Hitachi Digital Services Disrupted Series: Population Health & Deviceless Remote Patient Monitoring

Speakers List:

Patrick Burton: SVP, Business Development, Lightbeam Health Solutions
Paul Watson: VP, Global Head of Healthcare and Life Sciences, Hitachi Digital Services
Eratha Poongkuntran: Associate Director, Healthcare & Life Sciences, AVASANT
Vitor Domingos: Lead Solution Architect, Hitachi Digital Services

Here’s what you missed:

1. The Shift Towards Proactive and Preventive Care:

Innovation in digital solutions and AI can transform healthcare management on a large scale, moving from reactive treatment to proactive and preventive care.

• Patrick Burton of Lightbeam Health Solutions highlighted that non-clinical risk factors (social determinants of health) are often overlooked but have a significant impact on health and clinical outcomes. These include where patients live, employment status, social support, access to food, and transportation.
• Lightbeam has published outcomes using AI that can predict the likelihood of future hospitalization with approximately 75% accuracy using only a patient’s age, gender, race, and zip code, emphasizing the importance of social factors.
• Data fragmentation across disparate healthcare systems creates significant obstacles to delivering effective care at scale. It forces manual data reconciliation, leading to inefficiencies and lost time that could be spent engaging with patients.

2. Global Healthcare Trends:

Eratha from AVASANT identified key global trends:

• Aging population and chronic disease burden: Leading to increased demand for elderly care, chronic disease management, long-term services, home healthcare, and geriatric specialists.
• Healthcare workforce burnout and shortage: Evident globally, creating vulnerabilities in workforce planning, particularly in nations relying on international healthcare professionals.
• Importance of social determinants of health: Playing a vital role, with AI increasingly able to predict health issues based on these factors.
• Lessons from other countries like Japan, Germany, and Scandinavian nations include implementing preventive health programs, lifestyle modification, integrated elderly care models, and unified care approaches with coordinated primary care networks and social services.
• Technology, such as real-time location systems and AI, is aiding in scheduling, diagnostics, and shortening administrative cycles.

3. UK Healthcare Landscape and the Role of Integrated Care Systems (ICS):

Paul Watson from Hitachi Digital Services highlighted the formation of ICS as a significant recent change, fostering better coordination between healthcare providers, local government, social care, and emergency services.

• ICS are also taking a leadership role in coordinating digital initiatives at a regional level, aiming for common strategies in areas like ERP platforms.
• The NHS still faces a fragmented landscape of systems and processes with a lack of integration, hindering the scaled application of digital solutions. Structural and people aspects need to be addressed alongside digital transformation.
• National initiatives like the Federated Data Platform aim to solve data integration challenges and enable large-scale interventions by providing richer data for programs to build upon, ultimately leading to better health outcomes.

4. The Transformative Impact of Artificial Intelligence (AI) in Healthcare:

• AI is currently being targeted for specific applications like film reading in diagnostics and national screening programs.
• The opportunity for AI lies significantly in improving productivity and efficiency, freeing up capacity for healthcare professionals by reducing administrative burdens.

 
Examples of AI applications include:

• Automation of administrative tasks: Medical coding, billing, patient data entry, medical documentation (e.g., Microsoft’s Dragon co-pilot for digital scribing).
• Improved medical coding and billing: Automating code assignment, reducing errors, and speeding up claim processing.
• Driving real-world evidence: Empowering digital patient solutions and conversational AI for easier patient interaction.
• Diagnostics and medical imaging analysis.
• Remote patient monitoring and telemedicine.
• Patient virtual twins.
• Adoption of generative AI (GenAI) in healthcare and life sciences is growing, with a focus on personalization, improved customer engagement, and unlocking user productivity. Healthcare providers see an equal split between revenue impact and productivity/efficiency gains from GenAI.
• A notable UK initiative, the EDIT trial, utilizes AI to assist in mammogram screening, potentially reducing the need for two radiologists to one, increasing screening capacity.

