Design and development of patient health tracking, monitoring and big data storage using Internet of Things and real time cloud computing.

With the outbreak of the COVID-19 pandemic, social isolation and quarantine have become commonplace across the world. IoT health monitoring solutions eliminate the need for regular doctor visits and interactions among patients and medical personnel. Many patients in wards or intensive care units require continuous monitoring of their health. Continuous patient monitoring is a hectic practice in hospitals with limited staff; in a pandemic situation like COVID-19, it becomes much more difficult practice when hospitals are working at full capacity and there is still a risk of medical workers being infected. In this study, we propose an Internet of Things (IoT)-based patient health monitoring system that collects real-time data on important health indicators such as pulse rate, blood oxygen saturation, and body temperature but can be expanded to include more parameters. Our system is comprised of a hardware component that collects and transmits data from sensors to a cloud-based storage system, where it can be accessed and analyzed by healthcare specialists. The ESP-32 microcontroller interfaces with the multiple sensors and wirelessly transmits the collected data to the cloud storage system. A pulse oximeter is utilized in our system to measure blood oxygen saturation and body temperature, as well as a heart rate monitor to measure pulse rate. A web-based interface is also implemented, allowing healthcare practitioners to access and visualize the collected data in real-time, making remote patient monitoring easier. Overall, our IoT-based patient health monitoring system represents a significant advancement in remote patient monitoring, allowing healthcare practitioners to access real-time data on important health metrics and detect potential health issues before they escalate.

Improving our understanding of the social determinants of mental health: a data linkage study of mental health records and the 2011 UK census.

OBJECTIVES: To address the lack of individual-level socioeconomic information in electronic healthcare records, we linked the 2011 census of England and Wales to patient records from a large mental healthcare provider. This paper describes the linkage process and methods for mitigating bias due to non-matching. SETTING: South London and Maudsley NHS Foundation Trust (SLaM), a mental healthcare provider in Southeast London. DESIGN: Clinical records from SLaM were supplied to the Office of National Statistics for linkage to the census through a deterministic matching algorithm. We examined clinical (International Classification of Disease-10 diagnosis, history of hospitalisation, frequency of service contact) and socio-demographic (age, gender, ethnicity, deprivation) information recorded in Clinical Record Interactive Search (CRIS) as predictors of linkage success with the 2011 census. To assess and adjust for potential biases caused by non-matching, we evaluated inverse probability weighting for mortality associations. PARTICIPANTS: Individuals of all ages in contact with SLaM up until December 2019 (N=459 374). OUTCOME MEASURES: Likelihood of mental health records' linkage to census. RESULTS: 220 864 (50.4%) records from CRIS linked to the 2011 census. Young adults (prevalence ratio (PR) 0.80, 95% CI 0.80 to 0.81), individuals living in more deprived areas (PR 0.78, 95% CI 0.78 to 0.79) and minority ethnic groups (eg, Black African, PR 0.67, 0.66 to 0.68) were less likely to match to census. After implementing inverse probability weighting, we observed little change in the strength of association between clinical/demographic characteristics and mortality (eg, presence of any psychiatric disorder: unweighted PR 2.66, 95% CI 2.52 to 2.80; weighted PR 2.70, 95% CI 2.56 to 2.84). CONCLUSIONS: Lower response rates to the 2011 census among people with psychiatric disorders may have contributed to lower match rates, a potential concern as the census informs service planning and allocation of resources. Due to its size and unique characteristics, the linked data set will enable novel investigations into the relationship between socioeconomic factors and psychiatric disorders.

In safe hands: child health data storage, linkage and consent for use.

While there is potential for societal benefit from linkage and integration of large datasets, there are gaps in our understanding of the implications for children and young people, and limited inclusion of their views within this discourse. We aimed to understand the views and expectations of children, young people and their parents/caregivers in Aotearoa New Zealand regarding child health data storage, linkage and consent for use. This qualitative study included 24 Maori and non-Maori children, young people and their families across five focus groups, recruited from a community-based health service. A mixed Maori and non-Maori research team facilitated participant recruitment and data collection. Child, adolescent and parent/caregiver groups were held separately. Sessions were audio-recorded and the verbatim transcripts were analysed thematically. We identified three themes: (i) I am more than a number: seeing patients as people; (ii) In safe hands: data as power; and (iii) What are your intentions with my data? Consent as an active relationship. A key challenge was the reductive and stigmatizing potential of data integration for minoritised groups. Hypothetical discussions of data sharing and linkage were contingent on trust between the participant and the health professional, with negotiated data ownership. Consent was conceived as an active relationship needing renewal and renegotiation as children reached adulthood. Current consent processes for ongoing use of child data require further deliberation. Without a strong ethical and child rights-based approach to issues of child health data management, consent and linkage, we risk exacerbating health inequities and experiences of breach of trust.

