Leveraging Augmented Intelligence to Support Population Health Initiatives



“The AMA House of Delegates uses the term augmented intelligence (AI) as a conceptualization of artificial intelligence that focuses on AI’s assistive role, emphasizing that its design enhances human intelligence rather than replaces it” (AMA, 2024). Leveraging technology and tools will improve healthcare efficiencies, support clinical transformation, and drive evidence-based practice and outcomes. The need for ambulatory community-based care management services in healthcare markets far exceeds the contractual and service capacity of primary care teams. Leveraging AI technology to meet patient demand may be more equitable and efficient than adding additional clinical staffing, depending on the community and population needs (Schario et al, 2022).

Predictive risk modeling has historically been based on high-level demographic variables such as age, payer and having a primary care physician. Diagnoses, hospital and emergency department visits are also quantified into cost and utilization data through paid claims aggregation. By expanding the data to evaluate medications and use social influencers of health, and to include patient demographics such as sex, race, ethnicity, BMI, alcohol use, smoking status, zip codes, labs and tests, we can improve our population specificity for outreach and education (Morco & Sapir, 2023). Leveraging automated and personalized outreach using improved algorithms will support prioritized care team workflows.


Engaging patients requires building trust, establishing a relationship of shared decision making and interactions that connect patients based on their needs. Honoring patient preferences starts with identifying communication preferences. Care managers and care teams can quickly identify patients requiring additional touchpoints using AI tools that provide greater awareness of when patients may require a little extra help. Healthcare has lagged in adapting the technologies that other markets have as a standard way of engaging their customers. Care teams can then better focus on the human side of care and building longitudinal trusting relationships (Newman, 2021).

Depending on a person’s lifestyle and familiarity with technology, two-way texts and chat bots may better suit their communication style. Integrating a chat bot with access to request a live chat within an electronic medical record can provide immediate support to both new and prospective patients. Patients can quickly convert from the chat bot to live support when they need a little extra care and help. Chat bot technology can track patient progress and facilitate earlier interventions. When considering these types of systems, we need to ensure we have 24/7 support (Schario et al., 2022).

Along with electronic medical record patient portals, secure bidirectional two-way texting supports patient engagement for patients whose communication preference is texting. Integrating texting into patient services support can simplify appointment scheduling, decrease wait times on hold with front-desk staff and call centers along with telephonic voice and phone prompting features.

Healthcare systems have an opportunity to improve workflows and efficiencies within the electronic medical record. Best practice alerts based on diagnoses and prescribing patterns can alert clinicians to address recommended guideline-directed medical therapy. Integrated evidence-based practice using algorithms aligns with more precise medicine. Access to national genetic testing results would allow for more personalized care delivery impacting preventive care and prescribing patterns. The ability of a computer to understand written and verbal language is referred to as natural language processing (NLP) and is a component of AI. EMRs having NLP will improve how we communicate and code patient diagnoses.

Leveraging next site of care resources can help care managers find the next best site of care based on outcomes from similar patients. By evaluating outcomes from skilled nursing facilities and home care agencies, we can better match resources with patient needs. Imagine being able to offer more than star ratings for a SNF facility and knowing your patient will receive the best possible outcomes (Young, 2024).

Having the ability to query EMRs for individualized reports to aggregate populations for outreach will decrease the time of having to use external reports. We spend a significant amount of time exporting copious amounts of data, evaluating quality metrics and performing manual outreach. Leveraging bulk outreach tools including messaging with prompts will allow for immediate scheduling of preventive care, e.g., mammograms, colorectal screening, etc.


AI requires our clinicians and care teams to be receptive to change and embrace diverse ways technology can support the delivery of high-quality care. Change management requires that we collaborate and engage our care teams in these discussions to obtain their buy in. We need to continue to be diligent in ensuring that we address biases and ensure vulnerable members within our communities can access care by ensuring that we are delivering personalized care using the right resources at the right time to meet the needs of the communities served.

The use of AI tools will supplement clinicians’ knowledge, skills and competencies to improve our population outcomes, improve medical diagnosis and care and allow professionals to work to top of licensure. As AI continues to transform the healthcare sector, we need to be diligent to safeguard privacy, security, operate with integrity and protect our patient’s information from cybersecurity threats, biases or misinformation (WHO, 2023).


In summary, as technology/AI continues to advance, care management as a key stakeholder needs to have a voice at the table and evaluate our opportunities to continue to improve and expand how we care for our patients, caregivers, families and our clinicians. At the heart of care management is patient advocacy. Accessing innovative technology can improve access to equitable and appropriate resources and decrease health disparities (Newman, 2022).

Lisa Simmons-Fields, DNP, MSA, RN, CCM, CPHQserves as the director, System Population Health and Care Management for Trinity Health System, a multi-institutional Catholic health care delivery system spanning 25 states. In this role, Lisa collaborates with leadership across clinical, business, and community health domains to improve the health and well-being of our patient populations. This work includes standardizing evidence-based integrated care coordination programs that supports high quality patient care across the continuum of care.

Lisa has more than 25 years of experience in care management including ambulatory, acute, and post-acute environments. Lisa previously served as the director of quality for Ascension SE MI Physician Network leading primary care practice transformation, improving quality, and supporting care team redesign.

Lisa holds a Doctor of Nursing Practice, a Master of Science in Administration, is a Certified Case Manager, and a Certified Professional in Health Care Quality. Lisa has been inducted into the Honor Society of Nursing, Sigma Theta Tau International.

Lisa is sought out as a national speaker and author related to her innovative work in population health. She currently serves as President for the Care Management Society of America-Detroit Chapter and the Editorial Board of CMSA nationally. Lisa also serves as co-chair of the Epic Care Management Advisory Board.


American Medical Association (AMA) (2024). Augmented intelligence in medicine. https://www.ama-assn.org/practice-management/digital/augmented-intelligence-medicine.

Morco, K., & Sapir, T. (2023). Growth of remote therapeutic monitoring lands new opportunities for case management. Professional Case Management, 29(2), 63-69.

Newman, M. B. (2021). Artificial intelligence and case management: Embracing transformative practice. Case Management Society of America (CMSA), Highlights 26(6), 304-306.

Powell, S. K. (2023). AI and case management: From artificial intelligence to generative intelligence. Professional Case Management, 28(6) 259-261

Schario, M. E., Bahner, C. A., Widenhofer, T. V., Rajaballey, J. I., & Thatcher, E. J. (2022). Chatbot-assissted care management. Professional Case Management, 27(1), 19-23.

Treiger, T. M. (2023). AI and case management: New tools of our trade. Commission of Case Manager Certification (CCMC) News and Views. 28(6), 296-297.

World Health Organization (2023). WHO outlines considerations for regulation of artificial intelligence for health. https://www.who.int/news/item/19-10-2023-who-outlines-considerations-for-regulation-of-artificial-intelligence-for-health.

Young, M. (2024). Artificial intelligence could help case managers improve efficiency and outcomes. Hospital Case Management, 31(3), 33-48


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