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Interventions designed to address COVID-19 needed to be rapidly scaled up to the population level, and to address health equity by reaching historically marginalized populations most affected by the pandemic (e.g., racial/ethnic minorities and rural and low socioeconomic status populations). From February 2021 to June 2022, SCALE-UP Utah used text messaging interventions to reach 107 846 patients from 28 clinics within seven safety-net health care systems. Interventions provided informational and motivational messaging regarding COVID-19 testing and vaccination, and were developed using extensive community partner input. (Am J Public Health. 2024;114(11):1207–1211. https://doi.org/10.2105/AJPH.2024.307770) [ABSTRACT FROM AUTHOR]

Objective: To evaluate the cost-effectiveness of common obstetric transfusion preparedness strategies to prevent emergency-release transfusions.Methods: A decision analytic model compared five commonly used transfusion preparedness strategies in a general obstetric population. Patients were classified as being at low, moderate, or high risk for transfusion. The most prepared strategy used a policy of universal type and screen plus crossmatch for high-risk patients. Other strategies used universal type and screen only, universal hold clot plus crossmatch for high-risk patients, selective type and screen only in high-risk patients, or no routine admission testing. Strategies were compared using transfusion-related cost and probability estimates derived from patient-level data and from the published literature. The primary outcome was incremental cost per emergency-release transfusion prevented. A strategy was considered cost-effective if the cost was less than $1,500 per emergency-release transfusion avoided as determined by expert consensus. Emergency-release transfusion included universal donor or type-specific packed red cells that are not crossmatched to the recipient. Along with the base-case analyses, we also conducted one- and two-way sensitivity analyses and probabilistic sensitivity analyses using second-order Monte Carlo simulation. Variability in the willingness-to-pay threshold was explored in a cost-effectiveness acceptability analysis. The model was conducted from a hospital perspective.Results: In the base-case analysis, the strategy of universal type and screen with crossmatch for high-risk patients yielded an incremental cost of $115,541 per emergency-release transfusion prevented compared with a strategy of universal hold clot. The universal hold clot strategy yielded a cost of $2,878 per emergency-release transfusion prevented compared with a strategy of no routine admission testing. Strategies using universal type and screen were cost-effective in zero of the 10,000 simulations at a willingness-to-pay threshold of $1,500 per emergency-release transfusion prevented. Even at willingness to pay greater than $10,000 to prevent an emergency-release transfusion, universal type and screen strategies were not cost-effective.Conclusion: Transfusion preparedness with universal type and screen is not cost-effective in a general obstetric population across a wide range of assumptions and variable ranges. [ABSTRACT FROM AUTHOR]

Background: Research on social determinants of genetic testing uptake is limited, particularly among unaffected patients with inherited cancer susceptibility., Methods: We conducted a secondary analysis of the Broadening the Reach, Impact, and Delivery of Genetic Services (BRIDGE) trial at University of Utah Health and NYU Langone Health, involving 2,760 unaffected patients meeting genetic testing criteria for inherited cancer susceptibility and who were initially randomized to either an automated chatbot or an enhanced standard of care (SOC) genetic services delivery model. We used encounters from the electronic health record (EHR) to measure the uptake of genetic counseling and testing, including dichotomous measures of (1) whether participants initiated pre-test cancer genetic services, (2) completed pre-test cancer genetic services, (3) had genetic testing ordered, and (4) completed genetic testing. We merged zip codes from the EHR to construct census tract-weighted social measures of the Social Vulnerability Index. Multilevel models estimated associations between social vulnerability and genetic services utilization. We tested whether intervention condition (i.e., chatbot vs. SOC) moderated the association of social vulnerability with genetic service utilization. Covariates included study arm, study site, age, sex, race/ethnicity, language preference, rural residence, having a recorded primary care provider, and number of algorithm criteria met., Results: Patients living in areas of medium socioeconomic status (SES) vulnerability had lower odds of initiating pre-test genetic services (adjusted OR [aOR] = 0.81, 95% CI: 0.67, 0.98) compared to patients living in low SES vulnerability areas. Patients in medium household vulnerability areas had a lower likelihood of completing pre-test genetic services (aOR = 0.80, 95% CI: 0.66-0.97) and having genetic testing ordered (aOR = 0.79, 95% CI: 0.63-0.99) relative to patients in low household vulnerability areas. We did not find that social vulnerability associations varied by intervention condition., Conclusions: These results underscore the importance of investigating social and structural mechanisms as potential pathways to increasing genetic testing uptake among patients with increased inherited risk of cancer. Census information is publicly available but seldom used to assess social determinants of genetic testing uptake among unaffected populations. Existing and future cohort studies can incorporate census data to derive analytic insights for clinical scientists., Trial Registration: BRIDGE was registered as NCT03985852 on June 6, 2019 at clinicaltrials.gov., Competing Interests: Declarations. Ethics approval and consent to participate: The UHealth and NYULH Institutional Review Boards (IRBs) approved the BRIDGE trial study protocol as a single IRB protocol (IRB 00115509). Eligible individuals received a study invitation letter with a link to an IRB-approved consent cover letter and a questionnaire. Individuals reviewed the consent cover letter first and then indicated their consent to participate in the trial by completing the questionnaire. The trial has an approved waiver of documentation of consent so a signature is not required. This study was performed in accordance with the ethical standards laid down in the 1964 Declaration of Helsinki and its later amendments or comparable ethical standards. Consent for publication: Not applicable. Competing interests: The authors declare no competing interests., (© 2025. The Author(s).)