Evaluation of the Healthy Start voucher scheme on maternal vitamin use and child breastfeeding: a natural experiment using data linkage.

Background: Having a good start in life during pregnancy and infancy has been shown to be important for living both a healthy life and a longer life. Despite the introduction of many policies for the early-years age group, including voucher schemes, with the aim of improving nutrition, there is limited evidence of their impact on health. Objectives: To assess the effectiveness of the Healthy Start voucher scheme on infant, child and maternal outcomes, and to capture the lived experiences of the Healthy Start voucher scheme for low-income women. Design: This was a natural experiment study using existing data sets, linked to routinely collected health data sets, with a nested qualitative study of low-income women and an assessment of the health economics. Setting: Representative sample of Scottish children and UK children. Participants: Growing Up in Scotland cohort 2 (n = 2240), respondents to the 2015 Infant Feeding Study (n = 8067) and a sample of 40 participants in the qualitative study. Interventions: The Health Start voucher, a means-tested scheme that provides vouchers worth £3.10 per week to spend on liquid milk, formula milk, fruit and vegetables. Main outcome measures: Infant and child outcomes - breastfeeding initiation and duration; maternal outcomes - vitamin use pre and during pregnancy. Results: The exposed group were women receiving the Healthy Start voucher (R), with two control groups: eligible and not claiming the Healthy Start voucher (E) and nearly eligible. There was no difference in vitamin use during pregnancy for either comparison (receiving the Healthy Start voucher, 82%; eligible and not claiming the Healthy Start voucher, 86%; p = 0.10 vs. receiving the Healthy Start voucher, 87%; nearly eligible, 88%; p = 0.43) in the Growing Up in Scotland cohort. Proportions were similar for the Infant Feeding Study cohort (receiving the Healthy Start voucher, 89%; eligible and not claiming the Healthy Start voucher, 86%; p = 0.01 vs. receiving the Healthy Start voucher, 89%; nearly eligible, 87%; p = 0.01); although results were statistically significantly different, these were small effect sizes. There was no difference for either comparison in breastfeeding initiation or breastfeeding duration in months in Growing Up in Scotland, but there was a negative effect of the Healthy Start voucher in the Infant Feeding Survey. This contrast between data sets indicates that results are inconclusive for breastfeeding. The qualitative study found that despite the low monetary value the women valued the Healthy Start voucher scheme. However, the broader lives of low-income women are crucial to understand the constraints to offer a healthy diet. Limitations: Owing to the policy being in place, it was difficult to identify appropriate control groups using existing data sources, especially in the Infant Feeding Study. Conclusions: As the Healthy Start voucher scheme attempts to influence health behaviour, this evaluation can inform other policies aiming to change behaviour and use voucher incentives. The null effect of Healthy Start vouchers on the primary outcomes may be due to the value of the vouchers being insufficient to change the broader lives of low-income women to offer a healthy diet. Future work: The methods developed to undertake an economic evaluation alongside a natural experiment using existing data can be used to explore the cost-effectiveness of the Healthy Start voucher scheme. Funding: This project was funded by the National Institute for Health and Care Research (NIHR) Public Health Research programme and will be published in full in Public Health Research; Vol. 11, No. 11. See the NIHR Journals Library website for further project information.

