Natural Language Processing Accurately Measures Adherence to Best Practice Guidelines for Palliative Care in Trauma

Lee, Katherine C.; Udelsman, Brooks V.; Streid, Jocelyn; Chang, David C.; Salim, Ali; Livingston, David H.; Cooper, Zara

doi:10.1016/j.jpainsymman.2019.09.017

Cited by 24 publications

(26 citation statements)

References 28 publications

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“…An extensive literature review was conducted to determine which statistical software program and packages would be used to conduct this project, including both peer reviewed journals and grey literature, such as technical reports, blogs on natural language processing, and information technology related websites (Alsawas et al, 2016;Guetterman et al, 2018;Hong et al, 2018;Koleck et al, 2019;Lee et al, 2019;Matsutani et al, 2019;Miller & Brown, 2018;Sievert & Shirley, 2014). Our preference for software was an open source freeware platform as we hoped to develop a methodology which could be provided for other researchers to use and would not require significant financial expenditures.…”

Section: Methodsmentioning

confidence: 99%

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Methods to Integrate Natural Language Processing Into Qualitative Research

Abram

Mancini

Parker

2020

International Journal of Qualitative Methods

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Background: Qualitative methods analyze contextualized, unstructured data. These methods are time and cost intensive, often resulting in small sample sizes and yielding findings that are complicated to replicate. Integrating natural language processing (NLP) into a qualitative project can increase efficiency through time and cost savings; increase sample sizes; and allow for validation through replication. This study compared the findings, costs, and time spent between a traditional qualitative method (Investigator only) to a method pairing a qualitative investigator with an NLP function (Investigator +NLP). Methods: Using secondary data from a previously published study, the investigators designed an NLP process in Python to yield a corpus, keywords, keyword influence, and the primary topics. A qualitative researcher reviewed and interpreted the output. These findings were compared to the previous study results. Results: Using comparative review, our results closely matched the original findings. The NLP + Investigator method reduced the project time by a minimum of 120 hours and costs by $1,500. Discussion: Qualitative research can evolve by incorporating NLP methods. These methods can increase sample size, reduce project time, and significantly reduce costs. The results of an integrated NLP process create a corpus and code which can be reviewed and verified, thus allowing a replicable, qualitative study. New data can be added over time and analyzed using the same interpretation and identification. Off the shelf qualitative software may be easier to use, but it can be expensive and may not offer a tailored approach or easily interpretable outcomes which further benefits researchers.

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Section: Methodsmentioning

confidence: 99%

“…ML has been successful recently in drug discovery, evidence based medicine, and multiple medical healthcare treatments (Alsawas et al, 2016;Lee et al, 2019).…”

Section: Introductionmentioning

confidence: 99%

Methods to Integrate Natural Language Processing Into Qualitative Research

Abram

Mancini

Parker

2020

International Journal of Qualitative Methods

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“…Natural language processing (NLP) can offer an efficient, accurate alternative for identification of SIC in the EHR 14,15 , and has been used to identify care-planning discussions and palliative care delivery. 16,17,18 Despite early progress, more sophisticated approaches are needed to classify and evaluate SIC documentation. At this time, NLP approaches for identification of SIC predominantly rely on keywords derived from chart review.…”

Section: Natural Language Processingmentioning

confidence: 99%

“…Although these results are preliminary, the methodology employed here allows for greater real-world applicability than other reports of NLP approaches to SIC identification thus far, which have all been keyword-based. 15,16,17,18 Recent applications of these methods have seen success in patient groups drawn from pragmatic trials in oncology, 35,36 but due to their lexical basis these efforts have required manual annotation of hundreds of clinical notes, and may be weighted towards inpatient admissions or medical crises requiring treatment decisions. 35 Our method may lay the foundation for more nuanced identification of patient-specific priorities and prognostic communication more upstream in the disease trajectory, which would have significant utility across a wide array of clinical contexts.…”

Section: Clinical Applicationsmentioning

confidence: 99%

Using Natural Language Processing to Classify Serious Illness Communication with Oncology Patients

Davoudi

Tissot

Doucette

et al. 2021

Preprint

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One core measure of healthcare quality set forth by the Institute of Medicine is whether care decisions match patient goals. High-quality “serious illness communication” about patient goals and prognosis is required to support patient-centered decision-making, however current methods are not sensitive enough to measure the quality of this communication or determine whether care delivered matches patient priorities. Natural language processing (NLP) offers an efficient method for identification and evaluation of documented serious illness communication, which could serve as the basis for future quality metrics in oncology and other forms of serious illness. In this study, we trained NLP algorithms to identify and characterize serious illness communication with oncology patients.

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“…Another article [16], dealing with contradictory recommendations, focuses on the semi-automatic detection of inconsistencies in guidelines; these tools are applied to antibiotherapy in primary care. Another recent application of natural language processing [17,18] shows that one can accurately measure adherence to best practice guidelines in a context of palliative care, as well as try to assess the quality of care from discharge summaries.…”

Section: Text Analysis Of Medical Guidelinesmentioning

confidence: 99%

A Method for Computing Conceptual Distances between Medical Recommendations: Experiments in Modeling Medical Disagreement

et al. 2021

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Using natural language processing tools, we investigate the semantic differences in medical guidelines for three decision problems: breast cancer screening, lower back pain and hypertension management. The recommendation differences may cause undue variability in patient treatments and outcomes. Therefore, having a better understanding of their causes can contribute to a discussion on possible remedies. We show that these differences in recommendations are highly correlated with the knowledge brought to the problem by different medical societies, as reflected in the conceptual vocabularies used by the different groups of authors. While this article is a case study using three sets of guidelines, the proposed methodology is broadly applicable. Technically, our method combines word embeddings and a novel graph-based similarity model for comparing collections of documents. For our main case study, we use the CDC summaries of the recommendations (very short documents) and full (long) texts of guidelines represented as bags of concepts. For the other case studies, we compare the full text of guidelines with their abstracts and tables, summarizing the differences between recommendations. The proposed approach is evaluated using different language models and different distance measures. In all the experiments, the results are highly statistically significant. We discuss the significance of the results, their possible extensions, and connections to other domains of knowledge. We conclude that automated methods, although not perfect, can be applicable to conceptual comparisons of different medical guidelines and can enable their analysis at scale.

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Natural Language Processing Accurately Measures Adherence to Best Practice Guidelines for Palliative Care in Trauma

Cited by 24 publications

References 28 publications

Methods to Integrate Natural Language Processing Into Qualitative Research

Methods to Integrate Natural Language Processing Into Qualitative Research

Using Natural Language Processing to Classify Serious Illness Communication with Oncology Patients

A Method for Computing Conceptual Distances between Medical Recommendations: Experiments in Modeling Medical Disagreement

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