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Integrating behavioral health into primary care can improve access to behavioral health and patient health outcomes. We used 2017-2021 American Board of Family Medicine continuing certificate examination registration questionnaire responses to determine the characteristics of family physicians who work collaboratively with behavioral health professionals. With a 100% response rate, 38.8% of 25,222 family physicians reported working collaboratively with behavioral health professionals, with those working in independently owned practices and in the South having substantially lower rates. Future research exploring these differences could help develop strategies to support family physicians implement integrated behavioral health to improve care for patients in these communities.

Peer reviewed: True, Funder: Fred Hutchinson Cancer Research Center, The diagnosis of lung cancer in ambulatory settings is often challenging due to non-specific clinical presentation, but there are currently no clinical quality measures (CQMs) in the United States used to identify areas for practice improvement in diagnosis. We describe the pre-diagnostic time intervals among a retrospective cohort of 711 patients identified with primary lung cancer from 2012-2019 from ambulatory care clinics in Seattle, Washington USA. Electronic health record data were extracted for two years prior to diagnosis, and Natural Language Processing (NLP) applied to identify symptoms/signs from free text clinical fields. Time points were defined for initial symptomatic presentation, chest imaging, specialist consultation, diagnostic confirmation, and treatment initiation. Median and interquartile ranges (IQR) were calculated for intervals spanning these time points. The mean age of the cohort was 67.3 years, 54.1% had Stage III or IV disease and the majority were diagnosed after clinical presentation (94.5%) rather than screening (5.5%). Median intervals from first recorded symptoms/signs to diagnosis was 570 days (IQR 273-691), from chest CT or chest X-ray imaging to diagnosis 43 days (IQR 11-240), specialist consultation to diagnosis 72 days (IQR 13-456), and from diagnosis to treatment initiation 7 days (IQR 0-36). Symptoms/signs associated with lung cancer can be identified over a year prior to diagnosis using NLP, highlighting the need for CQMs to improve timeliness of diagnosis.

BackgroundReports on medium and long-term sequelae of SARS-CoV-2 infections largely lack quantification of incidence and relative risk. We describe the rationale and methods of the Innovative Support for Patients with SARS-CoV-2 Registry (INSPIRE) that combines patient-reported outcomes with data from digital health records to understand predictors and impacts of SARS-CoV-2 infection.MethodsINSPIRE is a prospective, multicenter, longitudinal study of individuals with symptoms of SARS-CoV-2 infection in eight regions across the US. Adults are eligible for enrollment if they are fluent in English or Spanish, reported symptoms suggestive of acute SARS-CoV-2 infection, and if they are within 42 days of having a SARS-CoV-2 viral test (i.e., nucleic acid amplification test or antigen test), regardless of test results. Recruitment occurs in-person, by phone or email, and through online advertisement. A secure online platform is used to facilitate the collation of consent-related materials, digital health records, and responses to self-administered surveys. Participants are followed for up to 18 months, with patient-reported outcomes collected every three months via survey and linked to concurrent digital health data; follow-up includes no in-person involvement. Our planned enrollment is 4,800 participants, including 2,400 SARS-CoV-2 positive and 2,400 SARS-CoV-2 negative participants (as a concurrent comparison group). These data will allow assessment of longitudinal outcomes from SARS-CoV-2 infection and comparison of the relative risk of outcomes in individuals with and without infection. Patient-reported outcomes include self-reported health function and status, as well as clinical outcomes including health system encounters and new diagnoses.ResultsParticipating sites obtained institutional review board approval. Enrollment and follow-up are ongoing.ConclusionsThis study will characterize medium and long-term sequelae of SARS-CoV-2 infection among a diverse population, predictors of sequelae, and their relative risk compared to persons with similar symptomatology but without SARS-CoV-2 infection. These data may inform clinical interventions for individuals with sequelae of SARS-CoV-2 infection.

Additional file 1. Table S1. P-values of gender and race groups by Fisher���s exact test.

Patient stakeholders are getting increasingly involved in research health data networks, particularly as research partners. However, tools do not exist to help effectively orient, educate, and engage patient stakeholders as they take on these roles. Using a human centered design approach, we conducted a patient stakeholder needs assessment qualitative study to identify key user needs to drive design recommendations for development of an online education and engagement tool for research health data networks. We found three key needs related to multiple role identities, motivations and expectations for participation on research teams, and patient journeys. Design recommendations derived from the needs assessment are discussed that can support future tool design and testing.

Lightning Talk for NSF SI2 PI Meeting 2018: Widespread disruption of drinking water distribution systems in Puerto Rico following Hurricane Maria poses a significant risk to human health. Thus, it is necessary to strategically archive and disseminate water resources data relevant to public health and environmental concerns. This project seeks to design and test a prototype scientific cyberinfrastructure that integrates existing hardware and software platforms for the storage and curation of water resources data and analytical tools following natural disasters that cause loss of public utilities.

Poster for NSF SI2 PI Meeting 2018: Widespread disruption of drinking water distribution systems in Puerto Rico following Hurricane Maria poses a significant risk to human health. Thus, it is necessary to strategically archive and disseminate water resources data relevant to public health and environmental concerns. This project seeks to design and test a prototype scientific cyberinfrastructure that integrates existing hardware and software platforms for the storage and curation of water resources data and analytical tools following natural disasters that cause loss of public utilities.