Health Care Equity in the Use of Advanced Analytics and Artificial Intelligence Technologies in Primary Care.

The integration of advanced analytics and artificial intelligence (AI) technologies into the practice of medicine holds much promise. Yet, the opportunity to leverage these tools carries with it an equal responsibility to ensure that principles of equity are incorporated into their implementation and use. Without such efforts, tools will potentially reflect the myriad of ways in which data, algorithmic, and analytic biases can be produced, with the potential to widen inequities by race, ethnicity, gender, and other sociodemographic factors implicated in disparate health outcomes. We propose a set of strategic assertions to examine before, during, and after adoption of these technologies in order to facilitate healthcare equity across all patient population groups. The purpose is to enable generalists to promote engagement with technology companies and co-create, promote, or support innovation and insights that can potentially inform decision-making and health care equity.

Screening, Diagnosis, and Intervention for Autism: Experiences of Black and Multiracial Families Seeking Care.

PURPOSE: Despite advances in screening and awareness, Black and multiracial families continue to experience challenges when seeking an autism diagnosis for their children. METHODS: We surveyed 400 Black and multiracial families of young children with autism from an existing research database in the United States about their retrospective diagnostic experiences. We gathered quantitative and qualitative data and engaged in iterative coding to understand timing and content of first concerns, families' experiences of care providers and systems, and the impact of race and culture on accessing care. RESULTS: Families provided examples of early developmental concern and described provider, systemic, and cultural barriers and facilitators to care. Families also provided insight into the influence of culture and made recommendations on how the medical system could better care for Black and multiracial families of children with autism. CONCLUSIONS: Results add to a growing body of literature supporting the need for culturally sensitive and accessible care related to developmental monitoring, diagnosis, and follow-up care for Black and multiracial children.

Developing Ethics and Equity Principles, Terms, and Engagement Tools to Advance Health Equity and Researcher Diversity in AI and Machine Learning: Modified Delphi Approach.

BACKGROUND: Artificial intelligence (AI) and machine learning (ML) technology design and development continues to be rapid, despite major limitations in its current form as a practice and discipline to address all sociohumanitarian issues and complexities. From these limitations emerges an imperative to strengthen AI and ML literacy in underserved communities and build a more diverse AI and ML design and development workforce engaged in health research. OBJECTIVE: AI and ML has the potential to account for and assess a variety of factors that contribute to health and disease and to improve prevention, diagnosis, and therapy. Here, we describe recent activities within the Artificial Intelligence/Machine Learning Consortium to Advance Health Equity and Researcher Diversity (AIM-AHEAD) Ethics and Equity Workgroup (EEWG) that led to the development of deliverables that will help put ethics and fairness at the forefront of AI and ML applications to build equity in biomedical research, education, and health care. METHODS: The AIM-AHEAD EEWG was created in 2021 with 3 cochairs and 51 members in year 1 and 2 cochairs and ~40 members in year 2. Members in both years included AIM-AHEAD principal investigators, coinvestigators, leadership fellows, and research fellows. The EEWG used a modified Delphi approach using polling, ranking, and other exercises to facilitate discussions around tangible steps, key terms, and definitions needed to ensure that ethics and fairness are at the forefront of AI and ML applications to build equity in biomedical research, education, and health care. RESULTS: The EEWG developed a set of ethics and equity principles, a glossary, and an interview guide. The ethics and equity principles comprise 5 core principles, each with subparts, which articulate best practices for working with stakeholders from historically and presently underrepresented communities. The glossary contains 12 terms and definitions, with particular emphasis on optimal development, refinement, and implementation of AI and ML in health equity research. To accompany the glossary, the EEWG developed a concept relationship diagram that describes the logical flow of and relationship between the definitional concepts. Lastly, the interview guide provides questions that can be used or adapted to garner stakeholder and community perspectives on the principles and glossary. CONCLUSIONS: Ongoing engagement is needed around our principles and glossary to identify and predict potential limitations in their uses in AI and ML research settings, especially for institutions with limited resources. This requires time, careful consideration, and honest discussions around what classifies an engagement incentive as meaningful to support and sustain their full engagement. By slowing down to meet historically and presently underresourced institutions and communities where they are and where they are capable of engaging and competing, there is higher potential to achieve needed diversity, ethics, and equity in AI and ML implementation in health research.

Design thinking in applied informatics: what can we learn from Project HealthDesign?

OBJECTIVE: The goals of this study are to describe the value and impact of Project HealthDesign (PHD), a program of the Robert Wood Johnson Foundation that applied design thinking to personal health records, and to explore the applicability of the PHD model to another challenging translational informatics problem: the integration of AI into the healthcare system. MATERIALS AND METHODS: We assessed PHD's impact and value in 2 ways. First, we analyzed publication impact by calculating a PHD h-index and characterizing the professional domains of citing journals. Next, we surveyed and interviewed PHD grantees, expert consultants, and codirectors to assess the program's components and the potential future application of design thinking to artificial intelligence (AI) integration into healthcare. RESULTS: There was a total of 1171 unique citations to PHD-funded work (collective h-index of 25). Studies citing PHD span medical, legal, and computational journals. Participants stated that this project transformed their thinking, altered their career trajectory, and resulted in technology transfer into the commercial sector. Participants felt, in general, that the approach would be valuable in solving contemporary challenges integrating AI in healthcare including complex social questions, integrating knowledge from multiple domains, implementation, and governance. CONCLUSION: Design thinking is a systematic approach to problem-solving characterized by cooperation and collaboration. PHD generated significant impacts as measured by citations, reach, and overall effect on participants. PHD's design thinking methods are potentially useful to other work on cyber-physical systems, such as the use of AI in healthcare, to propose structural or policy-related changes that may affect adoption, value, and improvement of the care delivery system.

