Electronic Health Record Alerts Research Articles

Electronic Health Record Alert to Promote Adoption of Limited Transthoracic Echocardiograms in Primary Care and Cardiology Clinics: A Mixed Methods Evaluation.

A limited transthoracic echocardiogram (TTE) can be an appropriate, lower-cost substitute for a full TTE. We assessed the impact of an electronic health record alternative alert promoting the adoption of limited TTEs on the ordering practices of cardiology clinicians and primary care providers and captured their perspectives on the initiative. The alert was deployed in a cardiology clinic and 4 primary care clinics at an academic medical center. The alert provided clinical guidance on the appropriate use of limited TTEs when a clinician selected a full TTE order. We used logistic regression to estimate the change in the proportion of limited versus full TTEs ordered between the baseline and intervention periods in clinics with and without the alert. We also conducted interviews with 24 clinicians (5 cardiologists and 19 primary care providers) to identify implementation barriers and facilitators. Cardiology clinicians ordered 10 654 and 3761 TTEs during the baseline and intervention periods, respectively, for 9100 patients. Primary care providers ordered 723 and 617 TTEs during the baseline and intervention periods for 1273 patients. The model estimated that the percentage of limited TTEs ordered increased by 16.1±2.3 percentage points (P<0.0001) in the cardiology clinic with the alert and by 13.2±1.5 percentage points (P<0.0001) in the primary care clinics with the alert from baseline to post-intervention. Ordering practices did not change in the cardiology (0.7±0.6 percentage points; P=0.24) or primary care (0.7±1.0 percentage points; P=0.52) clinics without the alert. Clinicians viewed the alert as acceptable. Cardiologists appreciated that the alert was concise, whereas primary care providers wanted more information from the alert. An alternative alert providing clinical guidance on the use of limited TTEs at the point of care increased the selection of this lower-cost test in cardiology and primary care clinics. Perspectives on the alert differed between specialists and nonspecialists, highlighting the importance of tailoring intervention design to clinical expertise.

Optimizing Decision Support Alerts to Reduce Telemetry Duration: A Multicenter Evaluation.

Telemetry monitoring is crucial for high-risk patients but excessive use beyond practice standards increases costs. Prior studies have shown that electronic health record (EHR) alerts reduce low-value telemetry monitoring. However, specific components of these alerts that contribute to effectiveness are unknown. We aimed to revise previously implemented EHR Best Practice Advisories (BPAs) to optimize their effectiveness in reducing telemetry duration. The secondary objective was to assess the impact on clinicians' alert burden. A multicenter retrospective study was conducted at Johns Hopkins Hospital (JHH), Johns Hopkins Bayview Medical Center (JHBMC), and Howard County General Hospital (HCGH). An EHR alert in the form of a BPA was previously implemented at JHH/JHBMC, firing at 24, 48, or 72 hours based on order indication. HCGH used an alert firing every 24 hours. A revised BPA was implemented at all hospitals optimizing the prior JHH/JHBMC alert by including patient-specific telemetry indications, restricting alerts to daytime hours (8:00 a.m.-6:00 p.m.), and embedding the discontinuation order within the BPA alert. A retrospective analysis from October 2018 to December 2021 was performed. The primary outcome was telemetry duration. The secondary outcome was the mean monthly BPA alerts per patient-day. Compared with the original BPA, the revised BPA reduced telemetry duration by a mean of 6.7 hours (95% CI: 5.2-9.1 hours, p < 0.001) at JHH/JHBMC, with a minimal increase of 0.06 mean monthly BPA alerts per patient-day (p < 0.001). The BPA acceptance rate increased from 7.8 to 31.3% postintervention at JHH/JHBMC (p < 0.0001). At HCGH, the intervention led to a mean monthly reduction of 20.2 hours in telemetry duration per hospitalization (95% CI: 19.1-22.8 hours, p < 0.0001). Optimizing EHR BPAs reduces unnecessary telemetry duration without substantially increasing clinician alert burden. This study highlights the importance of tailoring EHR alerts to enhance effectiveness and promote value-based care.

