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Genomic profiling information is frequently available to oncologists, enabling targeted cancer therapy. Because clinically relevant information is rapidly emerging in the literature and elsewhere, there is a need for informatics technologies to support targeted therapies. To this end, we have developed a system for Automated Identification of Molecular Effects of Drugs, to help biomedical scientists curate this literature to facilitate decision support.

Development of computational approaches and tools to effectively integrate multidomain data is urgently needed for the development of newly targeted cancer therapeutics.

The Information Systems Department at Memorial Sloan Kettering Cancer Center developed the DARWIN Cohort Management System (DCMS). The DCMS identifies and tracks cohorts of patients based on genotypic and clinical data. It assists researchers and treating physicians in enrolling patients to genotype-matched IRB-approved clinical trials. The DCMS sends automated, actionable, and secure email notifications to users with information about eligible or enrolled patients before their upcoming appointments. The system also [...]

As targeted cancer therapies and molecular profiling become widespread, the era of "precision oncology" is at hand. However, cancer genomes are complex, making mutation-specific outcomes difficult to track. We created a proof-of-principle, CUSTOM-SEQ: Continuously Updating System for Tracking Outcome by Mutation, to Support Evidence-based Querying, to automatically calculate and display mutation-specific survival statistics from electronic health record data.

"Precision medicine" (PM) requires researchers to identify actionable genetic risks and for clinicians to interpret genetic testing results to patients. Whether PM will equally benefit all populations or exacerbate existing disparities is uncertain.

Precision medicine approaches disease treatment and prevention by taking patients' individual variability in genes, environment, and lifestyle into account. Although the ideas underlying precision medicine are not new, opportunities for its more widespread use in practice have been enhanced by the development of large-scale databases, new methods for categorizing and representing patients, and computational tools for analyzing large datasets. New research methods may create uncertainty for both healthcare professionals and [...]

AMIA is leading the effort to strengthen the health informatics profession by creating an advanced health informatics certification (AHIC) for individuals whose informatics work directly impacts the practice of health care, public health, or personal health. The AMIA Board of Directors has endorsed a set of proposed AHIC eligibility requirements that will be presented to the future AHIC certifying entity for adoption. These requirements specifically establish who will be eligible [...]

Health care organizations must develop integrated health information systems to respond to the numerous government mandates driving the movement toward reimbursement models emphasizing value-based and accountable care. Success in this transition requires integrated data analytics, supported by the combination of health informatics, interoperability, business process design, and advanced decision support tools. This case study presents the development of a master's level cross- and multidisciplinary informatics program offered through a business [...]

Identifying disease-mutation relationships is a significant challenge in the advancement of precision medicine. The aim of this work is to design a tool that automates the extraction of disease-related mutations from biomedical text to advance database curation for the support of precision medicine.

(1) To develop an automated algorithm to predict a patient's response (ie, if the patient agrees or declines) before he/she is approached for a clinical trial invitation; (2) to assess the algorithm performance and the predictors on real-world patient recruitment data for a diverse set of clinical trials in a pediatric emergency department; and (3) to identify directions for future studies in predicting patients' participation response.