Electronic Health Record Systems Research Articles

Understanding the predictors of health professionals' intention to use electronic health record system: extend and apply UTAUT3 model

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.

Intelligent Threat Detection And Response Systems For Safeguarding Cloud-Hosted Electronic Health Records From Cyber Attacks

As more people use cloud-based electronic health record (EHR) systems, they make healthcare better, but they also make it easier for cybercriminals to attack. This article describes a system that uses artificial intelligence (AI) and machine learning (ML) to intelligently watch cloud-hosted EHR environments for bad behaviour, find cyberattacks, and automatically take the right steps to stop them. Using supervised machine learning models that have been trained on known threat indicators, the suggested framework constantly looks at log and system data. As soon as an attack is found, established containment and mitigation steps are carried out naturally to lower the harm. The test results show that the framework can correctly identify common EHR attack methods like ransomware and data theft, as well as quickly and effectively protect private patient data

Comparative effectiveness of treating prenatal depression with counseling versus antidepressants in relation to preterm delivery

BackgroundMaternal depression during pregnancy is prevalent and has been associated with increased risk of preterm delivery (PTD). However, comparative effectiveness of two commonly used treatment options, mental health counseling and use of antidepressants, in mitigating the risk of PTD associated with maternal depression remains uncertain. Although antidepressant use has been associated with increased risk of PTD in many previous studies, a direct head-to-head comparison between these two treatment options has not been investigated. Thus, the comparative risk-benefit profiles of those two treatment options remain unclear. ObjectivesTo determine the comparative effectiveness of two commonly used options for treating prenatal depression in limiting the risk of PTD associated with maternal depression. Study DesignA large prospective cohort study was conducted among 82,170 pregnant women at Kaiser Permanente Northern California (KPNC), an integrated health delivery system. Clinically diagnosed depression and its treatments (use of antidepressants and mental health counseling) were identified from the KPNC electronic health record system (EHR). Gestational age was also recorded for all deliveries and captured by EHRs for determining PTD. ResultsUsing Cox proportional hazards regression incorporating propensity score methodology to ensure comparability between comparison cohorts, relative to those without depression, pregnant women with untreated depression had 41% increased risk of PTD: adjusted hazard ratio (aHR)=1.41, 95% confidence interval (CI)=1.24-1.60, confirming increased risk of PTD associated underlying maternal depression. Relative to untreated depression, any mental health counseling was associated with a 18% of reduced risk of PTD: aHR=0.82 (0.71-0.96). The inverse association showed a dose-response pattern: increased number of counseling visits was associated with greater reduction in PTD risk with 43% reduction in PTD risk associated with 4 or more visits (aHR=0.57, 95% CI=0.45-0.73). In contrast, use of antidepressants during pregnancy was associated with an additional 31% increased risk of PTD independent of underlying depression: aHR=1.31, 95% CI=1.06-1.61. This positive association also showed a dose-response relationship: a longer duration of use was associated with an even higher risk. ConclusionsThis study provides much needed evidence regarding the comparative effectiveness of two common treatment options for prenatal depression in the context of PTD risk. The results indicate that, to reduce PTD risk due to maternal depression, mental health counseling is more effective. Use of antidepressants may add additional risk of PTD, independent of the underlying depression. The findings provide data for clinicians and pregnant women to make informed and evidence-based treatment decisions that take into account the risks and benefits to both maternal and fetal health.

Prognosis of fall risk in home care clients: A noninvasive approach using survival analysis

Abstract Aim The present study has designed, implemented, and evaluated a machine learning model that can predict fall risk and fall occurrence in community-dwelling elderly based on their home care usage. Subjects and methods A dataset consisting of 2542 weekly home care records for 1499 citizens (59% female, 41% male) with a mean age of 77 years (SD 10 years) was collected from a large municipality in Denmark. The data were recorded between January 1, 2021, and December 31, 2021. The dataset was divided into two cohorts. Subsequently, five machine learning-based survival analysis models were trained and evaluated using cross-validation. Results The CoxBoost model showed the best discriminative performance with a mean 0.64 (95% CI 0.57–0.72) Harrell’s concordance index, indicating better ranking than chance-level by 14% on average. However, the model could not accurately predict when the next fall would take place. Conclusions The proposed method enables professionals to assess individual fall risk by using home care records from an Electronic Health Record (EHR) system. This facilitates the initiation of targeted fall-prevention programs for those at highest risk. Additionally, it is expected that a risk-based approach can lead to a lower number needed to treat (NNT), indicating greater effectiveness of health interventions.

