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Disruptions to the health sector in Uganda during the COVID 19 pandemic affected health services in the early phases of the pandemic. Not much data exists on their effect on these same services during the later stages of the pandemic especially for children. To fill this gap, we set out to study missed appointments and their associated factors during the lockdown for children receiving Anti-Retroviral Therapy (ART). This was a retrospective cohort study from January 2022 to May 2022. We included all children aged 0-15 and adolescents aged 15-19 years who were on ART. Electronic Medical Records (EMR) for the participants in the last 12 months were extracted. Descriptive statistics are presented. Binary logistic regression was performed, and odds ratios were reported. Out of the 382 participants, 26 (6.8%) missed appointments during the study period. The likelihood of missing appointments was increased when drugs were given to last 4 months than when they were given to last one month (COR 3.207, P value 0.026, 95% CI 1.150-8.943). Patients were also more likely to miss appointments if their mode of receiving drugs was Facility based group (COR 3.174, P = 0.043, 95% CI 1.037-9.713). Not having a viral load in the last 12 months increased the likelihood of missing an appointment (COR 2.309, P = 0.049, CI 1.004-5.306). A drug refill of 4 months and being scheduled to receive drugs by Facility-based group model predisposed the participants to missing the next appointment. Home- or community-based ART delivery to clients as well as drug prescriptions for a longer period could reduce missed appointments. Timely viral load testing should be encouraged as it correlates with adherence to appointments. More research is needed on the safety, storage practices and efficacy of ART given to last more than 2 months.

The explosive growth of biomedical Big Data presents both significant opportunities and challenges in the realm of knowledge discovery and translational applications within precision medicine. Efficient management, analysis, and interpretation of big data can pave the way for groundbreaking advancements in precision medicine. However, the unprecedented strides in the automated collection of large‐scale molecular and clinical data have also introduced formidable challenges in terms of data analysis and interpretation, necessitating the development of novel computational approaches. Some potential challenges include the curse of dimensionality, data heterogeneity, missing data, class imbalance, and scalability issues. This overview article focuses on the recent progress and breakthroughs in the application of big data within precision medicine. Key aspects are summarized, including content, data sources, technologies, tools, challenges, and existing gaps. Nine fields—Datawarehouse and data management, electronic medical record, biomedical imaging informatics, Artificial intelligence‐aided surgical design and surgery optimization, omics data, health monitoring data, knowledge graph, public health informatics, and security and privacy—are discussed. The explosive growth of biomedical Big Data presents both significant opportunities and challenges in the realm of knowledge discovery and translational applications within precision medicine. Efficient management, analysis, and interpretation of big data can pave the way for groundbreaking advancements in precision medicine. The review focuses on the recent progress and breakthroughs in the application of big data within precision medicine.

From a technical perspective, for electronic medical records (EMR), this paper proposes an effective confidential management solution on the cloud, whose basic idea is to deploy a trusted local server between the untrusted cloud and each trusted client of a medical information management system, responsible for running an EMR cloud hierarchical storage model and an EMR cloud segmentation query model. (1) The EMR cloud hierarchical storage model is responsible for storing light EMR data items (such as patient basic information) on the local server, while encrypting heavy EMR data items (such as patient medical images) and storing them on the cloud, to ensure the confidentiality of electronic medical records on the cloud. (2) The EMR cloud segmentation query model performs EMR related query operations through the collaborative interaction between the local server and the cloud server, to ensure the accuracy and efficiency of each EMR query statement. Finally, both theoretical analysis and experimental evaluation demonstrate the effectiveness of the proposed solution for confidentiality management of electronic medical records on the cloud, i.e., which can ensure the confidentiality of electronic medical records on the untrusted cloud, without compromising the availability of an existing medical information management system. •An EMR hierarchical storage model is proposed to ensure the confidentiality of EMRs on the cloud.•An EMR segmentation query model is proposed to ensure the accuracy and efficiency of each EMR query statement.•Both theory analysis and experimental evaluation are performed to demonstrate the performance of the proposed solution.

Background Electronic Medical Records (EMRs) are systems to store patient information like medical histories, test results, and medications electronically. It helps to give quality service by improving data handling and communication in healthcare setting. EMR implementation in developing countries is increasing exponentially. But, only few of them are successfully implemented. Intention to use EMRs by health care provider is crucial for successful implementation and adoption of EMRs. However, intention of health care providers to use EMR in Ethiopia is unknown. Objective The aim of this study was to assess health care provider’s intention to use and its predictors towards Electronic Medical Record systems at three referral hospitals in north-west, Ethiopia, 2019. Methods Institutional based cross-sectional explanatory study design was conducted from March to September among 420 health care providers working at three referral hospitals in north-west Ethiopia. Data were analyzed using structural equation model (SEM). Simple and multiple SEM were used to assess the determinants of health care providers intention to use EMRs. Critical ratio and standardized coefficients were used to measure the association of dependent and independent variables, 95% confidence intervals and P -value were calculated to evaluate statistical significance. Qualitative data was analyzed using thematic analysis. Result The mean age of the study subjects was 32.4 years ±8.3 SD. More than two-third 293(69.8%) of the participants were male. Among 420 health care providers, only 167 (39.8%) were scored above the mean of intention to use EMRs. Factors positively associated with intention to use EMRs were performance expectancy (β = 0.39, p  < 0.001), effort expectancy (β = 0.24, p  < 0.001),social influence (β = 0.18, p  < 0.001),facilitating condition (β = 0.23, p  < 0.001), and computer literacy (β = 0.08, p  < 0.001). Performance expectancy was highly associated with intention to use EMRs. Conclusion Generally, about 40 % of health care providers were scored above the mean of intention to use EMRs. Performance expectancy played a major role in determining health care providers’ intention to use EMRs. The intention of health care providers to use EMRs was attributed by social influence, facilitating condition in the organization, effort expectancy, performance expectancy and computer literacy. Therefore, identifying necessary prerequisites before the actual implementation of EMRs will help to improve the implementation status.

