Electronic Medical Records in the American Health System: challenges and lessons learned

Paper-and-Pencil vs. Electronic Patient Records: Analyzing Time Efficiency, Personnel Requirements, and Usability Impacts on Healthcare Administration

Background: This study investigates the impact of transitioning from paper and pencil (P&P) methods to electronic patient records (EPR) on workflow and usability in surgical ward rounds. Methods: Surgical ward rounds were audited by two independent observers to evaluate the effects of transitioning from P&P to EPR. Key observations included the number of medical personnel and five critical workflow aspects before and after EPR implementation. Additionally, usability was assessed using the System Usability Scale (SUS) and the Post-Study System Usability Questionnaire (PSSUQ). Results: A total of 192 P&P and 160 EPR observations were analyzed. Physicians experienced increased administrative workload with EPR, while nurses adapted more easily. Ward teams typically consisted of two physicians and three or four nurses. Usability scores rated the system as "Not Acceptable" across all professional groups. Conclusions: The EPR system introduced usability challenges, particularly for physicians, despite potential benefits like improved data access. Usability flaws hindered system acceptance, highlighting the need for better workflow integration. Addressing these issues could improve efficiency and reduce administrative strain. As artificial intelligence becomes more integrated into clinical practice, healthcare professionals must critically assess AI-driven tools to ensure safe and effective patient care.

Cancer Diagnosis During Pregnancy and Livebirth Outcomes in the Adolescent and Young Adult Horizon Study

Objective: To describe patterns of cancer treatment and live birth outcomes that followed a cancer diagnosis during pregnancy. Study Design: The Adolescent and Young Adult (AYA) Horizon Study is an observational study evaluating outcomes in survivors of the five most common types of cancer in this age group (15-39 years old). Of the 23,629 individuals identified diagnosed with breast, lymphoma, thyroid, melanoma, or gynecological cancer in North Carolina (2000-2015) and California (2004-2016), we identified 555 live births to individuals who experienced cancer diagnosis during pregnancy. Births to individuals diagnosed with cancer during pregnancy were matched ∼1:5 on maternal age and year of delivery to live births to individuals without a cancer diagnosis (N = 2,667). Multivariable Poisson regression was used to compare birth outcomes between pregnancies affected by a cancer diagnosis and unaffected matched pregnancies. Results: Cancer diagnosis during pregnancy was associated with an increased risk of preterm delivery (prevalence ratio [PR] 2.70; 95% confidence interval [CI] 2.24, 3.26); very preterm delivery (PR 1.74; 95% CI 1.12, 2.71); induction of labor (PR 1.48; 95% CI 1.27, 1.73); low birth weight (PR 1.97; 95% CI 1.55, 2.50); and cesarean delivery (PR 1.18; 95% CI 1.04, 1.34) but not associated with low Apgar score (PR 0.90; 95% CI 0.39, 2.06). In our sample, 41% of patients received chemotherapy, half of whom initiated chemotherapy during pregnancy, and 86% received surgery, 58% of whom had surgery during pregnancy. Of the 19% who received radiation, all received radiation treatment following pregnancy. Conclusion: We identified an increased risk of birth outcomes, including preterm and very preterm delivery, induction of labor, low birth weight, and cesarean delivery, to those experiencing a cancer diagnosis during pregnancy. This analysis contributes to the available evidence for those experiencing a cancer diagnosis during pregnancy.

Closing the Gap: Foster Parents' Lived Experiences and Recommendations for Interdisciplinary Health-Care Providers

Purpose: This study explored barriers and facilitators to utilizing health-care services for foster youth, from the perspective of foster parents. Background: There are nearly 437,283 youths in the United States foster care system. Youths living in locations that are different from their familial home situations have a significantly higher occurrence of physical and mental health illnesses than children who remain in their familial homes, increasing risk for homelessness, substance use and abuse, chronic long-term illnesses, and incarceration, thus placing additional burden on the health-care system. Design and Methods: This study utilized a qualitative descriptive design with purposive sampling, with video focus groups to explore 15 foster parents' experiences in accessing health-care services for foster youth. Results: Barriers and facilitators to accessing health care and care coordination were identified, including logistics, transportation, electronic health record access, location of health care, and the impact of the social worker. Practice Implications: Nurses are well-positioned to aid in closing gaps in care for foster youth. Conclusions: These experiences provide important insights and implications for health-care providers and for future research and policy development.

Evaluating the utility and challenges associated with "unknown" and fictional patients in the electronic medical record

Electronic medical records (EMRs) allow for the creation of "fictional" and unknown patients within the EMR production environment. Surprisingly, there is sparse literature regarding the use cases for these patients or the challenges associated with their existence in the EMR. Here, we identified three classes of patients in regular use at our institution: true fictional patients with medical record numbers (MRNs) used to test EMR functions in the production environment, "confidential patients" used to store sensitive data, and "unknown" patients that are assigned temporary MRNs in emergency situations until additional information can be acquired. A further layer of complexity involving the merging of records for unknown patients once they are identified is also explored. Each class of patients, real or fictional, poses a variety of challenges from a clinical laboratory standpoint, which are often dealt with on a case-by-case basis. Here, we present a series of instructional cases adapted from actual patient safety events at our institution involving fictional, confidential, and unknown patient records. These illustrative cases highlight the utility of these fictional and unknown patients, as well as the challenges they pose on an institutional and individual level, including issues that arise from merging clinical data from temporary MRNs to identified patient charts. Lastly, we provide recommendations on how best to manage similar scenarios that may arise.

