The use of Electronic Health Records to Support Population Health: A Systematic Review of the Literature

Integrating Health Care Data in an Informatics for Integrating Biology & the Bedside (i2b2) Model Persisted Through Elasticsearch: Design, Implementation, and Evaluation in a French University Hospital

BACKGROUND

The volume of digital data in health care is continually growing. In addition to its use in health care, the health data collected can also serve secondary purposes, such as research. In this context, clinical data warehouses (CDWs) provide the infrastructure and organization necessary to enhance the secondary use of health data. Various data models have been proposed for structuring data in a CDW, including the Informatics for Integrating Biology & the Bedside (i2b2) model, which relies on a relational database. However, this persistence approach can lead to performance issues when executing queries on massive data sets.

OBJECTIVE

This study aims to describe the necessary transformations and their implementation to enable i2b2's search engine to perform the phenotyping task using data persistence in a NoSQL Elasticsearch database.

METHODS

This study compares data persistence in a standard relational database with a NoSQL Elasticsearch database in terms of query response and execution performance (focusing on counting queries based on structured data, numerical data, and free text, including temporal filtering) as well as material resource requirements. Additionally, the data loading and updating processes are described.

RESULTS

We propose adaptations to the i2b2 model to accommodate the specific features of Elasticsearch, particularly its inability to perform joins between different indexes. The implementation was tested and evaluated within the CDW of Bordeaux University Hospital, which contains data on 2.5 million patients and over 3 billion observations. Overall, Elasticsearch achieves shorter query execution times compared with a relational database, with particularly significant performance gains for free-text searches. Additionally, compared with an indexed relational database (including a full-text index), Elasticsearch requires less disk space for storage.

CONCLUSIONS

We demonstrate that implementing i2b2 with Elasticsearch is feasible and significantly improves query performance while reducing disk space usage. This implementation is currently in production at Bordeaux University Hospital.

Leveraging Electronic Health Records in International Humanitarian Clinics for Population Health Research: Cross-Sectional Study

Background

As more humanitarian relief organizations are beginning to use electronic medical records in their operations, data from clinical encounters can be leveraged for public health planning. Currently, medical data from humanitarian medical workers are infrequently available in a format that can be analyzed, interpreted, and used for public health.

objectives

This study aims to develop and test a methodology by which diagnosis and procedure codes can be derived from free-text medical encounters by medical relief practitioners for the purposes of data analysis.

Methods

We conducted a cross-sectional study of clinical encounters from humanitarian clinics for displaced persons in Mexico between August 3, 2021, and December 5, 2022. We developed and tested a method by which free-text encounters were reviewed by medical billing coders and assigned codes from the International Classification of Diseases, Tenth Revision (ICD-10) and the Current Procedural Terminology (CPT). Each encounter was independently reviewed in duplicate and assigned ICD-10 and CPT codes in a blinded manner. Encounters with discordant codes were reviewed and arbitrated by a more experienced medical coder, whose decision was used to determine the final ICD-10 and CPT codes. We used chi-square tests of independence to compare the ICD-10 codes for concordance across single-diagnosis and multidiagnosis encounters and across patient characteristics, such as age, sex, and country of origin.

Results

We analyzed 8460 encounters representing 5623 unique patients and 2774 unique diagnosis codes. These free-text encounters had a mean of 20.5 words per encounter in the clinical documentation. There were 58.78% (4973/8460) encounters where both coders assigned 1 diagnosis code, 18.56% (1570/8460) encounters where both coders assigned multiple diagnosis codes, and 22.66% (1917/8460) encounters with a mixed number of codes assigned. Of the 4973 encounters with a single code, only 11.82% (n=588) had a unique diagnosis assigned by the arbitrator that was not assigned by either of the initial 2 coders. Of the 1570 encounters with multiple diagnosis codes, only 3.38% (n=53) had unique diagnosis codes assigned by the arbitrator that were not initially assigned by either coder. The frequency of complete concordance across diagnosis codes was similar across sex categories and ranged from 30.43% to 46.05% across age groups and countries of origin.

Conclusions

Free-text electronic medical records from humanitarian relief clinics can be used to develop a database of diagnosis and procedure codes. The method developed in this study, which used multiple independent reviews of clinical encounters, appears to reliably assign diagnosis codes across a diverse patient population in a resource-limited setting.

The Impact of Trust and the Role of the Opt-Out Mechanism in Willingness to Share Health Data via Electronic Health Records in Germany: Telephone Survey Study

Background

Electronic health records (EHRs) offer a valuable resource for research and health care improvement. However, public acceptance of sharing personal health data is critical to the success of such initiatives. In Germany, automatic data sharing via EHRs will be implemented unless people opt out.

Objective

This study aims to assess the willingness of the German population to share health data via EHRs and to explore the role of trust in influencing these attitudes.

Methods

A computer-assisted telephone interview study was conducted in December 2023, with 1004 respondents aged 18 years and older, representative of the German population. Descriptive statistics and multivariate linear regression models were used to analyze the data.

Results

The survey shows that 43.4% (n=432) of respondents would be willing to share their health data via EHR, and a significant 34% (n=338) remain undecided. While the population is open to adoption of the EHR for personal health issues (n=483, 53% show interest in using it), the opt-out model for data sharing is viewed critically, with 44.7% (n=438) of respondents rejecting it. Socioeconomic status significantly influences the willingness to share data, with higher income, education, and digital literacy being associated with greater openness to data sharing. However, trust emerged as the most significant factor. Additionally, experiences with digital technologies increase the willingness to share personal health data.

Conclusions

The German population shows general openness toward EHRs and data sharing. Trust plays a critical role in promoting willingness to share health data. The findings highlight challenges in Germany's transition to an opt-out system.

A field study to explore user experiences with socially assistive robots for older adults: emphasizing the need for more interactivity and personalisation

Older adults often desire to remain in their homes for as long as possible, and Socially Assistive Robots (SARs) can play a role in supporting this goal. However, the acceptance and adoption rates of SARs remain relatively low, suggesting that current designs may not fully address all user needs. Field studies in Human-Robot Interaction, particularly those involving multiple end-users, remain limited. Nevertheless, such studies are crucial for identifying factors that shape the user experience with SARs, potentially improving their acceptance and adoption in healthcare settings. Therefore, this study aims to explore user perspectives, referred to as factors, that could guide design considerations for SAR development. We conducted a field study with 90 participants across Italy, Switzerland, and the Netherlands to identify these factors and their implications for improving the SAR user experience for older adults and their formal and informal caregivers. SARs were placed in the homes of older adults, and interviews were conducted with the three groups of primary end-users, at the beginning, midpoint, and end of the two-to six-week trial period. We initially focused on four factors (personalisation, interactivity, embodiment, and ethical considerations), identified in earlier design phases of the related 3-year Guardian project. Our findings confirmed the importance of these factors while uncovering additional ones. Personalisation and interactivity emerged as the most important ones among these factors. Based on our insights, we recommend involving all primary end-users in SAR research and design process and prioritising field studies to refine design elements. In conclusion, our study identified six factors for SAR design that can enhance the user experience: personalisation, interactivity, embodiment, ethical considerations, connectedness, and dignity. These findings provide valuable guidance for developing SARs that may better address the needs of older adults and their caregivers.

Population Health Management for Improving Kidney Health Outcomes

Chronic kidney disease (CKD) is globally prevalent, a leading cause of mortality, and is associated with poor patient outcomes and high healthcare costs. Gaps in guideline-concordant care are common across the continuum of CKD. These gaps lead to CKD progression, hospitalizations, and mortality, and are potentiated by existing racial and socioeconomic disparities. A thoughtfully designed population health management approach, that leverages electronic health record, can modernize CKD care delivery and improve outcomes. Such an approach can potentially provide timely, equitable, resource- and cost-efficient care across health systems in a way that is scalable and data driven. Herein, we share our experiences with the implementation of nephrology population health initiatives at the University of Pittsburgh Medical Center across the CKD spectrum, which include ongoing and planned programs in the primary care, kidney-palliative care, kidney transplantation, and transitions of care settings. Further, we discuss the challenges of population health management and future directions that can move healthcare toward personalized medicine.

Big data for neuroscience in the context of predictive, preventive, and personalized medicine

Accurate and precise diagnosis made the medicine the hallmark of evidence-based medicine. While attaining absolute patient satisfaction may seem impossible in the aspect of disease recurrent, personalized their mecidal conditions to their responsive treatment approach may save the day. The last generation approaches in medicine require advanced technologies that will lead to evidence-based medicine. One of the trending fields in this is the use of big data in predictive, preventive, and personalized medicine (3PM). This review dwelled through the practical examples in which big data tools harness neuroscience to add more individualized apporahes to the medical conditions in a bid to confer a more personalized treatment strategies. Moreover, the known breakthroughs of big data in 3PM, big data and 3PM in neuroscience, AI and neuroscience, limitations of big data with 3PM in neuroscience, and the challenges are thoroughly discussed. Finally, the prospects of incorporating big data in 3PM are as well discussed. The review could point out that the implications of big data in 3PM are still in their infancy and will require a holistic approach. While there is a need to carefully sensitize the community, convincing them will come under interdisciplinary and, to some extent, inter-professional collaborations, capacity building for professionals, and optimal coordination of the joint systems.

