Electronic health records (EHRs) are providing opportunities to revolutionize health care. However, they have brought with them a number of burdens – some expected and others unanticipated. The medical literature is replete with complaints about how important information in patient records is difficult to find, partly due to its absence and partly due to its obfuscation by a proliferation of low-value data in what is called “note bloat”. Other complaints focus on clinical alerting applications, which have proven to issue vastly more false alarms than true ones, leading to alert fatigue which results in clinicians missing the important warnings. Reuse of EHR data for research is also difficult. At this writing, multiple groups (ACT, eMERGE, All of Us, N3C and others) are working to automatically identify patients with COVID-19 (SARS Var-2 infection phenotype) using EHR data – a task that should be trivial, but clearly is not due to suboptimal EHR content and organization. Extensive effort to data has not succeeded in resolving these complaints about EHRs. The premise of the proposed work is that there is information about the clinicians’ thinking that is not readily available or is missing from the EHR and that if it can be added in a structured, computable way EHR improvements can follow. We refer to that information as the “why” of health care: why does the clinician think the patient has a sign or symptom, why is a particular test or treatment being chosen, why is a treatment being discontinued. The proposed work will explore way to represent patient data with this added knowledge to better understand what additional information must be added to the EHR, how the addition might be accomplished, and how the resulting knowledge base might be used. As a first step in usage, we will explore a knowledge- based method for improving the navigation of patient data in an EHR. The project will involve three sequential steps. First, we develop methods to break down the information in a patient record, including information from narrative text (notes), into individual medical entities (such as problems, tests and medications) to create patient data sets (PDSs). Next, we will build on our preliminary studies of the concepts of the clinical care context (patient findings and conditions, diagnostic tests and their results, and therapeutic plans) to add relationships between these entities that convey the clinical reasoning behind them (such as linking a problem to set of possible causes, a test intended to differentiate between the causes, and a treatment chosen on the basis of a test result) to create patient-specific knowledge bases (PSKBs). Finally, we will explore the practicality of creating PKSBs and their usability by creating PDSs and PKSBs for actual patients being seen by medical residents in clinic and providing the residents with a navigational tool that makes use of the knowledge base to help them better understand their patients’ cases. Evaluatio...