Engaging Stakeholders Using the Competency Matrix Model: A Framework for Current and Future Health Informaticists

Abstract

Objectives This study aims to explore how health informaticists collaborate with multiple stakeholder groups, each possessing varying levels of comfort and competence with health technology and data. stakeholder engagement is highlighted as a crucial skill for health informaticists, necessitated by the differing competency levels among stakeholders.

Methods The Competency Matrix Model was identified as a strategic tool to address the challenges faced by health informaticists in navigating the complexities of health information technology utilization. This framework was used to evaluate and enhance the technological competencies of various stakeholders within the health care domain.

Results The application of the Competency Matrix Model provides health informaticists with a structured approach to improving stakeholders' technological competencies. This approach facilitates a better understanding and utilization of health information technologies, contributing to improved health care outcomes and operational efficiency.

Conclusion This work demonstrates the applicability of the Competency Matrix Model in the health care domain by health informaticists to enhance the technological competencies of various stakeholders. Through strategic stakeholder engagement and competency development, health informaticists can effectively address the challenges of technology utilization in health care, ensuring a positive impact on health care delivery.

Keywords

health informatics - competency matrix model - stakeholder engagement in health IT - interoperability in health care systems - health information technology utilization

Protection of Human and Animal Subjects

No human subjects were involved in this project.

