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Documentation burden, defined as the need to complete unnecessary documentation elements in the electronic health record (EHR), is significant for nurses and contributes to decreased time with patients as well as burnout. Burden increases when new documentation elements are added, but unnecessary elements are not systematically identified and removed.

 Relaxation of laws and regulations around privacy and billing during the COVID-19 pandemic provide expanded opportunities to use telehealth to provide patient care at a distance. Many health systems have transitioned to providing outpatient care via telehealth; however, there is an opportunity to utilize telehealth for inpatients to promote physical distancing.

Patient attribution, or the process of attributing patient-level metrics to specific providers, attempts to capture real-life provider-patient interactions (PPI). Attribution holds wide-ranging importance, particularly for outcomes in graduate medical education, but remains a challenge. We developed and validated an algorithm using EHR data to identify pediatric resident PPIs (rPPIs).

Patient portals provide patients and their caregivers online access to limited health results. Health care employees with electronic health record (EHR) access may be able to view their health information not available in the patient portal by looking in the EHR.

Prior evaluations of automated speech recognition (ASR) to create hospital progress notes have not analyzed its effect on professional revenue billing codes. As ASR becomes a more common method of entering clinical notes, clinicians, hospital administrators, and payers should understand whether this technology alters charges associated with inpatient physician services.

Problem-oriented electronic health record (EHR) systems can help physicians to track a patient's status and progress, and organize clinical documentation, which could help improving quality of clinical data and enable data reuse. The problem list is central in a problem-oriented medical record. However, current problem lists remain incomplete because of the lack of end-user training and inaccurate content of underlying terminologies. This leads to modifications of diagnosis code descriptions and [...]

Medication nonadherence and unaffordability are prevalent, burdensome issues in primary care. In response, technology companies are capitalizing on clinical decision support (CDS) to deliver patient-specific information regarding medication adherence and costs to clinicians using electronic health records (EHRs). To maximize adoption and usability, these CDS tools should be designed with consideration of end users' values and preferences.

The increasing availability of molecular and clinical data of cancer patients combined with novel machine learning techniques has the potential to enhance clinical decision support, example, for assessing a patient's relapse risk. While these prediction models often produce promising results, a deployment in clinical settings is rarely pursued.

Early detection and efficient management of sepsis are important for improving health care quality, effectiveness, and costs. Due to its high cost and prevalence, sepsis is a major focus area across institutions and many studies have emerged over the past years with different models or novel machine learning techniques in early detection of sepsis or potential mortality associated with sepsis.