Implementation of Real-Time Electronic Clinical Decision Support for Emergency Department Patients with Pneumonia Across a Healthcare System.
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SUBMITTER: Dean NC
PROVIDER: S-EPMC7153076 | biostudies-literature | 2019
REPOSITORIES: biostudies-literature
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Implementation of Real-Time Electronic Clinical Decision Support for Emergency Department Patients with Pneumonia Across a Healthcare System.
Dean Nathan C NC Vines Caroline G CG Rubin Jenna J Collingridge Dave S DS Mankivsky Mark M Srivastava Raj R Jones Barbara E BE Kuttler Kathryn G KG Walker Missy M Jenson Nathan N Webb Brandon J BJ Allen Todd L TL Haug Peter J PJ
AMIA ... Annual Symposium proceedings. AMIA Symposium 20190101
A real-time electronic CDS for pneumonia (ePNa) identifies possible pneumonia patients, measures severity and antimicrobial resistance risk, and then recommends disposition, antibiotics, and microbiology studies. Use is voluntary, and clinicians may modify treatment recommendations. ePNa was associated with lower mortality in emergency department (ED) patients versus usual care (Annals EM 66:511). We adapted ePNa for the Cerner EHR, and implemented it across Intermountain Healthcare EDs (Utah, U ...[more]
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Project description:BackgroundThe use of big data and machine learning within clinical decision support systems (CDSSs) has the potential to transform medicine through better prognosis, diagnosis and automation of tasks. Real-time application of machine learning algorithms, however, is dependent on data being present and entered prior to, or at the point of, CDSS deployment. Our aim was to determine the feasibility of automating CDSSs within electronic health records (EHRs) by investigating the timing, data categorization, and completeness of documentation of their individual components of two common Clinical Decision Rules (CDRs) in the Emergency Department.MethodsThe CURB-65 severity score and HEART score were randomly selected from a list of the top emergency medicine CDRs. Emergency department (ED) visits with ICD-9 codes applicable to our CDRs were eligible. The charts were reviewed to determine the categorization components of the CDRs as structured and/or unstructured, median times of documentation, portion of charts with all data components documented as structured data, portion of charts with all structured CDR components documented before ED departure. A kappa score was calculated for interrater reliability.ResultsThe components of the CDRs were mainly documented as structured data for the CURB-65 severity score and HEART score. In the CURB-65 group, 26.8% of charts had all components documented as structured data, and 67.8% in the HEART score. Documentation of some CDR components often occurred late for both CDRs. Only 21 and 11% of patients had all CDR components documented as structured data prior to ED departure for the CURB-65 and HEART score groups, respectively. The interrater reliability for the CURB-65 score review was 0.75 and 0.65 for the HEART score.ConclusionOur study found that EHRs may be unable to automatically calculate popular CDRs-such as the CURB-65 severity score and HEART score-due to missing components and late data entry.
| S-EPMC6029277 | biostudies-literature
Project description:ObjectivesAdoption of emergency department (ED) initiation of buprenorphine (BUP) for opioid use disorder (OUD) into routine emergency care has been slow, partly due to clinicians' unfamiliarity with this practice and perceptions that it is complicated and time-consuming. To address these barriers and guide emergency clinicians through the process of BUP initiation, we implemented a user-centered computerized clinical decision support system (CDS). This study was conducted to assess the feasibility of implementation and to evaluate the preliminary efficacy of the intervention to increase the rate of ED-initiated BUP.MethodsAn interrupted time series study was conducted in an urban, academic ED from April 2018 to February 2019 (preimplementation phase), March 2019 to August 2019 (implementation phase), and September 2019 to December 2019 (maintenance phase) to study the effect of the intervention on adult ED patients identified by a validated electronic health record (EHR)-based computable phenotype consisting of structured data consistent with potential cases of OUD who would benefit from BUP treatment. The intervention offers flexible CDS for identification of OUD, assessment of opioid withdrawal, and motivation of readiness to start treatment and automates EHR activities related to ED initiation of BUP (including documentation, orders, prescribing, and referral). The primary outcome was the rate of ED-initiated BUP. Secondary outcomes were launch of the intervention, prescription for naloxone at ED discharge, and referral for ongoing addiction treatment.ResultsOf the 141,041 unique patients presenting to the ED over the preimplementation and implementation phases (i.e., the phases used in primary analysis), 906 (574 preimplementation and 332 implementation) met OUD phenotype and inclusion criteria. The rate of BUP initiation increased from 3.5% (20/574) in the preimplementation phase to 6.6% (22/332) in the implementation phase (p = 0.03). After the temporal trend of the number of physician's with X-waiver training and other covariates were adjusted for, the relative risk of BUP initiation rate was 2.73 (95% confidence interval [CI] = 0.62 to 12.0, p = 0.18). Similarly, the number of unique attendings who initiated BUP increased modestly 7/53 (13.0%) to 13/57 (22.8%, p = 0.10) after offering just-in-time training during the implementation period. The rate of naloxone prescribed at discharge also increased (6.5% preimplementation and 11.5% implementation; p < 0.01). The intervention received a system usability scale score of 82.0 (95% CI = 76.7 to 87.2).ConclusionImplementation of user-centered CDS at a single ED was feasible, acceptable, and associated with increased rates of ED-initiated BUP and naloxone prescribing in patients with OUD and a doubling of the number of unique physicians adopting the practice. We have implemented this intervention across several health systems in an ongoing trial to assess its effectiveness, scalability, and generalizability.
