Natural language processing (NLP) to facilitate abstract review in medical research: the application of BioBERT to exploring the 20-year use of NLP in medical research.

BACKGROUND: Abstract review is a time and labor-consuming step in the systematic and scoping literature review in medicine. Text mining methods, typically natural language processing (NLP), may efficiently replace manual abstract screening. This study applies NLP to a deliberately selected literature review problem, the trend of using NLP in medical research, to demonstrate the performance of this automated abstract review model. METHODS: Scanning PubMed, Embase, PsycINFO, and CINAHL databases, we identified 22,294 with a final selection of 12,817 English abstracts published between 2000 and 2021. We invented a manual classification of medical fields, three variables, i.e., the context of use (COU), text source (TS), and primary research field (PRF). A training dataset was developed after reviewing 485 abstracts. We used a language model called Bidirectional Encoder Representations from Transformers to classify the abstracts. To evaluate the performance of the trained models, we report a micro f1-score and accuracy. RESULTS: The trained models' micro f1-score for classifying abstracts, into three variables were 77.35% for COU, 76.24% for TS, and 85.64% for PRF. The average annual growth rate (AAGR) of the publications was 20.99% between 2000 and 2020 (72.01 articles (95% CI: 56.80-78.30) yearly increase), with 81.76% of the abstracts published between 2010 and 2020. Studies on neoplasms constituted 27.66% of the entire corpus with an AAGR of 42.41%, followed by studies on mental conditions (AAGR = 39.28%). While electronic health or medical records comprised the highest proportion of text sources (57.12%), omics databases had the highest growth among all text sources with an AAGR of 65.08%. The most common NLP application was clinical decision support (25.45%). CONCLUSIONS: BioBERT showed an acceptable performance in the abstract review. If future research shows the high performance of this language model, it can reliably replace manual abstract reviews.

A novel electronic health record-based, machine-learning model to predict severe hypoglycemia leading to hospitalizations in older adults with diabetes: A territory-wide cohort and modeling study.

BACKGROUND: Older adults with diabetes are at high risk of severe hypoglycemia (SH). Many machine-learning (ML) models predict short-term hypoglycemia are not specific for older adults and show poor precision-recall. We aimed to develop a multidimensional, electronic health record (EHR)-based ML model to predict one-year risk of SH requiring hospitalization in older adults with diabetes. METHODS AND FINDINGS: We adopted a case-control design for a retrospective territory-wide cohort of 1,456,618 records from 364,863 unique older adults (age ≥65 years) with diabetes and at least 1 Hong Kong Hospital Authority attendance from 2013 to 2018. We used 258 predictors including demographics, admissions, diagnoses, medications, and routine laboratory tests in a one-year period to predict SH events requiring hospitalization in the following 12 months. The cohort was randomly split into training, testing, and internal validation sets in a 7:2:1 ratio. Six ML algorithms were evaluated including logistic-regression, random forest, gradient boost machine, deep neural network (DNN), XGBoost, and Rulefit. We tested our model in a temporal validation cohort in the Hong Kong Diabetes Register with predictors defined in 2018 and outcome events defined in 2019. Predictive performance was assessed using area under the receiver operating characteristic curve (AUROC), area under the precision-recall curve (AUPRC) statistics, and positive predictive value (PPV). We identified 11,128 SH events requiring hospitalization during the observation periods. The XGBoost model yielded the best performance (AUROC = 0.978 [95% CI 0.972 to 0.984]; AUPRC = 0.670 [95% CI 0.652 to 0.688]; PPV = 0.721 [95% CI 0.703 to 0.739]). This was superior to an 11-variable conventional logistic-regression model comprised of age, sex, history of SH, hypertension, blood glucose, kidney function measurements, and use of oral glucose-lowering drugs (GLDs) (AUROC = 0.906; AUPRC = 0.085; PPV = 0.468). Top impactful predictors included non-use of lipid-regulating drugs, in-patient admission, urgent emergency triage, insulin use, and history of SH. External validation in the HKDR cohort yielded AUROC of 0.856 [95% CI 0.838 to 0.873]. Main limitations of this study included limited transportability of the model and lack of geographically independent validation. CONCLUSIONS: Our novel-ML model demonstrated good discrimination and high precision in predicting one-year risk of SH requiring hospitalization. This may be integrated into EHR decision support systems for preemptive intervention in older adults at highest risk.

Organizational Breast Cancer Data Mart: A Solution for Assessing Outcomes of Imaging and Treatment.

