Drivers and Barriers for Adopting Accreditation at Local Health Departments for Their Performance Improvement Effort.

CONTEXT: A national system of voluntary public health accreditation for state, local, and tribal health departments (local health departments [LHDs]) is part of a movement that aims to improve public health performance with ultimate impact on population health outcomes. Indiana is a good setting for the study of LHD accreditation adoption because several LHDs reported de-adopting accreditation in a recent statewide survey and because 71% of Indiana counties serve populations of 50 000 or less. DESIGN: A systematic method of analyzing qualitative data based on the Performance Improvement Model framework to expand our understanding of de-adoption of public health accreditation. SETTING/PARTICIPANTS: In 2015, we conducted a key informant interview study of the 3 LHDs that decided to delay their engagement in the accreditation based on findings from an Indiana survey on LHD accreditation adoption. The study is an exploration of LHD accreditation de-adoption and of the contributions made to its understanding by the Performance Improvement Model. RESULT: The study found that top management team members are those who champion accreditation adoption, and that organizational structure and culture facilitate the staff's embracing of the change. The Performance Improvement Model was found to enhance the elucidation of the inner domain elements of Consolidated Framework for Implementation Research in the context of de-adoption of public health accreditation. CONCLUSION: Governing entities' policies and priorities appear to mediate whether the LHDs are able to continue accreditation pursuit. Lacking any of these driving forces appears to be associated with decisions to de-adoption of accreditation. Further work is necessary to discern specific elements mediating decisions to pursue accreditation. This study demonstrates the added knowledge of Performance Improvement Model (PIM) to the CFIR framework. A large scale study is called to further clarify and discern supports of specific to the needs of individual LHDs for their performance improvement effort.

Telemedicine and health disparities: Association between the area deprivation index and primary care telemedicine utilization during the COVID-19 pandemic.

Introduction: The rapid implementation of telemedicine during the COVID-19 pandemic may have exacerbated the existing health disparities. This study investigated the association between the area deprivation index (ADI), which serves as a measure of socioeconomic deprivation within a geographic area, and the utilization of telemedicine in primary care. Methods: The study data source was electronic health records. The study population consisted of patients with at least one primary care visit between March 2020 and December 2021. The primary outcome of interest was the visit modality (office, phone, and video). The exposure of interest was the ADI score grouped into quartiles (one to four, with one being the least deprived). The confounders included patient sociodemographic characteristics (e.g., age, gender, race, ethnicity, insurance coverage, marital status). We utilized generalized estimating equations to compare the utilization of telemedicine visits with office visits, as well as phone visits with video visits. Results: The study population included 41,583 patients with 127,165 office visits, 39,484 phone visits, and 20,268 video visits. Compared to patients in less disadvantaged neighborhoods (ADI quartile = one), patients in more disadvantaged neighborhoods (ADI = two, three, or four) had higher odds of using phone visits vs office visits, lower odds of using video visits vs office visits, and higher odds of using phone visits vs video visits. Conclusions: Patients who resided in socioeconomically disadvantaged neighborhoods mainly relied on phone consultations for telemedicine visits with their primary care provider. Patient-level interventions are essential for achieving equitable access to digital healthcare, particularly for low-income individuals.

A Social Network Analysis Approach for Contact Tracing in the Hospital Setting.

Since the beginning of the COVID-19 pandemic, the State of Delaware has implemented various strategies including a stay-at-home order, mask-wearing requirements in public places, and community-based testing to control the spread of the disease. Health systems across the U.S. have taken actions including symptom monitoring and screening for visitors and healthcare workers, providing personal protection equipment (PPE), and contact tracing of confirmed infected individuals to provide maximum possible protection for healthcare workers. Despite such efforts, there remains a significant risk of intra-hospital transmission of COVID-19. Healthcare workers who contact patients with COVID-19 or were exposed to the disease in the community may transmit the infection to coworkers in the inpatient setting. In addition to universal and case-based precautions to prevent exposure and disease transmission, contact tracing is essential to minimizing the impact of outbreaks among healthcare workers and the community. A rapid increase in cases can quickly diminish hospital infection control and prevention program capacity to perform high-quality contact tracing. This article will describe an approach using the application of social network analysis (SNA) and Electronic Medical Records (EMR) to enhance the current efforts in COVID-19 contact tracings.

Impact of care provider network characteristics on patient outcomes: Usage of social network analysis and a multi-scale community detection.