Pharmacogenomic augmented machine learning in electronic health record alerts: A health system-wide usability survey of clinicians.

Pharmacogenomic (PGx) biomarkers integrated using machine learning can be embedded within the electronic health record (EHR) to provide clinicians with individualized predictions of drug treatment outcomes. Currently, however, drug alerts in the EHR are largely generic (not patient-specific) and contribute to increased clinician stress and burnout. Improving the usability of PGx alerts is an urgent need. Therefore, this work aimed to identify principles for optimal PGx alert design through a health-system-wide, mixed-methods study. Clinicians representing multiple practices and care settings (N = 1062) in urban, rural, and underserved regions were invited to complete an electronic survey comparing the usability of three drug alerts for citalopram, as a case study. Alert 1 contained a generic warning of pharmacogenomic effects on citalopram metabolism. Alerts 2 and 3 provided patient-specific predictions of citalopram efficacy with varying depth of information. Primary outcomes included the System's Usability Scale score (0-100 points) of each alert, the perceived impact of each alert on stress and decision-making, and clinicians' suggestions for alert improvement. Secondary outcomes included the assessment of alert preference by clinician age, practice type, and geographic setting. Qualitative information was captured to provide context to quantitative information. The final cohort comprised 305 geographically and clinically diverse clinicians. A simplified, individualized alert (Alert 2) was perceived as beneficial for decision-making and stress compared with a more detailed version (Alert 3) and the generic alert (Alert 1) regardless of age, practice type, or geographic setting. Findings emphasize the need for clinician-guided design of PGx alerts in the era of digital medicine.

Navigating Alert Fatigue: A Case Study in Electronic Health Record Alert Design Optimization.

Clinical decision support (CDS) systems play a crucial role in enhancing patient outcomes, but inadequate design contributes to alert fatigue, inundating clinicians with disruptive alerts that lack clinical relevance. This case study delves into a quality improvement (QI) project addressing nursing electronic health record (EHR) alert fatigue by strategically redesigning four high-firing/low action alerts. Employing a mixed-methods approach, including quantitative analysis, empathy mapping sessions, and user feedback, the project sought to understand and alleviate the challenges posed by these alerts. Virtual empathy mapping sessions with clinical nurses provided valuable insights into user experiences. Qualitative findings, CDS design principles, and organizational practice expectations informed the redesign process, resulting in the removal of all four identified disruptive alerts and redesign of passive alerts. This initiative released 877 unactionable disruptive nursing hours, emphasizing the significance of proper alert design and the necessity for organizational structures ensuring sustained governance in healthcare system optimization.

Factors Influencing Data Quality in Electronic Health Record Systems in 50 Health Facilities in Rwanda and the Role of Clinical Alerts: Cross-Sectional Observational Study.

Electronic health records (EHRs) play an increasingly important role in delivering HIV care in low- and middle-income countries. The data collected are used for direct clinical care, quality improvement, program monitoring, public health interventions, and research. Despite widespread EHR use for HIV care in African countries, challenges remain, especially in collecting high-quality data. We aimed to assess data completeness, accuracy, and timeliness compared to paper-based records, and factors influencing data quality in a large-scale EHR deployment in Rwanda. We randomly selected 50 health facilities (HFs) using OpenMRS, an EHR system that supports HIV care in Rwanda, and performed a data quality evaluation. All HFs were part of a larger randomized controlled trial, with 25 HFs receiving an enhanced EHR with clinical decision support systems. Trained data collectors visited the 50 HFs to collect 28 variables from the paper charts and the EHR system using the Open Data Kit app. We measured data completeness, timeliness, and the degree of matching of the data in paper and EHR records, and calculated concordance scores. Factors potentially affecting data quality were drawn from a previous survey of users in the 50 HFs. We randomly selected 3467 patient records, reviewing both paper and EHR copies (194,152 total data items). Data completeness was >85% threshold for all data elements except viral load (VL) results, second-line, and third-line drug regimens. Matching scores for data values were close to or >85% threshold, except for dates, particularly for drug pickups and VL. The mean data concordance was 10.2 (SD 1.28) for 15 (68%) variables. HF and user factors (eg, years of EHR use, technology experience, EHR availability and uptime, and intervention status) were tested for correlation with data quality measures. EHR system availability and uptime was positively correlated with concordance, whereas users' experience with technology was negatively correlated with concordance. The alerts for missing VL results implemented at 11 intervention HFs showed clear evidence of improving timeliness and completeness of initially low matching of VL results in the EHRs and paper records (11.9%-26.7%; P<.001). Similar effects were seen on the completeness of the recording of medication pickups (18.7%-32.6%; P<.001). The EHR records in the 50 HFs generally had high levels of completeness except for VL results. Matching results were close to or >85% threshold for nondate variables. Higher EHR stability and uptime, and alerts for entering VL both strongly improved data quality. Most data were considered fit for purpose, but more regular data quality assessments, training, and technical improvements in EHR forms, data reports, and alerts are recommended. The application of quality improvement techniques described in this study should benefit a wide range of HFs and data uses for clinical care, public health, and disease surveillance.