Association of Polypharmacy and Burden of Comorbidities on COVID-19 Adverse Outcomes in People with Type 1 or Type 2 Diabetes

INTRODUCTION

It is widely accepted that the higher the number of medications prescribed and taken by an individual, the higher the risk of poor health outcomes. We have investigated whether polypharmacy and comorbidities conveyed more risk of adverse health outcomes following COVID-19 infection (as a paradigm of serious viral infections in general) in people with type 1 diabetes (T1DM) or type 2 diabetes (T2DM).

METHODS

The Greater Manchester Care Record (GMCR) is an integrated database of electronic health records containing data collected from 433 general practices in Greater Manchester. Baseline demographic information (age, body mass index [BMI], gender, ethnicity, smoking status, deprivation index), hospital admission or death within 28 days of infection were extracted for adults (18+) diagnosed with either T1DM or T2DM.

RESULTS

The study cohort included individuals diagnosed as T1DM and T2DM separately. Across the Greater Manchester Region, a total of 145,907 individuals were diagnosed with T2DM and 9705 were diagnosed with T1DM. For the T2DM individuals, 45.2% were women and for the T1DM individuals, 42.7% were women. For T2DM, 16-20 medications (p = 0.005; odds ratio [OR] [95% confidence interval (CI) 2.375 [1.306-4.319]) and > 20 medications (p < 0.001; OR [95% CI] 3.141 [1.755-5.621]) were associated with increased risk of death following COVID-19 infection. Increased risk of hospital admissions in T2DM individuals was associated with 11 to 15 medications (p = 0.013; OR = 1.341 (95% CI) [1.063-1.692]). This was independent of comorbidities, metabolic and demographic factors. For T1DM, there was no association of polypharmacy with hospital admission. Additionally, respiratory, cardiovascular/cerebrovascular and gastrointestinal conditions were associated with increased risk of hospital admissions and deaths in T2DM (p < 0.001). Many comorbidities were common across both T1DM and T2DM.

CONCLUSIONS

We have shown in T2DM an independent association of multiple medications taken from 11 upwards with adverse health consequences following COVID-19 infection. We also found that individuals with diabetes develop comorbidities that were common across both T1DM and T2DM. This study has laid the foundation for future investigations into the way that complex pharmacological interactions may influence clinical outcomes in people with T2DM.

VIEWER: an extensible visual analytics framework for enhancing mental healthcare

OBJECTIVE

A proof-of-concept study aimed at designing and implementing Visual & Interactive Engagement With Electronic Records (VIEWER), a versatile toolkit for visual analytics of clinical data, and systematically evaluating its effectiveness across various clinical applications while gathering feedback for iterative improvements.

MATERIALS AND METHODS

VIEWER is an open-source and extensible toolkit that employs natural language processing and interactive visualization techniques to facilitate the rapid design, development, and deployment of clinical information retrieval, analysis, and visualization at the point of care. Through an iterative and collaborative participatory design approach, VIEWER was designed and implemented in one of the United Kingdom's largest National Health Services mental health Trusts, where its clinical utility and effectiveness were assessed using both quantitative and qualitative methods.

RESULTS

VIEWER provides interactive, problem-focused, and comprehensive views of longitudinal patient data (n = 409 870) from a combination of structured clinical data and unstructured clinical notes. Despite a relatively short adoption period and users' initial unfamiliarity, VIEWER significantly improved performance and task completion speed compared to the standard clinical information system. More than 1000 users and partners in the hospital tested and used VIEWER, reporting high satisfaction and expressed strong interest in incorporating VIEWER into their daily practice.

DISCUSSION

VIEWER provides a cost-effective enhancement to the functionalities of standard clinical information systems, with evaluation offering valuable feedback for future improvements.

CONCLUSION

VIEWER was developed to improve data accessibility and representation across various aspects of healthcare delivery, including population health management and patient monitoring. The deployment of VIEWER highlights the benefits of collaborative refinement in optimizing health informatics solutions for enhanced patient care.

Automated extraction of post-stroke functional outcomes from unstructured electronic health records

PURPOSE

Population level tracking of post-stroke functional outcomes is critical to guide interventions that reduce the burden of stroke-related disability. However, functional outcomes are often missing or documented in unstructured notes. We developed a natural language processing (NLP) model that reads electronic health records (EHR) notes to automatically determine the modified Rankin Scale (mRS).

METHOD

We included consecutive patients (⩾18 years) with acute stroke admitted to our center (2015-2024). mRS scores were obtained from the Get With the Guidelines registry and clinical notes (if documented), and used as the gold standard to compare against NLP-generated scores. We used text-based features from notes, along with age, sex, discharge status, and outpatient follow-up to train a logistic regression for prediction of good (0-2) versus poor (3-6) mRS, and a linear regression for the full range of mRS scores. The models were trained for prediction of mRS at hospital discharge and post-discharge. The models were externally validated in a dataset of patients with brain injuries from a different healthcare center.

FINDINGS

We included 5307 patients, 5006 in train and test and 301 in validation; average age was 69 (SD 15) and 65 (SD 17) years, respectively; 47% female. The logistic regression achieved an area under the receiver operating curve (AUROC) of 0.94 [CI 0.93-0.95] (test) and 0.94 [0.91-0.96] (validation), and the linear model a root mean squared error (RMSE) of 0.91 [0.87-0.94] (test) and 1.17 [1.06-1.28] (validation).

DISCUSSION AND CONCLUSION

The NLP-based model is suitable for use in large-scale phenotyping of stroke functional outcomes and population health research.

Gaps and Similarities in Research Use LOINC Codes Utilized in Korean University Hospitals: Towards Semantic Interoperability for Patient Care

BACKGROUND

The accuracy of Logical Observation Identifiers Names and Codes (LOINC) mappings is reportedly low, and the LOINC codes used for research purposes in Korea have not been validated for accuracy or usability. Our study aimed to evaluate the discrepancies and similarities in interoperability using existing LOINC mappings in actual patient care settings.

METHODS

We collected data on local test codes and their corresponding LOINC mappings from seven university hospitals. Our analysis focused on laboratory tests that are frequently requested, excluding clinical microbiology and molecular tests. Codes from nationwide proficiency tests served as intermediary benchmarks for comparison. A research team, comprising clinical pathologists and terminology experts, utilized the LOINC manual to reach a consensus on determining the most suitable LOINC codes.

RESULTS

A total of 235 LOINC codes were designated as optimal codes for 162 frequent tests. Among these, 51 test items, including 34 urine tests, required multiple optimal LOINC codes, primarily due to unnoted properties such as whether the test was quantitative or qualitative, or differences in measurement units. We analyzed 962 LOINC codes linked to 162 tests across seven institutions, discovering that 792 (82.3%) of these codes were consistent. Inconsistencies were most common in the analyte component (38 inconsistencies, 33.3%), followed by the method (33 inconsistencies, 28.9%), and properties (13 inconsistencies, 11.4%).

CONCLUSION

This study reveals a significant inconsistency rate of over 15% in LOINC mappings utilized for research purposes in university hospitals, underlining the necessity for expert verification to enhance interoperability in real patient care.

Delivering Guideline-Concordant Care for Patients With High-Risk HPV and Normal Cytologic Findings

Importance

As US health care systems shift to human papillomavirus (HPV)-based cervical cancer screening, more patients are receiving positive high-risk non-16/18 genotype HPV results and negative for intraepithelial lesion or malignancy (NILM) cytological findings. Risk-based management guidelines recommend 2 consecutive negative annual results to return to routine screening.

Objective

To quantify patterns of surveillance testing and associated outcomes for patients after an HPV-positive results and NILM cytologic findings.

Design, Setting, and Participants

This cohort study analyzed patients in the METRICS (Multi-level Optimization of the Cervical Cancer Screening Process in Diverse Settings and Populations) cohort of the PROSPR II (Population-Based Research to Optimize the Screening Process) Cervical Consortium. Population-based data were obtained from 3 diverse health care systems (Mass General Brigham [MGB] in Massachusetts, Kaiser Permanente Washington [KPWA] in Washington, and Parkland Health [PH] in Texas) in the METRICS cohort. Participants were patients aged 21 to 65 years who received an HPV-positive (non-16/18 or pooled genotypes) result and NILM cytologic finding from January 2010 to August 2018 and were followed up through December 2019. Data analyses were performed between April 2021 and November 2024.