| S-EPMC7496559 | biostudies-literature
Project description:ImportanceFatal human rabies infections can be prevented through appropriate rabies postexposure prophylaxis (PEP). Errors in patient selection and administration of human rabies immune globulin in the emergency department (ED) setting were identified in a previous study of rabies PEP administration.ObjectiveTo test the a priori hypothesis that implementation of a rabies PEP bundle in the ED would improve full adherence to 6 human rabies immune globulin quality indicators compared with preimplementation controls.Design, setting, and participantsThis quality improvement study was conducted in 15 EDs in a US multihospital health system. Patients who received human rabies immune globulin or rabies vaccine in the ED from January 2015 to June 2018 were included in the preimplementation control group and from December 2019 to November 2020 were included in the postimplementation intervention group. Data were analyzed in January 2021.ExposureThe PEP bundle was implemented in December 2019 and consisted of electronic health record enhancements, including clinical decision support, ED staff education, and patient education.Main outcomes and measuresFull adherence to 6 human rabies immune globulin quality indicators: patient selection, dose, timing, infiltration into wounds, administration distant from rabies vaccine site, and administration that avoids the buttock.ResultsThe study included 324 patients; 254 patients were in preimplementation group (mean [SD] age, 39 [21] years; 135 [53%] women) and 70 in the postimplementation group (mean [SD] age, 38 [19] years; 33 [47%] women). Most patients presented to EDs embedded in a community hospital (231 patients [71%]). Full adherence increased from 37% in the preimplementation group to 61% postimplementation (absolute increase, 24%; 95% CI, 11% to 37%; P < .001). Adherence improved for quality indicators for infiltration into wounds (137 of 254 patients [54%] to 50 of 70 patients [71%]; P = .009), administration distant from rabies vaccine site (180 of 254 [71%] to 58 of 70 [83%]; P = .04), and administration that avoids the buttock (168 of 254 [66%] to 58 of 70 [83%]; P = .007). No instances of sciatic nerve injury or compartment syndrome were observed.Conclusions and relevanceIn this quality improvement study, implementation of a rabies PEP bundle was associated with improved patient selection and delivery of human rabies immune globulin in EDs across a multihospital health system. Although the bundle included ED staff education and patient discharge education, the observed improvement was likely driven by clinical decision support from the rabies PEP ED order set. Future research should evaluate implementation of this clinical decision support at other health systems.
| S-EPMC9214583 | biostudies-literature
Project description:Background: To systematically review evidence on the feasibility and efficacy of real-time electronic notifications about patients at high risk of emergency department (ED) recidivism. Methods: Eight electronic databases were searched for empirical studies of real-time ED-based electronic tools, identifying adult patients at high risk of frequent utilization. Study selection and data extraction were performed independently by two reviewers. Qualitative data synthesis and assessment of strength of evidence were conducted through consensus discussion. Results: Of 2,256 records found through the search, 210 were duplicates, 2,004 were excluded based on abstract review, and 31 were excluded after full text review. The final sample consisted of 10 studies described in 11 articles describing the effect of real-time ED-based electronic notifications for high-risk patients. Three were randomized controlled trials (RCTs). All notifications were based on prespecified markers of risk. Seven studies integrated complex care plans into the electronic health record. Effect on ED use and length of stay (LOS) was mixed: nine studies reported decreased ED use, although results were statistically significant in only three studies; for LOS, one study reported a statistically significant reduction. Impact on cost and financial metrics was promising, with three (of three studies reporting this metric) showing improved organizational financial metrics. Three RCTs reported a reduction in opioid prescriptions. Conclusions: Real-time electronic notifications of ED providers regarding patients at high risk of ED recidivism are feasible. They may help reduce resource utilization and costs. Large knowledge gaps remain regarding patient- and provider-centered outcomes.
