Ensemble Evolutionary Algorithm for Feature Selection and Classification in Healthcare Data Mining

Introduction: The digital technology in healthcare has revolutionized during the last decade with the increased implementation of Electronic Health Record (EHR) systems. The growth of EHR has been from the simple database system to incorporating the applications of artificial intelligence and machine learning systems in the system. One of the most important aspects of digital system proves to be electronic health record system because of the intense data produced geographically which can be used for epidemiological studies and research. The EHRs also have a wide range of benefits such as effective communication between the clinicians, clinicians and laboratory, decreased human errors, effective tool for diagnosis and treatment planning, increased patient satisfaction, and quality care. Materials and Methods: The quantitative study included opinions and perspectives of dental clinicians toward EHR system. The data collection of the participants was from April to December 2024. The quantitative part involved a descriptive survey. After obtaining written informed consent from each participant, a structured survey e-questionnaire was given to dental clinicians to fill it through online mode by dental practitioners who had independent clinics. The sample size calculation done by www.openepi.com software quantitative study which included two hundred and eighty five (285) dentists participated. Results: The findings indicated a favorable disposition among dentists regarding the adoption of EHR; however, dental practitioners also emphasized the problems encountered during EHR implementation. Conclusion: EHRs are an effective way inside health information systems; however, they necessitate ongoing training, regular upgrades, and the integration of artificial intelligence and machine learning technologies in the future. The threat to data security continues to be a concern for dental practitioners due to the anticipated rise in cybercrimes, as patient-sensitive information is highly confidential.

BackgroundThe increasing adoption of electronic health record (EHR) systems enables automated, large scale, and meaningful analysis of regional population health. We explored how EHR systems could inform surveillance of trauma-related emergency department visits arising from seasonal, holiday-related, and rare environmental events.MethodsWe analyzed temporal variation in diagnosis codes over 24 years of trauma visit data at the three hospitals in the University of Washington Medicine system in Seattle, Washington, USA. We identified seasons and days in which specific codes and categories of codes were statistically enriched, meaning that a significantly greater than average proportion of trauma visits included a given diagnosis code during that time period.ResultsWe confirmed known seasonal patterns in emergency department visits for trauma. As expected, cold weather-related incidents (e.g. frostbite, snowboarding injury) were enriched in the winter, whereas fair weather-related incidents (e.g. bug bites, boating accidents, bicycle accidents) were enriched in the spring and summer. Our analysis of specific days of the year found that holidays were enriched for alcohol poisoning, assaults, and firework accidents. We also detected one time regional events such as the 2001 Nisqually earthquake and the 2006 Hanukkah Eve Windstorm.ConclusionsThough EHR systems were developed to prioritize operational rather than analytic priorities and have consequent limitations for surveillance, our EHR enrichment analysis nonetheless re-identified expected temporal population health patterns. EHRs are potentially a valuable source of information to inform public health policy, both in retrospective analysis and in a surveillance capacity.

BackgroundClinicians’ use of electronic health record (EHR) systems while multitasking may increase the risk of making errors, but silent EHR system use may lower patient satisfaction. Delaying EHR system use until after patient visits may increase clinicians’ EHR workload, stress, and burnout.ObjectiveWe aimed to describe the perspectives of clinicians, educators, administrators, and researchers about misses and near misses that they felt were related to clinician multitasking while using EHR systems.MethodsThis observational study was a thematic analysis of perspectives elicited from 63 continuing medical education (CME) participants during 2 workshops and 1 interactive lecture about challenges and strategies for relationship-centered communication during clinician EHR system use. The workshop elicited reflection about memorable times when multitasking EHR use was associated with “misses” (errors that were not caught at the time) or “near misses” (mistakes that were caught before leading to errors). We conducted qualitative analysis using an editing analysis style to identify codes and then select representative themes and quotes.ResultsAll workshop participants shared stories of misses or near misses in EHR system ordering and documentation or patient-clinician communication, wondering about “misses we don’t even know about.” Risk factors included the computer’s position, EHR system usability, note content and style, information overload, problematic workflows, systems issues, and provider and patient communication behaviors and expectations. Strategies to reduce multitasking EHR system misses included clinician transparency when needing silent EHR system use (eg, for prescribing), narrating EHR system use, patient activation during EHR system use, adapting visit organization and workflow, improving EHR system design, and improving team support and systems.ConclusionsCME participants shared numerous stories of errors and near misses in EHR tasks and communication that they felt related to EHR multitasking. However, they brainstormed diverse strategies for using EHR systems safely while preserving patient relationships.

