UNASSIGNED: To describe the demographics and clinical profile of patients with glaucoma presenting during the novel coronavirus (COVID-19) lockdown and unlock phases in India.
UNASSIGNED: This retrospective hospital-based comparative study included patients presenting between March 25, 2017, and March 31, 2021. All patients who presented with glaucoma disorders were included as cases. The demographic and clinical data of these glaucoma patients were collected using an electronic medical record system.
UNASSIGNED: Overall, 34,419 patients (mean 47 per day) diagnosed with glaucoma diseases presented to the network and were included for analysis. The mean age of the patients was 54.16 ± 18.74 years and most were male (n=21,140; 61.42%) from the urban region (n=12,871;37.4%). On categorizing based on the timeline of the COVID-19 pandemic, most of the patients presented pre-COVID-19 (n=29,122; 84.61%), followed by a minority (n=175; 0.51%) during the lockdown and the rest (n=5,122; 14.88%) during unlock phase. An increasing number of patients with secondary glaucoma (n=82; 46.86%) and presenting from the local intra-city (n=82; 46.86%) was seen during the lockdown. There was a 6.6-fold increase in neovascular glaucoma and a 2.7-fold increase in lens induced glaucoma during the lockdown phase ((p<0.001) for both). There was a significant increase in subjects in 4th decade (p<0.03) and a decrease in subjects in 7th decade (p<0.008) during the lockdown period.
UNASSIGNED: The presentation of patients with glaucoma disorders to the hospital is evolving due to the COVID-19 pandemic. The footfalls of patients during the unlock regained to two-thirds of the pre COVID-19 level. During the lockdown, the older patients were less, there was an increase in younger patients and those with secondary glaucoma, and the majority presenting from within the city.

UNASSIGNED: The Pediatric Surviving Sepsis Campaign supports the implementation of automated tools for early sepsis recognition. In 2019 the C.S. Mott Children\'s Hospital Pediatric Intensive Care Unit deployed an electronic medical record (EMR)-based screening for early recognition and treatment of sepsis.
UNASSIGNED: We analyzed all automated primary sepsis alerts, secondary screens, and bedside huddles from November 2019 to January 2020 (Cohort 1) and from November 2020 to January 2021 (Cohort 2) to identify barriers and facilitators for the use of this tool. We distributed surveys to frontline providers to gather feedback on end-user experience.
UNASSIGNED: In Cohort 1, 895 primary alerts were triggered, yielding 503 completed secondary screens and 40 bedside huddles. In Cohort 2, 925 primary alerts were triggered, yielding 532 completed secondary screens and 12 bedside huddles. Surveys assessing end-user experience identified the following facilitators: (1) 73% of nurses endorsed the bedside huddle as value added; (2) 74% of medical providers agreed the bedside huddle increased the likelihood of interventions. The greatest barriers to successful implementation included the (1) overall large number of primary alerts from the automated tool and (2) rate of false alerts, many due to routine respiratory therapy interventions.
UNASSIGNED: Our data suggests that the successful implementation of EMR-based sepsis screening tools requires countermeasures focusing on 3 key drivers for change: education, technology, and patient safety.
UNASSIGNED: While both medical providers and bedside nurses found merit in our EMR-based sepsis early recognition system, continued refinement is necessary to avoid sepsis alert fatigue.

BACKGROUND: Population viral load (VL), the most comprehensive measure of the HIV transmission potential, cannot be directly measured due to lack of complete sampling of all people with HIV.
OBJECTIVE: A given HIV clinic\'s electronic health record (EHR), a biased sample of this population, may be used to attempt to impute this measure.
METHODS: We simulated a population of 10,000 individuals with VL calibrated to surveillance data with a geometric mean of 4449 copies/mL. We sampled 3 hypothetical EHRs from (A) the source population, (B) those diagnosed, and (C) those retained in care. Our analysis imputed population VL from each EHR using sampling weights followed by Bayesian adjustment. These methods were then tested using EHR data from an HIV clinic in Delaware.
RESULTS: Following weighting, the estimates moved in the direction of the population value with correspondingly wider 95% intervals as follows: clinic A: 4364 (95% interval 1963-11,132) copies/mL; clinic B: 4420 (95% interval 1913-10,199) copies/mL; and clinic C: 242 (95% interval 113-563) copies/mL. Bayesian-adjusted weighting further improved the estimate.
CONCLUSIONS: These findings suggest that methodological adjustments are ineffective for estimating population VL from a single clinic\'s EHR without the resource-intensive elucidation of an informative prior.