Pragmatic Trial of Messaging to Providers About Treatment of Hyperlipidemia (PROMPT-LIPID): A Randomized Clinical Trial.

Lipid-lowering therapy (LLT) is underutilized for very high-risk atherosclerotic cardiovascular disease. PROMPT-LIPID (PRagmatic Trial of Messaging to Providers about Treatment of HyperLIPIDemia) sought to determine whether electronic health record (EHR) alerts improve 90-day LLT intensification in patients with very high-risk atherosclerotic cardiovascular disease. PROMPT-LIPID was a pragmatic trial in which cardiovascular and internal medicine clinicians within Yale New Haven Health (New Haven, CT) were cluster-randomized to receive an EHR alert with individualized LLT recommendations or no alert for outpatients with very high-risk atherosclerotic cardiovascular disease and LDL-C (low-density lipoprotein cholesterol), ≥70 mg/dL. The primary outcome was 90-day LLT intensification (change to high-intensity statin and addition of ezetimibe or PCSK9i [proprotein subtilisin/kexin type 9 inhibitors]). Secondary outcomes included LDL-C level, proportion of patients with LDL-C of <70 or < 55 mg/dL, rate of major adverse cardiovascular events, ED visit incidence, and 6-month mortality. Results were analyzed using logistic and linear regression clustered at the provider level. The no-alert group included 47 clinicians and 1370 patients (median age, 71 years; 50.1% female, median LDL-C, 93 mg/dL); the alert group included 49 clinicians and 1130 patients (median age, 72 years; 47% female, median LDL-C 91, mg/dL). The primary outcome was observed in 14.1% of patients in the alert group as compared with 10.4% in the no-alert group. There were no differences in any secondary outcomes at 6 months. Among 542 patients whose clinicians (n=46) did not dismiss the EHR alert recommendations, LLT intensification was significantly greater (21.2% versus 10.4%, odds ratio, 2.33 [95% CI, 1.48-3.66]). With a real-time, targeted, individualized EHR alert as compared with usual care, the proportion of patients with atherosclerotic cardiovascular disease with LLT intensification was numerically higher but not statistically significant. Among clinicians who did not dismiss the alert, there was a > 2-fold increase in LLT intensification. EHR alerts, coupled with strategies to reduce clinician dismissal, may help address persistent gaps in LDL-C management. URL: https://www.clinicaltrials.gov; Unique identifier: NCT04394715, https://www.clinicaltrials.gov/ct2/show/study/NCT04394715.

Evaluation of a Clinical Decision Support System and an Automated Electronic Health Record Alert on Outpatient Prescribing of Cefdinir.

Acute bacterial upper respiratory infections are common indications for antibiotics in pediatrics, and many prescriptions may be inappropriate. Novel approaches to outpatient antimicrobial stewardship interventions are needed. This quasi-experimental study of an order set and best practice advisory alert targeting cefdinir prescriptions demonstrated an 8.4% decrease in cefdinir prescribing (P ≤ .001).

Randomized Comparison of Electronic Health Record Alert Types in Eliciting Responses about Prognosis in Gynecologic Oncology Patients.

