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A proof-of-concept framework for the preference elicitation and evaluation of health informatics technologies: the online PRESENT patient experience dashboard as a case example.

ABSTRACT: BACKGROUND:Constrained budgets within healthcare systems and the need to efficiently allocate resources often necessitate the valuation of healthcare interventions and services. However, when a technological product is developed for which no market exists it is a challenge to understand how to place the product and which specifications are the most sought after and important for end users. This was the case for a dashboard we developed, displaying analyses of patient experience survey free-text comments. METHOD:We describe a customisation and evaluation process for our online dashboard that addresses this challenge, using a Discrete Choice Experiment (DCE). We were not interested in the exact content of the dashboard, which was determined in previous stages of our larger study, but on the availability of features and customization options and how they affect individuals' purchasing behaviours. RESULTS:Our DCE completion rate was 33/152 (22%). Certain features were highly desirable - the search function, filtering, and upload own data - and would contribute significant added value to the dashboard. Purchasing behaviour was dependent on the dashboard features, going from a 10 to 90% probability to purchase when we moved from a baseline to a fully-featured dashboard. The purchasing behaviour elicited in this study assumes individuals already have buy-in to the online dashboard, so we assessed only how the various features of our dashboard influence the probability of purchasing the product. Results were used to inform development of a generic checklist of desirable healthcare dashboard features as well as to refine the dashboard itself. Our study suggests the development of the online dashboard and its roll-out in the market would result in a positive net benefit in terms of utilities. The cost-benefit analysis offers a lower bound estimate of the net benefit as it does not acknowledge or incorporate non-monetary benefits that would result from the use of the online dashboard, such as from improved healthcare management. CONCLUSION:DCEs can be successfully used to inform development of an online dashboard by determining preferences for particular features and customisation options and how this affects individuals' purchasing behaviours. The process should be transferable to the development of other technologies.

SUBMITTER: Mentzakis E

PROVIDER: S-EPMC7245892 | biostudies-literature | 2020 May

REPOSITORIES: biostudies-literature

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A proof-of-concept framework for the preference elicitation and evaluation of health informatics technologies: the online PRESENT patient experience dashboard as a case example.

Mentzakis Emmanouil E Tkacz Daria D Rivas Carol C

BMC medical informatics and decision making 20200524 1

<h4>Background</h4>Constrained budgets within healthcare systems and the need to efficiently allocate resources often necessitate the valuation of healthcare interventions and services. However, when a technological product is developed for which no market exists it is a challenge to understand how to place the product and which specifications are the most sought after and important for end users. This was the case for a dashboard we developed, displaying analyses of patient experience survey fr ...[more]

PMID: 32448286

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Project description:BackgroundThe language gap between health consumers and health professionals has been long recognized as the main hindrance to effective health information comprehension. Although providing health information access in consumer health language (CHL) is widely accepted as the solution to the problem, health consumers are found to have varying health language preferences and proficiencies. To simplify health documents for heterogeneous consumer groups, it is important to quantify how CHLs are different in terms of complexity among various consumer groups.ObjectiveThis study aimed to propose an informatics framework (consumer health language complexity [CHELC]) to assess the complexity differences of CHL using syntax-level, text-level, term-level, and semantic-level complexity metrics. Specifically, we identified 8 language complexity metrics validated in previous literature and combined them into a 4-faceted framework. Through a rank-based algorithm, we developed unifying scores (CHELC scores [CHELCS]) to quantify syntax-level, text-level, term-level, semantic-level, and overall CHL complexity. We applied CHELCS to compare posts of each individual on online health forums designed for (1) the general public, (2) deaf and hearing-impaired people, and (3) people with autism spectrum disorder (ASD).MethodsWe examined posts with more than 4 sentences of each user from 3 health forums to understand CHL complexity differences among these groups: 12,560 posts from 3756 users in Yahoo! Answers, 25,545 posts from 1623 users in AllDeaf, and 26,484 posts from 2751 users in Wrong Planet. We calculated CHELCS for each user and compared the scores of 3 user groups (ie, deaf and hearing-impaired people, people with ASD, and the public) through 2-sample Kolmogorov-Smirnov tests and analysis of covariance tests.ResultsThe results suggest that users in the public forum used more complex CHL, particularly more diverse semantics and more complex health terms compared with users in the ASD and deaf and hearing-impaired user forums. However, between the latter 2 groups, people with ASD used more complex words, and deaf and hearing-impaired users used more complex syntax.ConclusionsOur results show that the users in 3 online forums had significantly different CHL complexities in different facets. The proposed framework and detailed measurements help to quantify these CHL complexity differences comprehensively. The results emphasize the importance of tailoring health-related content for different consumer groups with varying CHL complexities.

Dataset Information

A proof-of-concept framework for the preference elicitation and evaluation of health informatics technologies: the online PRESENT patient experience dashboard as a case example.

Publications

A proof-of-concept framework for the preference elicitation and evaluation of health informatics technologies: the online PRESENT patient experience dashboard as a case example.

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