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

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.

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.

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.

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.

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.

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.

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.

Thinking out of the box: revisiting health surveillance based on medical records

Despite the considerable advances in the last years, the health information systems for health surveillance still need to overcome some critical issues so that epidemic detection can be performed in real time. For instance, despite the efforts of the Brazilian Ministry of Health (MoH) to make COVID-19 data available during the pandemic, delays due to data entry and data availability posed an additional threat to disease monitoring. Here, we propose a complementary approach by using electronic medical records (EMRs) data collected in real time to generate a system to enable insights from the local health surveillance system personnel. As a proof of concept, we assessed data from São Caetano do Sul City (SCS), São Paulo, Brazil. We used the "fever" term as a sentinel event. Regular expression techniques were applied to detect febrile diseases. Other specific terms such as "malaria," "dengue," "Zika," or any infectious disease were included in the dictionary and mapped to "fever." Additionally, after "tokenizing," we assessed the frequencies of most mentioned terms when fever was also mentioned in the patient complaint. The findings allowed us to detect the overlapping outbreaks of both COVID-19 Omicron BA.1 subvariant and Influenza A virus, which were confirmed by our team by analyzing data from private laboratories and another COVID-19 public monitoring system. Timely information generated from EMRs will be a very important tool to the decision-making process as well as research in epidemiology. Quality and security on the data produced is of paramount importance to allow the use by health surveillance systems.

Electronic health record interoperability using FHIR and blockchain: A bibliometric analysis and future perspective

Electronic health records (EHRs) constitute vital statistics, current health condition, ongoing therapies, and patient data; hence, their interoperability could be useful for epidemiologic and clinical research. Fast Healthcare Interoperability Resources (FHIR) and blockchain are currently "in-use" and tested for exchange of such data. The annual scientific production of publications for both FHIR and blockchain shows steady growth. The data interoperability and electronic data interchange have been introduced in the field of EHR in 2020, hence inferring that data interoperability is relatively a new domain. The thematic mapping suggested "interoperability" of EHR is well-developed and important for the structure of the research field.

Segmenting Patients With Diabetes With the Navigator Service in Primary Care and a Description of the Self-Acting Patient Group: Cross-Sectional Study

BACKGROUND

The aim of patient segmentation is to recognize patients with similar health care needs. The Finnish patient segmentation service Navigator segregates patients into 4 groups, including a self-acting group, who presumably manages their everyday life and coordinates their health care. Digital services could support their self-care. Knowledge on self-acting patients' characteristics is lacking.

OBJECTIVE

The study aims are to describe how Navigator assigns patients with diabetes to the 4 groups at nurses' appointments at a health center, the self-acting patient group's characteristics compared with other patient groups, and the concordance between the nurse's evaluation of the patient's group and the actual group assigned by Navigator (criterion validity).

METHODS

Patients with diabetes ≥18 years old visiting primary care were invited to participate in this cross-sectional study. Patients with disability preventing informed consent for participation were excluded. Nurses estimated the patients' upcoming group results before the appointment. We describe the concordance (%) between the evaluation and actual groups. Nurses used Navigator patients with diabetes (n=304) at their annual follow-up visits. The self-acting patients' diabetes care values (glycated hemoglobin [HbA1c], urine albumin to creatinine ratio, low-density lipoprotein cholesterol, blood pressure, BMI), chronic conditions, medication, smoking status, self-rated health, disability (World Health Organization Disability Assessment Schedule [WHODAS] 2.0), health-related quality of life (EQ-5D-5L), and well-being (Well-being Questionnaire [WBQ-12]) and the patients' responses to Navigator's question concerning their digital skills as outcome variables were compared with those of the other patients. We used descriptive statistics for the patients' distribution into the 4 groups and demographic data. We used the Mann-Whitney U test with nonnormally distributed variables, independent samples t test with normally distributed variables, and Pearson chi-square tests with categorized variables to compare the groups.