To compare the ability of different electronic health record alert types to elicit responses from users caring for cancer patients benefiting from goals of care (GOC) conversations. A validated question asking if the user would be surprised by the patient's 6-month mortality was built as an Epic BestPractice Advisory (BPA) alert in three versions-(1) Required on Open chart (pop-up BPA), (2) Required on Close chart (navigator BPA), and (3) Optional Persistent (Storyboard BPA)-randomized using patient medical record number. Meaningful responses were defined as "Yes" or "No," rather than deferral. Data were extracted over 6 months. Alerts appeared for 685 patients during 1,786 outpatient encounters. Measuring encounters where a meaningful response was elicited, rates were highest for Required on Open (94.8% of encounters), compared with Required on Close (90.1%) and Optional Persistent (19.7%) (p < 0.001). Measuring individual alerts to which responses were given, they were most likely meaningful with Optional Persistent (98.3% of responses) and least likely with Required on Open (68.0%) (p < 0.001). Responses of "No," suggesting poor prognosis and prompting GOC, were more likely with Optional Persistent (13.6%) and Required on Open (10.3%) than with Required on Close (7.0%) (p = 0.028). Required alerts had response rates almost five times higher than optional alerts. Timing of alerts affects rates of meaningful responses and possibly the response itself. The alert with the most meaningful responses was also associated with the most interruptions and deferral responses. Considering tradeoffs in these metrics is important in designing clinical decision support to maximize success.

Interventions for Optimization of Guideline-Directed Medical Therapy

Implementation of guideline-directed medical therapy (GDMT) in real-world practice remains suboptimal. It is unclear which interventions are most effective at addressing current barriers to GDMT in patients with heart failure with reduced ejection fraction (HFrEF). To perform a systematic review to identify which types of system-level initiatives are most effective at improving GDMT use among patients with HFrEF. PubMed, Embase, Cochrane, CINAHL, and Web of Science databases were queried from January 2010 to November 2023 for randomized clinical trials that implemented a quality improvement intervention with GDMT use as a primary or secondary outcome. References from related review articles were also included for screening. Quality of studies and bias assessment were graded based on the Cochrane Risk of Bias tool and Oxford Centre for Evidence-Based Medicine. Twenty-eight randomized clinical trials were included with an aggregate sample size of 19 840 patients. Studies were broadly categorized as interdisciplinary interventions (n = 15), clinician education (n = 5), electronic health record initiatives (n = 6), or patient education (n = 2). Overall, interdisciplinary titration clinics were associated with significant increases in the proportion of patients on target doses of GDMT with a 10% to 60% and 2% to 53% greater proportion of patients on target doses of β-blockers and renin-angiotensin-aldosterone system inhibitors, respectively, in intervention groups compared with usual care. Other interventions, such as audits, clinician and patient education, or electronic health record alerts, were also associated with some improvements in GDMT utilization, though these findings were inconsistent across studies. This review summarizes interventions aimed at optimization of GDMT in clinical practice. Initiatives that used interdisciplinary teams, largely comprised of nurses and pharmacists, most consistently led to improvements in GDMT. Additional large, randomized studies are necessary to better understand other types of interventions, as well as their long-term efficacy and sustainability.

An Electronic Health Record Alert for Inpatient Coronavirus Disease 2019 Vaccinations Increases Vaccination Ordering and Uncovers Workflow Inefficiencies.