United Kingdom governments have introduced many policies to support infants and their families. Most of these policies have not been evaluated in terms of health outcomes. Therefore, there is limited evidence for policy-makers about whether or not the right policies are in place to make a difference to the health of young children and their families. We investigated the impact of the Healthy Start voucher scheme (worth £3.10 per week to spend on milk, fruit and vegetables) on the health of low-income mothers, and their infants and young children, in particular vitamin use of mothers and breastfeeding of infants. Using survey data, there were high rates of vitamin use during pregnancy, but fewer women taking vitamins before pregnancy. There was no effect of Healthy Start vouchers on taking vitamins before or during pregnancy. There was inconclusive evidence of the effect of Healthy Start vouchers on breastfeeding, indicating that use of the vouchers does not discourage breastfeeding in women with low incomes. From interviews with mothers, we found that they valued the Healthy Start vouchers and understood the aims of the policy. Healthy Start vouchers were not mentioned in decision-making around breastfeeding. Women's choice to breast or formula feed was based on a range of other factors, such as support to breastfeed. They wanted to provide a healthy diet for their families, but owing to living on low incomes did not always manage it. Policy-makers still need more evidence about the effects of voucher schemes to improve the health of low-income mothers, and their infants and young children. The decision-makers require evidence to determine where to allocate limited resources. There is a need to improve support for low-income families to provide their families with a healthy diet.

Medicare Data Linkages for Conducting Patient-Centered Outcomes Research on Economic Outcomes.

BACKGROUND: Medicare patients and other stakeholders often make health care decisions that have economic consequences. Research on economic variables that patients have identified as important is referred to as patient-centered outcomes research (PCOR) and can generate evidence that informs decision-making. Medicare fee-for-service (FFS) claims are widely used for research and are a potentially valuable resource for studying some economic variables, particularly when linked to other datasets. OBJECTIVE: The aim of this study was to identify and assess the characteristics of federally funded administrative and survey data sources that can be linked to Medicare claims for conducting PCOR on some economic outcomes. RESEARCH DESIGN: A targeted internet search was conducted to identify a list of relevant data sources. A technical panel and key informant interviews were used for guidance and feedback. RESULTS: We identified 12 survey and 6 administrative sources of linked data for Medicare FFS beneficiaries. A majority provide longitudinal data and are updated annually. All linked sources provide some data on social determinants of health and health equity-related factors. Fifteen sources capture direct medical costs (beyond Medicare FFS payments); 5 capture indirect costs (eg, lost wages from absenteeism), and 7 capture direct nonmedical costs (eg, transportation). CONCLUSIONS: Linking Medicare FFS claims data to other federally funded data sources can facilitate research on some economic outcomes for PCOR. However, few sources capture direct nonmedical or indirect costs. Expanding linkages to include additional data sources, and reducing barriers to existing data sources, remain important objectives for increasing high-quality, patient-centered economic research.

Improving model transferability for clinical note section classification models using continued pretraining.

OBJECTIVE: The classification of clinical note sections is a critical step before doing more fine-grained natural language processing tasks such as social determinants of health extraction and temporal information extraction. Often, clinical note section classification models that achieve high accuracy for 1 institution experience a large drop of accuracy when transferred to another institution. The objective of this study is to develop methods that classify clinical note sections under the SOAP ("Subjective," "Object," "Assessment," and "Plan") framework with improved transferability. MATERIALS AND METHODS: We trained the baseline models by fine-tuning BERT-based models, and enhanced their transferability with continued pretraining, including domain-adaptive pretraining and task-adaptive pretraining. We added in-domain annotated samples during fine-tuning and observed model performance over a varying number of annotated sample size. Finally, we quantified the impact of continued pretraining in equivalence of the number of in-domain annotated samples added. RESULTS: We found continued pretraining improved models only when combined with in-domain annotated samples, improving the F1 score from 0.756 to 0.808, averaged across 3 datasets. This improvement was equivalent to adding 35 in-domain annotated samples. DISCUSSION: Although considered a straightforward task when performing in-domain, section classification is still a considerably difficult task when performing cross-domain, even using highly sophisticated neural network-based methods. CONCLUSION: Continued pretraining improved model transferability for cross-domain clinical note section classification in the presence of a small amount of in-domain labeled samples.

Ethics information retrieval in HTA: state of current practice.