RESULTS

Most patients (259/304, 85.2%) were in the self-acting group. Hypertension, hyperlipidemia, and joint ailments were the most prevalent comorbidities among all patients. Self-acting patients had less ischemic cardiac disease (P=.001), depression or anxiety (P=.03), asthma or chronic obstructive pulmonary disease (P<.001), long-term pain (P<.001), and related medication. Self-acting patients had better self-rated health (P<.001), functional ability (P<.001), health-related quality of life (P<.001), and general well-being (P<.001). All patients considered their skills at using electronic services to be good.

CONCLUSIONS

The patients in the self-acting group had several comorbidities. However, their functional ability was not yet diminished compared with patients in the other groups. Therefore, to prevent diabetic complications and disabilities, support for patients' self-management should be emphasized in their integrated care services. Digital services could be involved in the care of patients willing to use them. The study was performed in 1 health center, the participants were volunteers, and most patients were assigned to self-acting patient group. These facts limit the generalizability of our results.

INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID)

RR2-10.2196/20570.

Graphical User Interface Development for a Hospital-Based Predictive Risk Tool: Protocol for a Co-Design Study

BACKGROUND

This co-design research method details the iterative process developed to identify health professional recommendations for the graphical user interface (GUI) of an artificial intelligence (AI)-enabled risk prediction tool. Driving the decision to include a co-design process is the belief that choices regarding the aesthetic and functionality of an intervention are best made by its intended users and that engaging these users in its design will promote the tool's adoption and use.

OBJECTIVE

The aim of this research is to identify health professional design and uptake recommendations for the GUI of an AI-enabled predictive risk tool.

METHODS

We will hold 3 research phases, each consisting of 2 workshops with health professionals, between mid-2023 and mid-2024. A total of 6 health professionals will be sought per workshop, resulting in a total enrollment of 36 health professionals at the conclusion of the research. A total of 7 workshop activities have been scheduled across the 3 workshops; these include context of use, notifiers, format, AI survey-Likert, prototype, AI survey-written, and testing. The first 6 of these activities will be repeated in each workshop to enable the iterative development and refinement of GUI. The last activity (testing) will be performed in the final workshop to examine health professionals' thoughts on the final GUI iteration. Qualitative and quantitative results data will be produced from tasks in each research activity. Qualitative data will be examined through inductive thematic analysis or deductive thematic analysis in accordance with the Nonadoption, Abandonment, and Challenges to the Scale-up, Spread, and Sustainability (NASSS) framework; visual data will be examined in accordance with "framework of interactivity;" and quantitative data will be examined using descriptive statistics.

RESULTS

Project registration with the Australia and New Zealand Clinical Trial Registry has been requested (#384098). Finalized design recommendations are expected in early to mid-2024, with a results manuscript to be submitted in mid-2024. This research method has human research ethics approval from the South Australian Department of Health and Wellbeing (#2022/HRE00131) as well as from the Human Research Ethics Committee of the University of South Australia (application ID#204143).

CONCLUSIONS

Understanding whether an intervention is needed in a particular situation is just the start; designing an intervention so that it is used within that situation is paramount. This co-design process engages end users to create a GUI that includes the aesthetic and functional details they need in a manner that aligns with their existing work practices. Indeed, interventions that fail to do this may be disliked, and at worst, they may be dangerous.

INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID)

PRR1-10.2196/47717.

FHIR-PYrate: a data science friendly Python package to query FHIR servers

BACKGROUND

We present FHIR-PYrate, a Python package to handle the full clinical data collection and extraction process. The software is to be plugged into a modern hospital domain, where electronic patient records are used to handle the entire patient's history. Most research institutes follow the same procedures to build study cohorts, but mainly in a non-standardized and repetitive way. As a result, researchers spend time writing boilerplate code, which could be used for more challenging tasks.

METHODS

The package can improve and simplify existing processes in the clinical research environment. It collects all needed functionalities into a straightforward interface that can be used to query a FHIR server, download imaging studies and filter clinical documents. The full capacity of the search mechanism of the FHIR REST API is available to the user, leading to a uniform querying process for all resources, thus simplifying the customization of each use case. Additionally, valuable features like parallelization and filtering are included to make it more performant.