Measuring Documentation Burden in Healthcare.

The enactment of the Health Information Technology for Economic and Clinical Health Act and the wide adoption of electronic health record (EHR) systems have ushered in increasing documentation burden, frequently cited as a key factor affecting the work experience of healthcare professionals and a contributor to burnout. This systematic review aims to identify and characterize measures of documentation burden. We integrated discussions with Key Informants and a comprehensive search of the literature, including MEDLINE, Embase, Scopus, and gray literature published between 2010 and 2023. Data were narratively and thematically synthesized. We identified 135 articles about measuring documentation burden. We classified measures into 11 categories: overall time spent in EHR, activities related to clinical documentation, inbox management, time spent in clinical review, time spent in orders, work outside work/after hours, administrative tasks (billing and insurance related), fragmentation of workflow, measures of efficiency, EHR activity rate, and usability. The most common source of data for most measures was EHR usage logs. Direct tracking such as through time-motion analysis was fairly uncommon. Measures were developed and applied across various settings and populations, with physicians and nurses in the USA being the most frequently represented healthcare professionals. Evidence of validity of these measures was limited and incomplete. Data on the appropriateness of measures in terms of scalability, feasibility, or equity across various contexts were limited. The physician perspective was the most robustly captured and prominently focused on increased stress and burnout. Numerous measures for documentation burden are available and have been tested in a variety of settings and contexts. However, most are one-dimensional, do not capture various domains of this construct, and lack robust validity evidence. This report serves as a call to action highlighting an urgent need for measure development that represents diverse clinical contexts and support future interventions.

Reinterpreting the Nursing Record for an Electronic Context: Development Principles.

The full potential for electronic health record systems in facilitating a positive transformation in care, with improvements in quality and safety, has yet to be realised. There remains a need to reconceptualise the structure, content and use of the nursing component of electronic health record systems. The aim of this study was to engage and involve a diverse group of stakeholders, including nurses and electronic health record system developers, in exploring together both issues and possible new approaches to documentation that better fit with practice, and that facilitate the optimal use of recorded data. Three focus groups were held in the UK and USA, using a semi-structured interview guide, and a common reflexive approach to analysis. The findings were synthesised into themes that were further developed into a set of development principles that might be used to inform a novel electronic health record system specification to support nursing practice.

Initial Findings of a Mixed-Methods Study to Reduce Nursing Documentation-Related Burden.

Amidst the current healthcare workforce crisis, nurses continue to experience burnout, with one contributor being the growing amount of required documentation and other electronic health record (EHR) tasks that must be completed. This study aims to identify ways in which nurses can be better supported through consideration for the burden that EHR systems may present due to increasing documentation requirements and areas of inefficient use. This study leverages nursing engagement to ensure the needs of nurses are identified and EHR efficiency is improved. Practical strategies and EHR system improvements will be proposed based on the recommendations and guidance of nursing staff.

Advancements in health informatics for monitoring perioperative patient data

Advancements in health informatics are revolutionizing perioperative care by enhancing patient monitoring, improving clinical decision-making, and promoting patient safety. The integration of electronic health records (EHR) facilitates seamless information flow across all perioperative stages, ensuring up-to-date patient data is accessible to the entire healthcare team. This accessibility reduces errors, improves coordination, and enhances overall patient outcomes. EHR systems also support standardized documentation, reducing variability in record-keeping and ensuring comprehensive patient information is consistently captured. Real-time data analytics and predictive modeling are powerful tools in perioperative settings, enabling clinicians to anticipate and manage potential complications proactively. By analyzing vast datasets, predictive models identify patterns and risk factors associated with adverse outcomes, allowing for targeted interventions and personalized care plans. Real-time analytics provide continuous monitoring of patient status during surgery, offering immediate feedback and enabling prompt adjustments to ensure patient stability. Wearable devices and remote monitoring systems further augment real-time data analytics by continuously tracking physiological parameters throughout the surgical process and into the postoperative period. This continuous data stream allows for ongoing assessment of patient status and early detection of complications, enhancing patient safety and outcomes. Health informatics solutions, including automated alerts and decision support tools, minimize human error and ensure adherence to clinical guidelines. Automated reminders for critical actions, such as timely antibiotic administration, significantly reduce the incidence of surgical site infections. Decision support tools provide real-time, evidence-based recommendations at the point of care, supporting clinicians in making informed decisions and improving patient outcomes. Additionally, health informatics facilitates continuous quality improvement by enabling the collection and analysis of performance data, informing targeted interventions and process improvements. Overall, advancements in health informatics are transforming perioperative care, offering numerous benefits in terms of efficiency, accuracy, and patient safety, ultimately leading to better patient outcomes and higher quality of care. Continued innovation in this field will be essential for maintaining and improving the standards of perioperative care.