PURPOSE: In the United States, a comprehensive national breast cancer registry (CR) does not exist. Thus, care and coverage decisions are based on data from population subsets, other countries, or models. We report a prototype real-world research data mart to assess mortality, morbidity, and costs for breast cancer diagnosis and treatment. METHODS: With institutional review board approval and Health Insurance Portability and Accountability Act (HIPPA) compliance, a multidisciplinary clinical and research data warehouse (RDW) expert group curated demographic, risk, imaging, pathology, treatment, and outcome data from the electronic health records (EHR), radiology (RIS), and CR for patients having breast imaging and/or a diagnosis of breast cancer in our institution from January 1, 2004, to December 31, 2020. Domains were defined by prebuilt views to extract data denormalized according to requirements from the existing RDW using an export, transform, load pattern. Data dictionaries were included. Structured query language was used for data cleaning. RESULTS: Five-hundred eighty-nine elements (EHR 311, RIS 211, and CR 67) were mapped to 27 domains; all, except one containing CR elements, had cancer and noncancer cohort views, resulting in a total of 53 views (average 12 elements/view; range, 4-67). EHR and RIS queries returned 497,218 patients with 2,967,364 imaging examinations and associated visit details. Cancer biology, treatment, and outcome details for 15,619 breast cancer cases were imported from the CR of our primary breast care facility for this prototype mart. CONCLUSION: Institutional real-world data marts enable comprehensive understanding of care outcomes within an organization. As clinical data sources become increasingly structured, such marts may be an important source for future interinstitution analysis and potentially an opportunity to create robust real-world results that could be used to support evidence-based national policy and care decisions for breast cancer.

[The use of standardized nursing languages in electronic medical records: an exploratory study on opportunities, limitations, and strategies]. / L'uso del linguaggio standardizzato infermieristico nelle cartelle cliniche elettroniche: studio esplorativo su opportunità, limiti e strategie.

. The use of standardized nursing languages in electronic medical records: an exploratory study on opportunities, limitations, and strategies. INTRODUCTION: Standardized nursing languages (SNLs) have found increasing application in electronic medical records in recent years. In Italy their use is still uneven and accompanied by a silent debate between positions 'against' and 'for' their use. AIM: To render visible the debate regarding SNLs in Italy, and the strategies to consider when digitized records are based on a SNL. METHOD: Data has been collected through audio-recorded semi-structured interviews, selecting three Italian nursing professors, four managers representing Italian healthcare settings that used a SNT and a representative of the Central committee of the National federation of orders of nursing professions. The thematic approach was used to analyze the data. RESULTS: Participants reported having introduced digitized records based on nursing diagnoses, integrated with the Nursing Interventions Classification System and Nursing Outcome Classification, Clinical Care Classification System, Nursing Sensitive Outcomes or mixed models. Divergent aspects emerge regarding: (1) using nursing languages vs a common language to other healthcare professions; (2) planning care vs enhancing clinical reasoning; (3) measuring nursing care vs accepting the variability of the practice, and (4) making documentation efficient vs dedicating more time. Some convergences have emerged and a set of indications for introducing electronic records when based on standardized languages. CONCLUSIONS: The introduction of electronic documentation requires the use of homogeneous languages. The debate on the potential and limits of SNL is still open and requires reflection among researchers, trainers, clinicians, and coordinators/managers of nursing care regarding the choices to be made which may have long-term effects on many nurses.

Replacing the Scalpel With a Computer Mouse: An Evaluation of Time Spent on Electronic Health Record for Plastic Surgery Residents and Its Impact on Resident Training.

