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Competition in health-related industries (i.e., health care services, pharmaceutical, biotechnology) has been increasing continuously in the past two to three decades. The health business has also become more complex and multilayered with public and private institutions, individual doctors and patients, as players. Consequently, decision processes on medical treatment may become more complicated or variable, being more difficult to predict which treatment or medication will be administered to patients. For example:

  • For many medical conditions there are likely to exist a few alternative brands or versions of the same type of prescribed medication. Depending on the health systems in different countries, and on additional situational factors, it may be decided by a physician, a health care provider and/or insurer, or a pharmacist what particular brand of medication a patient would use. In some cases the patient may be allowed to choose between a more expensive brand and an economic brand (e.g., original and generic brands, subsidised and non-subsidised brands).
  •  There are plenty of over-the-counter (OTC) medications, formulae and devices that patients can buy at their own discretion, possibly with a recommendation of a physician or pharmacist.
  • Public and private medical centers and clinics offer various clinical tests and treatments (e.g., prostate screening, MRI scanning, [virtual] colonoscopy), often going above the heads of general/family physicians of the concerned patients.
  • In more complex or serious conditions, a patient may choose between having a surgery at a public hospital or at a private hospital, depending on the coverage of his or her health insurance.

In the late 1990s, professionals, executives and researchers in health-related areas have developed an interest in methods for measuring preferences that would allow them to better understand how decisions are made by their prospect customers, especially doctors and patients (“end-consumers”). This knowledge serves (a) to address more closely the preferences of patients or requirements of physicians, and (b) to channel planning, product development or marketing efforts more effectively. In particular, they have become interested in methods of conjoint analysis and choice-based conjoint that have already been prevalent in marketing research for measuring and analysing preferences. Conjoint methods are based on two key principles: (a) making trade-offs between decision criteria, and (b) decomposition of stated preferences with respect to whole product concepts (e.g., a medication) by means of statistical techniques into utility values for levels of each attribute or criterion describing the product (e.g., administering 2 vs. 4 times in 24 hours). The methods differ, some argue quite distinctly, in terms of the form in which preferences are expressed (i.e., ranking or rating versus choice) and in the statistical models applied (e.g., choice-based conjoint is often identified by its application of discrete choice modelling). An important benefit for pharmaceutical companies, for example, is gained in learning what characteristics of a medication (e.g., anti-depressant) contribute more to convincing physicians to prescribe it, versus factors like risks or side-effects that lead them to avoid a medication.

The product concepts presented are hypothetical in the sense that they are specified by using controlled experimental techniques and do not necessarily match existing products at the time of study. This property is essential for deriving utility values for the various levels of product attributes studied, and to allow prediction by simulation of shares of preference (“market shares”) for future products. The forecasting power of conjoint models is considered their major appeal from a managerial perspective. In addition, conjoint data can be used for segmenting patients and designing refined targeted marketing strategies.

Interest in application of conjoint methods in a health context has grown in the past decade. According to a review research of conjoint studies reported in 79 articles published between 2005 and 2008, the number of studies nearly doubled from 16 in 2005 to 29 in 2007. The researchers estimated that by the end of 2008 the number of published studies would reach 40. The most frequent areas of application have been cancer (15%) and respiratory disorder (12%)(1). However, applications of conjoint techniques can be found also for guiding policy making and the design of health plans in a broader context of health-care services provided to patients (e.g., by HMOs).

Most conjoint studies in health (71%) apply choice experiments and modelling, becoming the dominant approach (close to 80%) particularly in 2008. A typical study includes 5 or 6 attributes with 2 or 3 levels for each attribute. Most studies in a choice-based approach involve 7 to 8 scenarios (choice sets) but studies with 10-11 or 14-15 scenarios are also frequent (2). A choice scenario normally includes 3 to 5 concepts from which a respondent has to choose a single most prefered concept.

Interpretation of conjoint studies among medical doctors needs a special qualification to be distinguished from studies of patients or consumers. That is because the physicians make professional judgements about the most appropriate treatment option for their patients.  Therefore, it is less appropriate to relate to personal preferences in this context. It is more sensible and suitable to talk about decision criteria that physicians apply, their priorities (i.e., represented by importance weights), and requirements of physicians from pharmaceutical or other treatment alternatives available in the market.

