Electronic Health Record Systems Research Articles

Integrating clinical research into electronic health record (EHR) workflows to support a learning health system

Abstract Objective Integrating clinical research into routine clinical care workflows within electronic health record systems (EHRs) can be challenging, expensive, and labor-intensive. This case study presents a large-scale clinical research project conducted entirely within a commercial EHR during the COVID-19 pandemic. Case Report The UCSD and UCSDH COVID-19 NeutraliZing Antibody Project (ZAP) aimed to evaluate antibody levels to SARS-CoV-2 virus in a large population at an academic medical center and examine the association between antibody levels and subsequent infection diagnosis. Results The project rapidly and successfully enrolled and consented over two thousand participants, integrating the research trial with standing COVID-19 testing operations, staff, lab, and mobile applications. EHR-integration increased enrollment, ease of scheduling, survey distribution and return of research results at a low cost by utilizing existing resources. Conclusion The case study highlights the potential benefits of EHR-integrated clinical research, expanding their reach across multiple health systems and facilitating rapid learning during a global health crisis.

Implementation of an electronic health record system during global surgical outreach: A prospective evaluation of success and sustainability.

The burden of musculoskeletal conditions continues to grow in low- and middle-income countries. Among thousands of surgical outreach trips each year, few organizations electronically track patient data to inform real-time care decisions and assess trip impact. We report the implementation of an electronic health record (EHR) system utilized at point of care during an orthopedic surgical outreach trip. In March 2023, we implemented an EHR on an orthopedic outreach trip to guide real-time care decisions. We utilized an effectiveness-implementation hybrid type 3 design to evaluate implementation success. Success was measured using outcomes adopted by the World Health Organization, including acceptability, appropriateness, feasibility, adoption, fidelity, and sustainability. Clinical outcome measures included adherence to essential quality measures and follow-up numerical rating system (NRS) pain scores. During the 5-day outreach trip, 76 patients were evaluated, 25 of which underwent surgery beforehand. The EHR implementation was successful as defined by: mean questionnaire ratings of acceptability (4.26), appropriateness (4.12), feasibility (4.19), and adoption (4.33) at least 4.00, WHO behaviorally anchored rating scale ratings of fidelity (6.8) at least 5.00, and sustainability (80%) at least 60% follow-up at 6months. All clinical quality measures were reported in greater than 80% of cases with all measures reported in 92% of cases. NRS pain scores improved by an average of 2.4 points. We demonstrate successful implementation of an EHR for real-time clinical use on a surgical outreach trip. Benefits of EHR utilization on surgical outreach trips may include improved documentation, minimization of medical errors, and ultimately improved quality of care.

Using an mHealth approach to collect patient-generated health data for predicting adverse health outcomes among adult survivors of childhood cancer

IntroductionCancer therapies predispose childhood cancer survivors to various treatment-related late effects, which contribute to a higher symptom burden, chronic health conditions (CHCs), and premature mortality. Regular monitoring of symptoms between clinic visits is useful for timely medical consultation and interventions that can improve quality of life (QOL). The Health Share Study aims to utilize mHealth to collect patient-generated health data (PGHD; daily symptoms, momentary physical health status) and develop survivor-specific risk prediction scores for mitigating adverse health outcomes including poor QOL and emergency room admissions. These personalized risk scores will be integrated into the hospital-based electronic health record (EHR) system to facilitate clinician communications with survivors for timely management of late effects.MethodsThis prospective study will recruit 600 adult survivors of childhood cancer from the St. Jude Lifetime Cohort study. Data collection include 20 daily symptoms via a smartphone, objective physical health data (physical activity intensity, sleep performance, and biometric data including resting heart rate, heart rate variability, oxygen saturation, and physical stress) via a wearable activity monitor, patient-reported outcomes (poor QOL, unplanned healthcare utilization) via a smartphone, and clinically ascertained outcomes (physical performance deficits, onset of/worsening CHCs) assessed in the survivorship clinic. Participants will complete health surveys and physical/functional assessments in the clinic at baseline, 2) report daily symptoms, wear an activity monitor, measure blood pressure at home over 4 months, and 3) complete health surveys and physical/functional assessments in the clinic 1 and 2 years from the baseline. Socio-demographic and clinical data abstracted from the EHR will be included in the analysis. We will invite 20 cancer survivors to investigate suitable formats to display predicted risk information on a dashboard and 10 clinicians to suggest evidence-based risk management strategies for adverse health outcomes.AnalysisMachine and statistical learning will be used in prediction modeling. Both approaches can handle a large number of predictors, including longitudinal patterns of daily symptoms/other PGHD, along with cancer treatments and socio-demographics.ConclusionThe individualized risk prediction scores and added communications between providers and survivors have the potential to improve survivorship care and outcomes by identifying early clinical presentations of adverse events.

