Patient Perspectives on a Patient-Facing Tool for Lung Cancer Screening.

BACKGROUND: Individuals with high risk for lung cancer may benefit from lung cancer screening, but there are associated risks as well as benefits. Shared decision-making (SDM) tools with personalized information may provide key support for patients. Understanding patient perspectives on educational tools to facilitate SDM for lung cancer screening may support tool development. AIM: This study aimed to explore patient perspectives related to a SDM tool for lung cancer screening using a qualitative approach. METHODS: We elicited patient perspectives by showing a provider-facing SDM tool. Focus group interviews that ranged in duration from 1.5 to 2 h were conducted with 23 individuals with high risk for lung cancer. Data were interpreted inductively using thematic analysis to identify patients' thoughts on and desires for a patient-facing SDM tool. RESULTS: The findings highlight that patients would like to have educational information related to lung cancer screening. We identified several key themes to be considered in the future development of patient-facing tools: barriers to acceptance, preference against screening and seeking empowerment. One further theme illustrated effects of patient-provider relationship as a limitation to meeting lung cancer screening information needs. Participants also noted several suggestions for the design of technology decision aids. CONCLUSION: These findings suggest that patients desire additional information on lung cancer screening in advance of clinical visits. However, there are several issues that must be considered in the design and development of technology to meet the information needs of patients for lung cancer screening decisions. PATIENT OR PUBLIC CONTRIBUTION: Patients, service users, caregivers or members of the public were not involved in the study design, conduct, analysis or interpretation of the data. However, clinical experts in health communication provided detailed feedback on the study protocol, including the focus group approach. The study findings contribute to a better understanding of patient expectations for lung cancer screening decisions and may inform future development of tools for SDM.

Evaluating Artificial Intelligence in Clinical Settings-Let Us Not Reinvent the Wheel.

Given the requirement to minimize the risks and maximize the benefits of technology applications in health care provision, there is an urgent need to incorporate theory-informed health IT (HIT) evaluation frameworks into existing and emerging guidelines for the evaluation of artificial intelligence (AI). Such frameworks can help developers, implementers, and strategic decision makers to build on experience and the existing empirical evidence base. We provide a pragmatic conceptual overview of selected concrete examples of how existing theory-informed HIT evaluation frameworks may be used to inform the safe development and implementation of AI in health care settings. The list is not exhaustive and is intended to illustrate applications in line with various stakeholder requirements. Existing HIT evaluation frameworks can help to inform AI-based development and implementation by supporting developers and strategic decision makers in considering relevant technology, user, and organizational dimensions. This can facilitate the design of technologies, their implementation in user and organizational settings, and the sustainability and scalability of technologies.

Considerations for using predictive models that include race as an input variable: The case study of lung cancer screening.

Indiscriminate use of predictive models incorporating race can reinforce biases present in source data and lead to an exacerbation of health disparities. In some countries, such as the United States, there is therefore a push to remove race from prediction models; however, there are still many prediction models that use race as an input. Biomedical informaticists who are given the responsibility of using these predictive models in healthcare environments are likely to be faced with questions like how to deal with race covariates in these models. Thus, there is a need for a pragmatic framework to help model users think through how to include race in their chosen model so as to avoid inadvertently exacerbating disparities. In this paper, we use the case study of lung cancer screening to propose a simple framework to guide how model users can approach the use (or non-use) of race inputs in the predictive models they are tasked with leveraging in electronic health records and clinical workflows.

Association of Mobile Workstations and Rounding-in-Flow with Resident Efficiency: A Controlled Study at an Academic Internal Medicine Department.