Usability problems do not heal by themselves: National survey on physicians’ experiences with EHRs in Finland

Adoption of electronic health record (EHR) systems can lead to health care savings, reduction in medical errors, and improvement in health. The intersection of health information technology (HIT) and health care quality is a result of the following two developments: the quality improvement movement and the maturation of HIT. The potential safety benefits of EHR systems focus largely on alerts, reminders, and other components of ambulatory computerized provider order entry. EHR systems are integral throughout the disease management process. Using HIT for near-term chronic disease management programs has the benefits of identifying people with a potential or active chronic disease, targeting services based on their level of risk, and monitoring their risk. The purposes of benchmarking and survey reports are to provide a quantifiable measure of performance and to quantify gaps between your practice and best practices.

Electronic health record implementation and hospitals' total factor productivity

This paper presents the impact of the Electronic Health Records (EHRs) systems jointly in the Spanish Primary Public Health System. Different EHRs that exist in each of the Spanish regions are discussed. Moreover, other purpose of this analysis is to identify the current state of knowledge about health information systems adoption in primary care in Spain. For the analysis and study of EHRs systems in Spain we have relied on the use of different sources, mostly items related to the study of EHRs systems in different areas. We will analyze some technical aspects of these and some of their major implications, both positive and negative. Moreover, we have resorted to make direct contact with the organizations that have implemented the EHRs systems. The result of this study leads to a main idea, the need for interoperability between different systems. We will delve into how we have reached this conclusion and that is the key to EHRs systems homogenization of Spanish territory. EHR systems used in different regions of Spain offer the access to medical information as well as provide a clinical analysis of each patient more quickly. The adoption of health information systems is seen world wide as one method to mitigate the widening health care demand and supply gap.

BackgroundThe implementation of Electronic Health Record (EHR) systems is a critical challenge, particularly in low-income countries, where behavioral intention plays a crucial role. To address this issue, we conducted a study to extend and apply the Unified Theory of Acceptance and Use of Technology 3 (UTAUT3) model in predicting health professionals' behavioral intention to use EHR systems.MethodsA quantitative research approach was employed among 423 health professionals in Southwest Ethiopia. We assessed the validity of the proposed model through measurement and structural model statistics. Analysis was done using SPSS AMOS version 23. Hypotheses were tested using structural equation modeling (SEM) analysis, and mediation and moderation effects were evaluated. The associations between exogenous and endogenous variables were examined using standardized regression coefficients (β), 95% confidence intervals, and p-values, with a significance level of p-value < 0.05.ResultsThe proposed model outperformed previous UTAUT models, explaining 84.5% (squared multiple correlations (R2) = 0.845) of the variance in behavioral intention to use EHR systems. Personal innovativeness (β = 0.215, p-value < 0.018), performance expectancy (β = 0.245, p-value < 0.001), and attitude (β = 0.611, p-value < 0.001) showed significant associations to use EHR systems. Mediation analysis revealed that performance expectancy, hedonic motivation, and technology anxiety had significant indirect effects on behavioral intention. Furthermore, moderation analysis indicated that gender moderated the association between social influence, personal innovativeness, and behavioral intention.ConclusionThe extended UTAUT3 model accurately predicts health professionals' intention to use EHR systems and provides a valuable framework for understanding technology acceptance in healthcare. We recommend that digital health implementers and concerned bodies consider the comprehensive range of direct, indirect, and moderating effects. By addressing personal innovativeness, performance expectancy, attitude, hedonic motivation, technology anxiety, and the gender-specific impact of social influence, interventions can effectively enhance behavioral intention toward EHR systems. It is crucial to design gender-specific interventions that address the differences in social influence and personal innovativeness between males and females.

One goal of public-policy makers in general and health care managers in particular is the adoption and efficient utilization of electronic health record (EHR) systems throughout the health care industry. Consequently, this investigation focused on the effects of known antecedents of technology adoption on physician satisfaction with EHR technology and the continued use of such systems. The American Academy of Family Physicians provided support in the survey of 453 physicians regarding their satisfaction with their EHR use experience. A conceptual model merging technology adoption and computer user satisfaction models was tested using structural equation modeling. Results indicate that effort expectancy (ease of use) has the most substantive effect on physician satisfaction and the continued use of EHR systems. As such, health care managers should be especially sensitive to the user and computer interface of prospective EHR systems to avoid costly and disruptive system selection mistakes.