OBJECTIVES: Though there have been longstanding discussions on the value of ethics in health technology assessment (HTA), less awareness exists on ethics information retrieval methods. This study aimed to scope available evidence and determine current practices for ethics information retrieval in HTA. METHODS: Literature searches were conducted in Ovid MEDLINE, LISTA, Scopus, and Google Scholar. Once a list of relevant articles was determined, citation tracking was conducted via Scopus. HTA agency websites were searched for published guidance on ethics searching, and for reports which included ethical analyses. Methods sections of each report were analyzed to determine the databases, subject headings, and keywords used in search strategies. The team also reached out to information specialists for insight into current search practices. RESULTS: Findings from this study indicate that there is still little published guidance from HTA agencies, few HTAs that contain substantial ethical analysis, and even less information on the methodology for ethics information retrieval. The researchers identified twenty-five relevant HTAs. Ten of these reports did not utilize subject-specific databases outside health sciences. Eight reports published ethics searches, with significant overlap in subject headings and text words. CONCLUSIONS: This scoping study of current practice in HTA ethics information retrieval highlights findings of previous studies-while ethics analysis plays a crucial role in HTA, methods for literature searching remain relatively unclear. These findings provide insight into the current state of ethics searching, and will inform continued work on filter development, database selection, and grey literature searching.

DAta Linkage to Enhance Cancer Care (DaLECC): Protocol of a Large Australian Data Linkage Study.

Cancer is a leading cause of global morbidity and mortality, accounting for 250 Disability-Adjusted Life Years and 10 million deaths in 2019. Minimising unwarranted variation and ensuring appropriate cost-effective treatment across primary and tertiary care to improve health outcomes is a key health priority. There are few studies that have used linked data to explore healthcare utilisation prior to diagnosis in addition to post-diagnosis patterns of care. This protocol outlines the aims of the DaLECC project and key methodological features of the linked dataset. The primary aim of this project is to explore predictors of variations in pre- and post-cancer diagnosis care, and to explore the economic and health impact of any variation. The cohort of patients includes all South Australian residents diagnosed with cancer between 2011 and 2020, who were recorded on the South Australian Cancer Registry. These cancer registry records are being linked with state and national healthcare databases to capture health service utilisation and costs for a minimum of one-year prior to diagnosis and to a maximum of 10 years post-diagnosis. Healthcare utilisation includes state databases for inpatient separations and emergency department presentations and national databases for Medicare services and pharmaceuticals. Our results will identify barriers to timely receipt of care, estimate the impact of variations in the use of health care, and provide evidence to support interventions to improve health outcomes to inform national and local decisions to enhance the access and uptake of health care services.

Leveraging natural language processing to augment structured social determinants of health data in the electronic health record.

OBJECTIVE: Social determinants of health (SDOH) impact health outcomes and are documented in the electronic health record (EHR) through structured data and unstructured clinical notes. However, clinical notes often contain more comprehensive SDOH information, detailing aspects such as status, severity, and temporality. This work has two primary objectives: (1) develop a natural language processing information extraction model to capture detailed SDOH information and (2) evaluate the information gain achieved by applying the SDOH extractor to clinical narratives and combining the extracted representations with existing structured data. MATERIALS AND METHODS: We developed a novel SDOH extractor using a deep learning entity and relation extraction architecture to characterize SDOH across various dimensions. In an EHR case study, we applied the SDOH extractor to a large clinical data set with 225 089 patients and 430 406 notes with social history sections and compared the extracted SDOH information with existing structured data. RESULTS: The SDOH extractor achieved 0.86 F1 on a withheld test set. In the EHR case study, we found extracted SDOH information complements existing structured data with 32% of homeless patients, 19% of current tobacco users, and 10% of drug users only having these health risk factors documented in the clinical narrative. CONCLUSIONS: Utilizing EHR data to identify SDOH health risk factors and social needs may improve patient care and outcomes. Semantic representations of text-encoded SDOH information can augment existing structured data, and this more comprehensive SDOH representation can assist health systems in identifying and addressing these social needs.

Development of Indirect Health Data Linkage on Health Product Use and Care Trajectories in France: Systematic Review.

