Getting Value From Electronic Health Records: Research Needed to Improve Practice

Assessment of Blood Pressure and Heart Rate Related Variables in Acute Stroke Patients Receiving Intravenous Antihypertensive Medication Infusions

BACKGROUND

We performed an analysis of a large intensive care unit electronic database to provide preliminary estimates of various blood pressure parameters in patients with acute stroke receiving intravenous (IV) antihypertensive medication and determine the relationship with in-hospital outcomes.

METHODS

We identified the relationship between pre-treatment and post-treatment systolic blood pressure (SBP) and heart rate (HR)-related variables and in-hospital mortality and acute kidney injury in patients with acute stroke receiving IV clevidipine, nicardipine, or nitroprusside using data provided in the Medical Information Mart for Intensive Care (MIMIC) IV database.

RESULTS

A total of 1830 patients were treated with IV clevidipine (n = 64), nicardipine (n = 1623), or nitroprusside (n = 143). The standard deviations [SDs] of pre-treatment SBP (16.3 vs. 13.7, p ≤ 0.001) and post-treatment SBP (15.4 vs. 14.4, p = 0.004) were higher in patients who died compared with those who survived, particularly in patients with intracerebral hemorrhage (ICH). The mean SBP was significantly lower post treatment compared with pre-treatment values for clevidipine (130.7 mm Hg vs. 142.5 mm Hg, p = 0.006), nicardipine (132.8 mm Hg vs. 141.6 mm Hg, p ≤ 0.001), and nitroprusside (126.2 mm Hg vs. 139.6 mm Hg, p ≤ 0.001). There were no differences in mean SDs post treatment compared with pre-treatment values for clevidipine (14.5 vs. 13.5, p = 0.407), nicardipine (14.2 vs. 14.6, p = 0.142), and nitroprusside (14.8 vs. 14.8, p = 0.997). The SDs of pre-treatment and post-treatment SBP were not significantly different in patients with ischemic stroke treated with IV clevidipine, nicardipine, or nitroprusside or for patients with ICH treated with IV clevidipine or nitroprusside. However, patients with ICH treated with IV nicardipine had a significantly higher SD of post-treatment SBP (13.1 vs. 14.2, p = 0.0032).

CONCLUSIONS

We found that SBP fluctuations were associated with in-hospital mortality in patients with acute stroke. IV antihypertensive medication reduced SBP but did not reduce SBP fluctuations in this observational study. Our results highlight the need for optimizing therapeutic interventions to reduce SBP fluctuations in patients with acute stroke.

Charting Tomorrow's Healthcare: A Traditional Literature Review for an Artificial Intelligence-Driven Future

Electronic health record (EHR) systems have developed over time in parallel with general advancements in mainstream technology. As artificially intelligent (AI) systems rapidly impact multiple societal sectors, it has become apparent that medicine is not immune from the influences of this powerful technology. Particularly appealing is how AI may aid in improving healthcare efficiency with note-writing automation. This literature review explores the current state of EHR technologies in healthcare, specifically focusing on possibilities for addressing EHR challenges through the automation of dictation and note-writing processes with AI integration. This review offers a broad understanding of existing capabilities and potential advancements, emphasizing innovations such as voice-to-text dictation, wearable devices, and AI-assisted procedure note dictation. The primary objective is to provide researchers with valuable insights, enabling them to generate new technologies and advancements within the healthcare landscape. By exploring the benefits, challenges, and future of AI integration, this review encourages the development of innovative solutions, with the goal of enhancing patient care and healthcare delivery efficiency.

A qualitative analysis of algorithm-based decision support usability testing for symptom management across the trajectory of cancer care: one size does not fit all

BACKGROUND

Adults with cancer experience symptoms that change across the disease trajectory. Due to the distress and cost associated with uncontrolled symptoms, improving symptom management is an important component of quality cancer care. Clinical decision support (CDS) is a promising strategy to integrate clinical practice guideline (CPG)-based symptom management recommendations at the point of care.

