scholarly journals 91. Development of an Electronic Algorithm to Identify Inappropriate Antibiotic Prescribing for Pediatric Otitis Media

2020 ◽  
Vol 7 (Supplement_1) ◽  
pp. S176-S177
Author(s):  
Ebbing Lautenbach ◽  
Jeffrey Gerber ◽  
Robert Grundmeier ◽  
Keith W Hamilton ◽  
Lauri Hicks ◽  
...  
Keyword(s):  
Electronic Health Record ◽  
Otitis Media ◽  
Chart Review ◽  
Antibiotic Use ◽  
Antibiotic Prescribing ◽  
Health Record ◽  
Test Characteristics ◽  
Academic Practices ◽  
Electronic Health ◽  
The Impact

Abstract Background Antibiotic stewardship (AS) interventions have primarily focused on acute care settings. The majority of antibiotic use, however, occurs in outpatients. The electronic health record (EHR) might provide an effective and efficient tool for outpatient AS. We aimed to develop and validate an electronic algorithm to identify inappropriate antibiotic use for pediatric outpatients with acute otitis media (AOM). Methods Within the Children’s Hospital of Philadelphia (CHOP) Care Network, we used ICD-10 diagnostic codes to identify patient encounters for AOM at any CHOP practice between 3/15/17 – 3/14/18. Exclusion criteria included underlying immunocompromising condition, comorbidities, and concurrent infections that might influence antibiotic use. We randomly selected 450 eligible subjects (150 each from academic practices, non-academic practices, and urgent care). Inappropriate antibiotic use based on CHOP and professional society guidelines were assessed via chart review and served as the basis for assessment of the electronic algorithm which was constructed using only data in the electronic health record (EHR). Criteria for appropriateness focused on the decision to prescribe, the choice of antibiotic, and duration of therapy. Results Of 450 subjects, median age was 2, 46% were female, and 88% were evaluated by a physician (vs. advanced practice provider). On chart review, the prescribing decision was correct in 438 (97%), of which 25 appropriately received no antibiotics. Of the 413 subjects who were appropriately prescribed an antibiotic, the choice of antibiotic was appropriate in 37 (9%). Finally, of the 413 patients who were appropriately treated, 412 (99.7%) received the correct duration. Test characteristics of the EHR algorithm (compared to chart review) are noted in the Table. Conclusion For children with AOM, an electronic algorithm for identification of inappropriate antibiotic prescribing is highly accurate. This algorithm can also highlight for which elements of prescribing the impact of an intervention might be greatest (i.e., choice of agent). Future work should validate this approach in other health systems and geographic regions and evaluate the impact of an audit and feedback intervention based on this tool. Table. Test Characteristics of Electronic Algorithm for Inappropriate Prescribing, Agent, and Duration Disclosures All Authors: No reported disclosures

scholarly journals 154. Development of an Electronic Algorithm to Target Outpatient Antimicrobial Stewardship Efforts for Adults with Acute Pharyngitis

2020 ◽  
Vol 7 (Supplement_1) ◽  
pp. S87-S87
Author(s):  
Ebbing Lautenbach ◽  
Keith W Hamilton ◽  
Robert Grundmeier ◽  
Melinda M Neuhauser ◽  
Lauri Hicks ◽  
...  
Keyword(s):  
Chart Review ◽  
Inappropriate Prescribing ◽  
Antibiotic Use ◽  
University Of Pennsylvania ◽  
Antibiotic Prescribing ◽  
Acute Pharyngitis ◽  
Test Characteristics ◽  
Appropriate Prescribing ◽  
Concurrent Infections ◽  
The Impact