Colorectal cancer health and care quality indicators in a federated setting using the Personal Health Train

ObjectiveHospitals and healthcare providers should assess and compare the quality of care given to patients and based on this improve the care. In the Netherlands, hospitals provide data to national quality registries, which in return provide annual quality indicators. However, this process is time-consuming, resource intensive and risks patient privacy and confidentiality. In this paper, we presented a multicentric ‘Proof of Principle’ study for federated calculation of quality indicators in patients with colorectal cancer. The findings suggest that the proposed approach is highly time-efficient and consume significantly lesser resources.Materials and methodsTwo quality indicators are calculated in an efficient and privacy presevering federated manner, by i) applying the Findable Accessible Interoperable and Reusable (FAIR) data principles and ii) using the Personal Health Train (PHT) infrastructure. Instead of sharing data to a centralized registry, PHT enables analysis by sending algorithms and sharing only insights from the data.ResultsETL process extracted data from the Electronic Health Record systems of the hospitals, converted them to FAIR data and hosted in RDF endpoints within each hospital. Finally, quality indicators from each center are calculated using PHT and the mean result along with the individual results plotted.Discussion and conclusionPHT and FAIR data principles can efficiently calculate quality indicators in a privacy-preserving federated approach and the work can be scaled up both nationally and internationally. Despite this, application of the methodology was largely hampered by ELSI issues. However, the lessons learned from this study can provide other hospitals and researchers to adapt to the process easily and take effective measures in building quality of care infrastructures.

Distributed Ledger Approach BlockChain- Based Integrated EHR: A Ethereum Smart Contract Method

Blockchain, on the other hand, is a groundbreaking technology that provides a distributed and Decentralized environment in which nodes in a list of networks can connect to each other without the need for a central authority. It has the potential to overcome the limits of Electronic Health Record (EHR) management and create a more secure, Decentralized, and safer environment for exchanging EHR data. Further, blockchain is a distributed ledger on which data can be stored and shared in a cryptographically secure, validated, and mutually agreed-upon manner across all mining nodes. The blockchain stores data with a high level of integrity and robustness, and it cannot be altered. Electronic health records (EHRs) are digitally saved health records that provide information about a person’s health. EHRs are generally shared among healthcare stakeholders, and thus are susceptible to power failures, data misuse, a lack of privacy, security, and an audit trail, among other problems. The aim of our proposed framework is firstly to implement blockchain technology for EHR and secondly to provide secure storage of electronic records by defining granular access rules for the users of the proposed framework. Moreover, this framework also discusses the scalability problem faced by the blockchain technology in general via use of off-chain storage of the records. This framework provides the EHR system with the benefits of having a scalable, secure and integral blockchain-based solution.

Data management for continuous learning in EHR systems

To gain a comprehensive understanding of a patient’s health, advanced analytics must be applied to the data collected by electronic health record (EHR) systems. However, managing and curating this data requires carefully designed workflows. While digitalization and standardization enable continuous health monitoring, missing data values and technical issues can compromise the consistency and timeliness of the data. In this paper, we propose a workflow for developing prognostic models that leverages the SMART BEAR infrastructure and the capabilities of the Big Data Analytics (BDA) engine to homogenize and harmonize data points. Our workflow improves the quality of the data by evaluating different imputation algorithms and selecting one that maintains the distribution and correlation of features similar to the raw data. We applied this workflow to a subset of the data stored in the SMART BEAR repository and examined its impact on the prediction of emerging health states such as cardiovascular disease and mild depression. We also discussed the possibility of model validation by clinicians in the SMART BEAR project, the transmission of subsequent actions in the decision support system, and the estimation of the required number of data points.