RESULTS

As an exemplary practical application, the package can be used to analyze the prognostic significance of routine CT imaging and clinical data in breast cancer with tumor metastases in the lungs. In this example, the initial patient cohort is first collected using ICD-10 codes. For these patients, the survival information is also gathered. Some additional clinical data is retrieved, and CT scans of the thorax are downloaded. Finally, the survival analysis can be computed using a deep learning model with the CT scans, the TNM staging and positivity of relevant markers as input. This process may vary depending on the FHIR server and available clinical data, and can be customized to cover even more use cases.

CONCLUSIONS

FHIR-PYrate opens up the possibility to quickly and easily retrieve FHIR data, download image data, and search medical documents for keywords within a Python package. With the demonstrated functionality, FHIR-PYrate opens an easy way to assemble research collectives automatically.

Identifying subgroups of patients with type 2 diabetes based on real-world traditional chinese medicine electronic medical records

Introduction: Type 2 diabetes (T2D) is a multifactorial complex chronic disease with a high prevalence worldwide, and Type 2 diabetes patients with different comorbidities often present multiple phenotypes in the clinic. Thus, there is a pressing need to improve understanding of the complexity of the clinical Type 2 diabetes population to help identify more accurate disease subtypes for personalized treatment. Methods: Here, utilizing the traditional Chinese medicine (TCM) clinical electronic medical records (EMRs) of 2137 Type 2 diabetes inpatients, we followed a heterogeneous medical record network (HEMnet) framework to construct heterogeneous medical record networks by integrating the clinical features from the electronic medical records, molecular interaction networks and domain knowledge. Results: Of the 2137 Type 2 diabetes patients, 1347 were male (63.03%), and 790 were female (36.97%). Using the HEMnet method, we obtained eight non-overlapping patient subgroups. For example, in H3, Poria, Astragali Radix, Glycyrrhizae Radix et Rhizoma, Cinnamomi Ramulus, and Liriopes Radix were identified as significant botanical drugs. Cardiovascular diseases (CVDs) were found to be significant comorbidities. Furthermore, enrichment analysis showed that there were six overlapping pathways and eight overlapping Gene Ontology terms among the herbs, comorbidities, and Type 2 diabetes in H3. Discussion: Our results demonstrate that identification of the Type 2 diabetes subgroup based on the HEMnet method can provide important guidance for the clinical use of herbal prescriptions and that this method can be used for other complex diseases.

Use of an electronic medical record to optimize a neonatal sepsis score for mortality prediction

OBJECTIVE

Late-onset sepsis (LOS) is a significant cause of mortality in preterm infants. The neonatal sequential organ failure assessment (nSOFA) provides an objective assessment of sepsis risk but requires manual calculation. We developed an EMR pipeline to automate nSOFA calculation for more granular analysis of score performance and to identify optimal alerting thresholds.

METHODS

Infants born <33 weeks of gestation with LOS were included. A SQL-based pipeline calculated hourly nSOFA scores 48 h before/after sepsis evaluation. Sensitivity analysis identified the optimal timing and threshold of nSOFA for LOS mortality.

RESULTS

Eighty episodes of LOS were identified (67 survivors, 13 non-survivor). Non-survivors had persistently elevated nSOFA scores, markedly increasing 12 h prior to culture. At sepsis evaluation, the AUC for nSOFA >2 was 0.744 (p = 0.0047); thresholds of >3 and >4 produced lower AUCs.

CONCLUSIONS

nSOFA is persistently elevated for infants with LOS mortality compared to survivors with an optimal alert threshold >2.

The digital future of nursing: making sense of taxonomies and key concepts

Digital technology is becoming increasingly common in routine nursing practice. The adoption of digital technologies such as video calling, and other digital communication, has been hastened by the recent COVID-19 pandemic. Use of these technologies has the potential to revolutionise nursing practice, leading to potentially more accurate patient assessment, monitoring processes and improved safety in clinical areas. This article outlines key concepts related to the digitalisation of health care and the implications for nursing practice. The aim of this article is to encourage nurses to consider the implications, opportunities and challenges associated with the move towards digitalisation and advances in technology. Specifically, this means understanding key digital developments and innovations associated with healthcare provision and appreciating the implications of digitalisation for the future of nursing practice.