Abstract Background Although most antibiotic use occurs in outpatients, antibiotic stewardship programs (ASPs) have primarily focused on inpatients. A major challenge for outpatient ASPs is lack of accurate and accessible electronic data to target interventions. We developed and validated an electronic algorithm to identify inappropriate antibiotic use for adult outpatients with acute pharyngitis. Methods In the University of Pennsylvania Health System, we used ICD-10 diagnostic codes to identify patient encounters for acute pharyngitis at outpatient practices between 3/15/17 – 3/14/18. Exclusion criteria included immunocompromising conditions, comorbidities, and concurrent infections that might require antibiotic use. We randomly selected 300 eligible subjects. Inappropriate antibiotic use based on chart review served as the basis for assessment of the electronic algorithm which was constructed using only data in the electronic health record (EHR). Criteria for appropriate prescribing, choice of antibiotic, and duration included positive streptococcal testing, use of penicillin/amoxicillin (absent b-lactam allergy), and 10 days maximum duration of therapy. Results Of 300 subjects, median age was 42, 75% were female, 64% were seen by internal medicine (vs. family medicine), and 69% were seen by a physician (vs. advanced practice provider). On chart review, 127 (42%) subjects received an antibiotic, of which 29 had a positive streptococcal test and 4 had another appropriate indication. Thus, 74% (94/127) of patients received antibiotics inappropriately. Of the 29 patients who received appropriate prescribing, 27 (93%) received an appropriate antibiotic. Finally, of the 29 patients who were appropriately treated, 29 (100%) received the correct duration. Test characteristics of the EHR algorithm (compared to chart review) are noted in the Table. Conclusion Inappropriate antibiotic prescribing for acute pharyngitis is common. An electronic algorithm for identifying inappropriate prescribing, antibiotic choice, and duration is highly accurate. This algorithm could be used to efficiently assess prescribing among practices and individual clinicians. The impact of interventions based on this algorithm should be tested in future work. Test Characteristics of Electronic Algorithm for Inappropriate Prescribing, Agent, and Duration Disclosures All Authors: No reported disclosures

scholarly journals Management of a parenteral opioid shortage using ASHP guidelines

2021 ◽  
Vol 78 (5) ◽  
pp. 426-435
Author(s):  
Peter Vo ◽  
Daniel A Sylvia ◽  
Loay Milibari ◽  
John Ryan Stackhouse ◽  
Paul Szumita ◽  
...  
Keyword(s):  
Electronic Health Record ◽  
Drug Product ◽  
Health Record ◽  
Large Hospital ◽  
Hospital Administrators ◽  
Utilization Data ◽  
Electronic Health ◽  
Opioid Medications ◽  
American Society ◽  
The Impact

Abstract Purpose Management of an acute shortage of parenteral opioid products at a large hospital through prescribing interventions and other guideline-recommended actions is described. Summary In early 2018, many hospitals were faced with a shortage of parenteral opioids that was predicted to last an entire year. The American Society of Health-System Pharmacists (ASHP) has published guidelines on managing drug product shortages. This article describes the application of these guidelines to manage the parenteral opioid shortage and the impact on opioid dispensing that occurred in 2018. Our approach paralleled that recommended in the ASHP guidelines. Daily dispensing reports generated from automated dispensing cabinets and from the electronic health record were used to capture dispenses of opioid medications. Opioid prescribing and utilization data were converted to morphine milligram equivalents (MME) to allow clinical leaders and hospital administrators to quickly evaluate opioid inventories and consumption. Action steps included utilization of substitute opioid therapies and conversion of opioid patient-controlled analgesia (PCA) and opioid infusions to intravenous bolus dose administration. Parenteral opioid supplies were successfully rationed so that surgical and elective procedures were not canceled or delayed. During the shortage, opioid dispensing decreased in the inpatient care areas from approximately 2.0 million MME to 1.4 million MME and in the operating rooms from 0.56 MME to 0.29 million MME. The combination of electronic health record alerts, increased utilization of intravenous acetaminophen and liposomal bupivacaine, and pharmacist interventions resulted in a 67% decline in PCA use and a 65% decline in opioid infusions. Conclusion A multidisciplinary response is necessary for effective management of drug shortages through implementation of strategies and practices for notifying clinicians of shortages and identifying optimal alternative therapies.

scholarly journals Validation of an Internationally Derived Patient Severity Phenotype to Support COVID-19 Analytics from Electronic Health Record Data

2021 ◽  
Author(s):  
Jeffrey G Klann ◽  
Griffin M Weber ◽  
Hossein Estiri ◽  
Bertrand Moal ◽  
Paul Avillach ◽  
...  
Keyword(s):  
Machine Learning ◽  
Electronic Health Record ◽  
Chart Review ◽  
Learning Approach ◽  
Health Record ◽  
Learning Approaches ◽  
Electronic Health Record Data ◽  
Icu Admission ◽  
Machine Learning Approach ◽  
Electronic Health

