Lung Cancer Screening Before and After a Multifaceted Electronic Health Record Intervention: A Nonrandomized Controlled Trial.

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.

Implementation of Lung Cancer Screening in Primary Care and Pulmonary Clinics: Pragmatic Clinical Trial of Electronic Health Record-Integrated Everyday Shared Decision-Making Tool and Clinician-Facing Prompts.

BACKGROUND: Although low-dose CT (LDCT) scan imaging lung cancer screening (LCS) can reduce lung cancer mortality, it remains underused. Shared decision-making (SDM) is recommended to assess the balance of benefits and harms for each patient. RESEARCH QUESTION: Do clinician-facing electronic health record (EHR) prompts and an EHR-integrated everyday SDM tool designed to support routine incorporation of SDM into primary care improve LDCT scan imaging ordering and completion? STUDY DESIGN AND METHODS: A preintervention and postintervention analysis was conducted in 30 primary care and four pulmonary clinics for visits with patients who met United States Preventive Services Task Force criteria for LCS. Propensity scores were used to adjust for covariates. Subgroup analyses were conducted based on the expected benefit from screening (high benefit vs intermediate benefit), pulmonologist involvement (ie, whether the patient was seen in a pulmonary clinic in addition to a primary care clinic), sex, and race and ethnicity. RESULTS: In the 12-month preintervention phase among 1,090 eligible patients, 77 patients (7.1%) had LDCT scan imaging orders and 48 patients (4.4%) completed screenings. In the 9-month intervention phase among 1,026 eligible patients, 280 patients (27.3%) had LDCT scan imaging orders and 182 patients (17.7%) completed screenings. Adjusted ORs were 4.9 (95% CI, 3.4-6.9; P < .001) and 4.7 (95% CI, 3.1-7.1; P < .001) for LDCT imaging ordering and completion, respectively. Subgroup analyses showed increases in ordering and completion for all patient subgroups. In the intervention phase, the SDM tool was used by 23 of 102 ordering providers (22.5%) and for 69 of 274 patients (25.2%) for whom LDCT scan imaging was ordered and who needed SDM at the time of ordering. INTERPRETATION: Clinician-facing EHR prompts and an EHR-integrated everyday SDM tool are promising approaches to improving LCS in the primary care setting. However, room for improvement remains. As such, further research is warranted. TRIAL REGISTRY: ClinicalTrials.gov; No.: NCT04498052; URL: www. CLINICALTRIALS: gov.

Using CDS Hooks to increase SMART on FHIR app utilization: a cluster-randomized trial.

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.

GARDE: a standards-based clinical decision support platform for identifying population health management cohorts.

Population health management (PHM) is an important approach to promote wellness and deliver health care to targeted individuals who meet criteria for preventive measures or treatment. A critical component for any PHM program is a data analytics platform that can target those eligible individuals. OBJECTIVE: The aim of this study was to design and implement a scalable standards-based clinical decision support (CDS) approach to identify patient cohorts for PHM and maximize opportunities for multi-site dissemination. MATERIALS AND METHODS: An architecture was established to support bidirectional data exchanges between heterogeneous electronic health record (EHR) data sources, PHM systems, and CDS components. HL7 Fast Healthcare Interoperability Resources and CDS Hooks were used to facilitate interoperability and dissemination. The approach was validated by deploying the platform at multiple sites to identify patients who meet the criteria for genetic evaluation of familial cancer. RESULTS: The Genetic Cancer Risk Detector (GARDE) platform was created and is comprised of four components: (1) an open-source CDS Hooks server for computing patient eligibility for PHM cohorts, (2) an open-source Population Coordinator that processes GARDE requests and communicates results to a PHM system, (3) an EHR Patient Data Repository, and (4) EHR PHM Tools to manage patients and perform outreach functions. Site-specific deployments were performed on onsite virtual machines and cloud-based Amazon Web Services. DISCUSSION: GARDE's component architecture establishes generalizable standards-based methods for computing PHM cohorts. Replicating deployments using one of the established deployment methods requires minimal local customization. Most of the deployment effort was related to obtaining site-specific information technology governance approvals.

Establishing a multidisciplinary initiative for interoperable electronic health record innovations at an academic medical center.