| S-EPMC6664825 | biostudies-literature
Project description:ObjectiveeCTAS is a real-time electronic decision-support tool designed to standardize the application of the Canadian Triage and Acuity Scale (CTAS). This study addresses the variability of CTAS score distributions across institutions pre- and post-eCTAS implementation.MethodsWe used population-based administrative data from 2016-2018 from all emergency departments (EDs) that had implemented eCTAS for 9 months. Following a 3-month stabilization period, we compared 6 months post-eCTAS data to the same 6 months the previous year (pre-eCTAS). We included triage encounters of adult (≥17 years) patients who presented with 1 of 16 pre-specified, high-volume complaints. For each ED, consistency was calculated as the absolute difference in CTAS distribution compared to the average of all included EDs for each presenting complaint. Pre-eCTAS and post-eCTAS change scores were compared using a paired-samples t-test. We also assessed if eCTAS modifiers were associated with triage consistency.ResultsThere were 363,214 (183,231 pre-eCTAS, 179,983 post-eCTAS) triage encounters included from 35 EDs. Triage scores were more consistent (P < 0.05) post-eCTAS for 6 (37.5%) presenting complaints: chest pain (cardiac features), extremity weakness/symptoms of cerebrovascular accident, fever, shortness of breath, syncope, and hyperglycemia. Triage consistency was similar pre- and post-eCTAS for altered level of consciousness, anxiety/situational crisis, confusion, depression/suicidal/deliberate self-harm, general weakness, head injury, palpitations, seizure, substance misuse/intoxication, and vertigo. Use of eCTAS modifiers was associated with increased triage consistency.ConclusionseCTAS increased triage consistency across many, but not all, high-volume presenting complaints. Modifier use was associated with increased triage consistency, particularly for non-specific complaints such as fever and general weakness.
| S-EPMC7593433 | biostudies-literature
Project description:Study objectiveAs electronic health records evolve, integration of computerized clinical decision support offers the promise of sorting, collecting, and presenting this information to improve patient care. We conducted a systematic review to examine the scope and influence of electronic health record-integrated clinical decision support technologies implemented in the emergency department (ED).MethodsA literature search was conducted in 4 databases from their inception through January 18, 2018: PubMed, Scopus, the Cumulative Index of Nursing and Allied Health, and Cochrane Central. Studies were included if they examined the effect of a decision support intervention that was implemented in a comprehensive electronic health record in the ED setting. Standardized data collection forms were developed and used to abstract study information and assess risk of bias.ResultsA total of 2,558 potential studies were identified after removal of duplicates. Of these, 42 met inclusion criteria. Common targets for clinical decision support intervention included medication and radiology ordering practices, as well as more comprehensive systems supporting diagnosis and treatment for specific disease entities. The majority of studies (83%) reported positive effects on outcomes studied. Most studies (76%) used a pre-post experimental design, with only 3 (7%) randomized controlled trials.ConclusionNumerous studies suggest that clinical decision support interventions are effective in changing physician practice with respect to process outcomes such as guideline adherence; however, many studies are small and poorly controlled. Future studies should consider the inclusion of more specific information in regard to design choices, attempt to improve on uncontrolled before-after designs, and focus on clinically relevant outcomes wherever possible.
| S-EPMC6610851 | biostudies-literature
Project description:PurposePatients with pneumonia often present to the emergency department (ED) and require prompt diagnosis and treatment. Clinical decision support systems for the diagnosis and management of pneumonia are commonly utilized in EDs to improve patient care. The purpose of this study is to investigate whether a deep learning model for detecting radiographic pneumonia and pleural effusions can improve functionality of a clinical decision support system (CDSS) for pneumonia management (ePNa) operating in 20 EDs.Materials and methodsIn this retrospective cohort study, a dataset of 7434 prior chest radiographic studies from 6551 ED patients was used to develop and validate a deep learning model to identify radiographic pneumonia, pleural effusions, and evidence of multilobar pneumonia. Model performance was evaluated against 3 radiologists' adjudicated interpretation and compared with performance of the natural language processing of radiology reports used by ePNa.ResultsThe deep learning model achieved an area under the receiver operating characteristic curve of 0.833 (95% confidence interval [CI]: 0.795, 0.868) for detecting radiographic pneumonia, 0.939 (95% CI: 0.911, 0.962) for detecting pleural effusions and 0.847 (95% CI: 0.800, 0.890) for identifying multilobar pneumonia. On all 3 tasks, the model achieved higher agreement with the adjudicated radiologist interpretation compared with ePNa.ConclusionsA deep learning model demonstrated higher agreement with radiologists than the ePNa CDSS in detecting radiographic pneumonia and related findings. Incorporating deep learning models into pneumonia CDSS could enhance diagnostic performance and improve pneumonia management.
