{"id":41,"date":"2017-08-31T18:19:20","date_gmt":"2017-08-31T18:19:20","guid":{"rendered":"https:\/\/dataquest-dev.iths.org\/?page_id=41"},"modified":"2026-02-18T19:31:14","modified_gmt":"2026-02-18T19:31:14","slug":"research-2","status":"publish","type":"page","link":"https:\/\/familymedicine.uw.edu\/stephensgroup\/research-2\/","title":{"rendered":"Research"},"content":{"rendered":"

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RESEARCH IN BEHAVIORAL HEALTH IN PRIMARY CARE, INFORMATICS,\u00a0CHRONIC PAIN, AND SUBSTANCE USE DISORDER<\/h1>\n

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Clinical Research Informatics\u00a0<\/strong><\/span><\/h2>\n

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Data QUEST<\/a><\/strong>
The Data QUEST system provides access to research datasets from a network of community-based, primary care clinics in the Washington, Wyoming, Alaska, Montana, and Idaho (WWAMI) region. Our partners within these clinics are interested in participating in research and helping catalyze health discovery. Rich data from electronic health record systems are vital to creating a dynamic learning healthcare environment that can propel discoveries forward. Impacting the health of the population requires interventions in primary care settings. Data QUEST is partnered with\u00a0the Institute of Translational Health Sciences<\/a> and <\/span>support<\/span> by a multitude of funded research projects.\u00a0<\/span><\/p>\n

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[\/et_pb_text][et_pb_toggle title=”More Information” open_toggle_background_color=”rgba(0,0,0,0)” closed_toggle_text_color=”#0c71c3″ closed_toggle_background_color=”rgba(0,0,0,0)” icon_color=”rgba(0,0,0,0)” _builder_version=”3.12.2″ title_font=”|700|||||||” title_text_align=”center” title_text_color=”#0c71c3″ body_font=”||||||||” body_text_color=”#666666″ body_font_size=”16px” body_line_height=”1.2em” border_width_all=”0px” border_color_all=”rgba(0,0,0,0)” text_orientation=”left” module_alignment=”center” custom_margin=”-30px|||”]<\/p>\n

Publications<\/strong>
Cole AM, Stephens KA<\/b>, Keppel<\/span> GA, Estiri H, Baldwin LM. Extracting Electronic Health Record Data in a Practice-Based Research Network: Processes to Support Translational Research across Diverse Practice Organizations. <\/a>EGEMS (Wash DC). 2016;4(2):1206.<\/p>\n

Lin CP, Stephens KA<\/b>, Baldwin LM, Keppel GA, Whitener RJ, Echo-Hawk A, Korngiebel D. Developing Governance for Federated Community-based EHR Data Sharing. <\/a>AMIA Jt Summits Transl Sci Proc. 2014;2014:71-6. eCollection 2014.<\/p>\n

Cole AM, Stephens KA<\/b>, Keppel GA, Lin CP, Baldwin LM. Implementation of a health data-sharing infrastructure across diverse primary care organizations. <\/a>J Ambul Care Manage. 2014 Apr-Jun;37(2):164-70.<\/p>\n

Stephens KA<\/b>, Lin CP, Baldwin LM, Echo-Hawk A, Keppel GA, Buchwald D, Whitener RJ, Korngiebel DM, Berg AO, Black RA, Tarczy-Hornoch P. LC Data QUEST: A Technical Architecture for Community Federated Clinical Data Sharing. <\/a>AMIA Jt Summits Transl Sci Proc. 2012;2012:57-62. Epub 2012 Mar 19.<\/p>\n

Lin CP, Black RA, Laplante J, Keppel GA, Tuzzio L, Berg AO, Whitener RJ, Buchwald DS, Baldwin LM, Fishman PA, Greene SM, Gennari JH, Tarczy-Hornoch P, Stephens KA<\/b>. Facilitating health data sharing across diverse practices and communities. <\/a>Summit Transl Bioinform. 2010 Mar 1;2010:16-20.<\/p>\n

[\/et_pb_toggle][\/et_pb_column][\/et_pb_row][et_pb_row custom_padding=”0|0px|8px|0px|false|false” _builder_version=”3.12.2″][et_pb_column type=”1_4″ _builder_version=”3.12.2″ parallax=”off” parallax_method=”on”][et_pb_image src=”https:\/\/familymedicine.uw.edu\/stephensgroup\/wp-content\/uploads\/sites\/11\/2021\/07\/model-brain-on-orange-scaled-e1626813734232.jpg” _builder_version=”3.12.2″ custom_margin=”10px|||”][\/et_pb_image][\/et_pb_column][et_pb_column type=”3_4″ _builder_version=”3.12.2″ parallax=”off” parallax_method=”on”][et_pb_text _builder_version=”3.12.2″ text_font=”||||||||” text_text_color=”#666666″ text_font_size=”20px” text_line_height=”1.2em” custom_margin=”|||”]<\/p>\n

Opioid Use following Traumatic Brain Injury (TBI) Study\u00a0\u00a0<\/b>\u00a0<\/b>
This observational study using retrospective chart review will combine data from the University of Washington EHR, prescription monitoring programs (PMP) and the Washington State Trauma Registry (WTR) to characterize opioid use following TBI in the trauma ICU and inpatient rehabilitation settings, and examine the association of opioid use during hospitalization on outpatient use. Funded by the Garvey Institute for Brain Health Solutions<\/a>.<\/span><\/p>\n

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Abstract<\/strong>
Starosta A, Humbert A, Prado MG, Stephens KA, Hoffman J. (2022, April). Characterization of opioid use during hospital care for traumatic brain injury<\/a>.<\/em>\u00a0[Abstract and poster]. Society of Behavioral Medicine 2022 Annual Meeting<\/p>\n

Publications<\/strong><\/p>\n

Starosta, AJ, Mata-Greve, F, Au, MA, Prado, MG, Mollis, B, Stephens, KA, <\/strong>& Hoffman, J. (2023). Intersectionality of Systemic Disadvantage on Mortality and Care Following TBI<\/a>.\u00a0<\/span>J Head Trauma Rehabil<\/i>. 2023;38(2):137-146. doi:10.1097\/HTR.0000000000000830<\/span><\/p>\n

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Flu Study\u00a0 <\/b><\/a>\u00a0<\/b>
Up to 80,000 people in the United States die from the flu each year, and more than 30 million people get sick and suffer. This study used predictive analytics to develop new tools to detect, monitor, and respond to flu outbreaks in order to enhance our ability to stop the spread of flu. Funded by the Brotman Baty Institute<\/a>.\u00a0<\/p>\n

[\/et_pb_text][et_pb_toggle title=”More Information” open_toggle_background_color=”rgba(0,0,0,0)” closed_toggle_text_color=”#0c71c3″ closed_toggle_background_color=”rgba(0,0,0,0)” icon_color=”rgba(0,0,0,0)” _builder_version=”3.12.2″ title_font=”|700|||||||” title_text_align=”center” title_text_color=”#0c71c3″ body_font=”||||||||” body_text_color=”#666666″ body_font_size=”16px” body_line_height=”1.2em” border_width_all=”0px” border_color_all=”rgba(0,0,0,0)” text_orientation=”left” module_alignment=”center” custom_margin=”-30px|||”]<\/p>\n

Publications<\/strong>
Stephens KA, Au MA, Yetisgen M, Lutz B, Zigman Suchlsland M, Ebell MH, Thompson M. Leveraging UMLS-driven NLP to enhance identification of influenza predictors derived from electronic medical record data.<\/a> bioRxiv 2020 Apr. Preprint.\u00a0<\/span><\/p>\n