Including monetary cost in conjoint studies on products and services in health-care may be subject to several complications and limitations. That may be the reason for the relatively low proportion of articles on conjoint studies in health that were found to include prices (40%)  (3). For instance, doctors do not take money out of their own pockets to pay for the medications they prescribe, so it is generally less relevant to include price in their studies. It may be sensible, however, to include cost in cases where doctors are allowed to purchase and hold a readily available  inventory of medications for their visiting patients in their private clinics (e.g., Switzerland). It may still be useful to examine how sensitive doctors are to the cost of medication that their patients will have to incur when prescribing them. However, this practice may be additionally complicated because the actual price patients pay for a specific medication is likely to change according to the coverage of their health plan or insurance. It is appropriate and recommended to include price in studies on OTC medications or health-related devices (e.g., for measuring blood pressure). Aspects of cost can be included in studies on health plans such as the percentage of discounts provided on medications and other types of clinical tests and treatments in the plan’s coverage.

An Example for a Conjoint Study on Health-Care Plans:

A choice-based conjoint study was conducted to help a health-care coverage provider assess the potential for a new modified heath plan it was considering to launch. Researchers Gates, McDaniel and Braunsberger (4)  designed a study with 11 attributes including provider names (the client and two competitors), network of physicians accessible, payment per doctor visit, prescription coverage, doctor quality, hospital choice, monthly premium, and additional attributes. Each respondent was introduced to 10 choice sets where in each set he or she had to choose one out of four plans. This setting was elected so that in subsequent simulations the researchers could more accurately test scenarios with existing plans of the three providers plus a new plan by the client-provider. The study was conducted among residents in a specific US region by mail. Yet beforehand a qualitative study (focus group discussions) and a telephone survey have been carried out to define, screen and refine the set of attributes to be included in the conjoint study. 506 health-care patients returned the mail questionnaire (71% response rate out of those in the phone survey who agreed to participate in the next phase).

The estimated (aggregate) utility function suggested to the researchers that the attributes could be divided into two classes of importance: primary criteria for choosing a health plan and secondary considerations. The primary criteria focused on access allowed to doctors in the region of residence and cost associated with the plan, representing the more immediate concerns to target consumers in the market in choosing a health-care plan by a HMO. It was mainly confirmed in the study that consumers are less concerned by narrowing the network of doctors they may visit, as long as they can keep their current family physician and are not forced to replace him or her with another on the list. Respondents appeared to rely less on reported quality ratings of doctors and hospitals. Vision tests and dental coverage were among the secondary considerations. Managers could thereby examine candidate modifications to their health plan and estimate their impact on market shares.

The conjoint methods offer professionals and managers in health-related organizations research tools for gaining valuable insights into patient preferences or criteria governing the clinical decisions of doctors on medications and other treatments. These methods can be particularly helpful in guiding the development of pharmaceutical products or instruments for performing clinical tests and treatments when issues of marketing and promoting them to decision makers come into play. As illustrated in the example, findings from conjoint studies can be useful in policy making on health-care services and designing attractive health plans to patients. This kind of research-based knowledge is acknowledged more widely as a key to success in the highly competitive environs of health-care.

Ron Ventura, Ph.D. (Marketing)

Notes:

(1)  Conjoint Analysis Applications in Health – How Are Studies Being Designed and Reported? An Update of Current Practice in the Published Literature Between 2005 and 2008, D. Marshall, J.F.P. Bridges, B. Hauber, R. Cameron, L. Donnalley, K. Fyie, and F.R. Johnson, 2010, The Patient: Patient-Centered Outcomes Research, 3 (4), 249-256

(2) Ibid. 1.

(3) Ibid. 1.

(4) Modeling Consumer Health Plan Choice Behavior to Improve Customer Value and Health Plan Market Share, Roger Gates, Carl McDaniel, and Karin Braunsberger, 2000, Journal of Business Research, 48, pp. 247-257 (The research was executed by DSS Research to which Gates is affiliated).

Additional sources:

A special report on conducting conjoint studies in health was prpared in 2011 by a task force of the International Society for Pharmaeconomics and Outcomes Research. The authors provide methodological recommendations for guiding the planning, design, and analysis and reporting conjoint studies in health-related domains.

Conjoint Analysis Applications in Health – A Checklist: A Report of the ISPOR Good Research Practices for Conjoint Analysis Task Force, John F.P. Bridges, and A. Brett Hauber et al., 2011, Value in Health, 14, pp. 403-413

http://www.ispor.org/taskforces/documents/ISPOR-CA-in-Health-TF-Report-Checklist.pdf

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