METHODS

The objectives of this project were to develop and evaluate the usability of two symptom management algorithms (constipation and fatigue) across the trajectory of cancer care in patients with active disease treated in comprehensive or community cancer care settings to surveillance of cancer survivors in primary care practices. A modified ADAPTE process was used to develop algorithms based on national CPGs. Usability testing involved semi-structured interviews with clinicians from varied care settings, including comprehensive and community cancer centers, and primary care. The transcripts were analyzed with MAXQDA using Braun and Clarke's thematic analysis method. A cross tabs analysis was also performed to assess the prevalence of themes and subthemes by cancer care setting.

RESULTS

A total of 17 clinicians (physicians, nurse practitioners, and physician assistants) were interviewed for usability testing. Three main themes emerged: (1) Algorithms as useful, (2) Symptom management differences, and (3) Different target end-users. The cross-tabs analysis demonstrated differences among care trajectories and settings that originated in the Symptom management differences theme. The sub-themes of "Differences between diseases" and "Differences between care trajectories" originated from participants working in a comprehensive cancer center, which tends to be disease-specific locations for patients on active treatment. Meanwhile, participants from primary care identified the sub-theme of "Differences in settings," indicating that symptom management strategies are care setting specific.

CONCLUSIONS

While CDS can help promote evidence-based symptom management, systems providing care recommendations need to be specifically developed to fit patient characteristics and clinical context. Findings suggest that one set of algorithms will not be applicable throughout the entire cancer trajectory. Unique CDS for symptom management will be needed for patients who are cancer survivors being followed in primary care settings.

Factors identified as barriers or facilitators to EMR/EHR based interprofessional primary care: a scoping review

As interprofessional collaboration (IPC) in primary care receives increasing attention, the role of electronic medical and health record (EMR/EHR) systems in supporting IPC is important to consider. A scoping review was conducted to synthesize the current literature on the barriers and facilitators of EMR/EHRs to interprofessional primary care. Four online databases (OVID Medline, EBSCO CINAHL, OVID EMBASE, and OVID PsycINFO) were searched without date restrictions. Twelve studies were included in the review. Of six facilitator and barrier themes identified, the key facilitator was teamwork support and a significant barrier was data management. Other important barriers included usability related mainly to interoperability, and practice support primarily in terms of patient care. Additional themes were organization attributes and user features. Although EMR/EHR systems facilitated teamwork support, there is potential for team features to be strengthened further. Persistent barriers may be partly addressed by advances in software design, particularly if interprofessional perspectives are included. Organizations and teams might also consider strategies for working with existing EMR/EHR systems, for instance by developing guidelines for interprofessional use. Further research concerning the use of electronic records in interprofessional contexts is needed to support IPC in primary care.

Digital health integration for noncommunicable diseases: Comprehensive process mapping for full-life-cycle management

AIM

To create a systematic digital health process mapping framework for full-life-cycle noncommunicable disease management grounded in key stakeholder engagement.

METHODS

A triphasic, qualitative methodology was employed to construct a process mapping framework for digital noncommunicable disease management in Shanghai, China. The first phase involved desk research to examine current guidance and practices. In the second phase, pivotal stakeholders participated in focus group discussions to identify prevalent digital touchpoints across lifetime noncommunicable disease management. In the final phase, the Delphi technique was used to refine the framework based on expert insights and obtain consensus.

RESULTS

We identified 60 digital touchpoints across five essential stages of full-life-cycle noncommunicable disease management. Most experts acknowledged the rationality and feasibility of these touchpoints.

CONCLUSIONS

This study led to the creation of a comprehensive digital health process mapping framework that encompasses the entire life cycle of noncommunicable disease management. The insights gained emphasize the importance of a systemic strategic, person-centered approach over a fragmented, purely technocentric approach. We recommend that healthcare professionals use this framework as a linchpin for efficient disease management and seamless technology incorporation in clinical practice.

Value of Electronic Health Records Measured Using Financial and Clinical Outcomes: Quantitative Study

BACKGROUND

The Health Information Technology for Economic and Clinical Health Act of 2009 was legislated to reduce health care costs, improve quality, and increase patient safety. Providers and organizations were incentivized to exhibit meaningful use of certified electronic health record (EHR) systems in order to achieve this objective. EHR adoption is an expensive investment, given the resources and capital that are invested. Due to the cost of the investment, a return on the EHR adoption investment is expected.

