Dataset Information

Capturing and missing the patient's story through outcome measures: A thematic comparison of patient-generated items in PSYCHLOPS with CORE-OM and PHQ-9.

ABSTRACT:

Background

There is increasing interest in individualized patient-reported outcome measures (I-PROMS), where patients themselves indicate the specific problems they want to address in therapy and these problems are used as items within the outcome measurement tool.

Objective

This paper examined the extent to which 279 items reported in an I-PROM (PSYCHLOPS) added qualitative information which was not captured by two well-established outcome measures (CORE-OM and PHQ-9).

Design

Comparison of items was only conducted for patients scoring above the "caseness" threshold on the standardized measures.

Setting and patients

107 patients were participating in therapy within addiction and general psychiatric clinical settings.

Main results

Almost every patient (95%) reported at least one item whose content was not covered by PHQ-9, and 71% reported at least one item not covered by CORE-OM.

Discussion

Results demonstrate the relevance of individualized outcome assessment for capturing data describing the issues of greatest concern to patients, as nomothetic measures do not always seem to capture the whole story.

SUBMITTER: Sales CM

PROVIDER: S-EPMC5980523 | biostudies-literature | 2018 Jun

REPOSITORIES: biostudies-literature

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Publications

Capturing and missing the patient's story through outcome measures: A thematic comparison of patient-generated items in PSYCHLOPS with CORE-OM and PHQ-9.

Sales Célia Md CM Neves Inês Td IT Alves Paula G PG Ashworth Mark M

Health expectations : an international journal of public participation in health care and health policy 20171122 3

<h4>Background</h4>There is increasing interest in individualized patient-reported outcome measures (I-PROMS), where patients themselves indicate the specific problems they want to address in therapy and these problems are used as items within the outcome measurement tool.<h4>Objective</h4>This paper examined the extent to which 279 items reported in an I-PROM (PSYCHLOPS) added qualitative information which was not captured by two well-established outcome measures (CORE-OM and PHQ-9).<h4>Design< ...[more]

PMID: 29165852

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Project description:Background: Natural Language Processing (NLP) methods are increasingly being utilized to mine knowledge from unstructured health-related texts. Recent advances in noisy text processing techniques are enabling researchers and medical domain experts to go beyond the information encapsulated in published texts (e.g., clinical trials and systematic reviews) and structured questionnaires, and obtain perspectives from other unstructured sources such as Electronic Health Records (EHRs) and social media posts. Objectives: To review the recently published literature discussing the application of NLP techniques for mining health-related information from EHRs and social media posts. Methods: Literature review included the research published over the last five years based on searches of PubMed, conference proceedings, and the ACM Digital Library, as well as on relevant publications referenced in papers. We particularly focused on the techniques employed on EHRs and social media data. Results: A set of 62 studies involving EHRs and 87 studies involving social media matched our criteria and were included in this paper. We present the purposes of these studies, outline the key NLP contributions, and discuss the general trends observed in the field, the current state of research, and important outstanding problems. Conclusions: Over the recent years, there has been a continuing transition from lexical and rule-based systems to learning-based approaches, because of the growth of annotated data sets and advances in data science. For EHRs, publicly available annotated data is still scarce and this acts as an obstacle to research progress. On the contrary, research on social media mining has seen a rapid growth, particularly because the large amount of unlabeled data available via this resource compensates for the uncertainty inherent to the data. Effective mechanisms to filter out noise and for mapping social media expressions to standard medical concepts are crucial and latent research problems. Shared tasks and other competitive challenges have been driving factors behind the implementation of open systems, and they are likely to play an imperative role in the development of future systems.

Dataset Information

Capturing and missing the patient's story through outcome measures: A thematic comparison of patient-generated items in PSYCHLOPS with CORE-OM and PHQ-9.

Background

Objective

Design

Setting and patients

Main results

Discussion

Publications

Capturing and missing the patient's story through outcome measures: A thematic comparison of patient-generated items in PSYCHLOPS with CORE-OM and PHQ-9.

Similar Datasets

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