Main Outcomes and Measures

Test receipt and outcomes delivered within 16 months after the index result (round 1 surveillance).

Results

The final sample across the 3 health care systems comprised 13 158 female patients (3228 Hispanic or Latine [24.5%], 1990 non-Hispanic African American or Black [15.1%], 749 non-Hispanic Asian [5.7%], and 6559 non-Hispanic White [49.8%] individuals). Sociodemographic characteristics varied by site, with more non-Hispanic White (2277 [63.7%] and 4061 [61.2%]) and commercially insured patients (3137 [87.8%] and 4365 [65.7%]) at KPWA and MGB, and more Hispanic or Latine (1664 [56.5%]) and uninsured patients (2352 [79.9%]) at PH. During round 1 surveillance, 43.7% of patients were tested, of whom 18.2% (2394) had HPV-negative results and NILM cytologic findings and 25.5% (3351) had abnormal results. Many patients remained in the cohort and were untested through round 1 surveillance (overall: 49.4% [6505]; across sites: 39.0% [1395] to 69.4% [2043]), while fewer exited the cohort (overall: 6.9% [908]; across sites: 0.2% [12] to 24.6% [879]). Groups with lower odds of timely testing were younger adults (aged 25-29 vs 30-39 years: adjusted odds ratio [AOR], 0.65; 95% CL, 0.53-0.81), non-Hispanic African American or Black compared with non-Hispanic White patients (AOR, 0.78; 95% CL, 0.68-0.89), and those with Medicaid compared with commercial insurance (AOR, 0.81; 95% CL, 0.72-0.91), while those with a primary care clinician were more likely to have timely testing (AOR, 1.44; 95% CL, 1.21-1.70). Cancer was diagnosed in 10 patients (0.2%) untested in round 1 surveillance compared with 0 cancers in those with an HPV-negative results and NILM cytologic findings.

Conclusions and Relevance

This cohort study found that among patients with HPV-positive results and NILM cytologic findings, less than half received a surveillance cotest during the guideline-recommended time frame. Health care systems should monitor annual surveillance and gather evidence on interventions to optimize the delivery of surveillance testing.

Improving electronic health record documentation and use to promote evidence-based pediatric care

OBJECTIVE

Electronic health records (EHRs) often lack the necessary functionalities to support the full implementation of national clinical guidelines for pediatric care outlined in the American Academy of Pediatrics Bright Futures Guidelines. Using HealthySteps (HS), an evidence-based pediatric primary care program, as an exemplar, this study aimed to enhance pediatric EHRs, identify facilitators and barriers to EHR enhancements, and improve data quality for delivering clinical care as part of HS implementation and evidence building.

METHODS

Three HS sites-each differing in location, setting, number of children served, and mix of child insurance coverage-participated in the study. Sites received technical assistance to support data collection and EHR updates. A comprehensive evaluation, including a process evaluation and outcomes monitoring, was conducted to gauge progress toward implementing study data requirements over time. Data sources included administrative records, surveys, and interviews.

RESULTS

All sites enhanced their EHRs yet relied on supplemental data systems to track care coordination. Sites improved documentation of required data, demonstrating reductions in missing data and increases in extractable data between baseline and follow-up assessments. For example, the percentage of missing social-emotional screening results ranged from 0% to 8.0% at study conclusion. Facilitators and barriers to EHR enhancements included organizational supports, leadership, and capacity building.

CONCLUSIONS

With significant investment of time and resources, practices modified their EHRs to better capture services aligned with HS and Bright Futures. However, more scalable digital solutions are necessary to support EHR updates to help drive improvements in clinical care and outcomes for children and families.

Electronic health record and primary care physician self-reported quality of care: a multilevel study in China

BACKGROUND

Health information technology is one of the building blocks of a high-performing health system. However, the evidence regarding the influence of an electronic health record (EHR) on the quality of care remains mixed, especially in low- and middle-income countries.

OBJECTIVE

This study examines the association between greater EHR functionality and primary care physician self-reported quality of care.

METHODS

A total of 224 primary care physicians from 38 community health centres (CHCs) in four large Chinese cities participated in a cross-sectional survey to assess CHC care quality. Each CHC director scored their CHC's EHR functionality on the availability of ten typical features covering health information, data, results management, patient access, and clinical decision support. Data analysis utilised hierarchical linear modelling.

RESULTS

The availability of five EHR features was positively associated with physician self-reported clinical quality: share records online with providers outside the practice (β = 0.276, p = 0.04), access records online by the patient (β = 0.325, p = 0.04), alert provider of potential prescription problems (β = 0.353, p = 0.04), send the patient reminders for care (β = 0.419, p = 0.003), and list patients by diagnosis or health risk (β = 0.282, p = 0.04). However, no association was found between specific features availability or total features score and physician self-reported preventive quality.

CONCLUSIONS

This study provides evidence that the availability of EHR systems, and specific features of these systems, was positively associated with physician self-reported quality of care in these 38 CHCs. Future longitudinal studies focused on standardised quality metrics, and designed to control known confounding variables, will further inform quality improvement efforts in primary care.

The state of mental health in Greece: An international comparative analysis using data from the Global Mental Health Countdown 2030

BACKGROUND

Effective mental health systems depend on the functioning of a variety of factors that can be systematically monitored across countries. Macro-level assessments are needed to identify potential areas for improvement in the health sector, particularly in countries that face significant access barriers such as Greece.

AIM

To analyze Greece's mental health-related indicators in comparison to countries with similar socioeconomic contexts and geography and identify priority areas for the national mental health system.

METHODS

Data was sourced from the Global Mental Health Countdown 2030, an initiative gathering 48 indicators from 193 countries, classifying metrics into four domains: mental health system performance, determinants of mental health, factors influencing the demand for care, and wellbeing. We analyzed 39 indicators available for Greece to perform a comparative analysis with three groups of countries (27 European Union, 55 high-income, and 52 upper-middle income nations). We employed content analysis to organize mental health system indicators into a framework to inform policy and practice.

RESULTS

Greece exhibited low performance in several indicators related to mental health provision, with four metrics falling below the 12.5th centile for all comparative groups ('interventions in primary care', 'policy implementation', 'promotion and prevention', and 'frequency of collection of data'). A content-analysis framework grouped indicators into categories related to the mental health system, with low-scoring metrics clustering around 'policy and planning', 'affordability of care', 'coordination of services', and 'data collection and quality assessment'.

CONCLUSION

This analysis provides a contextualized overview of Greece's mental health system, identifying areas for improvement based on a panel of evidence-based indicators. Priority policy actions should focus on enhancing mental health insurance coverage and freely-available mental health services, organizing provision into a stepped-care and coordinated service network, and establishing systematic data monitoring mechanisms with unified electronic registers.

The Role of Primary Care in Achieving Life's Essential 8: A Scientific Statement From the American Heart Association

To reduce morbidity and mortality rates of cardiovascular disease, an urgent need exists to improve cardiovascular health among US adults. In 2022, the American Heart Association issued Life's Essential 8, which identifies and defines 8 health behaviors and factors that, when optimized through a combination of primary prevention, risk factor management, and effective treatments, can promote ideal cardiovascular health. Because of its central role in patient care across the life span, primary care is in a strategic position to promote Life's Essential 8 and improve cardiovascular health in the United States. High-quality primary care is person-centered, team-based, community-aligned, and designed to provide affordable optimized health care. The purpose of this scientific statement from the American Heart Association is to provide evidence-based guidance on how primary care, as a field and practice, can support patients in implementing Life's Essential 8. The scientific statement aims to describe the role and functions of primary care, provide evidence for how primary care can be leveraged to promote Life's Essential 8, examine the role of primary care in providing access to care and mitigating disparities in cardiovascular health, review challenges in primary care, and propose solutions to address challenges in achieving Life's Essential 8.

Telemedicine in Italy: Healthcare authorization profiles in the modern medico-legal reading

BACKGROUND

The ruling n. 38485, 20 June 2019, of the Italian Supreme Court, III criminal section, addressed by the perspective of the law the very sensitive and new issue of telemedicine.

OBJECTIVE

This commentary deals with the issue of authorization of telemedicine activities by the health authority, starting from the Italian Court of Criminal Cassation, III section, decision n. 38485/2019. The case law explored the authorization of a health point, which carries out telemedicine services.

METHODS

Starting from the perspective discussed by Italian health regulations, the paper examines how the health act could be defined, with the possibilities offered by telecommunications, and how it now relates legally to the physical place where it takes place.

RESULTS

Even if telemedicine opens the way to virtual spaces of health practice, the Ministry of Health Italian Guidelines pose functional and logistical issues to guarantee users' safety and health care system accountability. Then, functional requirements for health legitimate practice, and their continuous monitoring, together with the responsibilities of the service centers, health professionals and health facilities, are discussed.