Maintaining a healthy weight postintentional weight loss is crucial for preventing chronic health conditions, yet many regain weight postintervention. Electronic health record (EHR) portals offer a promising avenue for weight management interventions, leveraging patient-primary care relationships. Our previous research demonstrated that coaching alongside self-monitoring improves weight maintenance compared to monitoring alone. Integrating weight management into routine clinical practice by training existing staff could enhance scalability and sustainability. However, challenges such as inconsistent staff qualifications and high coach turnover rates could affect intervention effectiveness. Standardizing services, training, and coaching continuity seem crucial for success. To report on developing, testing, and evaluating an EHR-based coaching training program for clinical staff, guided by an implementation tool for the MAINTAIN PRIME study. Conducted across 14 University of Utah primary care sites, we developed, tested, and evaluated a coaching training for clinical staff. Guided by a planning model and the Predisposing, Enabling, and Reinforcing (PER) tool, stakeholders actively participated in planning, ensuring alignment with clinic priorities. All clinical staff were invited to participate voluntarily. Evaluation measures included staff interest, training effectiveness, confidence, and readiness. Data collection utilized REDCap, with survey results analyzed using descriptive statistics. Despite increased clinical workload and reassignments posed by coronavirus disease 2019, we were able to train 39 clinical staff, with 34 successfully coaching patients. Feedback indicated high readiness and positive perceptions of coaching feasibility. Coaches reported satisfaction with training, support, and enjoyed establishing connections with patients. The PER strategies allowed us to implement a well-received training program found effective by primary care coaches., (© The Author(s) 2024. Published by Oxford University Press on behalf of the Society of Behavioral Medicine.)

