با استفاده از روش های تجارت کردن زمان به ارزیابی تنظیمات بیش از مراقبت های بهداشتی گزینه های تحویل: مطالعات امکان سنجی

Objectives Time trade-off (TTO) methods are often used for utility assessments of different health states to measure quality of life (QOL). They have not generally been used to assess social preferences with respect to options for health care delivery, although the need for quantifying these preferences is arguably just as important. Policymakers are increasingly faced with decisions about how much to invest in, and how much to incentivize, particular modes of health care delivery, generally with little evidence about user preferences. Methods This study draws on long-term care (LTC) delivery modes as an example. Focus groups were conducted to approach this issue both qualitatively and quantitatively. In a qualitative pilot study, two focus groups discussed issues of the LTC decision-making process and preferences among different LTC options. The TTO was then used to assess QOL for each LTC option, conditional on a specific health state, and then quantified user’s LTC preferences by differential QOL between the two options. Results This study found that the TTO-elicited utilities and their differences are consistent with the LTC preferences revealed from focus group discussions. These preferences depend on levels of disability and education. Conclusions The modified TTO technique seems a feasible method to quantify preferences over LTC delivery options. These methods may be applicable to various health care alternatives in which better evidence is needed to guide funding policy.

Allocation of resources across health care delivery modes is arguably as important as allocation of resources across diseases. For example, given a disease state such as advanced cancer, patients may experience significantly different quality of life (QOL) depending on whether the disease is treated at home, in a hospital, or in hospice [1]. Yet, the science of understanding and comparing the benefits of health care delivery modes is much less developed than that of specific disease states. This study aimed to explore the feasibility of using time trade-off (TTO) methods for utility elicitation in QOL to quantify user’s preferences under different existing health care delivery modes, focused on long-term care (LTC), contingent on a specific health state. This study was conducted in the context of LTC, a setting in which there is a clear need for better evidence on QOL to guide resource allocation. During the last two decades, the expansion of home- and community-based service (HCBS) alternatives to institutional care has been a priority for Medicaid, the US government’s health insurance program for US citizens with low incomes and the largest source of funding for medical and health-related services for these individuals. Although most Medicaid LTC dollars still go toward institutional care, the national percentage of Medicaid spending on HCBS has more than doubled over the last two decades [2] and [3]. These policy shifts are based on qualitative and survey research finding that older adults generally prefer HCBS to nursing home care [4], [5], [6], [7], [8] and [9], but these preferences have not been rigorously quantified. Effectiveness research in LTC has focused disproportionately on clinical outcomes that represent only a narrow range of the outcomes of interest and may or may not be correlated strongly with QOL or preferences. Potential benefits of different health care options need to be translated into comparable units such as quality-adjusted life-years (QALYs) across studies, so that researchers can better gauge the effectiveness of different approaches and provide valid scientific evidence to policymakers using this metric [10]. This study seeks to lay the groundwork for developing a valid scientific methodology for quantifying preferences across LTC options and health conditions, and to provide useful information to policymakers. Specifically, this study tests whether standard TTO methods can feasibly be extended to quantifying patients’ LTC preferences.

This study gathered information about potential adult users’ general preferences over types of LTC, dependent on specific disability states, and we found that utilities based on the TTO method reasonably reflected their preferences. For example, while participants voiced a strong aversion to nursing home care generally, they preferred that level of care for greater disability burden. Defining health care delivery modes in addition to defining health states naturally adds a layer of definitional complexity to the use of TTO methods, and it is a challenge to identify the appropriate balance of survey tractability and the number of potential combinations. Answering TTO questions about health care options may require more explanation of the questions and explanation of the intent. This pilot study was limited to a small sample in certain geographic areas. This study also excluded people with cognitive impairment, which is common for LTC placement. Testing these methods in a larger sample from other areas and further testing their validity will be important if these techniques are to be used more generally. Despite these remaining challenges, the TTO technique seems a feasible method to quantify preferences over LTC delivery options, thus enabling a more quantitative assessment of their cost-effectiveness. This study extends TTO methods to health conditions that include health care delivery modes. It therefore serves as a proof of concept for using TTO methods to assess important policy questions about allocating LTC resources more efficiently. These methods may be applicable to various health care alternatives in which better evidence is needed to guide funding policy. Source of financial support: Financial support for this study was provided entirely by a pilot grant from the Center for Health Administration Studies at the University of Chicago. The funding agreement ensured the authors’ independence in designing the study, interpreting the data, writing, and publishing the report.