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Colonoscopy is one of the primary methods of screening for colorectal cancer (CRC), a leading cause of cancer mortality in the United States. However, up to half of patients referred to colonoscopy fail to complete the procedure, and rates of adherence are lower in rural areas. Colonoscopy Outreach for Rural Communities (CORC) is a randomized controlled trial to test the effectiveness of a centralized patient navigation program provided remotely by a community-based organization to six geographically distant primary care organizations serving rural patients, to improve colonoscopy completion for CRC. CORC is a type 1 hybrid implementation-effectiveness trial. Participants aged 45–76 from six primary care organizations serving rural populations in the northwestern United States are randomized 1:1 to patient navigation or standard of care control. The patient navigation is delivered remotely by a trained lay-person from a community-based organization. The primary effectiveness outcome is completion of colonoscopy within one year of referral to colonoscopy. Secondary outcomes are colonoscopy completion within 6 and 9 months, time to completion, adequacy of patient bowel preparation, and achievement of cecal intubation. Analyses will be stratified by primary care organization. Trial results will add to our understanding about the effectiveness of patient navigation programs to improve colonoscopy for CRC in rural communities. The protocol includes pragmatic adaptations to meet the needs of rural communities and findings may inform approaches for future studies and programs. Trial registration: National Clinical Trial Identifier: NCT05453630. Trial registration: ClinicalTrials.gov. Identifier: NCT05453630. Registered July 6, 2022. [ABSTRACT FROM AUTHOR]

Introduction: The emergence of the COVID-19 pandemic and subsequent public health mitigation strategies resulted in rapid and significant changes to delivery of primary care. The field of primary care faced an unprecedented dual challenge of providing routine care to patients while ensuring patient and staff safety and managing patients with a highly transmissible disease. This study describes how a diverse group of primary care practices addressed these challenges at the start of the COVID-19 pandemic, in Spring 2020. Methods: A cross-sectional electronic survey of representatives from primary care practices in the WWAMI region Practice and Research Network (WPRN). Survey topics included clinical workforce, operations, and use of telemedicine in the first 3 months of the COVID-19 pandemic. Results: To safely manage patients with COVID-19 symptoms all clinics modified operations; 81.3% diverted patients with respiratory symptoms to a telemedicine evaluation, 68.8% diverted these patients to be seen in-person at another location, and 75% made in-clinic changes to maintain safety. The set of operational changes employed by clinics was diverse. To continue to provide routine patient care, all clinics employed telemedicine. Over 80% of clinics had never used telemedicine prior to March 2020. Conclusions: A diverse group of primary care clinics all rapidly implemented a variety of operational adaptations to address patient needs and maintain patient and staff safety at the onset of the COVID- 19 pandemic. Telemedicine, together with other measures, provided critical pathways for maintaining delivery of care. [ABSTRACT FROM AUTHOR]

We use prescription of statin medications and prescription of warfarin to explore the capacity of electronic health record data to (1) describe cohorts of patients prescribed these medications and (2) identify cohorts of patients with evidence of adverse events related to prescription of these medications. This study was conducted in the WWAMI region Practice and Research Network (WPRN)., a network of primary care practices across Washington, Wyoming, Alaska, Montana and Idaho DataQUEST, an electronic data-sharing infrastructure. We used electronic health record data to describe cohorts of patients prescribed statin or warfarin medications and reported the proportions of patients with adverse events. Among the 35,445 active patients, 1745 received at least one statin prescription and 301 received at least one warfarin prescription. Only 3 percent of statin patients had evidence of myopathy; 51 patients (17% of those prescribed warfarin) had a bleeding complication. Primary-care electronic health record data can effectively be used to identify patients prescribed specific medications and patients potentially experiencing medication adverse events. [ABSTRACT FROM AUTHOR]

Context: The widespread adoption of electronic health records (EHRs) offers significant opportunities to conduct research with clinical data from patients outside traditional academic research settings. Because EHRs are designed primarily for clinical care and billing, significant challenges are inherent in the use of EHR data for clinical and translational research. Efficient processes are needed for translational researchers to overcome these challenges. The Data QUEST Coordinating Center (DQCC), which oversees Data QUEST - a primary care EHR data sharing infrastructure - created processes that that guide EHR data extraction for clinical and translational research across these diverse practices. We describe these processes and their application in a case example. Case Description: The DQCC process for developing EHR data extractions not only supports researchers access to EHR data, but supports this access for the purpose of answering scientific questions. This process requires complex coordination across multiple domains, including: 1) understanding the context of EHR data; 2) creating and maintaining a governance structure to support exchange of EHR data; and 3) defining data parameters that are used in order to extract data from the EHR.1,2,3,4We use the Northwest-Alaska Pharmacogenomics Research Network (NWA-PGRN) as a case example that focuses on pharmacogenomic discovery and clinical applications to describe the DQCC process. The NWA-PGRN collaborates with Data QUEST to explore ways to leverage primary care EHR data to support pharmacogenomics research. Findings: Preliminary analysis on the case example shows that initial decisions about how researchers define the study population can influence study outcomes. Major Themes and Conclusions: The experience of the DQCC demonstrates that Coordinating Centers provide expertise in helping researchers understand the context of EHR data, create and maintain governance structures, and guide the definition of parameters for data extractions. This expertise is critical to support research with EHR data. Replication of these strategies through Coordinating Centers may lead to more efficient translational research. Investigators must also consider the impact of initial decisions in defining study groups that may potentially affect outcomes. Acknowledgements We acknowledge the Northwest Alaska Pharmacogenomics Research Network group for supporting the infrastructure and data collection, and Imara West for her assistance in data cleaning and analysis. This project was funded by the National Institute of General Medical Science (U01 GM092676) and the National Center for Advancing Translational Sciences of the National Institutes of Health (UL1TR000423). The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health [ABSTRACT FROM AUTHOR]