[\/et_pb_toggle][\/et_pb_column][\/et_pb_row][et_pb_row custom_padding=”0|0px|26px|0px|false|false” _builder_version=”3.12.2″][et_pb_column type=”1_4″ _builder_version=”3.12.2″ parallax=”off” parallax_method=”on”][et_pb_image src=”https:\/\/familymedicine.uw.edu\/stephensgroup\/wp-content\/uploads\/sites\/11\/2020\/07\/upper-body-944557_1280.jpg” _builder_version=”3.12.2″ custom_margin=”10px|||”][\/et_pb_image][\/et_pb_column][et_pb_column type=”3_4″ _builder_version=”3.12.2″ parallax=”off” parallax_method=”on”][et_pb_text _builder_version=”3.12.2″ text_font=”||||||||” text_text_color=”#666666″ text_font_size=”20px” text_line_height=”1.2em”]<\/p>\n

Development and pilot testing of a measurement tool for the diagnosis of lung cancer<\/b><\/a>
This project will develop and test a new method for measuring and tracking the timeliness of the patient\u2019s cancer trajectory from first onset of symptoms to final diagnosis of lung cancer. This measure will draw information from the electronic medical record as well as from patients and their caregivers. The goal of this measure is to identify places in the diagnostic pathway where improvements are needed to improve the diagnosis and eventual outcomes for lung cancer.\u00a0Funded by the Moore Foundation.<\/p>\n

[\/et_pb_text][et_pb_toggle title=”More Information” open_toggle_background_color=”rgba(0,0,0,0)” closed_toggle_text_color=”#0c71c3″ closed_toggle_background_color=”rgba(0,0,0,0)” icon_color=”rgba(0,0,0,0)” _builder_version=”3.12.2″ title_font=”|700|||||||” title_text_align=”center” title_text_color=”#0c71c3″ body_font=”||||||||” body_text_color=”#666666″ body_font_size=”16px” body_line_height=”1.2em” border_width_all=”0px” border_color_all=”rgba(0,0,0,0)” text_orientation=”left” module_alignment=”center” custom_margin=”-30px|||”]<\/p>\n

Publications<\/strong><\/p>\n

Thompson, M, Zigman Suchsland, M, Kowalski, L, Burkhardt, H, Prado, MG, Kessler, LG, Yetisgen, M, Au, MA, Stephens, KA, <\/strong>Farjah, F, Schleyer, AM, Walter, FM, Neal, RD, Lybarger, K, Thompson, C, Al Achkar, M, Sarma, EA, Turner, G. (in press). Laying the foundation for a clinical quality measure for timeliness of diagnosis of lung cancer: Cohort study of individual with lung cancer presenting in ambulatory care in the United States. Cancers (special issue). <\/em>PMCID: in progress<\/p>\n

Prado, MG, Kessler, LG, Au, MA, Burkhardt, H, Zigman Suchsland, M, Kowalski, L, Stephens, KA, <\/strong>Yetisgen, M, Walter, FM, Neal, RD, Lybarger, K, Thompson, C, Al Achkar, M, Sarma, EA, Turner, G, Farjah, F, & Thompson, M (2023). Symptoms and signs of lung cancer prior to diagnosis: Case-control study using electronic health records from ambulatory care within a large US-based tertiary care center.<\/a> BMJ Open, 13, <\/em>e068832. PMCID: PMC10124310<\/p>\n

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Patient-Centered Scalable National Network for Effectiveness Research (pSCANNER)<\/b><\/a>\u00a0<\/b>
This study developed a stakeholder governed federated network of electronic health data as part of PCORnet, a national patient-centered clinical research network. UW is creating an online website to improve health communication for patient and clinical stakeholders using the pSCANNER network. Funded by the Patient Centered Outcomes Research Institute.<\/p>\n

[\/et_pb_text][et_pb_toggle title=”More Information” open_toggle_background_color=”rgba(0,0,0,0)” closed_toggle_text_color=”#0c71c3″ closed_toggle_background_color=”rgba(0,0,0,0)” icon_color=”rgba(0,0,0,0)” _builder_version=”3.12.2″ title_font=”|700|||||||” title_text_align=”center” title_text_color=”#0c71c3″ body_font=”||||||||” body_text_color=”#666666″ body_font_size=”16px” body_line_height=”1.2em” border_width_all=”0px” border_color_all=”rgba(0,0,0,0)” text_orientation=”left” module_alignment=”center” custom_margin=”-30px|||”]<\/p>\n

Publications<\/strong>
Stephens KA<\/strong>, Osterhage KP, Fiore-Gartland B, Lovins TL, Keppel GA, Kim KK. <\/span>Examining the Needs of Patient Stakeholders as Research Partners in Health Data Networks for Translational Research. <\/a><\/span>AMIA Jt Summits Transl Sci Proc<\/span>. <\/span>2019;<\/span>2019<\/span>:363-369<\/span>. <\/span>eCollection 2019.<\/span><\/p>\n

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Leaf: An Open-Source, Model-Agnostic, Data-Driven Web Application for Cohort Discovery and Translational Biomedical Research<\/b><\/b><\/a>
Leaf, a successful production-supported tool at the University of Washington, collaborated with DataQUEST to develop a proof of concept use case for a multisite version of Leaf and a live OMOP network, yielding improved speed and self-service of cohort discovery within the Data QUEST Coordinating Center and the potential to support Leaf for community partners.<\/p>\n

[\/et_pb_text][et_pb_toggle title=”More Information” open_toggle_background_color=”rgba(0,0,0,0)” closed_toggle_text_color=”#0c71c3″ closed_toggle_background_color=”rgba(0,0,0,0)” icon_color=”rgba(0,0,0,0)” _builder_version=”3.12.2″ title_font=”|700|||||||” title_text_align=”center” title_text_color=”#0c71c3″ body_font=”||||||||” body_text_color=”#666666″ body_font_size=”16px” body_line_height=”1.2em” border_width_all=”0px” border_color_all=”rgba(0,0,0,0)” text_orientation=”left” module_alignment=”center” custom_margin=”-30px|||”]<\/p>\n

Publications<\/strong>
Dobbins NJ, Spital CH, Black RA, Morrison JM, de Veer B, Zampino E, Harrington RD, Britt BD, Stephens KA, Wilcox AB, Tarczy-Hornoch P, Mooney SD. Leaf: an open-source, model-agnostic, data-driven web application for cohort discovery and translational biomedical research. <\/a><\/span>J Am Med Inform Assoc. 2020 Jan 1;27(1):109-118.<\/p>\n

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Identification and Characterization of Patients Prescribed Statins and Warfarin<\/strong>
This study explored the capacity of electronic health record (EHR) data to describe cohorts of patients prescribed\u00a0statin medications warfarin, and to identify cohorts of patients with evidence of adverse events related to prescription of these medications.\u00a0This project was supported by the National Institutes of Health under Awards Number UL1 TR000423 and U01 GM092676-05.<\/p>\n