BACKGROUND: Following the integration of the electronic health record (EHR) into the healthcare system, concern has grown regarding EHR use on physician well-being. For surgical residents, time spent on the EHR increases the burden of a demanding, hourly restricted schedule and detracts from time spent honing surgical skills. To better characterize these burdens, we sought to describe EHR utilization patterns for plastic surgery residents. METHODS: Integrated plastic surgery resident EHR utilization from March 2019 to March 2020 was extracted via Cerner Analytics at a tertiary academic medical center. Time spent in the EHR on-duty (0600-1759) and off-duty (1800-0559) in the form of chart review, orders, documentation, and patient discovery was analyzed. Statistical analysis was performed in the form of independent t tests and Analysis of Variance (ANOVA). RESULTS: Twelve plastic surgery residents spent a daily average of 94 ± 84 minutes on the EHR, one-third of which was spent off-duty. Juniors (postgraduate years 1-3) spent 123 ± 99 minutes versus seniors (postgraduate years 4-6) who spent 61 ± 49 minutes (P < 0.01). Seniors spent 19% of time on the EHR off-duty, compared with 37% for juniors (P < 0.01). Chart review comprised the majority (42%) of EHR usage, followed by patient discovery (22%), orders (14%), documentation (12%), other (6%), and messaging (1%). Seniors spent more time on patient discovery (25% vs 21%, P < 0.001), while juniors spent more time performing chart review (48% vs 36%, P = 0.19). CONCLUSION: Integrated plastic surgery residents average 1.5 hours on the EHR daily. Junior residents spend 1 hour more per day on the EHR, including more time off-duty and more time performing chart review. These added hours may play a role in duty hour violations and detract from obtaining operative skill sets.

Validating claims-based definitions for deprescribing: Bridging the gap between clinical and administrative data.

BACKGROUND: Limited research has evaluated the validity of claims-based definitions for deprescribing. OBJECTIVES: Evaluate the validity of claims-based definitions of deprescribing against electronic health records (EHRs) for deprescribing of benzodiazepines (BZDs) after a fall-related hospitalization. METHODS: We used a novel data linkage between Medicare fee-for-service (FFS) and Part D with our health system's EHR. We identified patients aged ≥66 years with a fall-related hospitalization, continuous enrollment in Medicare FFS and Part D for 6 months pre- and post-hospitalization, and ≥2 BZD fills in the 6 months pre-hospitalization. Using a standardized EHR abstraction tool, we adjudicated deprescribing for a sub-sample with a fall-related hospitalization at UNC. We evaluated the validity of claims-based deprescribing definitions (e.g., gaps in supply, dosage reductions) versus chart review using sensitivity and specificity. RESULTS: Among 257 patients in the overall sample, 44% were aged 66-74 years, 35% had Medicare low-income subsidy, 79% were female. Among claims-based definitions using gaps in supply, the prevalence of BZD deprescribing ranged from 8.2% (no refills) to 36.6% (30-day gap). When incorporating dosage, the prevalence ranged from 55.3% to 65.8%. Among the validation sub-sample (n = 47), approximately one-third had BZDs deprescribed in the EHR. Compared to EHR, gaps in supply from claims had good sensitivity, but poor specificity. Incorporating dosage increased sensitivity, but worsened specificity. CONCLUSIONS: The sensitivity of claims-based definitions for deprescribing of BZDs was low; however, the specificity of a 90-day gap was >90%. Replication in other EHRs and for other low-value medications is needed to guide future deprescribing research.

Validation of alopecia coding in US claims data among women of childbearing age.

BACKGROUND: Accurately identifying alopecia in claims data is important to study this rare medication side effect. OBJECTIVES: To develop and validate a claims-based algorithm to identify alopecia in women of childbearing age. METHODS: We linked electronic health records from a large healthcare system in Massachusetts (Mass General Brigham) with Medicaid claims data from 2016 through 2018 to identify all women aged 18 to 50 years with an ICD-10 code for alopecia, including alopecia areata, androgenic alopecia, non-scarring alopecia, or cicatricial alopecia, from a visit to the MGB system. Using eight predefined algorithms to identify alopecia in Medicaid claims data, we randomly selected 300 women for whom we reviewed their charts to validate the alopecia diagnosis. Positive predictive values (PPVs) were computed for the primary algorithm and seven algorithm variations, stratified by race. RESULTS: Out of 300 patients with at least 1 ICD-10 code for alopecia in the Medicaid claims, 286 had chart-confirmed alopecia (PPV = 95.3%). The algorithm requiring two diagnosis codes plus one prescription claim for alopecia treatment identified 55 patients (PPV = 100%). The algorithm requiring 1 diagnosis code for alopecia plus 1 procedure claim for intralesional triamcinolone injection identified 35 patients (PPV = 100%). Across all 8 algorithms tested, the PPV varied between 95.3% and 100%. The PPV for alopecia ranged from 94% to 100% in White and 96%-100% in 48 non-White women. The exact date of alopecia onset was difficult to determine in charts. CONCLUSION: At least one recorded ICD-10 code for alopecia in claims data identified alopecia in women of childbearing age with high accuracy.

Identifying cancer patients who received palliative care using the SPICT-LIS in medical records: a rule-based algorithm and text-mining technique.

