Automated Detection of Substance-Use Status and Related Information from Clinical Text

This study aims to develop and evaluate an automated system for extracting information related to patient substance use (smoking, alcohol, and drugs) from unstructured clinical text (medical discharge records). The authors propose a four-stage system for the extraction of the substance-use status and related attributes (type, frequency, amount, quit-time, and period). The first stage uses a keyword search technique to detect sentences related to substance use and to exclude unrelated records. In the second stage, an extension of the NegEx negation detection algorithm is developed and employed for detecting the negated records. The third stage involves identifying the temporal status of the substance use by applying windowing and chunking methodologies. Finally, in the fourth stage, regular expressions, syntactic patterns, and keyword search techniques are used in order to extract the substance-use attributes. The proposed system achieves an F1-score of up to 0.99 for identifying substance-use-related records, 0.98 for detecting the negation status, and 0.94 for identifying temporal status. Moreover, F1-scores of up to 0.98, 0.98, 1.00, 0.92, and 0.98 are achieved for the extraction of the amount, frequency, type, quit-time, and period attributes, respectively. Natural Language Processing (NLP) and rule-based techniques are employed efficiently for extracting substance-use status and attributes, with the proposed system being able to detect substance-use status and attributes over both sentence-level and document-level data. Results show that the proposed system outperforms the compared state-of-the-art substance-use identification system on an unseen dataset, demonstrating its generalisability

Do financial incentives for delivering health promotion counselling work? Analysis of smoking cessation activities stimulated by the quality and outcomes framework

<p>Abstract</p> <p>Background</p> <p>A substantial fraction of UK general practitioners' salaries is now intended to reflect the quality of care provided. This performance-related pay system has probably improved aspects of primary health care but, using the observational data available, disentangling the impacts of different types of targets set within this unique payment system is challenging.</p> <p>Discussion</p> <p>Financial incentives undoubtedly influence GPs' activities, however, those aimed at encouraging GPs' delivery of health promotion counselling may not always have the effects intended. There is strong, observational evidence that targets and incentives intended to increase smoking cessation counselling by GPs have merely increased their propensity to record this activity in patients' medical records. The limitations of using financial incentives to stimulate the delivery of counselling in primary care are discussed and a re-appraisal of their use within UK GPs' performance-related pay system is argued for.</p> <p>Summary</p> <p>The utility of targets employed by the system for UK General Practitioners' performance related pay may be inappropriate for encouraging the delivery of health promotion counselling interventions. An evaluation of these targets is essential before they are further developed or added to.</p

Cigarette smoking as a risk factor for schizophrenia or all non-affective psychoses

BACKGROUND: Smoking tobacco is regarded as an epiphenomenon in patients with schizophrenia when it may be causal. We aimed to examine whether smoking status is related to the onset of schizophrenia or the broader diagnosis of non-affective psychosis, including schizophrenia. METHODS: We used data from The Health Improvement Network primary care database to identify people aged 15-24 between 1 January 2004 and 31 December 2009. We followed them until the earliest of: first diagnosis of schizophrenia (or psychosis), patient left the practice, practice left THIN, patient died or 31 December 2014. RESULTS: In men, incidence rates for schizophrenia per 100 000 person years at risk were higher in smoking initiators (non-smoker who became a smoker during the study) than in non-smokers (adjusted IRR 1.94; 95% CI 1.29-2.91) and higher still in smokers (adjusted IRR 3.32; 95% CI 2.67-4.14). Among women, the incidence rate of schizophrenia was higher in smokers than in non-smokers (adjusted IRR 1.50; 95% CI 1.06-2.12), but no higher in smoking initiators than non-smokers. For non-affective psychosis, the pattern was similar for men but more evident in women where psychosis incidence rates were higher in smoking initiators (adjusted IRR 1.90; 95% CI 1.40-2.56) and in smokers (adjusted IRR 2.13; 95% CI 1.76-2.57) than in non-smokers. CONCLUSIONS: We found an important and strong association between smoking and incidence of schizophrenia. Smoking may increase risk through as yet unknown pathways or smoking may share genetic risk with schizophrenia and non-affective psychoses

Healthcare use by people who use illicit opioids (HUPIO): development of a cohort based on electronic primary care records in England [version 1; peer review: 2 approved]