Ten years of the Citizen's Electronic Health Record e-SUS Primary Healthcare: in search of an electronic Unified Health System

OBJECTIVE

Contextualize the adherence to the Prontuário Eletrônico do Cidadão (PEC - Citizen's Electronic Health Record) by Brazilian municipalities and the evolution of the electronic strategy of the Unified Health System (e-SUS) for Primary Healthcare (PHC) during its 10 years.

METHODS

This descriptive study added information on adherence to the use of medical records extracted from the database of the Secretaria de Atenção Primária à Saúde (SAPS- Primary Healthcare Secretary) of the Federal Government between 2017 and 2022. We analized the number of computerized basic healthcare units that used some electronic medical records, the number of those that used simplified data collection (SDC), and those that implemented the citizen's electronic health record (PEC) in the same period. A descriptive synthesis of the functionalities and modules implemented in the system during its 10 years of development was also carried out.

RESULTS

The adherence of Brazilian municipalities to the PEC has grown exponentially in the last five years, going from 8,930 healthcare units in 2017 to 26,091 in 2022. As expected, while the main functionalities and improvements developed in this decade sought to implement new flows and modules of administrative, clinical care, and care management processes and health service administration, improving aspects of usability and technological infrastructure of the application architecture was also crucial for the success of the system.

CONCLUSIONS

In 2023, the milestone of a decade will be celebrated since the beginning of health records implementation by Brazilian municipalities, marked by technological and infrastructure challenges and improvements and new functionalities that highlight the technological evolution of the e-SUS PHC system and strategy. Despite many other tools, the PEC is arguably Brazil's leading electronic medical record today, as it has always invested in evolution, updating itself in technological and usability opportunities.

Social transition, socioeconomic status and self-rated health in China: evidence from a national cross-sectional survey (CGSS)

Background

Social transition is one of the multi-level mechanisms that influence health disparities. However, it has received less attention as one of the non-traditional social determinants of health. A few studies have examined China's social transition and its impact on health inequality in self-rated health (SRH). Therefore, this study explores the impact of China's market-oriented reforms-social transition and socioeconomic status (SES)-on residents' SRH.

Methods

Using the cross-sectional data from the Chinese General Social Survey (CGSS) in 2017, we analyzed the effects of social transition and SES on the SRH of Chinese residents using the RIF (Recentered influence function) method. The RIF decomposition method investigated health differences among different populations and their determinants.

Results

Social transition and SES have significant positive effects on the SRH of Chinese residents. The correlation between SES and the SRH of Chinese residents is moderated by social transition, implying that social transition can weaken the correlation between SES and the SRH of Chinese residents. The impacts of SES and social transition on SRH vary across populations.

Conclusion

Promoting social transition and favoring disadvantaged groups with more resources are urgently needed to promote equitable health outcomes.

MF-MNER: Multi-models Fusion for MNER in Chinese Clinical Electronic Medical Records

To address the problem of poor entity recognition performance caused by the lack of Chinese annotation in clinical electronic medical records, this paper proposes a multi-medical entity recognition method F-MNER using a fusion technique combining BART, Bi-LSTM, and CRF. First, after cleaning, encoding, and segmenting the electronic medical records, the obtained semantic representations are dynamically fused using a bidirectional autoregressive transformer (BART) model. Then, sequential information is captured using a bidirectional long short-term memory (Bi-LSTM) network. Finally, the conditional random field (CRF) is used to decode and output multi-task entity recognition. Experiments are performed on the CCKS2019 dataset, with micro avg Precision, macro avg Recall, weighted avg Precision reaching 0.880, 0.887, and 0.883, and micro avg F1-score, macro avg F1-score, weighted avg F1-score reaching 0.875, 0.876, and 0.876 respectively. Compared with existing models, our method outperforms the existing literature in three evaluation metrics (micro average, macro average, weighted average) under the same dataset conditions. In the case of weighted average, the Precision, Recall, and F1-score are 19.64%, 15.67%, and 17.58% higher than the existing BERT-BiLSTM-CRF model respectively. Experiments are performed on the actual clinical dataset with our MF-MNER, the Precision, Recall, and F1-score are 0.638, 0.825, and 0.719 under the micro-avg evaluation mechanism. The Precision, Recall, and F1-score are 0.685, 0.800, and 0.733 under the macro-avg evaluation mechanism. The Precision, Recall, and F1-score are 0.647, 0.825, and 0.722 under the weighted avg evaluation mechanism. The above results show that our method MF-MNER can integrate the advantages of BART, Bi-LSTM, and CRF layers, significantly improving the performance of downstream named entity recognition tasks with a small amount of annotation, and achieving excellent performance in terms of recall score, which has certain practical significance. Source code and datasets to reproduce the results in this paper are available at https://github.com/xfwang1969/MF-MNER .

Evaluation of activities of daily living using an electronic version of the Longshi Scale in patients with stroke: reliability, consistency, and preference

BACKGROUND

The Longshi Scale is a pictorial assessment tool for evaluating activities of daily living (ADL) in patients with stroke. The paper-based version presents challenges; thus, the WeChat version was created to enhance accessibility. Herein, we aimed to validate the inter-rater and test-retest reliabilities of the WeChat version of the Longshi Scale and explore its potential clinical applications.