OBJECTIVE

This study performed a value analysis of EHRs. The objective of this study was to investigate the relationship between EHR adoption levels and financial and clinical outcomes by combining both financial and clinical outcomes into one conceptual model.

METHODS

We examined the multivariate relationships between different levels of EHR adoption and financial and clinical outcomes, along with the time variant control variables, using moderation analysis with a longitudinal fixed effects model. Since it is unknown as to when hospitals begin experiencing improvements in financial outcomes, additional analysis was conducted using a 1- or 2-year lag for profit margin ratios.

RESULTS

A total of 5768 hospital-year observations were analyzed over the course of 4 years. According to the results of the moderation analysis, as the readmission rate increases by 1 unit, the effect of a 1-unit increase in EHR adoption level on the operating margin decreases by 5.38%. Hospitals with higher readmission payment adjustment factors have lower penalties.

CONCLUSIONS

This study fills the gap in the literature by evaluating individual relationships between EHR adoption levels and financial and clinical outcomes, in addition to evaluating the relationship between EHR adoption level and financial outcomes, with clinical outcomes as moderators. This study provided statistically significant evidence (P<.05), indicating that there is a relationship between EHR adoption level and operating margins when this relationship is moderated by readmission rates, meaning hospitals that have adopted EHRs could see a reduction in their readmission rates and an increase in operating margins. This finding could further be supported by evaluating more recent data to analyze whether hospitals increasing their level of EHR adoption would decrease readmission rates, resulting in an increase in operating margins. Hospitals would incur lower penalties as a result of improved readmission rates, which would contribute toward improved operating margins.

lab: an R package for generating analysis-ready data from laboratory records

Background

Electronic health records (EHRs) play a crucial role in healthcare decision-making by giving physicians insights into disease progression and suitable treatment options. Within EHRs, laboratory test results are frequently utilized for predicting disease progression. However, processing laboratory test results often poses challenges due to variations in units and formats. In addition, leveraging the temporal information in EHRs can improve outcomes, prognoses, and diagnosis predication. Nevertheless, the irregular frequency of the data in these records necessitates data preprocessing, which can add complexity to time-series analyses.

Methods

To address these challenges, we developed an open-source R package that facilitates the extraction of temporal information from laboratory records. The proposed lab package generates analysis-ready time series data by segmenting the data into time-series windows and imputing missing values. Moreover, users can map local laboratory codes to the Logical Observation Identifier Names and Codes (LOINC), an international standard. This mapping allows users to incorporate additional information, such as reference ranges and related diseases. Moreover, the reference ranges provided by LOINC enable us to categorize results into normal or abnormal. Finally, the analysis-ready time series data can be further summarized using descriptive statistics and utilized to develop models using machine learning technologies.

Results

Using the lab package, we analyzed data from MIMIC-III, focusing on newborns with patent ductus arteriosus (PDA). We extracted time-series laboratory records and compared the differences in test results between patients with and without 30-day in-hospital mortality. We then identified significant variations in several laboratory test results 7 days after PDA diagnosis. Leveraging the time series-analysis-ready data, we trained a prediction model with the long short-term memory algorithm, achieving an area under the receiver operating characteristic curve of 0.83 for predicting 30-day in-hospital mortality in model training. These findings demonstrate the lab package's effectiveness in analyzing disease progression.

Conclusions

The proposed lab package simplifies and expedites the workflow involved in laboratory records extraction. This tool is particularly valuable in assisting clinical data analysts in overcoming the obstacles associated with heterogeneous and sparse laboratory records.