Revolutionizing decision support: asystematic literature review of contextual implementation models for electronic health records systems.

This study aims to assess previously developed Electronic Health Records System (EHRS) implementation models and identify successful models for decision support. A systematic review followed the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. The data sources used were Scopus, PubMed and Google Scholar. The review identified peer-reviewed papers published in the English Language from January 2010 to April 2023, targeting well-defined implementation of EHRS with decision-support capabilities in healthcare. To comprehensively address the research question, we ensured that all potential sources of evidence were considered, and quantitative and qualitative studies reporting primary data and systematic review studies that directly addressed the research question were included in the review. By including these studies in our analysis, we aimed to provide a more thorough and reliable evaluation of the available evidence. The findings suggest that the success of EHRS implementation is determined by organizational and human factors rather than technical factors alone. Successful implementation is dependent on a suitable implementation framework and management of EHRS. The review identified the capabilities of Clinical Decision Support (CDS) tools as essential in the effectiveness of EHRS in supporting decision-making. This study contributes to the existing literature on EHRS implementation models and identifies successful models for decision support. The findings can inform future implementations and guide decision-making in healthcare facilities.

Continuous patient state attention model for addressing irregularity in electronic health records

BackgroundIrregular time series (ITS) are common in healthcare as patient data is recorded in an electronic health record (EHR) system as per clinical guidelines/requirements but not for research and depends on a patient’s health status. Due to irregularity, it is challenging to develop machine learning techniques to uncover vast intelligence hidden in EHR big data, without losing performance on downstream patient outcome prediction tasks.MethodsIn this paper, we propose Perceiver, a cross-attention-based transformer variant that is computationally efficient and can handle long sequences of time series in healthcare. We further develop continuous patient state attention models, using Perceiver and transformer to deal with ITS in EHR. The continuous patient state models utilise neural ordinary differential equations to learn patient health dynamics, i.e., patient health trajectory from observed irregular time steps, which enables them to sample patient state at any time.ResultsThe proposed models’ performance on in-hospital mortality prediction task on PhysioNet-2012 challenge and MIMIC-III datasets is examined. Perceiver model either outperforms or performs at par with baselines, and reduces computations by about nine times when compared to the transformer model, with no significant loss of performance. Experiments to examine irregularity in healthcare reveal that continuous patient state models outperform baselines. Moreover, the predictive uncertainty of the model is used to refer extremely uncertain cases to clinicians, which enhances the model’s performance. Code is publicly available and verified at https://codeocean.com/capsule/4587224.ConclusionsPerceiver presents a computationally efficient potential alternative for processing long sequences of time series in healthcare, and the continuous patient state attention models outperform the traditional and advanced techniques to handle irregularity in the time series. Moreover, the predictive uncertainty of the model helps in the development of transparent and trustworthy systems, which can be utilised as per the availability of clinicians.

Characterization of Post-COVID-19 Definitions and Clinical Coding Practices: Longitudinal Study.

