Using data science to improve outcomes for persons with opioid use disorder

References

• Understanding the Epidemic | Drug Overdose | CDC Injury Center. 2019. https://www.cdc.gov/drugoverdose/epidemic/index.html. Accessed August 12, 2019.
   Google Scholar
• Products – Vital Statistics Rapid Release – Provisional Drug Overdose Data. 2021. https://www.cdc.gov/nchs/nvss/vsrr/drug-overdose-data.htm. Accessed August 11, 2021.
   Google Scholar
• Chang HY, Kharrazi H, Bodycombe D, Weiner JP, Alexander GC. Healthcare costs and utilization associated with high-risk prescription opioid use: a retrospective cohort study. BMC Med. 2018;16(1), 69.
   PubMed Web of Science ®Google Scholar
• U.S. Department of Health and Human Services Office of the Surgeon General. Facing Addiction in America: The Surgeon General’s Spotlight on Opioids. 2018. https://addiction.surgeongeneral.gov/sites/default/files/Spotlight-on-Opioids_09192018.pdf. Accessed September 24, 2019.
   Google Scholar
• Volkow ND, Blanco C. The changing opioid crisis: development, challenges and opportunities. Mol Psychiatry. 2021;26(1):218–233.
   PubMed Web of Science ®Google Scholar
• Volkow ND, Frieden TR, Hyde PS, Cha SS. Medication-assisted therapies-tackling the opioid-overdose epidemic. N Engl J Med. 2014;370(22):2063–2066.
   PubMed Web of Science ®Google Scholar
• Volkow ND, Collins FS. The role of science in addressing the opioid crisis. N Engl J Med. 2017;377(4):391–394.
   PubMed Web of Science ®Google Scholar
• Barajas-Nava L. Oral substitution treatment of injecting opioid users for prevention of HIV infection. J Assoc Nurses AIDS Care. 2017;28(5):832–833.
   PubMed Web of Science ®Google Scholar
• Nielsen S, Larance B, Degenhardt L, Gowing L, Kehler C, Lintzeris N. Opioid agonist treatment for pharmaceutical opioid dependent people. Cochrane Database Syst Rev 2016;(5):CD011117.
   Google Scholar
• Linas BP, Savinkina A, Madushani RWMA, et al. Projected estimates of opioid mortality after community-level interventions. JAMA Netw Open. 2021;4(2):e2037259.
   PubMed Web of Science ®Google Scholar
• Manhapra A, Agbese E, Leslie DL, Rosenheck RA. Three-year retention in buprenorphine treatment for opioid use disorder among privately insured adults. Psychiatr Serv. 2018;69(7):768–776.
   PubMed Web of Science ®Google Scholar
• Manhapra A, Petrakis I, Rosenheck R. Three-year retention in buprenorphine treatment for opioid use disorder nationally in the Veterans Health Administration. Am J Addict. 2017;26(6):572–580.
   PubMed Web of Science ®Google Scholar
• O'Connor AM, Cousins G, Durand L, Barry J, Boland F. Retention of patients in opioid substitution treatment: a systematic review. PLoS One. 2020;15(5):e0232086.
   PubMed Web of Science ®Google Scholar
• Santo T, Clark B, Hickman M, et al. Association of opioid agonist treatment with all-cause mortality and specific causes of death among people with opioid dependence. JAMA Psychiatry. 2021;78(9):979–993.
   PubMed Web of Science ®Google Scholar
• Wyse JJ, Gordon AJ, Dobscha SK, et al. Medications for opioid use disorder in the Department of Veterans Affairs (VA) health care system: historical perspective, lessons learned, and next steps. Subst Abus. 2018;39(2):139–144.
   PubMed Web of Science ®Google Scholar
• Rizk A, Elragal A. Data science: developing theoretical contributions in information systems via text analytics. J Big Data.Data. 2020;7(1):1–26.
   Web of Science ®Google Scholar
• Agarwal R, Dhar V. Editorial-big data, data science, and analytics: the opportunity and challenge for IS research. Inf Syst Res. 2014;25(3):443–448.