BACKGROUND: Due to limited numbers of palliative care specialists and/or resources, accessing palliative care remains limited in many low and middle-income countries. Data science methods, such as rule-based algorithms and text mining, have potential to improve palliative care by facilitating analysis of electronic healthcare records. This study aimed to develop and evaluate a rule-based algorithm for identifying cancer patients who may benefit from palliative care based on the Thai version of the Supportive and Palliative Care Indicators for a Low-Income Setting (SPICT-LIS) criteria. METHODS: The medical records of 14,363 cancer patients aged 18 years and older, diagnosed between 2016 and 2020 at Songklanagarind Hospital, were analyzed. Two rule-based algorithms, strict and relaxed, were designed to identify key SPICT-LIS indicators in the electronic medical records using tokenization and sentiment analysis. The inter-rater reliability between these two algorithms and palliative care physicians was assessed using percentage agreement and Cohen's kappa coefficient. Additionally, factors associated with patients might be given palliative care as they will benefit from it were examined. RESULTS: The strict rule-based algorithm demonstrated a high degree of accuracy, with 95% agreement and Cohen's kappa coefficient of 0.83. In contrast, the relaxed rule-based algorithm demonstrated a lower agreement (71% agreement and Cohen's kappa of 0.16). Advanced-stage cancer with symptoms such as pain, dyspnea, edema, delirium, xerostomia, and anorexia were identified as significant predictors of potentially benefiting from palliative care. CONCLUSION: The integration of rule-based algorithms with electronic medical records offers a promising method for enhancing the timely and accurate identification of patients with cancer might benefit from palliative care.

E2EE enhanced patient-centric blockchain-based system for EHR management.

To secure sensitive medical records in the healthcare clouds, this paper proposes an End-to-End Encryption (E2EE) to enhance a patient-centric blockchain-based system for electronic health record (EHR) management. The suggested system with a focus on the patient enables individuals to oversee their medical records within various involved parties by authorizing or withdrawing permission for access to their records. Utilizing the inter-planetary file system (IPFS) for record storage is chosen due to its decentralized nature and its ability to guarantee the unchangeability of records. Then an E2EE enhancement maintains the medical data integrity using dual level-Hybrid encryption: symmetric Advanced Encryption Standard (AES) and asymmetric Elliptic Curve Cryptography (ECC) cryptographic techniques. The proposed system is implemented using the Ethereum blockchain system for EHR data sharing and integration utilizing a web-based interface for the patient and all users to initiate the EHR sharing transactions over the IPFS cloud. The proposed system performance is evaluated in a working system prototype. For different file sizes between 512 KB to 100 MB, the performance metrics used to evaluate the proposed system were the time consumed for generating key, encryption, and decryption. The results demonstrate the proposed system's superiority over other cutting-edge systems and its practical ability to share secure health data in cloud environments.

Overcoming denominator problems in refugee settings with fragmented electronic records for health and immigration data: a prediction-based approach.

BACKGROUND: Epidemiological studies in refugee settings are often challenged by the denominator problem, i.e. lack of population at risk data. We develop an empirical approach to address this problem by assessing relationships between occupancy data in refugee centres, number of refugee patients in walk-in clinics, and diseases of the digestive system. METHODS: Individual-level patient data from a primary care surveillance system (PriCarenet) was matched with occupancy data retrieved from immigration authorities. The three relationships were analysed using regression models, considering age, sex, and type of centre. Then predictions for the respective data category not available in each of the relationships were made. Twenty-one German on-site health care facilities in state-level registration and reception centres participated in the study, covering the time period from November 2017 to July 2021. RESULTS: 445 observations ("centre-months") for patient data from electronic health records (EHR, 230 mean walk-in clinics visiting refugee patients per month and centre; standard deviation sd: 202) of a total of 47.617 refugee patients were available, 215 for occupancy data (OCC, mean occupancy of 348 residents, sd: 287), 147 for both (matched), leaving 270 observations without occupancy (EHR-unmatched) and 40 without patient data (OCC-unmatched). The incidence of diseases of the digestive system, using patients as denominators in the different sub-data sets were 9.2% (sd: 5.9) in EHR, 8.8% (sd: 5.1) when matched, 9.6% (sd: 6.4) in EHR- and 12% (sd 2.9) in OCC-unmatched. Using the available or predicted occupancy as denominator yielded average incidence estimates (per centre and month) of 4.7% (sd: 3.2) in matched data, 4.8% (sd: 3.3) in EHR- and 7.4% (sd: 2.7) in OCC-unmatched. CONCLUSIONS: By modelling the ratio between patient and occupancy numbers in refugee centres depending on sex and age, as well as on the total number of patients or occupancy, the denominator problem in health monitoring systems could be mitigated. The approach helped to estimate the missing component of the denominator, and to compare disease frequency across time and refugee centres more accurately using an empirically grounded prediction of disease frequency based on demographic and centre typology. This avoided over-estimation of disease frequency as opposed to the use of patients as denominators.