Naloxone is a life-saving, yet underprescribed, medication that is recommended to be provided to patients at high risk of opioid overdose. We set out to evaluate the changes in prescriber practices due to the use of an electronic health record (EHR) advisory that prompted opioid prescribers to co-prescribe naloxone when prescribing a high-dose opioid. It also provided prescribers with guidance on decreasing opioid doses for safety. This was a retrospective chart abstraction study looking at all opioid prescriptions and all naloxone prescriptions written as emergency department (ED) discharge, inpatient hospital discharge, or outpatient medications, between July 1, 2018, and February 1, 2020. The EHR advisory went live on June 1, 2019. Included in the analysis were all adult patients seen in the abovementioned settings at a large county hospital and associated outpatient clinics. We performed an interrupted time series analysis looking at naloxone prescriptions and daily opioid dosing in morphine milligram equivalents (MMEs), before and after initiation of the EHR advisory. The EHR advisory was associated with changes in prescribers' behavior, leading to increased naloxone prescriptions and decreased prescribed opioid doses. EHR advisories are an effective systems-level intervention to enhance the safety of prescribed opioids and increase rates of naloxone prescribing.

Background In resources constrained settings, effectively implemented Electronic Medical Record systems have numerous benefits over paper-based record keeping. This system was implemented in the 2009 Gregorian Calendar in the two Ethiopian territory hospitals, Ayder and St. Paul’s. The pilot implementation and similar re-deployment efforts done in 2014 and 2017 Gregorian Calendar failed at St. Paul's. This study aimed to assess the current status, identify challenges, success factors and perception of health care providers to the system to inform on future roll-outs and scale-up plans. Methods A cross sectional study design with quantitative and qualitative methods was employed. A survey was administered October to December 2019 using a structured questionnaire. A total of 240 health care providers participated in the study based on a stratified random sampling technique. An interview was conducted with a total of 10 persons that include IT experts and higher managements of the hospital. Descriptive statistics were employed to summarize the survey data using SPSS V.21. Qualitative data were thematically presented. Results St. Paul’s hospital predominantly practiced the manual medical recording system. The majority of respondents (30.6%) declared that a lack of training and follow up, lack of management commitment, poor network infrastructure and hardware/software-related issues were challenges and contributed to EMR system failure at St. Paul’s. Results from the qualitative data attested to the above results. The system is found well-functioning at Ayder, and the majority of respondents (38%) noted that lack of training and follow-up was the most piercing challenge. As per the qualitative findings, ICT infrastructure, availability of equipment, incentive mechanisms, and management commitment are mentioned as supportive for successful implementation. At both hospitals, 70 to 95% of participants hold favorable perceptions and are willing to use the system. Conclusion Assessing the readiness of the hospital, selecting and acquiring standard and certified EMR systems, provision of adequate logistic requirements including equipment and supplies, and upgrading the hospital ICT infrastructure will allow sustainable deployment of an EMR system.

Background Electronic medical record systems (EMRS) have transformed healthcare by improving quality, efficiency, and safety through centralized patient data and streamlined workflows. Challenges such as budget constraints and staff resistance hinder its adoption, particularly in resource-limited settings like India; however, they have not been investigated thoroughly. The objective of our study is to explore the benefits and barriers perceived by medical doctors in implementing EMRS in an Indian private-sector hospital. Methods A cross-sectional study was conducted at a private hospital in a rural area of the Ujjain district, Madhya Pradesh, India. All 130 doctors pursuing postgraduate studies were invited to participate in person using a convenience sampling method. Responses from 105 doctors were received using a self-administered questionnaire. The survey captured participants’ attitudes, perceived benefits, and barriers to EMRS implementation in the facility. Descriptive statistics were used to analyse the data. Results Of the respondents, 93% expressed a desire for EMRS implementation. Identified barriers included financial constraints, insufficient infrastructure, staffing shortages, and technological challenges, such as unreliable internet access. Participants highlighted the anticipated benefits, such as improved data accessibility, enhanced operational efficiency, and preferences for digitizing lab reports and e-prescriptions. Conclusion Exploring and addressing the financial, organizational, and technological barriers as perceived by the participants are crucial steps to facilitate EMRS implementation in healthcare facilities. Larger in-depth studies are necessary to develop tailored strategies for overcoming these challenges in similar settings.