Abstract Introduction The Consortium for Clinical Characterization of COVID-19 by EHR (4CE) is an international collaboration addressing COVID-19 with federated analyses of electronic health record (EHR) data. Objective We sought to develop and validate a computable phenotype for COVID-19 severity. Methods Twelve 4CE sites participated. First we developed an EHR-based severity phenotype consisting of six code classes, and we validated it on patient hospitalization data from the 12 4CE clinical sites against the outcomes of ICU admission and/or death. We also piloted an alternative machine-learning approach and compared selected predictors of severity to the 4CE phenotype at one site. Results The full 4CE severity phenotype had pooled sensitivity of 0.73 and specificity 0.83 for the combined outcome of ICU admission and/or death. The sensitivity of individual code categories for acuity had high variability - up to 0.65 across sites. At one pilot site, the expert-derived phenotype had mean AUC 0.903 (95% CI: 0.886, 0.921), compared to AUC 0.956 (95% CI: 0.952, 0.959) for the machine-learning approach. Billing codes were poor proxies of ICU admission, with as low as 49% precision and recall compared to chart review. Discussion We developed a severity phenotype using 6 code classes that proved resilient to coding variability across international institutions. In contrast, machine-learning approaches may overfit hospital-specific orders. Manual chart review revealed discrepancies even in the gold-standard outcomes, possibly due to heterogeneous pandemic conditions. Conclusion We developed an EHR-based severity phenotype for COVID-19 in hospitalized patients and validated it at 12 international sites.

scholarly journals Development of an Electronic Algorithm to Identify Inappropriate Antibiotic Prescribing for Pediatric Pharyngitis

2020 ◽  
Vol 41 (S1) ◽  
pp. s188-s189
Author(s):  
Jeffrey Gerber ◽  
Robert Grundmeier ◽  
Keith Hamilton ◽  
Lauri Hicks ◽  
Melinda Neuhauser ◽  
...  
Keyword(s):  
Predictive Value ◽  
Gold Standard ◽  
Antibiotic Stewardship ◽  
Chart Review ◽  
Antibiotic Use ◽  
Audit And Feedback ◽  
Urgent Care ◽  
Antibiotic Prescribing ◽  
Tract Infections ◽  
The Impact

Background: Antibiotic overuse contributes to antibiotic resistance and unnecessary adverse drug effects. Antibiotic stewardship interventions have primarily focused on acute-care settings. Most antibiotic use, however, occurs in outpatients with acute respiratory tract infections such as pharyngitis. The electronic health record (EHR) might provide an effective and efficient tool for outpatient antibiotic stewardship. We aimed to develop and validate an electronic algorithm to identify inappropriate antibiotic use for pediatric outpatients with pharyngitis. Methods: This study was conducted within the Children’s Hospital of Philadelphia (CHOP) Care Network, including 31 pediatric primary care practices and 3 urgent care centers with a shared EHR serving >250,000 children. We used International Classification of Diseases, Tenth Revision (ICD-10) codes to identify encounters for pharyngitis at any CHOP practice from March 15, 2017, to March 14, 2018, excluding those with concurrent infections (eg, otitis media, sinusitis), immunocompromising conditions, or other comorbidities that might influence the need for antibiotics. We randomly selected 450 features for detailed chart abstraction assessing patient demographics as well as practice and prescriber characteristics. Appropriateness of antibiotic use based on chart review served as the gold standard for evaluating the electronic algorithm. Criteria for appropriate use included streptococcal testing, use of penicillin or amoxicillin (absent β-lactam allergy), and a 10-day duration of therapy. Results: In 450 patients, the median age was 8.4 years (IQR, 5.5–9.0) and 54% were women. On chart review, 149 patients (33%) received an antibiotic, of whom 126 had a positive rapid strep result. Thus, based on chart review, 23 subjects (5%) diagnosed with pharyngitis received antibiotics inappropriately. Amoxicillin or penicillin was prescribed for 100 of the 126 children (79%) with a positive rapid strep test. Of the 126 children with a positive test, 114 (90%) received the correct antibiotic: amoxicillin, penicillin, or an appropriate alternative antibiotic due to b-lactam allergy. Duration of treatment was correct for all 126 children. Using the electronic algorithm, the proportion of inappropriate prescribing was 28 of 450 (6%). The test characteristics of the electronic algorithm (compared to gold standard chart review) for identification of inappropriate antibiotic prescribing were sensitivity (99%, 422 of 427); specificity (100%, 23 of 23); positive predictive value (82%, 23 of 28); and negative predictive value (100%, 422 of 422). Conclusions: For children with pharyngitis, an electronic algorithm for identification of inappropriate antibiotic prescribing is highly accurate. Future work should validate this approach in other settings and develop and evaluate the impact of an audit and feedback intervention based on this tool.Funding: NoneDisclosures: None

scholarly journals Development of an Electronic Algorithm to Target Outpatient Antimicrobial Stewardship Efforts for Acute Bronchitis

