Kimberly Fryer, Chinyere N Reid, Chaitanya Chaphalkar, Jennifer Marshall, Laura Szalacha, Kimberly Johnson, Tanner Wright, Caitlin Read, Ayesha Khan, Anna Wilson, Meera Ratani, Kaitlyn Cox, Angela Tavolieri, Melanny Sampayo, Rachel Su, Kelly Campbell, Jason L Salemi
{"title":"开发基于电子病历的五步算法，以识别妊娠期阿片类药物使用障碍患者。","authors":"Kimberly Fryer, Chinyere N Reid, Chaitanya Chaphalkar, Jennifer Marshall, Laura Szalacha, Kimberly Johnson, Tanner Wright, Caitlin Read, Ayesha Khan, Anna Wilson, Meera Ratani, Kaitlyn Cox, Angela Tavolieri, Melanny Sampayo, Rachel Su, Kelly Campbell, Jason L Salemi","doi":"","DOIUrl":null,"url":null,"abstract":"<p><strong>Objectives: </strong>This study aimed to develop and validate an algorithm for the identification of opioid use disorder (OUD) in pregnant patients using electronic medical record (EMR) data.</p><p><strong>Materials and methods: </strong>A cohort of pregnant patients from a single institution was used to develop and validate the algorithm. Five algorithm components were used, and chart reviews were conducted to confirm OUD diagnoses based on established criteria. Positive predictive values (PPV) of each of the algorithm's components were assessed.</p><p><strong>Results: </strong>Of the 334 charts identified by the algorithm, 256 true cases were confirmed. The overall PPV of the algorithm was 76.6%, with 100% accuracy for outpatient medication lists, and high PPVs ranging from 81.3% to 93.4% across other algorithm components.</p><p><strong>Discussion and conclusion: </strong>The study highlights the significance of a multifaceted approach in identifying OUD among pregnant patients, aiming to improve patient care and target interventions for patients at risk.</p>","PeriodicalId":39246,"journal":{"name":"Journal of registry management","volume":"51 2","pages":"69-74"},"PeriodicalIF":0.0000,"publicationDate":"2024-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11343436/pdf/","citationCount":"0","resultStr":"{\"title\":\"Development of a 5-Step Electronic Medical Record-Based Algorithm to Identify Patients with Opioid Use Disorder in Pregnancy.\",\"authors\":\"Kimberly Fryer, Chinyere N Reid, Chaitanya Chaphalkar, Jennifer Marshall, Laura Szalacha, Kimberly Johnson, Tanner Wright, Caitlin Read, Ayesha Khan, Anna Wilson, Meera Ratani, Kaitlyn Cox, Angela Tavolieri, Melanny Sampayo, Rachel Su, Kelly Campbell, Jason L Salemi\",\"doi\":\"\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><strong>Objectives: </strong>This study aimed to develop and validate an algorithm for the identification of opioid use disorder (OUD) in pregnant patients using electronic medical record (EMR) data.</p><p><strong>Materials and methods: </strong>A cohort of pregnant patients from a single institution was used to develop and validate the algorithm. Five algorithm components were used, and chart reviews were conducted to confirm OUD diagnoses based on established criteria. Positive predictive values (PPV) of each of the algorithm's components were assessed.</p><p><strong>Results: </strong>Of the 334 charts identified by the algorithm, 256 true cases were confirmed. The overall PPV of the algorithm was 76.6%, with 100% accuracy for outpatient medication lists, and high PPVs ranging from 81.3% to 93.4% across other algorithm components.</p><p><strong>Discussion and conclusion: </strong>The study highlights the significance of a multifaceted approach in identifying OUD among pregnant patients, aiming to improve patient care and target interventions for patients at risk.</p>\",\"PeriodicalId\":39246,\"journal\":{\"name\":\"Journal of registry management\",\"volume\":\"51 2\",\"pages\":\"69-74\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2024-01-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11343436/pdf/\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Journal of registry management\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q4\",\"JCRName\":\"Medicine\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of registry management","FirstCategoryId":"1085","ListUrlMain":"","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q4","JCRName":"Medicine","Score":null,"Total":0}
Development of a 5-Step Electronic Medical Record-Based Algorithm to Identify Patients with Opioid Use Disorder in Pregnancy.
Objectives: This study aimed to develop and validate an algorithm for the identification of opioid use disorder (OUD) in pregnant patients using electronic medical record (EMR) data.
Materials and methods: A cohort of pregnant patients from a single institution was used to develop and validate the algorithm. Five algorithm components were used, and chart reviews were conducted to confirm OUD diagnoses based on established criteria. Positive predictive values (PPV) of each of the algorithm's components were assessed.
Results: Of the 334 charts identified by the algorithm, 256 true cases were confirmed. The overall PPV of the algorithm was 76.6%, with 100% accuracy for outpatient medication lists, and high PPVs ranging from 81.3% to 93.4% across other algorithm components.
Discussion and conclusion: The study highlights the significance of a multifaceted approach in identifying OUD among pregnant patients, aiming to improve patient care and target interventions for patients at risk.
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