Natural Language Processing of Clinical Notes to Identify Mental Illness and Substance Use Among People Living with HIV: Retrospective Cohort Study

Ridgway, Jessica P; Uvin, A Ziggy; Schmitt, Jessica; Oliwa, Tomasz; Almirol, Ellen; Devlin, Samantha; Schneider, John A.

doi:10.2196/23456

Cited by 29 publications

(15 citation statements)

References 35 publications

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“…Our findings suggest that, although substance use and mental health screening was occurring prior to TAPS/AOQ implementation, screening practices were highly variable, which is consistent with previous reports of substance use and mental health screening in similar settings [ 16 , 17 ]. As noted above, standard KPNC practice already routinely screened all primary care patients for alcohol and tobacco use [ 46 , 47 ], but the TAPS/AOQ enabled screening for tobacco, alcohol, and all major drug classes in a single instrument.…”

Section: Discussionsupporting

confidence: 89%

“…Routine screening is essential for identifying these comorbidities [10][11][12][13], but is often underutilized due to lack of resources, time constraints, and stigma [14,15]. When screening does occur, there is variability in question content, frequency, and documentation by providers [16,17], and patients often underreport symptoms, particularly alcohol and other drug use problems [15,18].…”

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confidence: 99%

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Patient and provider perspectives on self-administered electronic substance use and mental health screening in HIV primary care

Lea

Altschuler

Leibowitz

et al. 2022

Addict Sci Clin Pract

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Background Substance use disorders, depression and anxiety disproportionately affect people with HIV (PWH) and lead to increased morbidity and mortality. Routine screening can help address these problems but is underutilized. This study sought to describe patient and provider perspectives on the acceptability and usefulness of systematic electronic, self-administered screening for tobacco, alcohol, other substance use, and mental health symptoms among patients in HIV primary care. Methods Screening used validated instruments delivered pre-appointment by both secure messaging and clinic-based tablets, with results integrated into the electronic health record (EHR). Qualitative analysis of semi-structured interviews with 9 HIV primary care providers and 12 patients in the 3 largest HIV primary care clinics in the Kaiser Permanente Northern California health system who participated in a clinical trial evaluating computerized screening and behavioral interventions was conducted. Interviews were audio-recorded and transcribed. A thematic approach was utilized for coding and analysis of interview data using a combination of deductive and inductive methods. Results Four key themes were identified: (1) perceived clinical benefit of systematic, electronic screening and EHR integration for providers and patients; (2) usefulness of having multiple methods of questionnaire completion; (3) importance of the patient–provider relationship to facilitate completion and accurate reporting; and (4) barriers, include privacy and confidentiality concerns about reporting sensitive information, particularly about substance use, and potential burden from repeated screenings. Conclusions Findings suggest that electronic, self-administered substance use and mental health screening is acceptable to patients and may have clinical utility to providers. While offering different methods of screening completion can capture a wider range of patients, a strong patient–provider relationship is a key factor in overcoming barriers and ensuring accurate patient responses. Further investigation into facilitators, barriers, and utility of electronic screening for PWH and other high-priority patient populations is indicated. Trial registration ClinicalTrials.gov, NCT03217058. Registered 13 July 2017, https://clinicaltrials.gov/ct2/show/NCT03217058

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Section: Discussionsupporting

confidence: 89%

mentioning

confidence: 99%

Patient and provider perspectives on self-administered electronic substance use and mental health screening in HIV primary care

Lea

Altschuler

Leibowitz

et al. 2022

Addict Sci Clin Pract

View full text Add to dashboard Cite

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“…Hospitals and health systems have made, and continue to make, substantial investments in their EMR systems. Although a systematic collection of salient medical and social data remains a work in progress, successful efforts using NLP algorithm have enabled efficient mining of rich free-text medical notes for various risk assessment or decision-making tools aimed at reducing the occurrences of adverse health events and wasteful spending [22][23][24]. Our study aligns with this work to identify caregiver availability for patients whose well-being depends on caregivers.…”

Section: Comparison With Prior Workmentioning

confidence: 65%

Identifying Caregiver Availability Using Medical Notes With Rule-Based Natural Language Processing: Retrospective Cohort Study