Mortality and risk factors associated with peptic ulcer bleeding among adult inpatients of Damascus Hospital, Syria: A cross-sectional study

Peptic ulcer bleeding is associated with significant morbidity and mortality, while monitoring mortality is extremely beneficial to public health, and the latest estimates date back to 2010 for the Syrian population. This study aims to estimate the in-hospital mortality rate and risk factors associated with peptic ulcer bleeding among adult inpatients at Damascus Hospital, Syria. A cross-sectional study with systematic random sampling. Sample size (n) was calculated using the proportional equation: [n = Z2P (1 - P)/d2], with the following hypothesis: Z = 1.96 for the 95% confidence level, P = .253 for mortality in patients hospitalized with complicated peptic ulcers, a margin of error (d) = 0.05, 290 charts were reviewed, and the Chi-square test (χ2 test) was used for categorical variables, and the t test for continuous data. We reported the odds ratio in addition to mean and standard deviation with a 95% confidence. A P value less than .05 was considered statistically significant. Data were analyzed using a statistical package for the social sciences (SPSS). The mortality rate was 3.4%, and the mean age was 61.76 ± 16.02 years. The most frequent comorbidities were hypertension, diabetes mellitus, and ischemic heart disease. The most commonly used medications were NSAIDs, aspirin, and clopidogrel. 74 patients (25.52%) were using aspirin with no documented indication P < .01, odds ratio = 6.541, 95% CI [2.612-11.844]. There were 162 (56%) Smokers. Six patients (2.1%) suffered from recurrent bleeding, and 13 (4.5%) needed surgery. Raising awareness about the risks of using non-steroidal anti-inflammatory drugs may reduce the occurrence of peptic ulcers and, as a result, peptic ulcer complications. Larger, nationwide studies are needed to estimate the real mortality rate in complicated peptic ulcer patients in Syria. There is a lack of some critical data in the patients' charts, which necessitates action to correct.

Japanese Consumers' Attitudes towards Obtaining and Sharing Health Information Regarding Over-the-Counter Medication: Designing an Over-the-Counter Electronic Health Record

Designing an over-the-counter medication electronic health record (OTC-EHR) may help improve OTC usage. An online survey for the conceptual OTC-EHR design examined participant characteristics, attitudes towards obtaining user-shared OTC medication information, health-related application usage, and the inclination to share anonymized health information. Descriptive statistics, tests to assess statistical significance, and text mining were used to analyze the results. The findings revealed that Japanese consumers, particularly those with high eHealth literacy and women, possess relatively positive attitudes towards obtaining user-shared OTC medication information than those with low eHealth literacy (t (280.71) = -4.11, p < 0.001) and men (t (262.26) = -2.78, p = 0.006), respectively. Most consumers own smartphones but do not use health-related applications. A minority held positive attitudes about sharing anonymized health information. The perceived helpfulness of OTC-EHR was positively associated with the usage of health-related applications (χ² (4) = 18.35, p = 0.001) and attitude towards sharing anonymized health information (χ² (3) = 19.78, p < 0.001). The study findings contribute towards OTC-EHR's design to enhance consumers' self-medication and reduce risks, while the psychological barriers to sharing anonymized health information should be improved by increasing the OTC-EHR's penetration rate and engaging in appropriate information design.

Smart healthcare system using integrated and lightweight ECC with private blockchain for multimedia medical data processing

Cloud-based Healthcare 4.0 systems have research challenges with secure medical data processing, especially biomedical image processing with privacy protection. Medical records are generally text/numerical or multimedia. Multimedia data includes X-ray scans, Computed Tomography (CT) scans, Magnetic Resonance Imaging (MRI) scans, etc. Transferring biomedical multimedia data to medical authorities raises various security concerns. This paper proposes a one-of-a-kind blockchain-based secure biomedical image processing system that maintains anonymity. The integrated Healthcare 4.0 assisted multimedia image processing architecture includes an edge layer, fog computing layer, cloud storage layer, and blockchain layer. The edge layer collects and sends periodic medical information from the patient to the higher layer. The multimedia data from the edge layer is securely preserved in blockchain-assisted cloud storage through fog nodes using lightweight cryptography. Medical users then safely search such data for medical treatment or monitoring. Lightweight cryptographic procedures are proposed by employing Elliptic Curve Cryptography (ECC) with Elliptic Curve Diffie-Hellman (ECDH) and Elliptic Curve Digital Signature (ECDS) algorithm to secure biomedical image processing while maintaining privacy (ECDSA). The proposed technique is experimented with using publically available chest X-ray and CT images. The experimental results revealed that the proposed model shows higher computational efficiency (encryption and decryption time), Peak to Signal Noise Ratio (PSNR), and Meas Square Error (MSE).

