Developing and testing inter-rater reliability of a data collection tool for patient health records on end-of-life care of neurological patients in an acute hospital ward

AIM

Develop and test a data collection tool-Neurological End-Of-Life Care Assessment Tool (NEOLCAT)-for extracting data from patient health records (PHRs) on end-of-life care of neurological patients in an acute hospital ward.

DESIGN

Instrument development and inter-rater reliability (IRR) assessment.

METHOD

NEOLCAT was constructed from patient care items obtained from clinical guidelines and literature on end-of-life care. Expert clinicians reviewed the items. Using percentage agreement and Fleiss' kappa we calculated IRR on 32 nominal items, out of 76 items.

RESULTS

IRR of NEOLCAT showed 89% (range 83%-95%) overall categorical percentage agreement. The Fleiss' kappa categorical coefficient was 0.84 (range 0.71-0.91). There was fair or moderate agreement on six items, and moderate or almost perfect agreement on 26 items.

CONCLUSION

The NEOLCAT shows promising psychometric properties for studying clinical components of care of neurological patients at the end-of-life on an acute hospital ward but could be further developed in future studies.

Extracting Medical Information From Free-Text and Unstructured Patient-Generated Health Data Using Natural Language Processing Methods: Feasibility Study With Real-world Data

BACKGROUND

Patient-generated health data (PGHD) captured via smart devices or digital health technologies can reflect an individual health journey. PGHD enables tracking and monitoring of personal health conditions, symptoms, and medications out of the clinic, which is crucial for self-care and shared clinical decisions. In addition to self-reported measures and structured PGHD (eg, self-screening, sensor-based biometric data), free-text and unstructured PGHD (eg, patient care note, medical diary) can provide a broader view of a patient's journey and health condition. Natural language processing (NLP) is used to process and analyze unstructured data to create meaningful summaries and insights, showing promise to improve the utilization of PGHD.

OBJECTIVE

Our aim is to understand and demonstrate the feasibility of an NLP pipeline to extract medication and symptom information from real-world patient and caregiver data.

METHODS

We report a secondary data analysis, using a data set collected from 24 parents of children with special health care needs (CSHCN) who were recruited via a nonrandom sampling approach. Participants used a voice-interactive app for 2 weeks, generating free-text patient notes (audio transcription or text entry). We built an NLP pipeline using a zero-shot approach (adaptive to low-resource settings). We used named entity recognition (NER) and medical ontologies (RXNorm and SNOMED CT [Systematized Nomenclature of Medicine Clinical Terms]) to identify medication and symptoms. Sentence-level dependency parse trees and part-of-speech tags were used to extract additional entity information using the syntactic properties of a note. We assessed the data; evaluated the pipeline with the patient notes; and reported the precision, recall, and F₁ scores.

RESULTS

In total, 87 patient notes are included (audio transcriptions n=78 and text entries n=9) from 24 parents who have at least one CSHCN. The participants were between the ages of 26 and 59 years. The majority were White (n=22, 92%), had more than one child (n=16, 67%), lived in Ohio (n=22, 92%), had mid- or upper-mid household income (n=15, 62.5%), and had higher level education (n=24, 58%). Out of 87 notes, 30 were drug and medication related, and 46 were symptom related. We captured medication instances (medication, unit, quantity, and date) and symptoms satisfactorily (precision >0.65, recall >0.77, F₁>0.72). These results indicate the potential when using NER and dependency parsing through an NLP pipeline on information extraction from unstructured PGHD.

CONCLUSIONS

The proposed NLP pipeline was found to be feasible for use with real-world unstructured PGHD to accomplish medication and symptom extraction. Unstructured PGHD can be leveraged to inform clinical decision-making, remote monitoring, and self-care including medical adherence and chronic disease management. With customizable information extraction methods using NER and medical ontologies, NLP models can feasibly extract a broad range of clinical information from unstructured PGHD in low-resource settings (eg, a limited number of patient notes or training data).

Oncology Nursing Perceptions of Patient Electronic Portal Use: A Qualitative Analysis

PURPOSE/OBJECTIVES

To identify nursing staff reactions to and perceptions of electronic portal use in a cancer setting.
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RESEARCH APPROACH

Qualitative.
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SETTING

Outpatient clinic at the Harold C. Simmons Comprehensive Cancer Center of the University of Texas Southwestern Medical Center in Dallas.
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PARTICIPANTS

13 nurses with a range of credentials and experience, representing infusion, medical oncology, and bone marrow transplantation clinics.
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METHODOLOGIC APPROACH

Two focus groups were conducted. Theoretical thematic content analysis of data was performed.
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FINDINGS

Key themes that emerged for consideration of electronic portals included work volume and flow, patient expectations and safety, variation in use of communication technologies, and education and management.
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INTERPRETATION

The current study provides insight into the implications of electronic portals by identifying nursing staff reactions to this technology. These reactions are predominantly related to the impact on clinical workload and patient safety and expectations.
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IMPLICATIONS FOR NURSING

As clinical cancer facilities incorporate electronic portal technology into their operations, attention to the impact on staff workload, division of labor, patient safety, and patient expectations should be considered.