Medical images and signals are important data sources in the medical field, and they contain key information such as patients' physiology, pathology, and genetics. However, due to the complexity and diversity of medical images and signals, resulting in difficulties in medical knowledge acquisition and decision support. In order to solve this problem, this paper proposes an end-to-end framework based on BERT (Bidirectional Encoder Representation from Transformer) for NER and RE tasks in electronic medical records. Our framework employs the integration of natural and artificial cognitive systems to efficiently and accurately recognize named entities and relations in electronic medical records, thereby providing powerful support for medical image and signal processing. Our framework first integrates NER and RE tasks into a unified model, adopting an end-to-end processing manner, which removes the limitation and error propagation of multiple independent steps in traditional methods. Second, by pre-training and fine-tuning the BERT model on large-scale electronic medical record data, we enable the model to obtain rich semantic representation capabilities that adapt to the needs of medical fields and tasks.Finally, through multi-task learning, we enable the model to make full use of the correlation and complementarity between NER and RE tasks, and improve the generalization ability and effect of the model on different data sets. We conduct experimental evaluation on four electronic medical record datasets, and the model significantly outperforms other methods on different datasets in the NER task. In the RE task, the EMLB model also achieved advantages on different data sets, especially in the multi-task learning mode, its performance has been significantly improved, and the ETE and MTL modules performed well in terms of comprehensive precision and recall. Our research provides an innovative and efficient solution for automated processing and knowledge mining of medical image and signal data.

Background Deep learning algorithms significantly improve the accuracy of pathological image classification, but the accuracy of breast cancer classification using only single-mode pathological images still cannot meet the needs of clinical practice. Inspired by the real scenario of pathologists reading pathological images for diagnosis, we integrate pathological images and structured data extracted from clinical electronic medical record (EMR) to further improve the accuracy of breast cancer classification. Methods In this paper, we propose a new richer fusion network for the classification of benign and malignant breast cancer based on multimodal data. To make pathological image can be integrated more sufficient with structured EMR data, we proposed a method to extract richer multilevel feature representation of the pathological image from multiple convolutional layers. Meanwhile, to minimize the information loss for each modality before data fusion, we use the denoising autoencoder as a way to increase the low-dimensional structured EMR data to high-dimensional, instead of reducing the high-dimensional image data to low-dimensional before data fusion. In addition, denoising autoencoder naturally generalizes our method to make the accurate prediction with partially missing structured EMR data. Results The experimental results show that the proposed method is superior to the most advanced method in terms of the average classification accuracy (92.9%). In addition, we have released a dataset containing structured data from 185 patients that were extracted from EMR and 3764 paired pathological images of breast cancer, which can be publicly downloaded from http://ear.ict.ac.cn/?page_id=1663 . Conclusions We utilized a new richer fusion network to integrate highly heterogeneous data to leverage the structured EMR data to improve the accuracy of pathological image classification. Therefore, the application of automatic breast cancer classification algorithms in clinical practice becomes possible. Due to the generality of the proposed fusion method, it can be straightforwardly extended to the fusion of other structured data and unstructured data.

Background Osteoporotic fracture prediction calculators are poorly utilized in primary care, leading to underdiagnosis and undertreatment of those at risk for fracture. The use of these calculators could be improved if predictions were automated using the electronic health record (EHR). However, this approach is not well validated in multi-ethnic populations, and it is not clear if the adjustments for race or ethnicity made by calculators are appropriate. Objective To investigate EHR-generated fracture predictions in a multi-ethnic population. Design Retrospective cohort study using data from the EHR. Setting An urban, academic medical center in Philadelphia, PA. Participants 12,758 White, 7,844 Black, and 3,587 Hispanic patients seeking routine care from 2010 to 2018 with mean 3.8 years follow-up. Interventions None. Measurements FRAX and QFracture, two of the most used fracture prediction tools, were studied. Risk for major osteoporotic fracture (MOF) and hip fracture were calculated using data from the EHR at baseline and compared to the number of fractures that occurred during follow-up. Results MOF rates varied from 3.2 per 1000 patient-years in Black men to 7.6 in White women. FRAX and QFracture had similar discrimination for MOF prediction (area under the curve, AUC, 0.69 vs. 0.70, p=0.08) and for hip fracture prediction (AUC 0.77 vs 0.79, p=0.21) and were similar by race or ethnicity. FRAX had superior calibration than QFracture (calibration-in-the-large for FRAX 0.97 versus QFracture 2.02). The adjustment factors used in MOF prediction were generally accurate in Black women, but underestimated risk in Black men, Hispanic women, and Hispanic men. Limitations Single center design. Conclusions Fracture predictions using only EHR inputs can discriminate between high and low risk patients, even in Black and Hispanic patients, and could help primary care physicians identify patients who need screening or treatment. However, further refinements to the calculators may better adjust for race-ethnicity.