BACKGROUND: European national disparities in the integration of data linkage (ie, being able to match patient data between databases) into routine public health activities were recently highlighted. In France, the claims database covers almost the whole population from birth to death, offering a great research potential for data linkage. As the use of a common unique identifier to directly link personal data is often limited, linkage with a set of indirect key identifiers has been developed, which is associated with the linkage quality challenge to minimize errors in linked data. OBJECTIVE: The aim of this systematic review is to analyze the type and quality of research publications on indirect data linkage on health product use and care trajectories in France. METHODS: A comprehensive search for all papers published in PubMed/Medline and Embase databases up to December 31, 2022, involving linked French database focusing on health products use or care trajectories was realized. Only studies based on the use of indirect identifiers were included (ie, without a unique personal identifier available to easily link the databases). A descriptive analysis of data linkage with quality indicators and adherence to the Bohensky framework for evaluating data linkage studies was also realized. RESULTS: In total, 16 papers were selected. Data linkage was performed at the national level in 7 (43.8%) cases or at the local level in 9 (56.2%) studies. The number of patients included in the different databases and resulting from data linkage varied greatly, respectively, from 713 to 75,000 patients and from 210 to 31,000 linked patients. The diseases studied were mainly chronic diseases and infections. The objectives of the data linkage were multiple: to estimate the risk of adverse drug reactions (ADRs; n=6, 37.5%), to reconstruct the patient's care trajectory (n=5, 31.3%), to describe therapeutic uses (n=2, 12.5%), to evaluate the benefits of treatments (n=2, 12.5%), and to evaluate treatment adherence (n=1, 6.3%). Registries are the most frequently linked databases with French claims data. No studies have looked at linking with a hospital data warehouse, a clinical trial database, or patient self-reported databases. The linkage approach was deterministic in 7 (43.8%) studies, probabilistic in 4 (25.0%) studies, and not specified in 5 (31.3%) studies. The linkage rate was mainly from 80% to 90% (reported in 11/15, 73.3%, studies). Adherence to the Bohensky framework for evaluating data linkage studies showed that the description of the source databases for the linkage was always performed but that the completion rate and accuracy of the variables to be linked were not systematically described. CONCLUSIONS: This review highlights the growing interest in health data linkage in France. Nevertheless, regulatory, technical, and human constraints remain major obstacles to their deployment. The volume, variety, and validity of the data represent a real challenge, and advanced expertise and skills in statistical analysis and artificial intelligence are required to treat these big data.

Progress Note Understanding - Assessment and Plan Reasoning: Overview of the 2022 N2C2 Track 3 shared task.

Daily progress notes are a common note type in the electronic health record (EHR) where healthcare providers document the patient's daily progress and treatment plans. The EHR is designed to document all the care provided to patients, but it also enables note bloat with extraneous information that distracts from the diagnoses and treatment plans. Applications of natural language processing (NLP) in the EHR is a growing field with the majority of methods in information extraction. Few tasks use NLP methods for downstream diagnostic decision support. We introduced the 2022 National NLP Clinical Challenge (N2C2) Track 3: Progress Note Understanding - Assessment and Plan Reasoning as one step towards a new suite of tasks. The Assessment and Plan Reasoning task focuses on the most critical components of progress notes, Assessment and Plan subsections where health problems and diagnoses are contained. The goal of the task was to develop and evaluate NLP systems that automatically predict causal relations between the overall status of the patient contained in the Assessment section and its relation to each component of the Plan section which contains the diagnoses and treatment plans. The goal of the task was to identify and prioritize diagnoses as the first steps in diagnostic decision support to find the most relevant information in long documents like daily progress notes. We present the results of the 2022 N2C2 Track 3 and provide a description of the data, evaluation, participation and system performance.

National data linkage assessment of live births and deaths in Mexico: Estimating under-five mortality rate ratios for vulnerable newborns and trends from 2008 to 2019.

BACKGROUND: Linked datasets that enable longitudinal assessments are scarce in low and middle-income countries. OBJECTIVES: We aimed to assess the linkage of administrative databases of live births and under-five child deaths to explore mortality and trends for preterm, small (SGA) and large for gestational age (LGA) in Mexico. METHODS: We linked individual-level datasets collected by National statistics from 2008 to 2019. Linkage was performed based on agreement on birthday, sex, residential address. We used the Centre for Data and Knowledge Integration for Health software to identify the best candidate pairs based on similarity. Accuracy was assessed by calculating the area under the receiver operating characteristic curve. We evaluated completeness by comparing the number of linked records with reported deaths. We described the percentage of linked records by baseline characteristics to identify potential bias. Using the linked dataset, we calculated mortality rate ratios (RR) in neonatal, infants, and children under-five according to gestational age, birthweight, and size. RESULTS: For the period 2008-2019, a total of 24,955,172 live births and 321,165 under-five deaths were available for linkage. We excluded 1,539,046 records (6.2%) with missing or implausible values. We succesfully linked 231,765 deaths (72.2%: range 57.1% in 2009 and 84.3% in 2011). The rate of neonatal mortality was higher for preterm compared with term (RR 3.83, 95% confidence interval, [CI] 3.78, 3.88) and for SGA compared with appropriate for gestational age (AGA) (RR 1.22 95% CI, 1.19, 1.24). Births at <28 weeks had the highest mortality (RR 35.92, 95% CI, 34.97, 36.88). LGA had no additional risk vs AGA among children under five (RR 0.92, 95% CI, 0.90, 0.93). CONCLUSIONS: We demonstrated the utility of linked data to understand neonatal vulnerability and child mortality. We created a linked dataset that would be a valuable resource for future population-based research.

