Reflections on interactive visualization of electronic health records: past, present, future

Abstract

In the early 2000s, the transition to paperless documentation of patients’ health data begun at large scale, with the introduction of Electronic Health and Medical Records (EHR and EMR, respectively). This constituted a paradigm shift in how patient data was stored and exchanged among institutions. The impact of the so-called “Electronic Health Revolution” was significant. Standardization of personal health data allowed for a more uniform definition of diagnoses and their ensuing clinical process, with fewer mistakes in diagnosis and treatment, and a more reliable application of medical guidelines. For instance, in the United States (US), patients now have control over their information, with more mandated electronic access. Recent studies showed that online medical records by US adults doubled over the last 8 years. Simultaneously, a new generation of smart, affordable, and wearable devices, such as smartwatches, has emerged. These devices generate fine-grained and continuous data about the health status of their users, with minimal discomfort, eliminating the need for specialized equipment. The rapid evolution of Artificial Intelligence (AI) technologies is about to significantly impact healthcare as well. AI technologies present opportunities and challenges for both physicians and patients. AI models recognize patterns in complex datasets, potentially identifying a broader range of disease progression patterns that might not be immediately apparent to clinicians or patients. However, the inherent “black-box” nature of AI has slowed its adoption, as healthcare professionals often struggle to evaluate the underlying process that led to the AI recommendations. In essence, while it can be impressive what AI models predict, concerns remain about why the AI produces a particular output, and how. The considerable lack of transparency impedes trust-building, such that “the doctor just won’t accept that,” calling for explainable AI output.

Ähnliche Arbeiten