Risky business: a scoping review for communicating results of predictive models between providers and patients.

OBJECTIVE: Given widespread excitement around predictive analytics and the proliferation of machine learning algorithms that predict outcomes, a key next step is understanding how this information is-or should be-communicated with patients. MATERIALS AND METHODS: We conducted a scoping review informed by PRISMA-ScR guidelines to identify current knowledge and gaps in this domain. RESULTS: Ten studies met inclusion criteria for full text review. The following topics were represented in the studies, some of which involved more than 1 topic: disease prevention (N = 5/10, 50%), treatment decisions (N = 5/10, 50%), medication harms reduction (N = 1/10, 10%), and presentation of cardiovascular risk information (N = 5/10, 50%). A single study included 6- and 12-month clinical outcome metrics. DISCUSSION: As predictive models are increasingly published, marketed by industry, and implemented, this paucity of relevant research poses important gaps. Published studies identified the importance of (1) identifying the most effective source of information for patient communications; (2) contextualizing risk information and associated design elements based on users' needs and problem areas; and (3) understanding potential impacts on risk factor modification and behavior change dependent on risk presentation. CONCLUSION: An opportunity remains for researchers and practitioners to share strategies for effective selection of predictive algorithms for clinical practice, approaches for educating clinicians and patients in effectively using predictive data, and new approaches for framing patient-provider communication in the era of artificial intelligence.

Prospective Validation of an Electronic Health Record-Based, Real-Time Suicide Risk Model.

Importance: Numerous prognostic models of suicide risk have been published, but few have been implemented outside of integrated managed care systems. Objective: To evaluate performance of a suicide attempt risk prediction model implemented in a vendor-supplied electronic health record to predict subsequent (1) suicidal ideation and (2) suicide attempt. Design, Setting, and Participants: This observational cohort study evaluated implementation of a suicide attempt prediction model in live clinical systems without alerting. The cohort comprised patients seen for any reason in adult inpatient, emergency department, and ambulatory surgery settings at an academic medical center in the mid-South from June 2019 to April 2020. Main Outcomes and Measures: Primary measures assessed external, prospective, and concurrent validity. Manual medical record validation of coded suicide attempts confirmed incident behaviors with intent to die. Subgroup analyses were performed based on demographic characteristics, relevant clinical context/setting, and presence or absence of universal screening. Performance was evaluated using discrimination (number needed to screen, C statistics, positive/negative predictive values) and calibration (Spiegelhalter z statistic). Recalibration was performed with logistic calibration. Results: The system generated 115 905 predictions for 77 973 patients (42 490 [54%] men, 35 404 [45%] women, 60 586 [78%] White, 12 620 [16%] Black). Numbers needed to screen in highest risk quantiles were 23 and 271 for suicidal ideation and attempt, respectively. Performance was maintained across demographic subgroups. Numbers needed to screen for suicide attempt by sex were 256 for men and 323 for women; and by race: 373, 176, and 407 for White, Black, and non-White/non-Black patients, respectively. Model C statistics were, across the health system: 0.836 (95% CI, 0.836-0.837); adult hospital: 0.77 (95% CI, 0.77-0.772); emergency department: 0.778 (95% CI, 0.777-0.778); psychiatry inpatient settings: 0.634 (95% CI, 0.633-0.636). Predictions were initially miscalibrated (Spiegelhalter z = -3.1; P = .001) with improvement after recalibration (Spiegelhalter z = 1.1; P = .26). Conclusions and Relevance: In this study, this real-time predictive model of suicide attempt risk showed reasonable numbers needed to screen in nonpsychiatric specialty settings in a large clinical system. Assuming that research-valid models will translate without performing this type of analysis risks inaccuracy in clinical practice, misclassification of risk, wasted effort, and missed opportunity to correct and prevent such problems. The next step is careful pairing with low-cost, low-harm preventive strategies in a pragmatic trial of effectiveness in preventing future suicidality.

Organizational diagnostics: a systematic approach to identifying technology and workflow issues in clinical settings.

