Use of Natural Language Processing to Identify Sexual and Reproductive Health Information in Clinical Text.

OBJECTIVES: This study aimed to enable clinical researchers without expertise in natural language processing (NLP) to extract and analyze information about sexual and reproductive health (SRH), or other sensitive health topics, from large sets of clinical notes. METHODS: (1) We retrieved text from the electronic health record as individual notes. (2) We segmented notes into sentences using one of scispaCy's NLP toolkits. (3) We exported sentences to the labeling application Watchful and annotated subsets of these as relevant or irrelevant to various SRH categories by applying a combination of regular expressions and manual annotation. (4) The labeled sentences served as training data to create machine learning models for classifying text; specifically, we used spaCy's default text classification ensemble, comprising a bag-of-words model and a neural network with attention. (5) We applied each model to unlabeled sentences to identify additional references to SRH with novel relevant vocabulary. We used this information and repeated steps 3 to 5 iteratively until the models identified no new relevant sentences for each topic. Finally, we aggregated the labeled data for analysis. RESULTS: This methodology was applied to 3,663 Child Neurology notes for 971 female patients. Our search focused on six SRH categories. We validated the approach using two subject matter experts, who independently labeled a sample of 400 sentences. Cohen's kappa values were calculated for each category between the reviewers (menstruation: 1, sexual activity: 0.9499, contraception: 0.9887, folic acid: 1, teratogens: 0.8864, pregnancy: 0.9499). After removing the sentences on which reviewers did not agree, we compared the reviewers' labels to those produced via our methodology, again using Cohen's kappa (menstruation: 1, sexual activity: 1, contraception: 0.9885, folic acid: 1, teratogens: 0.9841, pregnancy: 0.9871). CONCLUSION: Our methodology is reproducible, enables analysis of large amounts of text, and has produced results that are highly comparable to subject matter expert manual review.

Preferences and Experiences Regarding Pregnancy Options Counseling in Adolescence and Young Adulthood: A Qualitative Study.

PURPOSE: Perspectives of adolescents and young adults (AYAs) experiencing pregnancy options counseling (POC) are absent from the literature. This study explores AYA experiences and preferences related to POC to inform best practice guidelines. METHODS: We conducted semistructured phone interviews in 2020-2021 among US-based individuals, 18-35 years old, who experienced a pregnancy less than 20 years of age. We performed qualitative descriptive analysis of positive and negative attributes of AYA's experiences with POC. RESULTS: Fifty participants reported 59 pregnancies (16 parenting, 19 abortions, 18 adoptions, three miscarriages) between the ages of 13 and 19 years. Positive attributes of POC experienced included: (1) provider communication that was compassionate, respectful, supportive, and attentive to nonverbal cues; (2) provider neutrality; (3) discussion of all pregnancy options; (4) asking about feelings, choice, life plans, and additional supports; (5) provision of informational materials; and (6) warm handoffs/follow-up facilitation. Negative attributes of POC experienced included: (1) judgmental, impersonal, or absent communication; (2) lack of counseling on all options and/or coercive/directive counseling; (3) insufficient time and supportive resources; and (4) confidentiality concerns. We identified no differences in these perspectives across pregnancy outcomes reported. Participants generally desired counseling about all options, with rare exceptions of ambivalence. DISCUSSION: Individuals who experienced an adolescent pregnancy described similar positive and negative attributes of POC regardless of preferred pregnancy outcome. Their perspectives highlight how crucial interpersonal communication skills are for effective POC for AYA. POC training across health care specialties should emphasize confidential, compassionate, and nonjudgmental care for AYA patients.

Sleep time and efficiency in patients undergoing laboratory-based polysomnography.

STUDY OBJECTIVES: Sleep quality in patients studied with laboratory-based polysomnography may differ from sleep quality in patients studied at home but remains clinically relevant and important to describe. We assessed objective sleep quality and explored factors associated with poor sleep in patients undergoing laboratory-based polysomnography. METHODS: We reviewed diagnostic polysomnography studies from a 10-year period at a single sleep center. Total sleep time (TST) and sleep efficiency (SE) were assessed as markers of sleep quality. Poor sleep was defined as TST ≤ 4 hours or SE ≤ 50%. Multivariable analysis was performed to determine associations between objective sleep quality as an outcome and multiple candidate predictors including age, sex, race, body mass index, comorbidities, severity of obstructive sleep apnea, and central nervous system medications. RESULTS: Among 4957 patients (age 53 ± 15 years), average TST and median SE were 5.8 hours and 79%, respectively. There were 556 (11%) and 406 (8%) patients who had poor sleep based on TST and SE, respectively. In multivariable analysis, those who were older (per 10 years: 1.48 [1.34, 1.63]), male (1.38 [1.14,1.68]), and had severe obstructive sleep apnea (1.76 [1.28, 2.43]) were more likely to have short sleep. Antidepressant use was associated with lower odds of short sleep (0.77 [0.59,1.00]). Older age (per 10 years: 1.48 [1.34, 1.62]), male sex (1.34 [1.07,1.68]), and severe obstructive sleep apnea (2.16 [1.47, 3.21]) were associated with higher odds of poor SE. CONCLUSIONS: We describe TST and SE from a single sleep center cohort. Multiple demographic characteristics were associated with poor objective sleep in patients during laboratory-based polysomnography. CITATION: Harrison EI, Roth RH, Lobo JM, et al. Sleep time and efficiency in patients undergoing laboratory-based polysomnography. J Clin Sleep Med. 2021;17(8):1591-1598.