The nursing profession faces a projected shortage of 78,610 full-time registered nurses by 2026, a crisis technology could alleviate if not for critical systemic barriers. This deficit follows an estimated decrease of 100,000 registered nurses in 2025, deepening the challenge for healthcare systems struggling to maintain staffing and patient care, especially in long-term settings. While Electronic Health Record (EHR) adoption is widespread in long-term care, the interoperable exchange of health information is not routine. This limits technology's full potential. Companies invest in technology to address the nursing shortage, but without foundational interoperability and robust governance, these investments risk becoming isolated solutions. They fail to deliver systemic improvements. The focus must shift from mere adoption to creating integrated, intelligent platforms that empower nurses and optimize care.
EHR Adoption: A Mixed Landscape
EHR adoption varies significantly across long-term care. Eighty percent of nursing homes and home health agencies have adopted EHRs for clinical and business needs, according to ahcancal. This figure aligns with 78% adoption in 2025 for nursing home, skilled nursing, and home health providers, also reported by ahcancal. However, residential care settings show only 26% adoption, with larger facilities having higher rates than smaller ones, This disparity reveals an uneven technological foundation. It complicates efforts to establish a unified digital infrastructure capable of supporting advanced nurse informatics solutions.
The Interoperability and Data Quality Gap
Despite high EHR adoption rates in some long-term care settings, these systems often fail to communicate effectively. This lack of seamless data exchange fundamentally limits advanced technologies' potential to support nursing practice. The interoperable exchange of health information is not routine in long-term post-acute care (LTPAC) settings, according to ahcancal. While EHRs capture data, they frequently fail to share it across platforms or providers. Artificial intelligence in nursing can optimize surgical scheduling, enhance decision support, improve predictive analytics, and streamline workflows, but it depends on clean, structured data, as stated by AORN. This absence of routine interoperable data exchange and the critical need for clean, structured data for AI tools exposes a fundamental weakness in current health information infrastructure. It prevents transforming raw data into actionable intelligence for nurses and patients.