Capturing Semantic Relationships in Electronic Health Records Using Knowledge Graphs: An Implementation Using MIMIC III Dataset and GraphDB

Electronic health records (EHRs) are an increasingly important source of information for healthcare professionals and researchers. However, EHRs are often fragmented, unstructured, and difficult to analyze due to the heterogeneity of the data sources and the sheer volume of information. Knowledge graphs have emerged as a powerful tool for capturing and representing complex relationships within large datasets. In this study, we explore the use of knowledge graphs to capture and represent complex relationships within EHRs. Specifically, we address the following research question: Can a knowledge graph created using the MIMIC III dataset and GraphDB effectively capture semantic relationships within EHRs and enable more efficient and accurate data analysis? We map the MIMIC III dataset to an ontology using text refinement and Protege; then, we create a knowledge graph using GraphDB and use SPARQL queries to retrieve and analyze information from the graph. Our results demonstrate that knowledge graphs can effectively capture semantic relationships within EHRs, enabling more efficient and accurate data analysis. We provide examples of how our implementation can be used to analyze patient outcomes and identify potential risk factors. Our results demonstrate that knowledge graphs are an effective tool for capturing semantic relationships within EHRs, enabling a more efficient and accurate data analysis. Our implementation provides valuable insights into patient outcomes and potential risk factors, contributing to the growing body of literature on the use of knowledge graphs in healthcare. In particular, our study highlights the potential of knowledge graphs to support decision-making and improve patient outcomes by enabling a more comprehensive and holistic analysis of EHR data. Overall, our research contributes to a better understanding of the value of knowledge graphs in healthcare and lays the foundation for further research in this area.

User-Centered Design of a Clinical Tool for Shared Decision-making About Diet in Primary Care

BACKGROUND

Health information technology is a leading cause of clinician burnout and career dissatisfaction, often because it is poorly designed by nonclinicians who have limited knowledge of clinicians' information needs and health care workflow.

OBJECTIVE

Describe how we engaged primary care clinicians and their patients in an iterative design process for a software application to enhance clinician-patient diet discussions.

DESIGN

Descriptive study of the steps followed when involving clinicians and their at-risk patients in the design of the content, layout, and flow of an application for collaborative dietary goal setting. This began with individual clinician and patient interviews to detail the desired informational content of the screens displayed followed by iterative reviews of intermediate and final versions of the program and its outputs.

PARTICIPANTS

Primary care clinicians practicing in an urban federally qualified health center and two academic primary care clinics, and their patients who were overweight or obese with diet-sensitive conditions.

MAIN MEASURES

Descriptions of the content, format, and flow of information from pre-visit dietary history to the display of evidence-based, guideline-driven suggested goals to final display of dietary goals selected, with information on how the patient might reach them and patients' confidence in achieving them.

KEY RESULTS

Through three iterations of design and review, there was substantial evolution of the program's content, format, and flow of information. This involved "tuning" of the information desired: from too little, to too much, to the right amount displayed that both clinicians and patients believed would facilitate shared dietary goal setting.

CONCLUSIONS

Clinicians' well-founded criticisms of the design of health information technology can be mitigated by involving them and their patients in the design of such tools that clinicians may find useful, and use, in their everyday medical practice.

Characterizing athletic healthcare: A perspective on methodological challenges, lessons learned, and paths forward

Recently, there has been an emphasis on collecting large datasets in the field of sports medicine. While there have been great advances in areas of sport performance and sport epidemiology, there have been fewer efforts dedicated to understanding the effectiveness and impact of athletic healthcare, including injury prevention programs and rehabilitation interventions provided at the point-of-care. In 2009, the Athletic Training Practice-Based Research Network (AT-PBRN) was launched to address this need, with the mission of improving the quality of care provided by athletic trainers. Unlike other research efforts in sports and medicine, such as sport epidemiology, there are fewer methodological best practices specifically related to clinical data in athletic healthcare. As a result, the AT-PBRN has encountered several methodological challenges during its tenure and has established guidelines based on various sources within the fields of sports and medicine to address these challenges. Therefore, the purpose of this perspective is to identify the challenges and describe strategies to address these challenges related to characterizing athletic healthcare using a large database. Specifically, challenges related to data entry (data quality and reliability) and data extraction and processing (data variability and missing data) will be discussed. Sharing challenges and perspectives on solutions for collecting and reporting on athletic healthcare data may facilitate a greater consistency in the approach used to collect, analyze, and report on clinical data in athletic healthcare, with the goal of improving patient outcomes and the quality of care provided by athletic trainers.

