Second-Generation Digital Health Platforms: Placing the Patient at the Center and Focusing on Clinical Outcomes

Abstract

Artificial intelligence (AI) digital health systems have drawn much attention over the last decade. However, their implementation into medical practice occurs at a much slower pace than expected. This paper reviews some of the achievements of first-generation AI systems, and the barriers facing their implementation into medical practice. The development of second-generation AI systems is discussed with a focus on overcoming some of these obstacles. Second-generation systems are aimed at focusing on a single subject and on improving patients' clinical outcomes. A personalized closed-loop system designed to improve end-organ function and the patient's response to chronic therapies is presented. The system introduces a platform which implements a personalized therapeutic regimen and introduces quantifiable individualized-variability patterns into its algorithm. The platform is designed to achieve a clinically meaningful endpoint by ensuring that chronic therapies will have sustainable effect while overcoming compensatory mechanisms associated with disease progression and drug resistance. Second-generation systems are expected to assist patients and providers in adopting and implementing of these systems into everyday care.

Keywords: algorithms; artificial intelligence; complex systems; precision medicine; variability.

Figure 1

Establishment of a second-generation AI platform. Depiction of a closed-loop platform where a patient with a chronic disease is placed at the center. The closed feedback loop is responsive to the effect of therapy on clinical outcomes. The algorithm adapts itself dynamically to the effects of therapy on the endpoint. The algorithm quantifies personalized variability patterns and implements them into therapeutic regimens. In parallel, the algorithm generates an insightful database which evolves from analyzing outputs on the endpoint. The dataset collects the relevant variability-based quantifiable parameters which are associated with improved clinical outcomes. The dataset is continuously being updated based on the clinical response of each of the treated subjects.