[\/et_pb_text][et_pb_toggle title=”More Information” open_toggle_background_color=”rgba(0,0,0,0)” closed_toggle_text_color=”#0c71c3″ closed_toggle_background_color=”rgba(0,0,0,0)” icon_color=”rgba(0,0,0,0)” _builder_version=”3.12.2″ title_font=”|700|||||||” title_text_align=”center” title_text_color=”#0c71c3″ body_font=”||||||||” body_text_color=”#666666″ body_font_size=”16px” body_line_height=”1.2em” border_width_all=”0px” border_color_all=”rgba(0,0,0,0)” text_orientation=”left” module_alignment=”center” custom_margin=”-30px|||”]Publications<\/strong>
\nFishbein\u00a0HA,\u00a0Jeffries\u00a0Birch R,\u00a0Mathew\u00a0SM,\u00a0Sawyer\u00a0HL,\u00a0Pulver\u00a0G,\u00a0Poling\u00a0J,\u00a0Kaelber D,\u00a0Mardon R,\u00a0Johnson MC,\u00a0Pace W,\u00a0Umbel KD,\u00a0Zhang X,\u00a0Siegel KR,\u00a0Imperatore G,\u00a0Shrestha S,\u00a0Proia K,\u00a0Cheng Y,\u00a0McKeever Bullard K,\u00a0Gregg EW,\u00a0Rolka D,\u00a0Pavkov ME.\u00a0The Longitudinal\u00a0Epidemiologic Assessment of Diabetes Risk (LEADR): Unique 1.4\u00a0M patient Electronic Health Record cohort<\/a>.\u00a0Healthcare. 2020 8(4).<\/em>[\/et_pb_toggle][\/et_pb_column][\/et_pb_row][et_pb_row custom_padding=”0|0px|0|0px|false|false” _builder_version=”3.12.2″][et_pb_column type=”1_4″ _builder_version=”3.12.2″ parallax=”off” parallax_method=”on”][et_pb_image src=”https:\/\/familymedicine.uw.edu\/stephensgroup\/wp-content\/uploads\/sites\/11\/2020\/07\/diabetes-777002_960_720.jpg” align=”center” _builder_version=”3.12.2″ custom_margin=”|||”][\/et_pb_image][\/et_pb_column][et_pb_column type=”3_4″ _builder_version=”3.12.2″ parallax=”off” parallax_method=”on”][et_pb_text _builder_version=”3.12.2″ text_font=”||||||||” text_text_color=”#666666″ text_font_size=”20px” text_line_height=”1.2em” custom_margin=”|||” custom_padding=”||0px|” custom_css_main_element=” body {|| padding: 30px;||}||||html {|| overflow-y: scroll;||}||||body {|| background: #EFEFEF;||}||||ul {|| margin-bottom: 0;||}||||.accordion {|| background: #FFF;|| border-radius: 5px;|| padding: 30px;||}||||.question {|| border-top: 1px solid #EEE;|| padding: 20px;|| cursor: pointer;|| position: relative;||}||||.question h2 {|| font-size: 20px;|| margin: 0;|| color: #C00C00;||}||||.question span {|| position: absolute;|| right: 20px;|| top: 0;|| height: 100%;|| display: flex;|| align-items: center;|| color: #C00C00;|| transition: .75s cubic-bezier(0.175, 0.885, 0.32, 1.275) all;||}||||.answer {|| max-height: 0;|| overflow: hidden;|| transition: .75s cubic-bezier(0.175, 0.885, 0.32, 1.275) all;||}||||.answer p {|| padding: 20px;||}||||.accordion li.open .question span {|| transform: rotate(180deg);||}||||.accordion li.open .answer {|| max-height: 150px;||}”]<\/p>\n

LEADR: Longitudinal Epidemiologic Assessment of Diabetes Risk<\/b><\/a>
This study created an electronic health record-generated patient cohort to study progression to diabetes and related morbidity, and examine the role that access to and delivery of preventive care has in the prevention and control of diabetes. Funded by the Centers for Disease Control.<\/p>\n

[\/et_pb_text][et_pb_toggle title=”More Information” open_toggle_background_color=”rgba(0,0,0,0)” closed_toggle_text_color=”#0c71c3″ closed_toggle_background_color=”rgba(0,0,0,0)” icon_color=”rgba(0,0,0,0)” _builder_version=”3.12.2″ title_font=”|700|||||||” title_text_align=”center” title_text_color=”#0c71c3″ body_font=”||||||||” body_text_color=”#666666″ body_font_size=”16px” body_line_height=”1.2em” border_width_all=”0px” border_color_all=”rgba(0,0,0,0)” text_orientation=”left” module_alignment=”center” custom_margin=”-30px|||”]Publications<\/strong>
\nFishbein\u00a0HA,\u00a0Jeffries\u00a0Birch R,\u00a0Mathew\u00a0SM,\u00a0Sawyer\u00a0HL,\u00a0Pulver\u00a0G,\u00a0Poling\u00a0J,\u00a0Kaelber D,\u00a0Mardon R,\u00a0Johnson MC,\u00a0Pace W,\u00a0Umbel KD,\u00a0Zhang X,\u00a0Siegel KR,\u00a0Imperatore G,\u00a0Shrestha S,\u00a0Proia K,\u00a0Cheng Y,\u00a0McKeever Bullard K,\u00a0Gregg EW,\u00a0Rolka D,\u00a0Pavkov ME.\u00a0The Longitudinal\u00a0Epidemiologic Assessment of Diabetes Risk (LEADR): Unique 1.4\u00a0M patient Electronic Health Record cohort<\/a>.\u00a0Healthcare. 2020 8(4).<\/em>[\/et_pb_toggle][\/et_pb_column][\/et_pb_row][et_pb_row custom_padding=”43px|0px|0|0px|false|false” _builder_version=”3.12.2″][et_pb_column type=”1_4″ _builder_version=”3.12.2″ parallax=”off” parallax_method=”on”][et_pb_image src=”https:\/\/familymedicine.uw.edu\/stephensgroup\/wp-content\/uploads\/sites\/11\/2020\/07\/uterus-3777765_960_720.jpg” _builder_version=”3.12.2″ custom_margin=”10px|||”][\/et_pb_image][\/et_pb_column][et_pb_column type=”3_4″ _builder_version=”3.12.2″ parallax=”off” parallax_method=”on”][et_pb_text _builder_version=”3.12.2″ text_font=”||||||||” text_text_color=”#666666″ text_font_size=”20px” text_line_height=”1.2em” custom_margin=”0px|||”]<\/p>\n

How Well are Contraceptive Guidelines Followed (CoACHD)<\/b>
This study used Data QUEST data to measure adherence to national contraceptive guidelines among women of childbearing age, assessed the validity of contraceptive data from the EHR compared to manually abstracted data. Funded by the Institute of Translational Health Sciences Community Partners in Research Pilot Grant.\u00a0<\/p>\n

[\/et_pb_text][et_pb_toggle title=”More Information” open_toggle_background_color=”rgba(0,0,0,0)” closed_toggle_text_color=”#0c71c3″ closed_toggle_background_color=”rgba(0,0,0,0)” icon_color=”rgba(0,0,0,0)” _builder_version=”3.12.2″ title_font=”|700|||||||” title_text_align=”center” title_text_color=”#0c71c3″ body_font=”||||||||” body_text_color=”#666666″ body_font_size=”16px” body_line_height=”1.2em” border_width_all=”0px” border_color_all=”rgba(0,0,0,0)” text_orientation=”left” module_alignment=”center” custom_margin=”-30px|||”]Publications<\/strong>
\nGodfrey EM, West II<\/b>, Holmes J, Keppel GA, Baldwin LM. Use of an electronic health record data sharing system for identifying current contraceptive use within the WWAMI region Practice and Research Network.<\/a> Contraception<\/i>. 2018;98(6):476\u2013481. PMID: 29936151; PMCID: PMC6913191.<\/p>\n

 [\/et_pb_toggle][\/et_pb_column][\/et_pb_row][et_pb_row custom_padding=”17px|0px|26px|0px|false|false” _builder_version=”3.12.2″][et_pb_column type=”1_4″ _builder_version=”3.12.2″ parallax=”off” parallax_method=”on”][et_pb_image src=”https:\/\/familymedicine.uw.edu\/stephensgroup\/wp-content\/uploads\/sites\/11\/2020\/07\/haley-lawrence-HTttz00wkzU-unsplash-scaled.jpg” _builder_version=”3.12.2″][\/et_pb_image][\/et_pb_column][et_pb_column type=”3_4″ _builder_version=”3.12.2″ parallax=”off” parallax_method=”on”][et_pb_text _builder_version=”3.12.2″ text_font=”||||||||” text_text_color=”#666666″ text_font_size=”20px” text_line_height=”1.2em” custom_padding=”13px|||”]Antibiotic Prescribing for Children in Family Medicine Clinics <\/b>
\nThis study aimed to provide a comprehensive characterization of antibiotic use for pediatric patients in family medicine clinics in order to identify opportunities for antibiotic stewardship. Funded by Seattle Children\u2019s Clinical Research Scholars Program Award.<\/p>\n

