Benjamin A Howell1,2, Erica A Abel3,4, Dongchan Park5,6, Sara N Edmond3,4, Leah J Leisch7,8, William C Becker3,7. 1. National Clinician Scholars Program, Yale School of Medicine, New Haven, CT, 06511, USA. benjamin.howell@yale.edu. 2. VA Connecticut Healthcare System, West Haven, CT, USA. benjamin.howell@yale.edu. 3. VA Connecticut Healthcare System, West Haven, CT, USA. 4. Department of Psychiatry, Yale School of Medicine, New Haven, CT, USA. 5. Edith Nourse Rogers Veterans Memorial Hospital, Bedford, MA, USA. 6. Boston University School of Medicine, Boston, MA, USA. 7. Section of General Internal Medicine, Yale School of Medicine, New Haven, CT, USA. 8. Alabama VA Medical Center, Birmingham, AL, USA.
Abstract
BACKGROUND: An important strategy to address the opioid overdose epidemic involves identifying people at elevated risk of overdose, particularly those with opioid use disorder (OUD). However, it is unclear to what degree OUD diagnoses in administrative data are inaccurate. OBJECTIVE: To estimate the prevalence of inaccurate diagnoses of OUD among patients with incident OUD diagnoses. SUBJECTS: A random sample of 90 patients with incident OUD diagnoses associated with an index in-person encounter between October 1, 2016, and June 1, 2018, in three Veterans Health Administration medical centers. DESIGN: Direct chart review of all encounter notes, referrals, prescriptions, and laboratory values within a 120-day window before and after the index encounter. Using all available chart data, we determined whether the diagnosis of OUD was likely accurate, likely inaccurate, or of indeterminate accuracy. We then performed a bivariate analysis to assess demographic or clinical characteristics associated with likely inaccurate diagnoses. KEY RESULTS: We identified 1337 veterans with incident OUD diagnoses. In the chart verification subsample, we assessed 26 (29%) OUD diagnoses as likely inaccurate; 20 due to systems error and 6 due to clinical error; additionally, 8 had insufficient information to determine accuracy. Veterans with likely inaccurate diagnoses were more likely to be younger and prescribed opioids for pain. Clinical settings associated with likely inaccurate diagnoses were non-mental health clinical settings, group visits, and non-patient care settings. CONCLUSIONS: Our study identified significant levels of likely inaccurate OUD diagnoses among veterans with incident OUD diagnoses. The majority of these cases reflected readily addressable systems errors. The smaller proportion due to clinical errors and those with insufficient documentation may be addressed by increased training for clinicians. If these inaccuracies are prevalent throughout the VHA, they could complicate health services research and health systems responses.
BACKGROUND: An important strategy to address the opioid overdose epidemic involves identifying people at elevated risk of overdose, particularly those with opioid use disorder (OUD). However, it is unclear to what degree OUD diagnoses in administrative data are inaccurate. OBJECTIVE: To estimate the prevalence of inaccurate diagnoses of OUD among patients with incident OUD diagnoses. SUBJECTS: A random sample of 90 patients with incident OUD diagnoses associated with an index in-person encounter between October 1, 2016, and June 1, 2018, in three Veterans Health Administration medical centers. DESIGN: Direct chart review of all encounter notes, referrals, prescriptions, and laboratory values within a 120-day window before and after the index encounter. Using all available chart data, we determined whether the diagnosis of OUD was likely accurate, likely inaccurate, or of indeterminate accuracy. We then performed a bivariate analysis to assess demographic or clinical characteristics associated with likely inaccurate diagnoses. KEY RESULTS: We identified 1337 veterans with incident OUD diagnoses. In the chart verification subsample, we assessed 26 (29%) OUD diagnoses as likely inaccurate; 20 due to systems error and 6 due to clinical error; additionally, 8 had insufficient information to determine accuracy. Veterans with likely inaccurate diagnoses were more likely to be younger and prescribed opioids for pain. Clinical settings associated with likely inaccurate diagnoses were non-mental health clinical settings, group visits, and non-patient care settings. CONCLUSIONS: Our study identified significant levels of likely inaccurate OUD diagnoses among veterans with incident OUD diagnoses. The majority of these cases reflected readily addressable systems errors. The smaller proportion due to clinical errors and those with insufficient documentation may be addressed by increased training for clinicians. If these inaccuracies are prevalent throughout the VHA, they could complicate health services research and health systems responses.
Entities:
Keywords:
administrative data; chart verification; opioid use disorder; validation study
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