OBJECTIVES: Healthcare organizations need to rapidly adapt to new technology, policy changes, evolving payment strategies, and other environmental changes. We report on the development and application of a structured methodology to support technology and process improvement in healthcare organizations, Systematic Iterative Organizational Diagnostics (SIOD). SIOD was designed to evaluate clinical work practices, diagnose technology and workflow issues, and recommend potential solutions. MATERIALS AND METHODS: SIOD consists of five stages: (1) Background Scan, (2) Engagement Building, (3) Data Acquisition, (4) Data Analysis, and (5) Reporting and Debriefing. Our team applied the SIOD approach in two ambulatory clinics and an integrated ambulatory care center and used SIOD components during an evaluation of a large-scale health information technology transition. RESULTS: During the initial SIOD application in two ambulatory clinics, five major analysis themes were identified, grounded in the data: putting patients first, reducing the chaos, matching space to function, technology making work harder, and staffing is more than numbers. Additional themes were identified based on SIOD application to a multidisciplinary clinical center. The team also developed contextually grounded recommendations to address issues identified through applying SIOD. DISCUSSION: The SIOD methodology fills a problem identification gap in existing process improvement systems through an emphasis on issue discovery, holistic clinic functionality, and inclusion of diverse perspectives. SIOD can diagnose issues where approaches as Lean, Six Sigma, and other organizational interventions can be applied. CONCLUSION: The complex structure of work and technology in healthcare requires specialized diagnostic strategies to identify and resolve issues, and SIOD fills this need.

Toward model-driven interventions for African Americans with cognitive-communicative disorders.

African American adults have a disproportionately high incidence and prevalence of cognitive-communicative disorders, yet their use of speech-language pathology services does not reflect their need for clinical intervention. The purpose of this article is to issue a call to action aimed at moving toward the development of model-informed interventions for African American adults with cognitive-communicative disorders. We propose the development of model-driven interventions that are designed to reflect the values and preferences of many African American adults in terms of culturally distinctive opportunities for activities and participation within their communities. Examples of culturally distinctive activities and participatory roles are offered as a starting point for establishing social validity and empirical support for underlying assumptions. Constructs from the INTERNATIONAL CLASSIFICATION OF FUNCTIONING, DISABILITY, AND HEALTH and evidence-based practice are juxtaposed to suggest their mutual relevance to developing clinical services that resonate with the values and preferences of many African Americans.

Age, working memory, figurative language type, and reading ability: influencing factors in African American adults' comprehension of figurative language.

This study investigated the cognitive and linguistic factors presumed to be associated with adult comprehension of figurative language, including age, working memory (WM), figurative language type, and reading comprehension (RC). Forty younger (M = 22 years) and 40 older (M = 63 years) healthy African American adults completed WM and reading tasks, and the 60-item forced-choice multiple-category (20 idioms, 20 metaphors, and 20 metonyms) Figurative Language Comprehension Test. After controlling for WM and RC, the older adults outperformed the younger adults on idioms and metonyms. Metaphor comprehension was comparable between the groups. Findings demonstrate that WM and RC underlie adults' comprehension of figurative language and should be considered when interpreting performance on tests assessing figurative language competence in this population.

Cultural Knowledge in African American Children.

PURPOSE: This study sought to determine whether typically developing African American children's culturally based mainstream and ethnocultural knowledge increased between grades four and six. Because a lack of mainstream cultural knowledge has been implicated in reduced reading comprehension among many African American children, this study also investigated the degree to which ethnocultural and mainstream cultural knowledge differed. METHOD: Fifty-eight African American children in grades four, five, and six responded to the Test of Core Knowledge (Bradford & Harris, 2000), a divergent task that required free associations about topics drawn from both mainstream and African American history, arts, and news events. Responses were audiotape-recorded, orthographically transcribed, and analyzed for accuracy and quantity of information. RESULTS: Participants' knowledge of both mainstream and African American cultural items increased significantly between grades four and five and their knowledge of history, news events, and African American arts increased significantly between grades four and six. Additionally, significant differences were found between grades five and six for mainstream and African American news events. CLINICAL IMPLICATIONS: This sample of African American children demonstrated mainstream cultural knowledge that often surpassed ethnocultural knowledge. Such broadbased mainstream cultural knowledge meets academic expectations and can be used to facilitate further development of language and reading comprehension skills. However, this finding also suggests the need for future investigations of the relationship between mainstream cultural knowledge and literacy, bicultural knowledge in African American children from lower socioeconomic status and/or more ethnocentric backgrounds, and bicultural knowledge in those children who have language and reading comprehension deficits.

Effects of Familiarity on Idiom Comprehension in African American and European American Fifth Graders.

In order to test the language experience hypothesis, the comprehension of high-, moderate-, and low-familiarity idioms was examined in African American (N=24) and European American (N= 24) fifth-grade students in the Mid-South. This study was designed to augment the existing literature on cross-cultural idiom comprehension, of which there is a paucity of research, and provide a look from a culturally diverse perspective at idiom comprehension in youth. Results indicate a significant effect of group on idioms rated as low-familiarity, whereas idioms rated as high- and moderate-familiarity did not distinguish the groups. Additionally, the current results indicate a pattern of comprehension that is different from that found in previous studies, based on levels of familiarity (Nippold & Rudzinski, 1993) that were established in a different U.S. geographic location. The influences of social and regional culture on idiom comprehension and familiarity is discussed, and a preliminary hypothesis is proposed to explain these influences.