Return of Results in Genomic Research Using Large-Scale or Whole Genome Sequencing: Toward a New Normal.

Genome sequencing is increasingly used in research and integrated into clinical care. In the research domain, large-scale analyses, including whole genome sequencing with variant interpretation and curation, virtually guarantee identification of variants that are pathogenic or likely pathogenic and actionable. Multiple guidelines recommend that findings associated with actionable conditions be offered to research participants in order to demonstrate respect for autonomy, reciprocity, and participant interests in health and privacy. Some recommendations go further and support offering a wider range of findings, including those that are not immediately actionable. In addition, entities covered by the US Health Insurance Portability and Accountability Act (HIPAA) may be required to provide a participant's raw genomic data on request. Despite these widely endorsed guidelines and requirements, the implementation of return of genomic results and data by researchers remains uneven. This article analyzes the ethical and legal foundations for researcher duties to offer adult participants their interpreted results and raw data as the new normal in genomic research.

[Methodology and Attribution Success of a Data Linkage of Clinical Registry Data with Health Insurance Data]. / Methodik und Zuordnungserfolg eines Linkage von Daten klinischer Krebsregister mit Abrechnungsdaten gesetzlicher Krankenkassen.

BACKGROUND: The aim of the project "Effectiveness of care in oncological centres" (WiZen), funded by the innovation fund of the federal joint committee, is to investigate the effectiveness of certification in oncology. The project uses nationwide data from the statuory health insurance AOK and data from clinical cancer registries from three different federal states from 2006-2017. To combine the strengths of both data sources, these will be linked for eight different cancer entities in compliance with data protection regulations. METHODS: Data linkage was performed using indirect identifiers and validated using the health insurance's patient ID ("Krankenversichertennummer") as a direct identifier and gold standard. This enables quantification of the quality of different linkage variants. Sensitivity and specificity as well as hit accuracy and a score addressing the quality of the linkage were used as evaluation criteria. The distributions of relevant variables resulting from the linkage were validated against the original distributions in the individual datasets. RESULTS: Depending on the combination of indirect identifiers, we found a range of 22,125 to 3,092,401 linkage hits. An almost perfect linkage could be achieved by combining information on cancer type, date of birth, gender and postal code. A total of 74,586 one-to-one linkages were achieved with these characteristics. The median hit quality for the different entities was more than 98%. In addition, both the age and sex distributions and the dates of death, if any, showed a high degree of agreement. DISCUSSION AND CONCLUSION: SHI and cancer registry data can be linked with high internal and external validity at the individual level. This robust linkage enables completely new possibilities for analysis through simultaneous access to variables from both data sets ("the best of both worlds"): Information on the UICC stage that stems from the registries can now be combined, for instance, with comorbidities from the SHI data at the individual level. Due to the use of readily available variables and the high success of the linkage, our procedure constitutes a promising method for future linkage processes in health care research.

Why and how we can use data linkage in oral health research: a narrative review.

OBJECTIVES: Poor oral health, impacting health and wellbeing across the life-course, is a costly and wicked problem. Data (or record) linkage is the linking of different sets of data (often administrative data gathered for non-research purposes) that are matched to an individual and may include records such as medical data, housing information and sociodemographic information. It often uses population-level data or 'big data'. Data linkage provides the opportunity to analyse complex associations from different sources for total populations. The aim of the paper is to explore data linkage, how it is important for oral health research and what promise it holds for the future. METHODS: This is a narrative review of an approach (data linkage) in oral health research. RESULTS: Data linkage may be a powerful method for bringing together various population datasets. It has been used to explore a wide variety of topics with many varied datasets. It has substantial current and potential application in oral health research. CONCLUSIONS: Use of population data linkage is increasing in oral health research where the approach has been very useful in exploring the complexity of oral health. It offers promise for exploring many new areas in the field.

