Electronic Health Reporter

By Michelle Barlow, RN, BSN, Director of Product Management Home Health, Homecare Homebase.

Home-based care clinicians are under growing strain, with recent reports showing that 40% of nurses intend to leave the workforce by 2029. Time lost on redundant administrative tasks only adds to this strain.

Care providers spend significant bandwidth on ineffective documentation, with 79% reporting time lost to unproductive charting, time that could otherwise be spent with patients.

In home-based care, time spent on inefficient administrative work can lead to reduced visits, delayed appointments, and fewer patients reached. As agencies work to relieve that burden, many are looking for practical ways to return time to clinicians without disrupting care delivery

Emerging software designed for healthcare, such as AI-driven clinical documentation platforms, can offer a path forward. Still, providers in highly regulated settings remain cautious about adopting tools that interact with sensitive patient information. In home-based care, adoption will depend not just on what AI can do, but on whether it is implemented with the right safeguards. Home-based care agencies should therefore implement AI that prioritizes compliance and clinician judgment, while reducing documentation burden.

Reimagining Documentation to Restore Time for Care

In home-based care, workforce shortages are a contributor to access to care limitations. Since documentation can play a significant role in clinician burnout, integrating AI documentation tools into an agency’s current software stack may help providers prioritize care and open up more capacity to help new patients. Doing so may help avoid an infrastructure overhaul that would further disrupt care delivery.

When effectively layered, these systems can save up to 30%-50% of a nurse’s bedside documentation time by generating draft language or structured suggestions for the Outcome and Assessment Information Set (OASIS) responses based on contemporaneous clinical inputs. AI can also play a constructive role in the revenue cycle, identifying missing claim information and automating eligibility, freeing more time for hands-on patient care.

Yet, there are certain concerns around whether AI will draft documentation for clinician review or independently determine a response. The former approach, where the clinician remains responsible for evaluating, editing, and confirming the final record, is what is needed in today’s healthcare environment to maintain high-quality, individualized care as well as regulatory compliance. Without this emphasis on accountability, automation will lack effectiveness.

Balancing Automation with Accountability

Given patient privacy concerns and stringent HIPAA regulations in decentralized environments, many agencies hesitate to adopt AI that interacts with clinical record systems. Organizations may delay pilots or even pause the adoption of low-risk tools altogether due to regulatory concerns, which can stall the use of workflow-support tools that could ease documentation burden. To address these concerns, agencies should implement solutions that focus on compliance. These approaches should include deliberate safeguards that promote transparency and preserve clinician oversight.

AI in home-based care must support clinician-led, human-in-the-loop processes to maintain compliance. This often looks like care providers monitoring AI-generated summaries and outputs to determine whether they are consistent with source data, suppress unsupported inferences, and avoid hallucinations not grounded in clinical records. Providers are expected to evaluate the suggested documentation content, make any necessary modifications, and confirm the final response.

These systems should also be based on interoperable, clinically meaningful data points. In home-based care, timely visibility into events such as hospital admissions, discharges, and other material changes in patient status. Without that access, AI may be limited in its ability to support preventive intervention or care coordination. At the same time, agencies need to ensure patient data is handled in ways that protect privacy and support compliance, while reducing biased recommendations and security breaches. When these conditions are met, organizations can help improve output accuracy, strengthen audit defensibility, and maintain consistency across records, all without compromising clinician judgment.

Putting Clinicians First in the Age of AI

In home-based settings, patients are medically fragile and reliant on coordinated assistance. Even slight disruptions in timing or service could trigger avoidable hospitalization. Home-based agencies cannot afford the effects of staffing shortages caused by the nurse burnout epidemic. To elevate patient care, home-based organizations should prioritize integrating solutions that ease administrative burden where appropriate and return time to the clinicians delivering care.

Integrating these intelligent systems is not about replacing clinical judgment, but about supporting agencies with tools that reduce unnecessary documentation burden and help reduce burnout. By implementing human-in-the-loop practices alongside AI outputs, home-based agencies can better prioritize provider well-being and, in turn, help patients receive the care they need.