Download PDF
Appl Clin Inform 2024; 15(01): 026-033
DOI: 10.1055/a-2207-7396
Case Report

Developing a Quality Improvement Implementation Taxonomy for Organizational Employee Wellness Initiatives

Grace Gao
1   Veteran Affairs Quality Scholars Program, Joseph Maxwell Cleland Atlanta VA Medical Center, Atlanta, Georgia, United States
2   Nell Hodgson Woodruff School of Nursing, Emory University, Atlanta, Georgia, United States
3   School of Nursing, St Catherine University, St Paul, Minnesota, United States
,
Lindsay Vaclavik
4   Department of Internal Medicine, Michael E. DeBakey VA Medical Center, Baylor College of Medicine, Houston, Texas, United States
,
Alvin D. Jeffery
5   Office of Nursing Services, Tennessee Valley Healthcare System, Nashville, Tennessee, United States
6   Vanderbilt University School of Nursing, Nashville, Tennessee, United States
7   Department of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, Tennessee, United States
,
Erica C. Koch
8   Veteran Affairs Quality Scholars Program, Tennessee Valley VA Healthcare System, Nashville, Tennessee, United States, Clinical Instructor of Emergency Medicine, Vanderbilt University School of Medicine, Nashville, Tennessee, United States
9   Emergency Medicine, Vanderbilt University School of Medicine, Nashville, Tennessee, United States
,
Katherine Schafer
8   Veteran Affairs Quality Scholars Program, Tennessee Valley VA Healthcare System, Nashville, Tennessee, United States, Clinical Instructor of Emergency Medicine, Vanderbilt University School of Medicine, Nashville, Tennessee, United States
,
Jeannie P. Cimiotti
1   Veteran Affairs Quality Scholars Program, Joseph Maxwell Cleland Atlanta VA Medical Center, Atlanta, Georgia, United States
2   Nell Hodgson Woodruff School of Nursing, Emory University, Atlanta, Georgia, United States
,
Neha Pathak
1   Veteran Affairs Quality Scholars Program, Joseph Maxwell Cleland Atlanta VA Medical Center, Atlanta, Georgia, United States
,
Ingrid Duva
1   Veteran Affairs Quality Scholars Program, Joseph Maxwell Cleland Atlanta VA Medical Center, Atlanta, Georgia, United States
2   Nell Hodgson Woodruff School of Nursing, Emory University, Atlanta, Georgia, United States
,
Christie L. Martin
10   School of Nursing, University of Minnesota, Minneapolis, Minnesota, United States
,
Roy L. Simpson
2   Nell Hodgson Woodruff School of Nursing, Emory University, Atlanta, Georgia, United States
› Author Affiliations Funding The project was supported by the VAQS fellowship, through the VA Office of Academic Affiliations Advanced Fellowships Program.
› Further Information
    Permissions and Reprints

Abstract

Background Standardized taxonomies (STs) facilitate knowledge representation and semantic interoperability within health care provision and research. However, a gap exists in capturing knowledge representation to classify, quantify, qualify, and codify the intersection of evidence and quality improvement (QI) implementation. This interprofessional case report leverages a novel semantic and ontological approach to bridge this gap.

Objectives This report had two objectives. First, it aimed to synthesize implementation barrier and facilitator data from employee wellness QI initiatives across Veteran Affairs health care systems through a semantic and ontological approach. Second, it introduced an original framework of this use-case-based taxonomy on implementation barriers and facilitators within a QI process.

Methods We synthesized terms from combined datasets of all-site implementation barriers and facilitators through QI cause-and-effect analysis and qualitative thematic analysis. We developed the Quality Improvement and Implementation Taxonomy (QIIT) classification scheme to categorize synthesized terms and structure. This framework employed a semantic and ontological approach. It was built upon existing terms and models from the QI Plan, Do, Study, Act phases, the Consolidated Framework for Implementation Research domains, and the fishbone cause-and-effect categories.

Results The QIIT followed a hierarchical and relational classification scheme. Its taxonomy was linked to four QI Phases, five Implementing Domains, and six Conceptual Determinants modified by customizable Descriptors and Binary or Likert Attribute Scales.

