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INTRODUCTION: Women with atypical hyperplasia (AH) or lobular carcinoma in situ (LCIS) have a significantly increased risk of breast cancer, which can be substantially reduced with antiestrogen therapy for chemoprevention. However, antiestrogen therapy for breast cancer risk reduction remains underutilized. Improving knowledge about breast cancer risk and chemoprevention among high-risk patients and their healthcare providers may enhance informed decision-making about this critical breast cancer risk reduction strategy. METHODS/DESIGN: We are conducting a cluster randomized controlled trial to evaluate the effectiveness and implementation of patient and provider decision support tools to improve informed choice about chemoprevention among women with AH or LCIS. We have cluster randomized 26 sites across the U.S. through the SWOG Cancer Research Network. A total of 415 patients and 200 healthcare providers are being recruited. They are assigned to standard educational materials alone or combined with the web-based decision support tools. Patient-reported and clinical outcomes are assessed at baseline, after a follow-up visit at 6 months, and yearly for 5 years. The primary outcome is chemoprevention informed choice after the follow-up visit. Secondary endpoints include other patient-reported outcomes, such as chemoprevention knowledge, decision conflict and regret, and self-reported chemoprevention usage. Barriers and facilitators to implementing decision support into clinic workflow are assessed through patient and provider interviews at baseline and mid-implementation. RESULTS/DISCUSSION: With this hybrid effectiveness/implementation study, we seek to evaluate if a multi-level intervention effectively promotes informed decision-making about chemoprevention and provide valuable insights on how the intervention is implemented in U.S. TRIAL REGISTRATION: NCT04496739.

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Genetic testing for hereditary breast and ovarian cancer (HBOC) is recommended for breast cancer patients diagnosed at age ≤ 50 years. Our objective was to examine racial/ethnic differences in genetic testing frequency and results among diverse breast cancer patients. A retrospective cohort study among women diagnosed with breast cancer at age ≤ 50 years from January 2007 to December 2017 at Columbia University in New York, NY. Among 1503 diverse young breast cancer patients, nearly half (46.2%) completed HBOC genetic testing. Genetic testing completion was associated with younger age, family history of breast cancer, and earlier stage, but not race/ethnicity or health insurance status. Blacks had the highest frequency of pathogenic/likely pathogenic (P/LP) variants (18.6%), and Hispanics and Asians had the most variants of uncertain significance (VUS), 19.0% and 21.9%, respectively. The percentage of women undergoing genetic testing increased over time from 15.3% in 2007 to a peak of 72.8% in 2015. Over the same time period, there was a significant increase in P/LP and VUS results. Due to uncertainty about the clinical implications of P/LP variants in moderate penetrance genes and VUSs, our findings underscore the need for targeted genetic counseling education, particularly among young minority breast cancer patients.

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OBJECTIVES: The prominence given to universal implementation of electronic health record (EHR) systems in U.S. health care reform, underscores the importance of devising reliable measures of factors that predict medical care providers' use of EHRs. This paper presents an easily administered provider survey instrument that includes measures corresponding to core dimensions of DeLone and McClean's (D & M) model of information system success. METHODS: Study data came from self-administered surveys completed by 460 primary care providers, who had recently begun using an EHR. RESULTS: Based upon assessment of psychometric properties of survey items, a revised D&M causal model was formulated that included four measures of the determinants of EHR use (system quality, IT support, ease of use, user satisfaction) and five indicators of provider beliefs about the impact on an individual's clinical practice. A structural equation model was estimated that demonstrated a high level of inter-correlation between the four scales measuring determinants of EHR use. All four variables had positive association with each of the five individual impact measures. Consistent with our revised D&M model, the association of system quality and IT support with the individual impact measures was entirely mediated by ease of use and user satisfaction. CONCLUSIONS: Survey research provides important insights into provider experiences with EHR. Additional studies are in progress to investigate how the variables constructed for this study are related to direct measures of EHR use.

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The American Medical Informatics Association 2001 Spring Congress brought together the public health and informatics communities to develop a national agenda for public health informatics. Discussions on funding and governance; architecture and infrastructure; standards and vocabulary; research, evaluation, and best practices; privacy, confidentiality, and security; and training and workforce resulted in 74 recommendations with two key themes: (1) all stakeholders need to be engaged in coordinated activities related to public health information architecture, standards, confidentiality, best practices, and research and (2) informatics training is needed throughout the public health workforce. Implementation of this consensus agenda will help promote progress in the application of information technology to improve public health.

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A panel was convened at the American Medical Informatics Association Spring Congress to discuss issues and opportunities that arise when informatics methods, theories, and applications are applied to public health functions. Panelists provided examples of applications that connect efforts between public health and clinical care, emphasizing the need for integration of clinical data with public health data and the analysis of those data to support surveillance and informed decision making. Benefits to be gained by both medical informatics and public health at the interface were evident; both encounter the same major issues including privacy, systems integration, standards, and many more.

