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Visual field testing that is not frequent enough results in delayed identification of open-angle glaucoma (OAG) progression. Guidelines recommend at least annual testing. It is not known how frequently patients with OAG across the United States receive visual field testing and how patient characteristics and circumstances influence this frequency. If US patients with OAG do not receive visual field tests frequently enough, interventions to increase this frequency or to develop other forms of testing visual function may reduce unidentified OAG vision loss.Retrospective cohort study.The TruvenHealth MarketScan Commercial Claims Database (IBM) contains demographic and claims data for160 million individuals across the United States from 2008 to 2017. We identified enrollees in the database with a recorded diagnosis of OAG (International Classification of Diseases, Ninth Revision, Clinical Modification and International Classification of Diseases, Tenth Revision, Clinical Modification codes 356.1x and H40.1x, respectively). We excluded those aged40 years at the time of their first OAG diagnosis, those without at least 1 confirmatory OAG diagnosis at a subsequent visit, and those with4 years of follow-up data after OAG diagnosis.We calculated the number of visual field tests that each enrollee with OAG underwent per year and categorized the enrollees based on that number (0,0 to0.9, ≥ 0.9 to ≤ 1.1,1.1 to ≤ 2.1, and2.1). We used negative binomial regression to investigate the demographic or health variables that were associated with the frequency of visual field tests that enrollees with OAG received.Frequency of visual field testing among enrollees with OAG.Of the 380 029 enrollees included in the study, 33 267 (8.8%) did not receive a visual field test during the study period, 259 349 (68.2%) underwent0 to0.9 visual field tests per year, 42 129 (11.1%) underwent ≥ 0.9 to ≤ 1.1 visual field tests per year, 42 301 (11.1%) underwent1.1 to ≤ 2.1 visual field tests per year, and 2983 (0.8%) underwent ≥ 2.1 visual field tests per year. The median number of visual field tests per year was 0.63 (interquartile range, 0.33-0.88; mean, 0.65).More than 75% of enrollees with OAG received1 visual field test per year and, thus, did not receive guideline-adherent glaucoma monitoring.

Purpose Inaccurate and nonspecific medication alerts contribute to high override rates, alert fatigue, and ultimately patient harm. Drug-drug interaction (DDI) alerts often fail to account for factors that could reduce risk; further, drugs that trigger alerts are often inconsistently grouped into value sets. Toward improving the specificity of DDI alerts, the objectives of this study were to (1) highlight the inconsistency of drug value sets for triggering DDI alerts and (2) demonstrate a method of classifying factors that can be used to modify the risk of harm from a DDI. Methods This was a proof-of-concept study focused on 15 well-known DDIs. Using 3 drug interaction references, we extracted 2 drug value sets and any available order- and patient-related factors for each DDI. Fleiss’ kappa was used to measure the consistency of value sets among references. Risk-modifying factors were classified as order parameters (eg, route and dose) or patient characteristics (eg, comorbidities and laboratory results). Results Seventeen value sets (56%) had nonsignificant agreement. Agreement among the remaining 13 value sets was on average moderate. Thirty-three factors that could reduce risk in 14 of 15 DDIs (93%) were identified. Most risk-modifying factors (67%) were classified as order parameters. Conclusion This study demonstrates the importance of increasing the consistency of drug value sets that trigger DDI alerts and how alert specificity and usefulness can be improved with risk-modifying factors obtained from drug references. It may be difficult to operationalize certain factors to reduce unnecessary alerts; however, factors can be used to support decisions by providing contextual information.

