AI-enabled care

Three ways automation and AI
advance patient-centered care

By Glenn David, Director of Digital Health Data and Analytics, Nordic

Automation is one of the fastest-growing areas for investment in healthcare today. According to Gartner, 57% of healthcare providers in 2024 are increasing investments in hyper-automation, a business-driven approach to automate as many processes as possible. As automation systems are intricately intertwined with AI technologies to enhance their capabilities, 77% of providers are also increasing investments in AI. Through intelligent automation and growing use cases for AI, health organizations can advance what is possible when prioritizing patient-centered initiatives.

This section outlines three areas that contribute to advancing patient-centered care and directly benefit from automation and AI deployment. Healthcare leaders should recognize these benefits and their far-reaching effects.

AI-enabled
workflow automation

Most automation initiatives focused on patient-facing workflows before and during the COVID-19 pandemic. As a result, the market for patient engagement platforms is strong, with a wide range of options depending on specific features.

While the patient’s needs continue to be a target for automation, the focus has shifted toward the clinician and care team in the face of persistent staffing shortages and increasing labor costs. Success with workflow automation requires transparency and interpretable models that have undergone rigorous validation. This type of automation needs to gain the trust of clinicians to garner long-term use and adoption. Health IT solutions deployed to support workflow automation depend on how the enterprise prioritizes use cases. For example, AI-based scheduling optimization can reduce clinician burnout and possibly increase physician engagement.

Medical research continues to show that physician burnout directly affects the quality of care, increases medical errors, and destabilizes patient-physician relationships. Including AI-based workflows to optimize scheduling efficiency is one way physicians can care for patients without the added consequences of burnout, ultimately improving the quality of healthcare delivery. New Orleans-based Ochsner Health System enhanced anesthesiologists’ self-reported engagement by 27% directly after implementing an AI-based scheduling system.

Predictive analytics and risk stratification

Advanced analytics techniques, such as predictive modeling and risk stratification, can forecast future patient health outcomes and resource needs. Automated workflows leverage these insights to allocate resources more efficiently and target interventions to the patients who will benefit most.

Automated workflows can use predictive analytics and risk stratification to advance patient-centered care through enhanced knowledge-sharing.

Automated algorithms continuously analyze patient data to identify high-risk individuals for adverse health outcomes or requiring targeted interventions.

Automated workflows prioritize interventions based on patients' risk profiles, clinical needs, and preferences, ensuring that resources are allocated efficiently and effectively.

Automated systems generate personalized care plans and treatment protocols for high-risk patients, considering their unique health needs, preferences, and social determinants of health.

Automated workflows track patients' progress over time and adjust interventions as needed based on changes in their health status, adherence to treatment plans, and response to interventions.

Automated systems can evaluate the effectiveness of individual health outcomes and tie that to the efficacy of population health management initiatives by measuring key performance indicators, such as improvements in health outcomes, reductions in healthcare costs, and increases in patient satisfaction.

Here's how automated workflows can use PCC insights:

Patient engagement and education
through remote monitoring

Remote patient monitoring (RPM) usage has skyrocketed in the last several years. Between 2019 and 2022, top RPM procedure claims volumes increased by nearly 1,300%. RPM's benefits for patients include constant and consistent screening of biological and physiological data markers that help tell their individual stories of health instantaneously and over time.

AI-driven RPM platforms can engage and educate patients by providing personalized health insights, feedback, and recommendations based on their health data. These platforms may use chatbots, virtual health assistants, or interactive tools to deliver health education materials, medication reminders, self-care tips, and lifestyle recommendations tailored to patients' needs and preferences. AI-driven RPM solutions enhance patient engagement, adherence to treatment plans, and overall health outcomes by empowering patients to manage their health actively.

AI integration with remote patient monitoring requires a multi-layer process that can begin with a specific medical discipline and diagnosis. For example, diabetes type II diagnoses and hypertension diagnoses are both top use cases for implementing RPM. The American Medical Association identifies further existing and potential use cases, such as generating triage data from RPM and electronic health record systems to identify cardiology patients at the highest risk for disease progression.

IT partners should work with individual departments to develop the criteria for real-time analysis, proactive risk management, and personalized interventions tailored to the unique patient population. These partnerships can also plan for long-term data capabilities, such as integrating RPM workflows into command centers and adding more complex AI technologies as they develop and validate.

Automation and AI technologies are pivotal in orchestrating healthcare operations and clinical delivery to prioritize the individual patient's needs. By streamlining administrative tasks, optimizing workflows, and personalizing treatment plans, these technologies ensure that patients receive tailored care that is efficient, effective, and patient-centered. Through real-time data analysis, predictive analytics, and personalized decision support, automation, and AI ultimately enable healthcare providers to improve health outcomes for each patient.

Glenn David is a healthcare data and analytics executive with over 20 years of management consulting experience. He has expertise in advanced analytics, long-term strategic roadmaps, and the implementation of large-scale technology-enabled solutions. Glenn successfully leads projects across the data lifecycle continuum, including data strategy, architecture, modeling, engineering, and visualization. He is passionate about his track record of delivering results for healthcare payer and provider organizations, including large health systems and academic medical centers.

Hakkennes, S. (2023, December 4). Healthcare provider CIO priorities 2024: Insights for technology and service providers’ product plans (G00801984). www.gartner.com

GARTNER® is a registered trademark and service mark of Gartner, Inc. and/or its affiliates in the U.S. and internationally and is used herein with permission. All rights reserved.

TAKE A POLL | Industry perspectives on AI

Which area of PCC do you think AI has the most potential to improve?

Diagnosis and treatment planning
Patient monitoring and follow-up
Administrative tasks and workflow efficiency
Other

What is the biggest challenge of integrating AI in PCC?

Privacy and data security
Accuracy and reliability of AI systems
Impact on patient-care team relationships
Cost and accessibility of AI technologies

How soon do you believe AI will become a standard part of PCC in healthcare?

Less than five years
Within the next six to 10 years
More than 10 years
Not sure