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Identifying patterns and predictors of lifestyle modification in electronic health record documentation using
Kimberly Shoenbill1, Yiqiang Song2, Mark Craven3
1Department of Biostatistics and Medical Informatics, School of Medicine and Public Health, University of Wisconsin-Madison, Madison, WI, USA.
Insights
Identifying predictors of lifestyle modification assessment in hypertension patients is key to improving blood pressure control. Machine learning models effectively identified factors influencing documentation of diet and exercise advice.
Area of Science:
- Cardiology
- Health Informatics
- Preventive Medicine
Background:
- Hypertension affects millions of US adults, with a significant portion having uncontrolled blood pressure.
- Uncontrolled hypertension elevates risks for severe cardiovascular events, including death, stroke, heart failure, and myocardial infarction.
- Current hypertension management guidelines emphasize lifestyle modifications like diet and exercise.
Purpose of the Study:
- To identify predictors and timing of lifestyle modification assessment or advice in adult hypertension patients.
- To inform tailored interventions for improving lifestyle modification documentation and hypertension control.
- To leverage electronic health record (EHR) data for enhanced understanding of hypertension care processes.
Main Methods:
- Analysis of EHR data from 14,360 adult hypertension patients at an academic medical center.
- Application of statistical and machine learning methods, including Random Forest and logistic regression.
- Evaluation of multiple time points for lifestyle modification documentation.
Main Results:
- Multiple patient, clinic, and provider variables were statistically significant predictors of lifestyle modification documentation.
- Random Forest achieved an Area Under the Receiver Operator Curve (AUROC) of 0.831 for classifying documentation at any time.
- Logistic regression demonstrated an AUROC of 0.685 for classifying documentation within ≤3 months.
Conclusions:
- Analyzing EHR data, including narrative and coded information, enhances understanding of lifestyle modification timing and associated factors.
- Identified predictors can guide the development of targeted interventions to improve hypertension care processes.
- Improved documentation of lifestyle modifications has the potential to enhance overall hypertension control.
Abstract:
Just under half of the 85.7 million US adults with hypertension have uncontrolled blood pressure using a hypertension threshold of systolic pressure ≥ 140 or diastolic pressure ≥ 90. Uncontrolled hypertension increases risks of death, stroke, heart failure, and myocardial infarction. Guidelines on hypertension management include lifestyle modification such as diet and exercise. In order to improve hypertension control, it is important to identify predictors of lifestyle modification assessment or advice to tailor future interventions using these effective, low-risk interventions. Electronic health record data from 14,360 adult hypertension patients at an academic medical center were analyzed using statistical and machine learning methods to identify predictors and timing of lifestyle modification. Multiple variables were statistically significant in analysis of lifestyle modification documentation at multiple time points. Random Forest was the best machine learning method to classify lifestyle modification documentation at any time with Area Under the Receiver Operator Curve (AUROC) 0.831. Logistic regression was the best machine learning method for classifying lifestyle modification documentation at ≤3 months with an AUROC of 0.685. Analyzing narrative and coded data from electronic health records can improve understanding of timing of lifestyle modification and patient, clinic and provider characteristics that are correlated with or predictive of documentation of lifestyle modification for hypertension. This information can inform improvement efforts in hypertension care processes, treatment implementation, and ultimately hypertension control.
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