2020 ◽  
Vol 41 (S1) ◽  
pp. s32-s32
Author(s):  
Ebbing Lautenbach ◽  
Keith Hamilton ◽  
Robert Grundmeier ◽  
Melinda Neuhauser ◽  
Lauri Hicks ◽  
...  
Keyword(s):  
Antimicrobial Stewardship ◽  
Predictive Value ◽  
Gold Standard ◽  
Chart Review ◽  
Acute Bronchitis ◽  
Antibiotic Use ◽  
Antibiotic Prescribing ◽  
Diagnostic Codes ◽  
Icd 10 ◽  
The Impact

Background: Antibiotic resistance has increased at alarming rates, driven predominantly by antibiotic overuse. Although most antibiotic use occurs in outpatients, antimicrobial stewardship programs have primarily focused on inpatient settings. A major challenge for outpatient stewardship is the lack of accurate and accessible electronic data to target interventions. We sought to develop and validate an electronic algorithm to identify inappropriate antibiotic use for outpatients with acute bronchitis. Methods: This study was conducted within the University of Pennsylvania Health System (UPHS). We used ICD-10 diagnostic codes to identify encounters for acute bronchitis at any outpatient UPHS practice between March 15, 2017, and March 14, 2018. Exclusion criteria included underlying immunocompromising condition, other comorbidity influencing the need for antibiotics (eg, emphysema), or ICD-10 code at the same visit for a concurrent infection (eg, sinusitis). We randomly selected 300 (150 from academic practices and 150 from nonacademic practices) eligible subjects for detailed chart abstraction that assessed patient demographics and practice and prescriber characteristics. Appropriateness of antibiotic use based on chart review served as the gold standard for assessment of the electronic algorithm. Because antibiotic use is not indicated for this study population, appropriateness was assessed based upon whether an antibiotic was prescribed or not. Results: Of 300 subjects, median age was 61 years (interquartile range, 50–68), 62% were women, 74% were seen in internal medicine (vs family medicine) practices, and 75% were seen by a physician (vs an advanced practice provider). On chart review, 167 (56%) subjects received an antibiotic. Of these subjects, 1 had documented concern for pertussis and 4 had excluding conditions for which there were no ICD-10 codes. One received an antibiotic prescription for a planned dental procedure. Thus, based on chart review, 161 (54%) subjects received antibiotics inappropriately. Using the electronic algorithm based on diagnostic codes, underlying and concurrent conditions, and prescribing data, the number of subjects with inappropriate prescribing was 170 (56%) because 3 subjects had antibiotic prescribing not noted based on chart review. The test characteristics of the electronic algorithm (compared to gold standard chart review) for identification of inappropriate antibiotic prescribing were the following: sensitivity, 100% (161 of 161); specificity, 94% (130 of 139); positive predictive value, 95% (161 of 170); and negative predictive value, 100% (130 of 130). Conclusions: For outpatients with acute bronchitis, an electronic algorithm for identification of inappropriate antibiotic prescribing is highly accurate. This algorithm could be used to efficiently assess prescribing among practices and individual clinicians. The impact of interventions based on this algorithm should be tested in future studies.Funding: NoneDisclosures: None

scholarly journals The Development and Piloting of the Ambulatory Electronic Health Record Evaluation Tool: Lessons Learned

2021 ◽  
Vol 12 (01) ◽  
pp. 153-163
Author(s):  
Zoe Co ◽  
A. Jay Holmgren ◽  
David C. Classen ◽  
Lisa P. Newmark ◽  
Diane L. Seger ◽  
...  
Keyword(s):  
Decision Support ◽  
Electronic Health Record ◽  
Medication Safety ◽  
Medication Reconciliation ◽  
The United States ◽  
Health Record ◽  
Evaluation Tool ◽  
Safety Test ◽  
Electronic Health ◽  
The Impact

