Endogenous retroviruses are dysregulated in ALS.

Amyotrophic lateral sclerosis (ALS) is a universally fatal neurodegenerative disease with no cure. Human endogenous retroviruses (HERVs) have been implicated in its pathogenesis but their relevance to ALS is not fully understood. We examined bulk RNA-seq data from almost 2,000 ALS and unaffected control samples derived from the cortex and spinal cord. Using different methods of feature selection, including differential expression analysis and machine learning, we discovered that transcription of HERV-K loci 1q22 and 8p23.1 were significantly upregulated in the spinal cord of individuals with ALS. Additionally, we identified a subset of ALS patients with upregulated HERV-K expression in the cortex and spinal cord. We also found the expression of HERV-K loci 19q11 and 8p23.1 was correlated with protein coding genes previously implicated in ALS and dysregulated in ALS patients in this study. These results clarify the association of HERV-K and ALS and highlight specific genes in the pathobiology of late-stage ALS.

Study protocol: Collaboration Oriented Approach to Controlling High blood pressure (COACH) in adults - a randomised controlled trial.

INTRODUCTION: Hypertension, the clinical condition of persistent high blood pressure (BP), is preventable yet remains a significant contributor to poor cardiovascular outcomes. Digital self-management support tools can increase patient self-care behaviours to improve BP. We created a patient-facing and provider-facing clinical decision support (CDS) application, called the Collaboration Oriented Approach to Controlling High BP (COACH), to integrate home BP data, guideline recommendations and patient-centred goals with primary care workflows. We leverage social cognitive theory principles to support enhanced engagement, shared decision-making and self-management support. This study aims to measure the effectiveness of the COACH intervention and evaluate its adoption as part of BP management. METHODS AND ANALYSIS: The study design is a multisite, two-arm hybrid type III implementation randomised controlled trial set within primary care practices across three health systems. Randomised participants are adults with high BP for whom home BP monitoring is indicated. The intervention arm will receive COACH, a digital web-based intervention with effectively enhanced alerts and displays intended to drive engagement with BP lowering; the control arm will receive COACH without the alerts and a simple display. Outcome measures include BP lowering (primary) and self-efficacy (secondary). Implementation preplanning and postevaluation use the Consolidated Framework for Implementation Research and Reach-Effectiveness-Adoption-Implementation-Maintenance metrics with iterative cycles for qualitative integration into the trial and its quantitative evaluation. The trial analysis includes logistic regression and constrained longitudinal data analysis. ETHICS AND DISSEMINATION: The trial is approved under a single IRB through the University of Missouri-Columbia, #2091483. Dissemination of the intervention specifications and results will be through open-source mechanisms. TRIAL REGISTRATION NUMBER: NCT06124716.

Advancing equity in breast cancer care: natural language processing for analysing treatment outcomes in under-represented populations.

OBJECTIVE: The study aimed to develop natural language processing (NLP) algorithms to automate extracting patient-centred breast cancer treatment outcomes from clinical notes in electronic health records (EHRs), particularly for women from under-represented populations. METHODS: The study used clinical notes from 2010 to 2021 from a tertiary hospital in the USA. The notes were processed through various NLP techniques, including vectorisation methods (term frequency-inverse document frequency (TF-IDF), Word2Vec, Doc2Vec) and classification models (support vector classification, K-nearest neighbours (KNN), random forest (RF)). Feature selection and optimisation through random search and fivefold cross-validation were also conducted. RESULTS: The study annotated 100 out of 1000 clinical notes, using 970 notes to build the text corpus. TF-IDF and Doc2Vec combined with RF showed the highest performance, while Word2Vec was less effective. RF classifier demonstrated the best performance, although with lower recall rates, suggesting more false negatives. KNN showed lower recall due to its sensitivity to data noise. DISCUSSION: The study highlights the significance of using NLP in analysing clinical notes to understand breast cancer treatment outcomes in under-represented populations. The TF-IDF and Doc2Vec models were more effective in capturing relevant information than Word2Vec. The study observed lower recall rates in RF models, attributed to the dataset's imbalanced nature and the complexity of clinical notes. CONCLUSION: The study developed high-performing NLP pipeline to capture treatment outcomes for breast cancer in under-represented populations, demonstrating the importance of document-level vectorisation and ensemble methods in clinical notes analysis. The findings provide insights for more equitable healthcare strategies and show the potential for broader NLP applications in clinical settings.

