Utility of electronic health records in improving diagnosis Bianca - PowerPoint PPT Presentation

Co-Authors Utility of electronic health records in improving diagnosis Bianca Duah 1 Kerri Sheahan 1 and treatment of opioid use Shafaq Tarar 1 Blanche Langenbach 1 Emily Forcht 1 Wyley Gates 1 disorders in HIV clinic settings David Perlman 1 Judith Aberg 1 Ana Ventuneac 1 Gavriella Hecht, BS Division of Infectious Diseases at Icahn School of Medicine at Mount Sinai 1 Icahn School of Medicine at Mount Sinai, Division of Infectious Diseases, New York, NY, USA 2 Opioid Use among People with HIV National Estimates of Opioid Use ▶ Between 2002 and 2012, the number of opioid analgesics ▶ People with HIV (PWH) received twice as many opioid dispensed by US pharmacies has skyrocketed from 142 prescriptions compared to those without HIV (CMS data, Canan et. al, 2019). million to 248 million (Saha et al., 2016). ▶ Nearly 25% report illicit drug use and misuse of prescription ▶ Estimates indicate that nearly 7% of all prescriptions in 2012 drugs, including opioids (Korthuis et. al, 2012). were opioids (Levy et al., 2015). – 11% of patients receiving HIV care reported misuse of ▶ Problematic opioid and substance use has also increased¹ prescription medications in the past month. Of these, 41% – 2 million people aged 12+ had a diagnosed opioid use reported misusing opiates/analgesics (Newville et. al, 2015). disorder (OUD) ▶ Out of those receiving HIV care in the US who reported opioid – 20.3 million people aged 12+ had a diagnosed substance misuse, 64.8% reported misusing prescription opioids, 29.1% use disorder (SUD) in the past year reported using heroin, and 6.1% reported using both. 23.1% reported injecting them (Lemons et. al, 2019). ¹NSDUH Annual National Report. (2018). Retrieved from https://www.samhsa.gov/data/report/2018-nsduh-annual-national-report 3 4 1

Limited Research on Opioid Use among Study Objectives People with HIV ▶ Current data on opioid use among PWH is limited. In 2012, Önen et. al conducted a review evaluating indications of opioid prescribing, types of ▶ Describe opioid prescriptions among patients opioid regimens, therapeutic response, and urine drug screen usage. receiving HIV care and assess its association ▶ A retrospective cross-sectional study of a convenience sample of patients: with substance use disorders, including opioid – ≥18 years in outpatient HIV care use disorders. – Completed behavioral assessment – ≥2 clinical visits between June 2008-June 2009 ▶ Discuss implications for leveraging electronic health records to identify patients with ▶ Major findings: – Documentation of opioid prescribing are lacking problematic substance and opioid use • Per medication lists, 8% of patients were prescribed opioids by multiple providers, including HIV providers. There was no mention of these prescriptions in clinical notes from HIV providers. – A lack of use in pain scales and – Limited follow up regarding therapeutic efficacy was found. 5 6 What is iCare? Institute for Advanced Medicine Five primary care clinics with ▶ Integrated Care at Mount Sinai (iCare): research over 300 care staff members study designed to develop and test a program serving over 10,000 diverse that integrates care between HIV primary care patients with HIV: and substance use treatment in an effort to better meet the needs of persons with HIV ▶ Race and ethnicity (PWH) who struggle with substance misuse. ▶ Age ▶ Gender identity ▶ Sexual identity 7 8 2

A Multisite Trial Utilizing a Stepped Wedge Design Methods: iCare Inclusion Criteria to test the iCare Intervention Components ▶ Criteria for inclusion of patient records in the iCare dataset for analysis consist of: – Patients with HIV diagnosis – Patients  18 years of age – Patients with at least 1 primary care visit at a clinic within IAM from July 1, 2016 - December 31, 2017 9 10 Methods: Opioid Rx Validation Data Analysis ▶ Retrospective analysis involved ICD-10 for ▶ 12,176 valid opioid Rx out of a total of 20,058 records substance abuse/dependence, Epic Smart about opioid Rx Forms for substance use screening, HIV labs ▶ Opioid Rx validated through 3-step chart review process (viral suppression), and opioid Rx given that some Rx had a discontinuation indicator – 1 st Round: Sample of records were randomly selected for each ▶ Binary variables: reason for Rx discontinuation category and then chart reviewed for validation status (i.e. valid, invalid) of the records – SUD-related ICD (yes/no) – 2 nd Round: Additional sample of records were chart reviewed for – Substance use screening (yes/no) all categories with valid records to determine if status was – Outcome Variable: Opioid Rx (yes/no) unanimous for each Rx discontinuation category – 3 rd Round: Full dataset of records for categories without ▶ Logistic regression was adjusted unanimous 2 nd round results chart reviewed for Rx validation – demographic characteristics: gender, age, ethnic and racial differences – years since diagnosed with HIV 11 12 3

Sociodemographic characteristics and Results: Patient Demographics HIV outcomes (n = 9,772) ▶ 9,772 patients total – 76% male – 34% Black/African American, 24% Hispanic, 22% White, and 20% multiracial or “other” – 53% ≥50 years of age – 81% virally suppressed (<50 copies/mL) at last viral load test. ▶ 1,239 (12.7%) patients received ≥1 opioid Rx 13 14 Substance Use Documentation in Epic Documented SUDs (n= 1,816; 19%) ▶ Substance use screening – 7,716 Smart Form screeners were administered in 15% of primary care visits among 2,972 (30%) patients – substance use was endorsed in 760 of the screeners (10%) ▶ ICD-10 for SUDs: 1,816 (19%) patients had a documented SUD, and of them – 30% had an alcohol use disorder, – 31% cocaine disorder, – 24% opioid disorder, – 22% cannabis disorder, – 16% amphetamine disorder, and – 24% other disorder or not specified. 15 16 4

Any Opioid Rx by Documented Opioid Rx (n = 1,239; 12.7%) Patient Characteristics Prevalence of ≥1 Opioid Rx by Gender χ 2 = 103.00*** 25 20 15 Percent 10 χ 2 = 49.37*** χ 2 = 37.37*** χ 2 = 6.15* 5 0 Codeine Oxycodone Tramadol Other Male (n = 7470)1 Cisgender Female (n = 2232) Transgender Female (n = 70) 1 Includes 7467 cisgender and 3 transgender males.*p < .05; **p < .01 1 Includes 7467 cisgender and 3 transgender males. 17 18 ***p < .001 Logistic Regression Summary Predicting ≥1 Opioid Rx ▶ A larger percentage of older patients, cis-gender and transgender women, and Hispanic patients received ≥ 1 opioid Rx compared to their counterparts ▶ Significantly higher odds of having received an opioid Rx among patients with OUDs and those with SUDs compared to those without OUDs or SUDs, after adjusting for differences by demographic characteristics 20 19 5