Despite mortality benefits, only 19.9% of U.S. adults are fully vaccinated against the coronavirus disease 2019 (COVID-19). The inpatient setting is an opportune environment to update vaccinations, and inpatient electronic health record (EHR) alerts have been shown to increase vaccination rates. Our objective was to evaluate whether an EHR alert could increase COVID-19 vaccinations in eligible hospitalized adults by prompting providers to order the vaccine. This was a quasiexperimental pre-post-interventional design study at an academic and community hospital in the western United States between 1 January, 2021 and 31 October, 2021. Inclusion criteria were unvaccinated hospitalized adults. A soft-stop, interruptive EHR alert prompted providers to order COVID-19 vaccines for those with an expected discharge date within 48 hours and interest in vaccination. The outcome measured was the proportion of all eligible patients for whom vaccines were ordered and administered before and after alert implementation. Vaccine ordering rates increased from 4.0 to 13.0% at the academic hospital (odds ratio [OR]: 4.01, 95% confidence interval [CI]: 3.39-4.74, p < 0.001) and from 7.4 to 11.6% at the community hospital (OR: 1.62, 95% CI: 1.23-2.13, p < 0.001) after alert implementation. Administration increased postalert from 3.6 to 12.7% at the academic hospital (OR: 3.21, 95% CI: 2.70-3.82, p < 0.001) but was unchanged at the community hospital, 6.7 to 6.7% (OR: 0.99, 95% CI: 0.73-1.37, p = 0.994). Further analysis revealed infrequent vaccine availability at the community hospital. Vaccine ordering rates improved at both sites after alert implementation. Vaccine administration rates, however, only improved at the academic hospital, likely due in part to vaccine dispensation inefficiency at the community hospital. This study demonstrates the potential impact of complex workflow patterns on new EHR alert success and provides a rationale for subsequent qualitative workflow analysis with alert implementation.

Artificial intelligence in early detection and prediction of pediatric/neonatal acute kidney injury: current status and future directions.

Acute kidney injury (AKI) has a significant impact on the short-term and long-term clinical outcomes of pediatric and neonatal patients, and it is imperative in these populations to mitigate the pathways leading to AKI and be prepared for early diagnosis and treatment intervention of established AKI. Recently, artificial intelligence (AI) has provided more advent predictive models for early detection/prediction of AKI utilizing machine learning (ML). By providing strong detail and evidence from risk scores and electronic alerts, this review outlines a comprehensive and holistic insight into the current state of AI in AKI in pediatric/neonatal patients. In the pediatric population, AI models including XGBoost, logistic regression, support vector machines, decision trees, naïve Bayes, and risk stratification scores (Renal Angina Index (RAI), Nephrotoxic Injury Negated by Just-in-time Action (NINJA)) have shown success in predicting AKI using variables like serum creatinine, urine output, and electronic health record (EHR) alerts. Similarly, in the neonatal population, using the "Baby NINJA" model showed a decrease in nephrotoxic medication exposure by 42%, the rate of AKI by 78%, and the number of days with AKI by 68%. Furthermore, the "STARZ" risk stratification AI model showed a predictive ability of AKI within 7days of NICU admission of AUC 0.93 and AUC of 0.96 in the validation and derivation cohorts, respectively. Many studies have reported the superiority of using biomarkers to predict AKI in pediatric patients and neonates as well. Future directions include the application of AI along with biomarkers (NGAL, CysC, OPN, IL-18, B2M, etc.) in a Labelbox configuration to create a more robust and accurate model for predicting and detecting pediatric/neonatal AKI.

B-328 Analytical Performance of a Screening Test for Lead Using Dried Blood Spots and ICP-MS

