Alyssa Bosold

Context: During the COVID-19 pandemic, communities of practice (CoPs) supported state, Tribal, local, and territorial (STLT) public health agencies. No studies have examined the collective role of these CoPs in helping STLT public health agencies translate guidance into practice. Objectives: This qualitative study examines the types of CoPs that supported STLT public health agencies during the COVID-19 response, how CoPs assisted in translating guidance into practice, and the characteristics of CoPs that made them valuable to STLT public health members. We report lessons for future public health emergencies (PHEs) for STLT public health agencies and membership organizations that represent them. Design: We conducted 21 in-depth interviews with CoP leaders, STLT public health participants, and federal agency sponsors and collaborators. Participants: We interviewed 9 CoP leads, 6 STLT participants, and 6 federal agency representatives. Results: Three types of CoPs, each with unique advantages, supported STLT public health agencies during the COVID-19 pandemic: (1) CoPs led by federal agencies, (2) CoPs led by membership organizations or associations that represent STLT public health agencies, and (3) CoPs led by other nonfederal organizations, such as philanthropic organizations and academic institutions. The most valuable CoPs to STLT public health agencies had a clear focus on issues of significance to their members, strong connections between members, and a structure tailored to the group’s goals. STLT public health agencies valued CoP support with implementing guidance-based policies and practices and facilitating bidirectional communication with federal agencies. STLT public health agencies also benefitted from tailored and implementation-focused resources developed through CoPs. Conclusion: Our study affirms the importance of CoPs in facilitating collaboration and information-sharing among multiple actors during PHEs. During the COVID-19 pandemic, CoPs helped STLT public health agencies implement guidance, tailor approaches to specific contexts, and generate practice-based discoveries to advance the field.

Context: During the COVID-19 pandemic, the United States relied on the public health workforce to control the spread of COVID-19 while continuing to provide foundational public health services. Facing longstanding staffing shortages, state, tribal, local, and territorial (STLT) public health agencies (PHAs) used various strategies and supports to surge the workforce in response to the pandemic. Objectives: The study explored (1) the types of strategies and supports STLT PHAs used to surge the public health workforce during the pandemic, (2) assessments of surge strategies and supports, and (3) approaches to using the range of surge strategies available. Design: We conducted 27 semi-structured interviews in 2023 and performed thematic analysis. Participants: Interviewees included STLT public health officials, leaders of organizations that directly supported the COVID-19 workforce surge, and public health workforce experts indirectly involved in the workforce surge. Results: To surge the workforce, interviewees relied on partnerships, staffing agencies, the National Guard, the Centers for Disease Control and Prevention Foundation’s COVID-19 Corps, the Medical Reserve Corps, and other strategies. Interviewees valued strategies that rapidly engaged staff and volunteers at no cost to their PHA and flexible funding from the federal government to support surge efforts but noted shortcomings in the strategies and supports available. Interviewees described using multiple strategies simultaneously but noted challenges in implementing these strategies due to inadequate planning and insufficient staff and resources. Conclusion: The study underscores the need for STLT PHAs to establish mechanisms to surge the workforce as part of ongoing planning for emergency preparedness. Focus areas include building administrative and hiring capacity within STLT PHAs and sustaining partnerships and contractual agreements that helped fill staffing gaps during the pandemic. To support efforts to build workforce capacity to meet future surge management needs, STLT PHAs should consider creative solutions to attract and retain staff, as well as opportunities to engage students in public health work.

Health-related social needs (HRSNs), like unstable housing, inability to afford utilities, food insecurity, unreliable transportation, and lack of personal safety, profoundly affect people’s health and well-being. Between 2017 and 2022, awardees of the Accountable Health Communities Model (AHC) addressed the health-related social needs of Medicare and Medicaid beneficiaries through screening, referral, and community navigation services. Using and sharing HRSN data between clinical and community partners was a critical component of these efforts. This article shares findings from focus groups and interviews with 19 AHC awardees and seven of their partners. It explores the following: 1. Whether sharing HRSN data with clinical partners informed clinical care 2. Successes and challenges related to sharing data with community-based organizations (CBOs) and clinical partners 3. How awardees collected and used HRSN data to advance health equity Half of awardees interviewed documented HRSNs in electronic health records and shared aggregated HRSN data with CBOs. HRSN data enabled some clinicians to adjust patient care, although most were uncertain about how to do so. Participants described how sharing HRSN data with communities informs program and funding priorities to improve equity. However, CBOs noted that they had limited incentive to participate in data-sharing platforms. Our work highlights opportunities to provide guidance to clinicians on how to use HRSN screening results in care, standardize HRSN screening results in electronic health records, and co-create data-sharing initiatives with CBOs and patients to ensure meaningful participation.