CONCLUSION

The questioning of States' health law, in a broad health system such as that of the Europe, characterized by autonomous health regulations, is extremely important for cross-border health policy with telemedicine, as overall regulatory compliance in health care is the ground criterion for risk prevention and patient safety, to be properly verified.

An open-source framework for end-to-end analysis of electronic health record data

With progressive digitalization of healthcare systems worldwide, large-scale collection of electronic health records (EHRs) has become commonplace. However, an extensible framework for comprehensive exploratory analysis that accounts for data heterogeneity is missing. Here we introduce ehrapy, a modular open-source Python framework designed for exploratory analysis of heterogeneous epidemiology and EHR data. ehrapy incorporates a series of analytical steps, from data extraction and quality control to the generation of low-dimensional representations. Complemented by rich statistical modules, ehrapy facilitates associating patients with disease states, differential comparison between patient clusters, survival analysis, trajectory inference, causal inference and more. Leveraging ontologies, ehrapy further enables data sharing and training EHR deep learning models, paving the way for foundational models in biomedical research. We demonstrate ehrapy's features in six distinct examples. We applied ehrapy to stratify patients affected by unspecified pneumonia into finer-grained phenotypes. Furthermore, we reveal biomarkers for significant differences in survival among these groups. Additionally, we quantify medication-class effects of pneumonia medications on length of stay. We further leveraged ehrapy to analyze cardiovascular risks across different data modalities. We reconstructed disease state trajectories in patients with severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) based on imaging data. Finally, we conducted a case study to demonstrate how ehrapy can detect and mitigate biases in EHR data. ehrapy, thus, provides a framework that we envision will standardize analysis pipelines on EHR data and serve as a cornerstone for the community.

Understanding Pain in Polycystic Ovary Syndrome: Health Risks and Treatment Effectiveness

Polycystic ovary syndrome (PCOS) is a prevalent endocrine disorder in women, often accompanied by various symptoms including significant pain, such as dysmenorrhea, abdominal, and pelvic pain, which remains underexplored. This retrospective study examines electronic health records (EHR) data to assess the prevalence of pain in women with PCOS. Conducted on May 29, 2024, using data from 120 Health Care Organizations within the TriNetX Global Network, the study involved 76,859,666 women from diverse racial backgrounds. The analysis focused on the prevalence of pain among women with PCOS, both overall and in those prescribed PCOS-related medications. Relative risk ratios (RR) were calculated for future health outcomes and stratified by self-reported race. The study found that 19.21% of women with PCOS experienced pain, with the highest prevalence among Black or African American (32.11%) and White (30.75%) populations. Both the PCOS and PCOS and Pain cohorts exhibited increased RR for various health conditions, with significant differences noted across racial groups for infertility, ovarian cysts, obesity, and respiratory diseases. Additionally, women with PCOS who were treated with PCOS-related medications showed a decrease in pain diagnoses following treatment. In conclusion, this study highlights the critical need to address pain in the diagnosis and management of PCOS due to its significant impact on patient health outcomes.

Enhancing EHR Interoperability and Security through Distributed Ledger Technology: A Review

The management and exchange of electronic health records (EHRs) remain critical challenges in healthcare, with fragmented systems, varied standards, and security concerns hindering seamless interoperability. These challenges compromise patient care and operational efficiency. This paper proposes a novel solution to address these issues by leveraging distributed ledger technology (DLT), including blockchain, to enhance data security, integrity, and transparency in healthcare systems. The decentralized and immutable nature of DLT enables more efficient and secure information exchange across platforms, improving decision-making and coordination of care. This paper outlines a strategic implementation approach, detailing timelines, resource requirements, and stakeholder involvement while addressing crucial privacy and security concerns like encryption and access control. In addition, it explores standards and protocols necessary for achieving interoperability, offering case studies that demonstrate the framework's effectiveness. This work contributes by introducing a DLT-based solution to the persistent issue of EHR interoperability, providing a novel pathway to secure and efficient health data exchanges. It also identifies the standards and protocols essential for integrating DLT with existing health information systems, thereby facilitating a smoother transition toward enhanced interoperability.

From Chronic Gallstone to Acute Ileus: A Case Report

Gallstone ileus is an uncommon cause of mechanical bowel obstruction, often presenting a diagnostic challenge due to its nonspecific symptoms and the variable presence of Rigler's triad (pneumobilia, small bowel obstruction, and ectopic gallstone). We report a case of an 80-year-old female who presented to the emergency department with a two-week history of vague abdominal pain. An initial CT scan revealed mild pneumobilia and a 2 cm calcified mass in the distal small bowel. The diagnosis of gallstone ileus was not initially apparent but was established after reviewing a previous CT scan that showed an identical large mass in the gallbladder. The patient underwent robotic exploration and enterotomy, resulting in the removal of a 4 cm gallstone. This case underscores the importance of reviewing historical imaging in diagnosing gallstone ileus. While Rigler's triad (pneumobilia, small bowel obstruction, and ectopic gallstone) is not always fully present, partial findings should raise suspicion. The unified electronic medical record (EMR) system played a crucial role in expediting the diagnosis by facilitating access to previous imaging studies. This report highlights the value of correlating current and historical imaging findings in diagnosing gallstone ileus, particularly in elderly patients with nonspecific abdominal symptoms. Future educational efforts should focus on increasing awareness of gallstone ileus among emergency physicians and radiologists, emphasizing the importance of correlating current findings with historical imaging data.

Unveiling barriers to EHR implementation for effective decision support in tanzanian primary healthcare: Insights from practitioners

This study investigates the barriers to implementing electronic health records (EHR) systems for decision support in Tanzanian primary healthcare (PHC) facilities and proposes strategies to address these challenges. A qualitative, inductive approach was used, guided by the Diffusion of Innovations (DOI) theory, the Technology Acceptance Model (TAM), and the Sociotechnical Systems theory. Using snowball sampling, data were collected from 14 participants through semi-structured interviews in Dodoma, Tanzania. Thematic analysis identified key barriers. Critical barriers to EHR implementation include lack of leadership support, poor network infrastructure, increased workload, and resistance to technology due to concerns over professional autonomy. Technical challenges, such as system downtime and lack of skilled personnel, hinder EHR use, resulting in inefficiencies and incomplete system adoption, negatively affecting patient outcomes. This study offers unique insights into barriers to EHR adoption in Tanzanian PHC facilities. Grounded in multiple theoretical frameworks, the findings contribute to health informatics discourse in low-resource settings and provide practical recommendations for improving EHR implementation. The study's implications are relevant for policymakers, healthcare leaders, and IT developers in similar contexts.

Nursing Records Regarding Decision-Making in Cancer Supportive Care: A Retrospective Study in Japan

OBJECTIVES

This study was performed to examine the content of decision-making support and patient responses, as documented in the nursing records of individuals with cancer. These patients had received outpatient treatment at hospitals that met government requirements for providing specialized cancer care.

METHODS

Nursing records from the electronic medical record system (in the subjective, objective, assessment, and plan [SOAP] format), along with data from interviews, were extracted for patients receiving outpatient care at the Department of Internal Medicine and Palliative Care and the Department of Breast Oncology. Data analysis involved simple tabulation and text mining, utilizing KH Coder version 3.beta.07d.

RESULTS

The study included 42 patients from palliative care internal medicine and 60 from breast oncology, with mean ages of 70.5 ± 12.2 and 55.8 ± 12.2 years, respectively. Decisions most frequently regarded palliative care unit admission (25 cases) and genetic testing (24 cases). The assessment category covered keywords including (1) "pain," "treatment," "future," "recuperation," and "home," as terms related to palliative care and internal medicine, as well as (2) "treatment," "relief," and "genetics" as terms related to breast oncology. The plan category incorporated keywords such as (1) "treatment," "relaxation," and "visit" and (2) "explanation," "confirmation," and "conveyance."

CONCLUSIONS

Nurses appear crucial in evaluating patients' symptoms and treatment paths during the decision-making support process, helping them make informed choices about future treatments, care settings, and genetic testing. However, when patients cannot make a decision solely based on the information provided, clinicians must address complex psychological concepts such as disease progression and the potential genetic impact on their children. Further detailed observational studies of nurses' responses to patients' psychological reactions are warranted.

Addressing Information Biases Within Electronic Health Record Data to Improve the Examination of Epidemiologic Associations With Diabetes Prevalence Among Young Adults: Cross-Sectional Study

Background

Electronic health records (EHRs) are increasingly used for epidemiologic research to advance public health practice. However, key variables are susceptible to missing data or misclassification within EHRs, including demographic information or disease status, which could affect the estimation of disease prevalence or risk factor associations.

Objective

In this paper, we applied methods from the literature on missing data and causal inference to assess whether we could mitigate information biases when estimating measures of association between potential risk factors and diabetes among a patient population of New York City young adults.

