ABSTRACT
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.
Subject(s)
Early Detection of Cancer , Electronic Health Records , Lung Neoplasms , Humans , Lung Neoplasms/diagnosis , Lung Neoplasms/diagnostic imaging , Early Detection of Cancer/methods , Early Detection of Cancer/statistics & numerical data , Female , Male , Aged , Middle Aged , Tomography, X-Ray Computed/statistics & numerical data , Aged, 80 and over , Decision Support Systems, Clinical , Utah , Interrupted Time Series AnalysisABSTRACT
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.
Subject(s)
Lung Neoplasms , Humans , Decision Making , Early Detection of Cancer/methods , Electronic Health Records , Lung Neoplasms/diagnostic imaging , Primary Health Care , United StatesABSTRACT
OBJECTIVE: The US Preventive Services Task Force (USPSTF) requires the estimation of lifetime pack-years to determine lung cancer screening eligibility. Leading electronic health record (EHR) vendors calculate pack-years using only the most recently recorded smoking data. The objective was to characterize EHR smoking data issues and to propose an approach to addressing these issues using longitudinal smoking data. MATERIALS AND METHODS: In this cross-sectional study, we evaluated 16â874 current or former smokers who met USPSTF age criteria for screening (50-80 years old), had no prior lung cancer diagnosis, and were seen in 2020 at an academic health system using the Epic® EHR. We described and quantified issues in the smoking data. We then estimated how many additional potentially eligible patients could be identified using longitudinal data. The approach was verified through manual review of records from 100 subjects. RESULTS: Over 80% of evaluated records had inaccuracies, including missing packs-per-day or years-smoked (42.7%), outdated data (25.1%), missing years-quit (17.4%), and a recent change in packs-per-day resulting in inaccurate lifetime pack-years estimation (16.9%). Addressing these issues by using longitudinal data enabled the identification of 49.4% more patients potentially eligible for lung cancer screening (P < .001). DISCUSSION: Missing, outdated, and inaccurate smoking data in the EHR are important barriers to effective lung cancer screening. Data collection and analysis strategies that reflect changes in smoking habits over time could improve the identification of patients eligible for screening. CONCLUSION: The use of longitudinal EHR smoking data could improve lung cancer screening.
Subject(s)
Early Detection of Cancer , Lung Neoplasms , Aged , Aged, 80 and over , Cross-Sectional Studies , Early Detection of Cancer/methods , Electronic Health Records , Humans , Lung Neoplasms/diagnosis , Mass Screening/methods , Middle Aged , SmokingABSTRACT
OBJECTIVE: Our objective was to develop an evaluation framework for electronic health record (EHR)-integrated innovations to support evaluation activities at each of four information technology (IT) life cycle phases: planning, development, implementation, and operation. METHODS: The evaluation framework was developed based on a review of existing evaluation frameworks from health informatics and other domains (human factors engineering, software engineering, and social sciences); expert consensus; and real-world testing in multiple EHR-integrated innovation studies. RESULTS: The resulting Evaluation in Life Cycle of IT (ELICIT) framework covers four IT life cycle phases and three measure levels (society, user, and IT). The ELICIT framework recommends 12 evaluation steps: (1) business case assessment; (2) stakeholder requirements gathering; (3) technical requirements gathering; (4) technical acceptability assessment; (5) user acceptability assessment; (6) social acceptability assessment; (7) social implementation assessment; (8) initial user satisfaction assessment; (9) technical implementation assessment; (10) technical portability assessment; (11) long-term user satisfaction assessment; and (12) social outcomes assessment. DISCUSSION: Effective evaluation requires a shared understanding and collaboration across disciplines throughout the entire IT life cycle. In contrast with previous evaluation frameworks, the ELICIT framework focuses on all phases of the IT life cycle across the society, user, and IT levels. Institutions seeking to establish evaluation programs for EHR-integrated innovations could use our framework to create such shared understanding and justify the need to invest in evaluation. CONCLUSION: As health care undergoes a digital transformation, it will be critical for EHR-integrated innovations to be systematically evaluated. The ELICIT framework can facilitate these evaluations.
Subject(s)
Information Technology , Medical Informatics , Commerce , Electronic Health Records , Humans , TechnologyABSTRACT
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.
