Quantifying the value of non-clinical outcomes challenges health ecosystem stakeholders, including Health Technology Assessment (HTA) organizations, payers, clinicians, and patients. Regulatory approvals focus on clinical endpoints; HTA bodies assess clinical endpoints and economic parameters, sometimes incorporating Quality Metrics (QMs) or Patient Reported Outcomes (PROs) into their assessments. Frameworks attempting to assess value, quality, and downstream impact (clinical or economic) of non-clinical outcomes, particularly those that may be administrative, quality of care, patient experience, or efficiency related, are often limited in applicability. We prototyped a taxonomy and scientific framework for the assessment of non-clinical outcomes and data points that measure such outcomes, such as (1) patient-reported or patient-generated digital data via apps or devices, (2) efficiency and quality measurements for practice performance and care delivery, (3) access to care metrics, including the removal of geographic and financial access barriers. Challenges identified in this effort included: (1) Non-clinical outcomes are often collected in an observational manner, and therefore lack comparator (control) groups and are not typically associated with testing a hypothesis. (2) Non-clinical data, particularly when collected in an observational manner, is difficult to attribute to specific causal factors, making downstream analyses difficult, and raising the risk of assuming false correlation. A related finding is that the lack of downstream attribution of impacts often prohibits associating credit or incentives to the components in the health care ecosystem that enable the non-clinical outcomes. These factors, while impossible to completely mitigate in real world data (RWD) scenarios, can potentially be accounted for with methods that apply weighting or discounting to datapoints depending on contextual factors. Next steps include incorporating this taxonomy into a simulation model to capture the value of these outcomes based on available RWD and gaining alignment with stakeholders around the applicability of this taxonomy in policy, health economic, and clinical decision-making.