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Can patient-reported data improve predictions about who will be a high-need, high-cost patient in British Columbia?
Logan Trenaman1,2, Daphne Guh3, Stirling Bryan4
1Department of Health Systems and Population Health, School of Public Health, University of Washington, 3980 15th Ave NE, Fourth Floor, Box 351621, Seattle, WA, 98195, USA. trenaman@uw.edu.
Insights
Accurately predicting high-need, high-cost (HNHC) patients is crucial. Patient-reported data significantly enhances predictive models, improving the identification of individuals with the highest healthcare costs.
Area of Science:
- Health Services Research
- Predictive Analytics
- Health Economics
Background:
- Identifying high-need, high-cost (HNHC) patients is essential for improving healthcare outcomes and resource allocation.
- Predictive modeling plays a key role in proactively identifying at-risk populations.
- Current models often rely on administrative and clinical data, potentially missing valuable patient perspectives.
Purpose of the Study:
- To develop and evaluate predictive models for identifying individuals at risk of becoming future high-need, high-cost (HNHC) patients.
- To compare the predictive performance of models incorporating patient-reported data versus those using standard demographic, clinical, and resource utilization data.
Main Methods:
- Utilized two patient-reported survey datasets (inpatient and emergency department) from British Columbia, Canada, linked with administrative data.
- Defined the outcome as being an HNHC patient (top 5% of population costs) in the year following survey completion.
- Compared standard predictor sets with enhanced sets including patient-reported data using logistic regression, assessing discrimination (c-statistic, cost capture) and calibration.
Main Results:
- Models demonstrated good discrimination and calibration.
- The inclusion of patient-reported data improved model discrimination, evidenced by an increase in the c-statistic (e.g., from 0.83 to 0.85 in the ED survey logistic regression model).
- Enhanced models showed improved cost capture and provided the highest net benefit across various clinical utility thresholds.
Conclusions:
- Patient-reported data significantly enhances the discriminative performance of models designed to predict high-need, high-cost (HNHC) patients.
- These enhanced models are particularly effective in identifying patients with the highest healthcare expenditures.
- Incorporating patient insights into predictive analytics offers a promising avenue for targeted healthcare interventions.
Purpose:
Improving the outcomes for high-need, high-cost (HNHC) patients requires accurately predicting who will become an HNHC patient. The objectives of this study are to: (1) develop models to predict individuals at risk of becoming future HNHC patients, and (2) compare the performance of predictive models with and without patient-reported data.
Methods:
We used data from two patient-reported surveys datasets from British Columbia, Canada (inpatient and emergency department (ED) surveys) and linked administrative data. Our outcome was being an HNHC patient in the year following survey completion (i.e., incurring costs in the top 5% of the population). We compared two predictor sets, including a standard set (demographic, clinical, and resource use/cost) and an enhanced set (which included patient-reported data), across five model types. We assessed performance using measures of discrimination (c-statistic, and cost capture) calibration (calibration curve), and clinical usefulness (decision curve analysis).
Results:
Our final sample size was 11,964 for the inpatient survey and 11,144 for the ED survey. Models exhibited good discrimination and calibration. The addition of patient-reported data improved discrimination as measured by the c-statistic (from 0.83, 95% CI: 0.77-0.86 to 0.85, 95% CI: 0.80-0.88 for the logistic regression model from the ED survey), and cost capture (from 0.52, 95% CI: 0.40-0.67 to 0.62, 95% CI: 0.48-0.76). The decision curve analysis demonstrated that the enhanced models provided the highest net benefit across a range of thresholds.
Conclusion:
Patient-reported data improved the discriminative performance of models to predict HNHC patients, particularly for those with the highest health care costs.
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