   Web of Science ®Google Scholar
• Cukier K. Data, Data Everywhere | The Economist.; 2010. https://www.economist.com/special-report/2010/02/27/data-data-everywhere. Accessed July 20, 2021.
   Google Scholar
• Hey T, Tansley S, Tolle K. The fourth paradigm: data-intensive scientific discovery. 2009.
   Google Scholar
• NIH Strategic Plan for Data Science | Data Science at NIH. 2022. https://datascience.nih.gov/nih-strategic-plan-data-science. Accessed January 5, 2022.
   Google Scholar
• NOT-DA-21-013: Notice of Special Interest (NOSI): high-priority interest to enhance data science research training in addiction research. 2021. https://grants.nih.gov/grants/guide/notice-files/NOT-DA-21-013.html. Accessed May 24, 2021.
   Google Scholar
• ABCD Study. 2021. https://abcdstudy.org/. Accessed May 24, 2021.
   Google Scholar
• Landmark study of adolescent brain development renews for additional seven years | National Institute on Drug Abuse (NIDA). 2022. https://www.drugabuse.gov/news-events/news-releases/2020/04/landmark-study-of-adolescent-brain-development-renews-for-additional-seven-years. Accessed January 11, 2022.
   Google Scholar
• Data Analysis, Informatics & Resource Center (DAIRC) – ABCD Study. 2021. https://abcdstudy.org/study-sites/daic/. Accessed July 20, 2021.
   Google Scholar
• Barker W, Johnson C. Variation in methods for health information management among U.S. Substance Abuse Treatment Centers 2017; 2017(52):e0224272.
   Google Scholar
• Walden G. H.R.6 – 115th Congress (2017–2018): SUPPORT for Patients and Communities Act. Published online 2018. https://www.congress.gov/bill/115th-congress/house-bill/6. Accessed January 5, 2022.
   Google Scholar
• Adoption of Electronic Health Record Systems among U.S. Non-Federal Acute Care Hospitals: 2008–2015 | HealthIT.gov. 2021. https://www.healthit.gov/data/data-briefs/adoption-electronic-health-record-systems-among-us-non-federal-acute-care-1. Accessed September 8, 2021.
   Google Scholar
• Parasrampuria S, Henry J. Hospitals’ use of electronic health records data, 2015–2017. Hospitals, Published online 2015;2015–2017.
   Google Scholar
• Office of the Federal Register NA and RA. 42 CFR – Table Of Contents. govinfo.gov. Published online October 1, 2007. https://%3A%2F%2Fwww.govinfo.gov%2Fapp%2Fdetails%2FCFR-2007-title42-vol1%2FCFR-2007-title42-vol1-part2-toc-id8. Accessed August 6, 2021.
   Google Scholar
• Ghitza U, Ghitza U, Sparenborg S, Tai B. Improving drug abuse treatment delivery through adoption of harmonized electronic health record systems. Subst Abuse Rehabil. 2011;2011(2):125–131.
   PubMedGoogle Scholar
• Venkatesh A, Malicki C, Hawk K, D’onofrio G, Kinsman J, Taylor A. Assessing the readiness of digital data infrastructure for opioid use disorder research. Addict Sci Clin Pract. 2020;15:24.
   PubMed Web of Science ®Google Scholar
• NJAMHAA: Electronic health record interoperability program benefits substance use treatment providers and individuals they serve – Insider NJ. 2022. https://www.insidernj.com/press-release/njamhaa-electronic-health-record-interoperability-program-benefits-substance-use-treatment-providers-individuals-serve/. Accessed January 5, 2022.
   Google Scholar
• Olsen LA, Aisner D, McGinnis JM. The Learning Healthcare System. Washington (DC): National Academies Press; 2007.
   Google Scholar
• About VHA – Veterans Health Administration. 2021. https://www.va.gov/health/aboutvha.asp. Accessed March 30, 2021.
   Google Scholar
• Strategic Analytics for Improvement and Learning (SAIL) – Quality of Care. 2021. https://www.va.gov/qualityofcare/measure-up/strategic_analytics_for_improvement_and_learning_sail.asp. Accessed May 24, 2021.