A Pilot Implementation and Evaluation of Speech Recognition Technology for Nurses and Health Disciplines.

This pilot study addresses the pervasive issue of burnout among nurses and health disciplines, often exacerbated by the use of electronic health record (EHR) systems. Recognizing the potential of dictation to alleviate documentation burden, the study focuses on the adoption of speech recognition technology (SRT) in a large Canadian urban mental health and addiction teaching hospital. Clinicians who participated in the pilot provided feedback on their experiences via a survey, and analytics data were examined to measure usage and adoption patterns. Preliminary feedback reveals a subset of participants rapidly embracing the technology, reporting decreased documentation times and increased efficiency. However, some clinicians experienced challenges related to initial setup time and the effort of adjusting to a novel documentation approach.

Efficient Workflow Analysis to Address Paper Persistence in Tuberculin Testing.

Despite widespread adoption and maturity, paper persistence endures in many Electronic Health Record (EHR) systems, particularly for complex workflows involving multiple steps from different stakeholders separated in time. In our health system, Latent Tuberculosis Infection (LTBI) testing was one such workflow where a Tuberculin Skin Test (TST) must be administered and then correctly read 48-72 hours later and documented. This paper discusses a low-resource workflow analysis and clinical decision support approach to replace a paper workflow and garner the benefits of the EHR for clearer documentation and retrieval of LTBI results. Our approach resulted in a significant increase in completed TST documentation, 57% (24/42) to 95% (18/19), P < 0.003. Human-centered design practices such as work system analysis and formative usability testing are feasible with limited resources and improve the likelihood of success of electronic workflows by designing solutions that fit existing clinical workflows and automating processes wherever possible.

Use of Electronic Health Records and Documentation Burden Among Nurses in Acute and Critical Care.

Nurses who provide the majority of hands-on care for hospitalized patients are disproportionately affected by the current state of electronic health records (EHRs), and little is known about their lived perception of EHR use. Using a mixed-methods research design, we conducted an in-depth analysis and synthesis of data from EHR usage log files, interviews, and surveys and assessed factors contributing to the nurse documentation burden in acute and critical at a large academic medical center. There remain substantial spaces where we can develop viable solutions for enhancing the usability of multi-component EHR systems.

Impact of EHRs on Nursing Burnout in a Hospital Setting in Saudi Arabia: Mixed-Methods Study.

This study explores the association between nursing burnout and Electronic Health Record (EHR) use in a Saudi Arabian hospital adopting an advanced EHR system. Utilising a mixed-methods approach, the research combines quantitative analysis of 282 survey responses and qualitative interviews from 21 registered nurses. Despite high EHR acceptance, negative perceptions and stress related to EHR use were identified. Findings indicate a weak link between EHR use and burnout, with resilience acting as a mitigating factor. Specific stressors, including documentation workload and usability issues, were countered by individual and organisational resilience. The study introduces a novel conceptual model emphasising the pivotal role of resilience at both levels in mitigating EHR-related burnout. Recommendations include fostering resilience-building strategies in EHR implementation processes and usability to prevent burnout, emphasising self-care practices, promoting work-life balance, and improving health information infrastructure.