Mahmoudi¹,

Wu²,

Najarian³

et al. 2022

JMIR Aging

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Background Identifying caregiver availability, particularly for patients with dementia or those with a disability, is critical to informing the appropriate care planning by the health systems, hospitals, and providers. This information is not readily available, and there is a paucity of pragmatic approaches to automatically identifying caregiver availability and type. Objective Our main objective was to use medical notes to assess caregiver availability and type for hospitalized patients with dementia. Our second objective was to identify whether the patient lived at home or resided at an institution. Methods In this retrospective cohort study, we used 2016-2019 telephone-encounter medical notes from a single institution to develop a rule-based natural language processing (NLP) algorithm to identify the patient’s caregiver availability and place of residence. Using note-level data, we compared the results of the NLP algorithm with human-conducted chart abstraction for both training (749/976, 77%) and test sets (227/976, 23%) for a total of 223 adults aged 65 years and older diagnosed with dementia. Our outcomes included determining whether the patients (1) reside at home or in an institution, (2) have a formal caregiver, and (3) have an informal caregiver. Results Test set results indicated that our NLP algorithm had high level of accuracy and reliability for identifying whether patients had an informal caregiver (F1=0.94, accuracy=0.95, sensitivity=0.97, and specificity=0.93), but was relatively less able to identify whether the patient lived at an institution (F1=0.64, accuracy=0.90, sensitivity=0.51, and specificity=0.98). The most common explanations for NLP misclassifications across all categories were (1) incomplete or misspelled facility names; (2) past, uncertain, or undecided status; (3) uncommon abbreviations; and (4) irregular use of templates. Conclusions This innovative work was the first to use medical notes to pragmatically determine caregiver availability. Our NLP algorithm identified whether hospitalized patients with dementia have a formal or informal caregiver and, to a lesser extent, whether they lived at home or in an institutional setting. There is merit in using NLP to identify caregivers. This study serves as a proof of concept. Future work can use other approaches and further identify caregivers and the extent of their availability.

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“…Our effectiveness results agree with the literature [ 83 , 88 ], in which a Macro-F1 score >80% is considered a successful extraction of medical records. Even though there is still a need to cover more tasks related to ICHOM patient-reported outcome measures [ 3 , 74 , 76 , 85 ], we hypothesized that these tasks comprise a feeling state, and the lack of normalization of data contained in EMRs may explain the fact that these task categories did not perform very well [ 70 , 89 ]. Medical records related to baseline characteristics and care processes typically contain much more structured data (eg, numerical values for tasks) than medical patient-reported outcomes, which focus more on unstructured data [ 83 , 90 ].…”

Section: Discussionmentioning

confidence: 99%

Stroke Outcome Measurements From Electronic Medical Records: Cross-sectional Study on the Effectiveness of Neural and Nonneural Classifiers

et al. 2021

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Background With the rapid adoption of electronic medical records (EMRs), there is an ever-increasing opportunity to collect data and extract knowledge from EMRs to support patient-centered stroke management. Objective This study aims to compare the effectiveness of state-of-the-art automatic text classification methods in classifying data to support the prediction of clinical patient outcomes and the extraction of patient characteristics from EMRs. Methods Our study addressed the computational problems of information extraction and automatic text classification. We identified essential tasks to be considered in an ischemic stroke value-based program. The 30 selected tasks were classified (manually labeled by specialists) according to the following value agenda: tier 1 (achieved health care status), tier 2 (recovery process), care related (clinical management and risk scores), and baseline characteristics. The analyzed data set was retrospectively extracted from the EMRs of patients with stroke from a private Brazilian hospital between 2018 and 2019. A total of 44,206 sentences from free-text medical records in Portuguese were used to train and develop 10 supervised computational machine learning methods, including state-of-the-art neural and nonneural methods, along with ontological rules. As an experimental protocol, we used a 5-fold cross-validation procedure repeated 6 times, along with subject-wise sampling. A heatmap was used to display comparative result analyses according to the best algorithmic effectiveness (F1 score), supported by statistical significance tests. A feature importance analysis was conducted to provide insights into the results. Results The top-performing models were support vector machines trained with lexical and semantic textual features, showing the importance of dealing with noise in EMR textual representations. The support vector machine models produced statistically superior results in 71% (17/24) of tasks, with an F1 score >80% regarding care-related tasks (patient treatment location, fall risk, thrombolytic therapy, and pressure ulcer risk), the process of recovery (ability to feed orally or ambulate and communicate), health care status achieved (mortality), and baseline characteristics (diabetes, obesity, dyslipidemia, and smoking status). Neural methods were largely outperformed by more traditional nonneural methods, given the characteristics of the data set. Ontological rules were also effective in tasks such as baseline characteristics (alcoholism, atrial fibrillation, and coronary artery disease) and the Rankin scale. The complementarity in effectiveness among models suggests that a combination of models could enhance the results and cover more tasks in the future. Conclusions Advances in information technology capacity are essential for scalability and agility in measuring health status outcomes. This study allowed us to measure effectiveness and identify opportunities for automating the classification of outcomes of specific tasks related to clinical conditions of stroke victims, and thus ultimately assess the possibility of proactively using these machine learning techniques in real-world situations.

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Natural Language Processing of Clinical Notes to Identify Mental Illness and Substance Use Among People Living with HIV: Retrospective Cohort Study

Cited by 29 publications

References 35 publications

Patient and provider perspectives on self-administered electronic substance use and mental health screening in HIV primary care

Patient and provider perspectives on self-administered electronic substance use and mental health screening in HIV primary care

Identifying Caregiver Availability Using Medical Notes With Rule-Based Natural Language Processing: Retrospective Cohort Study

Stroke Outcome Measurements From Electronic Medical Records: Cross-sectional Study on the Effectiveness of Neural and Nonneural Classifiers

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