METHODS

We recruited 115 patients with stroke in the study. The ADL results of each patient were assessed using both the WeChat and paper-based version of the Longshi Scale; each evaluation was conducted by 28 health professionals and 115 caregivers separately. To explore the test-retest reliability of the WeChat version, 22 patients were randomly selected and re-evaluated by health professionals using the WeChat version. All evaluation criteria were recorded, and all evaluators were surveyed to indicate their preference between the two versions.

RESULTS

Consistency between WeChat and the paper-based Longshi Scale was high for ADL scores by health professionals (ICC2,1 = 0.803-0.988) and caregivers (ICC2,1 = 0.845-0.983), as well as for degrees of disability (κw = 0.870 by professionals; κw = 0.800 by caregivers). Bland-Altman analysis showed no significant discrepancies. The WeChat version exhibited good test-retest reliability (κw = 0.880). The WeChat version showed similar inter-rater reliability in terms of the ADL score evaluated using the paper-based version (ICC2,1 = 0.781-0.941). The time to complete assessments did not differ significantly, although the WeChat version had a shorter information entry time (P < 0.001, 95% confidence interval: -43.463 to -15.488). Health professionals favored the WeChat version (53.6%), whereas caregivers had no significant preference.

CONCLUSIONS

The WeChat version of the Longshi Scale is reliable and serves as a suitable alternative for health professionals and caregivers to assess ADL levels in patients with stroke. The WeChat version of the Longshi Scale is considered user-friendly by health professionals, although it is not preferred by caregivers.

TRIAL REGISTRATION

This study was approved by the Ethics Committee of the Second People's Hospital of Shenzhen (approval number: 20210812003-FS01) and registered on the Clinical Trial Register Center website: clinicaltrials.gov on January 31, 2022 (registration no.: NCT05214638).

Improving Hypertension Control in the Black Patient Population: A Quality Improvement Study of Workflow Redesign Using the Electronic Health Record to Integrate Self-Monitoring, Education, and Reporting

BACKGROUND

Nearly half of American adults have hypertension (HTN), and non-Hispanic Black patients are diagnosed at a higher rate than others.

LOCAL PROBLEM

Our local clinic population reflected disproportionate rates of uncontrolled HTN among Black patients.

METHODS

A quality improvement pre-/postintervention design was used to evaluate an educational intervention to reduce blood pressure (BP) and improve self-monitoring of BP in Black patients using the Chronic Care Model.

INTERVENTIONS

A team-based approach was used to redesign clinic workflows and patient education, prescribe self-paced videos from an electronic health record (EHR) patient portal, and provide home BP cuffs.

RESULTS

Black participants (n = 79) improved viewing of prescribed videos (7.9% to 68.5%), knowledge scores (67.9 to 75.2), and mean systolic BP (-20.3 mm Hg; P > .001).

CONCLUSIONS

This team-based approach enhanced patient engagement, self-monitoring skills, EHR-reported BP, and overall BP control for a cohort of Black patients with uncontrolled BP.

Physicians' and Patients' Expectations From Digital Agents for Consultations: Interview Study Among Physicians and Patients

BACKGROUND

Physicians are currently overwhelmed by administrative tasks and spend very little time in consultations with patients, which hampers health literacy, shared decision-making, and treatment adherence.

OBJECTIVE

This study aims to examine whether digital agents constructed using fast-evolving generative artificial intelligence, such as ChatGPT, have the potential to improve consultations, adherence to treatment, and health literacy. We interviewed patients and physicians to obtain their opinions about 3 digital agents-a silent digital expert, a communicative digital expert, and a digital companion (DC).

METHODS

We conducted in-depth interviews with 25 patients and 22 physicians from a purposeful sample, with the patients having a wide age range and coming from different educational backgrounds and the physicians having different medical specialties. Transcripts of the interviews were deductively coded using MAXQDA (VERBI Software GmbH) and then summarized according to code and interview before being clustered for interpretation.

RESULTS

Statements from patients and physicians were categorized according to three consultation phases: (1) silent and communicative digital experts that are part of the consultation, (2) digital experts that hand over to a DC, and (3) DCs that support patients in the period between consultations. Overall, patients and physicians were open to these forms of digital support but had reservations about all 3 agents.

CONCLUSIONS

Ultimately, we derived 9 requirements for designing digital agents to support consultations, treatment adherence, and health literacy based on the literature and our qualitative findings.

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.

Impact of Surgical Resident Education and EMR Standardization in Enhancing ERAS Adherence and Outcomes in Colorectal Surgery

OBJECTIVE

Our study aimed at investigating the degree of adherence to ERAS pathway at our institution and to evaluate the role of providing resident education and a standardized EMR order set in improving adherence and patient surgical outcomes.

DESIGN

The study is prospective in nature and consists of two phases with a preintervention cohort to assess adherence to ERAS protocol and a postintervention cohort to evaluate improvement in adherence and patient outcomes. Adherence with the ERAS protocol was assessed across preoperative, intraoperative, and postoperative phases.

SETTING

The study took place at MedStar Franklin Square Medical Center in Baltimore, Maryland, involving inpatient care at a surgical ward.