Provider Perceptions of an Electronic Health Record Prostate Cancer Screening Tool.

OBJECTIVES: We conducted a focus group to assess the attitudes of primary care physicians (PCPs) toward prostate-specific antigen (PSA)-screening algorithms, perceptions of using decision support tools, and features that would make such tools feasible to implement. METHODS: A multidisciplinary team (primary care, urology, behavioral sciences, bioinformatics) developed the decision support tool that was presented to a focus group of 10 PCPs who also filled out a survey. Notes and audio-recorded transcripts were analyzed using Thematic Content Analysis. RESULTS: The survey showed that PCPs followed different guidelines. In total, 7/10 PCPs agreed that engaging in shared decision-making about PSA screening was burdensome. The majority (9/10) had never used a decision aid for PSA screening. Although 70% of PCPs felt confident about their ability to discuss PSA screening, 90% still felt a need for a provider-facing platform to assist in these discussions. Three major themes emerged: (1) confirmatory reactions regarding the importance, innovation, and unmet need for a decision support tool embedded in the electronic health record; (2) issues around implementation and application of the tool in clinic workflow and PCPs' own clinical bias; and (3) attitudes/reflections regarding discrepant recommendations from various guideline groups that cause confusion. CONCLUSION: There was overwhelmingly positive support for the need for a provider-facing decision support tool to assist with PSA-screening decisions in the primary care setting. PCPs appreciated that the tool would allow flexibility for clinical judgment and documentation of shared decision-making. Incorporation of suggestions from this focus group into a second version of the tool will be used in subsequent pilot testing.

How do opt-in versus opt-out settings nudge patients toward electronic health record adoption? An exploratory study of facilitators and barriers in Austria and France.

BACKGROUND: Electronic health records (EHR) are becoming an integral part of the health system in many developed countries, though implementations and settings vary across countries. Some countries have adopted an opt-out policy, in which patients are enrolled in the EHR system following a default nudge, while others have applied an opt-in policy, where patients have to take action to opt into the system. While opt-in systems may exhibit lower levels of active user requests for access, this contrasts with opt-out systems where a notable percentage of users may passively retain access. Thus, our research endeavor aims to explore facilitators and barriers that contribute to explaining EHR usage (i.e., actively accessing the EHR system) in two countries with either an opt-in or opt-out setting, exemplified by France and Austria. METHODS: A qualitative exploratory approach using a semi-structured interview guideline was undertaken in both countries: 1) In Austria, with four homogenously composed group discussions, and 2) in France, with 19 single patient interviews. The data were collected from October 2020 to January 2021. RESULTS: Influencing factors were categorized into twelve subcategories. Patients have similar experiences in both countries with regard to all facilitating categories, for instance, the role of health providers, awareness of EHR and social norms. However, we highlighted important differences between the two systems regarding hurdles impeding EHR usage, namely, a lack of communication as well as transparency or information security about EHR. CONCLUSION: Implementing additional safeguards to enhance privacy protection and supporting patients to improve their digital ability may help to diminish the perception of EHR-induced barriers and improve patients' health and commitment in the long term. PRACTICAL IMPLICATIONS: Understanding the differences and similarities will help to develop practical implications to tackle the problem of low EHR usage rates in the long run. This problem is prevalent in countries with both types of EHR default settings.

Predicting pressure injury risk in hospitalised patients using machine learning with electronic health records: a US multilevel cohort study.

