By S. Jackson, MSN, RN, NE-BC, CPHIMS, FSIEN, FFNMRCSI
Introduction
The long-term care (LTC) industry stands at a defining moment.
While hospitals and health systems continue to invest heavily in artificial intelligence (AI), predictive analytics, clinical decision support, population health management, and advanced informatics infrastructures, many nursing homes and skilled nursing facilities remain years behind in technology adoption and digital transformation.
Ironically, the setting that cares for some of the most medically complex, vulnerable, and data-intensive populations often possesses the least mature informatics infrastructure.
The future of long-term care will not be determined solely by staffing ratios, reimbursement models, or regulatory compliance. It will increasingly be determined by how effectively organizations collect, interpret, and leverage data to improve resident outcomes, operational efficiency, workforce engagement, and quality of care.
The question is no longer whether informatics and AI belong in long-term care.
The question is whether long-term care leaders are prepared to embrace them.
The Digital Gap in Long-Term Care
Despite decades of discussion surrounding healthcare technology, long-term care continues to lag behind acute care settings in digital maturity.
Research demonstrates that adoption of advanced Electronic Health Record (EHR) systems in long-term care has progressed much more slowly than in hospitals. Many facilities continue to struggle with inconsistent documentation practices, limited interoperability, inadequate reporting capabilities, and underutilization of available technology.
In many organizations, technology is still viewed primarily as a documentation requirement rather than a strategic asset.
As a result:
- Clinical data remain underutilized.
- Quality improvement efforts are often retrospective rather than proactive.
- Staff spend excessive time documenting rather than analyzing.
- Leaders rely on manual audits instead of real-time intelligence.
- Opportunities for predictive interventions are frequently missed.
Many facilities possess enormous amounts of resident data but lack the informatics capability to transform that data into actionable knowledge.
Data without interpretation is merely information.
Information without action produces no improvement.
Why Informatics Matters
Nursing informatics is the specialty that integrates nursing science, information science, and computer science to manage and communicate data, information, knowledge, and wisdom in healthcare.
At its core, informatics is not about technology.
It is about improving decision-making.
Effective informatics enables organizations to:
- Identify residents at risk for falls, pressure injuries, rehospitalization, or weight loss.
- Detect trends before adverse outcomes occur.
- Improve medication safety.
- Streamline workflows.
- Support regulatory compliance.
- Reduce documentation burden.
- Enhance communication among interdisciplinary teams.
Studies consistently demonstrate that properly implemented EHR systems can improve patient safety, quality outcomes, workflow efficiency, and clinician performance.
The true value of informatics is not in the software itself.
It is in the clinical decisions that become possible because of the software.
Artificial Intelligence: The Next Evolution
Artificial Intelligence represents the next major advancement in healthcare informatics.
AI is not a replacement for clinicians.
It is an enhancement of clinical judgment.
Emerging evidence suggests that AI can assist healthcare organizations by:
- Identifying early signs of clinical deterioration.
- Predicting hospital readmissions.
- Supporting staffing and workforce planning.
- Enhancing care planning.
- Improving medication management.
- Automating routine administrative tasks.
- Reducing documentation burden.
- Supporting clinical decision-making.
For long-term care specifically, AI offers significant opportunities because residents often experience multiple chronic conditions, polypharmacy, functional decline, cognitive impairment, and frequent transitions of care.
These are precisely the types of complex clinical scenarios where predictive analytics can provide substantial value.
Imagine a system capable of identifying residents at highest risk for falls within the next seven days.
Or predicting hospitalization risk before symptoms become obvious.
Or alerting staff to subtle changes in resident condition that might otherwise go unnoticed.
This is no longer science fiction.
The technology already exists.
The challenge is adoption.
The Greatest Barrier Is Not Technology
Many leaders assume that budget limitations are the primary obstacle to innovation.
In reality, the greatest barrier is often education.
Research consistently identifies inadequate training, workforce readiness, knowledge deficits, and change management challenges as major barriers to successful technology implementation.
Organizations frequently invest hundreds of thousands of dollars in software yet invest very little in teaching people how to use it effectively.
Technology implementation often follows a predictable pattern:
- Purchase software.
- Provide minimal training.
- Expect immediate adoption.
- Become frustrated with low utilization.
- Conclude the technology failed.
The technology rarely fails.
The implementation does.
Education must be viewed not as an expense but as an operational strategy.
Without education:
- Staff become resistant.
- Confidence decreases.
- Workarounds develop.
- Documentation quality deteriorates.
- Data integrity suffers.
- Return on investment diminishes.
Evidence suggests that multimodal education approaches—including simulation, peer mentoring, coaching, hands-on learning, and ongoing reinforcement—produce better outcomes than one-time classroom instruction alone.
Why Leadership Matters
Technology adoption is fundamentally a leadership challenge.
Successful informatics programs require leaders who understand that digital transformation is not an IT initiative.