Racial/ethnic minority, low socioeconomic status, and rural populations are disproportionately affected by COVID-19. Developing and evaluating interventions to address COVID-19 testing and vaccination among these populations are crucial to improving health inequities. The purpose of this paper is to describe the application of a rapid-cycle design and adaptation process from an ongoing trial to address COVID-19 among safety-net healthcare system patients. The rapid-cycle design and adaptation process included: (a) assessing context and determining relevant models/frameworks; (b) determining core and modifiable components of interventions; and (c) conducting iterative adaptations using Plan-Do-Study-Act (PDSA) cycles. PDSA cycles included: Plan. Gather information from potential adopters/implementers (e.g., Community Health Center [CHC] staff/patients) and design initial interventions; Do. Implement interventions in single CHC or patient cohort; Study. Examine process, outcome, and context data (e.g., infection rates); and, Act. If necessary, refine interventions based on process and outcome data, then disseminate interventions to other CHCs and patient cohorts. Seven CHC systems with 26 clinics participated in the trial. Rapid-cycle, PDSA-based adaptations were made to adapt to evolving COVID-19-related needs. Near real-time data used for adaptation included data on infection hot spots, CHC capacity, stakeholder priorities, local/national policies, and testing/vaccine availability. Adaptations included those to study design, intervention content, and intervention cohorts. Decision-making included multiple stakeholders (e.g., State Department of Health, Primary Care Association, CHCs, patients, researchers). Rapid-cycle designs may improve the relevance and timeliness of interventions for CHCs and other settings that provide care to populations experiencing health inequities, and for rapidly evolving healthcare challenges such as COVID-19.

Objectives: To review current studies about designing and implementing clinician-facing clinical decision support (CDS) integrated or interoperable with an electronic health record (EHR) to improve health care for populations facing disparities. Methods: We searched PubMed to identify studies published between January 1, 2011 and October 22, 2021 about clinician-facing CDS integrated or interoperable with an EHR. We screened abstracts and titles and extracted study data from articles using a protocol developed by team consensus. Extracted data included patient population characteristics, clinical specialty, setting, EHR, clinical problem, CDS type, reported user-centered design, implementation strategies, and outcomes. Results: There were 28 studies (36 articles) included. Most studies were performed at safety net institutions (14 studies) or Indian Health Service sites (6 studies). CDS tools were implemented in primary care outpatient settings in 24 studies (86%) for screening or treatment. CDS included point-of-care alerts (93%), order facilitators (46%), workflow support (39%), relevant information display (36%), expert systems (11%), and medication dosing support (7%). Successful outcomes were reported in 19 of 26 studies that reported outcomes (73%). User-centered design was reported during CDS planning (39%), development (32%), and implementation phase (25%). Most frequent implementation strategies were education (89%) and consensus facilitation (50%). Conclusions: CDS tools may improve health equity and outcomes for patients who face disparities. The present review underscores the need for high-quality analyses of CDS-associated health outcomes, reporting of user-centered design and implementation strategies used in low-resource settings, and methods to disseminate CDS created to improve health equity.