Facilitating Health Information Exchange to Improve Health Outcomes for School-Aged Children: School Nurse Electronic Health Record Access

BACKGROUND AND OBJECTIVES

School-aged children with chronic conditions require care coordination for health needs at school. Access to the student's accurate, real-time medical information is essential for school nurses to maximize their care of students. We aim to analyze school nurse access to medical records in a hospital-based electronic health record (EHR) and the effect on patient outcomes. We hypothesized that EHR access would decrease emergency department (ED) visits and inpatient hospitalizations.

METHODS

This retrospective secondary data analysis was conducted using EHR data 6 months pre- and post-school nurse access to students' hospital-based EHR. The main outcome measures were the ED visits and inpatient hospitalizations.

RESULTS

For the sample of 336 students in the study, there was a 34% decrease in ED visits from 190 visits before access to 126 ED visits after access (p <0.01). Inpatient hospitalizations decreased by 44% from 176 before access to 99 after access (p <0.001). The incident rate of ED visits decreased (IRR: 0.66; 95% CI: 0.53-0.83; p = 0.00035), and hospitalizations decreased (IRR: 0.56; 95% CI: 0.44-0.72; p <0.0001) from pre to post access. These findings suggest school nurse access to medical records is a positive factor in improving school-aged patient outcomes.

CONCLUSION

School nurse access to medical records through the hospital-based EHR may be a factor to improve patient outcomes by utilizing health information technology for more efficient and effective communication and care coordination for school-aged children with chronic medical conditions.

Dig Deeper: A Case Report of Finding (and Fixing) the Root Cause of Add-On Laboratory Failures

BACKGROUND

Venipunctures and the testing they facilitate are clinically necessary, particularly for hospitalized patients. However, excess venipunctures lead to patient harm, decreased patient satisfaction, and waste.

OBJECTIVES

We sought to identify contributors to excess venipunctures at our institution, focusing on electronic health record (EHR)-related factors. We then implemented and evaluated the impact of an intervention targeting one of the contributing factors.

METHODS

We employed the quality improvement (QI) methodology to find sources of excess venipunctures, specifically targeting add-on failures. Once an error was identified, we deployed an EHR-based intervention which was evaluated with retrospective pre- and postintervention analysis.

RESULTS

We identified an error in how the EHR evaluated the ability of laboratories across a health system to perform add-on tests to existing blood specimens. A review of 195,263 add-on orders placed prior to the intervention showed that 165,118 were successful and 30,145 failed, a failure rate of 15.4% (95% confidence interval [CI]: 15.1-15.6). We implemented an EHR-based modification that changed the criteria for add-on testing from a health-system-wide query of laboratory capabilities to one that incorporated only the capabilities of laboratories with feasible access to existing patient samples. In the 6 months following the intervention, a review of 87,333 add-on orders showed that 77,310 were successful, and 10,023 add-on orders failed resulting in a postintervention failure rate of 11.4% (95% CI: 11.1, 11.8) (p < 0.001).

CONCLUSION

EHR features such as the ability to identify possible add-on tests are designed to reduce venipunctures but may produce unforeseen negative effects on downstream processes, particularly as hospitals merge into health systems using a single EHR. This case report describes the successful identification and correction of one cause of add-on laboratory failures. QI methodology can yield important insights that reveal simple interventions for improvement.

Signal Detection of Pediatric Drug–Induced Coagulopathy Using Routine Electronic Health Records

Background: Drug-induced coagulopathy (DIC) is a severe adverse reaction and has become a significantly increased clinical problem in children. It is crucial to the detection of the DIC safety signal for drug post-marketing scientific supervision purposes. Therefore, this study aimed to detect potential signals for DIC in children using the routine electronic medical record (EMR) data.

Methods: This study extracted EMR data from Beijing Children’s Hospital between 2009 and 2020. A two-stage modeling method was developed to detect the signal of DIC. We calculated the crude incidence by mining cases of coagulopathy to select the potential suspected drugs; then, propensity score-matched retrospective cohorts of specific screened drugs from the first stage were constructed and estimated the odds ratio (OR) and 95% confidence interval (CI) using conditional logistic regression models. The current literature evidence was used to assess the novelty of the signal.