Background: Practice-based research networks (PBRNs) promote the conduct of research in real-world settings by engaging primary care clinicians as champion research collaborators. Card studies are brief surveys administered to patients or clinicians at the point of care. The objective of this paper is to describe the design and evaluation of a card study methodology that the WWAMI Region Practice and Research Network (WPRN) used to develop research partnerships across multiple member sites. Methods: We used a collaborative model to develop, implement and disseminate the results of a network-wide card study to assess patient preferences for weight loss in primary care. After the card study data collection was completed, we conducted individual and focus group interviews and a brief survey of participating practice champions. Results: Increased research engagement and personal and professional developments were the primary motivators for participating in the development of the card study. Increasing research activity at practices and learning information about patients were motivators for implementing the study. Their participation resulted in champions reporting increased confidence in collaborating on research projects as well as the development of new clinical services for patients. Discussion: This collaborative model positively influenced research capacity in the WPRN and may be a useful strategy for helping PBRNs conducted translational research. [ABSTRACT FROM AUTHOR]

Practice-based research networks bring together academic researchers and primary care clinicians to conduct research that improves health outcomes in real-world settings. The Washington, Wyoming, Alaska, Montana, and Idaho region Practice and Research Network implemented a health data-sharing infrastructure across 9 clinics in 3 primary care organizations. Following implementation, we identified challenges and solutions. Challenges included working with diverse primary care organizations, adoption of health information data-sharing technology in a rapidly changing local and national landscape, and limited resources for implementation. Overarching solutions included working with a multidisciplinary academic implementation team, maintaining flexibility, and starting with an established network for primary care organizations. Approaches outlined may generalize to similar initiatives and facilitate adoption of health data sharing in other practice-based research networks. [ABSTRACT FROM AUTHOR]

Background: Most people with alcohol or opioid use disorders (AUD or OUD) are not diagnosed or treated for these conditions in primary care. This study takes a critical step toward quantifying service gaps and directing improvement efforts for AUD and OUD by using electronic health record (EHR) data from diverse primary care organizations to quantify the extent to which AUD and OUD are underdiagnosed and undertreated in primary care practices.Methods: We extracted and integrated diagnosis, medication, and behavioral health visit data from the EHRs of 21 primary care clinics within four independent healthcare organizations representing community health centers and rural hospital-associated clinics in the Pacific Northwest United States. Rates of documented AUD and OUD diagnoses, pharmacological treatments, and behavioral health visits were evaluated over a two-year period (2015-2016).Results: Out of 47,502 adult primary care patients, 1476 (3.1%) had documented AUD; of these, 115 (7.8%) had orders for AUD medications and 271 (18.4%) had at least one documented visit with a non-physician behavioral health specialist. Only 402 (0.8%) patients had documented OUD, and of these, 107 (26.6%) received OUD medications and 119 (29.6%) had at least one documented visit with a non-physician behavioral health specialist. Rates of AUD diagnosis and AUD and OUD medications were higher in clinics that had co-located non-physician behavioral health specialists.Conclusions: AUD and OUD are underdiagnosed and undertreated within a sample of independent primary care organizations serving mostly rural patients. Primary care organizations likely need service models, technologies, and workforces, including non-physician behavioral health specialists, to improve capacities to diagnose and treat AUD and OUD. [ABSTRACT FROM AUTHOR]

Objective: To evaluate the ability of electronic health record (EHR) data extracted into a data-sharing system to accurately identify contraceptive use.Study Design: We compared rates of contraceptive use from electronic extraction of EHR data via a data-sharing system and manual abstraction of the EHR among 142 female patients ages 15-49 years from a family medicine clinic within a primary care practice-based research network (PBRN). Cohen's kappa coefficient measured agreement between electronic extraction and manual abstraction.Results: Manual abstraction identified 62% of women as contraceptive users, whereas electronic extraction identified only 27%. Long acting reversible (LARC) methods had 96% agreement (Cohen's kappa 0.78; confidence interval, 0.57-0.99) between electronic extraction and manual abstraction. EHR data extracted via a data-sharing system was unable to identify barrier or over-the-counter contraceptives.Conclusions: Electronic extraction found substantially lower overall rates of contraceptive method use, but produced more comparable LARC method use rates when compared to manual abstraction among women in this study's primary care clinic.Implications: Quality metrics related to contraceptive use that rely on EHR data in this study's data-sharing system likely under-estimated true contraceptive use. [ABSTRACT FROM AUTHOR]