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.

Design of an interface to communicate artificial intelligence-based prognosis for patients with advanced solid tumors: a user-centered approach.

OBJECTIVES: To design an interface to support communication of machine learning (ML)-based prognosis for patients with advanced solid tumors, incorporating oncologists' needs and feedback throughout design. MATERIALS AND METHODS: Using an interdisciplinary user-centered design approach, we performed 5 rounds of iterative design to refine an interface, involving expert review based on usability heuristics, input from a color-blind adult, and 13 individual semi-structured interviews with oncologists. Individual interviews included patient vignettes and a series of interfaces populated with representative patient data and predicted survival for each treatment decision point when a new line of therapy (LoT) was being considered. Ongoing feedback informed design decisions, and directed qualitative content analysis of interview transcripts was used to evaluate usability and identify enhancement requirements. RESULTS: Design processes resulted in an interface with 7 sections, each addressing user-focused questions, supporting oncologists to "tell a story" as they discuss prognosis during a clinical encounter. The iteratively enhanced interface both triggered and reflected design decisions relevant when attempting to communicate ML-based prognosis, and exposed misassumptions. Clinicians requested enhancements that emphasized interpretability over explainability. Qualitative findings confirmed that previously identified issues were resolved and clarified necessary enhancements (eg, use months not days) and concerns about usability and trust (eg, address LoT received elsewhere). Appropriate use should be in the context of a conversation with an oncologist. CONCLUSION: User-centered design, ongoing clinical input, and a visualization to communicate ML-related outcomes are important elements for designing any decision support tool enabled by artificial intelligence, particularly when communicating prognosis risk.

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.

A Systematic Review of Quantitative Methods for Evaluating Electronic Medication Administration Record and Bar-Coded Medication Administration Usability.

BACKGROUND: Although electronic medication administration records (eMARs) and bar-coded medication administration (BCMA) have improved medication safety, poor usability of these technologies can increase patient safety risks. OBJECTIVES: The objective of our systematic review was to identify the impact of eMAR and BCMA design on usability, operationalized as efficiency, effectiveness, and satisfaction. METHODS: We retrieved peer-reviewed journal articles on BCMA and eMAR quantitative usability measures from PsycInfo and MEDLINE (1946-August 20, 2019), and EMBASE (1976-October 23, 2019). Following Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines, we screened articles, extracted and categorized data into the usability categories of effectiveness, efficiency, and satisfaction, and evaluated article quality. RESULTS: We identified 1,922 articles and extracted data from 41 articles. Twenty-four articles (58.5%) investigated BCMA only, 10 (24.4%) eMAR only, and seven (17.1%) both BCMA and eMAR. Twenty-four articles (58.5%) measured effectiveness, 8 (19.5%) efficiency, and 17 (41.5%) satisfaction. Study designs included randomized controlled trial (n = 1; 2.4%), interrupted time series (n = 1; 2.4%), pretest/posttest (n = 21; 51.2%), posttest only (n = 14; 34.1%), and pretest/posttest and posttest only for different dependent variables (n = 4; 9.8%). Data collection occurred through observations (n = 19, 46.3%), surveys (n = 17, 41.5%), patient safety event reports (n = 9, 22.0%), surveillance (n = 6, 14.6%), and audits (n = 3, 7.3%). CONCLUSION: Of the 100 measures across the 41 articles, implementing BCMA and/or eMAR broadly resulted in an increase in measures of effectiveness (n = 23, 52.3%) and satisfaction (n = 28, 62.2%) compared to measures of efficiency (n = 3, 27.3%). Future research should focus on eMAR efficiency measures, utilize rigorous study designs, and generate specific design requirements.

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.

Implementing lung cancer screening in primary care: needs assessment and implementation strategy design.

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.

Adapting Nielsen's Design Heuristics to Dual Processing for Clinical Decision Support.

The study objective was to improve the applicability of Nielson's standard design heuristics for evaluating electronic health record (EHR) alerts and linked ordering support by integrating them with Dual Process theory. Through initial heuristic evaluation and a user study of 7 physicians, usability problems were identified. Through independent mapping of specific usability criteria to support for each of the Dual Cognitive processes (S1 and S2) and deliberation, agreement was reached on mapping criteria. Finally, usability errors from the heuristic and user study were mapped to S1 and S2. Adding a dual process perspective to specific heuristic analysis increases the applicability and relevance of computerized health information design evaluations. This mapping enables designers to measure that their systems are tailored to support attention allocation. System 1 will be supported by improving pattern recognition and saliency, and system 2 through efficiency and control of information access.