5. Challenges in Implementing AI Solutions:

• Lack of a clear strategy: Tendency to focus on point solutions without considering the wider context and long-term integration.
• Data security and data sharing issues: Persistent challenges that need to be addressed for successful AI implementation.
• Need for a common approach to technology: Reducing overlap and duplication of initiatives across different healthcare organizations to optimize investment and impact.
• Data quality: AI relies on good quality data, and issues with data recording practices need to be addressed. Natural language processing can help glean data from non-discrete sources, and data quality assessments are crucial.

6. Lightbeam Health Solutions: Automated Care Orchestration:

• Lightbeam enables automated care orchestration to optimize the workforce and expand capacity through digital-first care management.
• Key components of Lightbeam’s solution:
• Data Aggregation: Centralizing and normalizing data from various sources.
• Prescriptive AI Modeling: Goes beyond predictive risk scores by identifying unique, modifiable risk factors per patient (clinical and non-clinical) and suggesting the next best recommended actions, streamlining the work for clinicians.
• Deviceless Remote Patient Monitoring (RPM): Enables bidirectional patient engagement using existing phone technology (text or voice-based), with programs for chronic conditions and event-based follow-up.
• Workflow Automation (Cohort Builder): Automates actions based on identified patient characteristics and risk thresholds, notifying appropriate resources for timely interventions.

 
Example Use Case:
 
Lightbeam identifies rising risk patients based on COPD and lack of transportation, enrolls them in deviceless RPM, receives alerts about breathing issues and transportation needs, and automates workflows for proactive intervention, potentially preventing hospitalization.
 
Outcomes Achieved:

• A rural US organization saw a 39% relative reduction in hospital admissions with one less full-time equivalent employee, saving significant time per phone call.
• A large multi-state health system engaged over 54,000 patients, eliminating over $53 million in projected hospitalizations and significantly increasing the panel size for care managers.
• Lightbeam’s prescriptive AI, which identifies unique patient vulnerabilities and suggests appropriate care pathways, is a key differentiator.

7. Future Impact of Innovation in Healthcare:

• Coordinated and centralized care management, covering both clinical and non-clinical activities, is a key area of future emphasis.
• Technologies will enable better real-time tracking of clinical aspects, optimized patient flow, resource allocation, and enhanced care coordination across settings.
• Digital care models will evolve with virtual hospitals and home healthcare hubs, providing real-time consultations enabled by smart devices.
• Increased use of smart devices will allow for tracking patient conditions outside the hospital, enabling early interventions, better risk stratification, and personalized care.

 
Potential Actionable Steps for Healthcare Organizations:

• Focus on developing a clear and comprehensive digital health strategy that considers long-term goals and integration rather than isolated point solutions.
• Prioritize initiatives that aim to improve data integration, quality, governance, and secure data sharing practices.
• Explore opportunities to leverage AI, particularly for enhancing productivity and efficiency by automating administrative tasks and freeing up clinical staff.
• Consider adopting solutions like deviceless RPM to extend care reach and proactively engage with broader patient populations.
• Look for platforms that can integrate existing human and digital care models and provide frameworks for agile transitions towards more digitally led care.
• Engage relevant stakeholders across the healthcare ecosystem (including local government and social care) to address social determinants of health and improve care coordination.

 
Conclusion:
 
There is a critical need for innovation in population health management to address rising healthcare demands and resource constraints. Digital solutions and AI, particularly when focused on proactive and preventive care, addressing social determinants of health, and overcoming data fragmentation, hold significant promise. Lightbeam Health Solutions’ approach to automated care orchestration, with its emphasis on prescriptive AI and deviceless RPM, provides a concrete example of how these technologies can be applied to improve patient outcomes and healthcare efficiency. Healthcare organizations should focus on developing clear strategies, improving data infrastructure, and exploring integrated digital solutions to realize the transformative potential of AI and RPM in population health.
 
Here’s a short AI-generated podcast for you to listen to: