Despite improvements in its diagnosis and treatment, lung cancer remains the leading cause of cancer-related deaths worldwide. The high degree of mortality associated with this cancer, and the spectrum of different treatment options (which themselves involve significant morbidity), creates a difficult situation in which a patient is faced with critical, if not life-changing decisons. In an effort to make informed decisions, many turn to the Internet to find information on their disease. However, the quality of information is variable;the information can be difficult for the layperson to comprehend;and patients can have considerable problems understanding how the information they find is specifically applicable to their individual circumstances. The objective of this proposal is the development of a framework, named RUMI (Retrieving Understandable Medical Information), which challenges how cancer patients receive information today by making the process of care explicit to the patient, providing access to his/her medical record data in the direct context of a clinical guideline so they can see how decisions are made. The first step in our effort is the development of a comprehensive lung cancer knowledge and process model (LCKPM) that captures a clinical guideline and workflow. The LCKPM provides a foundation for connecting the patient's medical record to decision points and actions that occur over time, detailing the criteria in making a selection and the supporting rationale. Different layers in the LCKPM organize information, which provide links: to public information resources helping explicate unfamiliar medical jargon and concepts;to questions (and answers) that are frequently fielded by healthcare providers managing lung cancer patients;and to more meaningful clinical episodes as experienced by the patient. Based on the LCKPM, RUMI automatically maps a patient's medical record: free-text documents are analyzed via information extraction (IE) and natural language processing (NLP) methods, identifying key concepts and variables (e.g., as used in decisions). In parallel, we examine public web resources that provide consumer-level explanations and discussions of lung cancer;these sites are deconstructed into curated knowledge segments that can be used in more directed presentations to a given individual. The knowledge segments are used to explain medical concepts within a patient's reports;and the terminology within the clinical guideline process flow. Collectively, these developments implement an individually-tailored web portal that visualizes the patient's personal experience over the disease trajectory using a simplified event-driven timeline. The portal also provides customized information regarding clinical trials that the individual is eligible for, and pertinent past trials results. In addition to technical evaluation, the RUMI framework will be evaluated through the UCLA Lung Cancer Program's outpatient clinics in a controlled study to assess end impact. The result of this project will be a set of approaches to employ patients'own medical records and public information resources to inform and empower lung cancer patients as participants in their own healthcare and medical decision-making processes.
In spite of the improvements in early diagnostic and therapeutic methods, lung cancer remains the leading cause of cancer-related mortality in the United States. In an effort to make informed decisions, these cancer patients frequently turn to the Internet to find information about their disease and potential treatment options;however, finding quality information is often problematic, the information is difficult to understand, and the relevance of the information to a patient?s own circumstances is often lost. The focus of this research is an informatics- based infrastructure to provide lung cancer patients with an automatically created, individually-tailored web portal that makes explicit their process of care, explaining decisions and choices using public information resources geared towards laypersons, thus better equipping users with the knowledge needed to make decisions.
|Petousis, Panayiotis; Han, Simon X; Aberle, Denise et al. (2016) Prediction of lung cancer incidence on the low-dose computed tomography arm of the National Lung Screening Trial: A dynamic Bayesian network. Artif Intell Med 72:42-55|
|Zide, Mary; Caswell, Kaitlyn; Peterson, Ellen et al. (2016) Consumers' Patient Portal Preferences and Health Literacy: A Survey Using Crowdsourcing. JMIR Res Protoc 5:e104|
|McNamara, Mary; Arnold, Corey; Sarma, Karthik et al. (2015) Patient Portal Preferences: Perspectives on Imaging Information. J Assoc Inf Sci Technol 66:1606-1615|
|Meng, Frank; Morioka, Craig (2015) Automating the generation of lexical patterns for processing free text in clinical documents. J Am Med Inform Assoc 22:980-6|
|Garcia-Gathright, Jean I; Oh, Andrea; Abarca, Phillip A et al. (2015) Representing and extracting lung cancer study metadata: study objective and study design. Comput Biol Med 58:63-72|
|McNamara, Mary; Sarma, Karthik; Aberle, Denise R et al. (2014) Data model for personalized patient health guidelines: an exploratory study. AMIA Annu Symp Proc 2014:1835-44|
|Bui, Alex A T; Hsu, William; Arnold, Corey et al. (2013) Imaging-based observational databases for clinical problem solving: the role of informatics. J Am Med Inform Assoc 20:1053-8|
|Arnold, Corey W; McNamara, Mary; El-Saden, Suzie et al. (2013) Imaging informatics for consumer health: towards a radiology patient portal. J Am Med Inform Assoc 20:1028-36|