Section of Patient-Centered Analytics
The Section of Patient-Centered Analytics is the academic home for clinical researchers and operations within the Department of Symptom Research.
The section provides resources, training and support for a broad range of research and operational activities related to patient-centered outcomes and analytics. These outcomes help researchers be more aware of how patients are feeling and how their care teams should proceed.
The Section of Patient-Centered Analytics aligns the department's new cross-cutting methodological work, which includes artificial intelligence (AI) and data science, with its long-standing expertise in developing, validating and deploying patient-reported outcomes (PROs) in clinical practice and research. This alignment signals the department's varied scope of work and promotes a wide range of patient-centered outcomes, including financial toxicity, daily functioning, mental health and quality of life.
Data that come directly from the patient are invaluable when building systems to guide collaborative decision making.
We envision a future where cancer care is informed by individual patient priorities and where every patient will achieve the outcomes they most desire.
Patient-reported outcomes are remarkably versatile.
As a clinical intervention, they can improve patient quality of life, reduce symptoms and even increase survival. The data collected from PROs can then be re-used to conduct comparative research and quality improvement.
Our Research Goal
The goal of the Section of Patient-Centered Analytics is to improve the use and usefulness of patient-reported data in predicting and achieving the outcomes most important to patients.
Cancer-Related Symptom Burden
The Department of Symptom Research has decades of expertise in developing and using patient-reported outcomes (PROs) to characterize cancer-related and treatment-related symptoms and functional impairment during and after cancer therapy. Including PROs both in research and in the clinic is vital for understanding and incorporating the patient’s own voice and their perceptions about what they are experiencing.
Led by Dr. Xin Shelley Wang (quantitative prospective studies) and Dr. Loretta Williams (qualitative studies), our PRO clinical studies group:
- conducts a wide range of PRO-based research focused on symptoms, quality of life, and preferences for symptom control
- collaborates with MD Anderson principal investigators to investigate symptoms and toxicities in the clinical trial arena and in standard care
- explores technological applications for collecting actionable, real-time symptom data from patients
- develops and validates PRO questionnaires that are specific to disease type, procedure or expected toxicities.
There is great need to develop sensitive and clinically relevant patient-reported outcomes assessments and user-friendly, interactive implementation methods to enhance symptom management and improve patient outcomes, both during and after treatment.
About the Investigator
Xin Shelley Wang, M.D., M.P.H
Professor
Dr. Wang’s research focuses on the measurement of patient-reported symptom and functioning outcomes (PROs), the management of cancer-related and treatment-related symptoms (especially fatigue), the identification and management of clinically important PROs during and after curative cancer therapy, clinical trials for symptom management in patients and survivors, the impact of culture on symptom reporting, and clinical and translational research on inflammatory mechanism-driven interventions for cancer-related symptoms. She develops PRO assessment tools, including various language versions and modules of the MD Anderson Symptom Inventory (MDASI), the Brief Pain Inventory (BPI), and the Brief Fatigue Inventory (BFI), and designs methodologies for their use. Her efforts have laid the groundwork for integrating evidence-based PROs to improve patient care on an international scale.
Our Research
Our patient-reported outcomes (PRO) clinical studies represent a wide range of research focused on symptoms, quality of life, and preferences for symptom control:
- identifying clinically important symptom burden from disease or treatment
- developing electronic PRO-based pathways and methodological approaches that support PRO implementation in routine patient care
- investigating the impact of culture and language on symptom reporting
- using qualitative research methods to explore and understand the patient experience of disease and treatment
- using the symptom experience information gained from our qualitative research to develop PRO measures
- assessing PRO-based physical functioning and functional impairment
- understanding the effects of symptoms on families and caregivers of cancer patients
- surveying patients and health care professionals about their attitudes and practices related to symptom management at all stages of disease progression
- collaborating and consulting with clinical researchers on the use of PRO measures in their research and understanding the patient experience of clinical interventions
We collaborate with MD Anderson principal investigators to investigate symptoms and toxicities in the clinical trial arena and in standard care — particularly PROs related to aggressive therapeutic approaches during the acute phase and in survivorship.
We are also exploring the effectiveness of technologies, such as web-based patient portals like MyChart, tablet and phone apps, and telephone-based interactive voice response systems, for collecting actionable, real-time symptom data from patients when they are at home or otherwise away from the hospital or clinic.