Development and Optimization of Machine Learning Algorithms for Predicting In-hospital Patient Charges for Congestive Heart Failure Exacerbations, Chronic Obstructive Pulmonary Disease Exacerbations and Diabetic Ketoacidosis.

Background: Hospitalizations for exacerbations of congestive heart failure (CHF), chronic obstructive pulmonary disease (COPD) and diabetic ketoacidosis (DKA) are costly in the United States. The purpose of this study was to predict in-hospital charges for each condition using machine learning (ML) models. Results: We conducted a retrospective cohort study on national discharge records of hospitalized adult patients from January 1st, 2016, to December 31st, 2019. We used numerous ML techniques to predict in-hospital total cost. We found that linear regression (LM), gradient boosting (GBM) and extreme gradient boosting (XGB) models had good predictive performance and were statistically equivalent, with training R-square values ranging from 0.49-0.95 for CHF, 0.56-0.95 for COPD, and 0.32-0.99 for DKA. We identified important key features driving costs, including patient age, length of stay, number of procedures. and elective/nonelective admission. Conclusions: ML methods may be used to accurately predict costs and identify drivers of high cost for COPD exacerbations, CHF exacerbations and DKA. Overall, our findings may inform future studies that seek to decrease the underlying high patient costs for these conditions.

Creating a next-generation phenotype library: the health data research UK Phenotype Library.

Objective: To enable reproducible research at scale by creating a platform that enables health data users to find, access, curate, and re-use electronic health record phenotyping algorithms. Materials and Methods: We undertook a structured approach to identifying requirements for a phenotype algorithm platform by engaging with key stakeholders. User experience analysis was used to inform the design, which we implemented as a web application featuring a novel metadata standard for defining phenotyping algorithms, access via Application Programming Interface (API), support for computable data flows, and version control. The application has creation and editing functionality, enabling researchers to submit phenotypes directly. Results: We created and launched the Phenotype Library in October 2021. The platform currently hosts 1049 phenotype definitions defined against 40 health data sources and >200K terms across 16 medical ontologies. We present several case studies demonstrating its utility for supporting and enabling research: the library hosts curated phenotype collections for the BREATHE respiratory health research hub and the Adolescent Mental Health Data Platform, and it is supporting the development of an informatics tool to generate clinical evidence for clinical guideline development groups. Discussion: This platform makes an impact by being open to all health data users and accepting all appropriate content, as well as implementing key features that have not been widely available, including managing structured metadata, access via an API, and support for computable phenotypes. Conclusions: We have created the first openly available, programmatically accessible resource enabling the global health research community to store and manage phenotyping algorithms. Removing barriers to describing, sharing, and computing phenotypes will help unleash the potential benefit of health data for patients and the public.

Analysis of the effect of digital hospital efforts on paper savings in inpatient procedures and on the duration of nursing care services.