BACKGROUND: Electronic health records (EHRs) play an increasingly important role in delivering HIV care in low- and middle-income countries. The data collected are used for direct clinical care, quality improvement, program monitoring, public health interventions, and research. Despite widespread EHR use for HIV care in African countries, challenges remain, especially in collecting high-quality data.
OBJECTIVE: We aimed to assess data completeness, accuracy, and timeliness compared to paper-based records, and factors influencing data quality in a large-scale EHR deployment in Rwanda.
METHODS: We randomly selected 50 health facilities (HFs) using OpenMRS, an EHR system that supports HIV care in Rwanda, and performed a data quality evaluation. All HFs were part of a larger randomized controlled trial, with 25 HFs receiving an enhanced EHR with clinical decision support systems. Trained data collectors visited the 50 HFs to collect 28 variables from the paper charts and the EHR system using the Open Data Kit app. We measured data completeness, timeliness, and the degree of matching of the data in paper and EHR records, and calculated concordance scores. Factors potentially affecting data quality were drawn from a previous survey of users in the 50 HFs.
RESULTS: We randomly selected 3467 patient records, reviewing both paper and EHR copies (194,152 total data items). Data completeness was >85% threshold for all data elements except viral load (VL) results, second-line, and third-line drug regimens. Matching scores for data values were close to or >85% threshold, except for dates, particularly for drug pickups and VL. The mean data concordance was 10.2 (SD 1.28) for 15 (68%) variables. HF and user factors (eg, years of EHR use, technology experience, EHR availability and uptime, and intervention status) were tested for correlation with data quality measures. EHR system availability and uptime was positively correlated with concordance, whereas users\' experience with technology was negatively correlated with concordance. The alerts for missing VL results implemented at 11 intervention HFs showed clear evidence of improving timeliness and completeness of initially low matching of VL results in the EHRs and paper records (11.9%-26.7%; P<.001). Similar effects were seen on the completeness of the recording of medication pickups (18.7%-32.6%; P<.001).
CONCLUSIONS: The EHR records in the 50 HFs generally had high levels of completeness except for VL results. Matching results were close to or >85% threshold for nondate variables. Higher EHR stability and uptime, and alerts for entering VL both strongly improved data quality. Most data were considered fit for purpose, but more regular data quality assessments, training, and technical improvements in EHR forms, data reports, and alerts are recommended. The application of quality improvement techniques described in this study should benefit a wide range of HFs and data uses for clinical care, public health, and disease surveillance.

BACKGROUND: Surveillance of hospital-acquired pressure injuries (HAPI) is often suboptimal when relying on administrative health data, as International Classification of Diseases (ICD) codes are known to have long delays and are undercoded. We leveraged natural language processing (NLP) applications on free-text notes, particularly the inpatient nursing notes, from electronic medical records (EMRs), to more accurately and timely identify HAPIs.
OBJECTIVE: This study aimed to show that EMR-based phenotyping algorithms are more fitted to detect HAPIs than ICD-10-CA algorithms alone, while the clinical logs are recorded with higher accuracy via NLP using nursing notes.
METHODS: Patients with HAPIs were identified from head-to-toe skin assessments in a local tertiary acute care hospital during a clinical trial that took place from 2015 to 2018 in Calgary, Alberta, Canada. Clinical notes documented during the trial were extracted from the EMR database after the linkage with the discharge abstract database. Different combinations of several types of clinical notes were processed by sequential forward selection during the model development. Text classification algorithms for HAPI detection were developed using random forest (RF), extreme gradient boosting (XGBoost), and deep learning models. The classification threshold was tuned to enable the model to achieve similar specificity to an ICD-based phenotyping study. Each model\'s performance was assessed, and comparisons were made between the metrics, including sensitivity, positive predictive value, negative predictive value, and F1-score.
RESULTS: Data from 280 eligible patients were used in this study, among whom 97 patients had HAPIs during the trial. RF was the optimal performing model with a sensitivity of 0.464 (95% CI 0.365-0.563), specificity of 0.984 (95% CI 0.965-1.000), and F1-score of 0.612 (95% CI of 0.473-0.751). The machine learning (ML) model reached higher sensitivity without sacrificing much specificity compared to the previously reported performance of ICD-based algorithms.
CONCLUSIONS: The EMR-based NLP phenotyping algorithms demonstrated improved performance in HAPI case detection over ICD-10-CA codes alone. Daily generated nursing notes in EMRs are a valuable data resource for ML models to accurately detect adverse events. The study contributes to enhancing automated health care quality and safety surveillance.