Abstract Introduction There is no safe blood lead levels (BLL) in children and exposure can cause irreversible neurological harm. In 2021, the CDC decreased the blood lead reference value (BLRV) to 3.5 ug/dL from 5.0 ug/dL, based on the 97.5th percentile of BLL among US children under 5 years of age. Despite this, lead poisoning disproportionately affects Black children, those below poverty levels, and living in houses built before 1978. The American Academy of Pediatrics recommends targeted screening for BLL in children aged 12–24 months living in high prevalence areas. Children enrolled in Medicaid also require screening by 24 months of age. Only one-third of children who require screening receive it and lead screening declined by &amp;gt;50% during the COVD-19 pandemic. Quality improvement strategies have included electronic health record alerts and point of care testing. To improve screening rates in our pediatric population, we developed a screening method utilizing dried blood spot (DBS) cards. This study aimed to validate the accuracy and precision of this screening method with emphasis on the performance around the BLRV cut-offs, which would be used to determine the need to follow up with a confirmatory venous lead test. Methods The method was calibrated using certified reference material traceable to NIST SRM 3128 (VHG labs, Manchester, NH, USA) in whole blood. The calibrators, controls and patient samples were spotted onto Whatman 903 cards. A 6 mm filter paper disc in extraction buffer was vortexed, allowed to sit at room temperature for 30 min and centrifuged. The samples were analyzed on the Thermo Fisher iCAP RQ and TQ (Whaltman, MA) in kinetic energy discrimination mode. The assay has a linear range of 1–100 µg/dL, and the recovery ranged from 92%–102%. We evaluated this method for reproducibility, and accuracy across the measurement range. In this study, we focused on the reproducibility and accuracy of DBS at lead concentrations around the BLRV cut-offs. We used in 2 samples spiked with NIST CRM 3128 (Inorganic Ventures, Christiansburg, VA, USA) analyzed 10 times over 3 days and 13 leftover patient samples run over at least 5 days. The total allowable error was 2 µg/dL or 10%. Results In two samples spiked to BLL of 3.5 and 3.9 µg/dL, interday precision (n = 10) ranged from 3.4% to 17.4% (SD 0.1–0.7) in samples run for 3 days. Removal of an apparent outlier decreased the %CV ranged observed to &amp;lt;8.5%. The average bias for each day ranged from 0.1 to 0.6 µg/dL. In 13 patient samples with concentrations around the BLRV ranging from 1.4 to 5.8 µg/dL, the coefficient of variation (CV) ranged from 0.2% to 32% with the largest SD at 0.8 µg/dL. The average bias for this sample set was -0.3 (11.7%). Conclusion The assay’s analytical performance is sufficient for screening purposes, but the clinical performance and the impact of introducing this test to enhance screening rates during 1 and 2 years well visits need to be evaluated further.

Harnessing EHR for goals of care: A randomized evaluation of electronic health record alerts for patients with gynecologic malignancies at risk for death in 6 months (165)

Harnessing EHR for goals of care: A randomized evaluation of electronic health record alerts for patients with gynecologic malignancies at risk for death in 6 months (165)

Electronic health record alerts for management of heart failure with reduced ejection fraction in hospitalized patients: the PROMPT-AHF trial.

Patients hospitalized for acute heart failure (AHF) continue to be discharged on an inadequate number of guideline-directed medical therapies (GDMT) despite evidence that inpatient initiation is beneficial. This study aimed to examine whether a tailored electronic health record (EHR) alert increased rates of GDMT prescription at discharge in eligible patients hospitalized for AHF. Pragmatic trial of messaging to providers about treatment of acute heart failure (PROMPT-AHF) was a pragmatic, multicenter, EHR-based, and randomized clinical trial. Patients were automatically enrolled 48 h after admission if they met pre-specified criteria for an AHF hospitalization. Providers of patients in the intervention arm received an alert during order entry with relevant patient characteristics along with individualized GDMT recommendations with links to an order set. The primary outcome was an increase in the number of GDMT prescriptions at discharge. Thousand and twelve patients were enrolled between May 2021 and November 2022. The median age was 74 years; 26% were female, and 24% were Black. At the time of the alert, 85% of patients were on β-blockers, 55% on angiotensin-converting enzyme inhibitor/angiotensin receptor blocker/angiotensin receptor-neprilysin inhibitor, 20% on mineralocorticoid receptor antagonist (MRA) and 17% on sodium-glucose cotransporter 2 inhibitor. The primary outcome occurred in 34% of both the alert and no alert groups [adjusted risk ratio (RR): 0.95 (0.81, 1.12), P = .99]. Patients randomized to the alert arm were more likely to have an increase in MRA [adjusted RR: 1.54 (1.10, 2.16), P = .01]. At the time of discharge, 11.2% of patients were on all four pillars of GDMT. A real-time, targeted, and tailored EHR-based alert system for AHF did not lead to a higher number of overall GDMT prescriptions at discharge. Further refinement and improvement of such alerts and changes to clinician incentives are needed to overcome barriers to the implementation of GDMT during hospitalizations for AHF. GDMT remains suboptimal in this setting, with only one in nine patients being discharged on a comprehensive evidence-based regimen for heart failure.

