By Simon Trussler
Historically, clinicians have had very limited visibility into how patients with chronic conditions were doing in home or institutional care settings; they tracked patients’ status mainly during office visits or as a result of acute episodes. Preventive care and education initiatives, together with outbound nurse calling programs, could help reach the at-risk population in between visits, but they lacked the up-to-date patient data required for precise targeting of the most vulnerable population.
New technologies now offer us the prospect of opening up this “black box” of home-based care. Through low-cost remote monitoring of both care plan adherence and key health and movement indicators, they offer the potential for real-time “course corrections”; some of which can safely be patient initiated. The additional data also allows the care team to intervene earlier when the care plan is not working, helping to avoid expensive acute episodes and transitions to higher-cost care settings.
The challenge is the sheer volume of data that these kinds of tools can gather; we risk overwhelming hard-pressed clinician teams. This is where artificial intelligence (AI) approaches can make a real contribution. AI structures the flood of data in a more organized way, contextualizing it to each patient’s health situation and identifying clear next steps for both patients and doctors.
Senscio Systems has developed Ibis, a remote solution for behavior, health, and movement monitoring, to address this need. Ibis is built on a sophisticated patent-pending AI engine that efficiently stores and interprets the huge amounts of information generated by daily care plan activities and responses for large numbers of patients. The AI engine runs in real time, summarizing the data, learning patterns and deviations, and triggering alerts and protocols when needed. It also helps identify relative vulnerability and care gaps across the patient population, as a guide to allocating scarce care support resources.
This type of AI has several specific benefits for complex care management:
The combination of comprehensive health and behavioral monitoring with AI analytics provides an infinitely richer toolset for the management of complex conditions than programs based on retrospective claims-based data. It allows patients to effectively manage their own care, providing guidance only when necessary, while also creating a new window for clinicians to assess how their patients are doing in real time, with the ability to intervene before health issues escalate to the level of acute care.