Healthcare Technology Trends: Pioneering Change Across the Industry
WHITE PAPER
The future of healthcare technology is more promising than precarious
Change was a constant for the healthcare industry in 2023, as hospitals and health systems grappled with ever-evolving challenges and expectations after the pandemic. As last year drew to a close, many healthcare leaders looked ahead to this year and wondered what was in store.
Prior investments into digital transformation have paved the way for new technological possibilities, and forward-thinking organizations have the opportunity to innovate with AI/ML capabilities, data-driven personalized care, and improved workforce management solutions, to name a few. However, challenges abound, and healthcare operators will need a combination of foresight and the right technologies to overcome them – and unlock a future of possibilities for their organization.
Technology trends: Healthcare is poised to reap the benefits of digitization
Over the last few years, healthcare organizations have rapidly modernized to meet new patient demands, comply with evolving regulations, and resolve internal challenges like employee burnout and lack of interoperability. Thanks to these efforts, the industry is in excellent shape to pursue the many technological innovations emerging for the rest of 2024.
Here are our top healthcare technology trends for the future:
1. The maturity of telehealth and the emergence of hybrid care:
The pandemic has normalized virtual care, making it a valuable addition to in-person consultations for both patients and primary care physicians extending the shift to specialty areas as specialists seek enhanced patient access and collaboration.1
As telehealth’s accessibility attracts patients seeking opinions or specialists abroad, hospitals must anticipate the rise of hybrid care, blending virtual and in-person services to meet evolving patient expectations.2 Drivers include value-based care, talent shortages, and favorable regulations. Virtual care ensures cost-effective access globally, enabling hospitals to meet demands without significant financial impact.
As a result, interoperability is a big consideration for healthcare organizations seeking to embrace this trend. Ensuring patient data can flow seamlessly across the organization is critical for delivering personalized and coordinated patient care, and investments in technology and expertise that improve interoperability between healthcare systems must be a priority.
2. Experimental use of Generative AI across the healthcare organization:
Artificial Intelligence has taken business by storm, and soon healthcare. While few healthcare organizations currently utilize AI, almost 58% of healthcare executives reportedly look to adopt Generative AI this year.3 Predictably, adoption will be greatest among larger health systems racing to harness Generative AI to improve outcomes.
What’s behind the urgency? Generative AI promises to enrich patient self-service, make staff more efficient, and accelerate innovation and research. By automating administrative tasks, EHR management, and claims management with some human oversight, Generative AI can streamline operations. Clinically, AI could support manual, time-consuming tasks like prior authorization documentation or discharge procedures, allowing more time for patient care.
To realize AI’s benefits, healthcare organizations must rethink data management, as AI models require sizable, structured data to learn and optimize. It’s early days, so organizations should begin consolidating medical data, improving interoperability, and modernizing formats now to take advantage of this future opportunity.
3. Increased use of predictive analytics for population health management:
The pandemic cemented predictive analytics’ reputation for accurately modeling health and infection risks. Unsurprisingly, healthcare organizations plan implementations for various use cases, from predicting chronic disease progression,4 to clinical decisions, and even risks in readmissions, supply chain, and resource allocation.
A key use case is detecting early chronic disease signs by analyzing social determinants and historical data. By creating prediction models for risks like diabetes or cancer and readmissions for different patients, organizations can devise targeted treatment plans, adopt preventative care, and forecast resource utilization – reducing long-term costs of chronic diseases.
This requires improving system interoperability and data modernization to integrate, ingest, store, and analyze diverse data sources. Executives should pursue solutions providing full, scalable data capabilities so population health initiatives benefit fully from all data.