symptoms and to focus shared decision process discussions with their clinicians. Critical pathways describe the specific process and sequence of care that can be used to project the expected course of the illness and the associated symptoms and treatments. Chronic care management can take advantage of critical pathway information by tracking the level of correspondence between the actual and expected pathways and adjusting the future projections based on current health state and treatment plan.

The information component is used to provide information and guidance that is customized to the current health state of the patient. The information provided is constantly updated in response to new data acquired by the system.

The communication component provides an integrated mechanism for communicating with the clinician. E-mail interfaces are provided that allow patients to send their health status along with any questions or comments that they have to their clinician and thus serve as a portal into the healthcare provider. Many systems such as CHESS provide for a forum where the patient can network with other patients to provide and receive emotional and therapeutic support.

Decision – Making to Promote Health Behavior Change

Several models of health behavior change provide insight into individuals’ decision – making and motivation about changing health and lifestyle habits. More than a dozen theoretical models have been proposed for how to bring about change in health behaviors and lifestyles. According to Glanz and colleagues (Glanz et al., 1997), these proposed changes fall into three broad categories: individual change, interpersonal change, and community change. Four theories for individual change are the health belief model (Janz and Becker, 1984; Rosenstock, 1960), stages of change model (Prochaska, 1984), reasoned action (Fishbein, 1967), and stress and coping model of change (Lerman and Glanz, 1997). These theories focus on the individual and imply that change or the lack of it can be explained by individual characteristics. Three theories of interpersonal health behavior are social cognitive theory (Bandura, 1997), social support theory (Israel and Rounds, 1987; Cassel, 1976), and patient provider communication (Roter and Hall, 1997). These focus on the interaction of two or a group of individuals and how these interactions can promote change. Four theories of community or group intervention models are community organization (Garvin and Cox, 1995), diffusion of innovations (Rogers, 1983), organizations change (Kaluzny and Hernandez, 1998), and communication theory (Bryant and Zillman, 1994; Gerbner, 1983). These models are helpful for leaders who want to make changes in organizations. In addition, other scientists have combines several existing theories into more broad set of models for behavior change (Petraitis, Flay, and Miller, 1995; Stokols, 1992; Ewart, 1989).

To change habits, it is not enough to change a single act. All related decisions and reinforcements also must be examined and modified. Successful change requires careful study of reinforcements and an understanding of linkages among decisions so that all decisions support the same action. To maintain new habits, behaviors and decisions that promote the needed change should be coupled with or incorporated into existing routines so that they occur without thought and effort. An avalanche of related small and large decisions is made to make one habit change.

It is proposed that to change the system that maintains a habit one must (1) identify and examine the linkages among decisions, (2) measure and receive feedback about behaviors, (3) propose and try out new activities to improve these habits, and (4) build these decisions and behaviors into everyday routine that continue over a long period of time (Alemi et al., 2000). In this context, willpower and discipline are organized and enhanced by changing the system of linked decisions. The conscious decision is not in changing the behavior, but in changing the system that produces the behavior.

       Decision Support in Screening for Latent Health Conditions

Patient decision – making in the context of the application of screening tests has a different set of characteristics than those applied in acute, chronic disease management, or behavioral modification situations. Screening tests may have effects on a patient’s life that are far broader than just their state of health and carry a different set of side effects including the potential for the individual and familial psychologic harm (Richards and Williams, 2004) as well as for affecting a person’s ability to obtain insurance (Low, King, and Wilkie, 1998) and employment. These negative effects can occur irrespective of the actual outcome of the test and as a result the decision to undergo testing in and of itself requires adequate information and counseling to facilitate an informed choice by patients which reflects their personal value set. In the event the patient chooses to undergo screening, an additional layer of decision – making requiring decision support is undertaken to determine what clinical course of treatment is to be followed in the context of the test results.