Background: This study has two primary objectives. Firstly, it aims to measure the time savings achieved through the digitization of paper forms filled out by nurses in the inpatient care process. Secondly, it seeks to reveal the financial savings resulting from reduced paper consumption due to the digitalization. The Health Information Management System Society (HIMSS)-Electronic Medical Record Adaption Model (EMRAM), which makes stage-based (0-7) evaluations, serves as a tool to measure the rate of technology utilization in public hospitals in Turkey. The study is based on the HIMSS EMRAM criteria for 2018. Bahçelievler State Hospital, a public hospital in Turkey, was chosen as the research facility. In 2017, it was accredited as Stage 6 with HIMSS EMRAM. However, not all its wards have been digitalized. Initially, pilot selected wards were digitized. Therefore, digital and non-digital wards serve together. In this context, 4 wards were randomly selected and time, paper and toner savings before and after digitalization were measured. Method: A table was created in Microsoft Excel,listing the forms used by nurses in inpatient care and the time required to fill them out.The time spent for filling paper-based forms and digital-based forms was measured in randomly selected wards. Result: The analysis showed that digital forms saved more time, paper and toner. For example, filling out the patient history form took 45 min when using paper, compared to 12 min in digital environment. Approximately 27% time savings are achieved only for the patient history form. The total time savings delivered by digitalization for 1,153 inpatients during the year were found as 117 care days, and the savings on total paper consumption was 41.289 pages. For 1,153 inpatients throughout the year, the total time savings from digitalization was 117 care days and the total paper consumption savings was 41,289 pages. In addition, in 4 wards with a total bed capacity of 25, annual paper savings of $1,705.86 and toner savings of $283,736 were achieved. Discussion: This study reveals the benefits of digitalisation in hospitals for nurses. It saves the time that nurses allocate for filling out paper forms with digitalised forms. Thus, it is a good practice example in terms of using the time allocated for form filling for patient care.When we extend this study to Turkey in general, it can be considered that the time savings achieved by nurses by digitizing inpatient forms varies between 10.8% and 13%. The number of nurses working in public hospitals in Turkey is approximately 160,000. Assuming that 60% of the nurses work in the inpatient ward, it is understood that the annual savings achieved by digitizing the forms corresponds to a range of 398-559 nursing hours.

Centering Digital Health Equity During Technology Innovation: Protocol for a Comprehensive Scoping Review of Evidence-Based Tools and Approaches.

BACKGROUND: In the rush to develop health technologies for the COVID-19 pandemic, the unintended consequence of digital health inequity or the inability of priority communities to access, use, and receive equal benefits from digital health technologies was not well examined. OBJECTIVE: This scoping review will examine tools and approaches that can be used during digital technology innovation to improve equitable inclusion of priority communities in the development of digital health technologies. The results from this study will provide actionable insights for professionals in health care, health informatics, digital health, and technology development to proactively center equity during innovation. METHODS: Based on the Arksey and O'Malley framework, this scoping review will consider priority communities' equitable involvement in digital technology innovation. Bibliographic databases in health, medicine, computing, and information sciences will be searched. Retrieved citations will be double screened against the inclusion and exclusion criteria using Covidence (Veritas Health Innovation). Data will be charted using a tailored extraction tool and mapped to a digital health innovation pathway defined by the Centre for eHealth Research roadmap for eHealth technologies. An accompanying narrative synthesis will describe the outcomes in relation to the review's objectives. RESULTS: This scoping review is currently in progress. The search of databases and other sources returned a total of 4868 records. After the initial screening of titles and abstracts, 426 studies are undergoing dual full-text review. We are aiming to complete the full-text review stage by May 30, 2024, data extraction in October 2024, and subsequent synthesis in December 2024. Funding was received on October 1, 2023, from the Centre for Health Equity Incubator Grant Scheme, University of Melbourne, Australia. CONCLUSIONS: This paper will identify and recommend a series of validated tools and approaches that can be used by health care stakeholders and IT developers to produce equitable digital health technology across the Centre for eHealth Research roadmap. Identified evidence gaps, possible implications, and further research will be discussed. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): DERR1-10.2196/53855.

Brief communication: a near real time interactive dashboard for monitoring and anticipating demands in emergency care in the île-de-France region (France).

OBJECTIVE: Allow health professionals to monitor and anticipate demands for emergency care in the Île-de-France region of France. MATERIALS AND METHODS: Data from emergency departments and emergency medical services are automatically processed on a daily basis and visualized through an interactive online dashboard. Forecasting methods are used to provide 7 days predictions. RESULTS: The dashboard displays data at regional and departmental levels or for five different age categories. It features summary statistics, historical values, predictions, comparisons to previous years, and monitoring of common reasons for care and outcomes. DISCUSSION: A large number of health professionals have already requested access to the dashboard (n = 606). Although the quality of data transmitted may vary slightly, the dashboard has already helped improve health situational awareness and anticipation. CONCLUSIONS: The high access demand to the dashboard demonstrates the operational usefulness of real time visualization of multisource data coupled with advanced analytics.