   Google Scholar
• Uknowledge U, Hampton E. Identifying Veterans with Opioid Use Disorder That Could Benefit Identifying Veterans with Opioid Use Disorder That Could Benefit from Referral for Evidence-Based Medication-Assisted Treatment from Referral for Evidence-Based Medication-Assisted Treatment. 2021. https://uknowledge.uky.edu/cph_etds. Accessed May 24, 2021.
   Google Scholar
• Binswanger IA, Carroll NM, Ahmedani BK, et al. The association between medical comorbidity and Healthcare Effectiveness Data and Information Set (HEDIS) measures of treatment initiation and engagement for alcohol and other drug use disorders. Subst Abus. 2019;40(3):292–301.
   PubMed Web of Science ®Google Scholar
• Boyne DJ, Brenner DR, Sajobi TT, et al. Development of a model for predicting early discontinuation of adjuvant chemotherapy in Stage III colon cancer. JCO Clin Cancer Informatics. 2020;(4):972–984.
   PubMed Web of Science ®Google Scholar
• Hayes MT, Hunt LA, Foo J, Tychinskaya Y, Stubbs RS. A model for predicting the resolution of type 2 diabetes in severely obese subjects following Roux-en y gastric bypass surgery. Obes Surg. 2011;21(7):910–916.
   PubMed Web of Science ®Google Scholar
• Oliva EM, Bowe T, Tavakoli S, et al. Development and applications of the Veterans Health Administration’s Stratification Tool for Opioid Risk Mitigation (STORM) to improve opioid safety and prevent overdose and suicide. Psychol Serv. 2017;14(1):34–49.
   PubMed Web of Science ®Google Scholar
• Lo-Ciganic W-H, Huang JL, Zhang HH, et al. Evaluation of machine-learning algorithms for predicting opioid overdose risk among medicare beneficiaries with opioid prescriptions. JAMA Netw Open. 2019;2(3):e190968.
   PubMed Web of Science ®Google Scholar
• Glanz JM, Narwaney KJ, Mueller SR, et al. Prediction model for two-year risk of opioid overdose among patients prescribed chronic opioid therapy. J Gen Intern Med. 2018;33(10):1646–1653.
   PubMed Web of Science ®Google Scholar
• Li X, Chaovalitwongse W, Curran G, Tilford J, Felix H, Martin B. Using machine learning to predict opioid overdoses among prescription opioid users. Value Heal. 2018;21:S245.
   Web of Science ®Google Scholar
• Chowdhury GG. Natural language processing. Ann Rev Info Sci Tech. 2005;37(1):51–89.
   Google Scholar
• Nadkarni PM, Ohno-Machado L, Chapman WW. Natural language processing: an introduction. J Am Med Inform Assoc. 2011;18(5):544–551.
   PubMed Web of Science ®Google Scholar
• Liu F, Pradhan R, Druhl E, et al. Learning to detect and understand drug discontinuation events from clinical narratives. J Am Med Inform Assoc. 2019;26(10):943–951.
   PubMed Web of Science ®Google Scholar
• Carrell DS, Cronkite D, Palmer RE, et al. Using natural language processing to identify problem usage of prescription opioids. Int J Med Inform. 2015;84(12):1057–1064.
   PubMed Web of Science ®Google Scholar
• Palumbo SA, Adamson KM, Krishnamurthy S, et al. Assessment of probable opioid use disorder using electronic health record documentation. JAMA Netw Open. 2020;3(9):e2015909.
   PubMed Web of Science ®Google Scholar
• Farmer R, Mathur R, Bhaskaran K, Eastwood SV, Chaturvedi N, Smeeth L. Promises and pitfalls of electronic health record analysis. Diabetologia 2018;61(6):1241–1248.
   PubMed Web of Science ®Google Scholar
• Wakeman SE, Larochelle MR, Ameli O, et al. Comparative effectiveness of different treatment pathways for opioid use disorder. JAMA Netw Open. 2020;3(2):e1920622.
   PubMed Web of Science ®Google Scholar
• Heil J, Zajic S, Albertson E, et al. The Genomics of Opioid Addiction Longitudinal Study (GOALS): study design for a prospective evaluation of genetic and non-genetic factors for development of and recovery from opioid use disorder. BMC Med Genomics. 2021;14(1):1–8.