Methods

We estimated the odds ratio (OR) for diabetes by race or ethnicity and asthma status using EHR data from NYU Langone Health. Methods from the missing data and causal inference literature were then applied to assess the ability to control for misclassification of health outcomes in the EHR data. We compared EHR-based associations with associations observed from 2 national health surveys, the Behavioral Risk Factor Surveillance System (BRFSS) and the National Health and Nutrition Examination Survey, representing traditional public health surveillance systems.

Results

Observed EHR-based associations between race or ethnicity and diabetes were comparable to health survey-based estimates, but the association between asthma and diabetes was significantly overestimated (OREHR 3.01, 95% CI 2.86-3.18 vs ORBRFSS 1.23, 95% CI 1.09-1.40). Missing data and causal inference methods reduced information biases in these estimates, yielding relative differences from traditional estimates below 50% (ORMissingData 1.79, 95% CI 1.67-1.92 and ORCausal 1.42, 95% CI 1.34-1.51).

Conclusions

Findings suggest that without bias adjustment, EHR analyses may yield biased measures of association, driven in part by subgroup differences in health care use. However, applying missing data or causal inference frameworks can help control for and, importantly, characterize residual information biases in these estimates.

Systematically assessing the quality of dental electronic health record data for an investigation into oral health care disparities

OBJECTIVES

This work describes the process by which the quality of electronic health care data for a public health study was determined. The objectives were to adapt, develop, and implement data quality assessments (DQAs) based on the National Institutes of Health Pragmatic Trials Collaboratory (NIHPTC) data quality framework within the three domains of completeness, accuracy, and consistency, for an investigation into oral health care disparities of a preventive care program.

METHODS

Electronic health record data for eligible children in a dental accountable care organization of 30 offices, in Oregon, were extracted iteratively from January 1, 2014, through March 31, 2022. Baseline eligibility criteria included: children ages 0-18 with a baseline examination, Oregon home address, and either Medicaid or commercial dental benefits at least once between 2014 and 2108. Using the NIHPTC framework as a guide, DQAs were conducted throughout data element identification, extraction, staging, profiling, review, and documentation.

RESULTS

The data set included 91,487 subjects, 11 data tables comprising 75 data variables (columns), with a total of 6,861,525 data elements. Data completeness was 97.2%, the accuracy of EHR data elements in extracts was 100%, and consistency between offices was strong; 29 of 30 offices within 2 standard deviations of the mean (s = 94%).

CONCLUSIONS

The NIHPTC framework proved to be a useful approach, to identify, document, and characterize the dataset. The concepts of completeness, accuracy, and consistency were adapted by the multidisciplinary research team and the overall quality of the data are demonstrated to be of high quality.

Bridging Real-World Data Gaps: Connecting Dots Across 10 Asian Countries

The economic trend and the health care landscape are rapidly evolving across Asia. Effective real-world data (RWD) for regulatory and clinical decision-making is a crucial milestone associated with this evolution. This necessitates a critical evaluation of RWD generation within distinct nations for the use of various RWD warehouses in the generation of real-world evidence (RWE). In this article, we outline the RWD generation trends for 2 contrasting nation archetypes: "Solo Scholars"-nations with relatively self-sufficient RWD research systems-and "Global Collaborators"-countries largely reliant on international infrastructures for RWD generation. The key trends and patterns in RWD generation, country-specific insights into the predominant databases used in each country to produce RWE, and insights into the broader landscape of RWD database use across these countries are discussed. Conclusively, the data point out the heterogeneous nature of RWD generation practices across 10 different Asian nations and advocate for strategic enhancements in data harmonization. The evidence highlights the imperative for improved database integration and the establishment of standardized protocols and infrastructure for leveraging electronic medical records (EMR) in streamlining RWD acquisition. The clinical data analysis and reporting system of Hong Kong is an excellent example of a successful EMR system that showcases the capacity of integrated robust EMR platforms to consolidate and produce diverse RWE. This, in turn, can potentially reduce the necessity for reliance on numerous condition-specific local and global registries or limited and largely unavailable medical insurance or claims databases in most Asian nations. Linking health technology assessment processes with open data initiatives such as the Observational Medical Outcomes Partnership Common Data Model and the Observational Health Data Sciences and Informatics could enable the leveraging of global data resources to inform local decision-making. Advancing such initiatives is crucial for reinforcing health care frameworks in resource-limited settings and advancing toward cohesive, evidence-driven health care policy and improved patient outcomes in the region.

On the evaluation of synthetic longitudinal electronic health records

BACKGROUND

Synthetic Electronic Health Records (EHRs) are becoming increasingly popular as a privacy enhancing technology. However, for longitudinal EHRs specifically, little research has been done into how to properly evaluate synthetically generated samples. In this article, we provide a discussion on existing methods and recommendations when evaluating the quality of synthetic longitudinal EHRs.

METHODS

We recommend to assess synthetic EHR quality through similarity to real EHRs in low-dimensional projections, accuracy of a classifier discriminating synthetic from real samples, performance of synthetic versus real trained algorithms in clinical tasks, and privacy risk through risk of attribute inference. For each metric we discuss strengths and weaknesses, next to showing how it can be applied on a longitudinal dataset.

RESULTS

To support the discussion on evaluation metrics, we apply discussed metrics on a dataset of synthetic EHRs generated from the Medical Information Mart for Intensive Care-IV (MIMIC-IV) repository.

CONCLUSIONS

The discussion on evaluation metrics provide guidance for researchers on how to use and interpret different metrics when evaluating the quality of synthetic longitudinal EHRs.

Telemedicine in Italy: Healthcare authorization profiles in the modern medico-legal reading

BACKGROUND

The ruling n. 38485, 20 June 2019, of the Italian Supreme Court, III criminal section, addressed by the perspective of the law the very sensitive and new issue of telemedicine.

OBJECTIVE

This commentary deals with the issue of authorization of telemedicine activities by the health authority, starting from the Italian Court of Criminal Cassation, III section, decision n. 38485/2019. The case law explored the authorization of a health point, which carries out telemedicine services.

METHODS

Starting from the perspective discussed by Italian health regulations, the paper examines how the health act could be defined, with the possibilities offered by telecommunications, and how it now relates legally to the physical place where it takes place.

RESULTS

Even if telemedicine opens the way to virtual spaces of health practice, the Ministry of Health Italian Guidelines pose functional and logistical issues to guarantee users' safety and health care system accountability. Then, functional requirements for health legitimate practice, and their continuous monitoring, together with the responsibilities of the service centers, health professionals and health facilities, are discussed.

CONCLUSION

The questioning of States' health law, in a broad health system such as that of the Europe, characterized by autonomous health regulations, is extremely important for cross-border health policy with telemedicine, as overall regulatory compliance in health care is the ground criterion for risk prevention and patient safety, to be properly verified.

Application of a Data Quality Framework to Ductal Carcinoma In Situ Using Electronic Health Record Data From the All of Us Research Program

PURPOSE

The specific aims of this paper are to (1) develop and operationalize an electronic health record (EHR) data quality framework, (2) apply the dimensions of the framework to the phenotype and treatment pathways of ductal carcinoma in situ (DCIS) using All of Us Research Program data, and (3) propose and apply a checklist to evaluate the application of the framework.

METHODS

We developed a framework of five data quality dimensions (DQD; completeness, concordance, conformance, plausibility, and temporality). Participants signed a consent and Health Insurance Portability and Accountability Act authorization to share EHR data and responded to demographic questions in the Basics questionnaire. We evaluated the internal characteristics of the data and compared data with external benchmarks with descriptive and inferential statistics. We developed a DQD checklist to evaluate concept selection, internal verification, and external validity for each DQD. The Observational Medical Outcomes Partnership Common Data Model (OMOP CDM) concept ID codes for DCIS were used to select a cohort of 2,209 females 18 years and older.

RESULTS

Using the proposed DQD checklist criteria, (1) concepts were selected and internally verified for conformance; (2) concepts were selected and internally verified for completeness; (3) concepts were selected, internally verified, and externally validated for concordance; (4) concepts were selected, internally verified, and externally validated for plausibility; and (5) concepts were selected, internally verified, and externally validated for temporality.

CONCLUSION

This assessment and evaluation provided insights into data quality for the DCIS phenotype using EHR data from the All of Us Research Program. The review demonstrates that salient clinical measures can be selected, applied, and operationalized within a conceptual framework and evaluated for fitness for use by applying a proposed checklist.

Inferring Population HIV Viral Load From a Single HIV Clinic's Electronic Health Record: Simulation Study With a Real-World Example

BACKGROUND

Population viral load (VL), the most comprehensive measure of the HIV transmission potential, cannot be directly measured due to lack of complete sampling of all people with HIV.