Engaging with the public on their views on the collection, use and sharing of personal health information: Development of a scenario-based survey instrument

Background Members of the public differ regarding their views on the use and sharing of personal health information. Objective This paper describes the methodology employed to develop a telephone-based survey tool to capture views of the public on the collection, use and sharing of personal health information. Method A rigorous methodology comprising multiple stages was undertaken to develop a vignette/scenario-based survey instrument. These steps included a review of instruments used in other jurisdictions, focus groups, engagement meetings with healthcare professionals, cognitive testing and piloting the final instrument. Informed by the findings of each survey development phase, draft scenarios and accompanying questions were developed. Results The following scenarios were developed: ‘Circle of care,’ ‘Use of information beyond your direct care’ and ‘Digital records.’ Conclusion The findings from this survey will inform national policy in relation to health information and will inform the development and implementation of eHealth initiatives. In turn, this should support the delivery of high-quality, effective health and social care. The learnings from the development of this survey will contribute to future health information policy and governance in countries or jurisdictions considering the development of a national electronic health record system. Moreover, this research will support public and population health management by encouraging public engagement to support successful implementation of new health information systems.

Evaluation of technical approaches for real-time data transfer from electronic health record systems

Background and ObjectiveReal-time data (RTD) are data that are delivered immediately after creation. The key feature of RTD is low delivery latency. Information systems in health care are extremely time-sensitive and their building block is the electronic health record (EHR). Real-time data from EHRs play an important role to support decision-making, analytics and coordination of care. This is well mentioned in the literature, but the process has not yet been described, providing reference implementations and testing. Real-time data delivery can technically be achieved using several methods. The objective of this work is to evaluate the performance of different transfer methods of RTD from EHRs by measuring delivery latency. MethodsIn our work we used four approaches to transfer RTD from EHRs: REST hooks, WebSocket notifications, reverse proxy and database triggers. We deployed a Fast Health Interoperability Resources (FHIR) server as it is one of the most widely used EHR standard. For the reference implementations we used Python and Golang. Delivery latency was selected as performance metric, derived by subtracting the timestamp of the EHR resource creation from the timestamp of the EHR resource receipt in millisecond. The data was analyzed using descriptive statistics, cumulative distribution function (CDF), Kruskal-Wallis and post-hoc tests. ResultsThe database trigger approach had the best mean delivery latency 13.52±5.56 ms, followed by the reverse proxy 14.43±4.58 ms, REST hooks 19.26±5.76 ms and WebSocket 27.32±9.44 ms. The reverse proxy showed a tighter range of the values and lower variability. There were significant differences in the latencies between all pairs of approaches, except for reverse proxy and database trigger. ConclusionReal-time data transfer is vital for the development of robust and innovative healthcare applications. Properties of current EHR systems as a data source predefine the approaches for transfer. In our work for the first time the performance of RTD transfer from the EHRs with reference implementations is measured and evaluated. We found that database triggers achieve lowest delivery latency. Reverse proxy performed slightly slower, but offered more stability, followed by REST hooks and WebSocket notifications.

MoCab: A framework for the deployment of machine learning models across health information systems

Background and Objective:Machine learning models are vital for enhancing healthcare services. However, integrating them into health information systems (HISs) introduces challenges beyond clinical decision making, such as interoperability and diverse electronic health records (EHR) formats. We proposed Model Cabinet Architecture (MoCab), a framework designed to leverage fast healthcare interoperability resources (FHIR) as the standard for data storage and retrieval when deploying machine learning models across various HISs, addressing the challenges highlighted by platforms such as EPOCH®, ePRISM®, KETOS, and others. Methods:The MoCab architecture is designed to streamline predictive modeling in healthcare through a structured framework incorporating several specialized parts. The Data Service Center manages patient data retrieval from FHIR servers. These data are then processed by the Knowledge Model Center, where they are formatted and fed into predictive models. The Model Retraining Center is crucial in continuously updating these models to maintain accuracy in dynamic clinical environments. The framework further incorporates Clinical Decision Support (CDS) Hooks for issuing clinical alerts. It uses Substitutable Medical Apps Reusable Technologies (SMART) on FHIR to develop applications for displaying alerts, prediction results, and patient records. Results:The MoCab framework was demonstrated using three types of predictive models: a scoring model (qCSI), a machine learning model (NSTI), and a deep learning model (SPC), applied to synthetic data that mimic a major EHR system. The implementations showed how MoCab integrates predictive models with health data for clinical decision support, utilizing CDS Hooks and SMART on FHIR for seamless HIS integration. The demonstration confirmed the practical utility of MoCab in supporting clinical decision making, validated by its application in various healthcare settings. Conclusions:We demonstrate MoCab’s potential in promoting the interoperability of machine learning models and enhancing its utility across various EHRs. Despite facing challenges like FHIR adoption, MoCab addresses key challenges in adapting machine learning models within healthcare settings, paving the way for further enhancements and broader adoption.