UNASSIGNED: In many countries, health care professionals are legally obliged to share information from electronic health records with patients. However, concerns have been raised regarding the sharing of notes with adolescents in mental health care, and health care professionals have called for recommendations to guide this practice.
UNASSIGNED: The aim was to reach a consensus among authors of scientific papers on recommendations for health care professionals\' digital sharing of notes with adolescents in mental health care and to investigate whether staff at child and adolescent specialist mental health care clinics agreed with the recommendations.
UNASSIGNED: A Delphi study was conducted with authors of scientific papers to reach a consensus on recommendations. The process of making the recommendations involved three steps. First, scientific papers meeting the eligibility criteria were identified through a PubMed search where the references were screened. Second, the results from the included papers were coded and transformed into recommendations in an iterative process. Third, the authors of the included papers were asked to provide feedback and consider their agreement with each of the suggested recommendations in two rounds. After the Delphi process, a cross-sectional study was conducted among staff at specialist child and adolescent mental health care clinics to assess whether they agreed with the recommendations that reached a consensus.
UNASSIGNED: Of the 84 invited authors, 27 responded. A consensus was reached on 17 recommendations on areas related to digital sharing of notes with adolescents in mental health care. The recommendations considered how to introduce digital access to notes, write notes, and support health care professionals, and when to withhold notes. Of the 41 staff members at child and adolescent specialist mental health care clinics, 60% or more agreed with the 17 recommendations. No consensus was reached regarding the age at which adolescents should receive digital access to their notes and the timing of digitally sharing notes with parents.
UNASSIGNED: A total of 17 recommendations related to key aspects of health care professionals\' digital sharing of notes with adolescents in mental health care achieved consensus. Health care professionals can use these recommendations to guide their practice of sharing notes with adolescents in mental health care. However, the effects and experiences of following these recommendations should be tested in clinical practice.

BACKGROUND: Large language models have propelled recent advances in artificial intelligence technology, facilitating the extraction of medical information from unstructured data such as medical records. Although named entity recognition (NER) is used to extract data from physicians\' records, it has yet to be widely applied to pharmaceutical care records.
OBJECTIVE: In this study, we aimed to investigate the feasibility of automatic extraction of the information regarding patients\' diseases and symptoms from pharmaceutical care records. The verification was performed using Medical Named Entity Recognition-Japanese (MedNER-J), a Japanese disease-extraction system designed for physicians\' records.
METHODS: MedNER-J was applied to subjective, objective, assessment, and plan data from the care records of 49 patients who received cefazolin sodium injection at Keio University Hospital between April 2018 and March 2019. The performance of MedNER-J was evaluated in terms of precision, recall, and F1-score.
RESULTS: The F1-scores of NER for subjective, objective, assessment, and plan data were 0.46, 0.70, 0.76, and 0.35, respectively. In NER and positive-negative classification, the F1-scores were 0.28, 0.39, 0.64, and 0.077, respectively. The F1-scores of NER for objective (0.70) and assessment data (0.76) were higher than those for subjective and plan data, which supported the superiority of NER performance for objective and assessment data. This might be because objective and assessment data contained many technical terms, similar to the training data for MedNER-J. Meanwhile, the F1-score of NER and positive-negative classification was high for assessment data alone (F1-score=0.64), which was attributed to the similarity of its description format and contents to those of the training data.
CONCLUSIONS: MedNER-J successfully read pharmaceutical care records and showed the best performance for assessment data. However, challenges remain in analyzing records other than assessment data. Therefore, it will be necessary to reinforce the training data for subjective data in order to apply the system to pharmaceutical care records.