The impact of automatic history-taking software on data quality in the cardiology outpatient clinic: Retrospective observational study.

Background: Healthcare delivery now mandates shorter visits despite the need for more data entry, under-mining patient-provider interaction. Furthermore, enhancing access to the outcomes of prior tests and imaging conducted on the patient, along with accurately documenting medication history, will significantly elevate the quality of healthcare service delivery. Objective: To enhance the efficiency of clinic visits, we have devised a patient-provider portal that systematically gathers symptom and clinical data from patients through a computer algorithm known as Automated Assessment of Cardiovascular Examination (AACE). We intended to assess the quality of computer-generated Electronic Health Records (EHRs) with those documented by physicians. Methods: We conducted a cross-sectional study employing a paired-sample design, focusing on individuals seeking assessment for active cardiovascular symptoms at outpatient adult cardiovascular clinics. Participants initially completed the AACE, and subsequently, in the first protocol, patients were subjected to routine care without providing the AACE forms to examining physicians. In the second protocol, the AACE form was presented to the physician before the examination, and participants were subjected to routine care. We assessed the impact of AACE forms generated through computerized history-taking method on the examination, considering various clinical outcomes and satisfaction surveys. Results: We included non-randomized eligible patients who visited seven general cardiology outpatient clinics between September 18, 2023, and October 27, 2023. These clinics were staffed by the same physicians who were unaware of the content and details of the study. A total of 762 patients (394 patients in protocol 1 and 368 patients in protocol 2) were included in the study. The mean overall impression score for computer-generated EHRs was higher versus physician EHRs (4.2 vs. 2.6; p < .001). Our study demonstrated that EHRs created by physicians' exhibit inaccuracies or deficiencies in various pieces of information. In the second protocol, in which the AACE form was presented to the physician before the examination, it was determined that the examination time was shorter, the number of tests requested, and the number of new drugs prescribed were less. Conclusions: We observed that the patient-provider portal, systematically collecting symptom and clinical data from patients through a computer algorithm known as AACE, yielded records that were of higher quality, more comprehensive, better organized, and more relevant compared to those documented by physicians.

Bringing the Public Health Informatics and Technology Workforce Together: The PHIAT Conference.

The field of public health informatics has undergone significant evolution in recent years, and advancements in technology and its applications are imperative to address emerging public health challenges. Interdisciplinary approaches and training can assist with these challenges. In 2023, the inaugural Public Health Informatics and Technology (PHIAT) Conference was established as a hybrid 3-day conference at the University of California, San Diego, and online. The conference's goal was to establish a forum for academics and public health organizations to discuss and tackle new opportunities and challenges in public health informatics and technology. This paper provides an overview of the quest for interest, speakers and topics, evaluations from the attendees, and lessons learned to be implemented in future conferences.

Implementation barriers and facilitators of remote monitoring, remote consultation and digital care platforms through the eyes of healthcare professionals: a review of reviews.