| S-EPMC8940736 | biostudies-literature
Project description:BackgroundAccurate severity assessment is crucial to the initial management of community-acquired pneumonia (CAP). The CURB-65 (confusion, uremia, respiratory rate, BP, age ? 65 years) score contains data that are entered routinely in electronic medical records and are, thus, electronically calculable. The aim of this study was to determine whether an electronically generated severity estimate using CURB-65 elements as continuous and weighted variables better predicts 30-day mortality than the traditional CURB-65.MethodsIn a retrospective cohort study at a US university-affiliated community teaching hospital, we identified 2,069 patients aged 18 years or older with CAP confirmed by radiographic findings in the ED. CURB-65 elements were extracted from the electronic medical record, and 30-day mortality was identified with the Utah Population Database. Performance of a severity prediction model using continuous and weighted CURB-65 variables was compared with the traditional CURB-65 in the US derivation population and validated in the original 1,048 patients from the CURB-65 international derivation study.ResultsThe traditional, binary CURB-65 score predicted mortality in the US cohort with an area under the curve (AUC) of 0.82. Our severity prediction model generated from continuous, weighted CURB-65 elements was superior to the traditional CURB-65, with an out-of-bag AUC of 0.86 (P < .001). This finding was validated in the international database, with an AUC of 0.85 for the electronic model compared with 0.80 for the traditional CURB-65 (P = .01).ConclusionsUsing CURB-65 elements as continuous and weighted data improved prediction of 30-day mortality and could be used as a real-time, electronic decision support tool or to adjust outcomes by severity when comparing processes of care.
| S-EPMC3198487 | biostudies-literature
Project description:In the event of a new infectious disease outbreak, mathematical and simulation models are commonly used to inform policy by evaluating which control strategies will minimize the impact of the epidemic. In the early stages of such outbreaks, substantial parameter uncertainty may limit the ability of models to provide accurate predictions, and policymakers do not have the luxury of waiting for data to alleviate this state of uncertainty. For policymakers, however, it is the selection of the optimal control intervention in the face of uncertainty, rather than accuracy of model predictions, that is the measure of success that counts. We simulate the process of real-time decision-making by fitting an epidemic model to observed, spatially-explicit, infection data at weekly intervals throughout two historical outbreaks of foot-and-mouth disease, UK in 2001 and Miyazaki, Japan in 2010, and compare forward simulations of the impact of switching to an alternative control intervention at the time point in question. These are compared to policy recommendations generated in hindsight using data from the entire outbreak, thereby comparing the best we could have done at the time with the best we could have done in retrospect. Our results show that the control policy that would have been chosen using all the data is also identified from an early stage in an outbreak using only the available data, despite high variability in projections of epidemic size. Critically, we find that it is an improved understanding of the locations of infected farms, rather than improved estimates of transmission parameters, that drives improved prediction of the relative performance of control interventions. However, the ability to estimate undetected infectious premises is a function of uncertainty in the transmission parameters. Here, we demonstrate the need for both real-time model fitting and generating projections to evaluate alternative control interventions throughout an outbreak. Our results highlight the use of using models at outbreak onset to inform policy and the importance of state-dependent interventions that adapt in response to additional information throughout an outbreak.
| S-EPMC6075790 | biostudies-literature
Project description:BackgroundMinistries of health in low- and middle-income countries often lack timely quality data for data-driven decision making in healthcare networks. We describe the design and implementation of decision-support electronic tools by the Ministry of Health of the State of Chiapas, in Mexico, as part of Salud Mesoamerica Initiative.MethodsThree electronic decision-support tools were designed through an iterative process focused on streamlined implementation: 1) to collect and report health facility data at health facilities; 2) to compile and analyze data at health district and central level; and, 3) to support stratified sampling of health facilities. Data was collected for five composite indicators measuring availability of equipment, medicines, and supplies for maternal and child health. Quality Assurance Teams collected data, evaluated results and supported quality improvement. Data was also analyzed at the central level and health districts for decision-making.ResultsData from 300 health facilities in four health districts was collected and analyzed (November 2014-June 2015). The first wave revealed gaps on availability of equipment and supplies in more than half of health facilities. Electronic tools provided the ministry of health officers new ways to visualize data, identify patterns and make hypothesis on root-causes. Between the first and second measurement, the number of missing items decreased, and actions performed by quality improvement teams became more proactive. In the final measurement, 89.7-100% of all health facilities achieved all the required items for each indicator.ConclusionsOur experience could help guide others seeking to implement electronic decision-support tools in low- and middle-income countries. Electronic decision-support tools supported data-driven decision-making by identifying gaps on heatmaps and graphs at the health facility, subdistrict, district or state level. Through a rapid improvement process, the Ministry of Health met targets of externally verified indicators. Using available information technology resources facilitated prompt implementation and adoption of technology.
| S-EPMC7301503 | biostudies-literature