Studying Associations Between Heart Failure Self-Management and Rehospitalizations Using Natural Language Processing

Predicting emergency department visits and hospitalizations for patients with heart failure in home healthcare using a time series risk model

OBJECTIVES

Little is known about proactive risk assessment concerning emergency department (ED) visits and hospitalizations in patients with heart failure (HF) who receive home healthcare (HHC) services. This study developed a time series risk model for predicting ED visits and hospitalizations in patients with HF using longitudinal electronic health record data. We also explored which data sources yield the best-performing models over various time windows.

MATERIALS AND METHODS

We used data collected from 9362 patients from a large HHC agency. We iteratively developed risk models using both structured (eg, standard assessment tools, vital signs, visit characteristics) and unstructured data (eg, clinical notes). Seven specific sets of variables included: (1) the Outcome and Assessment Information Set, (2) vital signs, (3) visit characteristics, (4) rule-based natural language processing-derived variables, (5) term frequency-inverse document frequency variables, (6) Bio-Clinical Bidirectional Encoder Representations from Transformers variables, and (7) topic modeling. Risk models were developed for 18 time windows (1-15, 30, 45, and 60 days) before an ED visit or hospitalization. Risk prediction performances were compared using recall, precision, accuracy, F1, and area under the receiver operating curve (AUC).

RESULTS

The best-performing model was built using a combination of all 7 sets of variables and the time window of 4 days before an ED visit or hospitalization (AUC = 0.89 and F1 = 0.69).

DISCUSSION AND CONCLUSION

This prediction model suggests that HHC clinicians can identify patients with HF at risk for visiting the ED or hospitalization within 4 days before the event, allowing for earlier targeted interventions.

Natural Language Processing of Nursing Notes: An Integrative Review

Natural language processing includes a variety of techniques that help to extract meaning from narrative data. In healthcare, medical natural language processing has been a growing field of study; however, little is known about its use in nursing. We searched PubMed, EMBASE, and CINAHL and found 689 studies, narrowed to 43 eligible studies using natural language processing in nursing notes. Data related to the study purpose, patient population, methodology, performance evaluation metrics, and quality indicators were extracted for each study. The majority (86%) of the studies were conducted from 2015 to 2021. Most of the studies (58%) used inpatient data. One of four studies used data from open-source databases. The most common standard terminologies used were the Unified Medical Language System and Systematized Nomenclature of Medicine, whereas nursing-specific standard terminologies were used only in eight studies. Full system performance metrics (eg, F score) were reported for 61% of applicable studies. The overall number of nursing natural language processing publications remains relatively small compared with the other medical literature. Future studies should evaluate and report appropriate performance metrics and use existing standard nursing terminologies to enable future scalability of the methods and findings.

Nursing Students' Home Care Learning Delivered in an Innovative 360-Degree Immersion Experience

BACKGROUND

Health care is delivered to patients in the community, frequently in their own homes. New graduates need to be prepared for this unique nursing role to care for patients in the home health setting.

PURPOSE

The purpose of this study was to assess student satisfaction with an innovative pedagogy to teach home health nursing using an immersion experience. The immersion experience delivered the home health nursing activity using 360-degree footage and zoom in pictures presented via a web platform.

METHODS

This study used a descriptive cross-sectional design with convenience sampling of 81 nursing students enrolled in the third semester of an associate degree program. Data were collected through an online survey eliciting anonymous feedback.

RESULTS

Participant feedback revealed this new pedagogy engaged nursing students and received overwhelmingly positive responses. A review of student feedback through written comments provided further support that this immersion experience exceeded expectations.

CONCLUSION

The 360-degree immersion home care experience provided problem-based learning that positively impacted student self-reported satisfaction and perceived learning.

Factors associated with poor self-management documented in home health care narrative notes for patients with heart failure

BACKGROUND

Patients with heart failure (HF) who actively engage in their own self-management have better outcomes. Extracting data through natural language processing (NLP) holds great promise for identifying patients with or at risk of poor self-management.

OBJECTIVE

To identify home health care (HHC) patients with HF who have poor self-management using NLP of narrative notes, and to examine patient factors associated with poor self-management.

METHODS

An NLP algorithm was applied to extract poor self-management documentation using 353,718 HHC narrative notes of 9,710 patients with HF. Sociodemographic and structured clinical data were incorporated into multivariate logistic regression models to identify factors associated with poor self-management.