Post-COVID-19 condition (colloquially known as "long COVID-19") characterized as postacute sequelae of SARS-CoV-2 has no universal clinical case definition. Recent efforts have focused on understanding long COVID-19 symptoms, and electronic health record (EHR) data provide a unique resource for understanding this condition. The introduction of the International Classification of Diseases, Tenth Revision (ICD-10) code U09.9 for "Post COVID-19 condition, unspecified" to identify patients with long COVID-19 has provided a method of evaluating this condition in EHRs; however, the accuracy of this code is unclear. This study aimed to characterize the utility and accuracy of the U09.9 code across 3 health care systems-the Veterans Health Administration, the Beth Israel Deaconess Medical Center, and the University of Pittsburgh Medical Center-against patients identified with long COVID-19 via a chart review by operationalizing the World Health Organization (WHO) and Centers for Disease Control and Prevention (CDC) definitions. Patients who were COVID-19 positive with either a U07.1 ICD-10 code or positive polymerase chain reaction test within these health care systems were identified for chart review. Among this cohort, we sampled patients based on two approaches: (1) with a U09.9 code and (2) without a U09.9 code but with a new onset long COVID-19-related ICD-10 code, which allows us to assess the sensitivity of the U09.9 code. To operationalize the long COVID-19 definition based on health agency guidelines, symptoms were grouped into a "core" cluster of 11 commonly reported symptoms among patients with long COVID-19 and an extended cluster that captured all other symptoms by disease domain. Patients having ≥2 symptoms persisting for ≥60 days that were new onset after their COVID-19 infection, with ≥1 symptom in the core cluster, were labeled as having long COVID-19 per chart review. The code's performance was compared across 3 health care systems and across different time periods of the pandemic. Overall, 900 patient charts were reviewed across 3 health care systems. The prevalence of long COVID-19 among the cohort with the U09.9 ICD-10 code based on the operationalized WHO definition was between 23.2% and 62.4% across these health care systems. We also evaluated a less stringent version of the WHO definition and the CDC definition and observed an increase in the prevalence of long COVID-19 at all 3 health care systems. This is one of the first studies to evaluate the U09.9 code against a clinical case definition for long COVID-19, as well as the first to apply this definition to EHR data using a chart review approach on a nationwide cohort across multiple health care systems. This chart review approach can be implemented at other EHR systems to further evaluate the utility and performance of the U09.9 code.

Electronic health records and e-prescribing in Australia: An exploration of technological utilisation in Australian community pharmacies

ObjectiveThis study aimed to assess the utilisation and challenges associated with Electronic Health Records (EHR) and e-prescribing systems in Australian Community Pharmacies, focusing on their integration into daily practice and the impacts on operational efficiency. MethodsA mixed-methods online survey was carried out among community pharmacists throughout Australia to assess the utilisation of EHR and e-prescribing systems, including the benefits and challenges associated with their use. Data was analysed based on pharmacists' age, gender, and practice location (metropolitan vs. regional). The chi-square test was applied to examine the relationship between these demographic factors and the utilisation and operational challenges of EHR and e-prescribing systems. ResultsThe survey engaged 120 Australian community pharmacists. Of the participants, 67 % reported usability and efficiency issues with EHR systems. Regarding e-prescribing, 58 % of pharmacists faced delays due to slow software performance, while 42 % encountered errors in data transmission. Despite these challenges, the benefits of e-prescribing were evident, with 79 % of respondents noting the elimination of illegible prescriptions and 40 % observing a reduction in their workload. Issues with prescription quantity discrepancies and the reprinting process were highlighted, indicating areas for improvement in workflow and system usability. The analysis revealed no significant statistical relationship between the utilisation and challenges of EHR and e-prescribing systems with the demographic variables of age, gender and location (p > 0.05), emphasising the necessity for healthcare solutions that address the needs of all pharmacists regardless of specific demographic segments. ConclusionIn Australian community pharmacies, EHR and e-prescribing may enhance patient care but come with challenges such as data completeness, technical issues, and usability concerns. Implementing successful integration relies on user-centric design, standardised practices, and robust infrastructure. While demanding for pharmacists, the digital transition improves efficiency and quality of care. Ensuring user-friendly tools is crucial for the smooth utilisation of digital health.

A Two-Level Security Layer for Medical Data

Implementing a two-level security approach comprising cryptography and steganography in Electronic Health Record (EHR) systems ensures comprehensive protection of sensitive patient information. Cryptography encryption methods alone may not provide adequate protection, as they can be susceptible to attacks and also, steganography, has the visual quality issues after embedding. The combination of RSA cryptography and DWT steganography will offers a comprehensive solution for securing medical data, ensuring confidentiality, integrity, and compliance with privacy regulations, while DWT steganography provides covert embedding within medical images, preserving diagnostic integrity and enhancing data protection. In this paper, a secure two-level layer of medical data security is proposed to encrypt secret information using Rivest-Shamir-Adleman (RSA) while the encrypted secret information is embedded using Discrete Wavelet Transform (DWT). The performance of the proposed method was evaluated using Mean Square Error (MSE), Peak Signal to Noise Ratio (PSNR) and Normalized Cross-Correlation (NCC) metrics. The results obtained show that our method performs with a value of 0.105 for MSE, 49.713 for PSNR and 0.912 for NCC showing high visual quality and low imperceptibility.

