Patient Engagement Via Crowdsourcing

Overview

The study aims to advance pain research by exploring feasibility of crowdsourcing patient pain data via Amazon Mechanical Turk, the largest and most studied crowdsourcing platform in the U.S. We will leverage an existing NIH/NCCIH grant as a comparison data (RAND Center of Excellence in Research on CAM; CERC) to conduct a feasibility study of new methods for gathering and analyzing data on chronic pain and engaging pain patients in health policy.

The study aims to advance the pain research by exploring the crowdsourcing approach for eliciting and analyzing the way in which individuals experience and understand chronic pain. Investigators will leverage an existing NIH/NCCIH grant (RAND Center of Excellence in Research on CAM; CERC) to conduct a feasibility study of new methods for gathering and analyzing data on chronic pain and engaging pain patients in health policy processes through three specific aims: Aim 1 (Inclusion): Gain access to chronic pain patients using crowdsourcing platform Amazon Mechanical Turk (MTurk). This aim explores whether crowdsourcing provides a credible method for patient inclusion. People with low back pain will be accessed via the crowdsourcing platform MTurk and asked to take health surveys that were also administered to a national clinical sample of chiropractic patients within a RAND study. Equivalency of validated, self-reported measures of low back pain obtained from crowdsourced versus "gold standard" data from the RAND study will be assessed. Similarities and differences between demographics and other pain and function variables between crowdsourced and RAND data will be analyzed. Subsamples of crowdsourced data will be analyzed to assess reliability of the extent to which data yields the same results across repeated crowdsourced samples. Aim 2 (Participation): Engage chronic pain patients in inclusion criteria setting for national pain treatment programs. This aim will intends to facilitate patient participation in NIH criteria-setting for program inclusion. Crowdsourced patients will assist with qualitative coding of data responses to the question, "What does chronic pain mean to you?" Investigators will explore whether crowdsourcing provides a valid method by which coding may be conducted, first measuring reliability across crowd samples, second testing the accuracy of participant coding as compared with expert coders at RAND Corporation. A method of assessing face validity will be tested as participants may create additional codes and give feedback by rating the importance of each dimension. Aim 3: Assess efficiency and quality of crowdsourced data as compared to CERC data. Investigators will draw quantitative comparisons of cost (labor/incentives), time, data quality (amount of text, missing data) across online crowdsourced and CERC study samples. The proposed study utilizes the resources of an existing NIH grant by exploring the feasibility of using innovative online methods for eliciting patient perspectives on chronic pain and for engaging patients in analyses procedures. The study provides an opportunity to determine whether patient chronic pain experiences and perspectives can be gathered through crowdsourcing using Amazon Mechanical Turk (MTurk), in a valid, replicable, and resource efficient way. Although focused on chronic low back pain, the study findings will have broad implications for patient engagement more generally. If the crowdsourcing methods produce data that is comparable to "gold standard" methods used in the RAND Study entitled, "Center of Excellence in Research on Chiropractic" (RAND/CERC Study), this new experimental system has the potential to provide low-cost and time-efficient methods to advance democratically-oriented research, evaluation, policy and ultimately patient-centered clinical care.

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