BACKGROUND: Residents commonly use a batched workflow to round on hospitalized patients, creating redundancy and decreasing efficiency. OBJECTIVE: To improve resident efficiency through a novel workflow using mobile laptops and modified rounding-in-flow. DESIGN, SETTING, PARTICIPANTS: A controlled experimental study conducted at an academic medical center for 3 months. One internal medicine team served as the intervention group, and two other teams served as a control group; 34 interns and 20 senior residents participated. INTERVENTION: Residents in the intervention group were provided a novel workflow and a mobile laptop to allow them to round "in-flow." Control group residents rounded as usual (batched workflow without laptops). MAIN MEASURES: Fourteen interns were monitored for a time-motion study. Time-stamped electronic medical record (EMR) data were used to assess percentage of progress notes and orders placed during rounds (9 a.m.-12:30 p.m.) and percentage of discharge summaries signed within 24 h of discharge. A post-intervention survey measured perceived efficiency. RESULTS: A time-motion study showed non-significant differences between time in the intervention group and that in the control group: communication time with patients (128 min vs 105 min, p = 0.37) and computer time (289 min vs 306 min, p = 0.71). EMR data for 664 visits in the control group and 374 in the intervention group showed that rounding-in-flow was associated with an odds ratio (OR) of 1.5 for placing progress notes during rounds (95% CI: 1.2-1.7, p < 0.001), an OR of 1.1 for placing non-discharge orders during rounds (95% CI: 1.0-1.2, p = 0.01), and an OR of 3.9 for signing discharge summaries within 24 h of discharge (95% CI: 2.3-7.2, p < 0.001). Post-intervention survey, completed by 23 of 34 interns, showed that interns in the intervention group perceived that orders were completed during rounds more often than the control group (OR 7.8; 95% CI: 1.3-60.1, p = 0.03). CONCLUSIONS: Using mobile laptops with modified rounding-in-flow was associated with earlier completion of residents' work, suggesting improved efficiency.

Evaluation in Life Cycle of Information Technology (ELICIT) framework: Supporting the innovation life cycle from business case assessment to summative evaluation.

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.

Single-reviewer electronic phenotyping validation in operational settings: Comparison of strategies and recommendations.

OBJECTIVE: Develop evidence-based recommendations for single-reviewer validation of electronic phenotyping results in operational settings. MATERIAL AND METHODS: We conducted a randomized controlled study to evaluate whether electronic phenotyping results should be used to support manual chart review during single-reviewer electronic phenotyping validation (N=3104). We evaluated the accuracy, duration and cost of manual chart review with and without the availability of electronic phenotyping results, including relevant patient-specific details. The cost of identification of an erroneous electronic phenotyping result was calculated based on the personnel time required for the initial chart review and subsequent adjudication of discrepancies between manual chart review results and electronic phenotype determinations. RESULTS: Providing electronic phenotyping results (vs not providing those results) was associated with improved overall accuracy of manual chart review (98.90% vs 92.46%, p<0.001), decreased review duration per test case (62.43 vs 76.78s, p<0.001), and insignificantly reduced estimated marginal costs of identification of an erroneous electronic phenotyping result ($48.54 vs $63.56, p=0.16). The agreement between chart review and electronic phenotyping results was higher when the phenotyping results were provided (Cohen's kappa 0.98 vs 0.88, p<0.001). As a result, while accuracy improved when initial electronic phenotyping results were correct (99.74% vs 92.67%, N=3049, p<0.001), there was a trend towards decreased accuracy when initial electronic phenotyping results were erroneous (56.67% vs 80.00%, N=55, p=0.07). Electronic phenotyping results provided the greatest benefit for the accurate identification of rare exclusion criteria. DISCUSSION: Single-reviewer chart review of electronic phenotyping can be conducted more accurately, quickly, and at lower cost when supported by electronic phenotyping results. However, human reviewers tend to agree with electronic phenotyping results even when those results are wrong. Thus, the value of providing electronic phenotyping results depends on the accuracy of the underlying electronic phenotyping algorithm. CONCLUSION: We recommend using a mix of phenotyping validation strategies, with the balance of strategies based on the anticipated electronic phenotyping error rate, the tolerance for missed electronic phenotyping errors, as well as the expertise, cost, and availability of personnel involved in chart review and discrepancy adjudication.

Scalable and successful patient portal lifestyle coaching training for primary care clinical staff.

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.

This report describes a training program for medical staff like nurses and medical assistants. The goal is to teach them how to coach patients through an online portal to help them keep their weight off after making healthy lifestyle changes. We worked with different clinic groups and used a planning tool called PER worksheet (predisposing, enabling, and reinforcing) to set up the training program. From September 2021 to March 2023, we offered the training in 14 clinics, and most interested staff completed it. The results showed that the training worked well. People who took part felt they learned enough to coach patients and felt ready to coach. They liked the training and found it helpful. This study suggests that we can teach coaching skills in just four hours of training and that ongoing support and mentorship are important to the trained coaches. Furthermore, this training set-up allows new staff to be trained as they join, which is especially important in places where staff changes frequently. Overall, using the PER tool enabled us to create a training program that staff can use in outpatient clinics to help patients improve their weight management.

Implementing weight maintenance with existing staff and electronic health record tools in a primary care setting: Baseline results from the MAINTAIN PRIME trial.

