The rapid progress of Artificial Intelligence (AI) is set to reshape various healthcare sectors, with high expectations that AI will deliver on the promise of unlocking patient data for personalized medicine.
While stakeholder concerns about privacy, fraud and ethics accompany this advancement, industry leaders are proactively tackling these issues to meet the needs of all healthcare settings, particularly skilled nursing facilities.
In the dynamic healthcare landscape, AI emerges as a transformative force, offering enhanced efficiency, improved resident outcomes and streamlined workflows. However, the growing integration of AI in healthcare, including medical devices, diagnostic tools and electronic health records (EHR), presents considerations for resident safety, workforce dynamics, legal aspects and impending regulations.
Responsible AI involves integrating ethically developed AI into organizational planning, striving for transparent and accountable solutions that prioritize human interests and comply with applicable laws when developed, scientific standards and developing regulations. Despite its significance, responsible development and use of AI in healthcare is still in its early stages.
Safety first, ethics always
Resident safety is paramount in skilled nursing, and the integration of AI should enhance rather than compromise this core principle. When selecting and deploying AI tools, providers must prioritize solutions aligning with patient safety, considering the impact on clinical workflows, potential for automation of existing workflows, applicability to the population being served and system compatibility.
Ethical considerations, such as data privacy, consent and responsible AI use should be addressed through clear guidelines that ensure resident rights are protected and algorithm biases are identified and rectified. Additionally, providers must ensure residents continue to receive the applicable standard of care when AI is considered, introduced and evaluated.
Some AI systems prove value in enabling more consistent and accurate assessment, significantly reducing the administrative burden that can lead to burnout. However, in the absence of regulations, providers should run AI parallel to accepted standards of care and practices, minimizing legal exposure through informed consent.
Traversing legalities, workforce and efficiency
The unique ability AI holds to analyze data for informed decisions distinguishes it from mere automation, highlighting the importance of a thoughtful approach in leveraging health record information.
The adoption of AI introduces medicolegal considerations, emphasizing the need to understand liability issues. Transparent documentation in AI tool usage is crucial to mitigate legal risks. Collaboration with legal experts to develop robust policies ensures skilled nursing facilities navigate the evolving legal landscape effectively.
Effortless connection: streamlining communication
A vital decision facing skilled nursing facilities is whether to prioritize automation or more advanced AI solutions in their workflows. While automation streamlines routine tasks, AI applications offer in-depth data analysis with the aim of informing clinical decisions.
Facilitating streamlined data for more open communication across healthcare teams on treatment plans and progress ensures improved patient care and increased transparency.
While implementing these systems, providers and executives in skilled nursing facilities must anticipate and adapt to future regulatory changes, ensuring compliance for the continued delivery of safe and effective care.
Elevating resident care through AI integration
The integration of AI can be tailored to specific resident safety and quality initiatives. AI-powered diagnostic tools enhance disease detection, leading to earlier interventions. Collaborating with AI developers ensures customization to meet the specific needs and goals of skilled nursing facilities, contributing to enhanced patient safety and quality of care.
For instance, fall detection systems and predictive analytics for patient admissions are areas of focus in acute and long-term care settings. Interventions that include AI-powered sensors signaling movements strategically placed in patient rooms or common areas analyze movement patterns, detecting behaviors indicative of potential falls. Real-time alerts enable prompt intervention, reducing the risk of injuries.
Leveraging AI algorithms, healthcare facilities can analyze historical patient data to predict potential admissions. This proactive approach allows for optimized resource allocation, improved bed management and enhanced patient flow, leading to more efficient and personalized care.
Integrating AI algorithms with compassionate human interaction is essential for achieving a balanced approach that improves patient outcomes. Informed consent becomes the starting point, ensuring patients are aware of the role AI plays as an assistive tool for providers and their subsequent care decisions.
One exemplary instance of commitment to this balance is the implementation of a lighting solution in long-term care facilities. Using thermal imaging sensors embedded in ceiling lights, this solution keeps staff informed about patient movements during nighttime activities, offering valuable insights and timely assistance.
The integration of AI in skilled nursing facilities holds immense promise for healthcare improvement. While AI holds great potential, its integration requires a careful and responsible approach. Proactive measures, both in regulatory frameworks and individual healthcare practices, are essential to guide the ethical and effective integration of AI in patient care.
Amy Hester, PhD, RN, BC, FAAN, is the Chairwoman and CEO of HD Nursing. She has 25 years of nursing experience including over a decade of med/surge and neuro nursing followed by unit management and hospital administration. In 2015, she earned a Doctor of Philosophy in Nursing Science and has since published and spoken extensively on the subject of falls and injury prediction and prevention.
The opinions expressed in McKnight’s Long-Term Care News guest submissions are the author’s and are not necessarily those of McKnight’s Long-Term Care News or its editors.
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