ImportanceClinical decision support (CDS) algorithms are increasingly being implemented in health care systems to identify patients for specialty care. However, systematic differences in missingness of electronic health record (EHR) data may lead to disparities in identification by CDS algorithms.ObjectiveTo examine the availability and comprehensiveness of cancer family history information (FHI) in patients’ EHRs by sex, race, Hispanic or Latino ethnicity, and language preference in 2 large health care systems in 2021.Design, Setting, and ParticipantsThis retrospective EHR quality improvement study used EHR data from 2 health care systems: University of Utah Health (UHealth) and NYU Langone Health (NYULH). Participants included patients aged 25 to 60 years who had a primary care appointment in the previous 3 years. Data were collected or abstracted from the EHR from December 10, 2020, to October 31, 2021, and analyzed from June 15 to October 31, 2021.ExposuresPrior collection of cancer FHI in primary care settings.Main Outcomes and MeasuresAvailability was defined as having any FHI and any cancer FHI in the EHR and was examined at the patient level. Comprehensiveness was defined as whether a cancer family history observation in the EHR specified the type of cancer diagnosed in a family member, the relationship of the family member to the patient, and the age at onset for the family member and was examined at the observation level.ResultsAmong 144 484 patients in the UHealth system, 53.6% were women; 74.4% were non-Hispanic or non-Latino and 67.6% were White; and 83.0% had an English language preference. Among 377 621 patients in the NYULH system, 55.3% were women; 63.2% were non-Hispanic or non-Latino, and 55.3% were White; and 89.9% had an English language preference. Patients from historically medically undeserved groups—specifically, Black vs White patients (UHealth: 17.3% [95% CI, 16.1%-18.6%] vs 42.8% [95% CI, 42.5%-43.1%]; NYULH: 24.4% [95% CI, 24.0%-24.8%] vs 33.8% [95% CI, 33.6%-34.0%]), Hispanic or Latino vs non-Hispanic or non-Latino patients (UHealth: 27.2% [95% CI, 26.5%-27.8%] vs 40.2% [95% CI, 39.9%-40.5%]; NYULH: 24.4% [95% CI, 24.1%-24.7%] vs 31.6% [95% CI, 31.4%-31.8%]), Spanish-speaking vs English-speaking patients (UHealth: 18.4% [95% CI, 17.2%-19.1%] vs 40.0% [95% CI, 39.7%-40.3%]; NYULH: 15.1% [95% CI, 14.6%-15.6%] vs 31.1% [95% CI, 30.9%-31.2%), and men vs women (UHealth: 30.8% [95% CI, 30.4%-31.2%] vs 43.0% [95% CI, 42.6%-43.3%]; NYULH: 23.1% [95% CI, 22.9%-23.3%] vs 34.9% [95% CI, 34.7%-35.1%])—had significantly lower availability and comprehensiveness of cancer FHI (P Conclusions and RelevanceThese findings suggest that systematic differences in the availability and comprehensiveness of FHI in the EHR may introduce informative presence bias as inputs to CDS algorithms. The observed differences may also exacerbate disparities for medically underserved groups. System-, clinician-, and patient-level efforts are needed to improve the collection of FHI.

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Background and Objectives: The established treatment target for most patients with type 2 diabetes mellitus (T2DM) is controlling HbA1c levels to less than 7%; however, about half of patients fail to achieve this target. In order to support optimal prescription selection, we have been developing a prescription selection support system that predicts expected outcomes by treatment options. In this presentation, we report a novel analytical method named Treatment Pathway Based Estimation (TPGE) , which predicts the probability of achieving a treatment goal, such as HbA1c less than 7% in 3 months, using a treatment pathway graph built from the data. Methods: The TPGE model was built and evaluated using EHR data from 27,9 patients with T2DM. TPGE was compared with a conventional machine learning approach, gradient boosting decision tree (GBT) , across two dimensions. First, the prediction error of the probability of achieving treatment was evaluated by the Brier Score. Second, the characteristics of the prescriptions recommended with the highest probability were investigated, referring to the prescriptions actually prescribed by clinicians. Results: TPGE achieved a prediction error of 0.158, which is lower than the prediction error of 0.162 of GBT. In addition, the calibration error was 0.002 (84.6% reduction) , much lower than that of GBT (0.013) . A prescription to add two or more drugs was selected by clinicians for 0.7% of cases. GBT recommended that prescription for 42.2 % of cases, while TPGE recommended for 1.4%, which was closer to the clinicians’ decision. In summary, the superiority of TPGE over GBT was confirmed. Conclusion: We have developed a pharmacotherapy decision support technology for T2DM to help clinicians and patients select the optimal prescription. Disclosure S.Tarumi: None. W.Takeuchi: None. G.Chalkidis: Employee; Hitachi, Ltd. H.Ban: None. K.Kawamoto: Consultant; Korean Society of Medical Informatics, NORC at the University of Chicago, Pfizer Inc., RTI International, Security Risk Solutions, Other Relationship; MD Aware, Research Support; Hitachi, Ltd., Pfizer Inc.