RESULTS

There were 758 (7.8%) patients in this sample identified as having notes with language describing poor HF self-management. Younger age (OR 0.982, 95% CI 0.976-0.987, p < .001), longer length of stay in HHC (OR 1.036, 95% CI 1.029- 1.043, p < .001), diagnosis of diabetes (OR 1.47, 95% CI 1.3-1.67, p < .001) and depression (OR 1.36, 95% CI 1.09-1.68, p < .01), impaired decision-making (OR 1.64, 95% CI 1.37-1.95, p < .001), smoking (OR 1.7, 95% CI 1.4-2.04, p < .001), and shortness of breath with exertion (OR 1.25, 95% CI 1.1-1.42, p < .01) were associated with poor self-management.

CONCLUSIONS

Patients with HF who have poor self-management can be identified from the narrative notes in HHC using novel NLP methods. Meaningful information about the self-management of patients with HF can support HHC clinicians in developing individualized care plans to improve self-management and clinical outcomes.

Tasks as needs: reframing the paradigm of clinical natural language processing research for real-world decision support

Electronic medical records are increasingly used to store patient information in hospitals and other clinical settings. There has been a corresponding proliferation of clinical natural language processing (cNLP) systems aimed at using text data in these records to improve clinical decision-making, in comparison to manual clinician search and clinical judgment alone. However, these systems have delivered marginal practical utility and are rarely deployed into healthcare settings, leading to proposals for technical and structural improvements. In this paper, we argue that this reflects a violation of Friedman's "Fundamental Theorem of Biomedical Informatics," and that a deeper epistemological change must occur in the cNLP field, as a parallel step alongside any technical or structural improvements. We propose that researchers shift away from designing cNLP systems independent of clinical needs, in which cNLP tasks are ends in themselves-"tasks as decisions"-and toward systems that are directly guided by the needs of clinicians in realistic decision-making contexts-"tasks as needs." A case study example illustrates the potential benefits of developing cNLP systems that are designed to more directly support clinical needs.

Systematic review of current natural language processing methods and applications in cardiology

Natural language processing (NLP) is a set of automated methods to organise and evaluate the information contained in unstructured clinical notes, which are a rich source of real-world data from clinical care that may be used to improve outcomes and understanding of disease in cardiology. The purpose of this systematic review is to provide an understanding of NLP, review how it has been used to date within cardiology and illustrate the opportunities that this approach provides for both research and clinical care. We systematically searched six scholarly databases (ACM Digital Library, Arxiv, Embase, IEEE Explore, PubMed and Scopus) for studies published in 2015-2020 describing the development or application of NLP methods for clinical text focused on cardiac disease. Studies not published in English, lacking a description of NLP methods, non-cardiac focused and duplicates were excluded. Two independent reviewers extracted general study information, clinical details and NLP details and appraised quality using a checklist of quality indicators for NLP studies. We identified 37 studies developing and applying NLP in heart failure, imaging, coronary artery disease, electrophysiology, general cardiology and valvular heart disease. Most studies used NLP to identify patients with a specific diagnosis and extract disease severity using rule-based NLP methods. Some used NLP algorithms to predict clinical outcomes. A major limitation is the inability to aggregate findings across studies due to vastly different NLP methods, evaluation and reporting. This review reveals numerous opportunities for future NLP work in cardiology with more diverse patient samples, cardiac diseases, datasets, methods and applications.

A natural language processing pipeline to synthesize patient-generated notes toward improving remote care and chronic disease management: a cystic fibrosis case study

Objectives

Patient-generated health data (PGHD) are important for tracking and monitoring out of clinic health events and supporting shared clinical decisions. Unstructured text as PGHD (eg, medical diary notes and transcriptions) may encapsulate rich information through narratives which can be critical to better understand a patient’s condition. We propose a natural language processing (NLP) supported data synthesis pipeline for unstructured PGHD, focusing on children with special healthcare needs (CSHCN), and demonstrate it with a case study on cystic fibrosis (CF).

Materials and Methods

The proposed unstructured data synthesis and information extraction pipeline extract a broad range of health information by combining rule-based approaches with pretrained deep-learning models. Particularly, we build upon the scispaCy biomedical model suite, leveraging its named entity recognition capabilities to identify and link clinically relevant entities to established ontologies such as Systematized Nomenclature of Medicine (SNOMED) and RXNORM. We then use scispaCy’s syntax (grammar) parsing tools to retrieve phrases associated with the entities in medication, dose, therapies, symptoms, bowel movements, and nutrition ontological categories. The pipeline is illustrated and tested with simulated CF patient notes.