OBJECTIVES: Digital transformation in healthcare is a necessity considering the steady increase in healthcare costs, the growing ageing population and rising number of people living with chronic diseases. The implementation of digital health technologies in patient care is a potential solution to these issues, however, some challenges remain. In order to navigate such complexities, the perceptions of healthcare professionals (HCPs) must be considered. The objective of this umbrella review is to identify key barriers and facilitators involved in digital health technology implementation, from the perspective of HCPs. DESIGN: Systematic umbrella review following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses statement. DATA SOURCES: Embase.com, PubMed and Web of Science Core Collection were searched for existing reviews dated up to 17 June 2022. Search terms included digital health technology, combined with terms related to implementation, and variations in terms encompassing HCP, such as physician, doctor and the medical discipline. ELIGIBILITY CRITERIA: Quantitative and qualitative reviews evaluating digital technologies that included patient interaction were considered eligible. Three reviewers independently synthesised and assessed eligible reviews and conducted a critical appraisal. DATA EXTRACTION AND SYNTHESIS: Regarding the data collection, two reviewers independently synthesised and interpreted data on barriers and facilitators. RESULTS: Thirty-three reviews met the inclusion criteria. Barriers and facilitators were categorised into four levels: (1) the organisation, (2) the HCP, (3) the patient and (4) technical aspects. The main barriers and facilitators identified were (lack of) training (n=22/33), (un)familiarity with technology (n=17/33), (loss of) communication (n=13/33) and security and confidentiality issues (n=17/33). Barriers of key importance included increased workload (n=16/33), the technology undermining aspects of professional identity (n=11/33), HCP uncertainty about patients' aptitude with the technology (n=9/33), and technical issues (n=12/33). CONCLUSIONS: The implementation strategy should address the key barriers highlighted by HCPs, for instance, by providing adequate training to familiarise HCPs with the technology, adapting the technology to the patient preferences and addressing technical issues. Barriers on both HCP and patient levels can be overcome by investigating the needs of the end-users. As we shift from traditional face-to-face care models towards new modes of care delivery, further research is needed to better understand the role of digital technology in the HCP-patient relationship.

A Deep Learning Framework for Predicting Patient Decannulation on Extracorporeal Membrane Oxygenation Devices: Development and Model Analysis Study.

BACKGROUND: Venovenous extracorporeal membrane oxygenation (VV-ECMO) is a therapy for patients with refractory respiratory failure. The decision to decannulate someone from extracorporeal membrane oxygenation (ECMO) often involves weaning trials and clinical intuition. To date, there are limited prognostication metrics to guide clinical decision-making to determine which patients will be successfully weaned and decannulated. OBJECTIVE: This study aims to assist clinicians with the decision to decannulate a patient from ECMO, using Continuous Evaluation of VV-ECMO Outcomes (CEVVO), a deep learning-based model for predicting success of decannulation in patients supported on VV-ECMO. The running metric may be applied daily to categorize patients into high-risk and low-risk groups. Using these data, providers may consider initiating a weaning trial based on their expertise and CEVVO. METHODS: Data were collected from 118 patients supported with VV-ECMO at the Columbia University Irving Medical Center. Using a long short-term memory-based network, CEVVO is the first model capable of integrating discrete clinical information with continuous data collected from an ECMO device. A total of 12 sets of 5-fold cross validations were conducted to assess the performance, which was measured using the area under the receiver operating characteristic curve (AUROC) and average precision (AP). To translate the predicted values into a clinically useful metric, the model results were calibrated and stratified into risk groups, ranging from 0 (high risk) to 3 (low risk). To further investigate the performance edge of CEVVO, 2 synthetic data sets were generated using Gaussian process regression. The first data set preserved the long-term dependency of the patient data set, whereas the second did not. RESULTS: CEVVO demonstrated consistently superior classification performance compared with contemporary models (P<.001 and P=.04 compared with the next highest AUROC and AP). Although the model's patient-by-patient predictive power may be too low to be integrated into a clinical setting (AUROC 95% CI 0.6822-0.7055; AP 95% CI 0.8515-0.8682), the patient risk classification system displayed greater potential. When measured at 72 hours, the high-risk group had a successful decannulation rate of 58% (7/12), whereas the low-risk group had a successful decannulation rate of 92% (11/12; P=.04). When measured at 96 hours, the high- and low-risk groups had a successful decannulation rate of 54% (6/11) and 100% (9/9), respectively (P=.01). We hypothesized that the improved performance of CEVVO was owing to its ability to efficiently capture transient temporal patterns. Indeed, CEVVO exhibited improved performance on synthetic data with inherent temporal dependencies (P<.001) compared with logistic regression and a dense neural network. CONCLUSIONS: The ability to interpret and integrate large data sets is paramount for creating accurate models capable of assisting clinicians in risk stratifying patients supported on VV-ECMO. Our framework may guide future incorporation of CEVVO into more comprehensive intensive care monitoring systems.