Engagement strategies adopted by the nursing information officer team in preparing nurses for the implementation of an integrated electronic health record.

This article explores the critical role the nursing information officer (NIO)'s team played in facilitating the transition to an electronic health record system (EHR) at two NHS trusts in London. The article highlights that with the increasing importance of digital leadership in nursing, it is necessary to prepare nursing staff for the implementation of an EHR to enhance patient care and staff experience. It discusses various methodologies the NIO's team adopted, including 'show and tells', demos, walkabouts, induction sessions, 'CopyCat' charting, and a 'change and engagement' document. These engagement strategies are aimed at addressing diverse learning needs, increasing nurses' confidence and ensuring effective use of the new EHR. The successful implementation of an EHR depends on collaborative efforts among nursing staff, leadership and NIO teams. This emphasises the importance of embracing digital transformation and innovative strategies in navigating healthcare technology complexities.

Invited commentary: A future of data-rich pharmacoepidemiology studies- transitioning to large-scale linked EHR+claims data.

Electronic health record (EHR) data are seen as an important source for Pharmacoepidemiology studies. In the US healthcare system, EHR systems often only identify fragments of patients' health information across the care continuum, including primary care, specialist care, hospitalizations, and pharmacy dispensing. This leads to unobservable information in longitudinal evaluations of medication effects causing unmeasured confounding, misclassification, and truncated follow-up times. A remedy is to link EHR data with longitudinal claims data which record all encounters during a defined enrollment period across all care settings. We evaluate EHR and claims data sources in three aspects relevant to etiologic studies of medical products: data continuity, data granularity, and data chronology. Reflecting on the strengths and limitations of EHR and insurance claims data, it becomes obvious that they complement each other. The combination of both will improve the validity of etiologic studies and expand the range of questions that can be answered. As the research community transitions towards a future state with access to large-scale combined EHR+claims data, we outline analytic templates to improve the validity and broaden the scope of pharmacoepidemiology studies in the current environment where EHR data are available only for a subset of patients with claims data.

ARDSFlag: an NLP/machine learning algorithm to visualize and detect high-probability ARDS admissions independent of provider recognition and billing codes

BackgroundDespite the significance and prevalence of acute respiratory distress syndrome (ARDS), its detection remains highly variable and inconsistent. In this work, we aim to develop an algorithm (ARDSFlag) to automate the diagnosis of ARDS based on the Berlin definition. We also aim to develop a visualization tool that helps clinicians efficiently assess ARDS criteria.MethodsARDSFlag applies machine learning (ML) and natural language processing (NLP) techniques to evaluate Berlin criteria by incorporating structured and unstructured data in an electronic health record (EHR) system. The study cohort includes 19,534 ICU admissions in the Medical Information Mart for Intensive Care III (MIMIC-III) database. The output is the ARDS diagnosis, onset time, and severity.ResultsARDSFlag includes separate text classifiers trained using large training sets to find evidence of bilateral infiltrates in radiology reports (accuracy of 91.9%±0.5%) and heart failure/fluid overload in radiology reports (accuracy 86.1%±0.5%) and echocardiogram notes (accuracy 98.4%±0.3%). A test set of 300 cases, which was blindly and independently labeled for ARDS by two groups of clinicians, shows that ARDSFlag generates an overall accuracy of 89.0% (specificity = 91.7%, recall = 80.3%, and precision = 75.0%) in detecting ARDS cases.ConclusionTo our best knowledge, this is the first study to focus on developing a method to automate the detection of ARDS. Some studies have developed and used other methods to answer other research questions. Expectedly, ARDSFlag generates a significantly higher performance in all accuracy measures compared to those methods.

Leaving Against Medical Advice: Current Problems and Plausible Solutions.