In this work, for the first time, the specific impedances of various injection solutions as well as the surface and tissue impedance after injection of these solutions were analyzed and compared regarding the radio-frequency surgical cutting process. The impedances of 0.9% NaCl, 4% gelatine, 6% hydroxyethyl starch, 10% glycerol/5% fructose, 10% glucose, 5% and 20% albumin, blood, and blood plasma as well as aqua destillata have been tested in vitro. Even if EMR and ESD are routinely used in clinical practice, there is so far no easy, fast, and safe method to remove larger lesions en bloc. We show that the impedance of the injected solution shows to be a crucial factor for safe removal, especially of larger lesions (Ø > 20 mm) and more importantly in accordance with the requirements of oncology and pathology. KEY MESSAGES: Impedance is playing a crucial factor in the radio-frequency (RF)-surgery. With a higher Impedance there will be less current necessary to reach the aimed voltage. Injection solution Aqua destillata and 10% Glucose, show significantly higher Impedances. Higher impedances lead to less surgical related complications. Minor changes in existing method to improve patent safety.

Healthcare costs in the United States (US) exceed those of comparable nations without yielding better outcomes. Factors contributing to this include lack of cost transparency, limited outpatient resources due to primary care provider shortages, and high patient volumes, where patients are not educated on differentials and the stepwise process of workup. Addressing these issues could curb unnecessary hospitalizations and expenses. A 31-year-old woman with hypertension, alcohol use, anemia, and obesity experienced paresthesias in September 2022. At her first visit, the exam was consistent with decreased bilateral plantar sensation; however, there was no weakness or gait abnormality. This was not consistent with a focal neurologic distribution. Despite multiple ER visits, her condition persisted. Initial evaluations included potassium replacement ($80 for labs, $13 for tablet), nonacute head CT ($1500), and benign CT L-spine ($2500). Subsequent hospitalization led to brain MRI/MRA head/neck ($6700) and serum workup ($240), revealing deficiencies in vitamin D, folate, and B12. Treatment involved prednisone taper ($30) and supplemental vitamins ($35), with lifestyle recommendations ($0). After evaluating CompuNet lab costs and equivalent market imaging prices, potential savings exceeding $15,000 were identified through more focused and cost-conscious initial testing including vitamin studies and outpatient management, reducing hospitalizations and imaging expenses. Rising healthcare costs in the US are driven by various factors, yet fail to correlate with improved outcomes. Our case argues that enhancing access to primary care, promoting cost transparency, and educating patients on healthcare decisions are crucial for mitigating excessive spending.

With the rapid development of modern medical technology and the dramatic increase in the amount of medical data, traditional centralized medical information management is facing many challenges. In recent years blockchain, which is a peer-to-peer distributed database, has been increasingly accepted and adopted by different industries and use cases. Key areas of healthcare blockchain applications include electronic medical record (EMR) management, medical device supply chain management, remote condition monitoring, insurance claims and personal health data (PHD) management, among others. Even so, there are a number of challenges in applying blockchain concepts to healthcare and its data, including interoperability, data security privacy, scalability, TPS and so on. While these challenges may hinder the development of blockchain in healthcare scenarios, they can be improved with existing technologies In this paper, we propose a blockchain-based healthcare operations management framework that is combined with the Interplanetary File System (IPFS) for managing EMRs, protects data privacy through a distributed approach while ensuring that this medical ledger is tamper-proof. Doctors act as full nodes, patients can participate in network maintenance either as light nodes or as full nodes, and the hospital acts as the endpoint database of data, i.e., the IPFS node, which saves the arithmetic power of nodes and allows the data stored in the hospitals and departments to be shared with the other organizations that have uploaded the data. Therefore, the integration of blockchain and zero-knowledge proof proposed in this paper helps to protect data privacy and is efficient, better scalable, and more throughput.