Health Information System Strengthening During Antenatal Care in Haiti: Continuous Quality Improvement Study.

BACKGROUND: Journey to 9 Plus (J9) is an integrated reproductive, maternal, neonatal, and child health approach to care that has at its core the goal of decreasing the rate of maternal and neonatal morbidity and mortality in rural Haiti. For the maximum effectiveness of this program, it is necessary that the data system be of the highest quality. OpenMRS, an electronic medical record (EMR) system, has been in place since 2013 throughout a tertiary referral hospital, the Hôpital Universitaire de Mirebalais, in Haiti and has been expanded for J9 data collection and reporting. The J9 program monthly reports showed that staff had limited time and capacity to perform double charting, which contributed to incomplete and inconsistent reports. Initial evaluation of the quality of EMR data entry showed that only 18% (58/325) of the J9 antenatal visits were being documented electronically at the start of this quality improvement project. OBJECTIVE: This study aimed to improve the electronic documentation of outpatient antenatal care from 18% (58/325) to 85% in the EMR by J9 staff from November 2020 to September 2021. The experiences that this quality improvement project team encountered could help others improve electronic data collection as well as the transition from paper to electronic documentation within a burgeoning health care system. METHODS: A continuous quality improvement strategy was undertaken as the best approach to improve the EMR data collection at Hôpital Universitaire de Mirebalais. The team used several continuous quality improvement tools to conduct this project: (1) a root cause analysis using Ishikawa and Pareto diagrams, (2) baseline evaluation measurements, and (3) Plan-Do-Study-Act improvement cycles to document incremental changes and the results of each change. RESULTS: At the beginning of the quality improvement project in November 2020, the baseline data entry for antenatal visits was 18% (58/325). Ten months of improvement strategies resulted in an average of 89% (272/304) of antenatal visits documented in the EMR at point of care every month. CONCLUSIONS: The experiences that this quality improvement project team encountered can contribute to the transition from paper to electronic documentation within burgeoning health care systems. Essential to success was having a strong and dedicated nursing leadership to transition from paper to electronic data and motivated nursing staff to perform data collection to improve the quality of data and thus, the reports on patient outcomes. Engaging the nursing team closely in the design and implementation of EMR and quality improvement processes ensures long-term success while centering nurses as key change agents in patient care systems.

Evaluating the Prevalence of Burnout Among Health Care Professionals Related to Electronic Health Record Use: Systematic Review and Meta-Analysis.

BACKGROUND: Burnout among health care professionals is a significant concern, with detrimental effects on health care service quality and patient outcomes. The use of the electronic health record (EHR) system has been identified as a significant contributor to burnout among health care professionals. OBJECTIVE: This systematic review and meta-analysis aims to assess the prevalence of burnout among health care professionals associated with the use of the EHR system, thereby providing evidence to improve health information systems and develop strategies to measure and mitigate burnout. METHODS: We conducted a comprehensive search of the PubMed, Embase, and Web of Science databases for English-language peer-reviewed articles published between January 1, 2009, and December 31, 2022. Two independent reviewers applied inclusion and exclusion criteria, and study quality was assessed using the Joanna Briggs Institute checklist and the Newcastle-Ottawa Scale. Meta-analyses were performed using R (version 4.1.3; R Foundation for Statistical Computing), with EndNote X7 (Clarivate) for reference management. RESULTS: The review included 32 cross-sectional studies and 5 case-control studies with a total of 66,556 participants, mainly physicians and registered nurses. The pooled prevalence of burnout among health care professionals in cross-sectional studies was 40.4% (95% CI 37.5%-43.2%). Case-control studies indicated a higher likelihood of burnout among health care professionals who spent more time on EHR-related tasks outside work (odds ratio 2.43, 95% CI 2.31-2.57). CONCLUSIONS: The findings highlight the association between the increased use of the EHR system and burnout among health care professionals. Potential solutions include optimizing EHR systems, implementing automated dictation or note-taking, employing scribes to reduce documentation burden, and leveraging artificial intelligence to enhance EHR system efficiency and reduce the risk of burnout. TRIAL REGISTRATION: PROSPERO International Prospective Register of Systematic Reviews CRD42021281173; https://www.crd.york.ac.uk/prospero/display_record.php?ID=CRD42021281173.

