Lead Physician Informaticist Artificial Intelligence Ready Mount Sinai (AIR-MS)

The Mount Sinai Health System (MSHS) is one of the largest healthcare systems in the United States and is at the forefront of leveraging clinical excellence and cutting-edge technology to advance patient care research and innovation through data-driven insights. MSHS is home to the worlds fastest supercomputer Minerva at an academic medical center. Minerva and the Artificial Intelligence Ready Mount Sinai (AIRMS) research platform are part of a computational and data ecosystem that includes cohort query tools AI agents and thousands of applications. With these extensive resources we are well positioned to continue our quest to better understand and treat disease through the use of AI. Several multi-modal data sources such as electronic health record (EHR) data from our system-wide Epic instance and millions of digital pathology slides are linked together and available for analytics research and quality improvement initiatives. AIRMS serves as a key entry point and infrastructure for dozens of departments groups and institutes throughout MSHS.

We are seeking a highly-engaged and inspired physician informaticist who is expert at computation data analytics AI and the data resulting from U.S. health care processes. The Lead Physician Informaticist AIRMS will partner with researchers and clinicians for the efficient and effective use of AIRMS and the other components in the computational and data this role you will serve as a bridge between clinical operational and research needs for data from the clinical care processes for MSHS using your world-class support and collaboration skills. You will oversee researcher and clinician engagement across MSHS including but not limited to: working with researchers and clinicians 1:1 to understand their needs and partnering with them for solutions; identifying gaps to broader use; and developing roadmaps and communication strategies for the deployment of new technological functionalities in AIRMS. You will help researchers and clinicians translate complex questions into actionable data queries and help create Python/R scripts to help them gain insights into our Observational Medical Outcomes Partnership (OMOP) Common Data Model (CDM) warehouse in the AIRMS platform. This is a highly-collaborative position focused on empowering users to effectively utilize health data while ensuring accuracy compliance and clinical

• Oversees all aspects of researcher and clinician engagement including understanding user needs developing and executing on a communications plan identifying new technologies developing roadmaps and ensuring a world-class user experience.
• Partners 1:1 with end-users (clinicians researchers analysts) to elicit data and computational requirements refine research or operational questions and support the creation of tailored data solutions.
• Communicates complex data concepts effectively to end users with different levels of expertise especially with relation to their use of data produced by the clinical processes at Mount Sinai and stored in the OMOP CDM data warehouse reuse of multi-modal data types and formats for research and clinical translation.
• Designs writes and optimizes SQL queries and Python/R scripts against the OMOP CDM to extract and analyze clinical revenue and research data.
• Educates trains and mentors users on EHR data structures OMOP CDM concepts data limitations cohort definition cohort query tools analytical tools and best practices for self-service analytics.
• Helps to support researchers to address various challenges associated with data use and analysis across the translational spectrum (T0 to T4).
• Oversees a user ticketing system and ensures that users are helped in a reasonable amount of time.
• Develops training sessions on health care data and data warehouses including the use of query tools and direct SQL.
• Validates data outputs for accuracy completeness and appropriateness.
• Identifies and communicates potential biases or limitations in EHR-derived data.
• Collaborates with database architect and engineers software developers system administrators and others to improve user functionality data quality and/or completeness.
• Ensures compliance with regulations institutional policies and ethical standards in all data handling and dissemination.
• Contributes to documentation standard query libraries and training materials to enhance organizational data literacy.
• Has familiarity with modern artificial intelligence and machine-learning methodologies including the use of supervised and unsupervised methods large language models and transformers.
• Collaborate effectively with students technologists physicians administrators compliance staff and others throughout MSHS.
• May manage a small staff.

• Bachelors degree in a technical discipline; Masters degree preferred
• 12-15 years preferred of related experience including 8 years of demonstrated ability in technology area (clinical infomatics at a healthcare system and/or in biomedical research). In-depth knowledge of associated technology areas that could impact area of responsibility; healthcare technology experience preferred.

Preferred:

• Understands and has expertise in a broad spectrum of clinical data types available at academic medical centers and understands the common standards transformations and formats for these datasets as well as the data lifecycle.
• Demonstrated proficiency in SQL (including complex joins stored procedures window functions and performance optimization) with experience querying large healthcare datasets as well as using Python and R programming languages.
• Understands how to perform quality assurance and quality control assessments of queries data and data warehouses.
• Experience with the OMOP Common Data Model and standardized vocabularies (e.g. SNOMED ICD RxNorm LOINC) and ontologies. Experience with mapping concepts to OMOP.
• Must have experience with HIPAA Compliance and Cybersecurity requirements.
• Strong understanding of EHR data including clinical workflows documentation practices and common data quality issues in EHR derived data.
• Familiarity with Epic Clarity and Caboodle data models is preferred.
• Experience preferred with a variety of multi-modal data including but not limited to: Radiology DICOM tags Pathology metadata genomic/WES return of genetic results.
• Understands best practices for software development lifecycles.
• Experience working with students residents fellows staff faculty administrators with different levels of technological and medical expertise in a large academic medical system.
• Effective communication and collaboration skills.