   PubMed Web of Science ®Google Scholar
• Moningka H, Lichenstein S, Worhunsky PD, DeVito EE, Scheinost D, Yip SW. Can neuroimaging help combat the opioid epidemic? A systematic review of clinical and pharmacological challenge fMRI studies with recommendations for future research. Neuropsychopharmacology 2019;44(2):259–273.
   PubMed Web of Science ®Google Scholar
• Nichols​ TE, Poline​ J, Vul​ E, Yarkoni​ T. Scanning The Horizon: future challenges for neuroimaging research. bioRxiv 2016;4:5.
   Google Scholar
• Marsch LA, Campbell A, Campbell C, et al. The application of digital health to the assessment and treatment of substance use disorders: the past, current, and future role of the National Drug Abuse Treatment Clinical Trials Network. J Subst Abuse Treat. 2020;112S:4–11.
   PubMedGoogle Scholar
• Onnela J-P, Rauch SL. Harnessing smartphone-based digital phenotyping to enhance behavioral and mental health. Neuropsychopharmacology 2016;41(7):1691–1696.
   PubMed Web of Science ®Google Scholar
• Harari G, Lane N, Wang R, Crosier B, Campbell A, Gosling S. Using smartphones to collect behavioral data in psychological science: opportunities, practical considerations, and challenges. Perspect Psychol Sci. 2016;11(6):838–854.
   PubMedGoogle Scholar
• Nahum-Shani I, Smith SN, Spring BJ, et al. Just-in-Time Adaptive Interventions (JITAIs) in Mobile Health: key components and design principles for ongoing health behavior support. Ann Behav Med. 2018;52(6):446–462.
   PubMed Web of Science ®Google Scholar
• Nuamah J, Mehta R, Sasangohar F. Technologies for opioid use disorder management: mobile App search and scoping review. JMIR Mhealth Uhealth. 2020;8(6):e15752.
   PubMed Web of Science ®Google Scholar
• Gustafson DH, Landucci G, McTavish F, et al. The effect of bundling medication-assisted treatment for opioid addiction with mHealth: study protocol for a randomized clinical trial. Trials 2016;17(1):1–12.
   PubMedGoogle Scholar
• Johnston DC, Mathews WD, Maus A, Gustafson DH. Using smartphones to improve treatment retention among impoverished substance-using appalachian women: a naturalistic study. Subst Abuse. 2019;13:1178221819861377.
   Web of Science ®Google Scholar
• FDA clears mobile medical app to help those with opioid use disorder stay in recovery programs | FDA. 2022. https://www.fda.gov/news-events/press-announcements/fda-clears-mobile-medical-app-help-those-opioid-use-disorder-stay-recovery-programs. Accessed January 5, 2022.
   Google Scholar
• Schramm ZA, Leroux BG, Radick AC, et al. Video directly observed therapy intervention using a mobile health application among opioid use disorder patients receiving office-based buprenorphine treatment: protocol for a pilot randomized controlled trial. Addict Sci Clin Pract. 2020;15(1):30.
   PubMed Web of Science ®Google Scholar
• Schuman-Olivier Z, Borodovsky JT, Steinkamp J, et al. MySafeRx: a mobile technology platform integrating motivational coaching, adherence monitoring, and electronic pill dispensing for enhancing buprenorphine/naloxone adherence during opioid use disorder treatment: a pilot study. Addict Sci Clin Pract. 2018;13(1):21.
   PubMedGoogle Scholar
• Vilardaga R, Fisher T, Palenski PE, et al. Review of popularity and quality standards of opioid-related smartphone apps. Curr Addict Rep. 2020;7(4):486–496.
   PubMedGoogle Scholar
• Onnela J-P. Opportunities and challenges in the collection and analysis of digital phenotyping data. Neuropsychopharmacology. 2021;46(1):45–54.
   PubMed Web of Science ®Google Scholar
• Benoit J, Onyeaka H, Keshavan M, Torous J. Systematic review of digital phenotyping and machine learning in psychosis spectrum illnesses. Harv Rev Psychiatry. 2020;28(5):296–304.