OBJECTIVE: To establish an enterprise initiative for improving health and health care through interoperable electronic health record (EHR) innovations. MATERIALS AND METHODS: We developed a unifying mission and vision, established multidisciplinary governance, and formulated a strategic plan. Key elements of our strategy include establishing a world-class team; creating shared infrastructure to support individual innovations; developing and implementing innovations with high anticipated impact and a clear path to adoption; incorporating best practices such as the use of Fast Healthcare Interoperability Resources (FHIR) and related interoperability standards; and maximizing synergies across research and operations and with partner organizations. RESULTS: University of Utah Health launched the ReImagine EHR initiative in 2016. Supportive infrastructure developed by the initiative include various FHIR-related tooling and a systematic evaluation framework. More than 10 EHR-integrated digital innovations have been implemented to support preventive care, shared decision-making, chronic disease management, and acute clinical care. Initial evaluations of these innovations have demonstrated positive impact on user satisfaction, provider efficiency, and compliance with evidence-based guidelines. Return on investment has included improvements in care; over $35 million in external grant funding; commercial opportunities; and increased ability to adapt to a changing healthcare landscape. DISCUSSION: Key lessons learned include the value of investing in digital innovation initiatives leveraging FHIR; the importance of supportive infrastructure for accelerating innovation; and the critical role of user-centered design, implementation science, and evaluation. CONCLUSION: EHR-integrated digital innovation initiatives can be key assets for enhancing the EHR user experience, improving patient care, and reducing provider burnout.

Leveraging Artificial Intelligence to Improve Chronic Disease Care: Methods and Application to Pharmacotherapy Decision Support for Type-2 Diabetes Mellitus.

OBJECTIVES: Artificial intelligence (AI), including predictive analytics, has great potential to improve the care of common chronic conditions with high morbidity and mortality. However, there are still many challenges to achieving this vision. The goal of this project was to develop and apply methods for enhancing chronic disease care using AI. METHODS: Using a dataset of 27,904 patients with diabetes, an analytical method was developed and validated for generating a treatment pathway graph which consists of models that predict the likelihood of alternate treatment strategies achieving care goals. An AI-driven clinical decision support system (CDSS) integrated with the electronic health record (EHR) was developed by encapsulating the prediction models in an OpenCDS Web service module and delivering the model outputs through a SMART on FHIR (Substitutable Medical Applications and Reusable Technologies on Fast Healthcare Interoperability Resources) web-based dashboard. This CDSS enables clinicians and patients to review relevant patient parameters, select treatment goals, and review alternate treatment strategies based on prediction results. RESULTS: The proposed analytical method outperformed previous machine-learning algorithms on prediction accuracy. The CDSS was successfully integrated with the Epic EHR at the University of Utah. CONCLUSION: A predictive analytics-based CDSS was developed and successfully integrated with the EHR through standards-based interoperability frameworks. The approach used could potentially be applied to many other chronic conditions to bring AI-driven CDSS to the point of care.

Integrated displays to improve chronic disease management in ambulatory care: A SMART on FHIR application informed by mixed-methods user testing.

OBJECTIVE: The study sought to evaluate a novel electronic health record (EHR) add-on application for chronic disease management that uses an integrated display to decrease user cognitive load, improve efficiency, and support clinical decision making. MATERIALS AND METHODS: We designed a chronic disease management application using the technology framework known as SMART on FHIR (Substitutable Medical Applications and Reusable Technologies on Fast Healthcare Interoperability Resources). We used mixed methods to obtain user feedback on a prototype to support ambulatory providers managing chronic obstructive pulmonary disease. Each participant managed 2 patient scenarios using the regular EHR with and without access to our prototype in block-randomized order. The primary outcome was the percentage of expert-recommended ideal care tasks completed. Timing, keyboard and mouse use, and participant surveys were also collected. User experiences were captured using a retrospective think-aloud interview analyzed by concept coding. RESULTS: With our prototype, the 13 participants completed more recommended care (81% vs 48%; P < .001) and recommended tasks per minute (0.8 vs 0.6; P = .03) over longer sessions (7.0 minutes vs 5.4 minutes; P = .006). Keystrokes per task were lower with the prototype (6 vs 18; P < .001). Qualitative themes elicited included the desire for reliable presentation of information which matches participants' mental models of disease and for intuitive navigation in order to decrease cognitive load. DISCUSSION: Participants completed more recommended care by taking more time when using our prototype. Interviews identified a tension between using the inefficient but familiar EHR vs learning to use our novel prototype. Concept coding of user feedback generated actionable insights. CONCLUSIONS: Mixed methods can support the design and evaluation of SMART on FHIR EHR add-on applications by enhancing understanding of the user experience.

