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Osterhage KP, Hser YI, Mooney LJ, Sherman S, Saxon AJ, Ledgerwood M, Holtzer CC, Gehring MA, Clingan SE, Curtis ME, Baldwin LM. Identifying patients with opioid use disorder using International Classification of Diseases (ICD) codes: Challenges and opportunities. Addiction 2024; 119:160-168. [PMID: 37715369 PMCID: PMC10846664 DOI: 10.1111/add.16338] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/24/2023] [Accepted: 07/27/2023] [Indexed: 09/17/2023]
Abstract
BACKGROUND AND AIMS International Classification of Diseases (ICD) diagnosis codes are often used in research to identify patients with opioid use disorder (OUD), but their accuracy for this purpose is not fully evaluated. This study describes application of ICD-10 diagnosis codes for opioid use, dependence and abuse from an electronic health record (EHR) data extraction using data from the clinics' OUD patient registries and clinician/staff EHR entries. DESIGN Cross-sectional observational study. SETTING Four rural primary care clinics in Washington and Idaho, USA. PARTICIPANTS 307 patients. MEASUREMENTS This study used three data sources from each clinic: (1) a limited dataset extracted from the EHR, (2) a clinic-based registry of patients with OUD and (3) the clinician/staff interface of the EHR (e.g. progress notes, problem list). Data source one included records with six commonly applied ICD-10 codes for opioid use, dependence and abuse: F11.10 (opioid abuse, uncomplicated), F11.20 (opioid dependence, uncomplicated), F11.21 (opioid dependence, in remission), F11.23 (opioid dependence with withdrawal), F11.90 (opioid use, unspecified, uncomplicated) and F11.99 (opioid use, unspecified with unspecified opioid-induced disorder). Care coordinators used data sources two and three to categorize each patient identified in data source one: (1) confirmed OUD diagnosis, (2) may have OUD but no confirmed OUD diagnosis, (3) chronic pain with no evidence of OUD and (4) no evidence for OUD or chronic pain. FINDINGS F11.10, F11.21 and F11.99 were applied most frequently to patients who had clinical diagnoses of OUD (64%, 89% and 79%, respectively). F11.20, F11.23 and F11.90 were applied to patients who had a diagnostic mix of OUD and chronic pain without OUD. The four clinics applied codes inconsistently. CONCLUSIONS Lack of uniform application of ICD diagnosis codes make it challenging to use diagnosis code data from EHR to identify a research population of persons with opioid use disorder.
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Affiliation(s)
- Katie P Osterhage
- Department of Family Medicine, University of Washington, Seattle, Washington, USA
| | - Yih-Ing Hser
- Department of Psychiatry and Biobehavioral Sciences, University of California, Los Angeles, California, USA
| | - Larissa J Mooney
- Department of Psychiatry and Biobehavioral Sciences, University of California, Los Angeles, California, USA
- Veterans Affairs Greater Los Angeles Healthcare System, Los Angeles, California, USA
| | | | - Andrew J Saxon
- Department of Psychiatry and Behavioral Sciences, University of Washington School of Medicine, Seattle, Washington, USA
- Center of Excellence in Substance Addiction Treatment and Education, Veterans Affairs Puget Sound Health Care System, Seattle, Washington, USA
| | - Maja Ledgerwood
- Rural Social Service Solutions, LLC, New Meadows, Idaho, USA
| | - Caleb C Holtzer
- Providence Northeast Washington Medical Group, Colville, Washington, USA
| | | | - Sarah E Clingan
- Department of Psychiatry and Biobehavioral Sciences, University of California, Los Angeles, California, USA
| | - Megan E Curtis
- Department of Psychiatry and Biobehavioral Sciences, University of California, Los Angeles, California, USA
- Department of Emergency Medicine, University of Florida College of Medicine, Jacksonville, Florida, USA
| | - Laura-Mae Baldwin
- Department of Family Medicine, University of Washington, Seattle, Washington, USA
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Hser YI, Mooney LJ, Baldwin LM, Ober A, Marsch LA, Sherman S, Matthews A, Clingan S, Fei Z, Zhu Y, Dopp A, Curtis ME, Osterhage KP, Hichborn EG, Lin C, Black M, Calhoun S, Holtzer CC, Nesin N, Bouchard D, Ledgerwood M, Gehring MA, Liu Y, Ha NA, Murphy SM, Hanano M, Saxon AJ. Care coordination between rural primary care and telemedicine to expand medication treatment for opioid use disorder: Results from a single-arm, multisite feasibility study. J Rural Health 2023; 39:780-788. [PMID: 37074350 PMCID: PMC10718290 DOI: 10.1111/jrh.12760] [Citation(s) in RCA: 7] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 04/20/2023]
Abstract
PURPOSE The use of telemedicine (TM) has accelerated in recent years, yet research on the implementation and effectiveness of TM-delivered medication treatment for opioid use disorder (MOUD) has been limited. This study investigated the feasibility of implementing a care coordination model involving MOUD delivered via an external TM provider for the purpose of expanding access to MOUD for patients in rural settings. METHODS The study tested a care coordination model in 6 rural primary care sites by establishing referral and coordination between the clinic and a TM company for MOUD. The intervention spanned approximately 6 months from July/August 2020 to January 2021, coinciding with the peak of the COVID-19 pandemic. Each clinic tracked patients with OUD in a registry during the intervention period. A pre-/post-intervention design (N = 6) was used to assess the clinic-level outcome as patient-days on MOUD based on patient electronic health records. FINDINGS All clinics implemented critical components of the intervention, with an overall TM referral rate of 11.7% among patients in the registry. Five of the 6 sites showed an increase in patient-days on MOUD during the intervention period compared to the 6-month period before the intervention (mean increase per 1,000 patients: 132 days, P = .08, Cohen's d = 0.55). The largest increases occurred in clinics that lacked MOUD capacity or had a greater number of patients initiating MOUD during the intervention period. CONCLUSIONS To expand access to MOUD in rural settings, the care coordination model is most effective when implemented in clinics that have negligible or limited MOUD capacity.