 [\/et_pb_text][\/et_pb_column][\/et_pb_row][et_pb_row custom_padding=”7px|0px|0|0px|false|false” _builder_version=”3.12.2″][et_pb_column type=”1_4″ _builder_version=”3.12.2″ parallax=”off” parallax_method=”on”][et_pb_image src=”https:\/\/familymedicine.uw.edu\/stephensgroup\/wp-content\/uploads\/sites\/11\/2020\/07\/data-quality-word-cloud.jpg” _builder_version=”3.12.2″ filter_hue_rotate=”118deg” filter_saturate=”43%”][\/et_pb_image][\/et_pb_column][et_pb_column type=”3_4″ _builder_version=”3.12.2″ parallax=”off” parallax_method=”on”][et_pb_text _builder_version=”3.12.2″ text_font=”||||||||” text_text_color=”#666666″ text_font_size=”20px” text_line_height=”1.2em” custom_margin=”0px|||”]<\/p>\n

Data Quality Testing Tools and Procedures<\/b>
A series of studies have been used to define standardized data quality procedures and develop a tool to test electronic health records data as it is refreshed in a data sharing infrastructure.<\/p>\n

[\/et_pb_text][et_pb_toggle title=”More Information” open_toggle_background_color=”rgba(0,0,0,0)” closed_toggle_text_color=”#0c71c3″ closed_toggle_background_color=”rgba(0,0,0,0)” icon_color=”rgba(0,0,0,0)” _builder_version=”3.12.2″ title_font=”|700|||||||” title_text_align=”center” title_text_color=”#0c71c3″ body_font=”||||||||” body_text_color=”#666666″ body_font_size=”16px” body_line_height=”1.2em” border_width_all=”0px” border_color_all=”rgba(0,0,0,0)” text_orientation=”left” module_alignment=”center” custom_margin=”-30px|||”]<\/p>\n

Explore data quality tools on GitHub<\/strong><\/a><\/p>\n

Publications<\/b>
West I<\/b>. DQe-c Report Standard Operating Procedures V2<\/a>. December 2016<\/p>\n

Estiri H, Stephens KA<\/b>, Klann JG, Murphy SN. Exploring completeness in clinical data research networks with DQe-c. <\/a>J Am Med Inform Assoc. 2018 Jan 1;25(1):17-24. PMID: 29069394; PMCID: PMC6481389.<\/p>\n

Estiri H, Stephens K<\/b>. DQe<\/sup>-v: A Database-Agnostic Framework for Exploring Variability in Electronic Health Record Data Across Time and Site Location. <\/a>EGEMS (Wash DC). 2017 May 10;5(1):3. PMID: 29930954; PMCID: PMC5994933.<\/p>\n

Estiri H, Chan YF, Baldwin LM, Jung H, Cole A, Stephens KA<\/b>. Visualizing Anomalies in Electronic Health Record Data: The Variability Explorer Tool. <\/a>AMIA Jt Summits Transl Sci Proc. 2015;2015:56-60. eCollection 2015. PMID: 26306237; PMCID: PMC4525227.<\/p>\n

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[\/et_pb_toggle][\/et_pb_column][\/et_pb_row][\/et_pb_section][et_pb_section fb_built=”1″ _builder_version=”3.19.12″ background_color=”#ffffff” custom_padding=”0|0px|4px|0px|false|false” custom_css_main_element=”max-width: 1400px;||margin: auto;”][et_pb_row make_fullwidth=”on” custom_padding=”15px|0px|4px|70px|false|false” custom_margin=”|||” _builder_version=”3.12.2″ background_color=”#1ed2b0″][et_pb_column type=”4_4″ _builder_version=”3.12.2″ parallax=”off” parallax_method=”on”][et_pb_text _builder_version=”3.12.2″ text_font=”||||||||” text_text_color=”#353535″ header_font=”||||||||” header_text_color=”rgba(53,53,53,0.81)”]<\/p>\n

Integrated Behavioral Health<\/strong><\/a><\/span><\/h2>\n

[\/et_pb_text][\/et_pb_column][\/et_pb_row][et_pb_row custom_padding=”26px|0px|7px|0px|false|false” _builder_version=”3.12.2″][et_pb_column type=”1_4″ _builder_version=”3.12.2″ parallax=”off” parallax_method=”on”][et_pb_image src=”https:\/\/familymedicine.uw.edu\/stephensgroup\/wp-content\/uploads\/sites\/11\/2020\/07\/IBHPC.png” _builder_version=”3.12.2″ max_width=”86%”][\/et_pb_image][\/et_pb_column][et_pb_column type=”3_4″ _builder_version=”3.12.2″ parallax=”off” parallax_method=”on”][et_pb_text _builder_version=”3.12.2″ text_font=”||||||||” text_text_color=”#666666″ text_font_size=”20px” text_line_height=”1.2em” custom_padding=”10px|||”]<\/p>\n

IBH-PC: Integrating Behavioral Health and Primary Care<\/b>\u00a0<\/a>
This study implemented and evaluated specific practice improvement strategies to generate a greater degree of behavioral health-primary care integration than usual, co-located behavioral health and primary care. Funded by the Patient Centered Outcomes Research Institute.\u00a0<\/p>\n

[\/et_pb_text][et_pb_toggle title=”More Information” open_toggle_background_color=”rgba(0,0,0,0)” closed_toggle_text_color=”#0c71c3″ closed_toggle_background_color=”rgba(0,0,0,0)” icon_color=”rgba(0,0,0,0)” _builder_version=”3.12.2″ title_font=”|700|||||||” title_text_align=”center” title_text_color=”#0c71c3″ body_font=”||||||||” body_text_color=”#666666″ body_font_size=”16px” body_line_height=”1.2em” border_width_all=”0px” border_color_all=”rgba(0,0,0,0)” text_orientation=”left” module_alignment=”center” custom_margin=”-30px|||”]<\/p>\n

Publications<\/strong><\/p>\n

Littenberg, B, Clifton, J, Crocker, AM, Baldwin, L, Bonnell, LN, Breshears, RE, Callas P, Chakravarti, P, Clark\/Keefe, K, Cohen, DJ, deGruy, F, Eidt-Pearson, L, Elder W, Fox, C, Frisbie, S, Hekman, K, Hitt J, Jewiss, J, Kaelber, DC, Kelley, KS, Kessler, R, O\u2019Rourke-Lavoie, JB, Leibowitz, GS, Macchi, CR, Martin, MP, McGovern, M, Mollis, B, Mullin, D, Nagykaldi, Z, Natkin, LW, Pace, W, Pinckney, RG, Pomeroy, D, Reynolds, P, Rose, GL, Scholle, SH, Sieber, WJ, Soucie J, Stancin, T, Stange, KC, Stephens, KA,<\/strong> Teng, K, Waddell, EN, van Eeghen, C. A cluster randomized trial of primary care practice redesign to integrate behavioral health for those who need it most: patients with multiple chronic conditions.<\/a> Annals of<\/em> Family Medicine. 2023;21.<\/em>\u00a0doi:10.1370\/afm.3027<\/sup><\/span><\/p>\n

Ma, KPK, Mollis, B, Rolfes, J, Au, M, Croker, A, Scholle, SH, Kessler, R, Baldwin, L, & Stephens, KA.\u00a0<\/strong>(2022). Payment strategies for behavioral health integration in hospital-affiliated and non-hospital-affiliated primary care practices<\/a> [published correction appears in Transl Behav Med. 2023 Feb 28;13(2):122].\u00a0Transl Behav Med<\/i>. 2022;12(8):878-883. doi:10.1093\/tbm\/ibac053<\/sup><\/span><\/p>\n

Crocker AM, Kessler R, van Eeghen C, Bonnell LN, Breshears RE, Callas P, Clifton J, Elder W, Fox C, Frisbie S, Hitt J, Jewiss J,\u00a0 Kathol R, Clark Keefe K, O’Rourke-Lavoie J, Leibowitz GS, Macchi CR, McGovern M, Mollis B<\/strong>, Mullin DJ, Nagykaldi Z, Natkin LW, Pace W, Pinckney RG, Pomeroy D, Pond A, Postupack R, Reynolds P, Rose GL, Scholle SH, Sieber WJ, Stancin T, Stange KC, Stephens KA<\/strong>, Teng T, Waddell EN, Littenberg B. Integrating Behavioral Health and Primary Care (IBH-PC) to improve patient-centered outcomes in adults with multiple chronic medical and behavioral health conditions: study protocol for a pragmatic cluster-randomized control trial.<\/a> Trials. <\/em>2021 March; 22(200).<\/sup><\/span><\/p>\n