Importance: Lung cancer is the deadliest cancer in the US. Early-stage lung cancer detection with lung cancer screening (LCS) through low-dose computed tomography (LDCT) improves outcomes., Objective: To assess the association of a multifaceted clinical decision support intervention with rates of identification and completion of recommended LCS-related services., Design, Setting, and Participants: This nonrandomized controlled trial used an interrupted time series design, including 3 study periods from August 24, 2019, to April 27, 2022: baseline (12 months), period 1 (11 months), and period 2 (9 months). Outcome changes were reported as shifts in the outcome level at the beginning of each period and changes in monthly trend (ie, slope). The study was conducted at primary care and pulmonary clinics at a health care system headquartered in Salt Lake City, Utah, among patients aged 55 to 80 years who had smoked 30 pack-years or more and were current smokers or had quit smoking in the past 15 years. Data were analyzed from September 2023 through February 2024., Interventions: Interventions in period 1 included clinician-facing preventive care reminders, an electronic health record-integrated shared decision-making tool, and narrative LCS guidance provided in the LDCT ordering screen. Interventions in period 2 included the same clinician-facing interventions and patient-facing reminders for LCS discussion and LCS., Main Outcome and Measure: The primary outcome was LCS care gap closure, defined as the identification and completion of recommended care services. LCS care gap closure could be achieved through LDCT completion, other chest CT completion, or LCS shared decision-making., Results: The study included 1865 patients (median [IQR] age, 64 [60-70] years; 759 female [40.7%]). The clinician-facing intervention (period 1) was not associated with changes in level but was associated with an increase in slope of 2.6 percentage points (95% CI, 2.4-2.7 percentage points) per month in care gap closure through any means and 1.6 percentage points (95% CI, 1.4-1.8 percentage points) per month in closure through LDCT. In period 2, introduction of patient-facing reminders was associated with an immediate increase in care gap closure (2.3 percentage points; 95% CI, 1.0-3.6 percentage points) and closure through LDCT (2.4 percentage points; 95% CI, 0.9-3.9 percentage points) but was not associated with an increase in slope. The overall care gap closure rate was 175 of 1104 patients (15.9%) at the end of the baseline period vs 588 of 1255 patients (46.9%) at the end of period 2., Conclusions and Relevance: In this study, a multifaceted intervention was associated with an improvement in LCS care gap closure., Trial Registration: ClinicalTrials.gov Identifier: NCT04498052.

SMART on FHIR applications are standards-based tools integrated with electronic health record (EHR) systems and intended for dissemination across EHR platforms. A key challenge for disseminating many apps is that EHR vendors provide different levels of support for FHIR. Thus, app developers must balance functionality versus portability. In this case study, a feature-rich app for neonatal bilirubin management was developed prioritizing physician-requested functionality, with custom FHIR interfaces implemented within the EHR as needed. Following wide intra-institutional use, several approaches are being pursued for adapting the app for cross-institutional dissemination: user surveys and interviews to identify least-valued app features which could potentially be omitted; enabling the application to provide differential features depending on available EHR FHIR capabilities; replacing custom FHIR interfaces with native EHR FHIR interfaces as they became available; and using a canonical logical data model known as QUICK that can be mapped to different FHIR versions and profiles., (©2019 AMIA - All rights reserved.)

Background: Tobacco use remains the leading cause of death and disability in the USA and is disproportionately concentrated among low socioeconomic status (SES) populations. Community Health Centers (CHCs) are a key venue for reaching low SES populations with evidence-based tobacco cessation treatment such as Quitlines. Electronic health record (EHR)-based interventions at the point-of-care, text messaging (TM), and phone counseling have the potential to increase Quitline reach and are feasible to implement within CHCs. However, there is a lack of data to inform how, when, and in what combination these strategies should be implemented. The aims of this cluster-randomized trial are to evaluate multi-level implementation strategies to increase the Reach (i.e., proportion of tobacco-using patients who enroll in the Quitline) and Impact (i.e., Reach × Efficacy [efficacy is defined as the proportion of tobacco-using patients who enroll in Quitline treatment that successfully quit]) and to evaluate characteristics of healthcare system, providers, and patients that may influence tobacco-use outcomes., Methods: This study is a multilevel, three-phase, Sequential Multiple Assignment Randomized Trial (SMART), conducted in CHCs (N = 33 clinics; N = 6000 patients). In the first phase, clinics will be randomized to two different EHR conditions. The second and third phases are patient-level randomizations based on prior treatment response. Patients who enroll in the Quitline receive no further interventions. In phase two, patients who are non-responders (i.e., patients who do not enroll in Quitline) will be randomized to receive either TM or continued-EHR. In phase three, patients in the TM condition who are non-responders will be randomized to receive either continued-TM or TM + phone coaching., Discussion: This project will evaluate scalable, multi-level interventions to directly address strategic national priorities for reducing tobacco use and related disparities by increasing the Reach and Impact of evidence-based tobacco cessation interventions in low SES populations., Trial Registration: This trial was registered at ClinicalTrials.gov (NCT03900767) on April 4th, 2019.

