Ahead of The Curve:
2025 Innovation Imperatives for Healthcare

Many industries, including healthcare, are caught in the same balancing act: Leaders know AI can drive measurable operational efficiency in the near term and entirely new business models and revenue streams in the long term. At the same time, pursuing automation and efficiency at all costs threatens to undermine the human side of the customer experience. Given this dynamic, it’s no surprise that the healthcare leaders we speak to are laser-focused on getting their AI strategies right. They understand that, in the coming year, AI and data will be a throughline in all the major industry advancements, whether it’s building a better technology foundation for home health care, accelerating the claims process, enabling whole patient records, or pursuing alternative payment models. As we work with healthcare clients to advance their business strategies in the coming year, we expect much of our work to intersect with the following sea changes.

1. AI for patient care

As GenAI revolutionizes the entire healthcare ecosystem, one of the most promising areas is AI-powered patient care. AI is increasingly becoming integrated into tools providers and patients use, offering benefits such as fully automated appointment scheduling, chat bot-enabled follow-ups, and virtual assistants for tasks like post-acute follow-up, medication adherence, and chronic condition management. Moreover, AI-generated clinical encounter documentation can now capture and analyze the most important aspects of each doctor-patient conversation, placing the clinical assessment and symptom data directly into the patient’s electronic medical record. This has the potential to significantly enhance patient/clinician engagement and provide a vastly improved patient and provider experience, while also reducing clinician burnout by eliminating the burden of manual documentation. AI-assisted Clinical Decision Support (CDS) systems can already capture discreet datapoints from these conversations and can combine that data with current patient clinical information and medical history to provide treatment suggestions in real-time. These tools hold the promise of improving the efficiency and profitability of care delivery organizations. However, it's important to note that AI for patient care is not without risks that need to be managed, particularly around compliance (HIPPA, etc).

2. AI for payers

Generative AI brings new tools and unprecedented solutions to the payer value chain. From claims operations to compliance, workflow optimization, communications, and personalization, GenAI is genuinely changing the game. However, one emerging concern is the rise of AI-generated automated appeals processing. Payers must build their GenAI capabilities to proactively manage these new appeal volumes at scale as the appeal ecosystem evolves into a computerized submission and triage process. A leading payor customer, for example, has experienced a more than 300% increase in appeals volumes in less than a year, citing issues with third-party vendors that automate batch submission every single day until they are acted upon. AI can assess these claims at the point of entry, whether submitted via fax or discrete data portals and identify duplicate or low-risk appeals that can be removed from the queue, freeing up time for clinicians to act on prioritized appeals that directly impact patient care.

3. New approaches to home health care

Patients and providers alike are increasingly seeking to move care from the hospital setting to care venues that can deliver effective, efficient care at a lower cost. Not only is hospitalization costly, but data show that the downsides of extended hospital stays are significant, especially from a patient perspective.  Because connected devices can now transmit essential patient information directly to the provider, remote care (supplemented as needed by a daily in-person visit) will continue to accelerate. As we move toward this “smart hospital at home” era, there’s enormous potential for startups and healthcare system innovators to collaborate on remote care technologies. These technology interventions need to consider clinical issues and the patient’s social context. By considering these social determinants of health, providers can potentially mitigate some of the challenges that limit the concept of “hospital-to-home.” For example, even for patients who lack home internet access, user-friendly mobile applications can leverage GenAI to customize caregiver instructions for specific regions, populations, and languages, improving the quality of at-home care across all demographics. 

4. The promise of predictive analytics

Healthcare organizations are understandably concerned that the urgency to adopt predictive analytics could result in uncertainty and chaos. The march of analytics is the next transformational force in healthcare, and there is every reason to believe that the opportunities far outweigh the risks. Data and analytics are already reshaping medical care, and this transformation will continue across the care continuum, from the discovery phase (newly diagnosed diseases, new treatments, and new care plans) through successful patient recovery. Healthcare payers can identify and promote early interventions for high-risk patients with these same tools. 

5. Significant steps toward the “whole patient record” 

When it comes to patient data, we know numerous non-clinical factors can impact patient outcomes, and even clinical data is too often siloed in disconnected healthcare systems. At the same time, healthcare companies are asking how to better incorporate the IoT data from the many connected devices that now monitor our every move. The data volumes are massive, but the challenge isn’t insurmountable. With 5G connectivity and better data exchange frameworks, achieving a “whole patient record” is becoming increasingly possible and will dramatically advance population health management. Yes, sometimes more data means more noise — but thanks to AI advances, healthcare companies will have new capabilities to interpret this data in a way that leads to better outcomes. As we come closer to creating the whole patient record, we must continue to provide the protections necessary to prevent unauthorized use and build capabilities to ensure that the patient is the valid owner of their patient record, no matter where it is stored or by whom.