Conclusion This case report introduces a novel approach to standardize the process and taxonomy to describe evidence translation to QI implementation barriers and facilitators. This classification scheme reduces redundancy and allows semantic agreements on concepts and ontological knowledge representation. Integrating existing taxonomies and models enhances the efficiency of reusing well-developed taxonomies and relationship modeling among constructs. Ultimately, employing STs helps generate comparable and sharable QI evaluations for forecast, leading to sustainable implementation with clinically informed innovative solutions.

Keywords

evidence - quality improvement - standardized taxonomy - implementation barriers - implementation facilitators

Protection of Human and Animal Subjects

No human subjects were involved in this project.




Publication History

Received: 24 August 2023

Accepted: 07 November 2023

Accepted Manuscript online:
09 November 2023

Article published online:
31 January 2024

© 2024. Thieme. All rights reserved.

Georg Thieme Verlag KG
Rüdigerstraße 14, 70469 Stuttgart, Germany

 

  • References

  • 1 Executive Board 118. eHealth: Standardized Terminology: Report by the Secretariat,. 2006 . Accessed August, 23, 2023 at: https://iris.who.int/handle/10665/21530
    Google Scholar
  • 2 Sundling KE, Kurtycz DFI. Standardized terminology systems in cytopathology. Diagn Cytopathol 2019; 47 (01) 53-63
    Google Scholar
  • 3 Mezei T. Current classification systems and standardized terminology in cytopathology. Rom J Morphol Embryol 2020; 61 (03) 655-663
    Google Scholar
  • 4 SNOMED CT Starter Guide. SNOMED International, 2022 . Accessed August 23, 2023 at: https://confluence.ihtsdotools.org/display/DOCSTART/SNOMED+CT+Starter+Guide
  • 5 Arvanitis TN. Semantic interoperability in healthcare. Stud Health Technol Inform 2014; 202: 5-8
    Google Scholar
  • 6 Batalden PB, Davidoff F. What is “quality improvement” and how can it transform healthcare?. Qual Saf Health Care 2007; 16 (01) 2-3
    Google Scholar
  • 7 Institute of Medicine (IOM). Crossing the Quality Chasm: A New Health System for the 21st Century. Washington, D.C:: National Academy Press;; 2001
    Google Scholar
  • 8 Nilsen P. Making sense of implementation theories, models and frameworks. Implement Sci 2015; 10: 53
    Google Scholar
  • 9 Eccles MP, Mittman BS. Welcome to implementation science. Implement Sci 2006; 1: 1
    Google Scholar
  • 10 Washington V, DeSalvo K, Mostashari F, Blumenthal D. The HITECH era and the path forward. N Engl J Med 2017; 377 (10) 904-906
    Google Scholar
  • 11 Blumenthal D. Stimulating the adoption of health information technology. N Engl J Med 2009; 360 (15) 1477-1479
    Google Scholar
  • 12 Powell BJ, Fernandez ME, Williams NJ. et al. Enhancing the impact of implementation strategies in healthcare: a research agenda. Front Public Health 2019; 7: 3
    Google Scholar
  • 13 Proctor E, Silmere H, Raghavan R. et al. Outcomes for implementation research: conceptual distinctions, measurement challenges, and research agenda. Adm Policy Ment Health 2011; 38 (02) 65-76
    Google Scholar
  • 14 Waltz TJ, Powell BJ, Matthieu MM. et al. Use of concept mapping to characterize relationships among implementation strategies and assess their feasibility and importance: results from the Expert Recommendations for Implementing Change (ERIC) study. Implement Sci 2015; 10: 109
    Google Scholar
  • 15 Michie S, Johnston M, Abraham C, Lawton R, Parker D, Walker A. “Psychological Theory” Group. Making psychological theory useful for implementing evidence based practice: a consensus approach. Qual Saf Health Care 2005; 14 (01) 26-33
    Google Scholar
  • 16 Michie S, Fixsen D, Grimshaw JM, Eccles MP. Specifying and reporting complex behaviour change interventions: the need for a scientific method. Implement Sci 2009; 4 (01) 40
    Google Scholar
  • 17 Grimshaw J, Eccles M, Thomas R. et al. Toward evidence-based quality improvement. Evidence (and its limitations) of the effectiveness of guideline dissemination and implementation strategies 1966-1998. J Gen Intern Med 2006; 21 (Suppl. 02) S14-S20