   PubMed Web of Science ®Google Scholar
• Mitra A, Rawat BPS, McManus D, Kapoor A, Yu H. Bleeding entity recognition in electronic health records: a comprehensive analysis of end-to-end systems. AMIA Annu Symp Proc. 2020;2020:860–869.
   PubMedGoogle Scholar
• Mitra A, Rawat BPS, McManus DD, Yu H. Relation classification for bleeding events from electronic health records using deep learning systems: an empirical study. JMIR Med Inform. 2021;9(7):e27527.
   PubMed Web of Science ®Google Scholar
• Esteva A, Kuprel B, Novoa RA, et al. Dermatologist-level classification of skin cancer with deep neural networks. Nature 2017;542(7639):115–118.
   PubMed Web of Science ®Google Scholar
• Hu B, Bajracharya A, Yu H. Generating medical assessments using a neural network model: algorithm development and validation. JMIR Med Inform. 2020;8(1):e14971.
   PubMed Web of Science ®Google Scholar
• Yang Z, Yu H. Generating accurate electronic health assessment from medical graph. Proc Conf Empir Methods Nat Lang Process Conf Empir Methods Nat Lang Process. 2020;2020:3764.
   PubMedGoogle Scholar
• Miotto R, Li L, Kidd BA, Dudley JT. Deep patient: an unsupervised representation to predict the future of patients from the electronic health records. Sci Rep. Rep. 2016;6(1):1–10.
   PubMedGoogle Scholar
• Choi E, Taha Bahadori M, Kulas JA. RETAIN: An interpretable predictive model for healthcare using reverse time attention mechanism. 2016.
   Google Scholar
• Rongali S, Rose AJ, McManus DD, et al. Learning latent space representations to predict patient outcomes: model development and validation. J Med Internet Res. 2020;22(3):e16374.
   PubMed Web of Science ®Google Scholar
• Liao P, Greenewald K, Klasnja P, Murphy S. Personalized heartsteps: a reinforcement learning algorithm for optimizing physical activity. Proc Acm Interact Mob Wearable Ubiquitous Technol. 2020;4(1):1–22.
   PubMedGoogle Scholar
• Botvinick M, Ritter S, Wang JX, Kurth-Nelson Z, Blundell C, Hassabis D. Reinforcement learning, fast and slow. Trends Cogn Sci. 2019;23(5):408–422.
   PubMed Web of Science ®Google Scholar
• Sarker A, Gonzalez-Hernandez G, Ruan Y, Perrone J. Machine learning and natural language processing for geolocation-centric monitoring and characterization of opioid-related social media chatter. JAMA Netw Open. 2019;2(11):e1914672.
   PubMed Web of Science ®Google Scholar
• Yang Z, Nguyen L, Jin F. Predicting opioid relapse using social media data. Published online November 14, 2018. https://arxiv.org/abs/1811.12169v1. Accessed January 6, 2022.
   Google Scholar
• Fadahunsi KP, O’Connor S, Akinlua JT, et al. Information quality frameworks for digital health technologies: systematic review. J Med Internet Res. 2021;23(5):e23479.
   PubMed Web of Science ®Google Scholar
• Hale TM, Cotten SR, Drentea P, Goldner M. Rural-urban differences in general and health-related internet use. Am Behav Scient. 2010;53(9):1304–1325.
   Web of Science ®Google Scholar
• Hsu MH, Tien SW, Lin HC, Chang CM. Understanding the roles of cultural differences and socio-economic status in social media continuance intention. Inf Technol People. 2015;28(1):224–241.
   Web of Science ®Google Scholar
• Schradie J. The trend of class, race, and ethnicity in social media inequality. Infor Commun Soc. 2012;15(4):555–571.
   Web of Science ®Google Scholar
• Clinical Trials Network (CTN) | National Institute on Drug Abuse (NIDA). 2022. https://www.drugabuse.gov/about-nida/organization/cctn/clinical-trials-network-ctn. Accessed January 6, 2022.
   Google Scholar