OBJECTIVE

A given HIV clinic's electronic health record (EHR), a biased sample of this population, may be used to attempt to impute this measure.

METHODS

We simulated a population of 10,000 individuals with VL calibrated to surveillance data with a geometric mean of 4449 copies/mL. We sampled 3 hypothetical EHRs from (A) the source population, (B) those diagnosed, and (C) those retained in care. Our analysis imputed population VL from each EHR using sampling weights followed by Bayesian adjustment. These methods were then tested using EHR data from an HIV clinic in Delaware.

RESULTS

Following weighting, the estimates moved in the direction of the population value with correspondingly wider 95% intervals as follows: clinic A: 4364 (95% interval 1963-11,132) copies/mL; clinic B: 4420 (95% interval 1913-10,199) copies/mL; and clinic C: 242 (95% interval 113-563) copies/mL. Bayesian-adjusted weighting further improved the estimate.

CONCLUSIONS

These findings suggest that methodological adjustments are ineffective for estimating population VL from a single clinic's EHR without the resource-intensive elucidation of an informative prior.

A Qualitative Study of Physicians' Views on the Reuse of Electronic Health Record Data for Secondary Analysis

Electronic health records (EHRs) have become ubiquitous in clinical practice. Given the rich biomedical data captured for a large panel of patients, secondary analysis of these data for health research is also commonplace. Yet, there are many caveats to EHR data that the researchers must be aware of, such as the accuracy of and motive for documentation, and the reason for patients' visits to the clinic. The clinician-the author of the documentation-is thus central to the correct interpretation of EHR data for research purposes. In this study, I interviewed 11 physicians in various clinical specialties to bring attention to their view on the validity of research using EHR data. Qualitative, in-depth, one-on-one interviews were conducted with practicing physicians in inpatient and outpatient medicine. Content analysis using a data-driven, inductive approach to identify themes related to challenges and opportunities in the reuse of EHR data for secondary analysis generated seven themes. Themes that reflected challenges of EHRs for research included (1) audience, (2) accuracy of data, (3) availability of data, (4) documentation practices, and (5) representativeness. Themes that reflected opportunities of EHRs for research included (6) endorsement and (7) enablers. The greatest perceived barriers reflected the intended audience of the EHR, the interpretation and meaning of the data, and the quality of the data for research purposes. Physicians generally expressed more perceived challenges than opportunities in the reuse of EHR data for research purposes; however, they remained optimistic.

An Infrastructure Framework for Remote Patient Monitoring Interventions and Research

Remote patient monitoring (RPM) enables clinicians to maintain and adjust their patients' plan of care by using remotely gathered data, such as vital signs, to proactively make medical decisions about a patient's care. RPM interventions have been touted as a means to improve patient care and well-being while reducing costs and resource needs within the health care ecosystem. However, multiple interworking components must be successfully implemented for an RPM intervention to yield the desired outcomes, and the design and key driver of each component can vary depending on the medical context. This viewpoint and perspective paper presents a 4-component RPM infrastructure framework based on a synthesis of existing literature and practice related to RPM. Specifically, these components are identified and considered: (1) data collection, (2) data transmission and storage, (3) data analysis, and (4) information presentation. Interaction points to consider between components include transmission, interoperability, accessibility, workflow integration, and transparency. Within each of the 4 components, questions affecting research and practice emerge that can affect the outcomes of RPM interventions. This framework provides a holistic perspective of the technologies involved in RPM interventions and how these core elements interact to provide an appropriate infrastructure for deploying RPM in health systems. Further, it provides a common vocabulary to compare and contrast RPM solutions across health contexts and may stimulate new research and intervention opportunities.

A near real-time electronic health record-based COVID-19 surveillance system: An experience from a developing country

CONTEXT

Access to real-time data that provide accurate and timely information about the status and extent of disease spread could assist management of the COVID-19 pandemic and inform decision-making.

AIM

To demonstrate our experience with regard to implementation of technical and architectural infrastructure for a near real-time electronic health record-based surveillance system for COVID-19 in Iran.

METHOD

This COVID-19 surveillance system was developed from hospital information and electronic health record (EHR) systems available in the study hospitals in conjunction with a set of open-source solutions; and designed to integrate data from multiple resources to provide near real-time access to COVID-19 patients' data, as well as a pool of health data for analytical and decision-making purposes.

OUTCOMES

Using this surveillance system, we were able to monitor confirmed and suspected cases of COVID-19 in our population and to automatically notify stakeholders. Based on aggregated data collected, this surveillance system was able to facilitate many activities, such as resource allocation for hospitals, including managing bed allocations, providing and distributing equipment and funding, and setting up isolation centres.

CONCLUSION

Electronic health record systems and an integrated data analytics infrastructure are effective tools to enable policymakers to make better decisions, and for epidemiologists to conduct improved analyses regarding COVID-19.

IMPLICATIONS

Improved quality of clinical coding for better case finding, improved quality of health information in data sources, data-sharing agreements, and increased EHR coverage in the population can empower EHR-based COVID-19 surveillance systems.

Classifying early infant feeding status from clinical notes using natural language processing and machine learning

The objective of this study is to develop and evaluate natural language processing (NLP) and machine learning models to predict infant feeding status from clinical notes in the Epic electronic health records system. The primary outcome was the classification of infant feeding status from clinical notes using Medical Subject Headings (MeSH) terms. Annotation of notes was completed using TeamTat to uniquely classify clinical notes according to infant feeding status. We trained 6 machine learning models to classify infant feeding status: logistic regression, random forest, XGBoost gradient descent, k-nearest neighbors, and support-vector classifier. Model comparison was evaluated based on overall accuracy, precision, recall, and F1 score. Our modeling corpus included an even number of clinical notes that was a balanced sample across each class. We manually reviewed 999 notes that represented 746 mother-infant dyads with a mean gestational age of 38.9 weeks and a mean maternal age of 26.6 years. The most frequent feeding status classification present for this study was exclusive breastfeeding [n = 183 (18.3%)], followed by exclusive formula bottle feeding [n = 146 (14.6%)], and exclusive feeding of expressed mother's milk [n = 102 (10.2%)], with mixed feeding being the least frequent [n = 23 (2.3%)]. Our final analysis evaluated the classification of clinical notes as breast, formula/bottle, and missing. The machine learning models were trained on these three classes after performing balancing and down sampling. The XGBoost model outperformed all others by achieving an accuracy of 90.1%, a macro-averaged precision of 90.3%, a macro-averaged recall of 90.1%, and a macro-averaged F1 score of 90.1%. Our results demonstrate that natural language processing can be applied to clinical notes stored in the electronic health records to classify infant feeding status. Early identification of breastfeeding status using NLP on unstructured electronic health records data can be used to inform precision public health interventions focused on improving lactation support for postpartum patients.

The individuals' awareness and adoption of electronic health records in China: a questionnaire survey of 1,337 individuals

BACKGROUND

Electronic health records (EHRs) are digital records of individual health information. However, their adoption and utilization remain low. This study explores the factors influencing the implementation of EHRs through a questionnaire survey to enhance individual awareness and adoption of EHRs.

METHODS

A questionnaire and an expert rating scale were developed sequentially, and the consistency of the scores from five experts was calculated using Kendall's W to generate a final questionnaire. A non-parametric test was utilized to analyze differences in continuous data that did not follow a normal distribution. Categorical variables were expressed as percentages (%), the chi-square test was employed for group comparisons, and multiple logistic regression was implemented to assess individuals' awareness and adoption of EHRs.

RESULTS

In total, 1,341 survey questionnaires were distributed between January and December 2022, with 1,337 valid responses (99.7%). The results indicated that the proportion of participants who were aware of EHRs and had a bachelor's degree or higher education, an income of ≥$700 per month, residence in urban areas, possessed self-care abilities, and underwent annual physical examinations was significantly higher than that without awareness of EHRs (P < 0.05), while in hearing problems and walking abilities was markedly lower than that of participants without awareness of EHRs (P < 0.05). Additionally, the proportion of individuals willing to self-manage EHRs was significantly higher than those reluctant to do so (P < 0.05) among participants with a bachelor's degree or higher education, an income of ≥$700 per month, residence in urban areas, possession of self-care abilities, annual physical examinations, hearing problems, and poor walking abilities. Age (Odds Ratio [OR] = 1.104, 95% Confidence Interval [CI] 1.001-1.028, P = 0.033), hearing problems (OR = 0.604, 95% CI 0.377-0.967, P = 0.036), self-care ability (OR = 5.881, 95% CI 1.867-18.529, P = 0.002), and annual physical examinations (OR = 3.167, 95% CI 2.31-4.34, P < 0.001) were independently associated with willingness to self-manage EHRs. Annual physical examination (OR = 2.507, 95%CI 1.585-2.669, P < 0.001) also independently made a difference to the awareness of EHRs.

