- Athenahealth’s latest artificial intelligence (AI) advancements are improving patient experience and healthcare delivery by simplifying interactions.
- The EHR provider’s AI-driven features in athenaOne have reduced administrative tasks, leading to an 18% reduction in task completion across users.
- Looking ahead, athenahealth projects AI-generated reports for care meetings and predictive analytics guiding patient care.
- AI tools should be used alongside clinical expertise and human judgement for best outcomes, according to Paul Brient, athenahealth’s chief product officer.
Athenahealth, a leading electronic health records (EHR) provider, has harnessed the power of AI to enhance healthcare delivery, adding efficiency, and making the process more accessible. Paul Brient, the company’s chief product officer, shared the latest innovations at the company, stating that the AI-assisted features in the athenaOne system are already addressing the issue of physician burnout by cutting down time-consuming administrative tasks.
The system has successfully leveraged the ability of AI to proactively identify missing prior authorization information, leading to an 18% reduction in task completion across EHR users. The company is optimistic about future applications, including AI-generated reports to assist caregivers in preparing for care meetings, and the use of generative AI to reach out to patients even beyond the clinical setting.
The potential of predictive analytics is particularly noteworthy. This approach, guided by AI, can help suggest further services and treatment modalities based on patterns observed in similar patients. However, Brient emphasizes the importance of using AI as a supportive tool rather than a replacement for human judgement and clinical expertise. He insists on the need for transparency, explainability, and continuous validation of AI algorithms to ensure their ethical use in healthcare settings.
Athenahealth has been harnessing machine learning and AI technologies for nearly 10 years to streamline EHR and practice management systems. Examples include the automatic selection of insurance packages from the scan of a patient’s insurance card, voice commands for controlling mobile apps, and machine learning applications for document management. Recently implemented features like proactive identification of missing authorization information and pre-drafted patient case responses have shown considerable promise in improving operational efficiency and patient experience.
Future prospects include exploring generative AI potentials and increasing resource allocation efficiency by adjusting schedules to meet patient demand and reduce unutilized appointment slots. This, Brient indicates, could lead to improved operational efficiency, reduced wait times, and improved access to care.