This article also appears in Nordic's e-book, "Patient-centered excellence for a new era of care."
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 hyperautomation, 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.1 Through intelligent automation and growing use cases for AI, health organizations can advance what is possible when prioritizing patient-centered initiatives.
Below are 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 quality of care, increases medical errors, and destabilizes patient-physician relationships. Including AI-based workflows to optimize scheduling efficiency is one way that physicians can care for patients without the added consequences of burnout, ultimately improving the quality of healthcare delivery. New Orleans-based Ochsner Health System was able to improve 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 health outcomes and resource needs for patients. Automated workflows leverage these insights to allocate resources more efficiently and target interventions to the patients who stand to benefit the most.
Automated workflows can use predictive analytics and risk stratification to advance patient-centered care through enhanced knowledge-sharing. Here's how automated workflows can use these insights:
- Identify high-risk patients: Automated algorithms continuously analyze patient data to identify individuals who are at high risk for adverse health outcomes or require targeted interventions.
- Prioritize interventions: Automated workflows prioritize interventions based on patients' risk profiles, clinical needs, and preferences, ensuring that resources are allocated efficiently and effectively.
- Tailor care plans: Automated systems generate personalized care plans and treatment protocols for high-risk patients, considering their unique health needs, preferences, and social determinants of health.
- Monitor progress: 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.
- Measure impact: Automated systems can evaluate the effectiveness of individual health outcomes and tie that to the effectiveness of population health management initiatives by measuring key performance indicators (KPIs), such as improvements in health outcomes, reductions in healthcare costs, and increases in patient satisfaction.
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%. The benefits of RPM 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 individual 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' specific needs and preferences. AI-driven RPM solutions enhance patient engagement, adherence to treatment plans, and overall health outcomes by empowering patients to take an active role in managing their health.
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 (EHR) 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 that can be tailored to the unique patient population. These types of 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 become developed and validated.
Automation and AI technologies play a pivotal role 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 enable healthcare providers to ultimately improve health outcomes for each individual patient.
References
1. Hakkennes, S. (2023, December 4). Healthcare provider CIO priorities 2024: Insights for technology and service providers’ product plans (G00801984). www.gartner.com
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