The 2022 n2c2/UW shared task on extracting social determinants of health.

OBJECTIVE: The n2c2/UW SDOH Challenge explores the extraction of social determinant of health (SDOH) information from clinical notes. The objectives include the advancement of natural language processing (NLP) information extraction techniques for SDOH and clinical information more broadly. This article presents the shared task, data, participating teams, performance results, and considerations for future work. MATERIALS AND METHODS: The task used the Social History Annotated Corpus (SHAC), which consists of clinical text with detailed event-based annotations for SDOH events, such as alcohol, drug, tobacco, employment, and living situation. Each SDOH event is characterized through attributes related to status, extent, and temporality. The task includes 3 subtasks related to information extraction (Subtask A), generalizability (Subtask B), and learning transfer (Subtask C). In addressing this task, participants utilized a range of techniques, including rules, knowledge bases, n-grams, word embeddings, and pretrained language models (LM). RESULTS: A total of 15 teams participated, and the top teams utilized pretrained deep learning LM. The top team across all subtasks used a sequence-to-sequence approach achieving 0.901 F1 for Subtask A, 0.774 F1 Subtask B, and 0.889 F1 for Subtask C. CONCLUSIONS: Similar to many NLP tasks and domains, pretrained LM yielded the best performance, including generalizability and learning transfer. An error analysis indicates extraction performance varies by SDOH, with lower performance achieved for conditions, like substance use and homelessness, which increase health risks (risk factors) and higher performance achieved for conditions, like substance abstinence and living with family, which reduce health risks (protective factors).

Understanding compensable and non-compensable patient profiles, pathways and physical outcomes for transport and work-related injuries in Queensland, Australia through data linkage.

INTRODUCTION: In many jurisdictions, people experiencing an injury often pursue compensation to support their treatment and recovery expenses. Healthcare costs form a significant portion of payments made by compensation schemes. Compensation scheme regulators need accurate and comprehensive data on injury severity, treatment pathways and outcomes to enable scheme modelling, monitoring and forecasting. Regulators routinely rely on data provided by insurers which have limited healthcare information. Health data provide richer information and linking health data with compensation data enables the comparison of profiles, patterns, trends and outcomes of injured patients who claim and injured parties who are eligible but do not claim. METHODS AND ANALYSIS: This is a retrospective population-level epidemiological data linkage study of people who have sought ambulatory, emergency or hospital treatment and/or made a compensation claim in Queensland after suffering a transport or work-related injury, over the period 1 January 2011 to 31 December 2021. It will use person-linked data from nine statewide data sources: (1) Queensland Ambulance Service, (2) Emergency Department, (3) Queensland Hospital Admitted Patients, (4) Retrieval Services, (5) Hospital Costs, (6) Workers' Compensation, (7) Compulsory Third Party Compensation, (8) National Injury Insurance Scheme and (9) Queensland Deaths Registry. Descriptive, parametric and non-parametric statistical methods and geospatial analysis techniques will be used to answer the core research questions regarding the patient's health service use profile, costs, treatment pathways and outcomes within 2 years postincident as well as to examine the concordance and accuracy of information across health and compensation databases. ETHICS AND DISSEMINATION: Ethics approval was obtained from the Royal Brisbane and Women's Hospital Human Research Ethics Committee, and governance approval was obtained via the Public Health Act 2005, Queensland. The findings of this study will be used to inform key stakeholders across the clinical, research and compensation regulation area, and results will be disseminated through peer-reviewed journals, conference presentations and reports/seminars with key stakeholders.

How insights from the field of information behavior can enrich understanding of knowledge mobilization.