Co-design of human-centered, explainable AI for clinical decision support

eXplainable AI (XAI) involves two intertwined but separate challenges: the development of techniques to extract explanations from black-box AI models, and the way such explanations are presented to users, i.e., the explanation user interface. Despite its importance, the second aspect has received limited attention so far in the literature. Effective AI explanation interfaces are fundamental for allowing human decision-makers to take advantage and oversee high-risk AI systems effectively. Following an iterative design approach, we present the first cycle of prototyping-testing-redesigning of an explainable AI technique, and its explanation user interface for clinical Decision Support Systems (DSS). We first present an XAI technique that meets the technical requirements of the healthcare domain: sequential, ontology-linked patient data, and multi-label classification tasks. We demonstrate its applicability to explain a clinical DSS, and we design a first prototype of an explanation user interface. Next, we test such a prototype with healthcare providers and collect their feedback, with a two-fold outcome: first, we obtain evidence that explanations increase users’ trust in the XAI system, and second, we obtain useful insights on the perceived deficiencies of their interaction with the system, so that we can re-design a better, more human-centered explanation interface.

The Priorities of End Users of Emergency Department Electronic Health Records: Modified Delphi Study

BACKGROUND

The needs of the emergency department (ED) pose unique challenges to modern electronic health record (EHR) systems. A diverse case load of high-acuity, high-complexity presentations, and ambulatory patients, all requiring multiple transitions of care, creates a rich environment through which to critically examine EHRs.

OBJECTIVE

This investigation aims to capture and analyze the perspective of end users of EHR about the strengths, limitations, and future priorities for EHR in the setting of the ED.

METHODS

In the first phase of this investigation, a literature search was conducted to identify 5 key usage categories of ED EHRs. Using key usage categories in the first phase, a modified Delphi study was conducted with a group of 12 panelists with expertise in both emergency medicine and health informatics. Across 3 rounds of surveys, panelists generated and refined a list of strengths, limitations, and key priorities.

RESULTS

The findings from this investigation highlighted the preference of panelists for features maximizing functionality of basic clinical features relative to features of disruptive innovation.

CONCLUSIONS

By capturing the perspectives of end users in the ED, this investigation highlights areas for the improvement or development of future EHRs in acute care settings.

ICD-11: A catalyst for advancing patient safety surveillance globally

The World Health Organization's (WHO) international classification of disease version 11 (ICD-11) contains several features which enable improved classification of patient safety events. We have identified three suggestions to facilitate adoption of ICD-11 from the patient safety perspective. One, health system leaders at national, regional, and local levels should incorporate ICD-11 into all approaches to monitor patient safety. This will allow them to take advantage of the innovative patient safety classification methods embedded in ICD-11 to overcome several limitations related to existing patient safety surveillance methods. Two, application developers should incorporate ICD-11 into software solutions. This will accelerate adoption and utility of software-enabled clinical and administrative workflows relevant to patient safety management. This is enabled as a result of the ICD-11 application programming interface (or API) developed by the WHO. Third, health system leaders should adopt the ICD-11 using a continuous improvement framework. This will help leaders at national, regional and local levels to take advantage of specific existing initiatives which will be strengthened by ICD-11, including peer review comparisons, clinician engagement, and alignment of front-line safety efforts with post marketing surveillance of medical technologies. While the investment to adopt ICD-11 will be considerable, these will be offset by reducing the ongoing costs related to a lack of accurate routine information.

