RESUMO
The COVID-19 pandemic has focused attention on the use of immunization information systems (IIS) to record and consolidate immunization records from a variety of sources to generate comprehensive patient immunization histories. Operationalization of IIS in the United States is decentralized, and as such, there are over 60 different IIS with wide variations in enabling policies and functionalities. As such, the policies that inform the development and operation of those sub-national IIS exist at the state and sometimes city levels. A targeted literature review was conducted to identify IIS policies and functionalities and assess their impact. The authors identified articles published from 2012 to 2022 that discussed or evaluated IIS policies and functionalities and screened titles, abstracts, and full text for inclusion. When selected for inclusion, authors extracted IIS policy/functionality characteristics and qualitative or quantitative outcomes of their implementation, where applicable. The search terms yielded 86 articles, of which 39 were included in the analysis. The articles were heterogeneous with respect to study design, interventions, outcomes, and effect measures. Out of the 17 IIS policies and functional components identified in the targeted literature review, the most commonly evaluated were provider-based patient reminder/recall, IIS-based centralized reminder/recall, and clinical decision support. Patient reminder/recall had the most published research and was associated with increased vaccination rates and vaccine knowledge. Despite the lack of quantitative evidence, there is a consensus that immunization data interoperability is critical to supporting IIS data quality, access, and exchange. Significant evidence gaps remain about the effectiveness of IIS functionalities and policies. Future research should evaluate the impact of policies and functionalities to guide improved utilization of IIS, increase national interoperability and standardization, and ultimately improve vaccination coverage and population health.
RESUMO
OBJECTIVES: To evaluate the methodological soundness and performance of 3 obesity quality measures aimed at promoting improvements in obesity care. STUDY DESIGN: Retrospective, clinical, and administrative data-based observational research study to evaluate scientific soundness, feasibility, and performance of obesity quality measures. METHODS: Four test sites (clinicians/clinician groups) submitted clinical and administrative health data including patient demographics, diagnoses, and encounter information for patient panels encompassing individuals aged 18 to 79 years with at least 1 ambulatory visit between July 1, 2017, and June 30, 2018 (measurement period). Clinician/clinician group data were supplemented by an Optum data set contributing patient information from 21 health care organizations with approximately 6 million qualifying patients to assess the impact of using a larger data set for measure testing. Patients were excluded if they met any of the following criteria: were pregnant during the measurement period or in the 6 months prior to the measurement period, had died during the measurement year, or had evidence of palliative or hospice care during the measurement period. RESULTS: This study resulted in the identification of a clinician/clinician group-level measure, Documentation of Obesity Diagnosis, as being feasible and reliable; however, the measure requires additional evaluation and potential adjustments to determine validity. Other measures included in our evaluation had feasibility and methodological challenges due to data capture and coding limitations. CONCLUSIONS: Findings of our current study suggest that there are emerging opportunities to capture data and advance obesity measurement incrementally. A process measure focused on obesity diagnosis has the most potential for immediate implementation by clinicians, and additional measures focused on change in body mass index over time and use of evidence-based obesity treatment remain challenging to implement due to data capture and benefit coverage.