Abstract Background Substantial research has been performed about the impact of computerized physician order entry on medication safety in the inpatient setting; however, relatively little has been done in ambulatory care, where most medications are prescribed. Objective To outline the development and piloting process of the Ambulatory Electronic Health Record (EHR) Evaluation Tool and to report the quantitative and qualitative results from the pilot. Methods The Ambulatory EHR Evaluation Tool closely mirrors the inpatient version of the tool, which is administered by The Leapfrog Group. The tool was piloted with seven clinics in the United States, each using a different EHR. The tool consists of a medication safety test and a medication reconciliation module. For the medication test, clinics entered test patients and associated test orders into their EHR and recorded any decision support they received. An overall percentage score of unsafe orders detected, and order category scores were provided to clinics. For the medication reconciliation module, clinics demonstrated how their EHR electronically detected discrepancies between two medication lists. Results For the medication safety test, the clinics correctly alerted on 54.6% of unsafe medication orders. Clinics scored highest in the drug allergy (100%) and drug–drug interaction (89.3%) categories. Lower scoring categories included drug age (39.3%) and therapeutic duplication (39.3%). None of the clinics alerted for the drug laboratory or drug monitoring orders. In the medication reconciliation module, three (42.8%) clinics had an EHR-based medication reconciliation function; however, only one of those clinics could demonstrate it during the pilot. Conclusion Clinics struggled in areas of advanced decision support such as drug age, drug laboratory, and drub monitoring. Most clinics did not have an EHR-based medication reconciliation function and this process was dependent on accessing patients' medication lists. Wider use of this tool could improve outpatient medication safety and can inform vendors about areas of improvement.

AIRA, an Artificial Intelligence Realistic Assistant that will save your time, a before-and-after study during Pandemic (Preprint)

2021 ◽  
Author(s):  
Kaio Bin ◽  
Adler Araújo Ribeiro Melo ◽  
José Guilherme Franco Da Rocha ◽  
Renata Pivi De Almeida ◽  
Vilson Cobello Junior ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Electronic Health Record ◽  
Medical Care ◽  
Health Screening ◽  
Urgent Care ◽  
Health Record ◽  
Before And After ◽  
Electronic Health ◽  
The Impact ◽  
Before And After Study

BACKGROUND AIRA is an AI designed to reduce the time that doctors dedicate filling out EHR, winner of the first edition of MIT Hacking Medicine held in Brazil in 2020. As a proof of concept, AIRA was implemented in administrative process before its application in a medical process. OBJECTIVE The aim of the study is to determinate the impact of AIRA by eliminating the Medical Care Registration (MCR) on Electronic Health Record (EHR) by Administrative Officer. METHODS This is a comparative before-and-after study following the guidance “Evaluating digital health products” from Public Health England. An Artificial Intelligence named AIRA was created and implemented at CEAC (Employee Attention Center) from HCFMUSP. A total of 25,507 attendances were evaluated along 2020 for determinate AIRA´s impact. Total of MCR, time of health screening and time between the end of the screening and the beginning of medical care, were compared in the pre and post AIRA periods. RESULTS AIRA eliminated the need for Medical Care Registration by Administrative Officer in 92% (p<0.0001). The nurse´s time of health screening increased 16% (p<0.0001) during the implementation, and 13% (p<0.0001) until three months after the implementation, but reduced in 4% three months after implementation (p<0.0001). The mean and median total time to Medical Care after the nurse’ Screening was decreased in 30% (p<0.0001) and 41% (p<0.0001) respectively. CONCLUSIONS The implementation of AIRA reduced the time to medical care in an urgent care after the nurse´ screening, by eliminating non-value-added activity the Medical Care Registration on Electronic Health Record (EHR) by Administrative Officer.

scholarly journals Opinions, perceptions and attitudes toward an electronic health record system among practicing nurses

2017 ◽  
Vol 8 (3) ◽  
pp. 12
Author(s):  
Ahmad H. Abu Raddaha ◽  
Arwa Obeidat ◽  
Huda Al Awaisi ◽  
Jahara Hayudini
Keyword(s):  
Electronic Health Record ◽  
Clinical Information System ◽  
Research Work ◽  
Healthcare Delivery ◽  
Future Research ◽  
Health Record ◽  
Randomized Sampling ◽  
Positive Attitudes ◽  
Electronic Health ◽  
The Impact