It is an organizational strategy.
Nurse leaders must develop competencies in:
- Data analytics.
- Clinical informatics.
- AI literacy.
- Change management.
- Workflow redesign.
- Digital governance.
- Technology evaluation.
Studies demonstrate that when nurse leaders receive informatics education, they become more effective at guiding staff, improving adoption, enhancing utilization, and supporting patient safety initiatives.
Leaders who cannot interpret data cannot effectively lead in a data-driven environment.
Future healthcare leaders will need to be as comfortable reviewing dashboards as they are reviewing staffing schedules.
Operationalizing Informatics in Long-Term Care
For long-term care organizations seeking meaningful transformation, several strategies are essential.
1. Establish Informatics as a Strategic Priority
Technology initiatives should align directly with organizational goals.
Examples include:
- Reducing falls.
- Improving survey outcomes.
- Lowering rehospitalizations.
- Enhancing staff retention.
- Improving resident satisfaction.
Technology must support strategy—not exist separately from it.
2. Develop Informatics Champions
Every facility should identify and develop:
- Nurse informaticists.
- Super users.
- Clinical technology champions.
- Data-driven quality leaders.
These individuals become translators between clinical practice and technology.
3. Invest in Education Before Implementation
Training should occur:
- Before go-live.
- During implementation.
- After implementation.
- During onboarding.
- As part of annual competency validation.
Education must become continuous rather than event-based.
4. Build a Data-Driven Culture
Leaders should routinely review:
- Falls trends.
- Infection rates.
- Rehospitalizations.
- Antipsychotic utilization.
- Pressure injury rates.
- Staffing metrics.
- Quality measures.
Staff should understand not only what the numbers are but what they mean.
5. Integrate AI Responsibly
AI should begin with targeted, high-value applications:
- Predictive fall-risk monitoring.
- Rehospitalization prediction.
- Staffing optimization.
- Documentation assistance.
- Clinical decision support.
AI should augment clinical judgment—not replace it.
6. Prioritize Data Quality
Poor data produces poor outcomes.
AI systems are only as reliable as the data they analyze. Research consistently highlights data quality and standardization as foundational requirements for successful AI deployment.
Organizations must emphasize:
- Documentation accuracy.
- Standardized workflows.
- Consistent assessment practices.
- Timely data entry.
The Return on Investment
Organizations that successfully integrate informatics and AI can expect benefits beyond regulatory compliance.
Potential outcomes include:
Clinical Outcomes
- Reduced falls.
- Reduced hospitalizations.
- Earlier intervention.
- Improved medication safety.
- Better chronic disease management.
Operational Outcomes
- Increased efficiency.
- Reduced duplication of work.
- Better resource allocation.
- Improved staffing decisions.
Workforce Outcomes
- Reduced burnout.
- Improved workflow satisfaction.
- Increased confidence.
- Enhanced professional development.
Organizational Outcomes
- Improved quality metrics.
- Stronger survey performance.
- Better financial performance.
- Increased competitive advantage.
Informatics
AI and informatics should not be viewed as technology projects.
They are workforce transformation strategies.
Conclusion
Long-term care has historically been asked to do more with less.
Less staffing.
Less reimbursement.
Less technological investment.
Yet the residents served in these settings are among the most clinically complex individuals in healthcare.
The future of long-term care depends on moving beyond paper-based thinking in a digital age.
Artificial intelligence and informatics are no longer optional competencies.
They are essential capabilities.
However, technology alone will never transform an organization.
Education transforms people.
People transform practice.
Practice transforms outcomes.
The organizations that thrive in the coming decade will not necessarily be those with the most technology.
They will be those that invest in developing leaders and frontline staff who know how to use technology strategically, ethically, and effectively to improve resident care.
The future of long-term care will belong to organizations that understand a simple truth:
Technology is the tool. Informatics is the strategy. Education is the catalyst. Leadership is the driver.
Selected References
- Wei Q. et al. (2025). The Integration of AI in Nursing: Addressing Current Applications, Challenges, and Future Directions.
- American Nurses Association. Advancing Nursing Practice Through Artificial Intelligence (2025).
- Ramadan O. et al. (2024). Facilitators and Barriers to AI Adoption in Nursing Practice.
- Wilks J. (2025). Best Practices in Training Nurses to Use Electronic Health Records.
- HIMSS. Electronic Health Record Training for Nurse Leaders.
- Provenzano M. et al. (2024). Electronic Health Record Adoption and Its Effects on Healthcare Staff.
- McKnight’s Long-Term Care News (2024). Long-Term Care Providers’ EHR Adoption Has Stalled Out.
- Cherry B. et al. Factors Affecting Electronic Health Record Adoption in Long-Term Care Facilities.
- Wong KLY et al. (2024). Adoption of AI-Enabled Robots in Long-Term Care Homes.
