Full-Time
Clinical Systems Analyst
Blood Center Services, Blood Establishment Computer System
Stanford Health Care
10,001+ employees
Coordinated cancer, neuroscience, cardiovascular care
Compensation Overview
$55.85 - $74/hr
Palo Alto, CA, USA
In Person
 IT & Security (1) |
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- Minimum 3 years systems analyst experience with BECS (Blood Establishment Computer System).
- Minimum 3 years experience with lab information systems, blood manufacturing and testing operations.
- Deep understanding of FDA medical device software regulations.
- Bachelor’s degree in Information Technology, Computer Science, or a directly related field from an accredited college or university OR a minimum of 5 years of directly related work experience.
- 3 or more years of progressively responsible and directly related work experience.
- Knowledge of BECS and/or Laboratory Information Systems as well as other information systems, clinical software, and computer applications used in a testing lab environment.
- Mid-level Microsoft Office skills.
- Ability to diagnose and resolve routine technology problems.
- Ability and desire to learn to resolve specialized and advanced technology problems.
- Ability to establish a set of tasks and activities associated with an intended outcome and timeline.
- Ability to take action consistent with available facts, constraints, and anticipated consequences.
- Ability to use appropriate interpersonal skills to give information to and receive information from coworkers and clients in a tactfully and professional manner.
- Ability to use effective approaches for choosing a course of action or developing appropriate solutions and/or reaching conclusions.
- Ability to develop new skills and teach others.
- Ability to collaborate and build consensus with stakeholders.
- Ability to understand and adhere to operational standards, policies, and procedures.
- Ability to identify risks and issues.
- Ability to develop solutions for new and unfamiliar challenges.
- Knowledge of current issues and trends in blood and/or HLA industry and clinical operations.
- Provide tier-2 support of application incidents reported through the help desk; including 24/7 on call coverage as required
- Provide analytical assistance to junior team members to resolve application incidents, maintenance items, and enhancement requests
- Perform daily monitoring of applications in production use
- Coordinate application support with other information technology teams including Infrastructure, Integration, Reporting, and the help desk
- Lead small to medium complexity software upgrade initiatives or enhancements to workflows including the design, build, and test phases
- Modify tables/master files, make additions and changes under the direction of senior team members
- Implement changes using documented procedures that are compliant with department’s policies and procedures
- Take ownership of low to medium complexity issues and act as a liaison between customer and other support staff to facilitate resolution
- Work with and mentor junior staff members to document workflows
- Assist senior team members in developing and maintaining requirements/specifications and test cases for new or enhanced functionality to department applications
- Participate in team and cross-team meetings and maintain appropriate meeting records
- None
Stanford Health Care operates a network of hospitals and care facilities in the Bay Area, delivering a wide range of medical services including cancer care, neurosciences, cardiovascular medicine, surgery, organ transplant, medical specialties, and primary care. Its care model combines expert clinical teams with researchers, translating medical discoveries into real patient treatments and improving how care is delivered. Unlike some providers, Stanford Health Care emphasizes a close link with Stanford University to foster multidisciplinary collaboration and apply the latest scientific knowledge to patient care. The organization aims to help people heal through science and compassion, advancing health outcomes by integrating research, innovation, and high-quality clinical practice for patients one at a time.
Company Size
10,001+
Company Stage
N/A
Total Funding
N/A
Headquarters
Palo Alto, California
Founded
2012
Simplify's Take
What believers are saying
- ChatEHR reports 40-70% faster workflows and over 95% faster data retrieval.
- Transitions of Care can scale post-discharge support across three California counties.
- The St. Rose collaboration extends Stanford specialty capacity into the East Bay.
What critics are saying
- California licensing and CMS policy still constrain Stanford's home-based care model.
- Partnered home care across counties depends on external agency capacity and quality.
- Enterprise AI inside Epic concentrates compliance, safety, and trust risk in one system.
What makes Stanford Health Care unique
- Stanford embeds ChatEHR inside Epic, not as a separate pilot tool.
- Stanford built transitional home care around discharge coordination, monitoring, and recovery.
- Stanford pairs specialty expansion with local partners like Alameda Health System and St. Rose.
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People at Stanford Health Care who can refer or advise you
Benefits
Health Insurance
Paid Vacation
401(k) Retirement Plan
Flexible Work Hours
Remote Work Options
Wellness Program
Mental Health Support
Company News
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Stanford Health Care took the stage at HIMSS to share their EHR training success. Amplifire + Kahuna Partnership were honored to see this work come to life during Stanford Health Care's presentation at HIMSS Global Health Conference & Exhibition last week. Anne Hyland, Vice President of EHR Learning, and Michael Walker, Senior Director of New Business at Amplifire, were able to hear first-hand how their team handled the challenges and the measurable outcomes they've achieved. It was a powerful reminder of what's possible when innovation is paired with a clear focus on outcomes. What stands out most is Stanford Health Care's thoughtful approach to modernizing onboarding. Rather than accepting traditional, one-size-fits-all training, they've embraced a more personalized, data-driven model that meets clinicians where they are, respecting prior experience while ensuring mastery of critical workflows. The result is not only greater efficiency, but a more confident, prepared workforce ready to deliver high-quality care from day one. As highlighted in their broader work, this approach has also enabled earlier identification of struggling learners and more targeted support, ultimately strengthening both performance and satisfaction. Stanford Health Care continues to set the standard for innovation in workforce development, and their recent feature in Healthcare IT News highlights just how impactful that work has been. By reimagining EHR training, their team has successfully reduced training time by 50% while simultaneously improving learning retention, an achievement that speaks to both their strategic vision and deep commitment to clinician success. Congratulations to the entire Stanford Health Care team on this well-deserved recognition. Their leadership is not only advancing their own organization, but helping to shape the future of healthcare workforce development across the industry.