Results:In the study, from a total of 340 drugs, 22 drugs were initially screened as potentially inducing coagulopathy. In total, we identified 19 positive DIC associations. Of these, potential DIC risk of omeprazole (OR: 2.23, 95% CI: 1.88–2.65), chlorpheniramine (OR:3.04, 95% CI:2.56–3.60), and salbutamol sulfate (OR:1.36, 95% CI:1.07–1.73) were three new DIC signals in both children and adults. Twelve associations between coagulopathy and drugs, meropenem (OR: 3.38, 95% CI: 2.72–4.20), cefoperazone sulbactam (OR: 2.80, 95% CI: 2.30–3.41), fluconazole (OR: 2.11, 95% CI: 1.71–2.59), voriconazole (OR: 2.82, 95% CI: 2.20–3.61), ambroxol hydrochloride (OR: 2.12, 95% CI: 1.74–2.58), furosemide (OR: 2.36, 95% CI: 2.08–2.67), iodixanol (OR: 2.21, 95% CI: 1.72–2.85), cefamandole (OR: 1.82, 95% CI: 1.56–2.13), ceftizoxime (OR: 1.95, 95% CI: 1.44–2.63), ceftriaxone (OR: 1.95, 95% CI: 1.44–2.63), latamoxef sodium (OR: 1.76, 95% CI: 1.49–2.07), and sulfamethoxazole (OR: 1.29, 95% CI: 1.01–1.64), were considered as new signals in children.

Conclusion: The two-stage algorithm developed in our study to detect safety signals of DIC found nineteen signals of DIC, including twelve new signals in a pediatric population. However, these safety signals of DIC need to be confirmed by further studies based on population study and mechanism research.

Artificial intelligence: Emerging player in the diagnosis and treatment of digestive disease

Given the breakthroughs in key technologies, such as image recognition, deep learning and neural networks, artificial intelligence (AI) continues to be increasingly developed, leading to closer and deeper integration with an increasingly data-, knowledge- and brain labor-intensive medical industry. As society continues to advance and individuals become more aware of their health needs, the problems associated with the aging of the population are receiving increasing attention, and there is an urgent demand for improving medical technology, prolonging human life and enhancing health. Digestive system diseases are the most common clinical diseases and are characterized by complex clinical manifestations and a general lack of obvious symptoms in the early stage. Such diseases are very difficult to diagnose and treat. In recent years, the incidence of diseases of the digestive system has increased. As AI applications in the field of health care continue to be developed, AI has begun playing an important role in the diagnosis and treatment of diseases of the digestive system. In this paper, the application of AI in assisted diagnosis and the application and prospects of AI in malignant and benign digestive system diseases are reviewed.

Development an extended-information success system model (ISSM) based on nurses' point of view for hospital EHRs: a combined framework and questionnaire

Background

Understanding the hospital EHR success rate has great benefits for hospitals. The present study aimed to 1-Propose an extended-ISSM framework and a questionnaire in a systematic manner for EHR evaluation based on nurses’ perspectives, 2-Determine the EHR success rate, and 3-Explore the effective factors contributing to EHR success.

Methods

The proposed framework was developed using ISSM, TAM3, TTF, HOT-FIT, and literature review in seven steps. A self-administrated structured 65-items questionnaire was developed with CVI: 90.27% and CVR: 94.34%. Construct validity was conducted using EFA and CFA. Eleven factors were identified, collectively accounting for 71.4% of the total variance. In the EFA step, 15 questions and two questions in EFA were excluded. Finally, 48 items remained in the framework including dimensions of technology, human, organization, ease of use, usefulness, and net benefits. The overall Cronbach’s alpha value was 93.4%. In addition, the hospital EHR success rate was determined and categorized. In addition, effective factors on EHR success were explored.