OBJECTIVE: We assess healthcare provider collaboration and the impact on patient outcomes using social network analysis, a multi-scale community detection algorithm, and generalized estimating equations. MATERIAL AND METHODS: A longitudinal analysis of health claims data of a large employer over a 3 year period was performed to measure how provider relationships impact patient outcomes. The study cohort included 4,230 patients with 167 providers. Social network analysis with a multi-scale community detection algorithm was used to identify groups of healthcare providers more closely working together. Resulting measures of provider collaboration were: 1) degree, 2) betweenness, and 3) closeness centrality. The three patient outcome measures were 1) emergency department visit, 2) inpatient hospitalization, and 3) unplanned hospitalization. Relationships between provider collaboration and patient outcomes were assessed using generalized estimating equations. General practitioner, family practice, and internal medicine were labeled as primary care. Cardiovascular, endocrinologists, etc. were labeled as specialists, and providers such as radiology and social workers were labeled as others. RESULTS: Higher connectedness (degree) and higher access (closeness) to other providers in the community were significant for reducing inpatient hospitalization and emergency department visits. Patients of specialists (e.g. cardiovascular) and providers specified as others (e.g. social worker) had higher rate of hospitalization and emergency department visits compared to patients of primary care providers. CONCLUSION: Application of social network analysis for developing healthcare provider networks can be leveraged by community detection algorithms and predictive modeling to identify providers' network characteristics and their impacts on patient outcomes. The proposed framework presents multi-scale measures to assess characteristics of healthcare providers and their impact on patient outcomes. This approach can be used by implementation experts for informed decision-making regarding the design of insurance coverage plans, and wellness promotion programs. Health services researchers can use the study approach for assessment of provider collaboration and impacts on patient outcomes.

Data-driven modeling of diabetes care teams using social network analysis.

OBJECTIVE: We assess working relationships and collaborations within and between diabetes health care provider teams using social network analysis and a multi-scale community detection. MATERIALS AND METHODS: Retrospective analysis of claims data from a large employer over 2 years was performed. The study cohort contained 827 patients diagnosed with diabetes. The cohort received care from 2567 and 2541 health care providers in the first and second year, respectively. Social network analysis was used to identify networks of health care providers involved in the care of patients with diabetes. A multi-scale community detection was applied to the network to identify groups of health care providers more densely connected. Social network analysis metrics identified influential providers for the overall network and for each community of providers. RESULTS: Centrality measures identified medical laboratories and mail-order pharmacies as the central providers for the 2 years. Seventy-six percent of the detected communities included primary care physicians, and 97% of the communities included specialists. Pharmacists were detected as central providers in 24% of the communities. DISCUSSION: Social network analysis measures identified the central providers in the network of diabetes health care providers. These providers could be considered as influencers in the network that could enhance the implication of promotion programs through their access to a large number of patients and providers. CONCLUSION: The proposed framework provides multi-scale metrics for assessing care team relationships. These metrics can be used by implementation experts to identify influential providers for care interventions and by health service researchers to determine impact of team relationships on patient outcomes.

Identifying Key Players in the Care Process of Patients with Diabetes Using Social Network Analysis and Administrative Data.

Determining networks of healthcare providers quantitatively can identify impactful care processes that improve health outcomes for a high-risk populations such as elderly people with multiple chronic conditions. By applying social network analysis to health claim data of a large university in the Midwest, we measured healthcare provider networks of patients with diabetes for two consecutive years. Networks were built based on the assumption that having common patients may indicate potential working relationships between providers. Measures of the social network analysis including degree and betweenness centrality were utilized to identify healthcare providers with an important role in the care process. Both degree and betweenness centrality measures identified a supply center and three laboratories as the central providers of the network for both years. This study can positively impact informed decision-making of policymakers and insurance companies to better design their insurance coverage plans based on the collaboration patterns of the healthcare providers.

Impact of an Onsite Clinic on Utilization of Preventive Services.

OBJECTIVE: To assess impact of an onsite clinic on healthcare utilization of preventive services for employees of a public university and their dependents. METHOD: Descriptive statistics, logistic regression and classification tree techniques were used to assess health claim data to identify changes in patterns of healthcare utilization and factors impacting usage of onsite clinic. RESULT: Utilization of preventive services significantly increased for women and men employees by 9% and 14% one year after implementation of the onsite clinic. Hourly-paid employees, employees without diabetes, employees with spouse opt out or no coverage were more likely to go to the onsite clinic. CONCLUSION: Adapted framework for assessing performance of onsite clinics based on usage of health informatics would help to identify health utilization patterns and interaction between onsite clinic and offsite health providers.