[\/et_pb_toggle][\/et_pb_column][\/et_pb_row][et_pb_row custom_padding=”0|0px|0|0px|false|false” _builder_version=”3.12.2″][et_pb_column type=”1_4″ _builder_version=”3.12.2″ parallax=”off” parallax_method=”on”][et_pb_image src=”https:\/\/familymedicine.uw.edu\/stephensgroup\/wp-content\/uploads\/sites\/11\/2020\/07\/community-150125_1280.png” _builder_version=”3.12.2″ max_width=”80%”][\/et_pb_image][\/et_pb_column][et_pb_column type=”3_4″ _builder_version=”3.12.2″ parallax=”off” parallax_method=”on”][et_pb_text _builder_version=”3.12.2″ text_font=”||||||||” text_text_color=”#666666″ text_font_size=”20px” text_line_height=”1.2em”]<\/p>\n

Model Agnostic Framework for Integrated Behavioral Health<\/b>\u00a0<\/b><\/a>
Leveraging work with the IBH-PC study, this study culminated in the development of a model agnostic framework for fully integrated mental and medical care within primary care and other settings. This set of processes and structures stretch across models of integrated care and was adopted as the community standard by the Institute for Clinical Systems Improvement (ICSI) in Minnesota, who lead the MN Health Collaborative, which includes a statewide effort to improve IBH in primary care through their Mental Health Collaborative.<\/p>\n

[\/et_pb_text][et_pb_toggle title=”More Information” open_toggle_background_color=”rgba(0,0,0,0)” closed_toggle_text_color=”#0c71c3″ closed_toggle_background_color=”rgba(0,0,0,0)” icon_color=”rgba(0,0,0,0)” _builder_version=”3.12.2″ title_font=”|700|||||||” title_text_align=”center” title_text_color=”#0c71c3″ body_font=”||||||||” body_text_color=”#666666″ body_font_size=”16px” body_line_height=”1.2em” border_width_all=”0px” border_color_all=”rgba(0,0,0,0)” text_orientation=”left” module_alignment=”center” custom_margin=”-30px|||”]<\/p>\n

Publications<\/strong><\/p>\n

Stephens KA, van Eeghen C, Mollis B, Au M, Brennhofer SA, Martin M, Clifton J, Witwer E, Hansen A, Monkman J, Buchanan G, Kessler R.\u00a0Defining and measuring core processes and structures in integrated behavioral health in primary care: a cross-model framework.\u00a0<\/a>Transl Behav Med. 2020 Aug 7;10(3):527-538.<\/sup><\/span><\/p>\n

In the news<\/strong>
Framework helps primary care practices integrate behavioral health<\/a>\u00a0<\/span><\/p>\n

[\/et_pb_toggle][\/et_pb_column][\/et_pb_row][\/et_pb_section][et_pb_section fb_built=”1″ _builder_version=”3.19.12″ background_color=”#ffffff” custom_padding=”3px|0px|4px|0px|false|false” custom_css_main_element=”max-width: 1400px;||margin: auto;”][et_pb_row make_fullwidth=”on” custom_padding=”15px|0px|4px|70px|false|false” custom_margin=”|||” _builder_version=”3.12.2″ background_color=”#1ed2b0″][et_pb_column type=”4_4″ _builder_version=”3.12.2″ parallax=”off” parallax_method=”on”][et_pb_text _builder_version=”3.12.2″ text_font=”||||||||” text_text_color=”#353535″ header_font=”||||||||” header_text_color=”rgba(53,53,53,0.81)”]<\/p>\n

Chronic Pain<\/strong><\/span><\/h2>\n

<\/a>[\/et_pb_text][\/et_pb_column][\/et_pb_row][et_pb_row custom_padding=”26px|0px|11px|0px|false|false” _builder_version=”3.12.2″][et_pb_column type=”1_4″ _builder_version=”3.12.2″ parallax=”off” parallax_method=”on”][et_pb_image src=”https:\/\/familymedicine.uw.edu\/stephensgroup\/wp-content\/uploads\/sites\/11\/2021\/07\/cell-phone-app-scaled.jpg” _builder_version=”3.12.2″ custom_margin=”20px|||”][\/et_pb_image][\/et_pb_column][et_pb_column type=”3_4″ _builder_version=”3.12.2″ parallax=”off” parallax_method=”on”][et_pb_text _builder_version=”3.12.2″ text_font=”||||||||” text_text_color=”#666666″ text_font_size=”20px” text_line_height=”1.2em”]<\/p>\n

2Morrow<\/b><\/a>
This study evaluates a digital therapeutic, the \u201cSalty\u201d mobile app, created by 2Morrow Inc.<\/a>, aimed at improving the quality of life of patients with chronic pain through evidence-based behavioral change.<\/span>\u00a0Salty<\/span> delivers chronic pain education, screening tools, self-management with the goal of <\/span>facilitating<\/span> PCPs\u2019 ability to manage their patients with chronic pain effectively.<\/span><\/span>\u00a0<\/span><\/p>\n

[\/et_pb_text][et_pb_toggle title=”More Information” open_toggle_background_color=”rgba(0,0,0,0)” closed_toggle_text_color=”#0c71c3″ closed_toggle_background_color=”rgba(0,0,0,0)” icon_color=”rgba(0,0,0,0)” _builder_version=”3.12.2″ title_font=”|700|||||||” title_text_align=”center” title_text_color=”#0c71c3″ body_font=”||||||||” body_text_color=”#666666″ body_font_size=”16px” body_line_height=”1.2em” border_width_all=”0px” border_color_all=”rgba(0,0,0,0)” text_orientation=”left” module_alignment=”center” custom_margin=”-30px|||”]<\/p>\n

Publications<\/strong><\/p>\n

Ma, KPK., Stephens, KA<\/strong>, Geyer, R, Prado, MG, Mollis, BL, Zbikowski, SM, Waters, D, Masterson, J & Zhang, Y. (2023). Developing digital therapeutics for chronic pain in primary care:<\/a> A qualitative, human-centered design study of providers\u2019 motivations and challenges. JMIR Formative Research, Feb 3, 2023<\/em>, e41788. PMCID: PMC9938436<\/p>\n

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[\/et_pb_toggle][\/et_pb_column][\/et_pb_row][et_pb_row custom_padding=”26px|0px|11px|0px|false|false” _builder_version=”3.12.2″][et_pb_column type=”1_4″ _builder_version=”3.12.2″ parallax=”off” parallax_method=”on”][et_pb_image src=”https:\/\/familymedicine.uw.edu\/stephensgroup\/wp-content\/uploads\/sites\/11\/2020\/07\/istockphoto-1165527764-170667a.jpg” _builder_version=”3.12.2″][\/et_pb_image][\/et_pb_column][et_pb_column type=”3_4″ _builder_version=”3.12.2″ parallax=”off” parallax_method=”on”][et_pb_text _builder_version=”3.12.2″ text_font=”||||||||” text_text_color=”#666666″ text_font_size=”20px” text_line_height=”1.2em”]<\/p>\n

Lumbar Imaging with Reporting of Epidemiology (LIRE)<\/b><\/a>
A large pragmatic, cluster-randomized controlled trial testing the effectiveness of a simple, inexpensive, and easy to deploy intervention to reduce subsequent tests and treatments for back pain. The goal of the trial is to reduce subsequent tests and treatments, including cross-sectional imaging (such as magnetic resonance and computed tomography), opioid prescriptions, spinal injections, or surgery.<\/p>\n