PARTICIPANTS

During the preintervention phase, patients undergoing elective colorectal surgery were identified over 6 months (N = 77), and their adherence to the ERAS protocol was assessed. Following the intervention of surgical resident and faculty education sessions on the ERAS protocol and the implementation of a standardized order set in the Electronic Medical Record, a postintervention cohort (N = 54) was selected for comparison over another 6 months.

RESULTS

Among 77 patients who underwent elective colorectal surgery, the adherence rate to ERAS protocol was notably below 80% for most elements of the postoperative phase. When pre- and postintervention cohorts were compared, there were no significant differences in the baseline demographics and perioperative variables. After the implementation of our intervention, adherence rates were significantly improved in 7 out of 8 ERAS protocol elements of the postintervention phase. Among primary outcome measures, readmission rate (24.7% vs.9.4%; p = 0.022) and length of stay (7.3 ± 4.5 vs. 5.5 ± 3.6; p = 0.014) were significantly lower in the postintervention cohort. Although the rate of postoperative complications did not decrease significantly (33.8% vs. 31.5%; p = 0.284), there were fewer patients with postoperative ileus and surgical site infections. Outcomes were evaluated based on an 8-point score of postoperative ERAS elements. A significant decrease in mean length of stay and readmission rates is observed when at least 5 elements are completed, emphasizing the ERAS pathway's importance as a complementary bundle.

CONCLUSION

Our study highlights the impact of resident education and electronic medical record standardization on ERAS adherence in colorectal surgery. This multidisciplinary approach improves adherence, reduces hospital stay, and enhances communication among healthcare providers for better patient outcomes.

Radiology E-Consults: Reinterpretation of Outside CT and MRI Exams to Improve Quality, Reduce Unneeded Imaging, and Lower Costs

OBJECTIVE

Advanced imaging examinations commonly uncover incidental findings. Without access to prior imaging that is external to their healthcare system, radiologists may struggle to assess the significance of these findings. This can lead to unnecessary imaging follow-up recommendations and management uncertainty. We created a program to facilitate outside advanced imaging reinterpretation in the context of both internal and external prior imaging, resulting in more complete and accurate follow-up recommendations.

METHODS

Our value-based care organization created a program to facilitate outside advanced imaging reinterpretation, including importing the outside examinations and reports into our PACS/RIS platform. A total of 222 of these reinterpretations were performed between November 2020 and June 2022. In each case, a radiologist reinterpreted the entire examination and placed an official report into the electronic medical record, comparing their conclusions and recommendations with those of the outside imaging report. The reinterpretation report included a trackable code for future data mining purposes.

RESULTS

These E-consults seamlessly fitted into a radiologist's normal workflow and resulted in revenue generation as well as wRVU credit. The E-consults resulted in a change in management recommendations for approximately 46% of the overall number of cases. Approximately 37% of all examinations resulted in a recommendation of no further imaging required, or adjustment of the imaging modality/interval.

DISCUSSION

An official radiology E-consult program can synthesize disparate sources of imaging data, transforming it into clear and documented recommendations which providers can use to improve patient safety and reduce unnecessary imaging and costs.

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.

Use of Electronic Medical Records (EMR) in Gerontology: Benefits, Considerations and a Promising Future

Electronic medical records (EMRs) have many benefits in clinical research in gerontology, enabling data analysis, development of prognostic tools and disease risk prediction. EMRs also offer a range of advantages in clinical practice, such as comprehensive medical records, streamlined communication with healthcare providers, remote data access, and rapid retrieval of test results, ultimately leading to increased efficiency, enhanced patient safety, and improved quality of care in gerontology, which includes benefits like reduced medication use and better patient history taking and physical examination assessments. The use of artificial intelligence (AI) and machine learning (ML) approaches on EMRs can further improve disease diagnosis, symptom classification, and support clinical decision-making. However, there are also challenges related to data quality, data entry errors, as well as the ethics and safety of using AI in healthcare. This article discusses the future of EMRs in gerontology and the application of AI and ML in clinical research. Ethical and legal issues surrounding data sharing and the need for healthcare professionals to critically evaluate and integrate these technologies are also emphasized. The article concludes by discussing the challenges related to the use of EMRs in research as well as in their primary intended use, the daily clinical practice.

Supporting professionals to implement integrated, person-centered care for people with chronic conditions: the TARGET pilot study

OBJECTIVE

The TARGET program for integrated, person-centered care for people with chronic conditions offers primary care (PC) professionals a set of tools and trainings to actively engage in population segmentation and person-centered needs assessments (PCNAs). A pilot study was conducted to gain insight into the program's feasibility and acceptability, and identify preconditions for successful implementation.

DESIGN AND SETTING

Seven Dutch PC practices participated in a half-year pilot study starting in August 2020. We performed a review of the population segmentation tool, observed four training sessions and 15 PCNAs, and interviewed 15 professionals and 12 patients.

RESULTS

Regarding feasibility and acceptability, we found that the tools and trainings provided professionals with skills to use the segmentation tool and take a more coaching role in the well-appreciated PCNAs. Concerning implementation preconditions, we found that team commitment and network connections need improvement, although work pleasure increased and professionals generally wanted the program to continue.

CONCLUSIONS

While the content of the TARGET program is supported by its users, the implementation process, for instance team commitment to the program, needs more attention in future upscaling efforts.

Harnessing the power of electronic health records and open natural language data mining to capture meaningful patient experience during routine clinical care

INTRODUCTION

Electronic health records (EHR) are a rich data source for both quality improvement and clinical research. Natural language processing can be harnessed to extract data from these previously difficult to access sources.

