Cancer pain is still poorly managed, likely due to the fact that it is multidimensional, dynamic and individualized. A personalized care approach that considers the complexity and trajectory of cancer pain is much needed. Using existing longitudinal patient data to examine similar trajectories of cancer pain will provide clinically useful information for richly characterizing cancer pain and identifying factors associated with pain trajectories. Data from electronic health records (EHRs) have the potential for revealing new patient-stratification principles and unknown correlations between factors that influence cancer pain outcomes. But EHR data are not readily available for research and require intensive data preparation before analysis. We propose an innovative strategy to facilitate personalized cancer pain care by better understanding complex pain trajectories using massive preprocessed EHR data. Our long-term goal is to reduce inadequately controlled cancer pain through better understanding of pain characteristics derived from clinical data. The short- term goals are to (1) examine the availability and quality of EHR data and (2) develop a Research data Repository for Cancer Pain research (R2CancerPain) with preprocessed EHR data, and (3) characterize distinct pain trajectories informed by the Dynamic Symptoms Model (DSM). The central hypothesis is that the EHR contains essential patient data for identifying cancer pain trajectories and can be used to examine the factors contributing to individual pain trajectories.
The specific aims are to: (1) examine availability and quality of EHR data for developing personalized cancer pain care and (2) identify factors contributing to groups of patients sharing similar pain trajectories. This K01 proposal aligns with the NINR mission of enhancing symptom science using innovative methodologies to facilitate the development of personalized care. This award will support a highly accomplished junior nursing scientist with nursing informatics background and clinical experience in oncology nursing. My long- term career goal is to become an independent nurse researcher in advancing symptom management research through reduce inadequately controlled cancer pain through better understanding of pain characteristics derived from massive patient data. My short-term career goals are to transition to an independent investigator by leveraging my prior research and training in pain science and informatics research. I have assembled a mentoring team, composed of interdisciplinary experts from cancer pain research, informatics, and data science. My career development aims are to (1) strengthen my existing knowledge in pain-related symptom science, (2) acquire advanced knowledge and skills in big data science and advanced statistical techniques, and (3) strengthen scientific research skills. This K01 will present solid preliminary data and my sufficient training for a future R01 in implementing personalized cancer pain care.
Personalized cancer pain care is needed to address inadequately controlled cancer pain which is multidimensional, dynamic and individualized. Using existing longitudinal patient data from electronic health records (EHRs) to examine similar trajectories of cancer pain will reveal unknown correlations between factors that influence cancer pain outcomes, but EHR data require intensive data preparation for research. This project will facilitate personalized cancer pain care by better understanding complex pain trajectories through using massive preprocessed EHR data from a research data repository, R2CancerPain.