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Affiliation(s)
- Yih-Ing Hser
- Department of Psychiatry and Biobehavioral Sciences, University of California, Los Angeles, California, USA
| | - Larissa J. Mooney
- Department of Psychiatry and Biobehavioral Sciences, University of California, Los Angeles, California, USA
- VA Greater Los Angeles Healthcare System, Los Angeles, California, USA
| | - Laura-Mae Baldwin
- Department of Family Medicine, University of Washington, Seattle, Washington, USA
| | | | - Lisa A. Marsch
- Center for Technology and Behavioral Health, Geisel School of Medicine, Dartmouth College, Lebanon, New Hampshire, USA
| | - Seth Sherman
- Data and Statistical Center, the Emmes Company, Rockville, Maryland, USA
| | - Abigail Matthews
- Data and Statistical Center, the Emmes Company, Rockville, Maryland, USA
| | - Sarah Clingan
- Department of Psychiatry and Biobehavioral Sciences, University of California, Los Angeles, California, USA
| | - Zhe Fei
- Department of Biostatistics, University of California, Los Angeles, California, USA
| | - Yuhui Zhu
- Department of Psychiatry and Biobehavioral Sciences, University of California, Los Angeles, California, USA
| | - Alex Dopp
- RAND Corporation, Santa Monica, California, USA
| | - Megan E. Curtis
- Department of Psychiatry and Biobehavioral Sciences, University of California, Los Angeles, California, USA
| | - Katie P. Osterhage
- Department of Family Medicine, University of Washington, Seattle, Washington, USA
| | - Emily G. Hichborn
- Center for Technology and Behavioral Health, Geisel School of Medicine, Dartmouth College, Lebanon, New Hampshire, USA
| | - Chunqing Lin
- Semel Institute for Neuroscience and Human Behavior, Center for Community Health, University of California, Los Angeles, California, USA
| | - Megan Black
- Department of Psychiatry and Biobehavioral Sciences, University of California, Los Angeles, California, USA
| | - Stacy Calhoun
- Department of Psychiatry and Biobehavioral Sciences, University of California, Los Angeles, California, USA
| | | | - Noah Nesin
- Penobscot Community Health Care, Bangor, Maine, USA
| | | | - Maja Ledgerwood
- Rural Social Service Solutions, LLC, New Meadows, Idaho, USA
| | | | - Yanping Liu
- Center for Clinical Trials Network, National Institute on Drug Abuse, Bethesda, Maryland, USA
| | - Neul Ah Ha
- Clinical Coordinating Center, Emmes Company, Rockville, Maryland, USA
| | - Sean M. Murphy
- Department of Population Health Sciences, Weill Cornell Medical College, New York, New York, USA
| | - Maria Hanano
- Department of Psychiatry and Biobehavioral Sciences, University of California, Los Angeles, California, USA
| | - Andrew J. Saxon
- Veterans Affairs Puget Sound Health Care System, Seattle, Washington, USA
- Department of Psychiatry and Behavioral Sciences, University of Washington School of Medicine, Seattle, Washington, USA
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Stickney HL, Schmutz J, Woods IG, Holtzer CC, Dickson MC, Kelly PD, Myers RM, Talbot WS. Rapid mapping of zebrafish mutations with SNPs and oligonucleotide microarrays. Genome Res 2002; 12:1929-34. [PMID: 12466297 PMCID: PMC187572 DOI: 10.1101/gr.777302] [Citation(s) in RCA: 101] [Impact Index Per Article: 4.6] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/24/2022]
Abstract
Large-scale genetic screens in zebrafish have identified thousands of mutations in hundreds of essential genes. The genetic mapping of these mutations is necessary to link DNA sequences to the gene functions defined by mutant phenotypes. Here, we report two advances that will accelerate the mapping of zebrafish mutations: (1) The construction of a first generation single nucleotide polymorphism (SNP) map of the zebrafish genome comprising 2035 SNPs and 178 small insertions/deletions, and (2) the development of a method for mapping mutations in which hundreds of SNPs can be scored in parallel with an oligonucleotide microarray. We have demonstrated the utility of the microarray technique in crosses with haploid and diploid embryos by mapping two known mutations to their previously identified locations. We have also used this approach to localize four previously unmapped mutations. We expect that mapping with SNPs and oligonucleotide microarrays will accelerate the molecular analysis of zebrafish mutations.
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Affiliation(s)
- Heather L Stickney
- Department of Developmental Biology, Stanford University, Stanford, California 94305, USA
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