Home Research Research Library Rapid Sense Making: A Feasible, Efficient Approach for Analyzing Large Data Sets of Open-Ended Comments Rapid Sense Making: A Feasible, Efficient Approach for Analyzing Large Data Sets of Open-Ended Comments 2018 Author(s) Etz, Rebecca S, Gonzalez, Martha M, Eden, Aimee R, and Winship, J Topic(s) Role of Primary Care Keyword(s) Measurement Volume 17(1):1609406918765509 Source International Journal of Qualitative Methods This article shares the problem-solving process and resultant rapid sensemaking methodology created by an interdisciplinary research team faced with qualitative “big data.” Confronted with a data set of over half a million free text comments, within an existing data set of 320,500 surveys, our team developed a process to structure the naturally occurring variability within the data, to identify and isolate meaningful analytic units, and to group subsets of our data amenable to automated coding using a template-based process. This allowed a significant portion of the data to be rapidly assessed while still preserving the ability to explore the more complex free text comments with a grounded theory informed emergent process. In this discussion, we focus on strategies useful to other teams interested in fielding open-ended questions as part of large survey efforts and incorporating those findings as part of an integrated analysis. ABFM Research Read all 2014 Medical specialty boards can help measure graduate medical education outcomes Go to Medical specialty boards can help measure graduate medical education outcomes 2020 Incorporating machine learning and social determinants of health indicators into prospective risk adjustment for health plan payments Go to Incorporating machine learning and social determinants of health indicators into prospective risk adjustment for health plan payments 2013 Do residents who train in safety net settings return for practice? Go to Do residents who train in safety net settings return for practice? 2019 Payment Structures That Support Social Care Integration With Clinical Care: Social Deprivation Indices and Novel Payment Models Go to Payment Structures That Support Social Care Integration With Clinical Care: Social Deprivation Indices and Novel Payment Models
Author(s) Etz, Rebecca S, Gonzalez, Martha M, Eden, Aimee R, and Winship, J Topic(s) Role of Primary Care Keyword(s) Measurement Volume 17(1):1609406918765509 Source International Journal of Qualitative Methods
ABFM Research Read all 2014 Medical specialty boards can help measure graduate medical education outcomes Go to Medical specialty boards can help measure graduate medical education outcomes 2020 Incorporating machine learning and social determinants of health indicators into prospective risk adjustment for health plan payments Go to Incorporating machine learning and social determinants of health indicators into prospective risk adjustment for health plan payments 2013 Do residents who train in safety net settings return for practice? Go to Do residents who train in safety net settings return for practice? 2019 Payment Structures That Support Social Care Integration With Clinical Care: Social Deprivation Indices and Novel Payment Models Go to Payment Structures That Support Social Care Integration With Clinical Care: Social Deprivation Indices and Novel Payment Models
2014 Medical specialty boards can help measure graduate medical education outcomes Go to Medical specialty boards can help measure graduate medical education outcomes
2020 Incorporating machine learning and social determinants of health indicators into prospective risk adjustment for health plan payments Go to Incorporating machine learning and social determinants of health indicators into prospective risk adjustment for health plan payments
2013 Do residents who train in safety net settings return for practice? Go to Do residents who train in safety net settings return for practice?
2019 Payment Structures That Support Social Care Integration With Clinical Care: Social Deprivation Indices and Novel Payment Models Go to Payment Structures That Support Social Care Integration With Clinical Care: Social Deprivation Indices and Novel Payment Models
