Adoption of an Electronic Medical Record Tool for Childhood Obesity by Primary Care Providers

Amy Williams; Christy Turer; Jamie Smith; Isabelle Nievera; Laura McCulloch; Nuha Wareg; Megan Clary; Anuradha Rajagopalan; Ross C. Brownson; Richelle J. Koopman; Sarah Hampl

doi:10.1055/s-0040-1705106

Applied Clinical Informatics, Table of Contents

Appl Clin Inform 2020; 11(02): 210-217
DOI: 10.1055/s-0040-1705106

Research Article

Georg Thieme Verlag KG Stuttgart · New York

Adoption of an Electronic Medical Record Tool for Childhood Obesity by Primary Care Providers

Authors

Amy Williams

¹Department of Family and Community Medicine, University of Missouri, Columbia, Missouri, United States
Christy Turer

²Department of Internal Medicine-Pediatrics, University of Texas Southwestern, Dallas, Texas, United States
Jamie Smith

¹Department of Family and Community Medicine, University of Missouri, Columbia, Missouri, United States
Isabelle Nievera

³University of Missouri School of Medicine, Columbia, Missouri, United States
Laura McCulloch

⁴Columbia/Boone County Public Health and Human Services, Columbia, Missouri, United States
Nuha Wareg

¹Department of Family and Community Medicine, University of Missouri, Columbia, Missouri, United States
Megan Clary

⁵Department of Child Health, University of Missouri, Columbia, Missouri, United States
Anuradha Rajagopalan

⁵Department of Child Health, University of Missouri, Columbia, Missouri, United States
Ross C. Brownson

⁶Department of Surgery and Alvin J. Siteman Cancer Center, Washington University in St. Louis, St. Louis, Missouri, United States

⁷Division of Public Health Sciences, Department of Surgery, Alvin J. Siteman Cancer Center, Washington University School of Medicine, Washington University in St. Louis, St. Louis, Missouri, United States
Richelle J. Koopman

¹Department of Family and Community Medicine, University of Missouri, Columbia, Missouri, United States
Sarah Hampl

⁸General Pediatrics and Weight Management, Children's Mercy Hospital Center for Children's Healthy Lifestyles and Nutrition, Kansas City, Missouri, United States

⁹Department of Pediatrics, University of MO-Kansas City School of Medicine, Kansas City, Missouri, United States

Abstract

Background Primary care providers are tasked with the increasingly difficult job of addressing childhood obesity during clinic visits. Electronic medical record (EMR)-enabled decision-support tools may aid providers in this task; however, information is needed regarding whether providers perceive such tools to be useful for addressing nutrition and physical activity lifestyle behaviors.

Objectives This study aimed to evaluate the usefulness and usability of FitTastic, an EMR-enabled tool to support prevention and management of childhood obesity in primary care.

Methods In this mixed-method study, we implemented the FitTastic tool in two primary-care clinics, then surveyed and conducted focused interviews with providers. Validated Technology Acceptance Model perceived usefulness and National Aeronautics and Space Administration (NASA) perceived usability survey questions were e-mailed to 60 providers. In-depth provider interviews with family medicine and pediatric physicians (n = 12) were used to further probe adoption of FitTastic.

Results Surveys were completed by 73% of providers (n = 44). The mean score for FitTastic's usefulness was 3.3 (standard deviation [SD] = 0.54, scale 1–5, where 5 is strongly agree) and usability, 4.8 (SD = 0.86, scale 1–7, where 7 is strongly agree). Usefulness and usability scores were associated with intention to use FitTastic (correlation for both, p < 0.05). Data from provider interviews indicated that useful features of FitTastic included: standardizing the approach to childhood obesity, and facilitating conversations about weight management, without increasing cognitive workload. However, use of FitTastic required more time from nurses to input lifestyle data.

Conclusion FitTastic is perceived as a useful and usable EMR-based lifestyle behavior tool that standardizes, facilitates, and streamlines healthy lifestyle conversations with families. Perceived usability and usefulness scores correlated with provider intention-to-use the technology. These data suggest that EMR-based child obesity prevention and management tools can be feasible to use in the clinic setting, with potential for scalability. Usefulness can be optimized by limiting amount of time needed by staff to input data.

Keywords

electronic health records and systems - infants and children - ambulatory care/primary care - adoption - implementation and deployment