CONCLUSIONS

Our findings suggest that annual physical examinations, age, hearing problems, and self-care abilities are significant factors in assessing individuals' awareness and adoption of EHRs. Understanding the characteristics of individuals who are aware of or are willing to take advantage of EHRs plays a positive role in promoting their popularization and application.

Improving healthcare quality by unifying the American electronic medical report system: time for change

BACKGROUND

In recent years, innovation in healthcare technology has significantly improved the efficiency of the healthcare system. Advancements have led to better patient care and more cost-effective services. The electronic medical record (EMR) system, in particular, has enhanced interoperability and collaboration across healthcare departments by facilitating the exchange and utilization of patient data. The COVID-19 pandemic further accelerated this trend, leading to a surge in telemedicine services, which rely on electronic communication to deliver healthcare remotely.

MAIN BODY

Integrating artificial intelligence (AI) and machine learning (ML) in healthcare have been instrumental in analyzing vast data sets, allowing for identifying patterns and trends that can improve care delivery and pinpoint potential issues. The proposal of a unified EMR system in the USA aims to capitalize on these technological advancements. Such a system would streamline the sharing of patient information among healthcare providers, improve the quality and efficiency of care, and minimize the likelihood of errors in patient treatment.

CONCLUSION

A unified electronic medical record system represents a promising avenue for enhancing interoperability within the US healthcare sector. By creating a more connected and accessible network of patient information, it sets the stage for a transformation in healthcare delivery. This change is imperative for maintaining the momentum of progress in healthcare technology and realizing the full potential of recent advancements in patient care and system efficiency.

Electronic Medical Record Data Missingness and Interruption in Antiretroviral Therapy Among Adults and Children Living With HIV in Haiti: Retrospective Longitudinal Study

Background

Children (aged 0-14 years) living with HIV often experience lower rates of HIV diagnosis, treatment, and viral load suppression. In Haiti, only 63% of children living with HIV know their HIV status (compared to 85% overall), 63% are on treatment (compared to 85% overall), and 48% are virally suppressed (compared to 73% overall). Electronic medical records (EMRs) can improve HIV care and patient outcomes, but these benefits are largely dependent on providers having access to quality and nonmissing data.

Objective

We sought to understand the associations between EMR data missingness and interruption in antiretroviral therapy treatment by age group (pediatric vs adult).

Methods

We assessed associations between patient intake record data missingness and interruption in treatment (IIT) status at 6 and 12 months post antiretroviral therapy initiation using patient-level data drawn from iSanté, the most widely used EMR in Haiti. Missingness was assessed for tuberculosis diagnosis, World Health Organization HIV stage, and weight using a composite score indicator (ie, the number of indicators of interest missing). Risk ratios were estimated using marginal parameters from multilevel modified Poisson models with robust error variances and random intercepts for the facility to account for clustering.

Results

Data were drawn from 50 facilities and comprised 31,457 patient records from people living with HIV, of which 1306 (4.2%) were pediatric cases. Pediatric patients were more likely than adult patients to experience IIT (n=431, 33% vs n=7477, 23.4% at 6 months; P<.001). Additionally, pediatric patient records had higher data missingness, with 581 (44.5%) pediatric records missing at least 1 indicator of interest, compared to 7812 (25.9%) adult records (P<.001). Among pediatric patients, each additional indicator missing was associated with a 1.34 times greater likelihood of experiencing IIT at 6 months (95% CI 1.08-1.66; P=.008) and 1.24 times greater likelihood of experiencing IIT at 12 months (95% CI 1.05-1.46; P=.01). These relationships were not statistically significant for adult patients. Compared to pediatric patients with 0 missing indicators, pediatric patients with 1, 2, or 3 missing indicators were 1.59 (95% CI 1.26-2.01; P<.001), 1.74 (95% CI 1.02-2.97; P=.04), and 2.25 (95% CI 1.43-3.56; P=.001) times more likely to experience IIT at 6 months, respectively. Among adult patients, compared to patients with 0 indicators missing, having all 3 indicators missing was associated with being 1.32 times more likely to experience IIT at 6 months (95% CI 1.03-1.70; P=.03), while there was no association with IIT status for other levels of missingness.

Conclusions

These findings suggest that both EMR data quality and quality of care are lower for children living with HIV in Haiti. This underscores the need for further research into the mechanisms by which EMR data quality impacts the quality of care and patient outcomes among this population. Efforts to improve both EMR data quality and quality of care should consider prioritizing pediatric patients.

Cross-institution natural language processing for reliable clinical association studies: a methodological exploration

OBJECTIVES

Natural language processing (NLP) of clinical notes in electronic medical records is increasingly used to extract otherwise sparsely available patient characteristics, to assess their association with relevant health outcomes. Manual data curation is resource intensive and NLP methods make these studies more feasible. However, the methodology of using NLP methods reliably in clinical research is understudied. The objective of this study is to investigate how NLP models could be used to extract study variables (specifically exposures) to reliably conduct exposure-outcome association studies.

STUDY DESIGN AND SETTING

In a convenience sample of patients admitted to the intensive care unit of a US academic health system, multiple association studies are conducted, comparing the association estimates based on NLP-extracted vs. manually extracted exposure variables. The association studies varied in NLP model architecture (Bidirectional Encoder Decoder from Transformers, Long Short-Term Memory), training paradigm (training a new model, fine-tuning an existing external model), extracted exposures (employment status, living status, and substance use), health outcomes (having a do-not-resuscitate/intubate code, length of stay, and in-hospital mortality), missing data handling (multiple imputation vs. complete case analysis), and the application of measurement error correction (via regression calibration).

RESULTS

The study was conducted on 1,174 participants (median [interquartile range] age, 61 [50, 73] years; 60.6% male). Additionally, up to 500 discharge reports of participants from the same health system and 2,528 reports of participants from an external health system were used to train the NLP models. Substantial differences were found between the associations based on NLP-extracted and manually extracted exposures under all settings. The error in association was only weakly correlated with the overall F1 score of the NLP models.

CONCLUSION

Associations estimated using NLP-extracted exposures should be interpreted with caution. Further research is needed to set conditions for reliable use of NLP in medical association studies.

German primary care data collection projects: a scoping review

BACKGROUND

The widespread use of electronic health records (EHRs) has led to a growing number of large routine primary care data collection projects globally, making these records a valuable resource for health services and epidemiological and clinical research. This scoping review aims to comprehensively assess and compare strengths and limitations of all German primary care data collection projects and relevant research publications that extract data directly from practice management systems (PMS).

METHODS

A literature search was conducted in the electronic databases in May 2021 and in June 2022. The search string included terms related to general practice, routine data, and Germany. The retrieved studies were classified as applied studies and methodological studies, and categorised by type of research, subject area, sample of publications, disease category, or main medication analysed.

RESULTS

A total of 962 references were identified, with 241 studies included from six German projects in which databases are populated by EHRs from PMS. The projects exhibited significant heterogeneity in terms of size, data collection methods, and variables collected. The majority of the applied studies (n = 205, 85%) originated from one database with a primary focus on pharmacoepidemiological topics (n = 127, 52%) including prescription patterns (n = 68, 28%) and studies about treatment outcomes, compliance, and treatment effectiveness (n = 34, 14%). Epidemiological studies (n = 77, 32%) mainly focused on incidence and prevalence studies (n = 41, 17%) and risk and comorbidity analysis studies (n = 31, 12%). Only 10% (n = 23) of studies were in the field of health services research, such as hospitalisation.

CONCLUSION

The development and durability of primary care data collection projects in Germany is hindered by insufficient public funding, technical issues of data extraction, and strict data protection regulations. There is a need for further research and collaboration to improve the usability of EHRs for health services and research.

The Feasibility and Acceptability of a Data Capture Methodology in Pediatric Cancer Patients Treated with Targeted Agents and Immunotherapies

As childhood cancer treatments have improved to include new and innovative agents, the need for more advanced monitoring of their long-term effects and related research has increased. This has resulted in a need for evidence-based research methodologies for the longitudinal care of childhood cancer patients treated with targeted agents and immunotherapies. The rationale for this pilot study was to determine the feasibility and acceptability of a data capture methodology for pediatric, adolescent, and young adult cancer patients treated with targeted agents and immunotherapy as there is little research to inform this delivery of care. Data were collected from thirty-two patients and two providers for descriptive statistics and thematic analyses. Feasibility was characterized by expected participant attrition. Key drivers of acceptability were (1) providers' language and clarity of communication and (2) convenient participation requirements. Long-term follow-up research practices developed with input from key stakeholders, including patients, caregivers, and providers, can lead to acceptable and feasible research protocols that optimize successful participant recruitment. These evidence-based research practices can result in high participant satisfaction and can be implemented as program development initiatives across centers caring for childhood cancer survivors.