Results

The proposed hybrid deep-learning rule-based approach can operate over a variety of natural language note types and allow customization for a given patient or cohort. Viable information was successfully extracted from simulated CF notes. This hybrid pipeline is robust to misspellings and varied word representations and can be tailored to accommodate the needs of a specific patient, cohort, or clinician.

Discussion

The NLP pipeline can extract predefined or ontology-based entities from free-text PGHD, aiming to facilitate remote care and improve chronic disease management. Our implementation makes use of open source models, allowing for this solution to be easily replicated and integrated in different health systems. Outside of the clinic, the use of the NLP pipeline may increase the amount of clinical data recorded by families of CSHCN and ease the process to identify health events from the notes. Similarly, care coordinators, nurses and clinicians would be able to track adherence with medications, identify symptoms, and effectively intervene to improve clinical care. Furthermore, visualization tools can be applied to digest the structured data produced by the pipeline in support of the decision-making process for a patient, caregiver, or provider.

Conclusion

Our study demonstrated that an NLP pipeline can be used to create an automated analysis and reporting mechanism for unstructured PGHD. Further studies are suggested with real-world data to assess pipeline performance and further implications.

Nursing documentation of symptoms is associated with higher risk of emergency department visits and hospitalizations in homecare patients

BACKGROUND

Nurses often document patient symptoms in narrative notes.

PURPOSE

This study used a technique called natural language processing (NLP) to: (1) Automatically identify documentation of seven common symptoms (anxiety, cognitive disturbance, depressed mood, fatigue, sleep disturbance, pain, and well-being) in homecare narrative nursing notes, and (2) examine the association between symptoms and emergency department visits or hospital admissions from homecare.

METHOD

NLP was applied on a large subset of narrative notes (2.5 million notes) documented for 89,825 patients admitted to one large homecare agency in the Northeast United States.

FINDINGS

NLP accurately identified symptoms in narrative notes. Patients with more documented symptom categories had higher risk of emergency department visit or hospital admission.

DISCUSSION

Further research is needed to explore additional symptoms and implement NLP systems in the homecare setting to enable early identification of concerning patient trends leading to emergency department visit or hospital admission.

Efficient goal attainment and engagement in a care manager system using unstructured notes

OBJECTIVE

To improve efficient goal attainment of patients by analyzing the unstructured text in care manager (CM) notes (CMNs). Our task is to determine whether the goal assigned by the CM can be achieved in a timely manner.

MATERIALS AND METHODS

Our data consists of CM structured and unstructured records from a private firm in Orlando, FL. The CM data is based on phone interactions between the CM and the patient. A portion of the data has been manually annotated to indicate engagement. We present 2 machine learning classifiers: an engagement model and a goal attainment model.

RESULTS

We can successfully distinguish automatically between engagement and lack of engagement. Subsequently, incorporating engagement and features from textual information from the unstructured notes significantly improves goal attainment classification.

DISCUSSION

Two key challenges in this task were the time-consuming annotation effort for engagement classification and the limited amount of data for the more difficult goal attainment class (specifically, for people who take a long time to achieve their goals). We successfully explore domain adaptation and transfer learning techniques to improve performance on the under-represented classes. We also explore the value of using features from unstructured notes to improve the model and interpretability.

CONCLUSIONS

Unstructured CMNs can be used to improve accuracy of our classification models for predicting patient self-management goal attainment. This work can be used to help identify patients who may require special attention from CMs to improve engagement in self-management.

Thirty-day readmission, length of stay and self-management behaviour among patients with acute coronary syndrome and type 2 diabetes mellitus: A scoping review

AIMS AND OBJECTIVES

To summarise the current evidence on comorbid type 2 diabetes mellitus (T2DM) related to 30-day readmission and hospital length of stay (LOS) among patients with acute coronary syndrome (ACS) and evidence on the effectiveness of self-management programmes for patients with both conditions.

BACKGROUND

Acute coronary syndrome and T2DM remain two major diseases leading to serious consequences. Thirty-day readmission and LOS were considered indicators of the quality of care, with the understanding that the potential significant effects of these outcomes could be varied.

DESIGN

This scoping review followed the methodology described by Arksey and O'Malley.

METHODS

Five databases including PubMed, Embase, Cochrane Library, Web of Science and CINAHL were searched, and a total of 20 articles involving 913,807 patients were included. Results were reported in accordance with PRISMA-ScR guidelines.

RESULTS

The results indicated that patients with both ACS and T2DM have prolonged LOS and increased 30-day readmission rates. The findings supported that improvements in patient self-management behaviour for optimal health outcomes were partially successful by effective self-management programmes; however, few articles on intervention programmes specifically designed for patients with two conditions were found.