Full Text

References

References
1 Hill SG, Phan TT, Datto GA, Hossain J, Werk LN, Abatemarco D. Integrating childhood obesity resources into the patient-centered medical home: provider perspectives in the United States. J Child Health Care 2019; 23 (01) 63-78
2 Beasley JW, Wetterneck TB, Temte J. , et al. Information chaos in primary care: implications for physician performance and patient safety. J Am Board Fam Med 2011; 24 (06) 745-751
3 Story MT, Neumark-Stzainer DR, Sherwood NE. , et al. Management of child and adolescent obesity: attitudes, barriers, skills, and training needs among health care professionals. Pediatrics 2002; 110 (1, Pt 2): 210-214
4 Barlow SE, Salahuddin M, Butte NF, Hoelscher DM, Pont SJ. Improvement in primary care provider self-efficacy and use of patient-centered counseling to address child overweight and obesity after practice-based changes: Texas childhood obesity research demonstration study. Child Obes 2018; 14 (08) 518-527
5 Morais A, Kelly J, Bost JE, Vaidya SS. Characteristics of correctly identified pediatric obesity and overweight status and management in an academic general pediatric clinic. Clin Pediatr (Phila) 2018; 57 (10) 1168-1175
6 Thaker VV, Lee F, Bottino CJ. , et al. Impact of an electronic template on documentation of obesity in a primary care clinic. Clin Pediatr (Phila) 2016; 55 (12) 1152-1159
7 Lingren T, Thaker V, Brady C. , et al. Developing an algorithm to detect early childhood obesity in two tertiary pediatric medical centers. Appl Clin Inform 2016; 7 (03) 693-706
8 Dugan TM, Mukhopadhyay S, Carroll A, Downs S. Machine learning techniques for prediction of early childhood obesity. Appl Clin Inform 2015; 6 (03) 506-520
9 Holden RJ, Karsh BT. The technology acceptance model: its past and its future in health care. J of Biomedical Inform 2009; 43 (01) 159-172
10 Yarbrough AK, Smith TB. Technology acceptance among physicians: a new take on TAM. Med Care Res Rev 2007; 64 (06) 650-672
11 Lee JD, Kirlik A, Danioff MJ. , eds. The Oxford Handbook of Cognitive Engineering. New York, NY: Oxford University Press; 2013
12 Jex HR. Measuring mental workload: problems, progress, and promises. In: Hancock PA, Meshkati N. , eds. Human Mental Workload. Amsterdam, The Netherlands: Elsevier; 1988: 5-39
13 Noyes JM, Bruneau DP. A self-analysis of the NASA-TLX workload measure. Ergonomics 2007; 50 (04) 514-519
14 Horsky J, Kaufman DR, Oppenheim MI, Patel VL. A framework for analyzing the cognitive complexity of computer-assisted clinical ordering. J Biomed Inform 2003; 36 (1,2): 4-22
15 Barlow SE. ; Expert Committee. Expert committee recommendations regarding the prevention, assessment, and treatment of child and adolescent overweight and obesity: summary report. Pediatrics 2007; 120 (Suppl. 04) S164-S192
16 Office of evidence based practice (EBP) – critically appraised topic: dairy intake and body composition. Available at: https://www.childrensmercy.org/contentassets/752849f69a344ba48856b523af8c70c1/dairy.pdf . Accessed February 10, 2020
17 Shook RP, Halpin K, Carlson JA. , et al. Adherence with multiple national healthy lifestyle recommendations in a large pediatric center electronic health record and reduced risk of obesity. Mayo Clin Proc 2018; 93 (09) 1247-1255
18 Davis FD. Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS Quart Manage Inf Syst. 1989; 13 (03) 319-339
19 Moroney WF, Biers DW, Eggemeier FT. Some measurement and methodological considerations in the application of subjective workload measurement techniques. Int J Aviat Psychol 1995; 5 (01) 87-106
20 Xiao YM, Wang ZM, Wang MZ, Lan YJ. [The appraisal of reliability and validity of subjective workload assessment technique and NASA-task load index] (Chinese). Zhonghua Lao Dong Wei Sheng Zhi Ye Bing Za Zhi 2005; 23 (03) 178-181
21 Fusch PI, Ness LR. Are we there yet? Data saturation in qualitative research. Qual Rep 2015; 20 (09) 1408-1416
22 Arndt BG, Beasley JW, Watkinson MD. , et al. Tethered to the EHR: Primary care physician workload assessment using EHR event log data and time-motion observations. Ann Fam Med 2017; 15 (05) 419-426
23 Milat AJ, King L, Bauman AE, Redman S. The concept of scalability: increasing the scale and potential adoption of health promotion interventions into policy and practice. Health Promot Int 2013; 28 (03) 285-298
24 Milat AJ, Bauman A, Redman S. Narrative review of models and success factors for scaling up public health interventions. Implement Sci 2015; 10: 113
25 Sharifi M, Franz C, Horan CM. , et al. Cost-effectiveness of a clinical childhood obesity intervention. Pediatrics 2017; 140 (05) e20162998
26 Rogers VW, Hart PH, Motyka E, Rines EN, Vine J, Deatrick DA. Impact of Let's Go! 5-2-1-0: a community-based, multisetting childhood obesity prevention program. J Pediatr Psychol 2013; 38 (09) 1010-1020
27 Venkatesh V, Davis FD. A theoretical extension of the technology acceptance model: four longitudinal field studies. Manage Sci 2000; 46 (02) 186-204
28 Davis FD, Bagozzi RP, Warshaw PR. Extrinsic and intrinsic motivation to use computers in the workplace. J Appl Soc Psychol 1992; 22 (14) 1111-1132