Association of an Electronic Health Record Add-on App for Neonatal Bilirubin Management With Physician Efficiency and Care Quality.

Importance: The usefulness of electronic health record (EHR) systems could be significantly enhanced by innovative, third-party EHR add-on apps. Objective: To evaluate whether an EHR add-on app for neonatal bilirubin management can save clinicians time and improve patient care. Design, Setting, and Participants: This quality improvement study was conducted at the University of Utah Health Well Baby nursery and outpatient clinics and consisted of 4 substudies: (1) time savings were estimated in an experimental task-timing study comparing the time required for physicians to manage newborns' bilirubin levels with and without the add-on app, (2) app use was estimated from app logs, (3) health care use measures and guideline compliance were compared retrospectively before and after the intervention, and (4) clinician-perceived usability was measured through System Usability Scale surveys. The study took place between April 1, 2016, and September 3, 2019. Data analyses were conducted from October 30, 2018, to September 23, 2019. Interventions: At baseline, clinicians used a manual approach to ensure compliance with an evidence-based clinical guideline for neonatal bilirubin management. To facilitate guideline compliance, an EHR add-on app that automatically retrieves, organizes, and visualizes relevant patient data was developed. The app provides patient-specific assessments and recommendations, including the risk of rebound hyperbilirubinemia following phototherapy based on a predictive model. The add-on app was integrated with the University of Utah Health EHR on April 12, 2017. Main Outcomes and Measures: Clinician time savings, app use, health care use measures, guideline-compliant phototherapy ordering, and perceived usability as measured by the System Usability Scale survey. The survey is composed of 10 statements with responses ranging from 1 (strongly disagree) to 5 (strongly agree). The survey results in a single score ranging from 0 to 100, with ratings described as worst imaginable (mean System Usability Scale score, 12.5), awful (20.3), poor (35.7), okay (50.9), good (71.4), excellent (85.5), and best imaginable (90.9). Results: In 2018, the application was used 20 516 times by clinicians for 91.84% of eligible newborns. Use of the app saved 66 seconds for bilirubin management tasks compared with a commonly used tool (95% CI, 53-79 seconds; P < .001). Following the intervention, health care use rates remained stable, while orders for clinically appropriate phototherapy during hospitalization increased for newborns with bilirubin levels above the guideline-recommended threshold (odds ratio, 1.84; 95% CI, 1.16-2.90; P = .009). Surveys indicated excellent usability (System Usability Scale score, 83.90; 95% CI, 81.49-86.31). Conclusions and Relevance: Well-designed EHR add-on apps may save clinicians time and improve patient care. If time-saving apps, such as the bilirubin app, were implemented widely across institutions and care domains, the potential association with improved patient care and clinician efficiency could be significant. The University of Utah Health bilirubin app is being prepared for release into EHR app stores as free-to-use software.

Challenges and Solutions in Optimizing Execution Performance of a Clinical Decision Support-Based Quality Measurement (CDS-QM) Framework.

Given the close relationship between clinical decision support (CDS) and quality measurement (QM), it has been proposed that a standards-based CDS Web service could be leveraged to enable QM. Benefits of such a CDS-QM framework include semantic consistency and implementation efficiency. However, earlier research has identified execution performance as a critical barrier when CDS-QM is applied to large populations. Here, we describe challenges encountered and solutions devised to optimize CDS-QM execution performance. Through these optimizations, the CDS-QM execution time was optimized approximately three orders of magnitude, such that approximately 370,000 patient records can now be evaluated for 22 quality measure groups in less than 5 hours (approximately 2 milliseconds per measure group per patient). Several key optimization methods were identified, with the most impact achieved through population-based retrieval of relevant data, multi-step data staging, and parallel processing. These optimizations have enabled CDS-QM to be operationally deployed at an enterprise level.

Clinical Decision Support-based Quality Measurement (CDS-QM) Framework: Prototype Implementation, Evaluation, and Future Directions.

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.

Federating clinical data from six pediatric hospitals: process and initial results for microbiology from the PHIS+ consortium.

Microbiology study results are necessary for conducting many comparative effectiveness research studies. Unlike core laboratory test results, microbiology results have a complex structure. Federating and integrating microbiology data from six disparate electronic medical record systems is challenging and requires a team of varied skills. The PHIS+ consortium which is partnership between members of the Pediatric Research in Inpatient Settings (PRIS) network, the Children's Hospital Association and the University of Utah, have used "FURTHeR' for federating laboratory data. We present our process and initial results for federating microbiology data from six pediatric hospitals.