Background: Inappropriate laboratory testing is a contributor to waste in healthcare., Objective: To evaluate the impact of a multifaceted laboratory reduction intervention on laboratory costs., Design: A retrospective, controlled, interrupted time series (ITS) study., Setting: University of Utah Health Care, a 500-bed academic medical center in Salt Lake City, Utah., Population: All patients 18 years or older admitted to the hospital to a service other than obstetrics, rehabilitation, or psychiatry., Intervention: Multifaceted quality-improvement initiative in a hospitalist service including education, process change, cost feedback, and financial incentive., Measurements: Primary outcomes of lab cost per day and per visit. Secondary outcomes of number of basic metabolic panel (BMP), comprehensive metabolic panel (CMP), complete blood count (CBC), and prothrombin time/international normalized ratio tests per day; length of stay (LOS); and 30-day readmissions., Results: A total of 6310 hospitalist patient visits (intervention group) were compared to 25,586 nonhospitalist visits (control group). Among the intervention group, the unadjusted mean cost per day was reduced from $138 before the intervention to $123 after the intervention (P < 0.001), and the unadjusted mean cost per visit decreased from $618 to $558 (P = 0.005). The ITS analysis showed significant reductions in cost per day, cost per visit, and the number of BMP, CMP, and CBC tests per day (P = 0.034, 0.02, <0.001, 0.004, and <0.001). LOS was unchanged and 30-day readmissions decreased in the intervention group., Conclusion: A multifaceted approach to laboratory reduction demonstrated a significant reduction in laboratory cost per day and per visit, as well as common tests per day at a major academic medical center. Journal of Hospital Medicine 2016;11:348-354. © 2016 Society of Hospital Medicine., (© 2016 Society of Hospital Medicine.)

Objective: To develop expeditiously a pragmatic, modular, and extensible software framework for understanding and improving healthcare value (costs relative to outcomes)., Materials and Methods: In 2012, a multidisciplinary team was assembled by the leadership of the University of Utah Health Sciences Center and charged with rapidly developing a pragmatic and actionable analytics framework for understanding and enhancing healthcare value. Based on an analysis of relevant prior work, a value analytics framework known as Value Driven Outcomes (VDO) was developed using an agile methodology. Evaluation consisted of measurement against project objectives, including implementation timeliness, system performance, completeness, accuracy, extensibility, adoption, satisfaction, and the ability to support value improvement., Results: A modular, extensible framework was developed to allocate clinical care costs to individual patient encounters. For example, labor costs in a hospital unit are allocated to patients based on the hours they spent in the unit; actual medication acquisition costs are allocated to patients based on utilization; and radiology costs are allocated based on the minutes required for study performance. Relevant process and outcome measures are also available. A visualization layer facilitates the identification of value improvement opportunities, such as high-volume, high-cost case types with high variability in costs across providers. Initial implementation was completed within 6 months, and all project objectives were fulfilled. The framework has been improved iteratively and is now a foundational tool for delivering high-value care., Conclusions: The framework described can be expeditiously implemented to provide a pragmatic, modular, and extensible approach to understanding and improving healthcare value., (© The Author 2014. Published by Oxford University Press on behalf of the American Medical Informatics Association.)

Electronic quality measurement (QM) and clinical decision support (CDS) are closely related but are typically implemented independently, resulting in significant duplication of effort. While it seems intuitive that technical approaches could be re-used across these two related use cases, such reuse is seldom reported in the literature, especially for standards-based approaches. Therefore, we evaluated the feasibility of using a standards-based CDS framework aligned with anticipated EHR certification criteria to implement electronic QM. The CDS-QM framework was used to automate a complex national quality measure (SCIP-VTE-2) at an academic healthcare system which had previously relied on time-consuming manual chart abstractions. Compared with 305 manually-reviewed reference cases, the recall of automated measurement was 100%. The precision was 96.3% (CI:92.6%-98.5%) for ascertaining the denominator and 96.2% (CI:92.3%-98.4%) for the numerator. We therefore validated that a standards-based CDS-QM framework can successfully enable automated QM, and we identified benefits and challenges with this approach.