Exploring the Impact of In Basket Metrics on the Adoption of a New Electronic Health Record System Among Specialists in a Tertiary Hospital in Alberta: Descriptive Study.

Health care organizations implement electronic health record (EHR) systems with the expectation of improved patient care and enhanced provider performance. However, while these technologies hold the potential to create improved care and system efficiencies, they can also lead to unintended negative consequences, such as patient safety issues, communication problems, and provider burnout. This study aims to document metrics related to the In Basket communication hub (time in In Basket per day, time in In Basket per appointment, In Basket messages received per day, and turnaround time) of the EHR system implemented by Alberta Health Services, the province-wide health delivery system called Connect Care (Epic Systems). The objective was to identify how a newly implemented EHR system was used, the timing of its use, and the duration of use specifically related to In Basket activities. A descriptive study was conducted. Due to the diversity of specialties, the providers were grouped into medical and surgical based on previous similar studies. The participants were further subgrouped based on their self-reported clinical full-time equivalent (FTE ) measure. This resulted in 3 subgroups for analysis: medical FTE <0.5, medical FTE >0.5, and surgical (all of whom reported FTE >0.5). The analysis was limited to outpatient clinical interactions and explicitly excluded inpatient activities. A total of 72 participants from 19 different specialties enrolled in this study. The providers had, on average, 8.31 appointments per day during the reporting periods. The providers received, on average, 21.93 messages per day, and they spent 7.61 minutes on average in the time in In Basket per day metric and 1.84 minutes on average in the time in In Basket per appointment metric. The time for the providers to mark messages as done (turnaround time) was on average 11.45 days during the reporting period. Although the surgical group had, on average, approximately twice as many appointments per scheduled day, they spent considerably less connected time (based on almost all time metrics) than the medical group. However, the surgical group took much longer than the medical group to mark messages as done (turnaround time). We observed a range of patterns with no consistent direction. There does not seem to be evidence of a "learning curve," which would have shown a consistent reduction in time spent on the system over time due to familiarity and experience. While this study does not show how the included metrics could be used as predictors of providers' satisfaction or feelings of burnout, the use trends could be used to start discussions about future Canadian studies needed in this area.

How do unintended consequences emerge from EHR implementation? An affordance perspective

AbstractDrawing upon an affordance‐actualisation perspective, we aim to advance our knowledge of the emergence of unintended consequences from the implementation of Electronic Health Record (EHR) systems. Prior research has not yet deeply understood how these unintended consequences unfold. We investigate how the (non‐)actualisation of affordances produces unintended consequences. Our exploratory case study of an EHR system implemented in Italy reveals four types of actions (flexing, bypassing, avoiding, and reorganising) through which different types of unintended consequences occur with the (non‐)actualisation of affordances. We explain and theorise how interactions among technology features and psychosocial and organisational constraints/enablers contribute to users' perception of affordances and technological constraints. This, in turn, influences different types of user actions, leading to unintended consequences. Our findings and insights contribute to the literature on unintended consequences and help organisations better manage implementing new systems.

Facing the digital frontier: exploring user acceptance of electronic health records in an urban, rural and remote setting in the Philippines.

A thorough understanding of user needs and behavioural intent-to-use underpins the development of a responsive health information system. This study aimed to examine health workers' intent-to-use an electronic health record (EHR) system in an urban, rural and remote setting in the Philippines. Following the Unified Theory of Acceptance and Use of Technology framework, user acceptance and the factors influencing intent-to-use the EHR were examined through a self-administered questionnaire. A total of 128 EHR users, comprising physicians, nurses, midwives, barangay health workers and administrative staff, were surveyed. Median scores for each domain were compared across the sites using the Kruskal-Wallis test. Ridge regression analysis was used to identify factors associated with behavioural intent-to-use. Over 94% of users across all sites reported their intent-to-use the EHR in the near future. Of the seven predictor variables examined, only self-efficacy was found to be significantly associated with behavioural intent-to-use. Intent-to-use the EHR increased by 31% (p=0.007) for each unit increase in self-efficacy score among participants. Acceptance was high across the three sites, with self-efficacy being a predictor of intent-to-use the technology. This suggests that users are more likely to adopt an EHR if they believe they have the capacity to successfully navigate the technology and perform their designated tasks with it. Co-producing interventions with primary care providers is crucial in sustaining EHR systems. Rather than developing a technology based on the surveillance needs of policymakers, an EHR developed from the grassroots was shown to be well-received by end-users.