Nikesh Kotecha on blueprint for trust: how ChatEHR establishes a framework for Responsible AI in clinical care. Stanford Health Care, part of Stanford Medicine, was awarded a 2025 InterSystems Impact Award for its AXIOM initiative for fast access to electronic health records. In turn, AXIOM underpins the ChatEHR medical chatbot that Nikesh Kotecha, head of the Data Science team at Stanford Health Care, presents below. The Impact Award recognizes the work of the team to deliver low-latency, comprehensive access to patient data from complex electronic health record (EHR) systems. This foundation has been essential to enabling the ChatEHR platform. Contribution by Nikesh Kotecha The integration of Large Language Models (LLMs) into health systems holds the promise of reducing administrative burden and cognitive overload for clinical teams. However, deploying such tools in clinical workflows requires a commitment to safety, governance, and continuous monitoring. Stanford Medicine's ChatEHR platform, an initiative developed as part of an enterprise investment in establishing a Data Science pillar, serves as an essential case study for any health system aiming for responsible AI adoption. Setting up a responsible clinical AI system requires focus on three key areas: a robust platform architecture, transparent governance, and continuous evaluation. A significant barrier to integrating AI tools into clinical workflows is often fragmented data sources and latency in data access. Traditional reporting systems were unsuitable for the real-time applications needed at the point of care. The ChatEHR Platform tackles this with a sophisticated, four-pillar architecture: * LLM Router: Provides secure access to a variety of models, standardizing calls and handling centralized logging. * Real-Time Data Access: Fetches and organizes clinical information using Fast Healthcare Interoperability Resources (FHIR). This enables near real-time responsiveness by combining FHIR and HL7v2 messaging with optimized query performance. * Function Server: Transforms generic AI capabilities into healthcare-specific functions and task-specific endpoints, powering workflow automations. * EHR Integration: Manages secure connections and integrates the custom UI directly into Epic Hyperspace, maintaining authentication and patient context. This scalable architecture, underpinned by Stanford's AXIOM framework, proved critical for performance. For instance, the system cut retrieval times by over 95% - from nearly two minutes to four seconds - for some AI workflows. This focus on low-latency data access is fundamental to clinical decision-making and the AXIOM initiative, which provides this breakthrough data capability, was recognized as an InterSystems Impact Award winner in 2025. Responsible AI deployment starts with organizational frameworks and strong governance models. ChatEHR is explicitly guided by Stanford Medicine's Responsible AI Lifecycle. Oversight is managed through the Data Science Executive Committee (DSEC), ensuring transparency and alignment throughout the project life cycle. Furthermore, all system operations adhere to HIPAA and HITECH standards, utilizing role-based access controls inherited directly from the EHR to limit data exposure. This structured approach ensures that AI deployment is treated not as a one-off technical build, but as a governed, enterprise-wide strategy. Crucial to maintaining trust and safety is developing approaches for evaluating LLM performance, a task made challenging by the flexibility and variability of generative AI outputs.ChatEHR implemented MedHELM (Medical Holistic Evaluation of Language Models), a generative AI evaluation framework developed in collaboration with the Stanford Center for Biomedical Informatics Research (BMIR) and the Institute for Human-Centered AI (HAI) By combining governance, an embedded platform for low-latency data access, and monitoring via the MedHELM framework, the ChatEHR initiative provides a replicable blueprint for health systems seeking to translate AI innovation into measurable clinical and operational impact while upholding standards of safety and responsibility. Nikesh Kotecha, Head of Data Science at Stanford Health Care, tasked with building a group to develop and deploy AI-guided models into clinical and operational workflows. Prior to that, Nikesh started and led the informatics efforts at the Parker Institute for Cancer Immunotherapy, a non-profit organization bringing together 7 top cancer centers Organization: Stanford Health Care Stanford Health Care's AXIOM initiative (Advance Extraction for Intelligent Orchestration and Medical Insight) is a transformative solution that enables fast access to complex electronic health record (EHR) data. Using a unique recursive data retrieval method, AXIOM minimizes duplication and reduces query times from minutes or hours to seconds. It leverages a FHIR (Fast Health Interoperability Resources) repository on InterSystems IRIS, to power advanced AI and large language model (LLM) applications. This architecture enables real-time clinical insights, enhances care team collaboration, and supports use cases such as augmented triage and optimized patient flow across a variety of care settings. Sponsored by InterSystems and selected by an independent panel of judges from Massachusetts Institute of Technology (MIT).
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