Background: Despite worldwide expanding implementation of electronic health record (EHR) systems, healthcare professionals conducted limited number of studies to explore factors that might facilitate or jeopardize using these systems. This study underscores the impact of nurses’ opinions, perceptions, and computer competencies on their attitudes toward using an EHR system.Methods: With randomized sampling, a cross-sectional exploratory design was used. The sample consisted of 169 nurses who worked at a public teaching hospital in Oman. They completed self-administered questionnaire. Several standardized valid and reliable instruments were utilized.Results: Seventy-four percent of our study nurses had high positive attitudes toward the EHR system. The least ranked perception scores (60.4%) were linked to perceiving that suggestions made by nurses about the system would be taken into account. Nurses who reported that the hospital sought for suggestions for customization of the system [OR: 2.54 (95% CI: 1.09, 5.88), p = .03], who found the system as an easy-to-use clinical information system [OR: 6.53 (95% CI: 1.72, 24.75), p = .01], who reported the presence of good relationship with the system’s managing personnel [OR: 3.59 (95% CI: 1.13, 11.36), p = .03] and who reported that the system provided all needed health information [OR: 2.97 (95% CI: 1.16, 7.62), p = .02] were more likely to develop high positive attitudes toward the system.Conclusions: To better develop plans to foster the EHR system’s use facilitators and overcome its usage barriers by nursing professionals, more involvement of nurses in system’s customization endeavors is highly suggested. When the system did not disrupt workflows, it would decrease clinical errors and expand nursing productivity. In order to maximize the utilization of the system in healthcare delivery, future research work to investigate the effect of the system on other healthcare providers and inter-professional communications is pressingly needed.

scholarly journals Beyond Getting Rid of Stupid Stuff in the Electronic Health Record (Beyond-GROSS): Protocol for a User-Centered, Mixed-Method Intervention to Improve the Electronic Health Record System

10.2196/25148 ◽  
2021 ◽  
Vol 10 (3) ◽  
pp. e25148
Author(s):  
Ahmed Umar Otokiti ◽  
Catherine K Craven ◽  
Avniel Shetreat-Klein ◽  
Stacey Cohen ◽  
Bruce Darrow
Keyword(s):  
Health Care ◽  
Data Analysis ◽  
Electronic Health Record ◽  
Health Record ◽  
User Centered Design ◽  
Pilot Phase ◽  
Direct Input ◽  
Electronic Health ◽  
The Impact ◽  
User Centered

Background Up to 60% of health care providers experience one or more symptoms of burnout. Perceived clinician burden resulting in burnout arises from factors such as electronic health record (EHR) usability or lack thereof, perceived loss of autonomy, and documentation burden leading to less clinical time with patients. Burnout can have detrimental effects on health care quality and contributes to increased medical errors, decreased patient satisfaction, substance use, workforce attrition, and suicide. Objective This project aims to improve the user-centered design of the EHR by obtaining direct input from clinicians about deficiencies. Fixing identified deficiencies via user-centered design has the potential to improve usability, thereby increasing satisfaction by reducing EHR-induced burnout. Methods Quantitative and qualitative data will be obtained from clinician EHR users. The input will be received through a form built in a REDCap database via a link embedded in the home page of the EHR. The REDCap data will be analyzed in 2 main dimensions, based on nature of the input, what section of the EHR is affected, and what is required to fix the issue(s). Identified issues will be escalated to relevant stakeholders responsible for rectifying the problems identified. Data analysis, project evaluation, and lessons learned from the evaluation will be incorporated in a Plan-Do-Study-Act (PDSA) manner every 4-6 weeks. Results The pilot phase of the study began in October 2020 in the Gastroenterology Division at Mount Sinai Hospital, New York City, NY, which includes 39 physicians and 15 nurses. The pilot is expected to run over a 4-6–month period. The results of the REDCap data analysis will be reported within 1 month of completing the pilot phase. We will analyze the nature of requests received and the impact of rectified issues on the clinician EHR user. We expect that the results will reveal which sections of the EHR have the highest deficiencies while also highlighting issues about workflow difficulties. Perceived impact of the project on provider engagement, patient safety, and workflow efficiency will also be captured by evaluation survey and other qualitative methods where possible. Conclusions The project aims to improve user-centered design of the EHR by soliciting direct input from clinician EHR users. The ultimate goal is to improve efficiency, reduce EHR inefficiencies with the possibility of improving staff engagement, and lessen EHR-induced clinician burnout. Our project implementation includes using informatics expertise to achieve the desired state of a learning health system as recommended by the National Academy of Medicine as we facilitate feedback loops and rapid cycles of improvement. International Registered Report Identifier (IRRID) PRR1-10.2196/25148

Sign in / Sign up

Export Citation Format

Share Document