Results

In total, 86 nurses participated in the study. On average, the “total hospital EHR success rate” was moderate. The total EHR success rates was ranging from 47.09 to 74.96%. The results of the Kruskal–Wallis test showed that there was a significant relationship between “gender” and “self-efficacy” (p-value: 0.042). A reverse relation between “years of experience using computers” and “training” (p-value: 0.012) was observed. “Years of experience using EHR” as well as “education level” (p-value: 0.001) and “ease of use” had a reverse relationship (p-value: 0.034).

Conclusions

Our findings underscore the EHR success based on nurses’ viewpoint in a developing country. Our results provide an instrument for comparison of EHR success rates in various hospitals.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12911-022-01800-1.

National electronic primary health care database in monitoring performance of primary care in Kyrgyzstan

Aim:

The aim of this study was to assess the feasibility of the national electronic primary health care (PHC) database in Kyrgyzstan in producing information on the disease burden of the patient population and on the processes and quality of care of noncommunicable diseases (NCDs) in PHC.

Background:

Strengthening of the PHC is essential for low- and middle-income countries (LMICs) to tackle the increasing burden of NCDs. Capacity building and quality improvement require timely data on processes and quality of care.

Methods:

A data extraction was carried out covering four PHC clinics in Bishkek in 2019 to pilot the use of the national data for quality assessment purposes. The data included patient-level information on all appointments in the clinics during the year 2018 and consisted of data of altogether 48 564 patients. Evaluation indicators of the WHO Package of Essential NCD Interventions framework were used to assess the process and outcome indicators of patients with hypertension or diabetes.

Findings:

The extracted data enabled the identification of different patient populations and analyses of various process and outcome indicators. The legibility of data was good and the structured database enabled easy data extraction and variable formation on patient level. As an example of process and outcome indicators of those with hypertension, the blood pressure was measured at least on two occasions of 90% of women and 89% of men, and blood pressure control was achieved among 61% of women and 53% of men with hypertension. This study showed that a rather basic system gathering nationally patient-level data to an electronic database can serve as an excellent information source for national authorities. Investments should be made to develop electronic health records and national databases also in LMICs.

First study of the RuPatient health information system with optical character recognition of medical records based on machine learning

RuPatient health information system (HIS) is a computer program consisting of a doctor-patient web user interface, which includes algorithms for recognizing medical record text and entering it into the corresponding fields of the system.

Aim. To evaluate the effectiveness of RuPatient HIS in actual clinical practice.

Material and methods. The study involved 10 cardiologists and intensivists of the department of cardiology and сardiovascular intensive care unit of the L. A. Vorokhobov City Clinical Hospital 67 We analyzed images (scanned copies, photos) of discharge reports from patients admitted to the relevant departments in 2021. The following fields of medical documentation was recognized: Name, Complaints, Anamnesis of life and illness, Examination, Recommendations. The correctness and accuracy of recognition of entered information were analyzed. We compared the recognition quality of RuPatient HIS and a popular optical character recognition application (FineReader for Mac).

Results. The study included 77 pages of discharge reports of patients from various hospitals in Russia from 50 patients (men, 52%). The mean age of patients was 57,7±7,9 years. The number of reports with correctly recognized fields in various categories using the program algorithms was distributed as follows: Name — 14 (28%), Diagnosis — 13 (26%), Complaints — 40 (80%), Anamnesis — 14 (28%), Examination — 24 (48%), Recommendations — 46 (92%). Data that was not included in the category was also recognized and entered in the comments field. The number of recognized words was 549±174,9 vs 522,4±215,6 (p=0,5), critical errors in words — 2,1±1,6 vs 4,4±2,8 (p<0,001), non-critical errors — 10,3±4,3 vs 5,6±3,3 (p<0,001) for RuPatient HIS and optical character recognition application for a personal computer, respectively.

Conclusion. The developed RuPatient HIS, which includes a module for recognizing medical records and entering data into the corresponding fields, significantly increases the document management efficiency with high quality of optical character recognition based on neural network technologies and the automation of filling process.

