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A Rigorous Approach to Large-Scale Elicitation and Analysis of Patient Narratives.

ABSTRACT: Patient narratives have emerged as promising vehicles for making health care more responsive by helping clinicians to better understand their patients' expectations, perceptions, or concerns and encouraging consumers to engage with information about quality. A growing number of websites incorporate patients' comments. But existing comments have fragmentary content, fail to represent less vocal patients, and can be manipulated to "manage" providers' reputations. In this article, we offer the first empirical test of the proposition that patient narratives can be elicited rigorously and reliably using a five-question protocol that can be incorporated into large-scale patient experience surveys. We tested whether elicited narratives about outpatient care are complete (report all facets of patient experience), balanced (convey an accurate mix of positive and negative events), meaningful (have a coherent storyline), and representative (draw fulsome narratives from all relevant subsets of patients). The tested protocol is strong on balance and representativeness, more mixed on completeness and meaningfulness.

SUBMITTER: Schlesinger M

PROVIDER: S-EPMC6731154 | biostudies-literature | 2020 Oct

REPOSITORIES: biostudies-literature

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A Rigorous Approach to Large-Scale Elicitation and Analysis of Patient Narratives.

Schlesinger Mark M Grob Rachel R Shaller Dale D Martino Steven C SC Parker Andrew M AM Rybowski Lise L Finucane Melissa L ML Cerully Jennifer L JL

Medical care research and review : MCRR 20181006 5

Patient narratives have emerged as promising vehicles for making health care more responsive by helping clinicians to better understand their patients' expectations, perceptions, or concerns and encouraging consumers to engage with information about quality. A growing number of websites incorporate patients' comments. But existing comments have fragmentary content, fail to represent less vocal patients, and can be manipulated to "manage" providers' reputations. In this article, we offer the firs ...[more]

PMID: 30293521

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Project description:BackgroundProper understanding of the roles of, and interactions between genetic, lifestyle, environmental and psycho-social factors in determining the risk of development and/or progression of chronic diseases requires access to very large high-quality databases. Because of the financial, technical and time burdens related to developing and maintaining very large studies, the scientific community is increasingly synthesizing data from multiple studies to construct large databases. However, the data items collected by individual studies must be inferentially equivalent to be meaningfully synthesized. The DataSchema and Harmonization Platform for Epidemiological Research (DataSHaPER; http://www.datashaper.org) was developed to enable the rigorous assessment of the inferential equivalence, i.e. the potential for harmonization, of selected information from individual studies.MethodsThis article examines the value of using the DataSHaPER for retrospective harmonization of established studies. Using the DataSHaPER approach, the potential to generate 148 harmonized variables from the questionnaires and physical measures collected in 53 large population-based studies (6.9 million participants) was assessed. Variable and study characteristics that might influence the potential for data synthesis were also explored.ResultsOut of all assessment items evaluated (148 variables for each of the 53 studies), 38% could be harmonized. Certain characteristics of variables (i.e. relative importance, individual targeted, reference period) and of studies (i.e. observational units, data collection start date and mode of questionnaire administration) were associated with the potential for harmonization. For example, for variables deemed to be essential, 62% of assessment items paired could be harmonized.ConclusionThe current article shows that the DataSHaPER provides an effective and flexible approach for the retrospective harmonization of information across studies. To implement data synthesis, some additional scientific, ethico-legal and technical considerations must be addressed. The success of the DataSHaPER as a harmonization approach will depend on its continuing development and on the rigour and extent of its use. The DataSHaPER has the potential to take us closer to a truly collaborative epidemiology and offers the promise of enhanced research potential generated through synthesized databases.

Project description:BackgroundThe visualization of large volumes of data is a computationally challenging task that often promises rewarding new insights. There is great potential in the application of new algorithms and models from combinatorial optimisation. Datasets often contain "hidden regularities" and a combined identification and visualization method should reveal these structures and present them in a way that helps analysis. While several methodologies exist, including those that use non-linear optimization algorithms, severe limitations exist even when working with only a few hundred objects.Methodology/principal findingsWe present a new data visualization approach (QAPgrid) that reveals patterns of similarities and differences in large datasets of objects for which a similarity measure can be computed. Objects are assigned to positions on an underlying square grid in a two-dimensional space. We use the Quadratic Assignment Problem (QAP) as a mathematical model to provide an objective function for assignment of objects to positions on the grid. We employ a Memetic Algorithm (a powerful metaheuristic) to tackle the large instances of this NP-hard combinatorial optimization problem, and we show its performance on the visualization of real data sets.Conclusions/significanceOverall, the results show that QAPgrid algorithm is able to produce a layout that represents the relationships between objects in the data set. Furthermore, it also represents the relationships between clusters that are feed into the algorithm. We apply the QAPgrid on the 84 Indo-European languages instance, producing a near-optimal layout. Next, we produce a layout of 470 world universities with an observed high degree of correlation with the score used by the Academic Ranking of World Universities compiled in the The Shanghai Jiao Tong University Academic Ranking of World Universities without the need of an ad hoc weighting of attributes. Finally, our Gene Ontology-based study on Saccharomyces cerevisiae fully demonstrates the scalability and precision of our method as a novel alternative tool for functional genomics.

Dataset Information

A Rigorous Approach to Large-Scale Elicitation and Analysis of Patient Narratives.

Publications

A Rigorous Approach to Large-Scale Elicitation and Analysis of Patient Narratives.

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