610 From Flames to Facts: Unveiling Discrepancies in Burn Patient Documentation

Abstract Introduction Comprehensive and accurate burn documentation is essential for initial and ongoing patient care. However, the challenge of inaccurate and incomplete records poses preventable risks. Our study evaluates burn documentation at a tertiary care burn centre, focusing on discrepancies between initial Emergency Department (ED) assessments and final evaluations by the Plastic Surgery Burn Consultant (PS). We hypothesize that the ED reports inaccurate and incomplete burn injury details, leading to differences in burn size and severity compared to PS assessments. Methods We conducted a retrospective review of our provincial burn registry from January 1, 2016, to December 31, 2021. We included patients admitted for burns warranting a PS consultation, excluding isolated first-degree, ocular, and inhalational burns, and those not requiring burn unit admission. Data covering time, date, etiology, injury details, treatment, and follow-up were collected and compared between ED and PS records. Incomplete entries lacked burn-specific data points. Mann-Whitney and Kruskal-Wallis H tests were used to compare continuous outcomes, while Pearson’s Chi-Square test was employed for categorical outcomes. Wilcoxon’s Signed-Rank test was used to identify significant variations in TBSA estimates, with PS considered the "gold" standard. Statistical significance was set at p&amp;lt; 0.05. Results 358 patients were included, with most burns in male patients (76%) occurring at home and involving the head and neck. Burn etiology, circumstances, place, and anatomic location were well reported and consistent across PS and ED documentation. However, there were significant differences in TBSA estimates. The ED calculated a median TBSA of 20 (IQR: 19.8), while PS estimated a median TBSA of 14 (IQR: 16) (p&amp;lt; 0.0019). Notably, TBSA estimates for burns &amp;lt; 10% and 10-25% showed significant differences (p&amp;lt; 0.0001), tending toward overestimation by the ED. Deeper burns were consistently over-reported during initial ED assessments compared to the final PS determinations. Furthermore, 81% of the initial records were incomplete: 66% lacked initial treatment data, 49% missed TBSA, and 39% omitted burn depth. Conclusions Significant discrepancies were appreciated in the initial ED documentation of burn injuries at our tertiary care burn centre, with overestimations in TBSA and burn depth. Over 80% of initial documentation was incomplete, with TBSA omitted in 49% of charts in both local and peripheral transfer consultations. There is a collective urgent need for enhanced awareness and education on the importance of accurate and comprehensive burn patient documentation. Applicability of Research to Practice This research emphasizes the need for improved documentation strategies for burn patients seen in the acute care setting, raising the potential to venture into modalities such as artificial intelligence and electronic health record systems to enhance burn documentation.

Using Electronic Health Record Data for Occupational Therapy Health Services Research: Invited Commentary.

Health services research (HSR) is a field of study that examines how social factors, financing systems, organizational structures and processes, health technologies, and personal behaviors affect access to health care, the quality and cost of health care, and health and well-being. HSR approaches can help build the occupational therapy evidence base, particularly in relation to population health. Data from electronic health record (EHR) systems provide a rich resource for applying HSR approaches to examine the value of occupational therapy services. Transparency about data preparation procedures is important for interpreting results. Based on our findings, we describe a six-step cleaning protocol for preparing EHR and billing data from an inpatient rehabilitation facility for research and provide recommendations for the field based on our experience. Using and reporting similar strategies across studies will improve efficiency and transparency, and facilitate comparability of results.

Effect of the COVID-19 pandemic on utilization of essential health services in Iran evidence from an interrupted time series analysis

BackgroundThe COVID-19 disrupted the provision of essential health services in numerous countries, potentially leading to outbreaks of deadly diseases. This study aims to investigate the effect of the COVID-19 pandemic on the utilization of essential health services in Iran.MethodsAn analytical cross-sectional study was conducted using interrupted time series (ITS) analysis. Data about five indicators, including 'childhood vaccination, infant care, hypertension screening, diabetes screening, and breast cancer screening,' were obtained from the electronic health record System in two-time intervals: 15 months before (November 2018 to January 2020) and 15 months after (January 2020 to May 2021) the onset of the COVID-19 pandemic. The data were analyzed by utilizing ITS. In addition, a Poisson model was employed due to the usage of count data. The Durbin-Watson (DW) test was used to identify the presence of lag-1 autocorrelation in the time series data. All statistical analysis was performed using R 4.3.1 software, considering a 5% significance level.ResultsThe ITS analysis showed that the COVID-19 pandemic significantly affected the utilization of all essential health services (P < 0.0001). The utilization of hypertension screening (RR = 0.51, p < 0.001), diabetes screening (RR = 0.884, p < 0.001), breast cancer screening (RR = 0.435, p < 0.001), childhood vaccination (IRR = 0.947, p < 0.001), and infant care (RR = 1.666, p < 0.001), exhibited a significant decrease in the short term following the pandemic (P < 0.0001). However, the long-term trend for all service utilization, except breast cancer screening (IRR = 0.952, p < 0.001), demonstrated a significant increase.ConclusionsThe COVID-19 pandemic affected utilization of essential health care in Iran. It is imperative to utilize this evidence to develop policies that will be translated into targeted planning and implementation to sustain provision and utilization of essential health services during public health emergencies. It is also vital to raise awareness and public knowledge regarding the consequences of interruptions in essential health services. In addition, it is important to identify the supply- and demand-side factors contributing to these disruptions.

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

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

Surveillance of catheter-associated bloodstream infections: development and validation of a fully automated algorithm

BackgroundMost surveillance systems for catheter-related bloodstream infections (CRBSI) and central line-associated bloodstream infections (CLABSI) are based on manual chart review. Our objective was to validate a fully automated algorithm for CRBSI and CLABSI surveillance in intensive care units (ICU).MethodsWe developed a fully automated algorithm to detect CRBSI, CLABSI and ICU-onset bloodstream infections (ICU-BSI) in patients admitted to the ICU of a tertiary care hospital in Switzerland. The parameters included in the algorithm were based on a recently performed systematic review. Structured data on demographics, administrative data, central vascular catheter and microbiological results (blood cultures and other clinical cultures) obtained from the hospital’s data warehouse were processed by the algorithm. Validation for CRBSI was performed by comparing results with prospective manual BSI surveillance data over a 6-year period. CLABSI were retrospectively assessed over a 2-year period.ResultsFrom January 2016 to December 2021, 854 positive blood cultures were identified in 346 ICU patients. The median age was 61.7 years [IQR 50–70]; 205 (24%) positive samples were collected from female patients. The algorithm detected 5 CRBSI, 109 CLABSI and 280 ICU-BSI. The overall CRBSI and CLABSI incidence rates determined by automated surveillance for the period 2016 to 2021 were 0.18/1000 catheter-days (95% CI 0.06–0.41) and 3.86/1000 catheter days (95% CI: 3.17–4.65). The sensitivity, specificity, positive predictive and negative predictive values of the algorithm for CRBSI, were 83% (95% CI 43.7–96.9), 100% (95% CI 99.5–100), 100% (95% CI 56.5–100), and 99.9% (95% CI 99.2–100), respectively. One CRBSI was misclassified as an ICU-BSI by the algorithm because the same bacterium was identified in the blood culture and in a lower respiratory tract specimen. Manual review of CLABSI from January 2020 to December 2021 (n = 51) did not identify any errors in the algorithm.ConclusionsA fully automated algorithm for CRBSI and CLABSI detection in critically-ill patients using only structured data provided valid results. The next step will be to assess the feasibility and external validity of implementing it in several hospitals with different electronic health record systems.

Electronic Health Records (EHR) System: Analysis of Gender Differences in EHR Physician/Provider Utilization in a Neurology Department (P7-4.001)

Electronic Health Records (EHR) System: Analysis of Gender Differences in EHR Physician/Provider Utilization in a Neurology Department (P7-4.001)