Title: Closing the Divide: The Potential and Accountability of AI in Home Health Care

By Dr. Sreeram Mullankandy

Having moved from clinical practice into health technology leadership, I’ve witnessed firsthand the significant transformations altering the health care landscape. A crucial understanding I’ve gathered is this: the future of health care extends beyond the confines of hospitals or clinics—it is increasingly being formulated within the patient’s residence.

However, this changing model presents a paradox. While we adopt artificial intelligence (AI) and embrace digital evolution in medicine, we may inadvertently neglect the very groups that stand to gain the most from these innovations—unless we establish equitable technology solutions as a core principle.

The Importance of Home Health Care

Home-based care is evolving from a mere option to a critical necessity, especially for patients who:

– Have restricted access to conventional care facilities
– Encounter socioeconomic difficulties
– Require management for chronic conditions
– Are discharged prematurely due to hospital capacity constraints

These patients shouldn’t be labeled as “non-compliant” when they miss appointments or struggle to adhere to treatments—they may be a mother of three without childcare, a rural farmer lacking internet access, or an elderly patient unable to maneuver up a lengthy staircase.

Conventional electronic health record (EHR) systems frequently neglect these contextual factors. This gap is not just a technical issue—it’s also a human one, with social determinants of health (SDOH) influencing up to 80% of health outcomes.

Examining the Unseen: Social Determinants of Health in the Home

During the development of an EHR designed for home health, I discovered significant blind spots within conventional systems. Data related to social risks—such as housing instability, language obstacles, or food access—were often inadequately documented or entirely omitted.

In a hospital setting, it’s easy to disregard a moldy ceiling or a lack of electricity in a patient’s home. Yet during a home visit, these issues become painfully apparent. Unfortunately, clinical documentation rarely reflects them accurately, and most EHRs do not effectively prompt for this information. Consequently, we get an incomplete view of the patient, resulting in less efficient care and often increased costs.

AI as a Catalyst for Equity in Home Health

Despite apprehensions regarding AI in some clinical environments, when thoughtfully designed, artificial intelligence can serve as a vital instrument for promoting health care equity—especially in home settings. Here’s how:

1. Uncovering Hidden SDOH Patterns

Traditional screening tools require patients to disclose sensitive details voluntarily. AI, through natural language processing (NLP) of visit notes and structured data, can detect unexpressed needs. For instance, analyzing caregiver notes might uncover signs of food insecurity or social isolation—insights easily overlooked in time-constrained clinical conversations.

Research indicates that NLP can recognize SDOH elements within unstructured clinical text with up to 87% accuracy. Such capabilities enable timely interventions and help avert patient decline.

2. Overcoming Language and Literacy Challenges

A staggering number of Americans—over 36 million—experience limited English proficiency or inadequate health literacy. AI-enhanced translation tools and user-oriented educational platforms can dismantle these obstacles. Beyond simple translation, these systems can customize information based on patients’ understanding levels and identify when instructions are unclear, prompting clinicians to provide clarification.

3. Supporting Community Health Workers

Community Health Workers (CHWs) often act as the crucial link in underserved communities. AI can enhance their effectiveness by:

– Streamlining documentation
– Suggesting personalized resources
– Providing real-time decision support

This allows CHWs to devote more time to what is truly important: listening to, advocating for, and assisting their patients.

Caution Against Bias: Challenges in AI Development

Despite its potential, there is a troubling side: poorly conceived AI can exacerbate existing health inequities. Many algorithms are trained on data sets that fail to adequately represent minorities or perpetuate historical biases. An AI model optimized for suburban, middle-income patients may significantly underperform when applied to urban, low-income communities.

To ensure equity, any AI deployment in home health care must:

– Undergo ongoing fairness assessments
– Utilize representative data sets
– Monitor model outputs across different demographic groups
– Involve diverse perspectives in the development phase

Financial Considerations and the Economic Argument for Equity

While the ethical imperative is compelling, the economic rationale is equally persuasive. Ignoring social determinants leads directly to preventable hospitalizations and soaring costs. Research indicates that addressing SDOH could save the U.S. health care system $1.7 trillion over ten years.

However, a significant hurdle lies in aligning incentives. Current fee-for-service models do not incentivize preventive or supportive care delivered at home. Although value-based care frameworks are starting to develop, their adoption in the health tech sector remains slow. For success, technology companies must create sustainable financial models that are capable of scaling in underserved communities and safety-net environments.

Designing for Justice: Guidelines for the Future

As we move towards a digitally empowered