OBJECTIVE

The objective of this study was to create and apply a natural language search query to extract EHR data to ask and answer quality improvement questions at a pediatric aerodigestive center.

METHODS

We developed a combined natural language search query to extract clinically meaningful data along with International Statistical Classification of Diseases (ICD10) and Current Procedural Terminology (CPT) code data. This search query was applied to a single pediatric aerodigestive center to answer key clinical questions asked by families. Data were extracted from EHR data from first clinic visit, operative note, microbiology lab report, and pathology report for all new patients from 2020 to 2021. Included as three queries were: 1) if I bring my child to a pediatric aerodigestive center, how often will my child obtain a medical diagnosis without needing an intervention? 2) if my child has a diagnostic procedure, how often will a diagnosis be made? 3) if a diagnosis is made, can it be addressed during that endoscopic intervention?

RESULTS

For the 711 new patients coming to the pediatric aerodigestive center from 2020 to 2021, only 26-32% required an interventional triple endoscopy (rigid/flexible bronchoscopy with esophagoduodenoscopy). Of these triple endoscopies, 75.7% resulted in a positive finding that enabled optimization of that child's care. Of the 221 patients who underwent diagnostic triple endoscopies, 40.7% underwent intervention at the same time for laryngeal cleft (injection or suture, dependent upon age).

CONCLUSION

Here we created an effective model of open language search query to extract meaningful metrics of patient experience from EHR data. This model easily allows the EHR to be harnessed to create retrospective and prospective databases that can be readily queried to answer clinical questions important to patients. Such databases are widely applicable not just to pediatric aerodigestive centers but to any clinical care setting using an EHR.

Artificial Intelligence Models in Health Information Exchange: A Systematic Review of Clinical Implications

Electronic health record (EHR) systems collate patient data, and the integration and standardization of documents through Health Information Exchange (HIE) play a pivotal role in refining patient management. Although the clinical implications of AI in EHR systems have been extensively analyzed, its application in HIE as a crucial source of patient data is less explored. Addressing this gap, our systematic review delves into utilizing AI models in HIE, gauging their predictive prowess and potential limitations. Employing databases such as Scopus, CINAHL, Google Scholar, PubMed/Medline, and Web of Science and adhering to the PRISMA guidelines, we unearthed 1021 publications. Of these, 11 were shortlisted for the final analysis. A noticeable preference for machine learning models in prognosticating clinical results, notably in oncology and cardiac failures, was evident. The metrics displayed AUC values ranging between 61% and 99.91%. Sensitivity metrics spanned from 12% to 96.50%, specificity from 76.30% to 98.80%, positive predictive values varied from 83.70% to 94.10%, and negative predictive values between 94.10% and 99.10%. Despite variations in specific metrics, AI models drawing on HIE data unfailingly showcased commendable predictive proficiency in clinical verdicts, emphasizing the transformative potential of melding AI with HIE. However, variations in sensitivity highlight underlying challenges. As healthcare's path becomes more enmeshed with AI, a well-rounded, enlightened approach is pivotal to guarantee the delivery of trustworthy and effective AI-augmented healthcare solutions.

Health professionals' readiness to implement electronic medical record system in Gamo zone public hospitals, southern Ethiopia: an institution based cross-sectional study

BACKGROUND

The adoption of Electronic Medical Records (EMR) by the healthcare sector can improve patient care and safety, facilitate structured research, and effectively plan, monitor, and assess disease. EMR adoptions in low-income countries like Ethiopia were delayed and failing more frequently, despite their critical necessity. The most popular way to solve the issue is to evaluate user preparedness prior to the adoption of EMR. However, little is known regarding the EMR readiness of healthcare professionals in this study setting. Therefore, the objective of this study was to assess the readiness and factors associated with health professional readiness toward EMR in Gamo Zone, Ethiopia.

METHODS

An institution-based cross-sectional survey was conducted by using a pretested self-administered questionnaire on 416 study participants at public hospital hospitals in southern Ethiopia. STAT version 14 software was used to conduct the analysis after the data was entered using Epi-data version 3.2. A binary logistic regression model was fitted to identify factors associated with readiness. Finally, the results were interpreted using an adjusted odds ratio (AOR) with a 95% confidence interval (CI) and p-value less than 0.05.

RESULTS

A total of 400 participants enrolled in the study, with a response rate of 97.1%. A total of 65.25% (n = 261) [95% CI: 0.60, 0.69] participants had overall readiness, 68.75% (n = 275) [95% CI: 0.64, 0.73] had engagement readiness, and (69.75%) (n = 279) [95% CI: 0.65, 0.74] had core EMR readiness. Computer skills (AOR: 3.06; 95% CI: 1.49-6.29), EMR training (AOR: 2.00; 95% CI: 1.06-3.67), good EMR knowledge (AOR: 2.021; 95% CI: 1.19-3.39), and favorable attitude (AOR: 3.00; 95% CI: 1.76-4.97) were factors significantly associated with EMR readiness.

CONCLUSION

Although it was deemed insufficient, more than half of the respondents indicated a satisfactory level of overall readiness for the adoption of EMR. Moreover, having computer skills, having EMR training, good EMR knowledge, and favorable EMR attitude were all significantly and positively related to EMR readiness.

Big Data in Transfusion Medicine and Artificial Intelligence Analysis for Red Blood Cell Quality Control

Background

"Artificial intelligence" and "big data" increasingly take the step from just being interesting concepts to being relevant or even part of our lives. This general statement holds also true for transfusion medicine. Besides all advancements in transfusion medicine, there is not yet an established red blood cell quality measure, which is generally applied.

Summary

We highlight the usefulness of big data in transfusion medicine. Furthermore, we emphasize in the example of quality control of red blood cell units the application of artificial intelligence.

Key Messages

A variety of concepts making use of big data and artificial intelligence are readily available but still await to be implemented into any clinical routine. For the quality control of red blood cell units, clinical validation is still required.

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.

Incongruous identities: Mental distress and burnout disparities in LGBTQ+ health care professional populations

Health care professionals are chronically overworked due to structural workplace demands and institutional challenges [1]. During the COVID-19 pandemic, US biomedical health care professionals experienced additional environmental strain [2]. Health care professionals who occupy socio-politically minoritized identities are more likely to report symptoms of distress and workplace overburden than their counterparts [2]. While minority stress and identity formation theories explain the relationship between socially constructed identity and environmental strain, these theories remain largely unexplored in LGBTQ+ health care professional populations. Furthermore, contemporary investigations into health care professional burnout and mental distress fail to include differential impacts of identity-based stress, particularly within LGBTQ+ groups. This paper proposes a theoretical explanation for differential stress experiences by health care professionals and calls for research to investigate identity congruence as a key aspect of professionalization in medical schools. Health professions researchers need to attend to identity-based stress models to address discriminatory experiences with burnout and mental distress.

An Overview of the Use of Precision Population Medicine in Cancer Care: First of a Series

Advances in science and technology in the past century and a half have helped improve disease management, prevention, and early diagnosis and better health maintenance. These have led to a longer life expectancy in most developed and middle-income countries. However, resource- and infrastructure-scarce countries and populations have not enjoyed these benefits. Furthermore, in every society, including in developed nations, the lag time from new advances, either in the laboratory or from clinical trials, to using those findings in day-to-day medical practice often takes many years and sometimes close to or longer than a decade. A similar trend is seen in the application of "precision medicine" (PM) in terms of improving population health (PH). One of the reasons for such lack of application of precision medicine in population health is the misunderstanding of equating precision medicine with genomic medicine (GM) as if they are the same. Precision medicine needs to be recognized as encompassing genomic medicine in addition to other new developments such as big data analytics, electronic health records (EHR), telemedicine, and information communication technology. By leveraging these new developments together and applying well-tested epidemiological concepts, it can be posited that population/public health can be improved. In this paper, we take cancer as an example of the benefits of recognizing the potential of precision medicine in applying it to population/public health. Breast cancer and cervical cancer are taken as examples to demonstrate these hypotheses. There exists significant evidence already to show the importance of recognizing "precision population medicine" (PPM) in improving cancer outcomes not only in individual patients but also for its applications in early detection and cancer screening (especially in high-risk populations) and achieving those goals in a more cost-efficient manner that can reach resource- and infrastructure-scarce societies and populations. This is the first report of a series that will focus on individual cancer sites in the future.

The evolution of the medical record from paper to digital: an ENT perspective

OBJECTIVES

A national electronic health record is being procured for Health Service Executive hospitals in Ireland. A number of hospitals have implemented an electronic document management system. This study aimed to investigate the efficiency and safety of the electronic document management system in our centre.

METHODS

A retrospective audit was performed of patients operated on at Galway University Hospital. The availability and location of patients' admission data on the electronic document management system were recorded. These data were analysed using Microsoft Excel software, version 16.45.

RESULTS

The records of 100 patients were analysed. The main findings were: 5 per cent of operation notes were missing, 80 per cent were in the incorrect section, while 15 per cent were in the correct 'procedure' section on the electronic document management system.

CONCLUSION

This study shows there is potential for error with 'paper-light' solutions, whereby delayed scanning, misfiling of scanned records and missing records may lead to significant delays in treatment and potential patient safety issues.

Specialized care and health transport from a Health Care Network integration perspective

Abstract The aim of this study was to analyze the organization and provision of specialized care (SC) and health transport in regional polyclinics in the state of Bahia, Brazil. We conducted a qualitative exploratory single case study of a polyclinic based on semi-structured interviews with key informants in municipal and state health services. We sought to identify elements that characterize network-based models of SC. A number of advances were identified, including: an increase in the provision of SC and the scope and quality of services; effective retention of health professionals; provision of health transport; monitoring of SC; and improvements in the regulation of access to care through the use of computerized systems. The following challenges were observed: tailoring SC planning to regional health needs; the promotion of care coordination by Primary Health Care (PHC); the development of strategies to improve communication between SC and PHC professionals; institutionalization of counter-referral; engagement between the polyclinic and spaces for citizen participation; and fostering matrix support, permanent education, teaching-service integration, and research. Despite the intrinsic challenges of SC and the need to strengthen the central role of PHC, the regional polyclinic arrangement is better suited to the integration of the health care networks.