PURPOSE: The authors review the literature on information behavior, an autonomous body of work developed mainly in library studies and compare it with work on knowledge mobilization. The aim is to explore how information behavior can contribute to understanding knowledge mobilization in healthcare management. DESIGN/METHODOLOGY/APPROACH: The authors conducted a narrative review using an exploratory, nonkeyword "double-sided systematic snowball" method. This is especially useful in the situation when the two traditions targeted are broad and relies on distinct vocabulary. FINDINGS: The authors find that the two bodies of work have followed similar trajectories and arrived at similar conclusions, with a linear view supplemented first by a social approach and then by a sensitivity to practice. Lessons from the field of information behavior can be used to avoid duplication of effort, repeating the same errors and reinventing the wheel among knowledge translation scholars. This includes, for example, focusing on sources of information or ignoring the mundane activities in which managers and policymakers are involved. ORIGINALITY/VALUE: The study is the first known attempt to build bridges between the field of information behavior and the study of knowledge mobilization. The study, moreover, foregrounds the need to address knowledge mobilization in context-sensitive and social rather than technical terms, focusing on the mundane work performed by a variety of human and nonhuman agents.

Utilização de banco de dados policiais como perspectiva para redução do sub-registro da violência contra mulheres / Using police databases as a prospect to reduce under-recording of violence against women / Utilizar las bases de datos policiales como perspectiva para reducir el subregistro de la violencia contra las mujeres

Objetivo: buscou-se avaliar a concordância entre o Sistema de Informação de Agravo de Notificação (SINAN) e dados policiais para casos de violência física e sexual contra mulheres, bem como o perfil das mulheres agredidas, das agressões e dos agressores. Método: foi realizado estudo transversal com casos ocorridos em uma cidade de Minas Gerais, notificados no SINAN e/ou identificados na base policial, entre os anos de 2015 e 2016. Criou-se uma base consolidada, composta pelos casos elegíveis das duas fontes, tendo sido realizadas análises descritivas. Em uma base pareada contendo casos comuns aos dois bancos, foi realizada análise de concordância pelo teste Fleiss'Kappa. Resultados: 1.185 casos compuseram a base consolidada e 56 constituíram a base pareada. Houve sub-registro de 83,54% nos dados do SINAN, além de incompletude importante de informações. A base policial apresentou cerca de oito vezes maior captação. A concordância de informações foi elevada/moderada para sete de 11 características avaliadas para os casos comuns. Na base consolidada, as vítimas foram predominantemente negras, solteiras ou viúvas, com idade entre 18 e 39 anos. Os homens, especialmente (ex)parceiros e familiares, foram os principais agressores. Conclusão: o sub-registro e a incompletude de informações sobre violência contra mulher no SINAN é uma realidade que precisa ser tratada. O cruzamento com fontes de dados policiais é uma alternativa para melhorar a qualidade das informações, reduzindo o sub-registro. Apesar dos dados subestimados, percebeu-se que a violência física e doméstica, cometida por (ex)parceiro contra mulheres jovens e negras continua sendo prevalente, atentando para o fato que se deve manter foco de políticas públicas.(AU)

Objective: this study sought to evaluate the agreement between the Information System for Notifiable Health Problems (Sistema de Informação de Agravo de Notificação, SINAN) and Police data for cases of physical and sexual violence against women, as well as the profile of the assaulted women, the aggressions and the aggressors. Method: a cross-sectional study was conducted with cases in a city from Minas Gerais, notified to the SINAN and/or identified in the Police database between 2015 and 2016. A consolidated database was created, comprised by the eligible cases from both sources, with performance of descriptive analyses. An agreement analysis by means of the Fleiss Kappa test was performed in a paired database containing cases common to both databases. Results: a total of 1,185 cases comprised the consolidated database, whereas 56 were included in the paired one. There was 83.54% under-recording in the SINAN data, in addition to important information incompleteness. The Police database presented nearly eight times more recording of cases. Agreement of all the information was high/moderate for seven out of 11 characteristics evaluated for the common cases. In the consolidated databases, the victims were predominantly black-skinned, single or widowed, and aged between 18 and 39 years old. The main aggressors were men, mainly (former) partners and family members. Conclusion: under-recording and incompleteness of diverse information about violence against women in the SINAN is a reality that needs to be dealt with. Cross-referencing with Police data sources represent an alternative to improve quality of the information, reducing under-recording. Despite the underestimated data, it was noticed that physical and domestic violence, perpetrated by (former) partners against young and black-skinned women, continues to be prevalent, pointing to the fact that it should remain as the focus of public policies.(AU)

Objetivo: se buscó evaluar la concordancia entre el Sistema de Informação de Agravo de Notificação (SINAN) y los datos policiales para los casos de violencia física y sexual contra las mujeres, así como el perfil de las mujeres maltratadas, de las agresiones y agresores. Método...(AU)