BACKGROUND: There is need for interventions that can assist with long-term maintenance of healthy body weight and be sustainably integrated into existing primary care teams. The goal of MAINTAIN PRIME (Promoting Real (World) IMplEmentation) is to evaluate whether a successful electronic health record (EHR)-based weight maintenance intervention can be adapted to a new clinical setting with primary care staff serving as coaches. METHODS: EHR tools include tracking tools, standardized surveys, and standardized "SmartPhrases" for coaching. Inclusion criteria were age 18-75 years, voluntary 5% weight loss in the past 2 years with prior BMI ≥ 25 kg/m2, and no bariatric procedures in past 2 years. Participants were randomized 1:1 to tailored online coaching with EHR tracking tools (coaching) or EHR tracking tools alone (tracking). RESULTS: We screened 405 individuals between September 2021 and April 2023; 269 participants enrolled (134 coaching; 135 tracking). The most common reason for not enrolling was ineligibility (55%). At baseline, participants were 50.3 (SD 15.02) years old, 66.4% female, and 84% White; 83.7% reported moderate physical activity. Average weight and BMI at baseline were 205.0 (SD 48.9) lbs. and 33.2 (6.8) kg/m2, respectively. Participants lost an average of 10.7% (SD 5.2) of their body weight before enrolling. We recruited 39 primary care coaches over the same period. Conclusion The study successfully identified and recruited primary care patients with recent intentional weight loss for participation in a weight maintenance program that uses EHR-based tools. We also successfully recruited and trained primary care staff as coaches.

Design and implementation of electronic health record-based tools to support a weight management program in primary care.

Objectives: This paper reports on a mixed methods formative evaluation to support the design and implementation of information technology (IT) tools for a primary care weight management intervention delivered through the patient portal using primary care staff as coaches. Methods: We performed a qualitative needs assessment, designed the IT tools to support the weight management program, and developed implementation tracking metrics. Implementation tracking metrics were designed to use real world electronic health record (EHR) data. Results: The needs assessment revealed IT requirements as well as barriers and facilitators to implementation of EHR-based weight management interventions in primary care. We developed implementation metrics for the IT tools. These metrics were used in weekly project team calls to make sure that project resources were allocated to areas of need. Conclusion: This study identifies the important role of IT in supporting weight management through patient identification, weight and activity tracking in the patient portal, and the use of the EHR as a population management tool. An intensive multi-level implementation approach is required for successful primary care-based weight management interventions including well-designed IT tools, comprehensive involvement of clinic leadership, and implementation tracking metrics to guide the process of workflow integration. This study helps to bridge the gap between informatics and implementation by using socio-technical formative evaluation methods early in order to support the implementation of IT tools. Trial registration: clinicaltrials.gov, NCT04420936. Registered June 9, 2020.

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.

A peer-educator network HIV prevention intervention among injection drug users: results of a randomized controlled trial in St. Petersburg, Russia.

We evaluated the efficacy of a peer-educator network intervention as a strategy to reduce HIV acquisition among injection drug users (IDUs) and their drug and/or sexual networks. A randomized controlled trial was conducted in St. Petersburg, Russia among IDU index participants and their risk network participants. Network units were randomized to the control or experimental intervention. Only the experimental index participants received training sessions to communicate risk reduction techniques to their network members. Analysis includes 76 index and 84 network participants who were HIV uninfected. The main outcome measure was HIV sero-conversion. The incidence rates in the control and experimental groups were 19.57 (95 % CI 10.74-35.65) and 7.76 (95 % CI 3.51-17.19) cases per 100 p/y, respectively. The IRR was 0.41 (95 % CI 0.15-1.08) without a statistically significant difference between the two groups (log rank test statistic X(2) = 2.73, permutation p value = 0.16). Retention rate was 67 % with a third of the loss due to incarceration or death. The results show a promising trend that this strategy would be successful in reducing the acquisition of HIV among IDUs.

One Health: Insights from Organizational & Social, Technology Assessment and Human Factors Perspectives.

OBJECTIVES: To offer diverse but complementary perspectives on how biomedical and health informatics can be informed by and help to achieve the vision of One Health. METHODS: Overview of key considerations and critical discussion of common themes, barriers and opportunities, based on collaborative review by International Medical Informatics Association (IMIA) working group members active in related fields. RESULTS: Health and care systems are complex sociotechnical systems that need explicit design and implementation strategies to align with the goals of One Health. The evidence-based health informatics paradigm and associated frameworks for evaluation of digital health technologies need to broaden their scope to take full account of the One Health approach. Informatics has specific contributions to make to One Health, for example by improved user experience reducing energy consumption and effective app design enhancing medication adherence. CONCLUSIONS: One Health is inherently intertwined with ergonomic, sociotechnical and evaluation perspectives in biomedical and health informatics. Health is a planetary issue that requires interdisciplinary collaborative action. The theories and principles of biomedical and health informatics offer many opportunities to transform digital health technology to better serve the One Health agenda.