Subject(s)
Decision Support Systems, Clinical , Population Health Management , Delivery of Health Care , Electronic Health Records , Humans , Information Storage and RetrievalABSTRACT
Lung cancer screening with low-dose computed tomography (CT) could help avert thousands of deaths each year. Since the implementation of screening is complex and underspecified, there is a need for systematic and theory-based strategies. Explore the implementation of lung cancer screening in primary care, in the context of integrating a decision aid into the electronic health record. Design implementation strategies that target hypothesized mechanisms of change and context-specific barriers. The study had two phases. The Qualitative Analysis phase included semi-structured interviews with primary care physicians to elicit key task behaviors (e.g., ordering a low-dose CT) and understand the underlying behavioral determinants (e.g., social influence). The Implementation Strategy Design phase consisted of defining implementation strategies and hypothesizing causal pathways to improve screening with a decision aid. Three key task behaviors and four behavioral determinants emerged from 14 interviews. Implementation strategies were designed to target multiple levels of influence. Strategies included increasing provider self-efficacy toward performing shared decision making and using the decision aid, improving provider performance expectancy toward ordering a low-dose CT, increasing social influence toward performing shared decision making and using the decision aid, and addressing key facilitators to using the decision aid. This study contributes knowledge about theoretical determinants of key task behaviors associated with lung cancer screening. We designed implementation strategies according to causal pathways that can be replicated and tested at other institutions. Future research is needed to evaluate the effectiveness of these strategies and to determine the contexts in which they can be effectively applied.
Subject(s)
Early Detection of Cancer , Lung Neoplasms , Decision Making , Early Detection of Cancer/methods , Humans , Lung Neoplasms/diagnostic imaging , Mass Screening , Needs Assessment , Primary Health CareABSTRACT
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.
ABSTRACT
INTRODUCTION: Prostate cancer screening using prostate-specific antigen (PSA) testing remains widespread. The prevalence of PSA testing in young men is unknown and may be an appropriate target for improving health care by decreasing low-value testing in this age group. The purpose of this study was to determine PSA testing rates in men younger than current guidelines support. MATERIALS AND METHODS: Health Informational National Trends Surveys (HINTS) from 2011 to 2014 and 2017 were analyzed to establish the prevalence of PSA testing in young men and to evaluate the differences in testing rates based on race. RESULTS: The combined survey data included 5178 men, with 2393 reporting previous PSA screening. Of men ages 18-39, 7% recalled receipt of PSA testing. Twenty-two percent of men between the ages of 40 and 44 had been tested. Among men under age 40, PSA testing was more common among black men (14%) compared to white men (7%), Hispanics (6%), and men of Asian descent (8%). Logistic regression modeling demonstrates that black men under the age of 40 were more likely to undergo PSA testing than other racial or ethnic groups (odds ratio 2.14; 95% CI 1.17, 3.93). CONCLUSIONS: Current guidelines do not recommend routine PSA testing in average-risk men under the age of 40. This study found that a significant number of young men are exposed to testing, with the greatest risk among black men. This suggests that there is an opportunity to improve the value of PSA testing by decreasing testing in young men.
Subject(s)
Prostate-Specific Antigen/blood , Prostatic Neoplasms/blood , Adult , Age Factors , Aged , Asian People/statistics & numerical data , Black People/statistics & numerical data , Confidence Intervals , Hispanic or Latino/statistics & numerical data , Humans , Logistic Models , Male , Middle Aged , Odds Ratio , Practice Guidelines as Topic , Prostatic Neoplasms/ethnology , Surveys and Questionnaires/statistics & numerical data , White People/statistics & numerical data , Young AdultABSTRACT
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.
Subject(s)
Chronic Disease/therapy , Decision Support Systems, Clinical , Disease Management , Electronic Health Records , Health Information Interoperability , Adult , Ambulatory Care , Attitude of Health Personnel , Computer Graphics , Electronic Health Records/organization & administration , Faculty, Medical , Female , Health Information Exchange , Humans , Male , Middle Aged , Software , User-Computer InterfaceABSTRACT
There is limited guidance available in the literature for establishing clinical decision support (CDS) governance and improving CDS effectiveness in a pragmatic, resource-efficient manner. Here, we describe how University of Utah Health established enterprise CDS governance in 2015 leveraging existing resources. Key components of the governance include a multi-stakeholder CDS Committee that vets new requests and reviews existing content; a requirement that proposed CDS is actually desired by intended recipients; coordination with other governance bodies; basic data analytics to identify high-frequency, low-value CDS and monitor progress; active solicitation of user issues; the transition of alert and reminder content to other, more appropriate areas in the electronic health record; and the judicious use of experimental designs to guide decision-making regarding CDS effectiveness. In the three years since establishing this governance, new CDS has been continuously added while the overall burden of clinician-facing alerts and reminders has been reduced by 53.8%.