Reviewing the Impact of Informatics on Substance Abuse Disorders and the Opioid Epidemic

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Drug overdoses are now the top reason for unintentional mortality in the United States, and prescription opioid abuse is a major contributor to the public health crisis (Sun et al., 2018). This review explores the contributions of informatics in combating substance abuse disorders and the opioid epidemic. Substance abuse informatics incorporates the availability and implementation of educational and preventative resources, analyzes associations and trends, identifies predictors and treatment outcomes, and establishes prescription drug monitoring programs.


The review was conducted on articles identified by PubMed and Medline Ovid.

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Articles were chosen based on quality and relevance.


Educating both healthcare providers and the public has increased awareness of corrective drugs like Narcan®. It has also raised awareness of available educational programs that may reduce substance abuse-related deaths and disorders. The identification of key predictors of successful treatment and potential abusers is still being researched. However, many studies have identified relevant factors associated with successful outcomes. These include decreased overdose deaths, decreased SADs, and a decrease in inappropriate prescription patterns. Through the use of available patient data, geographic systems, and informatics tools, associations and trends were analyzed. These were used to identify those with the highest risk of abuse and the greatest need for assistance. A review of treatment programs and outcomes revealed the need for greater participation in educational programs, improved patient management, and increased use of residential treatment facilities. Lastly, prescription drug monitoring programs require a higher rate of participation by healthcare providers and patients to be effective.


Informatics is constantly improving and positively contributing to research efforts to reduce substance abuse. Despite the advances in informatics in recent years, there remains a need for the discovery and incorporation of more effective uses of informatics to combat the substance abuse epidemic.


The review investigates recently-published papers that assess preventative measures, treatment, and public health issues surrounding the substance abuse crisis. Current studies are producing promising results regarding where and to whom epidemics are occurring, educating providers and the public, identifying abusers before the problem becomes too severe to manage, and determining which treatments are the most effective. The review summarizes current findings and recommends future research in specific areas, such as research on a national level, implementation of PDMP standards, mandatory healthcare provider participation, identifying predictors, and treatment outcomes to combat substance abuse disorders.


Substance abuse has become a significant issue affecting economic and social aspects of society. Due to the exponential increase in individuals with Substance Abuse Disorders (SADs), researchers and healthcare professionals are working to prevent, manage, and successfully treat these disorders. According to the National Institute on Drug Abuse (NIDA), abuse of tobacco, alcohol, and illicit drugs is costly to our nation, exacting more than $740 billion annually in costs related to crime, lost work productivity, and healthcare. Although tobacco use accounts for the majority of the economic burden, illicit drugs and opioids contribute to an overall cost of $271.5 billion as of 2013. Another alarming statistic published by NIDA estimated that 24.6 million Americans aged 12 or older (9.4% of the population) have used an illicit drug in the past month. Drug abuse is highest among people in their late teens and twenties, but abuse is rapidly increasing among people in their fifties and early sixties. NIDA continues to address the large “treatment gap” in the United States, with an estimated 8.6% of Americans needing treatment for a problem related to drugs or alcohol, but only 0.9% receiving help.

Biomedical informatics, a useful tool for understanding SADs, deals with storage, retrieval, and optimal use of data, knowledge for discovery, problem solving, and decision-making. Informatics aids in the analysis of patient data (e.g., overdose rates, frequently used drugs, comorbidities, user demographics, and treatment outcomes). Clinical informatics created prescription drug monitoring programs that collect, monitor, and analyze the prescribing and dispensing of controlled substances. A basic literature search reveals a large number of recent patient data analyses that have allowed physicians and healthcare professionals to identify demographic predictors of future substance abusers. Frequently, healthcare providers are unable to intervene until the substance abuse has become so severe that a patient overdoses or serious social and financial repercussions occur. If healthcare providers identify risk factors earlier, the quality of patient care may improve. The funding for and amount of substance abuse research has significantly increased in the last few decades and has contributed to more effective treatment program characteristics and improved patient care quality. Effectively managing substance abuse disorders through the proper use of informatics can improve patient care by offering effective treatment programs and preventative measures, decrease the financial burden, and benefit physicians.

The purpose of this review is to investigate and critique current research and the role that informatics plays regarding the education, prevention, management, and treatment outcomes of programs and studies focused on Substance Abuse Disorders, with an emphasis on prescription opioid abuse.