Dimensions of Uncertainty: A spatiotemporal review of five COVID-19 datasets.

COVID-19 surveillance across the U.S. is essential to tracking and mitigating the pandemic, but data representing cases and deaths may be impacted by attribute, spatial, and temporal uncertainties. COVID-19 case and death data are essential to understanding the pandemic and serve as key inputs for prediction models that inform policy-decisions; consistent information across datasets is critical to ensuring coherent findings. We implement an exploratory data analytic approach to characterize, synthesize, and visualize spatial-temporal dimensions of uncertainty across commonly used datasets for case and death metrics (Johns Hopkins University, the New York Times, USAFacts, and 1Point3Acres). We scrutinize data consistency to assess where and when disagreements occur, potentially indicating underlying uncertainty. We observe differences in cumulative case and death rates to highlight discrepancies and identify spatial patterns. Data are assessed using pairwise agreement (Cohen's kappa) and agreement across all datasets (Fleiss' kappa) to summarize changes over time. Findings suggest highest agreements between CDC, JHU, and NYT datasets. We find nine discrete type-components of information uncertainty for COVID-19 datasets reflecting various complex processes. Understanding processes and indicators of uncertainty in COVID-19 data reporting is especially relevant to public health professionals and policymakers to accurately understand and communicate information about the pandemic.

Enhancing healthcare process analysis through object-centric process mining: Transforming OMOP common data models into object-centric event logs.

OBJECTIVES: This study aims to enhance the analysis of healthcare processes by introducing Object-Centric Process Mining (OCPM). By offering a holistic perspective that accounts for the interactions among various objects, OCPM transcends the constraints of conventional patient-centric process mining approaches, ensuring a more detailed and inclusive understanding of healthcare dynamics. METHODS: We develop a novel method to transform the Observational Medical Outcomes Partnership Common Data Models (OMOP CDM) into Object-Centric Event Logs (OCELs). First, an OMOP CDM4PM is created from the standard OMOP CDM, focusing on data relevant to generating OCEL and addressing healthcare data's heterogeneity and standardization challenges. Second, this subset is transformed into OCEL based on specified healthcare criteria, including identifying various object types, clinical activities, and their relationships. The methodology is tested on the MIMIC-IV database to evaluate its effectiveness and utility. RESULTS: Our proposed method effectively produces OCELs when applied to the MIMIC-IV dataset, allowing for the implementation of OCPM in the healthcare industry. We rigorously evaluate the comprehensiveness and level of abstraction to validate our approach's effectiveness. Additionally, we create diverse object-centric process models intricately designed to navigate the complexities inherent in healthcare processes. CONCLUSION: Our approach introduces a novel perspective by integrating multiple viewpoints simultaneously. To the best of our knowledge, this is the inaugural application of OCPM within the healthcare sector, marking a significant advancement in the field.

FHIRing up OpenMRS: Architecture, Implementation and Real-World Use-Cases in Global Health.

HL7 FHIR was created almost a decade ago and is seeing increasingly wide use in high income settings. Although some initial work was carried out in low and middle income (LMIC) settings there has been little impact until recently. The need for reliable and easy to implement interoperability between health information systems in LMICs is growing with large scale deployments of EHRs, national reporting systems and mHealth applications. The OpenMRS open source EHR has been deployed in more than 44 LMIC with increasing needs for interoperability with other HIS. We describe here the development and deployment of a new FHIR module supporting the latest standards and its use in interoperability with laboratory systems, mHealth applications, pharmacy dispensing system and as a tool for supporting advanced user interface designs. We also show how it facilitates date science projects and deployment of machine leaning based CDSS and precision medicine in LMICs.