[\/et_pb_text][et_pb_toggle title=”More Information” open_toggle_background_color=”rgba(0,0,0,0)” closed_toggle_text_color=”#0c71c3″ closed_toggle_background_color=”rgba(0,0,0,0)” icon_color=”rgba(0,0,0,0)” _builder_version=”3.12.2″ title_font=”|700|||||||” title_text_align=”center” title_text_color=”#0c71c3″ body_font=”||||||||” body_text_color=”#666666″ body_font_size=”16px” body_line_height=”1.2em” border_width_all=”0px” border_color_all=”rgba(0,0,0,0)” text_orientation=”left” module_alignment=”center” custom_margin=”-30px|||”]Publications<\/strong>
\nJarvik JG, Meier EN, James KT, Gold LS, Tan KW, Kessler LG, Suri P, Kallmes DF, Cherkin DC, Deyo RA, Sherman KJ, Halabi SS, Comstock BA, Luetmer PH, Avins AL, Rundell SD, Griffith B, Friedly JL, Lavallee DC, Stephens KA<\/strong>, Turner JA, Bresnahan BW, Heagerty PJ. The Effect of Including Benchmark Prevalence Data of Common Imaging Findings in Spine Image Reports on Health Care Utilization Among Adults Undergoing Spine Imaging: A Stepped-Wedge Randomized Clinical Trial.<\/a> JAMA Netw Open. 2020 Sep 1;3(9):e2015713.<\/p>\n

Jarvik JG, Comstock BA, James KT, Avins AL, Bresnahan BW, Deyo RA, Luetmer PH, Friedly JL, Meier EN, Cherkin DC, Gold LS, Rundell SD, Halabi SS, Kallmes DF, Tan KW, Turner JA, Kessler LG, Lavallee DC, Stephens KA<\/b>, Heagerty PJ. Lumbar Imaging With Reporting Of Epidemiology (LIRE)–Protocol for a pragmatic cluster randomized trial. <\/a>Contemp Clin Trials. 2015 Nov;45(Pt B):157-163.<\/p>\n

 [\/et_pb_toggle][\/et_pb_column][\/et_pb_row][et_pb_row custom_padding=”1px|0px|0|0px|false|false” _builder_version=”3.12.2″][et_pb_column type=”1_4″ _builder_version=”3.12.2″ parallax=”off” parallax_method=”on”][et_pb_image src=”https:\/\/familymedicine.uw.edu\/stephensgroup\/wp-content\/uploads\/sites\/11\/2020\/07\/pills-2333023__480.jpg” _builder_version=”3.12.2″][\/et_pb_image][\/et_pb_column][et_pb_column type=”3_4″ _builder_version=”3.12.2″ parallax=”off” parallax_method=”on”][et_pb_text _builder_version=”3.12.2″ text_font=”||||||||” text_text_color=”#666666″ text_font_size=”20px” text_line_height=”1.2em” custom_padding=”0px|||”]<\/p>\n

Pilot Trial of Opioid Taper Support (POTS)<\/b>
A randomized controlled trial for a prescription opioid taper support intervention. This intervention aims to prevent opioid misuse and adverse events among patients receiving chronic opioid therapy for chronic non-cancer pain without evidence of current substance abuse. Funded by the National Institute on Drug Abuse<\/a>.\u00a0<\/p>\n

[\/et_pb_text][et_pb_toggle title=”More Information” open_toggle_background_color=”rgba(0,0,0,0)” closed_toggle_text_color=”#0c71c3″ closed_toggle_background_color=”rgba(0,0,0,0)” icon_color=”rgba(0,0,0,0)” _builder_version=”3.12.2″ title_font=”|700|||||||” title_text_align=”center” title_text_color=”#0c71c3″ body_font=”||||||||” body_text_color=”#666666″ body_font_size=”16px” body_line_height=”1.2em” border_width_all=”0px” border_color_all=”rgba(0,0,0,0)” text_orientation=”left” module_alignment=”center” custom_margin=”-30px|||”]<\/p>\n

Publications<\/strong>
Sullivan MD, Turner JA, DiLodovico C, D’Appollonio A, Stephens K<\/b>, Chan YF. Prescription Opioid Taper Support for Outpatients With Chronic Pain: A Randomized Controlled Trial. <\/a>J Pain. 2017 Mar;18(3):308-318.<\/p>\n

Stephens KA,<\/b> Ward A. Patient selection for spinal cord stimulators: mental health perspective. <\/a>Curr Pain\u00a0\u00a0\u00a0 Headache Rep. 2014 Mar;18(3):398.<\/p>\n

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[\/et_pb_toggle][\/et_pb_column][\/et_pb_row][et_pb_row custom_padding=”45px|0px|0|0px|false|false” _builder_version=”3.12.2″][et_pb_column type=”1_4″ _builder_version=”3.12.2″ parallax=”off” parallax_method=”on”][et_pb_image src=”https:\/\/familymedicine.uw.edu\/stephensgroup\/wp-content\/uploads\/sites\/11\/2020\/07\/back-pain-5318474__480.png” _builder_version=”3.12.2″][\/et_pb_image][\/et_pb_column][et_pb_column type=”3_4″ _builder_version=”3.12.2″ parallax=”off” parallax_method=”on”][et_pb_text _builder_version=”3.12.2″ text_font=”||||||||” text_text_color=”#666666″ text_font_size=”20px” text_line_height=”1.2em” custom_padding=”0px|||”]<\/p>\n

Collaborative Care for Chronic Pain and Behavioral Health in Washington State<\/b>
The goal of this study is to launch a new behavioral health stepped collaborative care program for injured workers in Washington State who suffer from chronic pain and behavioral health issues. Funded by the WA State Department of Labor and Industries<\/a>.\u00a0<\/p>\n

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[\/et_pb_text][\/et_pb_column][\/et_pb_row][\/et_pb_section][et_pb_section fb_built=”1″ _builder_version=”3.19.12″ background_color=”#ffffff” custom_padding=”0|0px|4px|0px|false|false” custom_css_main_element=”max-width: 1400px;||margin: auto;”][et_pb_row make_fullwidth=”on” custom_padding=”15px|0px|4px|70px|false|false” custom_margin=”|||” _builder_version=”3.12.2″ background_color=”#1ed2b0″][et_pb_column type=”4_4″ _builder_version=”3.12.2″ parallax=”off” parallax_method=”on”][et_pb_text _builder_version=”3.12.2″ text_font=”||||||||” text_text_color=”#353535″ header_font=”||||||||” header_text_color=”rgba(53,53,53,0.81)”]<\/p>\n

COVID-19<\/strong><\/span><\/h2>\n

<\/a><\/p>\n

[\/et_pb_text][\/et_pb_column][\/et_pb_row][et_pb_row custom_padding=”31px|0px|8px|0px|false|false” _builder_version=”3.12.2″][et_pb_column type=”1_4″ _builder_version=”3.12.2″ parallax=”off” parallax_method=”on”][et_pb_image src=”https:\/\/familymedicine.uw.edu\/stephensgroup\/wp-content\/uploads\/sites\/11\/2020\/12\/coronavirus-4914026_1280-e1626814099123.jpg” _builder_version=”3.12.2″ custom_margin=”30px|||”][\/et_pb_image][\/et_pb_column][et_pb_column type=”3_4″ _builder_version=”3.12.2″ parallax=”off” parallax_method=”on”][et_pb_text _builder_version=”3.12.2″ text_font=”||||||||” text_text_color=”#666666″ text_font_size=”20px” text_line_height=”1.2em” custom_margin=”|||”]<\/p>\n

COVID Study\u00a0\u00a0<\/b>\u00a0<\/b>
This study examines symptom patterns documented in routine care of individuals tested for SARS CoV-2 cohort within a large integrated health care system in Seattle, the ground zero for COVID infections in the US. Using data from electronic medical records (EMR) to capture symptom data using coded and free text data when patients presented for care, this study utilized innovations in informatics to rapidly extract and explore patterns of clinical presentation, access to testing, and variations in clinical presentation with individuals\u2019 characteristics such as age and race.\u00a0<\/span><\/p>\n