BACKGROUND: People who use illicit opioids such as heroin have substantial health needs, but there are few longitudinal studies of general health and healthcare in this population. Most research to date has focused on a narrow set of outcomes, including overdoses and HIV or hepatitis infections. We developed and validated a cohort using UK primary care electronic health records (Clinical Practice Research Datalink GOLD and AURUM databases) to facilitate research into healthcare use by people who use illicit opioid use (HUPIO). METHODS: Participants are patients in England with primary care records indicating a history of illicit opioid use. We identified codes including prescriptions of opioid agonist therapies (methadone and buprenorphine) and clinical observations such as ‘heroin dependence’. We constructed a cohort of patients with at least one of these codes and aged 18-64 at cohort entry, with follow-up between January 1997 and March 2020. We validated the cohort by comparing patient characteristics and mortality rates to other cohorts of people who use illicit opioids, with different recruitment methods. RESULTS: Up to March 2020, the HUPIO cohort included 138,761 patients with a history of illicit opioid use. Demographic characteristics and all-cause mortality were similar to existing cohorts: 69% were male; the median age at index for patients in CPRD AURUM (the database with more included participants) was 35.3 (IQR 29.1-42.6); the average age of new cohort entrants increased over time; 76% had records indicating current tobacco smoking; patients disproportionately lived in deprived neighbourhoods; and all-cause mortality risk was 5.4 (95% CI 5.3-5.5) times the general population of England. CONCLUSIONS: Primary care data offer new opportunities to study holistic health outcomes and healthcare of this population. The large sample enables investigation of rare outcomes, whilst the availability of linkage to external datasets allows investigation of hospital use, cancer treatment, and mortality

Improving Community Advisory Board Engagement In Precision Medicine Research To Reduce Health Disparities

Community Advisory Boards (CABs) are used in efforts to reduce health disparities; however, there is little documentation in the literature regarding their use in precision medicine research. In this case study, an academic-CAB partnership developed a questionnaire and patient educational materials for two precision smoking cessation interventions that involved use of genetic information. The community-engaged research (CEnR) literature provided a framework for enhancing benefits to CAB members involved in developing research documents for use with a low-income, ethnically diverse population of smokers. The academic partners integrated three CEnR strategies: 1) in-meeting statements acknowledging their desire to learn from community partners, 2) in-meeting written feedback to and from community partners, and 3) a survey to obtain CAB member feedback post-meetings. Strategies 1 and 2 yielded modifications to pertinent study materials, as well as suggestions for improving meeting operations that were then adopted, as appropriate, by the academic partners. The survey indicated that CAB members valued the meeting procedure changes which appeared to have contributed to improvements in attendance and satisfaction with the meetings. Further operationalization of relevant partnership constructs and development of tools for measuring these aspects of community-academic partnerships is warranted to support community engagement in precision medicine research studies

The prognosis of oral epithelial dysplasia and oral squamous cell carcinoma in individuals with oral lichen planus: a single-centre observational study and a pioneer preliminary exploration of UK national Electronic Health Records

Head and neck squamous cell carcinoma (HNSCC) is a significant public health problem worldwide due to its high mortality and morbidity. A notable proportion of HNSCC, particularly oral squamous cell carcinoma (OSCC), is preceded by several long-standing chronic oral mucosal diseases including oral lichen planus (OLP). However, it remains largely unknown regarding the impact of a pre-existing OLP upon the prognosis and behaviour of OSCC and its precursor, oral epithelial dysplasia (OED). Therefore, this PhD thesis has sought to determine the influence of OLP on the long-term behaviour and prognosis of oral epithelial dysplasia (OED) and oral squamous cell carcinoma (OSCC) using data from a single UK tertiary care centre. Additionally, this thesis has made the first steps towards providing understanding of the epidemiology of HNSCC in a representative sample with common chronic oral mucosal conditions including oral lichen planus, oral submucous fibrosis, leukoplakia and periodontal diseases of the UK population. A retrospective cohort study of 299 patients with OED revealed that individuals with OED arising on a background of OLP appeared to be at higher risk of developing new primary OEDs up to 3 years (in the early years) after the first diagnosis of OED compared to those without OLP. However, the risk of malignant progression was similar between the two groups. This thesis built on these findings by investigating the impact of OLP in determining the long-term behaviour and prognosis of OSCC using a retrospective cohort study of 285 patients with OSCC. The results indicated that patients with OSCC-associated OLP were more likely to develop multiple and multifocal new primary dysplastic and OSCC events following their first oral malignancy. Despite this, there seems to be no significant association between OLP and mortality. In order to reveal more about the relationship between long-standing oral mucosal conditions and HNSCC using national-scale data, this thesis went beyond single data sources. This project provides a detailed method for appropriate data handling and curation of a linked national database of a UK population (the CALIBER platform). This allowed the development and validation of a reliable phenotype algorithm to identify patients with HNSCC from this data platform. Taken together, these findings advance understanding of the impact of OLP on the behaviour and prognosis of OED and OSCC. In addition, the HSNCC phenotype algorithm developed here represents an important step towards understanding the association between common chronic oral mucosal conditions and HNSCC in the UK

