Utilizing decentralized care models for diabetes, hypertension, and other chronic conditions could hold the key to achieving sustainable health outcomes.
USA map with stethoscope, national health care concept, 3D rendering
The United States is currently facing a crisis in chronic diseases, with over 60% of adults dealing with conditions such as diabetes, hypertension, and obesity. These chronic illnesses are responsible for 90% of the nation’s $4.3 trillion healthcare expenditures, putting healthcare outcomes below those of similar nations (CDC).
Despite the lack of certain cited studies in the Trump administration’s “Make America Healthy Again” report, as confirmed by ABC News, the life expectancy in the United States significantly trails behind that of other developed countries. Prior to COVID-19, the average life expectancy in the United States was 78.8 years, while comparable countries averaged 82.6 years. This disparity, as noted by the President’s Make America Healthy Again Commission, translates to 1.25 billion fewer life years for the U.S. population.
Traditional healthcare systems were not designed to handle continuous care, behavior change, or real-time patient engagement, all of which contribute to the rising costs in the U.S. healthcare system. Despite leading in healthcare technology, the U.S. still lacks the necessary emerging technology and government mandates to bring about radical changes towards improving health outcomes. These emerging technologies provide a crucial missing link to drive change: individual accountability.
We are now entering an era where intelligence intersects with infrastructure, and patients play an active role in managing their own health.
The Role of AI: From Prediction to Precision
Artificial Intelligence excels at recognizing patterns, analyzing real-time data, and personalizing care. In chronic disease management, AI can:
- Predict risks before diagnosis using wearable and EHR data
- Provide real-time interventions based on biometrics and behavior
- Adjust care plans according to each individual’s lifestyle and response to treatment
For instance, AI-driven platforms can continuously monitor glucose levels and recommend personalized dietary changes. In the case of hypertension, AI can identify stress indicators or medication non-adherence and trigger alerts or reminders.
However, AI requires high-quality, secure, and longitudinal data. This is where blockchain technology comes into play.
Blockchain Adoption
The current issue with healthcare AI lies in its fragmented nature. Each provider, device, or health plan possesses only a portion of a patient’s data. Blockchain offers a decentralized data infrastructure where:
- Patients own and manage their health data
- Providers access only necessary information through permissioned smart contracts
- Data is secure, interoperable, and longitudinal
This framework not only enhances the performance of AI but also safeguards patient privacy, addressing a major obstacle to data sharing.
Decentralized Disease Management in Action
Let’s consider diabetes as an example.
A decentralized care model could function as follows:
- A patient stores their biometric data (glucose, sleep, diet, activity) in a personal blockchain wallet
- AI analyzes patterns and recommends interventions or flags risks
- Providers, coaches, and pharmacists can access this data stream as needed, without owning the data
- Patients receive crypto rewards (maintaining HIPAA compliance) for achieving health goals or sharing anonymized insights for research
The outcome? Continuous, community-driven care that adapts in real-time and aligns incentives among patients, providers, and payers.
In a recent study on decentralized diabetes care published in the NIH National Library of Medicine, it was determined that diabetic care services can be decentralized to non-specialized facilities such as primary hospitals without compromising the quality of care. This finding is supported by studies conducted in both low- and high-income countries, suggesting that decentralizing diabetes care to primary healthcare facilities could provide quality services within budget constraints in the U.S. It would also reduce transportation and accommodation costs for patients by offering services at nearby healthcare facilities. Additionally, this approach would allow general and referral hospitals to focus on more severe and complex cases.
As healthcare systems globally face mounting financial pressures, with healthcare expenditures consuming a larger portion of countries’ GDPs (OECD.org), decentralized care frameworks challenge the traditional hospital-centric care model. Rather than requiring patients to visit a central location, distributed care brings healthcare to the patient. We are increasingly witnessing healthcare delivery through a decentralized network of ambulatory clinics, retail settings, and home-based monitoring, coaching, and treatment. The cohesive element of this network is the end-to-end experiences of the patients it serves throughout their care journey (Phillips):
- 40% of Hospitals are expected to transition 20% of their beds to patients’ homes by 2025
- 60% of patients find virtual care more convenient than in-person visits
- 80% of patients are willing to seek care for minor ailments at retail clinics
The Path Forward
Healthcare does not require more dashboards; it necessitates an intelligent, transparent, and patient-centric infrastructure.
- AI provides the intelligence for personalization and prediction
- Blockchain offers the infrastructure for security and sharing
- Together, they facilitate a new healthcare system: decentralized, data-driven, and focused on prevention
Amid America’s pursuit of solutions to its chronic disease crisis, we must move beyond pilot initiatives and establish platforms that empower individuals to take charge of their health—with privacy, accuracy, and purpose.
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