BackgroundOne of the primary obstacles to the widespread adoption of openEHR methodology is the lack of practical persistence solutions for future-proof electronic health record (EHR) systems as described by the openEHR specifications. This paper presents an archetype relational mapping (ARM) persistence solution for the archetype-based EHR systems to support healthcare delivery in the clinical environment.MethodsFirst, the data requirements of the EHR systems are analysed and organized into archetype-friendly concepts. The Clinical Knowledge Manager (CKM) is queried for matching archetypes; when necessary, new archetypes are developed to reflect concepts that are not encompassed by existing archetypes. Next, a template is designed for each archetype to apply constraints related to the local EHR context. Finally, a set of rules is designed to map the archetypes to data tables and provide data persistence based on the relational database.ResultsA comparison study was conducted to investigate the differences among the conventional database of an EHR system from a tertiary Class A hospital in China, the generated ARM database, and the Node + Path database. Five data-retrieving tests were designed based on clinical workflow to retrieve exams and laboratory tests. Additionally, two patient-searching tests were designed to identify patients who satisfy certain criteria. The ARM database achieved better performance than the conventional database in three of the five data-retrieving tests, but was less efficient in the remaining two tests. The time difference of query executions conducted by the ARM database and the conventional database is less than 130 %. The ARM database was approximately 6–50 times more efficient than the conventional database in the patient-searching tests, while the Node + Path database requires far more time than the other two databases to execute both the data-retrieving and the patient-searching tests.ConclusionsThe ARM approach is capable of generating relational databases using archetypes and templates for archetype-based EHR systems, thus successfully adapting to changes in data requirements. ARM performance is similar to that of conventionally-designed EHR systems, and can be applied in a practical clinical environment. System components such as ARM can greatly facilitate the adoption of openEHR architecture within EHR systems.

BackgroundWhile machine learning (ML) technologies have shifted from development to real-world deployment over the past decade, US health care providers and hospital administrators have increasingly embraced ML, particularly through its integration with electronic health record (EHR) systems. This evolving landscape underscores the need for empirical evidence on ML adoption and its determinants; however, the relationship between hospital characteristics and ML integration within EHR systems remains insufficiently explored.ObjectiveThis study aimed to examine the current state of ML adoption within EHR systems across US general acute care hospitals and to identify hospital characteristics associated with ML implementation.MethodsWe used linked data between the 2022‐2023 American Hospital Association Annual Survey and the 2023‐2024 American Hospital Association Information Technology Supplement Survey. The sample includes 2562 general and acute care hospitals in the United States with a total of 4055 observations over 2 years. Applying inverse probability weighting to address nonresponse bias, we used descriptive statistics to assess ML adoption patterns and multivariate logistic regression models to identify hospital characteristics associated with ML adoption.ResultsOverall, about 75% of the hospitals had adopted ML functions within their EHR systems in 2023‐2024, and the majority tended to adopt both clinical and operational ML functions simultaneously. The most commonly adopted individual functions were predicting inpatient risks and outpatient follow-ups. ML model evaluation practices, while still limited overall, showed notable improvement. Multivariate regression estimates indicate that hospitals were more likely to adopt any ML if they were not-for-profit (4.4 percentage points, 95% CI 0.6-8.2; P=.02), large hospitals (15 percentage points, 95% CI 9.4-21; P<.001), operated in metropolitan areas (4.3 percentage points, 95% CI 0.8-7.8; P=.02), contracted with leading EHR vendors (20.6 percentage points, 95% CI 17.1-24; P<.001), and affiliated with a health system (26.8 percentage points, 95% CI 22.4-31.3; P<.001). Similar patterns were observed for predicting the adoption of both clinical and operative ML. We also identified specific hospital characteristics associated with the adoption of individual ML functions.ConclusionsML adoption in hospitals is influenced by organizational resources and strategic priorities, raising concerns about potential digital inequities. Limited quality control and evaluation practices highlight the need for stronger regulatory oversight and targeted support for underresourced hospitals. As the integration of ML into EHR systems expands, disparities in both adoption and oversight become increasingly critical. To ensure the equitable, safe, and effective implementation of ML technologies in health care, well-designed policies must address these gaps and promote inclusive innovation across all hospital settings.

Nurses are one of the largest user groups of the electronic health record (EHR) system, relying on its tools to support patient care and nursing workflows. Recent studies suggested that the redesign of nursing documentation may reduce the time spent in the EHR system and improve nurse satisfaction. We aimed to assess nurses' perceptions of the redesigned EHR, evaluate the impact of documentation interventions, and identify future improvement needs. Guided by the American Nursing Informatics Association's Six Domains of Burden conceptual framework, this multimethod project combined both qualitative and quantitative approaches. Registered nurses across the academic health system were recruited via email invitations to participate in focus group discussions. The focus groups were conducted via a web conference and ranged from 60 to 90 minutes in duration. The focus group discussions were transcribed and analyzed through thematic analysis. The EHR vendor's time data were used to analyze nurses' time spent in documentation. In total, 20 registered nurses participated in the focus group discussions, and 17 nurses completed the demographic survey; 88% (15/17) of participants had ≥3 years of EHR experience at the academic health system, and 53% (9/17) self-reported being competent in the EHR system. The following six themes emerged: positive feedback, usability and workflow opportunities, nuisance, training and education, communication, and time spent in the system. EHR vendor time data revealed that the time spent in flowsheets averaged 31.11% per 12-hour shift. Overall, participants reported a positive experience and that the EHR supported patient care. There are opportunities to further reduce redundancies in documentation and implement programs that support continuous learning about EHR and health technology tools. Specific suggestions include optimizing the oral health assessment tool. Analyzing frontline nursing perspectives in the redesign of EHR workflows is imperative for identifying interventions that support nurses' satisfaction with the EHR.

