Enhancing data practices for Whole Health: Strategies for a transformative future.
Lei GuoKavitha P ReddyTheresa Van IseghemWhitney N PiercePublished in: Learning health systems (2024)
We explored the challenges and solutions for managing data within the Whole Health System (WHS), which operates as a Learning Health System and a patient-centered healthcare approach that combines conventional and complementary approaches. Addressing these challenges is critical for enhancing patient care and improving outcomes within WHS. The proposed solutions include prioritizing interoperability for seamless data exchange, incorporating patient-centered comparative clinical effectiveness research and real-world data to personalize treatment plans and validate integrative approaches, and leveraging advanced data analytics tools to incorporate patient-reported outcomes, objective metrics, robust data platforms. Implementing these measures will enable WHS to fulfill its mission as a holistic and patient-centered healthcare model, promoting greater collaboration among providers, boosting the well-being of patients and providers, and improving patient outcomes.
Keyphrases
- healthcare
- electronic health record
- big data
- patient reported outcomes
- public health
- randomized controlled trial
- end stage renal disease
- chronic kidney disease
- type diabetes
- machine learning
- climate change
- data analysis
- prognostic factors
- peritoneal dialysis
- health information
- deep learning
- social media
- current status
- replacement therapy
- patient reported