Systematic review and meta-analysis comparing educational and reminder digital interventions for promoting HPV vaccination uptake

Global Human papillomavirus (HPV) vaccination rates remain low despite available WHO-approved vaccines. Digital interventions for promoting vaccination uptake offer a scalable and accessible solution to this issue. Here we report a systematic review and meta-analysis examining the efficacy of digital interventions, comparing educational and reminder approaches, for promoting HPV vaccination uptake (HVU). This study also identifies factors influencing the effectiveness of these interventions. We searched PubMed, PsycInfo, Web of Science, and the Cochrane Library from each database’s inception to January 2023. Three raters independently evaluate the studies using a systematic and blinded method for resolving disagreements. From 1929 references, 34 unique studies (281,280 unique participants) have sufficient data. Client reminder (OR, 1.41; 95% CI, 1.23–1.63; P < 0.001), provider reminder (OR, 1.39; 95% CI, 1.11–1.75; P = 0.005), provider education (OR, 1.18; 95% CI, 1.05–1.34; P = 0.007), and client education plus reminder interventions (OR, 1.29; 95% CI, 1.04–1.59; P = 0.007) increase HVU, whereas client education interventions do not (OR, 1.08; 95% CI, 0.92–1.28; P = 0.35). Digital intervention effectiveness varies based on participants’ gender and the digital platform used. Interventions targeting male or mixed-gender participants demonstrate greater benefit, and reminder platforms (SMS, preference reminders, or electronic health record alerts) are more effective in increasing HVU. Digital interventions, particularly client and provider reminders, along with provider education, prove significantly more effective than client education alone. Incorporating digital interventions into healthcare systems can effectively promote HPV vaccination uptake. Reminder interventions should be prioritized for promoting HVU.

Electronic Health Record Embedded Strategies for Improving Care of Patients With Heart Failure.

A majority of clinical decisions use the electronic health record (EHR) and there is an unmet need to use its capability to help providers to make evidence-based decisions that improve care for heart failure patients. These electronic nudges are rooted in the human psychology of decision-making and often target specific cognitive biases. This review outlines the development of novel EHR nudges and specific lessons learned from each experience to inform the development of future interventions. There have been several randomized clinical trials examining the impact of EHR alerts on quality of care for heart failure patients. These interventions have targeted both clinicians and patients. There are features of each trial that inform best practices and future directions for EHR nudges. Recent clinical trials have demonstrated that some EHR alerts can improve care for heart failure patients. These trials utilized default options, involved clinicians in the alert design process, provided actionable recommendations, and aimed to minimize disruptions to typical workflow. Alerts aimed at improving care should be examined in a randomized fashion in order to evaluate their impact on clinician satisfaction and patient care.

QIM23-150: Electronic Health Record Alerts Compared to Chart Review for Lung Cancer Screening Eligibility

QIM23-150: Electronic Health Record Alerts Compared to Chart Review for Lung Cancer Screening Eligibility

HCV universal EHR prompt successfully increases screening, highlights potential disparities.

Screening for hepatitis C virus is the first critical decision point for preventing morbidity and mortality from HCV cirrhosis and hepatocellular carcinoma and will ultimately contribute to global elimination of a curable disease. This study aims to portray the changes over time in HCV screening rates and the screened population characteristics following the 2020 implementation of an electronic health record (EHR) alert for universal screening in the outpatient setting in a large healthcare system in the US mid-Atlantic region. Data was abstracted from the EHR on all outpatients from 1/1/2017 through 10/31/2021, including individual demographics and their HCV antibody (Ab) screening dates. For a limited period centered on the implementation of the HCV alert, mixed effects multivariable regression analyses were performed to compare the timeline and characteristics of those screened and un-screened. The final models included socio-demographic covariates of interest, time period (pre/post) and an interaction term between time period and sex. We also examined a model with time as a monthly variable to look at the potential impact of COVID-19 on screening for HCV. Absolute number of screens and screening rate increased by 103% and 62%, respectively, after adopting the universal EHR alert. Patients with Medicaid were more likely to be screened than private insurance (ORadj 1.10, 95% CI: 1.05, 1.15), while those with Medicare were less likely (ORadj 0.62, 95% CI: 0.62, 0.65); and Black (ORadj 1.59, 95% CI: 1.53, 1.64) race more than White. Implementation of universal EHR alerts could prove to be a critical next step in HCV elimination. Those with Medicare and Medicaid insurance were not screened proportionately to the national prevalence of HCV in these populations. Our findings support increased screening and re-testing efforts for those at high risk of HCV.