Impact of electronic health record-coaching features in weight change: A secondary analysis from the MAINTAIN-pc randomized trial.

OBJECTIVE: This study aimed to evaluate whether coaching features were successfully transmitted via electronic health record (EHR) communication and to evaluate their relationship with weight change in a previously tested EHR-based coaching intervention. METHODS: A secondary analysis from the Maintaining Activity and Nutrition through Technology-Assisted Innovation in Primary Care (MAINTAIN-pc) study randomized clinical trial was conducted in nine primary care practices and one specialty practice (endocrinology) affiliated with the University of Pittsburgh Medical Center. Eligibility included age 18 to 75 years, intentional 5% weight loss in the previous 2 years, access to an internet-connected computer, and receipt of care from a University of Pittsburgh Medical Center primary care provider. Survey data were collected during the randomized clinical trial. RESULTS: Participants content with intervention delivery via the EHR and those who felt a strong connection to their coach had significantly less weight regain (p = 0.013 and p < 0.01, respectively). Participants who had needs unmet by the intervention (e.g., "in-person" support in a group setting or individual settings) regained more weight (p < 0.01). CONCLUSIONS: The data suggest heterogeneity in the patient population regarding preference for in-person versus EHR-based coaching formats. Such heterogeneity might result in the differential success of EHR-based interventions.

The need to strengthen the evaluation of the impact of Artificial Intelligence-based decision support systems on healthcare provision.

Despite the renewed interest in Artificial Intelligence-based clinical decision support systems (AI-CDS), there is still a lack of empirical evidence supporting their effectiveness. This underscores the need for rigorous and continuous evaluation and monitoring of processes and outcomes associated with the introduction of health information technology. We illustrate how the emergence of AI-CDS has helped to bring to the fore the critical importance of evaluation principles and action regarding all health information technology applications, as these hitherto have received limited attention. Key aspects include assessment of design, implementation and adoption contexts; ensuring systems support and optimise human performance (which in turn requires understanding clinical and system logics); and ensuring that design of systems prioritises ethics, equity, effectiveness, and outcomes. Going forward, information technology strategy, implementation and assessment need to actively incorporate these dimensions. International policy makers, regulators and strategic decision makers in implementing organisations therefore need to be cognisant of these aspects and incorporate them in decision-making and in prioritising investment. In particular, the emphasis needs to be on stronger and more evidence-based evaluation surrounding system limitations and risks as well as optimisation of outcomes, whilst ensuring learning and contextual review. Otherwise, there is a risk that applications will be sub-optimally embodied in health systems with unintended consequences and without yielding intended benefits.

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.

Electronic Health Record-Integrated Clinical Decision Support for Clinicians Serving Populations Facing Health Care Disparities: Literature Review.

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.

Assessing perceived effectiveness of career development efforts led by the women in American Medical Informatics Association Initiative.

OBJECTIVE: We sought to ascertain perceived factors affecting women's career development efforts in the American Medical Informatics Association (AMIA) and to provide recommendations for improvements. MATERIALS AND METHODS: Data were collected using a 27-item survey administered via the AMIA newsletter and other social channels. Survey questions comprised 3 demographics, 15 Likert-scale, and 9 open-ended items. Likert-scale responses were summarized across respondent ages, career stages, and career domains, and open-ended responses were thematically analyzed. RESULTS: We received survey responses from 109 AMIA women members. Our findings demonstrate that AMIA had made strides in promoting career development, and the most effective AMIA efforts included social events (83%), panel discussions (80%), and scientific sessions (79%). However, despite these efforts, women members perceived that gender-specific challenges persisted within AMIA, and recognized the need for increased networking opportunities (96%), raising awareness of gender-specific challenges (95%), and encouraging gender proportional representation in leadership (92%). DISCUSSION: International and national biomedical informatics professional communities have put forth efforts to address gender-specific issues in career development. Yet, our study identified that some of these, including the deep-rooted gender power hierarchy and bias, are still perceived as profound in AMIA. CONCLUSION: Even though existing career development efforts for women are highly effective, important perceived gender-specific career development issues require further attention and investigation to improve existing AMIA activities.

Inaccuracies in electronic health records smoking data and a potential approach to address resulting underestimation in determining lung cancer screening eligibility.

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 16 874 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.