Objective HL7 SMART on FHIR apps have the potential to improve healthcare delivery and EHR usability, but providers must be aware of the apps and use them for these potential benefits to be realized. The HL7 CDS Hooks standard was developed in part for this purpose. The objective of this study was to determine if contextually relevant CDS Hooks prompts can increase utilization of a SMART on FHIR medical reference app (MDCalc for EHR). Materials and Methods We conducted a 7-month, provider-randomized trial with 70 providers in a single emergency department. The intervention was a collection of CDS Hooks prompts suggesting the use of 6 medical calculators in a SMART on FHIR medical reference app. The primary outcome was the percentage of provider–patient interactions in which the app was used to view a recommended calculator. Secondary outcomes were app usage stratified by individual calculators. Results Intervention group providers viewed a study calculator in the app in 6.0% of interactions compared to 2.6% in the control group (odds ratio = 2.45, 95% CI, 1.2–5.2, P value .02), an increase of 130%. App use was significantly greater for 2 of 6 calculators. Discussion and Conclusion Contextually relevant CDS Hooks prompts led to a significant increase in SMART on FHIR app utilization. This demonstrates the potential of using CDS Hooks to guide appropriate use of SMART on FHIR apps and was a primary motivation for the development of the standard. Future research may evaluate potential impacts on clinical care decisions and outcomes.

Introduction: Obesity is a significant health problem in the US. Sustainable interventions are needed to support longer-term maintenance after weight loss. We designed and tested a successful electronic health record (EHR)-based weight maintenance intervention with coaching delivered by research staff. Our next step is to determine whether coaching can be done by primary care staff during routine clinical work. We address 3 feasibility questions for using staff as coaches: coach recruitment, efficiency and effectiveness of coach training, and workflow integration and burden. Methods: MAINTAIN PRIME is an RCT comparing EHR-based coaching plus tracking vs tracking alone for primary care patients with recent weight loss. Implementation in 2 pilot clinics included: 1) approval of clinic leaders; 2) advertising to clinic staff for volunteers; 3) development and delivery of a self-paced coach training program; 4) coaching pilot patients for one month. Training (with a $400 one-time stipend) consisted of: 1) in-person orientation; 2) six core interactive online learning modules; 3) in-person review session; and 4) hands-on EHR tool training. Topics included weight management and behavior change, online communication, and structure of coaching messages. We obtained quantitative and qualitative feedback for both training and pilot coaching experience. Results: Recruitment: All clinical leaders (n=9) agreed to staff participation. Thirteen (10 medical assistants (MAs), 2 nurses, and 1 care manager) volunteered, out of 96 total clinical staff. Training : Six (46%) staff completed training. Reported confidence was very high (4.6/5) after training and the likelihood of recommending training to others was moderate (6.8/10). A common theme to improve training was to consolidate; we eliminated in-person review and added case studies to online modules. Staff found hands-on EHR tool training and research team support most valuable components of training. Coaching Experience: Integration of coaching into routine clinic work varied extensively, with some coaching throughout the day, and others saving it for end of day or once a week. All reported that coaching was easy to do with tools provided and took between 10-20 minutes/day. Most believed that they could coach between 10 to 20 patients at a time. Confidence levels were high (5.8/7) while coaching and belief in usefulness of coaching was very high (6.4/7). Of the 6 coaches trained, 3 elected to continue coaching after pilot conclusion. Conclusions: Coaching by primary care staff appears to be feasible with clinical leaders supporting staff engagement and staff coaching during routine clinical work. The training program was well received and reported to be effective by coaches both right after the training and during coaching. The relatively low volunteer levels and coach attrition may be barriers and further incentives may need to be considered.

Aim: Wild-type transthyretin amyloid cardiomyopathy (ATTRwt-CM) is frequently misdiagnosed, and delayed diagnosis is associated with substantial morbidity and mortality. At three large academic medical centers, combinations of phenotypic features were implemented in electronic health record (EHR) systems to identify patients with heart failure at risk for ATTRwt-CM. Methods: Phenotypes/phenotype combinations were selected based on strength of correlation with ATTRwt-CM versus non-amyloid heart failure; different clinical decision support and reporting approaches and data sources were evaluated on Cerner and Epic EHR platforms. Results: Multiple approaches/sources showed potential usefulness for incorporating predictive analytics into the EHR to identify at-risk patients. Conclusion: These preliminary findings may guide other medical centers in building and implementing similar systems to improve recognition of ATTRwt-CM in patients with heart failure.