CONCLUSION

Prolonged LOS and increased 30-day readmission rates are found among patients with ACS and T2DM. Based on few pilot studies building on each other, the effectiveness of self-management programmes in promoting self-care behaviour, self-efficacy and knowledge for patients with ACS and T2DM cannot be concluded.

RELEVANCE TO CLINICAL PRACTICE

Findings from this review provide valuable information on and a better understanding of readmissions and LOS among patients with ACS and T2DM for healthcare providers. Future developments and implementations of effective self-management programmes should target patients with dual diagnoses to improve health behaviour and reduce readmission and LOS.

Natural Language Processing of Clinical Notes on Chronic Diseases: Systematic Review

Background

Novel approaches that complement and go beyond evidence-based medicine are required in the domain of chronic diseases, given the growing incidence of such conditions on the worldwide population. A promising avenue is the secondary use of electronic health records (EHRs), where patient data are analyzed to conduct clinical and translational research. Methods based on machine learning to process EHRs are resulting in improved understanding of patient clinical trajectories and chronic disease risk prediction, creating a unique opportunity to derive previously unknown clinical insights. However, a wealth of clinical histories remains locked behind clinical narratives in free-form text. Consequently, unlocking the full potential of EHR data is contingent on the development of natural language processing (NLP) methods to automatically transform clinical text into structured clinical data that can guide clinical decisions and potentially delay or prevent disease onset.

Objective

The goal of the research was to provide a comprehensive overview of the development and uptake of NLP methods applied to free-text clinical notes related to chronic diseases, including the investigation of challenges faced by NLP methodologies in understanding clinical narratives.

Methods

Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines were followed and searches were conducted in 5 databases using “clinical notes,” “natural language processing,” and “chronic disease” and their variations as keywords to maximize coverage of the articles.

Results

Of the 2652 articles considered, 106 met the inclusion criteria. Review of the included papers resulted in identification of 43 chronic diseases, which were then further classified into 10 disease categories using the International Classification of Diseases, 10th Revision. The majority of studies focused on diseases of the circulatory system (n=38) while endocrine and metabolic diseases were fewest (n=14). This was due to the structure of clinical records related to metabolic diseases, which typically contain much more structured data, compared with medical records for diseases of the circulatory system, which focus more on unstructured data and consequently have seen a stronger focus of NLP. The review has shown that there is a significant increase in the use of machine learning methods compared to rule-based approaches; however, deep learning methods remain emergent (n=3). Consequently, the majority of works focus on classification of disease phenotype with only a handful of papers addressing extraction of comorbidities from the free text or integration of clinical notes with structured data. There is a notable use of relatively simple methods, such as shallow classifiers (or combination with rule-based methods), due to the interpretability of predictions, which still represents a significant issue for more complex methods. Finally, scarcity of publicly available data may also have contributed to insufficient development of more advanced methods, such as extraction of word embeddings from clinical notes.

Conclusions

Efforts are still required to improve (1) progression of clinical NLP methods from extraction toward understanding; (2) recognition of relations among entities rather than entities in isolation; (3) temporal extraction to understand past, current, and future clinical events; (4) exploitation of alternative sources of clinical knowledge; and (5) availability of large-scale, de-identified clinical corpora.

Making Sense of Big Textual Data for Health Care: Findings from the Section on Clinical Natural Language Processing

Objectives: To summarize recent research and present a selection of the best papers published in 2016 in the field of clinical Natural Language Processing (NLP). Method: A survey of the literature was performed by the two section editors of the IMIA Yearbook NLP section. Bibliographic databases were searched for papers with a focus on NLP efforts applied to clinical texts or aimed at a clinical outcome. Papers were automatically ranked and then manually reviewed based on titles and abstracts. A shortlist of candidate best papers was first selected by the section editors before being peer-reviewed by independent external reviewers. Results: The five clinical NLP best papers provide a contribution that ranges from emerging original foundational methods to transitioning solid established research results to a practical clinical setting. They offer a framework for abbreviation disambiguation and coreference resolution, a classification method to identify clinically useful sentences, an analysis of counseling conversations to improve support to patients with mental disorder and grounding of gradable adjectives. Conclusions: Clinical NLP continued to thrive in 2016, with an increasing number of contributions towards applications compared to fundamental methods. Fundamental work addresses increasingly complex problems such as lexical semantics, coreference resolution, and discourse analysis. Research results translate into freely available tools, mainly for English.