IntroductionThe ability to utilize the electronic health record (EHR) without compromising the doctor-patient relationship (DPR) is an essential skill of all physicians and trainees, yet little time is spent on educating or assessing learners on needed techniques. To address this gap, we developed a conventional OSCE station coupled with a simulated patient chart within the Epic program in order to assess our students' skills utilizing the EHR during a patient encounter.MethodsOf third-year medical students, 119 were given full access to the patient's simulated chart 24 hours in advance of their OSCE to review clinical data. During an in-person OSCE with a standardized patient (SP), students performed a focused history and physical, using the EHR to verify allergies and medications. Students completed an electronic patient note graded by faculty. SPs evaluated the students on communication and interpersonal skills with specific rubric elements. Faculty graded the students' notes to evaluate their expression of clinical reasoning in the assessment and plan.ResultsTraining SPs and faculty to assess students on EHR skills was feasible. After implementation of a comprehensive curriculum focused on EHR and DPR, there was a significant difference on EHR-related communication skills (M = 76.4, SD = 17.6) versus (M = 37, SD = 28.9) before curriculum enhancement t (117.9) = −12.4, p <.001.DiscussionThe EHR OSCE station provided a standardized method of assessing students' EHR skills during a patient encounter. Challenges still exist in the technological requirements to develop and deliver cases in today's EHR platform.

Patients engaged in self-care through information technology can potentially improve the quality of healthcare they receive. This study aimed to examine how electronic health record (EHR) system functionalities help hospitals mediate the impact of patient engagement on quality outcomes-notably, readmission rates. A pooled cross-sectional study design employed data containing 3,547 observations from general acute care hospitals (2014-2018). The breadth of patient engagement functionalities adopted by a hospital was used as the independent variable and the degree of EHR presence was used as the mediating variable. Mean time to readmission for acute myocardial infarction (AMI), pneumonia, and heart failure were the dependent variables. The Baron and Kenny method was used to test mediation. Patient engagement was associated with reduced AMI readmission rates both directly and via EHR system presence. Mediation effects were present, in that a 1-unit increase in patient engagement through EHR system presence was associated with a 0.33% decrease in AMI readmission rates (p <.05). For other disease categories (heart failure and pneumonia), a significant effect was not found. For hospitals with a comprehensive EHR system, patient engagement through information technology can potentially reduce readmission rates for some diseases. More research is needed to determine which specific clinical conditions are amenable to quality improvement through patient engagement. Synergies between patient engagement functionalities and an EHR system positively affect quality outcomes. Therefore, practitioners and hospital managers should leverage hospital investments made in their EHR system infrastructure and use it to engage patients in self-care.

Electronic health records (EHRs) are a promising tool to improve the quality of health care, although it remains unclear who will benefit from this new technology. Given that a small group of providers care for most racial/ethnic minorities, we sought to determine whether minority-serving providers adopt EHR systems at comparable rates to other providers. We used survey data from stratified random sample of all medical practices in Massachusetts in 2005. We determined rates of EHR adoption, perceived barriers to adoption, and satisfaction with EHR systems. Physicians who reported patient panels of more than 40% black or Hispanic had comparable levels of EHR adoption than other physicians (27.9% and 21.8%, respectively, P = 0.46). Physicians from minority-serving practices identified financial and other barriers to implementing EHR systems at similar rates, although these physicians were less likely to be concerned with privacy and security concerns of EHRs (47.1% vs. 64.4%, P = 0.01). Finally, physicians from high-minority practices had similar perceptions about the positive impact of EHRs on quality (73.7% vs. 76.6%, P = 0.43) and costs (46.9% vs. 51.5%, P = 0.17) of care. In a state with a diverse minority population, we found no evidence that minority-serving providers had lower EHR adoption rates, faced different barriers to adoption or were less satisfied with EHRs. Given the importance of ensuring that minority-serving providers have equal access to EHR systems, we failed to find evidence of a new digital divide.