What is the clinical evidence from this study?

Study design: Other. Population: Cardiovascular disease. Intervention: Modular data architecture.

What does this research mean for the field?

A modular data architecture integrating personal digital health devices, indoor temperature sensors, and electronic medical records can enable the development of clinical tools to evaluate and address extreme heat-related health risks in older adults with cardiovascular disease. Novelty: ClaimNovelty.METHODOLOGICAL. Consensus alignment: ConsensusAlignment.NEUTRAL.

What question did this study set out to answer?

To describe a data-intensive system architecture designed to link diverse environmental and clinical data streams for mitigating extreme heat-related health risks in vulnerable older adults.

September 10, 2026Medical Care

Bridging Data Systems to Develop Interventions for Older Adults Exposed to Extreme Heat

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Key result

Integrating wearables, sensors, and EMR data enables clinical tools targeting extreme heat risks in CVD.

Population

Older adults with cardiovascular disease (CVD) exposed to extreme heat events (EHEs)

Design

Other

Authors

AGArnab K. GhoshCornell University NGNikhil GargCornell University EPEmma PiersonCornell University

Discussion

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Implication

Integrating digital health devices, environmental sensors, and electronic medical records into a modular data architecture could help develop clinical tools to mitigate extreme heat-related risks in vulnerable older adults with CVD.

Key Points

To describe a data-intensive system architecture designed to link diverse environmental and clinical data streams for mitigating extreme heat-related health risks in vulnerable older adults.
Designed a modular system architecture integrating data streams from personal digital health wearables, ambient indoor temperature monitors, and electronic medical records linked through unique patient identifiers.
Outlined data management procedures to address processing demands, data quality hurdles, and healthcare system attribution for multilevel risk assessment.
Established a multi-stream framework capable of tracking physiological indicators, including cardiac and sleep parameters, to anticipate heat-related healthcare utilization.
Formulated methodologies to assess individual heat tolerance thresholds based on comorbid conditions, medication regimens, and discrepancies between indoor and outdoor ambient temperatures.

Structured PICO

Population

Older adults with cardiovascular disease (CVD) exposed to extreme heat events (EHEs)

Intervention

Modular data architecture integrating personal digital health devices, indoor temperature sensors, and electronic medical records

Limitations

Data quality
Processing requirements
Health care system attribution

Cite This Study

Ghosh et al. (2026) studied Cardiovascular disease. Modular data architecture was evaluated. A modular data architecture integrating wearables, sensors, and electronic medical records can enable clinical tools to address extreme heat-related health risks in older adults with CVD.

synapsesocial.com/papers/6aa27b3558559d80afc744ce https://doi.org/10.1097/mlr.0000000000002320

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Bridging Data Systems to Develop Interventions for Older Adults Exposed to Extreme Heat

Discussion

Member takes

Implication

Key Points

Structured PICO

Limitations

Cite This Study

Bridging Data Systems to Develop Interventions for Older Adults Exposed to Extreme Heat

Discussion

Member takes

Implication

Key Points

Structured PICO

Limitations

Cite This Study

Also Consider

Also Consider