OUP accepted manuscript

The increasing volume and richness of healthcare data collected during routine clinical practice have not yet translated into significant numbers of actionable insights that have systematically improved patient outcomes. An evidence-practice gap continues to exist in healthcare. We contest that this gap can be reduced by assessing the use of nudge theory as part of clinical decision support systems (CDSS). Deploying nudges to modify clinician behaviour and improve adherence to guideline-directed therapy represents an underused tool in bridging the evidence-practice gap. In conjunction with electronic health records (EHRs) and newer devices including artificial intelligence algorithms that are increasingly integrated within learning health systems, nudges such as CDSS alerts should be iteratively tested for all stakeholders involved in health decision-making: clinicians, researchers, and patients alike. Not only could they improve the implementation of known evidence, but the true value of nudging could lie in areas where traditional randomized controlled trials are lacking, and where clinical equipoise and variation dominate. The opportunity to test CDSS nudge alerts and their ability to standardize behaviour in the face of uncertainty may generate novel insights and improve patient outcomes in areas of clinical practice currently without a robust evidence base.

Electronic health records, mobile health, and the challenge of improving global health

Technology continues to impact healthcare around the world. This provides great opportunities, but also risks. These risks are compounded in low-resource settings where errors in planning and implementation may be more difficult to overcome. Global Health Informatics provides lessons in both opportunities and risks by building off of general Global Health. Global Health Informatics also requires a thorough understanding of the local environment and the needs of low-resource settings. Forming effective partnerships and following the lead of local experts are necessary for sustainability; it also ensures that the priorities of the local community come first. There is an opportunity for partnerships between low-resource settings and high income areas that can provide learning opportunities to avoid the pitfalls that plague many digital health systems and learn how to properly implement technology that truly improves healthcare.

Excess Patient Visits for Cough and Pulmonary Disease at a Large US Health System in the Months Prior to the COVID-19 Pandemic: Time-Series Analysis

BACKGROUND

Accurately assessing the regional activity of diseases such as COVID-19 is important in guiding public health interventions. Leveraging electronic health records (EHRs) to monitor outpatient clinical encounters may lead to the identification of emerging outbreaks.

OBJECTIVE

The aim of this study is to investigate whether excess visits where the word "cough" was present in the EHR reason for visit, and hospitalizations with acute respiratory failure were more frequent from December 2019 to February 2020 compared with the preceding 5 years.

METHODS

A retrospective observational cohort was identified from a large US health system with 3 hospitals, over 180 clinics, and 2.5 million patient encounters annually. Data from patient encounters from July 1, 2014, to February 29, 2020, were included. Seasonal autoregressive integrated moving average (SARIMA) time-series models were used to evaluate if the observed winter 2019/2020 rates were higher than the forecast 95% prediction intervals. The estimated excess number of visits and hospitalizations in winter 2019/2020 were calculated compared to previous seasons.

RESULTS

The percentage of patients presenting with an EHR reason for visit containing the word "cough" to clinics exceeded the 95% prediction interval the week of December 22, 2019, and was consistently above the 95% prediction interval all 10 weeks through the end of February 2020. Similar trends were noted for emergency department visits and hospitalizations starting December 22, 2019, where observed data exceeded the 95% prediction interval in 6 and 7 of the 10 weeks, respectively. The estimated excess over the 3-month 2019/2020 winter season, obtained by either subtracting the maximum or subtracting the average of the five previous seasons from the current season, was 1.6 or 2.0 excess visits for cough per 1000 outpatient visits, 11.0 or 19.2 excess visits for cough per 1000 emergency department visits, and 21.4 or 39.1 excess visits per 1000 hospitalizations with acute respiratory failure, respectively. The total numbers of excess cases above the 95% predicted forecast interval were 168 cases in the outpatient clinics, 56 cases for the emergency department, and 18 hospitalized with acute respiratory failure.

CONCLUSIONS

A significantly higher number of patients with respiratory complaints and diseases starting in late December 2019 and continuing through February 2020 suggests community spread of SARS-CoV-2 prior to established clinical awareness and testing capabilities. This provides a case example of how health system analytics combined with EHR data can provide powerful and agile tools for identifying when future trends in patient populations are outside of the expected ranges.