OBJECTIVE: To predict the risk of hospital-acquired pressure injury using machine learning compared with standard care. DESIGN: We obtained electronic health records (EHRs) to structure a multilevel cohort of hospitalised patients at risk for pressure injury and then calibrate a machine learning model to predict future pressure injury risk. Optimisation methods combined with multilevel logistic regression were used to develop a predictive algorithm of patient-specific shifts in risk over time. Machine learning methods were tested, including random forests, to identify predictive features for the algorithm. We reported the results of the regression approach as well as the area under the receiver operating characteristics (ROC) curve for predictive models. SETTING: Hospitalised inpatients. PARTICIPANTS: EHRs of 35 001 hospitalisations over 5 years across 2 academic hospitals. MAIN OUTCOME MEASURE: Longitudinal shifts in pressure injury risk. RESULTS: The predictive algorithm with features generated by machine learning achieved significantly improved prediction of pressure injury risk (p<0.001) with an area under the ROC curve of 0.72; whereas standard care only achieved an area under the ROC curve of 0.52. At a specificity of 0.50, the predictive algorithm achieved a sensitivity of 0.75. CONCLUSIONS: These data could help hospitals conserve resources within a critical period of patient vulnerability of hospital-acquired pressure injury which is not reimbursed by US Medicare; thus, conserving between 30 000 and 90 000 labour-hours per year in an average 500-bed hospital. Hospitals can use this predictive algorithm to initiate a quality improvement programme for pressure injury prevention and further customise the algorithm to patient-specific variation by facility.

Influence of Disease-Related Stigma on Patients' Decisions to Upload Medical Reports to the German Electronic Health Record: Randomized Controlled Trial.

BACKGROUND: The rollout of the electronic health record (EHR) represents a central component of the digital transformation of the German health care system. Although the EHR promises more effective, safer, and faster treatment of patients from a systems perspective, the successful implementation of the EHR largely depends on the patient. In a recent survey, 3 out of 4 Germans stated that they intend to use the EHR, whereas other studies show that the intention to use a technology is not a reliable and sufficient predictor of actual use. OBJECTIVE: Controlling for patients' intention to use the EHR, we investigated whether disease-specific risk perceptions related to the time course of the disease and disease-related stigma explain the additional variance in patients' decisions to upload medical reports to the EHR. METHODS: In an online user study, 241 German participants were asked to interact with a randomly assigned medical report that varied systematically in terms of disease-related stigma (high vs low) and disease time course (acute vs chronic) and to decide whether to upload it to the EHR. RESULTS: Disease-related stigma (odds ratio 0.154, P<.001) offset the generally positive relationship between intention to use and the upload decision (odds ratio 2.628, P<.001), whereas the disease time course showed no effect. CONCLUSIONS: Even if patients generally intend to use the EHR, risk perceptions such as those related to diseases associated with social stigma may deter people from uploading related medical reports to the EHR. To ensure the reliable use of this key technology in a digitalized health care system, transparent and easy-to-comprehend information about the safety standards of the EHR are warranted across the board, even for populations that are generally in favor of using the EHR.

Medicine Package Recommendation via Dual-Level Interaction Aware Heterogeneous Graph.

Medicine package recommendation aims to assist doctors in clinical decision-making by recommending appropriate packages of medicines for patients. Current methods model this task as a multi-label classification or sequence generation problem, focusing on learning relationships between individual medicines and other medical entities. However, these approaches uniformly overlook the interactions between medicine packages and other medical entities, potentially resulting in a lack of completeness in recommended medicine packages. Furthermore, medicine commonsense knowledge considered by current methods is notably limited, making it challenging to delve into the decision-making processes of doctors. To solve these problems, we propose DIAGNN, a Dual-level Interaction Aware heterogeneous Graph Neural Network for medicine package recommendation. Specifically, DIAGNN explicitly models interactions of medical entities within electronic health records(EHRs) at two levels, individual medicine and medicine package, leveraging a heterogeneous graph. A dual-level interaction aware graph convolutional network is utilized to capture semantic information in the medical heterogeneous graph. Additionally, we incorporate medication indications into the medical heterogeneous graph as medicine commonsense knowledge. Extensive experimental results on real-world datasets validate the effectiveness of the proposed method.

Is obesity more likely among children sharing a household with an older child with obesity? Cross-sectional study of linked National Child Measurement Programme data and electronic health records.