Publication History

Received: 17 April 2024

Accepted: 04 December 2024

Article published online:
23 April 2025

• References

• 1 Walker DM, Yeager VA, Lawrence J, McAlearney AS. Identifying opportunities to strengthen the public health informatics infrastructure: exploring hospitals' challenges with data exchange. Milbank Q 2021; 99 (02) 393-425
• 2 Nundy S, Cooper LA, Mate KS. The quintuple aim for health care improvement: a new imperative to advance health equity. JAMA 2022; 327 (06) 521-522
• 3 Hong YA, Jiang S, Liu PL. Use of patient portals of electronic health records remains low from 2014 to 2018: results from a national survey and policy implications. Am J Health Promot 2020; 34 (06) 677-680
• 4 Wang YH, Lin GY. Exploring AI-healthcare innovation: natural language processing-based patents analysis for technology-driven roadmapping. Kybernetes 2023; 52 (04) 1173-1189
• 5 Dai T, Abràmoff MD. Incorporating artificial intelligence into healthcare workflows: models and insights. In: Tutorials in Operations Research: Advancing the Frontiers of OR/MS: From Methodologies to Applications. INFORMS. October 2023:pp. 133–155
• 6 Pai DR, Rajan B, Chakraborty S. Do EHR and HIE deliver on their promise? Analysis of Pennsylvania acute care hospitals. Int J Prod Econ 2022; 245: 108398
• 7 Feldman SS, Hikmet N, Modi S, Schooley B. Impact of provider prior use of HIE on system complexity, performance, patient care, quality and system concerns. Inf Syst Front 2022; 24 (01) 121-131
• 8 Baig MM, GholamHosseini H, Moqeem AA, Mirza F, Lindén M. a systematic review of wearable patient monitoring systems - current challenges and opportunities for clinical adoption. J Med Syst 2017; 41 (07) 115
• 9 McGreevey III JD, Mallozzi CP, Perkins RM, Shelov E, Schreiber R. Reducing alert burden in electronic health records: state of the art recommendations from four health systems. Appl Clin Inform 2020; 11 (01) 1-12
• 10 Brender J, Ammenwerth E, Nykänen P, Talmon J. Factors influencing success and failure of health informatics systems–a pilot Delphi study. Methods Inf Med 2006; 45 (01) 125-136
• 11 Dymek C, Kim B, Melton GB, Payne TH, Singh H, Hsiao CJ. Building the evidence-base to reduce electronic health record-related clinician burden. J Am Med Inform Assoc 2021; 28 (05) 1057-1061
• 12 Sieja A, Markley K, Pell J. et al. Optimization sprints: improving clinician satisfaction and teamwork by rapidly reducing electronic health record burden. Mayo Clin Proc 2019; 94 (05) 793-802
• 13 Brommeyer M, Whittaker M, Mackay M, Ng F, Liang Z. Building health service management workforce capacity in the era of health informatics and digital health - A scoping review. Int J Med Inform 2023; 169: 104909
• 14 Hollander AD, Hoy C, Huber PR. et al. Toward smart foodsheds: Using stakeholder engagement to improve informatics frameworks for regional food systems. Ann Assoc Am Geogr 2020; 110 (02) 535-546
• 15 Georgiou A, Magrabi F, Hyppönen H. et al. The safe and effective use of shared data underpinned by stakeholder engagement and evaluation practice. Yearb Med Inform 2018; 27 (01) 25-28
• 16 Wark K, Cheung K, Wolter E, Avey JP. “Engaging stakeholders in integrating social determinants of health into electronic health records: a scoping review”. Int J Circumpolar Health 2021; 80 (01) 1943983
• 17 Denecke K, Gabarron E, Petersen C, Merolli M. Defining participatory health informatics - a scoping review. Inform Health Soc Care 2021; 46 (03) 234-243
• 18 Campbell JP, Wiernik BM. The modeling and assessment of work performance. Annu Rev Organ Psychol Organ Behav 2015; 2: 47-74
• 19 Salman M, Ganie SA, Saleem I. The concept of competence: a thematic review and discussion. European J Train Dev 2020; 44 (6/7) 717-742
• 20 Ho SS, Frampton KM. A competency model for the information technology workforce: Implications for training and selection. Assoc Inf Syst 2010; 27: 63-80
• 21 McClelland DC. Testing for competence rather than for “intelligence”. Am Psychol 1973; 28 (01) 1-14
• 22 Brewer LC, Fortuna KL, Jones C. et al. Back to the future: achieving health equity through health informatics and digital health. JMIR Mhealth Uhealth 2020; 8 (01) e14512
• 23 Kuruba M. Role Competency Matrix: A Step-By-Step Guide to an Objective Competency Management System. Springer; 2019
• 24 Dullabh P, Sandberg SF, Heaney-Huls K. et al. Challenges and opportunities for advancing patient-centered clinical decision support: findings from a horizon scan. J Am Med Inform Assoc 2022; 29 (07) 1233-1243
• 25 Davies A, Mueller J, Moulton G. Core competencies for clinical informaticians: A systematic review. Int J Med Inform 2020; 141: 104237
• 26 Strudwick G, Nagle LM, Morgan A. et al. Adapting and validating informatics competencies for senior nurse leaders in the Canadian context: results of a Delphi study. Int J Med Inform 2019; 129: 211-218
• 27 Bell K. Public policy and health informatics. Semin Oncol Nurs 2018; 34 (02) 184-187
• 28 American Medical Informatics Association (AMIA). Clinical Informatics Occupational Code. 2023 . Accessed February 27, 2024 at: https://amia.org/public-policy/clinical-informatics-occupational-code
• 29 Wood EA, Ange BL, Miller DD. Are we ready to integrate artificial intelligence literacy into medical school curriculum: students and faculty survey. J Med Educ Curric Dev 2021; 8: 23 821205211024078
• 30 Seth P, Hueppchen N, Miller SD. et al. Data science as a core competency in undergraduate medical education in the age of artificial intelligence in health care. JMIR Med Educ 2023; 9: e46344
• 31 Hirose J, Wakata Y, Tagi M, Tamaki Y. The role of medical informatics in the management of medical information. J Med Invest 2020; 67 (1.2): 27-29
• 32 Liu S, Keil M, Wang L, Lu Y. Understanding critical risks of business process outsourcing from the vendor perspective: a dyadic comparison Delphi study. Inf Manage 2023; 60 (06) 103837