Recommendations for healthy aging as documented by health professionals: a summative content analysis of health records

AIM

To identify what type of recommendations were recorded in older adults' health records by health professionals during preventive home visits.

BACKGROUND

To promote health and prevent ill health, health professionals can give support and recommendations to older adults. The preventive home visit for older adults is one example of an intervention where health professionals such as nurses, social workers, and assistant nurses can give recommendations. By exploring what recommendations are recorded and within what areas, we can also gain knowledge about areas where provision of recommendations seems lacking. This knowledge would provide health professionals with guidance in their counseling with the older adult.

METHODS

Records from preventive home visits (n = 596; mean age 78.71) were qualitatively and quantitatively analyzed.

FINDINGS

The most frequently recorded recommendations were related to physical or mental illness, falls, and then nutrition. The results showed that recommendations could be sorted into ten sub-categories related to physical or mental illness, falls, nutrition, physical activity, preparation for the future, social participation, finances, getting help from others, municipal services, and security at home. These ten sub-categories were classified into the International Classification of Functioning, Disability, and Health categories body functions & structure (including one sub-category), activity (including four sub-categories), participation (including three sub-categories), and environmental factors (including two sub-categories). From the results, we could conclude that the major focus was on risk prevention and less focus was on health promotion. Thus, the visitor's recommendations most likely mirror the older adult's explicit needs 'here and now' to a great extent. However, health visitors also need to focus on intrinsic capacities to promote health. Besides recommendations relating to the person's intrinsic capacities, environmental aspects should be focused upon, to improve healthy aging.

Factors Influencing Electronic Health Record Workflow Integration Among Nurses in Saudi Arabia: Cross-Sectional Study

Introduction

Globally, healthcare organizations have transitioned from paper-based documentation to electronic health records (EHR), including in Saudi Arabia. However, the adoption of EHR at the national level in Saudi Arabia needs more attention. Thus, this study aimed to determine the workflow integration of EHR and associated factors.

Objectives

The specific aims were to examine the level of EHR use and workflow integration among nurses, to determine the differences in EHR use and workflow integration based on nurses' demographic characteristics, and to determine the association between the predictive factors and EHR workflow integration.

Methods

This is a cross-sectional, correlational descriptive study. The data were collected from 293 nurses using the convenience sampling method. The participating nurses completed a questionnaire that included two measures: the Information System Use Survey and the Workflow Integration Survey (WIS). The data were analyzed using descriptive and multivariate statistics with SPSS software.

Results

The nurses had a positive perception of EHR use and workflow. The EHR use scores differed based on workplace (P < .01), education level (P < .05), and area of practice (P < .001). Similarly, the EHR workflow integration scores varied according to workplace (P < .05), education level (P < .05), and area of practice (P < .001). Education level and workplace significantly predicted information system use. Furthermore, education level and information system use significantly predicted the EHR integration into nurses' workflow.

Conclusion

The nurses expressed a greater perceived use of EHR regarding the integrated health information system, which was a predictor of EHR integration into nurses' workflow.

An Assessment of Success Factors and Challenges in Implementation of Electronic Medical Record System in Referral Hospital in Northern Tanzania

Introduction

The Electronic Medical Record (EMR) has significant benefits in improving the quality of hospital services in low resources settings. Despite efforts to implement various EMRs in different health facilities, there is scarce information on the challenges and success factors regarding EMR Implementation in Regional hospitals. The aim of this study is to assess the success and challenging factors in the implementation of an electronic medical record system at the regional referral Hospital.

Methodology

This was a cross-sectional design study involving qualitative and quantitative approaches that was conducted at Haydom Lutheran Hospital a Regional Referral Hospital in northern Tanzania. The semi-structured questionnaires and the Key Informant Interview Guide questions were used for quantitative and qualitative data collection respectively. The quantitative data were analyzed using Stata Version 13.0. The quantitative data was summarized using descriptive statistics. Thematic method was used to analyze the qualitative data.

Results

Among 303 participants more than half were male 167(55.1%) and 119(39.3%) aged between 31 and 40 years. The nurses and medical attendants were the predominant group 188(62%). Most of the staff were on full-time employment 273(90.1%) and more than thirty percent 118(38.09%) have worked for over 10 years. The age group of between 31-60 years had a higher influence on the EMR net benefit compared to respondents aged 20 to 30 years and 60 years. The easy use, learning, usefulness, and relevance to work as well as leadership, staff involvement in processes, and use of champions were among of success factors for EMR implementation. Challenges include inadequate training, lack of funding, and inadequate IT equipment. The net benefit includes increases in efficiency in service delivery and better resource management.

Conclusion

Staff involvement, use of champions and the fact that the system is easy to use contributed to the success of EMR system. In order to scale up and sustain the EMR system in hospitals, adequate funding, training as well as continuous support to all staff in the hospital is required.

Prediction and diagnosis of depression using machine learning with electronic health records data: a systematic review

BACKGROUND

Depression is one of the most significant health conditions in personal, social, and economic impact. The aim of this review is to summarize existing literature in which machine learning methods have been used in combination with Electronic Health Records for prediction of depression.

METHODS

Systematic literature searches were conducted within arXiv, PubMed, PsycINFO, Science Direct, SCOPUS and Web of Science electronic databases. Searches were restricted to information published after 2010 (from 1st January 2011 onwards) and were updated prior to the final synthesis of data (27th January 2022).

RESULTS

Following the PRISMA process, the initial 744 studies were reduced to 19 eligible for detailed evaluation. Data extraction identified machine learning methods used, types of predictors used, the definition of depression, classification performance achieved, sample size, and benchmarks used. Area Under the Curve (AUC) values more than 0.9 were claimed, though the average was around 0.8. Regression methods proved as effective as more developed machine learning techniques.

LIMITATIONS

The categorization, definition, and identification of the numbers of predictors used within models was sometimes difficult to establish, Studies were largely Western Educated Industrialised, Rich, Democratic (WEIRD) in demography.

CONCLUSION

This review supports the potential use of machine learning techniques with Electronic Health Records for the prediction of depression. All the selected studies used clinically based, though sometimes broad, definitions of depression as their classification criteria. The reported performance of the studies was comparable to or even better than that found in primary care. There are concerns with generalizability and interpretability.

Evaluation of a method to identify midwives in national provider identifier data

OBJECTIVES

Comparison of national midwife workforce data from the National Provider Identifier file determined it undercounted midwives compared to national data available from the American Midwifery Certification Board. This undercount may be due to the existence of three taxonomy categories for midwives when registering for the National Provider Identifier. The objective of this study was to obtain an accurate count of advanced practice midwives using the National Provider Identifier Data.

METHODS

A recode strategy was created using the NPPES Data Dissemination File for November 7, 2021. The strategy identified advanced practice midwives using education and certification information provided in the "credentials" field. The strategy was validated using the NPPES Data Dissemination File for August 7, 2022 and the gold standard was the American Midwifery Certification Board count of midwives by state for August, 2022. Validation compared the accuracy and precision of the recode to the accuracy and precision of using the advanced practice midwife taxonomy category.

RESULTS

The recode strategy improved the accuracy and precision of the count of advanced practice midwives compared to the identification of advanced practice midwives using the advanced practice midwife taxonomy category.

CONCLUSIONS FOR PRACTICE

Recoding the NPPES Data Dissemination File provides a more accurate and precise count of advanced practice midwives than relying on the existing advanced practice midwife taxonomy classification. Researchers can use the NPPES Data Dissemination File when studying the midwifery workforce.

Examining Ethnic Disparities in Digital Healthcare Services Utilization: Insights from Israel

Purpose

The purpose of this study was to examine ethnic disparities in the utilization of digital healthcare services (DHS) in Israel and explore the characteristics and factors influencing DHS use among the Arab minority and Jewish majority populations.

Methods

A cross-sectional correlational design was employed to collect data from 606 Israeli participants, 445 Jews, and 161 Arabs. Participants completed a digital questionnaire that assessed DHS utilization, digital health literacy, attitudes towards DHS, and demographic variables.

Results

The findings reveal significant disparities in DHS utilization and attitudes between these ethnic groups, with Jewish participants demonstrating higher rates of utilization and positive attitudes toward DHS. The study also explores the predictive role of digital health literacy and attitudes in DHS use while considering ethnicity as a potential moderator. Significant predicting factors related to DHS utilization among Jews include positive attitudes and high health literacy. Among the Arabs, only attitudes towards DHS significantly predict the extent of DHS use. Digital health literacy affects the extent of use through attitudes at the two groups of the moderator significantly, but it is stronger among the Arab group.

Conclusion

To improve healthcare outcomes and reduce disparities, efforts should focus on ensuring equitable access to DHS for the Arab minority population. Targeted interventions, including digital literacy education, removing technology access barriers, offering services in Arabic, and collaborating with community organizations, can help bridge the gap and promote equal utilization of DHS.