BACKGROUND/OBJECTIVES: We identified household members from electronic health records linked to National Child Measurement Programme (NCMP) data to estimate the likelihood of obesity among children living with an older child with obesity. METHODS: We included 126 829 NCMP participants in four London boroughs and assigned households from encrypted Unique Property Reference Numbers for 115 466 (91.0%). We categorised the ethnic-adjusted body mass index of the youngest and oldest household children (underweight/healthy weight <91st, ≥91st overweight <98th, obesity ≥98th centile) and estimated adjusted ORs and 95% CIs of obesity in the youngest child by the oldest child's weight status, adjusting for number of household children (2, 3 or ≥4), youngest child's sex, ethnicity and school year of NCMP participation. RESULTS: We identified 19 702 households shared by two or more NCMP participants (% male; median age, range (years)-youngest children: 51.2%; 5.2, 4.1-11.8; oldest children: 50.6%; 10.6, 4.1-11.8). One-third of youngest children with obesity shared a household with another child with obesity (33.2%; 95% CI: 31.2, 35.2), compared with 9.2% (8.8, 9.7) of youngest children with a healthy weight. Youngest children living with an older child considered overweight (OR: 2.33; 95% CI: 2.06, 2.64) or obese (4.59; 4.10, 5.14) were more likely to be living with obesity. CONCLUSIONS: Identifying children sharing households by linking primary care and school records provides novel insights into the shared weight status of children sharing a household. Qualitative research is needed to understand how food practices vary by household characteristics to increase understanding of how the home environment influences childhood obesity.

Distributions of recorded pain in mental health records: a natural language processing based study.

OBJECTIVE: The objective of this study is to determine demographic and diagnostic distributions of physical pain recorded in clinical notes of a mental health electronic health records database by using natural language processing and examine the overlap in recorded physical pain between primary and secondary care. DESIGN, SETTING AND PARTICIPANTS: The data were extracted from an anonymised version of the electronic health records of a large secondary mental healthcare provider serving a catchment of 1.3 million residents in south London. These included patients under active referral, aged 18+ at the index date of 1 July 2018 and having at least one clinical document (≥30 characters) between 1 July 2017 and 1 July 2019. This cohort was compared with linked primary care records from one of the four local government areas. OUTCOME: The primary outcome of interest was the presence of recorded physical pain within the clinical notes of the patients, not including psychological or metaphorical pain. RESULTS: A total of 27 211 patients were retrieved. Of these, 52% (14,202) had narrative text containing relevant mentions of physical pain. Older patients (OR 1.17, 95% CI 1.15 to 1.19), females (OR 1.42, 95% CI 1.35 to 1.49), Asians (OR 1.30, 95% CI 1.16 to 1.45) or black (OR 1.49, 95% CI 1.40 to 1.59) ethnicities, living in deprived neighbourhoods (OR 1.64, 95% CI 1.55 to 1.73) showed higher odds of recorded pain. Patients with severe mental illnesses were found to be less likely to report pain (OR 0.43, 95% CI 0.41 to 0.46, p<0.001). 17% of the cohort from secondary care also had records from primary care. CONCLUSION: The findings of this study show sociodemographic and diagnostic differences in recorded pain. Specifically, lower documentation across certain groups indicates the need for better screening protocols and training on recognising varied pain presentations. Additionally, targeting improved detection of pain for minority and disadvantaged groups by care providers can promote health equity.

Medicolegal aspects of documentation and the electronic health record / Aspectos medicolegales de la documentación y la historia clínica electrónica

Introduction: The busiest times in the hospital are often met by the greatest challenges in complete and comprehensive documentation of the patient care event. The near complete transition to the Electronic Health Record (EHR) was to be the solution to a host of provider documentation concerns. It is clear the EHR provides reliability, reproducibility, integration, evidence based decision-making, multidisciplinary contribution across the entire healthcare spectrum.Methods: The use of a consensus of expert opinion supplemented by focused literature review allows a balanced evidence based presentation of data. Results: Documentation is not a perfect tool however, as issues with efficiency, reliability, use of shortcut maneuvers and potential for increased medico-legal risk have been raised. The solution is attention to documentation detail, and creation of systems that facilitate excellence. The focus on electronic documentation systems should include continual evaluation, ongoing improvement, involvement of a multidisciplinary patient care team and vendor receptiveness to in EHR development and operations. Conclusion: The most effective use of the EHR as a risk management tool requires documentation knowledge, targeted analysis, product improvement and co-development of clinical-commercial resource.(AU)

