Technology, Humanity, and the Path to Better Home Health Care

Home health care in the United States is struggling. We already know that. There are so many compounding pressures on this niche part of the healthcare system. Caregivers in the home setting, namely Home Health Aides, are in increasingly short supply. Who can blame them? They are systemically underpaid, and overworked. Government support of this kind of healthcare is shrinking with every new legislative cycle, squeezing the margins on an already razor thin business. And then, lastly, as we all know, our population is aging. Demand for in-home elder care is growing steadily at 7.5% a year according to Peterson-KFF Health System Tracker (2025). This is driving those who are still working to burn out and call it quits.

It doesn’t take a medical degree to understand that we need innovative ideas and solutions to help tackle these compounding challenges. As a professional working with AI and ML solutions, specifically in the application of AI in Adtech, my instinct is to try and figure out how AI and technology can help solve these problems.

I am tempted to frame the Home Healthcare industry challenges in the same way as Adtech. There are complex processes, and regulations, and a ton of outdated technology in both industries. I want to think “If we can just get our machines in there and automate and upgrade, we should be able to unstick everything. Right?

It’s not that simple. Home healthcare is such an intimate service. Caregivers enter the home of their patients. Services are provided BY human beings TO human beings, often in one-on-one situations. Introducing purely technical solutions is not the right approach.

I’ve spent the last year looking into this question, reading research papers and studies within the US and around the world. It’s been fascinating and inspiring. There are so many great companies and research teams out there working on these challenges.

There was one key observation that I wanted to share. It seems to me that the most sustainable and positive technology solutions out there are those that are deeply humanized. That is to say those that are focused not just on efficiency and cost effectiveness but on improving the lived experience of the human beings in the system, the caregivers, the patients, and their families.   

Algorithmic Care Coordination: Designing Schedules for Humans, Not Just Numbers

For many agencies, scheduling is a game of Tetris—manual, reactive, and often demoralizing for staff. It prioritizes coverage over consistency, leaving caregivers stretched thin and patients cycling through a rotating cast of strangers. That’s where technology offers hope.

Clapper et al. (2023) developed a model-based evolutionary algorithm to optimize home health schedules. Tested on real-world data from a large U.S. agency (3,200 daily visits), the algorithm did more than cut travel time by 11% and overtime by 17%. It increased the continuity index—meaning patients saw the same caregiver more often—by seven percentage points.

This matters because continuity isn’t just a nice-to-have; it’s a lifeline. Continuity builds trust, reduces errors, and helps caregivers feel connected to their work. This algorithm wasn’t just about squeezing costs out of the system, it created schedules that were more predictable for caregivers, less fragmented for patients, and ultimately more humane for everyone involved.

Team-Based Care: Sharing the Weight of Complex Work

If scheduling is the “when” of home health care, team-based care is the “who.” Many older adults need more than a single nurse making occasional visits. They need small, resilient teams—RNs, aides, therapists, and social workers—working together to manage complex care.

Farré et al. (2024) tested this with a randomized controlled trial for frail older adults. Patients assigned to coordinated interdisciplinary teams—including a nurse, physiotherapist, GP liaison, and social worker—experienced a 23% reduction in hospital admissions compared to those receiving standard nurse-led care.

Team-based care distributes the emotional and physical labor of caregiving. It reduces isolation for providers, spreads expertise across disciplines, and gives families confidence that their loved one’s needs are covered from multiple angles.

Technology can supercharge this model—making it easier to share notes across disciplines, align visit schedules, and maintain consistent team rosters, but again, the point isn’t just efficiency. It’s about sharing the weight—ensuring caregivers aren’t carrying it all alone, and patients aren’t left piecing together fragmented care.

Skill Mix Optimization: Matching the Right Person to the Right Task

One of the biggest inefficiencies in home health care isn’t in the number of visits—it’s in who performs them. Historically, home care roles have been rigid: nurses do one set of tasks, aides another. But as patient needs grow more complex, and staffing gets tighter, optimizing the mix of skills within a team is essential.

Van der Kluit et al. (2018) studied Dutch home care teams that broadened their staff skill mix—adding higher-qualified professionals where needed, cross-training aides, and boosting interdisciplinary communication. These teams saw a 7% improvement in patient quality-of-life scores, greater functional independence, and higher staff job satisfaction.

Technology can help here too. Scheduling platforms can assign tasks based on “optimal match”—not just minimum credentialing. That means deploying the lowest-cost, fully qualified provider for each task while reserving higher-credentialed staff for the interventions that truly require their expertise. This isn’t about devaluing aides; it’s about honoring every role—ensuring each caregiver operates at the top of their training and is recognized for their contribution.

Retention Strategies: Stability as the Ultimate Quality Metric

High turnover doesn’t just hurt morale—it compromises safety and drives up costs.

Bergman et al. (2021) found that day-to-day schedule volatility—fluctuations in mileage, caseload, and start times—significantly predicted nurse turnover. For full-time nurses, every 10-point increase in volatility raised the odds of leaving by 15%.

Meanwhile, Gusoff et al. (2025) showed how worker-owned cooperatives cut turnover nearly in half (28% vs. the national average of 50–60%) by giving caregivers autonomy, profit sharing, and a voice in governance.

Retention, in other words, is not just about pay. It’s about stability, respect, and agency. Technology can’t create culture on its own, but it can make retention easier: giving staff predictable schedules, better matching them with patients, and streamlining communication so they feel supported instead of overwhelmed.

Putting It Together: Technology in Service of People

These are just four examples of places where tech can be helpful. There are a great deal of other innovations happening in the space. However, these four —algorithmic scheduling, team-based care, skill mix optimization, and stable scheduling— highlight a key insight:

Technology works best when it’s designed to improve the lived experience of care. We need to take care of our caregivers as well as those they care for. I’m more convinced than ever that we can improve our current model in this regard.

Algorithms can optimize routes, build balanced teams, and match skills to tasks. But as someone who intends to contribute to the future of Home Healthcare, I will take these learnings and make sure to focus on improving the human experience of care—for the caregivers who deliver it, the patients who receive it, and the families who rely on it.

References

• Clapper, et al. (2023). Model-based evolutionary algorithm for home health care routing and scheduling. ScienceDirect.

• Farré, et al. (2024). Interdisciplinary home-care model for frail older adults: A randomized controlled trial. PubMed.

• Van der Kluit, et al. (2018). Diversified skill mix and its effects on quality of care in home health teams. BioMed Central.

• Bergman, et al. (2021). Schedule volatility and its impact on nurse turnover in home health care. PMC.

• Chen, et al. (2024). Continuity of home-care workers and patient outcomes in long-term home health. Oxford Academic.

• Gusoff, et al. (2025). Worker-owned cooperatives and turnover in home health agencies. JAMA Network.

• CHCA Case Study. UCLA Clinical and Translational Science Institute (CTSI).

• Alliance Homecare. Thrive Global. Why Your Organization Should Live and Breathe the People Rule.

• Peterson-KFF Health System Tracker. (2025). “How has U.S. spending on healthcare changed over time?”