Specialized care and health transport from a Health Care Network integration perspective

The aim of this study was to analyze the organization and provision of specialized care (SC) and health transport in regional polyclinics in the state of Bahia, Brazil. We conducted a qualitative exploratory single case study of a polyclinic based on semi-structured interviews with key informants in municipal and state health services. We sought to identify elements that characterize network-based models of SC. A number of advances were identified, including: an increase in the provision of SC and the scope and quality of services; effective retention of health professionals; provision of health transport; monitoring of SC; and improvements in the regulation of access to care through the use of computerized systems. The following challenges were observed: tailoring SC planning to regional health needs; the promotion of care coordination by Primary Health Care (PHC); the development of strategies to improve communication between SC and PHC professionals; institutionalization of counter-referral; engagement between the polyclinic and spaces for citizen participation; and fostering matrix support, permanent education, teaching-service integration, and research. Despite the intrinsic challenges of SC and the need to strengthen the central role of PHC, the regional polyclinic arrangement is better suited to the integration of the health care networks.

Machine Learning Model for the Prediction of Hemorrhage in Intensive Care Units

OBJECTIVES

Early hemorrhage detection in intensive care units (ICUs) enables timely intervention and reduces the risk of irreversible outcomes. In this study, we aimed to develop a machine learning model to predict hemorrhage by learning the patterns of continuously changing, real-world clinical data.

METHODS

We used the Medical Information Mart for Intensive Care databases (MIMIC-III and MIMIC-IV). A recurrent neural network was used to predict severe hemorrhage in the ICU. We developed three machine learning models with an increasing number of input features and levels of complexity: model 1 (11 features), model 2 (18 features), and model 3 (27 features). MIMIC-III was used for model training, and MIMIC-IV was split for internal validation. Using the model with the highest performance, external verification was performed using data from a subgroup extracted from the eICU Collaborative Research Database.

RESULTS

We included 5,670 ICU admissions, with 3,150 in the training set and 2,520 in the internal test set. A positive correlation was found between model complexity and performance. As a measure of performance, three models developed with an increasing number of features showed area under the receiver operating characteristic (AUROC) curve values of 0.61-0.94 according to the range of input data. In the subgroup extracted from the eICU database for external validation, an AUROC value of 0.74 was observed.

CONCLUSIONS

Machine learning models that rely on real clinical data can be used to predict patients at high risk of bleeding in the ICU.

Time Spent by Intensive Care Unit Nurses on the Electronic Health Record

BACKGROUND

The amount of time spent on the electronic health record is often cited as a contributing factor to burnout and work-related stress in nurses. Increased electronic health record use also reduces the time nurses have for direct contact with patients and families. There has been minimal investigation into the amount of time intensive care unit nurses spend on the electronic health record.

OBJECTIVE

To quantify the amount of time spent by intensive care unit nurses on the electronic health record.

METHODS

In this observational study, active electronic health record use time was analyzed for 317 intensive care unit nurses in a single institution from January 2019 through July 2020. Monthly data on electronic health record use by nurses in the medical, neurosurgical, and surgical-trauma intensive care units were evaluated.

RESULTS

Full-time intensive care unit nurses spent 28.9 hours per month on the electronic health record, about 17.5% of their clinical shift, for a total of 346.3 hours per year. Part-time nurses and those working as needed spent 20.5 hours per month (17.6%) and 7.4 hours per month (14.2%) on the electronic health record, respectively. Neurosurgical and medical intensive care unit nurses spent 25.0 hours and 19.9 hours per month, respectively. Nurses averaged 23 clicks per minute during use. Most time was spent on the task of documentation at 12.3 hours per month, which was followed by medical record review at 2.6 hours per month.

CONCLUSION

Intensive care unit nurses spend at least 17% of their shift on the electronic health record, primarily on documentation. Future interventions are necessary to reduce time spent on the electronic health record and to improve nurse and patient satisfaction.

Documentation of adherence to infection prevention best practice in patient records: a mixed-methods investigation

Background

Healthcare-associated infections remain a preventable cause of patient harm in healthcare. Full documentation of adherence to evidence-based best practices for each patient can support monitoring and promotion of infection prevention measures. Thus, we reviewed the extent, nature, and determinants of the documentation of infection prevention (IP) standards in patients with HAI.

Methods

We reviewed electronic patient records (EMRs) of patients included in four annual point-prevalence studies 2013–2016 who developed a device- or procedure-related HAI (surgical site infection (SSI), catheter-associated urinary tract infection (CAUTI), ventilator-associated infection (VAP), catheter-related bloodstream infection (CRBSI)). We examined the documentation quality of mandatory preventive measures published as institutional IP standards. Additionally, we undertook semi-structured interviews with healthcare providers and a two-step inductive (grounded theory) and deductive (Theory of Planned Behaviour) content analysis.

Results

Of overall 2972 surveyed patients, 249 (8.4%) patients developed 272 healthcare-associated infections. Of these, 116 patients met the inclusion criteria, classified as patients with SSI, CAUTI, VAP, CRBSI in 78 (67%), 21 (18%), 10 (9%), 7 (6%), cases, respectively. We found documentation of IP measures in EMRs in 432/1308 (33%) cases. Documentation of execution existed in the study patients’ EMRs for SSI, CAUTI, VAP, CRBSI, and overall, in 261/931 (28%), 27/104 (26%), 46/122 (38%), 26/151 (17%), and 360/1308 (28%) cases, respectively, and documentation of non-execution in 67/931 (7%), 2/104 (2%), 0/122 (0%), 3/151 (2%), and 72/1308 (6%) cases, respectively. Healthcare provider attitudes, subjective norms, and perceived behavioural control indicated reluctance to document IP standards.

