DOI 10.3389/fpubh.2026.1895129
Assessing the public health informatics workforce in Texas local health departments.
Аннотация
INTRODUCTION
As health disparities persist and our technological capabilities continue to evolve, a well-trained and representative workforce in public health informatics has become crucial.
METHODS
A mixed-methods approach with purposive sampling was utilized to explore the current public health informatics infrastructure within Texas local health departments (Objective 1) and to identify current workforce assets and challenges (Objective 2). Data was collected via an online survey and virtual interviews with key informants. Survey questions included organizational-level questions relating to informatics-related practices and corresponded to specific maturity levels from the Capability Maturity Model Integration (CMMI) framework. Descriptive statistics and thematic analysis were utilized to summarize and synthesize insights derived from the data.
RESULTS
For questions regarding data collection, data sharing, and information dissemination, over half of local health departments (LHDs) selected responses at the "systematic" or "optimized" capability maturity levels (i.e., have department-wide processes supported by policies). However, almost half of LHDs selected responses in the "absent" or "initial" capability maturity levels (i.e., only ad hoc efforts exist) for questions regarding support for informatics-related training and support. Interviews further revealed that a wide range of staff and programs beyond epidemiology supported informatics-related practices; however, resource and personnel shortages posed challenges for the workforce.
DISCUSSION: Our findings highlight public health informatics as an applied practice integrated across various departmental programs in LHDs. Given multiple resource-related constraints, Texas LHDs face challenges in training and supporting their informatics workforce. These challenges may ultimately hinder capabilities to carry out informatics work at the optimized level.
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