[\/et_pb_text][et_pb_toggle title=”More Information” open_toggle_background_color=”rgba(0,0,0,0)” closed_toggle_text_color=”#0c71c3″ closed_toggle_background_color=”rgba(0,0,0,0)” icon_color=”rgba(0,0,0,0)” _builder_version=”3.12.2″ title_font=”|700|||||||” title_text_align=”center” title_text_color=”#0c71c3″ body_font=”||||||||” body_text_color=”#666666″ body_font_size=”16px” body_line_height=”1.2em” border_width_all=”0px” border_color_all=”rgba(0,0,0,0)” text_orientation=”left” module_alignment=”center” custom_margin=”-30px|||”]<\/p>\n

Abstract<\/strong>
Burkhardt HA, Dobbins N, Mollis B, Au M, Ma PK, Yetisgen M, Singh A, Thompson M, Stephens KA\u00a0(2021, March). Extracting COVID-19 Related Symptoms from EHR Data: A Comparison of<\/em>
Three Methods<\/em><\/a>. [Abstract and poster]. American Medical Informatics Association 2021 Informatics Summit, (Virtual Meeting)<\/p>\n

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[\/et_pb_toggle][\/et_pb_column][\/et_pb_row][et_pb_row custom_padding=”26px|0px|11px|0px|false|false” _builder_version=”3.12.2″][et_pb_column type=”1_4″ _builder_version=”3.12.2″ parallax=”off” parallax_method=”on”][et_pb_image src=”https:\/\/familymedicine.uw.edu\/stephensgroup\/wp-content\/uploads\/sites\/11\/2023\/12\/INSPIRE-logo.png” _builder_version=”3.12.2″ custom_margin=”20px|||”][\/et_pb_image][\/et_pb_column][et_pb_column type=”3_4″ _builder_version=”3.12.2″ parallax=”off” parallax_method=”on”][et_pb_text _builder_version=”3.12.2″ text_font=”||||||||” text_text_color=”#666666″ text_font_size=”20px” text_line_height=”1.2em”]<\/p>\n

INSPIRE<\/b><\/a>
The Innovative Support For Patients with SARS-CoV-2 Infections (INSPIRE) Study aims to describe the long-term effects of COVID-19 infection. Participants complete online surveys and share their medical information through a secure, confidential, cloud-based personal health platform. Funded by the CDC.\u00a0<\/span><\/p>\n

[\/et_pb_text][et_pb_toggle title=”More Information” open_toggle_background_color=”rgba(0,0,0,0)” closed_toggle_text_color=”#0c71c3″ closed_toggle_background_color=”rgba(0,0,0,0)” icon_color=”rgba(0,0,0,0)” _builder_version=”3.12.2″ title_font=”|700|||||||” title_text_align=”center” title_text_color=”#0c71c3″ body_font=”||||||||” body_text_color=”#666666″ body_font_size=”16px” body_line_height=”1.2em” border_width_all=”0px” border_color_all=”rgba(0,0,0,0)” text_orientation=”left” module_alignment=”center” custom_margin=”-30px|||”]<\/p>\n

Publications<\/strong><\/p>\n

*Gottlieb, M, *Wang, R, *Wisk, LE, Yu, H, Spatz, ES, Montoy, JC, Rodriguez, R, Chang, A M, Elmore, JG, Hannikainen, PA, Hill, M, Huebinger, RM, Idris, AH, Lin, Z, Koo, K, McDonald, S, O\u2019Laughlin, K, Plumb, ID, Santangelo, M, Saydah, S, Willis, M, Wisk, LE, Venkatesh, A, *Stephens, KA,<\/strong> *Weinstein, RA. (in press). Distribution of prolonged symptoms at three months among patient with different SARS-CoV-2 variants. Clinical Infectious Diseases. <\/em>PMCID: in progress<\/p>\n

Gottlieb, M, Spatz, ES, Yu, H, Wisk, LE, Elmore, JG, Gentile, NL, Hill, M, Huebinger, RM, Idris, AH, Kean, ER, Koo, K, Li, S, McDonald, S, Montoy, JC, Nichol, G, O\u2019Laughlin, KN, Plumb, ID, Rising, KL, Santangelo, M, Saydah, S, Wang, RC, *Venkatesh, A, *Stephens, K A, <\/strong>*Weinstein, RA. Long COVID clinical phenotypes up to six months after infection identified by latent class analysis of self-reported symptoms. Open Forum Infectious Diseases. <\/em>2023;10(7):ofad277. Published 2023 May 31. doi:10.1093\/ofid\/ofad277<\/p>\n

*Wang, R, *Gottlieb, M, Montoy, JC, Rodriguez, R, Yu, H, Spatz, E, Chandler, CW, Elmore, JG, Hannikainen, PA, Chang, AM, Hill, M, Huebinger, RM, Idris, AH, Koo, K, Li, S, McDonald, S, Nichol, G, O\u2019Laughlin, K, Plumb, ID, Santangelo, M, Saydah, S, Stephens, K A, <\/strong>*Venkatesh, A, *Weinstein, RA. Association between SARS-CoV-2 variants and frequency of acute symptoms: Analysis of a multi-institutional prospective cohort study \u2013 December 20, 2020 \u2013 June 20, 2022. Open Forum Infectious Diseases. <\/em>2023;10(7):ofad275. Published 2023 May 23. doi:10.1093\/ofid\/ofad275<\/p>\n

Gottlieb, M, Wang, R, Yu, H, Spatz, ES, Montoy, JC, Rodriguez, R, Chang, AM, Elmore, J G, Hannikainen, PA, Hill, M, Huebinger, RM, Idris, AH, Lin, Z, Koo, K, McDonald, S, O\u2019Laughlin, KN, Plumb, ID, Santangelo, M, Saydah, S, Willis, M, Wisk, LE, Venkatesh, A, *Stephens, KA,<\/strong> *Weinstein, RA. Severe Fatigue and Persistent Symptoms at Three Months Following SARS-CoV-2 Infections During the Pre-Delta, Delta, and Omicron Time Periods: A Multicenter Prospective Cohort Study. Clinical Infectious Diseases<\/em>, <\/em>2023;11(1930-1941). PMCID:\u00a0PMC10249989<\/a><\/p>\n

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[\/et_pb_toggle][\/et_pb_column][\/et_pb_row][et_pb_row make_fullwidth=”on” custom_padding=”15px|0px|4px|70px|false|false” custom_margin=”|||” _builder_version=”3.12.2″ background_color=”#1ed2b0″][et_pb_column type=”4_4″ _builder_version=”3.12.2″ parallax=”off” parallax_method=”on”][et_pb_text _builder_version=”3.12.2″ text_font=”||||||||” text_text_color=”#353535″ header_font=”||||||||” header_text_color=”rgba(53,53,53,0.81)”]<\/p>\n

Substance Use Disorders<\/strong><\/span><\/h2>\n

<\/a>[\/et_pb_text][\/et_pb_column][\/et_pb_row][et_pb_row custom_padding=”26px|0px|0|0px|false|false” _builder_version=”3.12.2″][et_pb_column type=”1_4″ _builder_version=”3.12.2″ parallax=”off” parallax_method=”on”][et_pb_image src=”https:\/\/familymedicine.uw.edu\/stephensgroup\/wp-content\/uploads\/sites\/11\/2020\/07\/Opioid-Team-based.png” _builder_version=”3.12.2″][\/et_pb_image][\/et_pb_column][et_pb_column type=”3_4″ _builder_version=”3.12.2″ parallax=”off” parallax_method=”on”][et_pb_text _builder_version=”3.12.2″ text_font=”||||||||” text_text_color=”#666666″ text_font_size=”20px” text_line_height=”1.2em” custom_padding=”0px|||”]<\/p>\n

Six Building Blocks: A Team-Based Approach to Improving Opioid Management in Primary Care<\/a>
This study implemented and evaluated a \u201cteam-based best practices\u201d approach to safe opioid prescribing for chronic non-cancer pain across 20 rural health clinics. Funded by the Agency for Health Care Research and Quality.<\/p>\n