Transformers for cardiac patient mortality risk prediction from heterogeneous electronic health records

With over 17 million annual deaths, cardiovascular diseases (CVDs) dominate the cause of death statistics. CVDs can deteriorate the quality of life drastically and even cause sudden death, all the while inducing massive healthcare costs. This work studied state-of-the-art deep learning techniques to predict increased risk of death in CVD patients, building on the electronic health records (EHR) of over 23,000 cardiac patients. Taking into account the usefulness of the prediction for chronic disease patients, a prediction period of six months was selected. Two major transformer models that rely on learning bidirectional dependencies in sequential data, BERT and XLNet, were trained and compared. To our knowledge, the presented work is the first to apply XLNet on EHR data to predict mortality. The patient histories were formulated as time series consisting of varying types of clinical events, thus enabling the model to learn increasingly complex temporal dependencies. BERT and XLNet achieved an average area under the receiver operating characteristic curve (AUC) of 75.5% and 76.0%, respectively. XLNet surpassed BERT in recall by 9.8%, suggesting that it captures more positive cases than BERT, which is the main focus of recent research on EHRs and transformers.

Risk of COVID-19 breakthrough infection and hospitalization in individuals with comorbidities

BACKGROUND

The successful development of multiple COVID-19 vaccines has led to a global vaccination effort to reduce severe COVID-19 infection and mortality. However, the effectiveness of the COVID-19 vaccines wane over time leading to breakthrough infections where vaccinated individuals experience a COVID-19 infection. Here we estimate the risks of breakthrough infection and subsequent hospitalization in individuals with common comorbidities who had completed an initial vaccination series.

METHODS

Our study population included vaccinated patients between January 1, 2021 to March 31, 2022 who are present in the Truveta patient population. Models were developed to describe 1) time from completing primary vaccination series till breakthrough infection; and 2) if a patient was hospitalized within 14 days of breakthrough infection. We adjusted for age, race, ethnicity, sex, and year-month of vaccination.

RESULTS

Of 1,218,630 patients in the Truveta Platform who had completed an initial vaccination sequence between January 1, 2021 and March 31, 2022, 2.85, 3.42, 2.75, and 2.88 percent of patients with CKD, chronic lung disease, diabetes, or are in an immunocompromised state experienced breakthrough infection, respectively, compared to 1.46 percent of the population without any of these four comorbidities. We found an increased risk of breakthrough infection and subsequent hospitalization in individuals with any of the four comorbidities when compared to individuals without these four comorbidities.

CONCLUSIONS

Vaccinated individuals with any of the studied comorbidities experienced an increased risk of breakthrough COVID-19 infection and subsequent hospitalizations compared to the people without any of the studied comorbidities. Individuals with immunocompromising conditions and chronic lung disease were most at risk of breakthrough infection, while people with CKD were most at risk of hospitalization following breakthrough infection. Patients with multiple comorbidities have an even greater risk of breakthrough infection or hospitalization compared to patients with none of the studied comorbidities. Individuals with common comorbidities should remain vigilant against infection even if vaccinated.

Use of the clinical care classification in South Korean nursing practice: Challenges and opportunities

BACKGROUND AND OBJECTIVES

A government-driven standardization of nursing terminology including the Clinical Care Classification (CCC) was endorsed in South Korea in 2015, but the number of hospitals who have adopted this standard terminology remains unknown. This study aimed to determine the CCC awareness, adoption, and utilization statuses and its association with patient experience in South Korea.

DESIGN, SETTING, AND PARTICIPANTS

A nationwide telephone survey was conducted from January 13 to February 12, 2022 among 217 tertiary and secondary hospitals participating in the health information exchange network. The survey questionnaire included 22 items in 3 categories: current status of electronic nursing records, awareness and adoption of standard terminology, and open-ended questions regarding standard usage and dissemination. General characteristics and experience scores of the patients of the surveyed hospitals were collected from the publicly available data sources. Data analysis was performed using descriptive statistics, t-test, and generalized linear regression.

MAIN OUTCOMES AND MEASURES

The rates of awareness and adoption in hospitals to the nursing terminology standard of the CCC were calculated, and the current status of electronic nursing records used in practice was examined. The relationships between CCC awareness and the characteristics of hospitals in their patient experiences of health services were also identified.

