JMIR Publications

ABSTRACT

Background:

Both chronic obstructive pulmonary disease (COPD) and asthma incur heavy healthcare burdens on society. To support tailored preventive care for these two diseases, predictive modeling is widely used to give warnings and to identify patients for care management. However, three gaps exist in current modeling methods due to rarely factoring in temporal aspects showing trends and early health change: 1) Existing models seldom use temporal features and often give late warnings, making care reactive. A health risk is often found at a relatively late stage of declining health, when the risk of a poor outcome is high and resolving the issue is difficult and costly. A typical model predicts patient outcomes in the next, say, 12 months. This often does not warn early enough. If a patient will actually be hospitalized for COPD next week, intervening now could be too late to avoid the hospitalization. If temporal features were used, this patient could potentially be identified a few weeks earlier to institute preventive therapy. 2) Existing models often miss many temporal features with high predictive power and have low accuracy. This makes care management enroll many patients not needing it and overlook over half of patients needing it the most. 3) Existing models often give no information on why a patient is at high risk, nor about possible interventions to mitigate risk, causing busy care managers to spend more time reviewing charts and to miss suited interventions. Typical automatic explanation methods cannot handle longitudinal attributes and fully address these issues.

Objective:

To fill these gaps so that more COPD and asthma patients will receive more appropriate and timely care, we will develop comprehensible data-driven methods to provide accurate early warnings of poor outcomes and to suggest tailored interventions, making care more proactive, efficient, and effective.

Methods:

By conducting secondary data analysis and surveys, the study will: a) use temporal features to provide accurate early warnings of poor outcomes, and assess potential impact on prediction accuracy, risk warning timeliness, and outcomes; b) automatically identify actionable temporal risk factors for each patient at high risk for future hospital use and assess the impact on prediction accuracy and outcomes; c) assess actionable information’s impact on clinicians’ acceptance of early warnings and on perceived care plan quality.

Results:

We are currently obtaining clinical and administrative data sets from three leading healthcare systems’ enterprise data warehouses. We plan to finish our study in about six years.

Conclusions:

Techniques that will be developed in this study can boost risk warning timeliness, model accuracy, and generalizability; improve patient finding for preventive care; help form tailored care plans; advance machine learning for many clinical applications; and generalize to many other chronic diseases.