[\/et_pb_text][et_pb_toggle title=”More Information” open_toggle_background_color=”rgba(0,0,0,0)” closed_toggle_text_color=”#0c71c3″ closed_toggle_background_color=”rgba(0,0,0,0)” icon_color=”rgba(0,0,0,0)” _builder_version=”3.12.2″ title_font=”|700|||||||” title_text_align=”center” title_text_color=”#0c71c3″ body_font=”||||||||” body_text_color=”#666666″ body_font_size=”16px” body_line_height=”1.2em” border_width_all=”0px” border_color_all=”rgba(0,0,0,0)” text_orientation=”left” module_alignment=”center” custom_margin=”-30px|||”]<\/p>\n

Publications<\/strong>
Stephens, KA, <\/strong>Ike, B, Baldwin, L, Packer, C, Parchman, ML. Challenges and approaches to population management of patients on long-term opioid therapy in primary care<\/a>. Journal of the American Board of Family Medicine. 2023;34(89-98). PMCID: PMC8117725<\/p>\n

Parchman ML, Penfold RB, Ike B, Tauben D, Von Korff M, Stephens M, Stephens KA<\/b>, Baldwin LM. Team-Based Clinic Redesign of Opioid Medication Management in Primary Care: Effect on Opioid Prescribing. <\/a>Ann Fam Med. 2019 Jul;17(4):319-325. PMID: 31285209; PMCID: PMC6827656.<\/p>\n

Parchman, ML, Ike, B, Osterhage, KP, Baldwin LM, Stephens, KA,<\/strong> Sutton, S. Barriers and facilitators to implementing changes in opioid prescribing in rural primary care clinics<\/a>. Journal of Clinical and Translational Science. 2019;17(319-325). PMCID: PMC6827656<\/p>\n

[\/et_pb_toggle][\/et_pb_column][\/et_pb_row][et_pb_row custom_padding=”36px|0px|0|0px|false|false” _builder_version=”3.12.2″][et_pb_column type=”1_4″ _builder_version=”3.12.2″ parallax=”off” parallax_method=”on”][et_pb_image src=”https:\/\/familymedicine.uw.edu\/stephensgroup\/wp-content\/uploads\/sites\/11\/2020\/07\/online-marketing-hIgeoQjS_iE-unsplash-scaled.jpg” _builder_version=”3.12.2″][\/et_pb_image][\/et_pb_column][et_pb_column type=”3_4″ _builder_version=”3.12.2″ parallax=”off” parallax_method=”on”][et_pb_text _builder_version=”3.12.2″ text_font=”||||||||” text_text_color=”#666666″ text_font_size=”20px” text_line_height=”1.2em” custom_padding=”|||”]<\/p>\n

Incidence<\/b> of Alcohol Use Disorder (AUD) Diagnosis and Treatment <\/b>\u00a0<\/b>
This study evaluated the incidence of AUD and alcohol-related medical diagnoses, use of FDA-approved and off-label AUD medications, and use of behavioral health services. Funded by the Pacific Northwest Node of the NIDA Clinical Trials Network.\u00a0<\/p>\n

[\/et_pb_text][et_pb_toggle title=”More Information” open_toggle_background_color=”rgba(0,0,0,0)” closed_toggle_text_color=”#0c71c3″ closed_toggle_background_color=”rgba(0,0,0,0)” icon_color=”rgba(0,0,0,0)” _builder_version=”3.12.2″ title_font=”|700|||||||” title_text_align=”center” title_text_color=”#0c71c3″ body_font=”||||||||” body_text_color=”#666666″ body_font_size=”16px” body_line_height=”1.2em” border_width_all=”0px” border_color_all=”rgba(0,0,0,0)” text_orientation=”left” module_alignment=”center” custom_margin=”-30px|||”]<\/p>\n

Publications<\/strong>
Hallgren KA, Witwer E, West I<\/b>, Baldwin LM, Donovan D, Stuvek B, Keppel GA, Mollis B, Stephens KA<\/b>. Prevalence of documented alcohol and opioid use disorder diagnoses and treatments in a regional primary care practice-based research network.<\/a> J Subst Abuse Treat. 2020 Mar;110:18-27. PMID: 31952624. PMCID: PMC7255441<\/p>\n

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[\/et_pb_toggle][\/et_pb_column][\/et_pb_row][et_pb_row custom_padding=”40px|0px|1px|0px|false|false” _builder_version=”3.12.2″][et_pb_column type=”1_4″ _builder_version=”3.12.2″ parallax=”off” parallax_method=”on”][et_pb_image src=”https:\/\/familymedicine.uw.edu\/stephensgroup\/wp-content\/uploads\/sites\/11\/2020\/09\/heart-1143648_1280.png” _builder_version=”3.12.2″][\/et_pb_image][\/et_pb_column][et_pb_column type=”3_4″ _builder_version=”3.12.2″ parallax=”off” parallax_method=”on”][et_pb_text _builder_version=”3.12.2″ text_font=”||||||||” text_text_color=”#666666″ text_font_size=”20px” text_line_height=”1.2em”]<\/p>\n

Chronic Disease Management for Patients with Substance Use Disorders<\/b>
This study explored whether primary care patients with chronic diseases (i.e., hypertension, diabetes, obesity) have worse outcomes if diagnosed with a substance use disorder, and to identify predictive factors associated with good outcomes in chronic disease management, including integrated behavioral health services. Funded by the Pacific Northwest Node of the NIDA Clinical Trials Network.<\/p>\n

[\/et_pb_text][et_pb_toggle title=”More Information” open_toggle_background_color=”rgba(0,0,0,0)” closed_toggle_text_color=”#0c71c3″ closed_toggle_background_color=”rgba(0,0,0,0)” icon_color=”rgba(0,0,0,0)” _builder_version=”3.12.2″ title_font=”|700|||||||” title_text_align=”center” title_text_color=”#0c71c3″ body_font=”||||||||” body_text_color=”#666666″ body_font_size=”16px” body_line_height=”1.2em” border_width_all=”0px” border_color_all=”rgba(0,0,0,0)” text_orientation=”left” module_alignment=”center” custom_margin=”-30px|||”]<\/p>\n

Publications<\/strong>
Stephens KA<\/strong>, West I<\/b>, Hallgren KA, Mollis B<\/b>, Ma K, Donovan D, Stuvek B, Baldwin LM. Service Utilization and Chronic Condition Outcomes among Primary Care Patients with Substance Use Disorders and Co-Occurring Chronic Conditions<\/a>. J Subst Abuse Treat. 2020 Mar;\u00a0112S:49-55.\u00a0PMC7274163<\/span><\/p>\n

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[\/et_pb_toggle][\/et_pb_column][\/et_pb_row][\/et_pb_section][et_pb_section fb_built=”1″ _builder_version=”3.12.2″ background_image=”https:\/\/familymedicine.uw.edu\/stephensgroup\/wp-content\/uploads\/sites\/11\/2020\/09\/Picture11.jpg” custom_padding=”80px|0px|53px|0px|false|false” custom_css_main_element=”max-width: 1400px;||margin: auto;”][et_pb_row custom_padding=”120px|0px|120px|0px” _builder_version=”3.19.12″ module_alignment=”center”][et_pb_column type=”4_4″ _builder_version=”3.0.47″ parallax=”off” parallax_method=”on”][et_pb_button button_url=”\/stephensgroup\/training\/” url_new_window=”on” button_text=”Learn more about Training opportunities” button_alignment=”center” admin_label=”About” _builder_version=”3.12.2″ custom_button=”on” button_text_size=”30″ button_text_color=”#ffffff” button_bg_color=”#008e63″ button_border_width=”3″ button_border_color=”#1abc9c” button_border_radius=”0″ button_font=”||||||||” button_use_icon=”off” button_bg_color_hover=”rgba(26,188,156,0.45)” 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