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This paper describes how theory facilitated the development of educational content for the MI-HEART project, a tailored Web-based intervention designed to favorably influence the appropriateness and rapidity of decision-making in patients suffering from symptoms of acute myocardial infarction. There were five steps involved: 1) formulating the behavioral goal, 2) defining intervention objectives based on an analyses of the determinants of behavior, 3) developing an assessment tool to measure a person's status on these determinants, 4) creating tailored content that address individual variation on determinants of the health behavior and, 5) developing algorithms and a computer program that link responses from the assessment to specific tailored communication. The approach we describe largely distinguishes Web-based applications that are designed to change health behavior from those that simply impart information. Developers of Web-based applications that propose to improve health status by modifying health-related behaviors need the understanding that although it is said that we live in an "information age", simply increasing knowledge has not been effective in changing behaviors in most instances. Furthermore, the one-size fits all approach to developing educational content cannot address the needs, concerns and interests of different individuals. With informatics technology, our ability to collect information from individuals and provide educational content tailored to the specific information collected is not only possibly, but practical.

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BACKGROUND: Although interest in Consumer Health Informatics (CHI) has increased, a consensus definition of CHI does not yet exist. PURPOSE: To conduct a hypothesis-generating survey of AMIA members regarding definition and research agenda for CHI. METHODS: We solicited participation among AMIA members in an Internet-based survey focusing on issues related to a definition of CHI. RESULTS: One hundred thirty-five AMIA members responded. Participants indicated a broad spectrum of topics important to CHI including "self-help for disease management" and "patient access to their own medical records." CHI research was felt to rely heavily on public health methods such as epidemiology and outcomes research, a paradigm shift from traditional medical informatics. Responses indicated a perceived lack of funding and need for further research in CHI. CONCLUSIONS: A working definition should emphasize the multidisciplinary nature of CHI, include consumer input into CHI design, and focus on public health approaches to evaluation.

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This paper addresses a process in which we combined educational guidelines (EG) from heterogeneous sources in one set of coherent computable statements to support dynamically generated and precisely tailored patient education material. The Guideline Interchange Format (GLIF), predicate logic and decision tables were assessed. An extended formalism of GLIF was applied to break up composite sentences of the educational material in atomic sentences. The differentiation of atomic sentences and combinations of atomic sentences from heterogeneous sources lead to a simplified overall content and model, and a significant reduction of conditional sentences in the EG. The resulting streamlined and personalized guidelines are expected to provide an improved user experience.

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OBJECTIVE: The purpose of this study was to test the adequacy of the Clinical LOINC (Logical Observation Identifiers, Names, and Codes) semantic structure as a terminology model for standardized assessment measures. METHODS: After extension of the definitions, 1, 096 items from 35 standardized assessment instruments were dissected into the elements of the Clinical LOINC semantic structure. An additional coder dissected at least one randomly selected item from each instrument. When multiple scale types occurred in a single instrument, a second coder dissected one randomly selected item representative of each scale type. RESULTS: The results support the adequacy of the Clinical LOINC semantic structure as a terminology model for standardized assessments. Using the revised definitions, the coders were able to dissect into the elements of Clinical LOINC all the standardized assessment items in the sample instruments. Percentage agreement for each element was as follows: component, 100 percent; property, 87.8 percent; timing, 82.9 percent; system/sample, 100 percent; scale, 92.6 percent; and method, 97.6 percent. DISCUSSION: This evaluation was an initial step toward the representation of standardized assessment items in a manner that facilitates data sharing and re-use. Further clarification of the definitions, especially those related to time and property, is required to improve inter-rater reliability and to harmonize the representations with similar items already in LOINC.

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We are examining ways in which a clinical information system can favorably influence the appropriateness and rapidity of decision-making in patients suffering from symptoms of acute myocardial infarction. In order to do so, we have developed a theoretically based cognitive model for patient decision making. Our model includes somatic and emotional awareness, perceived threat (vulnerability and susceptibility), expectations of symptoms, self-efficacy and response efficacy to explain the response of an individual their symptoms. Variables are explained within a framework that details how they are interrelated in the context of other moderating variables. With an understanding of the decision process, we are able to collect, maintain and access patient specific data to tailor technology-based interventions unique to the requirements of each individual at various phases of the decision process. Existing clinical information systems at Columbia-Presbyterian Medical Center already address issues related to patient relevant on-line data. Other patient specific information will be collected through on-line questionnaires. By basing our approach on the use of a cognitive model, we can assess the capacity of our interventions to modify variables important to the decision-making process, allowing us to pinpoint which interventions are effective and the reasons why they are ineffective.

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