Managing Chronic Disease In A System Built For Appointments

As chronic disease management becomes more continuous, healthcare delivery models built around periodic visits are struggling to deliver consistent improvements in clinical outcomes

Effective medical treatment depends on frequent, incremental adjustments such as changes in dosage, timing, or therapy that reflect shifts in physiology, lifestyle, and environment. These decisions accumulate over time and materially affect outcomes. Yet most healthcare delivery models remain centered on episodic encounters rather than ongoing oversight.

This mismatch becomes clearer as chronic disease grows more prevalent and complex. Consider a person living with diabetes. Their day begins with a glucose reading before breakfast. Food choices, physical activity, stress, and sleep all influence what follows. A meal that was manageable yesterday may require a different insulin dose today. Afternoon exercise can reduce evening glucose sensitivity. Illness, travel, or a disrupted routine may further shift requirements.

Most of these decisions are made independently, and data interpretation occurs in real time, often without clinical input. The next formal interaction with a clinician may be monthsaway, by which point dozens of small decisions have already shaped outcomes. Continuous judgment within a system designed for periodic review is the everyday reality of chronic care and a source of frustration for many. It is not surprising that the current care model for diabetes hasn't improved quality of care over the past 2 decades, with roughly 16% of adults with type 2 diabetes achieving recommended glycemic targets, according to American Diabetes Association-aligned benchmarks [1 - see source below].

When Self-Management Becomes the Default

In response to the growing gap between continuous clinical need and episodic access, technological solutions have emerged. Following its introduction a decade ago, data have been collected on the impact of automated insulin delivery technology on glycemic control in people with type 1 diabetes. Automated insulin delivery is a wearable device that combines a continuous glucose monitor (CGM) and an insulin pump to automatically adjust insulin doses based on real-time blood sugar levels. It mimics the pancreas by automatically increasing, decreasing, or pausing insulin delivery to maintain blood glucose within a healthy range, primarily for people with Type 1 diabetes. 

Data comparing glucose control in people before the introduction of this technology (years 2016-2017) with 5 years later (years 2021-2022) show, for the first time, a significant improvement in clinical outcomes at the population level [2 - see source below]. This represents one of the clearest population-level demonstrations that technology-enabled self-management can translate into measurable outcome improvements. The natural question that follows is whether similar continuity-support tools can be extended to the much larger type 2 population that requires a similar level of care and tools. Clinical decision support and self-management systems have emerged as potential solutions. Rather than introducing new treatment paradigms, these tools focus on translating existing guidelines and individualized treatment rules into structured recommendations that can be applied directly to patients, offering a similar self-management opportunity.

Regulation has played a defining role in shaping this category. Software that influences treatment decisions is subject to oversight and must demonstrate safety and clinical validity, limiting adoption to systems that meet established standards. A notable example is DreaMed, which currently has FDA clearance for decision-support platforms focused on insulin management in diabetes care, granted after the FDA created this product code category in 2018. The system is designed to support dose adjustments based on patient data while remaining aligned with clinician-approved protocols. While today's recommendations are reviewed within a defined clinical framework, clinicians involved with DreaMed have recently presented at academic conferences the company's intent to transform its physician-facing decision-support technology into a patient self-management tool.

In the U.S. healthcare system, payment structures play a central role in determining which forms of care can scale and what impacts the feasibility of such models. Over time, new opportunities have been introduced to recognize digital and remote clinical support, providing a mechanism for integrating technology-enabled continuity into standard practice. Programs such as CMS Rural Health Transformation, CMS Access Model, and a new Class III CPT Code 0740T illustrate how emerging categories of clinical support have been formalized once their roles and values have been sufficiently defined.

A Structural Shift Underway

Taken together, these developments indicate a gradual structural adjustment in the delivery of chronic care. The current periodic care model remains constrained, while the demands of chronic disease management continue to require ongoing oversight and timely intervention. Emerging outcome data increasingly suggest that episodic care alone may be insufficient to bridge this gap.

In chronic disease management, continuity is no longer a secondary consideration; tools that support decision-making between visits are increasingly being used to sustain clinical intent over time. It is becoming a design requirement, shaped by the realities of system capacity and the everyday demands of long-term care.

***

Sources:

[1]https://www.sciencedirect.com/science/article/pii/S2666970625000095

[2]https://journals.sagepub.com/doi/10.1089/dia.2023.0320?url_ver=Z39.88-2003&rfr_id=ori:rid:crossref.org&rfr_dat=cr_pub%20%200pubme