Conclusions

EMRs rarely included conclusive data about adherence to IP standards. Documentation had to be established indirectly through data captured for other reasons. Mandatory institutional documentation protocols or technically automated documentation may be necessary to address such shortcomings in patient safety documentation.

Precision Medicine in Solid Tumors: How Far We Traveled So Far?

The future of disease management in solid tumors will rely heavily on how effectively we understand precision medicine and how successfully we can deliver personalized medicine [...]

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

Aim:

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

Background:

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

Methods:

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

Findings:

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

A Research Agenda for Diagnostic Excellence in Critical Care Medicine

Diagnosing critically ill patients in the intensive care unit is difficult. As a result, diagnostic errors in the intensive care unit are common and have been shown to cause harm. Research to improve diagnosis in critical care medicine has accelerated in past years. However, much work remains to fully elucidate the diagnostic process in critical care. To achieve diagnostic excellence, interdisciplinary research is needed, adopting a balanced strategy of continued biomedical discovery while addressing the complex care delivery systems underpinning the diagnosis of critical illness.

Systematic Review of Lifestyle Interventions for Gestational Diabetes Mellitus in Pregnancy and the Postpartum Period

OBJECTIVE

To examine the characteristics and effectiveness of lifestyle interventions for gestational diabetes mellitus (GDM) in pregnancy and the postpartum period to prevent Type 2 diabetes.

DATA SOURCES

We conducted searches in seven databases, including Ovid MEDLINE, CINAHL, Ovid Embase, Cochrane Central, Web of Science, Ovid PsycInfo, and ProQuest Dissertations and Theses for articles published from inception to January 2021.

STUDY SELECTION

We included articles on controlled intervention studies in which researchers evaluated a lifestyle intervention provided during pregnancy and the postpartum period for women with or at risk for GDM that were published in English.

DATA EXTRACTION

Twelve articles that were reports of seven studies met the inclusion criteria. In some cases, more than one article was selected from the same study. For example, articles reported different outcomes from the same study. We extracted data with the use of a data collection form and compared and synthesized data on study design, purpose, sample, intervention characteristics, recruitment and retention, and outcomes.

DATA SYNTHESIS

All seven studies focused on weight management and/or healthy lifestyle behaviors (diet and physical activity). Outcomes included glucose regulation, weight, lifestyle behaviors, and knowledge. The interventions varied in duration/dosage, strategies, and modes of delivery. In four studies, researchers reported interventions that had significant effects on improving glucose regulation and/or weight change. Some characteristics from the four effective interventions included goal setting, individualized care, and good retention rates. In the other three studies, limitations included low rates of participant retention, lack of personalized interventions, and limited population diversity or lack of culturally sensitive care.

CONCLUSION

Lifestyle interventions provided during and after pregnancy to reduce the risk associated with GDM have the potential to improve outcomes. Health care counseling to promote healthy lifestyle behaviors related to the prevention of Type 2 diabetes is needed at different stages of maternity care for women with GDM. Additional high-quality studies are needed to address the limitations of current studies.

Obstacles and features of health information systems: A systematic literature review

BACKGROUND

Currently many healthcare systems are supported by an increasing set of Health Information Sys-tems (HISs), which assist the activities for multiple stakeholders. The literature on HISs is, however, fragmented and a solid overview of the current state of HISs is missing. This impedes the understanding and characterization of the required HISs for the healthcare domain.

METHODS

In this article, we present the results of a Systematic Literature Review (SLR) that identifies the HISs, their domains, stakeholders, features, and obstacles.

RESULTS

In the SLR, we identified 1340 papers from which we selected 136 studies, on which we performed a full-text analysis. After the synthesis of the data, we were able to report on 33 different domains, 41 stakeholders, 73 features, and 69 obstacles. We discussed how these domains, features, and obstacles interact with each other and presented suggestions to overcome the identified obstacles. We recognized five groups of obstacles: technical problems, operational functionality, maintenance & support, usage problems, and quality problems. Obstacles from all groups require to be solved to pave the way for further research and application of HISs.

CONCLUSION

This study shows that there is a plentitude of HISs with unique features and that there is no consensus on the requirements and types of HISs in the literature.

Bases para a Reforma da Atenção Primária à Saúde no Brasil em 2019: mudanças estruturantes após 25 anos do Programa de Saúde da Família

O ano de 2019 marcou a estruturação das bases para uma profunda reforma na Atenção Primária à Saúde (APS) do Brasil. Os desafios enfrentados através dessa reforma foram a falta de priorização política real da APS, o financiamento insuficiente da APS e focado na estrutura de serviços, os obstáculos ao acesso de primeiro contato, a escassez de profissionais qualificados, a necessidade de maior informatização da APS e a ausência de integração de dados clínicos, a fragilidade clínica e necessidade de ampliação do escopo profissional e a falta de informação de qualidade para tomada de decisão clínica e gerencial. Com ações direcionadas a cada um desses desafios, a Secretaria de Atenção Primária à Saúde do Ministério da Saúde criou estratégias sistêmicas e integradas entre si que representam mudanças estruturantes e investimentos em governança clínica para transformar a APS brasileira, garantindo mais e melhor saúde para a população, com mecanismos transparentes e técnicos para seu financiamento, organização, oferta, monitoramento e avaliação.