RESULTS

The survey response rate was 24.9 % (54/217). Two out of three hospitals (68.5 %) were aware of the CCC. These hospitals had 800 beds or more, and higher scores for patient experience. CCC awareness was significantly related to increases in the overall scores for patient experiences (t = 2.70, p =.0103), but no significance with sub-score for nursing service (t = 1.23, p =.1594).

CONCLUSIONS

With a high adoption rate of electronic medical record systems, two-third hospitals acknowledged their CCC awareness, but were still lagged in adoption and usage of it in practice with operational challenges. The CCC awareness has potential relationships with positive patient experience.

Patient-facing cancer mobile apps that enable patient reported outcome data to be collected: A systematic review of content, functionality, quality, and ability to integrate with electronic health records

PURPOSE

Enabling cancer patients to self-manage symptoms through mobile applications can result in more informed, autonomous patients who are partners in their care, consequently reducing the burden on health services. Electronic patient reported outcomes completed before a clinical review can increase the frequency and quality of holistic assessments, while integration into electronic health records can maximise clinical utility. The ability of apps to integrate with electronic health records is key to providing a real-time interface between patient reports and healthcare response. This review identifies patient-facing cancer apps which can record patient reported outcomes, and explores their purpose, functionality, quality, and ability to integrate with electronic health records.

METHODS

A systematic app review and content synthesis was conducted on patient-facing cancer apps available in the United Kingdom. Where applicable, the review aligned with the Preferred Reporting Items for Systematic Reviews and meta-Analysis. Two validated scales assessed functionality and quality: The IMS Institute for Healthcare Informatics functionality score and the Mobile App Rating Scale. Flesch-Kincaid metrics explored readability.

RESULTS

Apple App and Google Play stores identified 405 apps, of which 12 met the eligibility criteria. All were free to download, 1 (8%) had in-app purchases/subscriptions. Nine (75%) were affiliated with a professional health body/charity. Six (50%) analysed inputted data and provided medical advice based on answers. The average Flesch Reading Ease score was 42.7 out of 100. The apps had an average of 7.3 functions each and a mean MARS score of 4/5. None integrated with electronic health records.

CONCLUSION

While many cancer apps exist, few enable patient reported outcomes to be recorded and shared with clinicians in real-time. Further research is warranted to explore the feasibility of integrating with electronic health records, as this function can improve patient experience and outcomes, and increase efficiency of hospital resources through more proactive care.

Smart product-service systems in the healthcare industry: Intelligent connected products and stakeholder communication drive digital health service adoption

Smart product-service systems (PSSs) have emerged as a solution for the ongoing digitalization of products and services, especially during the COVID-19 pandemic and under social distancing. However, the conditions for smart PSS adoption remain unclear, requiring the identification of driving attributes and the interrelationships of the attributes for smart PSS implementation in the healthcare industry. This study contributes by determining the cause-effect interrelationship among smart PSS attributes and by identifying and prioritizing the criteria that drive smart PSS adoption in chronic disease management. The study constructed a five-aspect theoretical model to deepen the understanding of digital health service adoption drivers. Data were collected from 233 healthcare industry practitioners to validate the smart PSS adoption attributes. Exploratory factor analysis (EFA) determined the structure of the attributes, the reliability of the criteria, and the validity of the aspects. The EFA result suggested 24 valid and reliable criteria drivers of smart PSS adoption in the healthcare industry, and they were grouped into five aspects. Following the smart PSS literature and stakeholder theory, the aspects are named digital health service adoption, intelligent connected products, stakeholder communication, environmental benefits, and use schemes. In addition, 17 practitioners treating patients with chronic conditions were interviewed to understand the interrelationships among the aspects and criteria. The fuzzy decision-making trial and evaluation laboratory (FDEMATEL) determined the cause-effect interrelationships based on their dependence and driving power. The FDEMATEL results indicated that intelligent connected products and stakeholder communication are the causal and focal attributes of improving digital health service adoption and providing alternative use schemes. For patients and physicians, the driving criteria include managing data, multifunctionality, data reliability, interoperability, patient communication, and resource efficiency. The theoretical and managerial implications are discussed.

