Closed
AN11--Kaiser Permanente R&D Statistical Expertise EHR Evaluation
- NAICS
- 541720
- Closed
- Jul 29, 2026
- Posted
- Jul 14, 2026
Description
2 Notice of Sources Sought The purpose of this Sources Sought Announcement is for market research to make appropriate acquisition decisions and to gain knowledge of potential qualified Service-Disabled Veteran Owned Small Businesses (SDVOSB), Veteran Owned Small Businesses (VOSB), Women Owned Small Businesses (WOSB) and Small Businesses interested and capable of providing the services requested, as well as any large businesses. The results from this source sought notice will be used to determine the appropriate set-aside. The intended contract is a firm-fixed price R&D service contract. The SBA Non-Manufacturer Rule is not applicable as the intended contract is for services. The Department of Veterans Affairs, NCO 20 is looking for Kaiser Permanente s authorized service providers that can provide below R&D services. Statement of Work: Contract Title Expertise in Measurement Error to Support VHA Digital Health Office Immersive Program Evaluation Project: VHA Digital Health Office Immersive Program Evaluation Background Virtual reality (VR) is an emerging technology that has been used as a treatment in diverse pain populations and for diverse indications. VA's Digital Health Office is exploring offering Veterans a variety of VR therapies through conducting multiple pilot initiatives. To date over 8,000 Veterans have engaged in VR activities through engaging in headset technologies in clinics and at home. Quantifying the potential benefit of disseminating VR technologies requires careful assessment of the VR activities, how they are being used in combination with other therapies such as mental health treatment and physical therapy, and monitoring of outcomes including patient reported changes in health, pain and well-being, healthcare utilization changes and overall costs. The Digital Health Office is providing tools to measure VR engagement and has requested evaluation of the VHA Immersive Program and to generate insights about how to best support further XR implementation. The evaluation activities will leverage data collected as part of VHA Immersive Program activities, data about participants linked to VHA administrative data, and primary data collected by the evaluation team. The evaluation will span three fiscal years, and will be comprised of retrospective analyses, mixed-methods evaluation design, and prospective data collection. To understand how population-level coding and documentation of VR engagement reflect true exposure levels, fidelity, and overall VR dose we require a statistical expert. We are requesting a contract with Kaiser Permanente Washington Health Research Institute (KPWHRI) in order for Pamela Shaw, PhD to work with this project and provide expertise in statistical methods and study design. Dr. Shaw is a Senior Investigator in the Division of Biostatistics at KPWHRI and also Affiliate Professor in the Department of Biostatistics at the University of Washington School of Public Health. Dr. Shaw s research focuses on the design and analysis of studies leveraging electronic health records (EHR) data to support rigorous, cost-effective, clinical and epidemiologic research to improve the health and guide treatment decisions for US citizens. Dr. Shaw brings expertise in complex survey design, statistical methods and study design to address measurement error and outcome misclassification, and the evaluations of interventions using clinical trial and observational data across a variety of health care settings and populations. Her work integrates methodological rigor with practical relevance, advancing the use of electronic health records data to public health and clinical decision making. Selected research projects done under Dr. Shaw s leadership: Williamson B, Krakauer C, Johnson E, Gruber S, Shepherd BE, van der Laan MJ, Lumley T, Lee H, Hernandez Munoz JJ, Zhao F, Dutcher SK, Desai, R, Simon GE, Shortreed SM, Nelson JC, Shaw PA. Assessing treatment effects in observational data with missing confounders: A...
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