Evaluation of Policies Limiting Opioid Exposure on Opioid Prescribing and Patient Pain in Opioid-Naive Patients Undergoing Elective Surgery in a Large American Health System.

Overprescribing to opioid-naive surgical patients substantially contributes to opioid use disorders, which have become increasingly prevalent. Opioid stewardship programs (OSPs) within healthcare settings provide an avenue for introducing interventions to regulate prescribing. This study examined the association of OSP policies limiting exposure on changes in surgery-related opioid prescriptions and patient pain. We evaluated policies implemented by an OSP in a large American healthcare system between 2016 and 2018: nonopioid medication during surgery, decrease of available opioid dosage vials in operating rooms, standardization of opioid in-patient practices through electronic health record alerts, and limit to postsurgery opioid supply. Generalized linear mixed effects models examined the association of interventions with outcome changes in 9262 opioid-naive patients undergoing elective surgery. Outcomes were discharge pain, morphine milligram equivalent in the first prescription postsurgery, and opioid prescription refills. Decreases in all prescription outcomes and discharge pain were observed following onset of OSP interventions ( P 's < 0.001). Among individual policies, standardization of in-patient prescribing practices was associated with the strongest decrease in prescribed morphine milligram equivalent. Importantly, there was no evidence of an increase in discharge pain related to any intervention. This study promotes the potential of OSP formation and policies to reduce opioid prescribing without compromising patient pain. The most effective policy, standardization of in-patient prescribing practices through alerts, suggests that reminding prescribers to re-evaluate the patient's need is effective in changing behavior. The findings offer considerations for OSP formation and policy implementation across health systems to improve quality and safety in opioid prescribing.

2066. Introduction of a Universal HIV Screening Electronic Health Record Alert at a Midwest Academic Health System

Abstract Background Universal human immunodeficiency virus (HIV) testing is recommended for patients ages 13-64 at least once by the Centers for Disease Control and Prevention. Screening lags behind these recommendations. Methods We performed an analysis of the current state of HIV screening at three university-based internal medicine and one internal medicine/pediatrics clinics serving counties with HIV prevalence in 2020 from 51/100,000 to 176/100,000. We completed a chart review to examine current screening practices and conducted an electronic survey of primary care providers inquiring about awareness of universal screening recommendations and barriers to screening. Based on this analysis, we implemented an automated electronic health record (EHR) reminder for HIV screening for patients aged 15-64 during health maintenance exams. Our primary outcome was the number of patients screened for HIV pre-intervention (2017-July 2021) and post-intervention (August 2021-December 2021). We used odds ratio to compare the proportion of tests which were false positive and true positive pre- vs post-intervention. To evaluate the effect of the intervention on total HIV tests ordered across the institution, we used control chart methodology. Results Survey response rate was 59% for residents (n=97) and 18% for faculty (n=29). 93% were aware of the screening recommendation. The most common barrier to screening was forgetting to screen (Figure 1). Pre-intervention, 25% of reviewed charts (n=4393) received recommended HIV screening. Post-intervention, HIV testing increased across the institution by 49% (Figure 2) and weekly testing was consistently three standard deviations above the mean compared to pre-intervention (Figure 3). The proportion of true positive tests decreased (p&amp;lt; 0.05). Figure 1.Common Reasons for Not Performing Universal HIV ScreeningFigure 2.Summary of HIV Testing 2017-2021Figure 3.Control Chart for HIV Testing by Week 2017-2021 Conclusion Implementation of an automated HIV screening EHR reminder increased HIV screening. The proportion of true positive tests decreased, likely due to the low prevalence of HIV in our community Disclosures All Authors: No reported disclosures.