Health-related social needs (HRSNs), such as food insecurity and housing instability, drive health and well-being. The socioeconomic impacts of the COVID-19 pandemic increased the prevalence of HRSNs and highlighted the critical need for strategies to address those needs, particularly in communities experiencing health disparities. Implementing HRSN screening requires adopting effective strategies to overcome common challenges. This report synthesizes promising implementation approaches and lessons learned from the Accountable Health Communities Model, a national effort funded by the Centers for Medicare & Medicaid Services Innovation Center to systematically screen for and address HRSNs in clinical settings. Key strategies include increasing patient engagement and building trust through culturally tailored language and outreach; using and sharing data for monitoring and improvement; using technology to expand access to screening and referrals; dedicating staff to screening roles; integrating screening into existing workflows; and building buy-in among staff by communicating the impact of screening and encouraging peer connections.

Background: In the United States, language barriers pose challenges to communication in emergency response and impact emergency care delivery and quality for individuals who are limited English proficient (LEP). There is a growing interest among Emergency Medical Services (EMS) personnel in using automated translation tools to improve communications with LEP individuals in the field. However, little is known about whether automated translation software can be used successfully in EMS settings to improve communication with LEP individuals. Objective: The objective of this work is to use scenario-based methods with EMS providers and nonnative English-speaking users who identified themselves as LEP (henceforth referred to as LEP participants) to evaluate the potential of two automated translation technologies in improving emergency communication. Methods: We developed mock emergency scenarios and enacted them in simulation sessions with EMS personnel and Spanish-speaking and Chinese-speaking (Mandarin) LEP participants using two automated language translation tools: an EMS domain-specific fixed-sentence translation tool (QuickSpeak) and a statistical machine translation tool (Google Translate). At the end of the sessions, we gathered feedback from both groups through a postsession questionnaire. EMS participants also completed the System Usability Scale (SUS). Results: We conducted a total of 5 group sessions (3 Chinese and 2 Spanish) with 12 Chinese-speaking LEP participants, 14 Spanish-speaking LEP participants, and 17 EMS personnel. Overall, communications between EMS and LEP participants remained limited, even with the use of the two translation tools. QuickSpeak had higher mean SUS scores than Google Translate (65.3 vs 48.4; P=.04). Although both tools were deemed less than satisfactory, LEP participants showed preference toward the domain-specific system with fixed questions (QuickSpeak) over the free-text translation tool (Google Translate) in terms of understanding the EMS personnel’s questions (Chinese 11/12, 92% vs 3/12, 25%; Spanish 12/14, 86% vs 4/14, 29%). While both EMS and LEP participants appreciated the flexibility of the free-text tool, multiple translation errors and difficulty responding to questions limited its usefulness. Conclusions: Technologies are emerging that have the potential to assist with language translation in emergency response; however, improvements in accuracy and usability are needed before these technologies can be used safely in the field.

BACKGROUND In the United States, language barriers pose challenges to communication in emergency response and impact emergency care delivery and quality for individuals who are limited English proficient (LEP). There is a growing interest among Emergency Medical Services (EMS) personnel in using automated translation tools to improve communications with LEP individuals in the field. However, little is known about whether automated translation software can be used successfully in EMS settings to improve communication with LEP individuals. OBJECTIVE The objective of this work is to use scenario-based methods with EMS providers and nonnative English-speaking users who identified themselves as LEP (henceforth referred to as LEP participants) to evaluate the potential of two automated translation technologies in improving emergency communication. METHODS We developed mock emergency scenarios and enacted them in simulation sessions with EMS personnel and Spanish-speaking and Chinese-speaking (Mandarin) LEP participants using two automated language translation tools: an EMS domain-specific fixed-sentence translation tool (QuickSpeak) and a statistical machine translation tool (Google Translate). At the end of the sessions, we gathered feedback from both groups through a postsession questionnaire. EMS participants also completed the System Usability Scale (SUS). RESULTS We conducted a total of 5 group sessions (3 Chinese and 2 Spanish) with 12 Chinese-speaking LEP participants, 14 Spanish-speaking LEP participants, and 17 EMS personnel. Overall, communications between EMS and LEP participants remained limited, even with the use of the two translation tools. QuickSpeak had higher mean SUS scores than Google Translate (65.3 vs 48.4; P=.04). Although both tools were deemed less than satisfactory, LEP participants showed preference toward the domain-specific system with fixed questions (QuickSpeak) over the free-text translation tool (Google Translate) in terms of understanding the EMS personnel’s questions (Chinese 11/12, 92% vs 3/12, 25%; Spanish 12/14, 86% vs 4/14, 29%). While both EMS and LEP participants appreciated the flexibility of the free-text tool, multiple translation errors and difficulty responding to questions limited its usefulness. CONCLUSIONS Technologies are emerging that have the potential to assist with language translation in emergency response; however, improvements in accuracy and usability are needed before these technologies can be used safely in the field.

Clinical informatics, decision support for health professionals, electronic health records, and ehealth infrastructures.