Using a chatbot as a digital tool at the primary health care level.

OBJECTIVE: Aim: To analyze the feasibility of utilizing a digital tool such as a chatbot at the primary health care level as part of a health program. PATIENTS AND METHODS: Materials and Methods: With the involvement of a general practitioner and the use of a digital tool, a chatbot, a three-month health program was conducted for employees of an IT company. The chatbot was used to collect information, monitor the health status of participants and provide personalized health recommendations. To evaluate the program's effectiveness survey was conducted to compare participants answers before and after using standardized evaluation scales. A questionnaire based on the Evaluation and Management Services Guide was created to collect medical information on the health status of participants before and after the program. RESULTS: Results: After the program, the average total score of participants' health complaints and symptoms decreased (from 27.1 to 16.1, p=0.019). The average severity of the chief complaint on a scale of 0 to 10 decreased from 5.08 to 2.27, or by 55.3% (p=0.00676). The frequency of individual complaints such as eye pain, decreased concentration, increased fatigue and irritability also dropped. CONCLUSION: Conclusions: The chatbot enabled the primary care physician to respond promptly to participants' health complaints. The results demonstrated the potential of chatbots as innovative and accessible digital tools at the primary health care level for providing recommendations, monitoring health, and contacting a primary care physician in a timely manner.

Collaborative learning from distributed data with differentially private synthetic data.

BACKGROUND: Consider a setting where multiple parties holding sensitive data aim to collaboratively learn population level statistics, but pooling the sensitive data sets is not possible due to privacy concerns and parties are unable to engage in centrally coordinated joint computation. We study the feasibility of combining privacy preserving synthetic data sets in place of the original data for collaborative learning on real-world health data from the UK Biobank. METHODS: We perform an empirical evaluation based on an existing prospective cohort study from the literature. Multiple parties were simulated by splitting the UK Biobank cohort along assessment centers, for which we generate synthetic data using differentially private generative modelling techniques. We then apply the original study's Poisson regression analysis on the combined synthetic data sets and evaluate the effects of 1) the size of local data set, 2) the number of participating parties, and 3) local shifts in distributions, on the obtained likelihood scores. RESULTS: We discover that parties engaging in the collaborative learning via shared synthetic data obtain more accurate estimates of the regression parameters compared to using only their local data. This finding extends to the difficult case of small heterogeneous data sets. Furthermore, the more parties participate, the larger and more consistent the improvements become up to a certain limit. Finally, we find that data sharing can especially help parties whose data contain underrepresented groups to perform better-adjusted analysis for said groups. CONCLUSIONS: Based on our results we conclude that sharing of synthetic data is a viable method for enabling learning from sensitive data without violating privacy constraints even if individual data sets are small or do not represent the overall population well. Lack of access to distributed sensitive data is often a bottleneck in biomedical research, which our study shows can be alleviated with privacy-preserving collaborative learning methods.

Scholarly communication between health informatics and information systems: A bibliometric study.

The challenges of IT adoption in the healthcare sector have generated much interest across a range of research communities, including Information Systems (IS) and Health Informatics (HI). Given their long-standing interest in IT design, development, implementation, and adoption to improve productivity and support organisational transformation, the IS and HI fields are highly correlated in their research interests. Nevertheless, the two fields serve different academic audiences, have different research foci, and theorise IT artifacts differently. We investigate the dyadic relationship between health information systems (HIS) research in IS and HI through the communication patterns between the two fields. We present the citation analysis results of HIS research published in IS and HI journals between 2000 and 2020. The results revealed that despite the two fields sharing a common interest, communication between them is limited and only about specific topics. Potentially relevant ideas and theories generated in IS have not yet been sufficiently recognised by HI scholars and incorporated into the HI literature. However, the upward trend of HIS publications in IS indicates that IS has the potential to contribute more to HI.