Utilization of a Best Practice Alert (BPA) at Point-of-Care for Recruitment into a US-Based Autism Research Study

Provider referral is one of the most influential factors in research recruitment. To ease referral burden on providers, we adapted the Best Practice Alert (BPA) in the EPIC Electronic Health Record and assessed its utility in recruiting pediatric patients with autism spectrum disorder for the national SPARK study. During a year-long surveillance, 1203 (64.0%) patients were Interested in SPARK and 223 enrolled. Another 754 participants not recruited via the BPA also enrolled; 35.5% of these participants completed their participation compared to 58.3% of BPA-referred participants. Results suggest that (a) a BPA can successfully engage providers in the study-referral process and (b) families who learn about research through their providers may be more engaged and effectively retained.

Reducing ethnic and racial disparities by improving undertreatment, control, and engagement in blood pressure management with health information technology (REDUCE-BP) hybrid effectiveness-implementation pragmatic trial: Rationale and design

BACKGROUND

While racial/ethnic disparities in blood pressure control are documented, few interventions have successfully reduced these gaps. Under-prescribing, lack of treatment intensification, and suboptimal follow-up care are thought to be central contributors. Electronic health record (EHR) tools may help address these barriers and may be enhanced with behavioral science techniques.

OBJECTIVE

To evaluate the impact of a multicomponent behaviorally-informed EHR-based intervention on blood pressure control.

TRIAL DESIGN

Reducing Ethnic and racial Disparities by improving Undertreatment, Control, and Engagement in Blood Pressure management with health information technology (REDUCE-BP) (NCT05030467) is a two-arm cluster-randomized hybrid type 1 pragmatic trial in a large multi-ethnic health care system. Twenty-four clinics (>350 primary care providers [PCPs] and >10,000 eligible patients) are assigned to either multi-component EHR-based intervention or usual care. Intervention clinic PCPs will receive several EHR tools designed to reduce disparities delivered at different points, including a: (1) dashboard of all patients visible upon logging on to the EHR displaying blood pressure control by race/ethnicity compared to their PCP peers and (2) set of tools in an individual patient's chart containing decision support to encourage treatment intensification, ordering home blood pressure measurement, interventions to address health-related social needs, default text for note documentation, and enhanced patient education materials. The primary outcome is patient-level change in systolic blood pressure over 12 months between arms; secondary outcomes include changes in disparities and other clinical outcomes.

CONCLUSION

REDUCE-BP will provide important insights into whether an EHR-based intervention designed using behavioral science can improve hypertension control and reduce disparities.

The Implementation of Integrated Health Information Systems - Research Studies from 7 Countries Involving the InterRAI Assessment System

Introduction

In the past years, governments from several countries have shown interest in implementing integrated health information systems. The interRAI Suite of instruments fits this concept, as it is a set of standardised, evidence-based assessments, which have been validated for different care settings. The system allows the electronic transfer of information across care settings, enabling integration of care and providing support for care planning and quality monitoring. The main purpose of this research is to describe the recent implementation process of the interRAI instruments in seven countries: Belgium, Switzerland, France, Ireland, Iceland, Finland and New Zealand.

Methods

The study applied a case study methodology with the focus on the implementation strategies in each country. Principal investigators gathered relevant information from multiple sources and summarised it according to specific aspects of the implementation process, comparing them across countries. The main implementation aspects are described, as well as the main advantages and barriers perceived by the users.

Results

The seven case studies showed that adequate staffing, appropriate information technology, availability of hardware, professional collaboration and continuous training are perceived as important factors which can contribute to the implementation of the interRAI instruments. In addition, the use of electronic standardised assessment instruments such as the interRAI Suite provided evidence to improve decision-making and quality of care, enabling resource planning and benchmarking.

Conclusion

In practice, the implementation of health information systems is a process that requires a cultural shift of policymakers and professional caregivers at all levels of health policy and service delivery. Information about the implementation process of the interRAI Suite in different countries can help investigators and policymakers to better plan this implementation. This research sheds light on the advantages and pitfalls of the implementation of the interRAI Suite of instruments and proposes approaches to overcome difficulties.