Associations Between Natural Language Processing (NLP) Enriched Social Determinants of Health and Suicide Death among US Veterans

Importance: Social determinants of health (SDOH) are known to be associated with increased risk of suicidal behaviors, but few studies utilized SDOH from unstructured electronic health record (EHR) notes. Objective: To investigate associations between suicide and recent SDOH, identified using structured and unstructured data. Design: Nested case-control study. Setting: EHR data from the US Veterans Health Administration (VHA). Participants: 6,122,785 Veterans who received care in the US VHA between October 1, 2010, and September 30, 2015. Exposures: Occurrence of SDOH over a maximum span of two years compared with no occurrence of SDOH. Main Outcomes and Measures: Cases of suicide deaths were matched with 4 controls on birth year, cohort entry date, sex, and duration of follow-up. We developed an NLP system to extract SDOH from unstructured notes. Structured data, NLP on unstructured data, and combining them yielded six, eight and nine SDOH respectively. Adjusted odds ratios (aORs) and 95% confidence intervals (CIs) were estimated using conditional logistic regression. Results: In our cohort, 8,821 Veterans committed suicide during 23,725,382 person-years of follow-up (incidence rate 37.18/100,000 person-years). Our cohort was mostly male (92.23%) and white (76.99%). Across the five common SDOH as covariates, NLP-extracted SDOH, on average, covered 80.03% of all SDOH occurrences. All SDOH, measured by structured data and NLP, were significantly associated with increased risk of suicide. The SDOH with the largest effects was legal problems (aOR=2.66, 95% CI=.46-2.89), followed by violence (aOR=2.12, 95% CI=1.98-2.27). NLP-extracted and structured SDOH were also associated with suicide. Conclusions and Relevance: NLP-extracted SDOH were always significantly associated with increased risk of suicide among Veterans, suggesting the potential of NLP in public health studies.Comment: Submitted to JAMA Network Ope

Social and Behavioral Domains in Acute Care Electronic Health Records: Barriers, Facilitators, Relevance, and Value.

Ph.D. Thesis. University of Hawaiʻi at Mānoa 2018

Social and behavioral determinants of health in the era of artificial intelligence with electronic health records: A scoping review

Background: There is growing evidence that social and behavioral determinants of health (SBDH) play a substantial effect in a wide range of health outcomes. Electronic health records (EHRs) have been widely employed to conduct observational studies in the age of artificial intelligence (AI). However, there has been little research into how to make the most of SBDH information from EHRs. Methods: A systematic search was conducted in six databases to find relevant peer-reviewed publications that had recently been published. Relevance was determined by screening and evaluating the articles. Based on selected relevant studies, a methodological analysis of AI algorithms leveraging SBDH information in EHR data was provided. Results: Our synthesis was driven by an analysis of SBDH categories, the relationship between SBDH and healthcare-related statuses, and several NLP approaches for extracting SDOH from clinical literature. Discussion: The associations between SBDH and health outcomes are complicated and diverse; several pathways may be involved. Using Natural Language Processing (NLP) technology to support the extraction of SBDH and other clinical ideas simplifies the identification and extraction of essential concepts from clinical data, efficiently unlocks unstructured data, and aids in the resolution of unstructured data-related issues. Conclusion: Despite known associations between SBDH and disease, SBDH factors are rarely investigated as interventions to improve patient outcomes. Gaining knowledge about SBDH and how SBDH data can be collected from EHRs using NLP approaches and predictive models improves the chances of influencing health policy change for patient wellness, and ultimately promoting health and health equity. Keywords: Social and Behavioral Determinants of Health, Artificial Intelligence, Electronic Health Records, Natural Language Processing, Predictive ModelComment: 32 pages, 5 figure