Effective AI implementation can boost efficiency, increase profitability, and improve patient care, while poorly implemented AI can introduce systemic errors, bias, and risks.

Fremont, CA: Healthcare delivery is transforming significantly due to digital technology, shifting from volume to value-based payment, focusing on patient-centered care, proactive intervention, outcome data, care coordination, non-facility care, and total cost of care. Digital health investment has increased significantly, with point solutions and infrastructure investments being developed.

Patient Experience (Consumerism)

The patient experience in healthcare is crucial, involving timely appointments, easy information access, and good communication with providers. With technology advancements and increased out-of-pocket costs, healthcare access is becoming more similar to retail consumers. Dissatisfaction can lead to alternative providers and market share loss. To maximize self-service capabilities, an omnichannel "digital front door" is being implemented in contact centers.

Usability (and Interoperability) of Electronic Medical Records

Electronic medical records (EMRs) are crucial for physician documentation, order entry, and closed-loop medication administration. However, they face intuitiveness, usability, and workflow issues, leading to physician burnout. A survey found that EHRs negatively impact patient-provider relationships, clinical workflows, and productivity. Custom dashboards and advanced voice activation technology are used to improve documentation and retrieval.

Revenue Cycle Automation

AI is increasingly used in revenue cycle management (RCM) to enhance efficiency and accuracy and reduce revenue leakage. It includes medical coding, billing, insurance verification, denial management, appeals, etc. However, RCM practitioners face both opportunities and challenges. Effective AI implementation can boost efficiency, increase profitability, and improve patient care, while poorly implemented AI can introduce systemic errors, bias, and risks.

Predictive and Prescriptive Algorithms (AI/ML) and RPA

AI and ML have significantly improved healthcare by identifying needs and solutions faster and more accurately. Robotic process automation (RPA) increases performance accuracy and speed. Clinical predictive and prescriptive analytics are increasingly used in secondary and tertiary prevention, care transitions, behavioral health issues, self-management, and palliative care. Machine learning applications are also being developed in radiology.

Telehealth Is Here to Stay

Telehealth visits among Medicare beneficiaries have increased 63-fold. Compared to primary care providers and other specialists, Telehealth services make up a third of visits to behavioral health specialists. A recent survey showed 23.1% of respondents used telehealth services within the last four weeks. The focus should be optimizing IT platforms for improved care experience and operational efficiency.