Frustration in Seeking Information about Health Conditions and Health Insurance: Methodological Presentation of a Mind Genomics Cartography

Abstract

The paper uses the emerging science of Mind Genomics to understand emotional responses (frustration) and prevention of decisions experienced when the respondent reads test vignettes describing websites which provide medical information (health) and/or medical insurance information (healthrelated finances). Respondents read and evaluated combinations of 2–4 messages (answers to questions), with the messages combined according to an experimental design. The ratings on a fivepoint scale provided an assessment of both estimated ‘frustration’ and estimated ‘difficulty to make a decision.’ The analysis related the presence/absence of the messages to both frustration and to inability to make a decision. Three mind-sets emerged, suggesting that the estimated frustration encountered in difficult web searches for healthcare information is not unidimensional. The three emergent mind-sets are: MS1 (moderate latent frustration), MS2 (little latent frustration but easily & strongly frustrated) and MS3 (a great deal of latent frustration, doing best with a very simple, direct search process). The paper concludes with the presentation of the PVI, personal viewpoint identifier, which allows the healthcare provider to understand the sensitivities of the prospect, in terms of what problems increase frustration for that prospect. The objective of the PVI is to improve the user experience by understanding the mind of the user. Introduction The use of the Internet for searching and finding health information is rising and is accompanied by the realization that the ‘experience’ itself must be made easier. We are no longer in the birthing years of the 1970’s – 1990’s, when simply having access to a large world of information sufficed, astonishing those who had grown up in a world where information was to be sought after, no matter what the difficulty [1]. Experts have been replaced by websites, by chat advisors, by guided searches, so much so that often there is no expert but rather guidance embedded in the software and the instructions emerging from the software. People often first search the internet for information about diseases, then talk to their friends, and then encounter the doctors [2]. For diseases such as cancer, in earlier days a death sentence for many has spawned an entire network of communications and information [3–5]. The same goes for diabetes [6] and for heart disease [7]. As a consequence, medical information, may be getting increasingly dense over time as medicine advances and the literature and alternative options become overwhelming, for example the “BELONG” community of cancer patients [8]. Much of this this transition and new world is contained within the words ‘user experience,’ a phrase which encompasses the range from one’s impression of the website to one’s experience with the website to achieve certain goals. In the previous generations of science this area would have been subsumed under the rubric ‘man-machine interaction’ in the world of ‘human factors.’ This paper focuses specifically on one aspect of the user experience, the source for diagnosis and treatment information, both in terms of medical information and in terms of medical coverage information. The objective is to quantify the important of different aspects of the search as they drive expected frustration and expected inability to make a decision [9]. A traditional strategy to obtain the information is by a guided interview. The research instructs respondents to answer questions about needs, asks about sources of frustration, and experienced challenges in choosing an answer. The study would also measure responses when participants are exposed to the actual information and instructed to make a decision. Our approach complements this typical study just described. Our experiment presents respondents with vignettes defining the situation and instructs the respondents to select the likely outcome based upon the description of the experience. The analysis deconstructs the response Howard Moskowitz (2019) Frustration in Seeking Information about Health Conditions and Health Insurance: Methodological Presentation of a Mind Genomics Cartography Ageing Sci Ment Health Stud, Volume 3(4): 2–13, 2019 to these vignettes into the contribution of the different elements of the vignette as drivers of expected frustration. Mind Genomics as an emerging science traces its history to a combination of statistics (experimental design [10] conjoint measurement as ways to study decision-making [11, 12]. Mind Genomics expands topics from the laboratory out to everyday life. Furthermore, Mind Genomics expands the capabilities of design of experiments, using individual permutations of a basic, fundamental design. The consequence is that one need not overthink the selection of combinations of elements to go into the design. The permutation covers a wider amount of the space, analogous to the way the MRi in medicine takes many pictures of underlying tissue, not just the ‘correct one’, which may not even be known [13,14] The result has been the creation of a new science with applications from policy to products, from law to health and everyday life [15,16]. Mind Genomics method Mind Genomics approaches the problem by an easy to construct, easy to analyze experiment. The experiment comprises a topic (sources of frustration and choice prevention during the search for medical information on the Internet), a set of four questions which ‘tell a story’ (the Socratic approach), and then requires four simple answers to each question, or a total of 16 answers. The Mind Genomics paradigm is designed to be fast, iterative, provide optimal results, and powerful results terms of a measure of the ability of each answer to ‘drive’ the response, a measure of conscious judgment, as well as measuring response time, a metric which reflects deeper cognitive processing or emotions. The set of four questions and the 16 answers, four answers to each question, appears in Table 1. The objective is to work with untrained respondents, over the Internet, requiring that the answers be simple, direct, and easy to comprehend. The questions in Table 1 never appear in the test stimuli. Rather, the test stimuli comprise simple vignettes, combinations of the answers, 2–4 answers for any vignette. Each vignette has at most one answer from a question, but for many of the vignettes one or two questions do not contribute an answer. This design structure is deliberate, for statistical reasons, specifically to increase the strength of the analytic tool, OLS (ordinary least-squares) regression. Figure 1 (left panel) shows the screen shot requiring the researcher to ask four questions. Figure 1 (right panel) shows one question, and the four answers to that question. The answers should be stand-alone phrases that can be understood, in and of themselves. The set-up system for Mind Genomics thus encourages critical thinking, and in the end, a deeper understanding of the topic by combining this thinking with affordable, rapid experimentation among prospective customers. Experimental design Each respondent evaluated a unique set of 24 combinations or vignettes. Each vignette comprised 2–4 elements or answers, no more than one answer from any question. The answers were stacked atop each other. The experimental design ensures that for each individual the 16 answers appear several times, and an equal number of times. The incompleteness of the design, with some vignettes absent one or two answers, ensures that the OLS (ordinary least-squares) regression will run without any problem. When the researcher requires each vignette to contain exactly one answer or element from each question, a very common practice, the sad, actually destructive outcome is that the OLS regression can return only with relative values for the coefficients, not absolute values, the cause being the multi-collinearity among the elements due to the requirement that each vignette be ‘complete’ with exactly one answer from each question. Table 1. The four questions and the four answers to each question Question 1 Stage of Life