Introducción: Los momentos de mayor actividad en el hospital a menudo se enfrentan con los mayores desafíos en cuanto a la documentación completa y exhaustiva del evento de atención al paciente. La transición casi completa a la historia clínica electrónica (HCE) iba a ser la solución a una serie de preocupaciones sobre la documentación de los proveedores. Está claro que la HCE proporciona confiabilidad, reproducibilidad, integración, toma de decisiones basada en la evidencia y contribución multidisciplinaria en todo el espectro de la atención médica.Métodos: El uso de un consenso de opinión de expertos complementado con una revisión de la literatura enfocada permite una presentación equilibrada de los datos basada en la evidencia.Resultados: La documentación no es una herramienta perfecta, ya que se han planteado problemas de eficiencia, confiabilidad, uso de maniobras abreviadas y la posibilidad de un mayor riesgo medicolegal. La solución es la atención al detalle de la documentación y la creación de sistemas que faciliten la excelencia. El enfoque en los sistemas de documentación electrónica debe incluir evaluación continua, mejora continua, participación de un equipo multidisciplinario de atención al paciente y receptividad de los proveedores en el desarrollo y las operaciones de la HCE. Conclusión: El uso más eficaz de la HCE como herramienta de gestión de riesgos requiere conocimiento de la documentación, análisis específicos, mejora del producto y desarrollo conjunto de recursos clínico-comerciales.(AU)

Accesibilidad, disponibilidad y asequibilidad de medicamentos para Niños con Enfermedad Renal Crónica en Guatemala / Access, availability and affordability of medications for children with Advanced Chronic Kidney Disease in Guatemala

El artículo tiene como objetivo analizar la disponibilidad, acceso y asequibilidad de los medicamentos para niños con Enfermedad Renal Crónica (ERC) en tratamiento con hemodiálisis (HD) en un país de bajos a medianos ingresos. Se llevó a cabo un estudio transversal para determinar los medicamentos más utilizados en una unidad de hemodiálisis pediátrica, incluyendo el nombre del medicamento, dosis, frecuencia, forma farmacéutica y vía de administración. Dos farmacias dentro del perímetro del hospital, una pública y una privada, fueron consultadas para determinar el costoy disponibilidad de medicamentos genéricos y de marca. De un total de 30 pacientes de la unidad de hemodiálisis, 22 expedientes fueron revisados. En general 94% de marca se encontraban disponibles en las farmacias consultadas en comparación a un 52% de los medicamentos genéricos. En farmacias públicas, 41% de medicamentos de marca y 29% de medicamentos genéricos se encontraban disponibles. El costo promedio para un mes de tratamiento con medicamentos de marca adquiridos en una farmacia privada era de $495.00 vs $299.00 en una farmacia pública. Para medicamentos genéricos, el costo promedio correspondía a $414.00 y $239.00 en farmacias privadas y públicas respectivamente. En promedio, los medicamentos de marca adquiridos en una farmacia privada requieren 41 días de trabajo en un mes a comparación de 25 días si se adquieren en una farmacia pública. Los medicamentos genéricos adquiridos en farmacias privadas corresponden a 34 días de trabajo vs 20 días en farmacias públicas. En general existió un acceso limitado a medicamentos genéricos y los medicamentos poseen un costo general más elevado a comparación de otros países lo que implica un posible impacto en la adherencia terapéutica y los padecimientos secundarios de la ERC en los pacientes pediátricos en Guatemala. Esta realidad se puede aplicar a otros países de bajos a medianos ingresos.

This article aims to analyze the availability, access, and affordability of medications for children with advanced Chronic Kidney Disease (CKD) treated with hemodialysis (HD) in a low to middle income country (LMIC). A cross- sectional chart review was carried out to determine the most common medications used in an HD pediatric unit, including medication name, dose, frequency, dosage form, and route of administration. Two pharmacies within the hospital perimeter, one public and one private, were consulted to determine medication cost and availability for generic and brand-name equivalents. From 30 patients attending the HD unit, 22 records were reviewed. Overall, 94 % of brand name medications were available at pharmacies consulted, versus and 52% of generic medications. In public pharmacies, 41% of brand name, and 29% of generic medications were available. The average cost for a full month´s treatment for brand name drugs in the private pharmacy was 495.00 USD versus 299.00 USD in the public pharmacy. For generic drugs, the average cost was 414.00 USD, and 239.00 USD in private and public pharmacies respectively. On average, brand-name drugs in the private pharmacy cost 41 days' wages versus 25 in the public pharmacy. Generic drugs in the private pharmacy cost 34 days' wages versus 20 in the public pharmacy. Overall, there was limited access to generic medications, medications had an overall high cost compared to other countries both of which have the potential to impact treatment adherence and overall outcomes of CKD5 pediatric patients in Guatemala. This reality can be translated to other LMIC.