Leave against medical advice (LAMA) is defined as 'a decision to leave the hospital before the treating physician recommends discharge', and is associated with higher rates of readmission, longer subsequent hospitalization, and worse health outcomes. In addition to this, they also contribute to poor healthcare resource utilization. We conducted a single-center audit to establishpatient demographics and contributing factors of patients leaving against medical advice from our emergency department (ED). We benchmarked our data against locally available clinical policy guidelines.We interrogated our electronic health record system (known as Salamtak®), which is a Cerner-based platform (Cerner Corporation, Kansas City, MO 64138)for patients who signed LAMA from ED from 2018 to 2023. We selected a convenience pilot sample of 120 subjects. Based on a literature review, we identified patient demographics (age, gender, nationality, socioeconomic status, marital status, religion), possible contributing factors (time of attendance, insurance status, length of ED stay), and patient outcomes (reattendances within 1 week and mortality) to evaluate. Based on locally available guidance, we formulated six criteria to audit with a standard set at 100% for each. A team of emergency medicine residents collected data that was anonymized on an Excel spreadsheet (Microsoft Excel,Microsoft Corporation. (2018). Basic descriptive statistics were used to collate results. About 93 patients (77.5%) were 16 years and above, and 27 patients (22.5%) were below 16 years. There was a slight preponderance of males (64 patients, 53.3%) than females (56 patients, 46.6%). The majority of LAMA cases presented in the evening and night (97 patients, 80.8%). About 57 (47.5%) patients had an ED length of stay of 3 hours or more. The average ED length of stay for these patients was 3.4 hours. About 73 patients (60.3%) were insured. Out of 120 patients, only 12 (10%) had a mental capacity assessment documented. The commonest reason for signing LAMA was a social reason in 45 (37.5%) cases. In the remaining cases, the causes were a combination of family, financial, waiting, or other/undocumented reasons). When faced with a decision to LAMA, the involvement of a Public Relationship Officer (PRO) was only documented to be consulted in seven (5.8%) cases. About 14 cases were re-attended within 1 week (11.6%) and no mortalities were reported in any of the reattendances. LAMA is a not-so-rare phenomenon often occurring in EDs, and often a cause of trepidation for healthcare workers. Treating this as an aberrant behavior on the part of the patient, or laying the responsibility for this action on the healthcare provider is primitive, counter-productive, and not patient-centric. Familiarity with local guidelines around this contentious area is essential. Revised nomenclature like 'premature discharge' may be less stigmatizing for the patient.Where possible, a harm reduction approach should be used and frontline healthcare workers must be prepared with an escalation plan.In the United Arab Emirates, familiarity with Wadeema's Law as a child protection measure is essential.

A Multidisciplinary Approach to Increase Dysphagia Compliance in Stroke Patients.

BACKGROUND: The objective was to optimize conditions that increase dysphagia compliance after stroke-a measure recognized by The Joint Commission in designating institutions as primary or comprehensive stroke centers and by the American Heart Association that promotes stroke treatment adherence and bestows achievement and quality awards. Failure to meet stroke measures, including medication documentation, may produce poor patient outcomes that directly affect consumer confidence and choice of treatment facility. Stroke patient care requires multidisciplinary team collaboration to manage multiple factors; variations in practice create fallouts that need correction, the absence of which jeopardizes standards for maintaining stroke center designation. PURPOSE: Aims were to determine the nature of dysphagia fallouts in our quaternary academic teaching hospital, assess multidisciplinary workflows and barriers to success, and develop strategies to enhance dysphagia compliance. DESCRIPTION: We used a multidisciplinary approach using James Reason's Swiss cheese model to examine compliance. The clinical nurse specialist interviewed physicians, advanced practice providers, pharmacists, speech-language pathologists (SLPs), nurses, and informaticists to ascertain discipline-specific success barriers, targeting top issues for each discipline to develop solutions. Unit educators unified their dysphagia reduction strategies; physicians, pharmacists, and informaticists collaborated to clarify standardized medication route orders and to increase electronic health record swallow screen visibility to multidisciplinary users; and SLPs adjusted workflow and documentation. EVALUATION: Outcomes included reinforcing correct nursing charting, pharmacy oral-to-feeding tube conversion order revision, optimization of delivery and awareness of medication routes, electronic health record system enhancements, and SLPs aligning documentation with nursing medication administration. The multidisciplinary approach proved successful and increased dysphagia compliance by 9.6% ( P = .001). These outcomes contributed to a successful The Joint Commission survey, redesignation as a comprehensive stroke center, and American Heart Association Gold Plus achievement award.