Finally, we develop PRO questionnaires that are specific to disease type, procedure, or expected toxicities yet concise enough to produce minimum burden for patients and their care teams. We then validate these measures psychometrically in terms of sensitivity to change over time, validity, reliability and ability to predict the most clinically relevant symptoms among all that were assessed.
Support Staff
Shu-En (Annie) Shen, M.S.
Data Analyst
Annie is a biostatistician with extensive experience in statistical methods and applications in health research. She joined Dr. Wang's team in January 2022. As a data analyst, Annie assists the team with data management, performs statistical analyses, and translates results into written reports for manuscripts.
"Stay curious!"
Laila Z. Noor, M.D., M.S.
Supervisor, Clinical Studies
Laila brings more than 20 years of diverse experience spanning various research domains, including breast medical oncology, urology, and pulmonary medicine. Her background also extends to research laboratory environments, and she has a wealth of expertise in protocol submission and regulatory affairs. Notably, she has extensive expertise using REDCap to develop databases for PI-initiated studies, some of which have been utilized by study collaborators nationally and internationally.
In her current capacity, Laila oversees and ensures the efficient operation and execution of all clinical research activities within the department and its collaborative units. This involves conducting meticulous quality assurance assessments, providing comprehensive progress updates, adeptly troubleshooting any project-related challenges, and lends a hand with tasks such as Material Transfer Agreement submissions, protocol submissions, completing regulatory documents, and updating the departmental standard operation policies.
"When you do things from your soul, you feel a river moving in you, a joy." – Rumi
Cancer Treatment-Related Toxicity
It is a well-known fact that patients undergoing cancer treatment experience potentially severe toxicities related to that treatment. Toxicity-related symptoms often have sudden onset, peak during the first days after a treatment session, and can have unknown trajectories over time and long-term adverse effects. Thus, clinically relevant patient-reported outcome (PRO) measures are necessary for close, real-time monitoring and early capture of symptoms, including symptomatic adverse effects as well as functional and cognitive impairment. We seek to understand the critical PROs that best characterize therapy-related symptom burden, establish the value of timely PRO monitoring for predicting the development of therapy-induced toxicities, and define the impact of cognitive and functional impairment on patient well being. Ultimately, we seek to improve clinical outcomes for patients, regardless of the type of treatment they are receiving.
About the Investigator
Goldy C. George, Ph.D.
Assistant Professor
Dr. George brings more than five years of experience with clinical-trial related research within the MD Anderson Phase I program into her position within the Department of Symptom Research, where she has facilitated numerous collaborations between the two departments. She has published multiple scientific articles related to Phase I clinical trials and to symptoms and toxicities in the early-phase clinical trial arena, including several publications summarizing the results of early-phase clinical trials that examined toxicities and efficacy associated with certain molecularly targeted therapeutic agents. Other research interests include sleep quality and nutritional intake and dietary behavior in a variety of populations.
Our Research
Dr. George is investigating the symptomatic effects of sleep quality, nutritional intake and dietary behavior.
Support Staff
Grace C. Appleton, B.S.
Senior Coordinator, Research Data
Grace has worked on Dr. George’s team since 2021. She earned her dual B.S. in psychology and cell and molecular biology from The University of Texas at Austin in 2019. Her undergraduate research centered on investigating health literacy in underserved and uninsured populations. In her current role, she focuses on coordinating patient recruitment and ensuring accurate data collection.
Artificial Intelligence & Data Science
In three decades of well-funded research, the Department of Symptom Research established an efficacious, standardized approach for symptom data collection and management in patients across the cancer center as well as at remote sites. We have now extended that approach to incorporate modern computational techniques, such as AI-based decision aids. machine learning, computerized adaptive testing, and updated psychometric methodologies, to evaluate the effectiveness of PRO measures in clinical practice and develop strategies for improving their acceptability.
We are currently recruiting for leadership and staff positions in this area of reasearch. Contact Carrie Howard, Department Administrator, to inquire.
“Learning” healthcare systems — from small group practices to large national providers — combine diverse data sources with complex machine-learning algorithms to optimize biomedical research, public health and health care quality improvement.