    Google Scholar
  • 18 Breimaier HE, Heckemann B, Halfens RJ, Lohrmann C. The Consolidated Framework for Implementation Research (CFIR): a useful theoretical framework for guiding and evaluating a guideline implementation process in a hospital-based nursing practice. BMC Nurs 2015; 14: 43
    Google Scholar
  • 19 Damschroder LJ, Aron DC, Keith RE, Kirsh SR, Alexander JA, Lowery JC. Fostering implementation of health services research findings into practice: a consolidated framework for advancing implementation science. Implement Sci 2009; 4: 50
    Google Scholar
  • 20 Hammad R, Barhoush M, Abed-Alguni BH. A semantic-based approach for managing healthcare big data: a survey. J Healthc Eng 2020; 2020: 8865808
    Google Scholar
  • 21 Liyanage H, Krause P, De Lusignan S. Using ontologies to improve semantic interoperability in health data. J Innov Health Inform 2015; 22 (02) 309-315
    Google Scholar
  • 22 Jacobson I. Object-oriented development in an industrial environment. Paper presented at: OOPSLA '87: Conference Proceedings on Object-oriented Programming Systems, Languages and Applications. December 1987: 183-191 . Orlando, Fl
    Google Scholar
  • 23 Jacobson I. Use cases - Yesterday, today, and tomorrow. SoSyM 2004; 3 (03) 210-220
    Google Scholar
  • 24 Mandel JC, Kreda DA, Mandl KD, Kohane IS, Ramoni RB. SMART on FHIR: a standards-based, interoperable apps platform for electronic health records. J Am Med Inform Assoc 2016; 23 (05) 899-908
    Google Scholar
  • 25 Bestek M, Grönvall E, Saad-Sulonen J. Commoning semantic interoperability in healthcare. Int J Commons 2022; 16 (01) 225-242
    Google Scholar
  • 26 Christoff P. Running PDSA cycles. Curr Probl Pediatr Adolesc Health Care 2018; 48 (08) 198-201
    Google Scholar
  • 27 Harel Z, Silver SA, McQuillan RF. et al. How to diagnose solutions to a quality of care problem. Clin J Am Soc Nephrol 2016; 11 (05) 901-907
    Google Scholar
  • 28 Consolidated Framework for Implementation Research. Qualitative data. 2023 . Accessed January 2023 at: https://cfirguide.org/evaluation-design/qualitative-data/
    Google Scholar
  • 29 Ridde V, Pérez D, Robert E. Using implementation science theories and frameworks in global health. BMJ Glob Health 2020; 5 (04) e002269
    Google Scholar
  • 30 Safaeinili N, Brown-Johnson C, Shaw JG, Mahoney M, Winget M. CFIR simplified: pragmatic application of and adaptations to the Consolidated Framework for Implementation Research (CFIR) for evaluation of a patient-centered care transformation within a learning health system. Learn Health Syst 2019; 4 (01) e10201
    Google Scholar
  • 31 Cox M, Sandberg K. Modeling causal relationships in quality improvement. Curr Probl Pediatr Adolesc Health Care 2018; 48 (07) 182-185
    Google Scholar
  • 32 Patrician PA, Dolansky M, Estrada C. et al. Interprofessional education in action: the VA Quality Scholars fellowship program. Nurs Clin North Am 2012; 47 (03) 347-354
    Google Scholar
  • 33 Watkins KE, Pincus HA, Smith B. et al. Veterans Health Administration Mental Health Program Evaluation: Capstone Report. Santa Monica, CA: : RAND Corporation, TR-956-VHA; 2011
    Google Scholar
  • 34 Holleran L, Baker S, Cheng C. et al. Using multisite process mapping to aid care improvement: an examination of inpatient suicide-screening procedures. J Healthc Qual 2019; 41 (02) 110-117
    Google Scholar
  • 35 Reddy KP, Schult TM, Whitehead AM, Bokhour BG. Veterans Health Administration's Whole Health System of Care: supporting the health, well-being, and resiliency of employees. Glob Adv Health Med 2021; 10: 21 649561211022698
    Google Scholar
  • 36 Joosten T, Bongers I, Janssen R. Application of lean thinking to health care: issues and observations. Int J Qual Health Care 2009; 21 (05) 341-347
    Google Scholar
  • 37 Gao G, Kerr MJ, Lindquist RA. et al. A strengths-based data capture model: mining data-driven and person-centered health assets. JAMIA Open 2018; 1 (01) 11-14
    Google Scholar