Abstract
Nationally, over fifty percent of primary care practices in rural, frontier, and inner city locations are not part of organized health care systems and encounter challenges in accessing healthcare services and advanced data access technology that supports decision support, monitoring, and risk prediction. Lack of access to available information is a leading cause of medical errors.

Patients in these practices are among the 68% of the adult population with two or more chronic diseases, often requiring enhanced care to function optimally. Primary care practices are left with limited access to information technology necessary to support risk identification and decision support for enhanced care. Experience from the EvidenceNOW Southwest project confirmed the challenges faced by practices to report even the most basic data, such as blood pressure. These clinical and access to technology challenges place large numbers of patients at an elevated risk of inadequate access and delivery of healthcare, particularly in the insufficient treatment of chronic health conditions. With behavioral co-morbidities.  Quality of life is compromised, outcomes are limited, and there is an increase in avoidable healthcare utilization.

Problem
The current focus of care is on treating advanced and often costly stages of illnesses, yielding outcomes of varying efficacy. A leading factor in medical errors is lack of access to needed data. Insufficient attention is devoted to strategies that prioritize early detection and intervention. This deficiency is compounded by a lack of time to utilize available technologies for upstream identification and interventions aimed at early disease detection and intervention.

Presently, there is no broadly used mechanism to discern high-risk patients within a panel, and a notable underutilization of technology in assessing and intervening with these high-risk patients. Addressing these issues is imperative for improving the overall effectiveness and efficiency of our healthcare system.

The Opportunity
We are implementing a cloud-based, interoperable data platform resulting in AI-supported assessment and interventions aimed at early risk identification, decision support, and rapid intervention for patients. We will utilize the RE-AIMPRISM framework to evaluate practice implementation, patient outcomes, and utilization. This work expands the framework used in the Diabetes Prevention Project, which demonstrated early risk identification and intervention follow-up decreases risk, can impact disease progression, and diminishes overall spending.

Plan
Using a rapid cycle learning approach, we have developed a learning community that includes 5 small independent primary care practices in rural and inner-city locations to determine and implement workflows resulting in early panel-wide identification of high-risk patients or patients moving towards becoming high-risk. This project will use technology and AI in the identification of risk and develop workflows that efficiently manage these processes and then evaluate the impact on clinical, patient-reported, and organizational outcomes, costs/utilization, and responsiveness.

Specific Aims

• Aim 1: Assess whether the use of AI-assisted risk identification and resulting enabled workflows to improve care delivery can be successfully implemented in community-based primary care practices with limited resources./p>
• Aim 2: Determine the effectiveness of this infrastructure to improve workflows that increase efficiency, effectiveness, and utilization within rural primary care practices and optimize care for at-risk patients with multiple chronic diseases and negative social determinants of health.
• Aim 3: Identify patient, provider, organization, and system facilitators and barriers associated with practice and patient outcomes, utilization, and costs.
• Aim 4: Create workflows and AI tools to support efficiencies in the electronic consult programs, automate data retrieval summarization and decision supports, and share those summaries with specialists and primary care providers.