The review included articles selected from PubMed and Medline Ovid. PubMed is a database found on the National Center for Biotechnology Information, NCBI. PubMed was chosen due to its popularity, relevance, and the availability of a large number of recent and topic-relevant articles. Medline Ovid is the Medical Literature Analysis and Retrieval System Online. This database is compiled by the United States National Library of Medicine and promotes the availability of biomedical information.

Search results were narrowed to peer-reviewed articles published in the last ten years (2008-current). Both clinical trials and reviews were included. Articles were grouped based on their relevance to education, identifying problem usage, treatment outcomes, and prescription drug monitoring programs. The central question of this research was, “What is the role of informatics in substance abuse disorders?” Articles were selected based on research methods, validity of results, and populations studied. Results were limited to articles published in the English language and studies that included human subjects exclusively.


1. Substance Abuse Education and Naloxone Distribution

A geographic information system (GIS) was developed to help pharmacists effectively distribute naloxone (Narcan®), a lifesaving opioid antagonist that rapidly reverses opioid overdose. The study, conducted in Pennsylvania, used overdose death data and ZIP Code Tabulation Areas (ZCTAs). The article stated that analysis will continue over the next five years to better understand the association between pharmacies that carry naloxone and opioid-related deaths. The researchers argued that the number of overdose deaths in ZCTAs with naloxone-distributing pharmacies was significantly higher than the average number of deaths in all ZCTAs in Allegheny County: 7.38 deaths versus 4.84 deaths, respectively (P = 0.021) (Burrell et al., 2017). The article recommends further consideration of naloxone in opioid-dependent populations. To prevent opioid-related mortality, Olivia et al. (2017) summarized the development of a national opioid overdose education and Naloxone distribution program. The program focused on facilitating a health care system-based approach to distributing naloxone, developing patient and provider education resources, and implementing and evaluating resources. A total of 172 overdose reversals were completed through this program. The success of the program was attributed to the use of informatics, which transformed a primarily community-based public health approach into a health care system-based approach to combat SADs.

In 2011, Kothari et al. used Medline to review studies of substance abuse disorders directed towards a future healthcare professionals’ population: undergraduate medical students. Kothari et al. (2011) suggested improved methods of curriculum evaluation and publication guidelines. The Substance Abuse Research Education and Training Program (SARET) was developed in 2012 by Truncali et al. to motivate current healthcare professionals to contribute more to substance abuse research. A significant result of SARET was an interactive web-based module series on different research substance abuse topics. Results of focus groups and online surveys were analyzed and showed that an online program increased interest in substance abuse professionals by 35-38%. In 2014, Bruckner et al. reviewed educational standards for substance abuse prevention programs in public schools. Before this article was published, no research systematically coded the educational standards across grades and states. The researchers found that most states fell far below the recommended amount of instruction and, overall, states varied significantly in their content and standard coverage of substance abuse. The authors advocate research that examines the relationship between state alcohol, tobacco, and other drug use standards, classroom instruction, and adolescent drug use.

Informatics research continues to investigate many of the drugs involved in SADs. Informatics has significantly improved the education of substance abuse disorders and increased the distribution of naloxone through educational standard reviews, data analysis, and program implementation. In 2017, Cepeda et al. assessed the impact of a Risk Evaluation and Mitigation Strategy (REMS), for extended release and long-acting opioids. REMS evaluates prescribers, training courses, patient counseling and education, drug utilization, and surveillance monitoring. REMS consists of education, monitoring, proper medication disposal, and enforcement. The authors found that the implementation of REMS resulted in the leveling off and even decrease in opioid abuse. Led by the American Academy of Addiction Psychiatry, the Provider’s Clinical Support System for Medication Assisted Treatment initiative focused on training and mentoring health professionals in the treatment of opioid use disorders (Levin et al., 2016). The researchers aimed to increase evidence-based practices with medications for opioid use disorders. Levin et al. (2017) reviewed current initiatives that included training and mentoring for primary care physicians, outreach to multidisciplinary professional organizations, and the creation of a resource portal for families, patients, and communities. The article recommends working with health care providers to offer Medication Assisted Therapy (MAT).

2. Identifying Predictors of Problem Usage

Recognizing key predictors of successful treatment can serve to identify disparities, strengths, and weaknesses in service delivery, leading to treatment success and reducing unmet treatment needs (Arndt, 3013). In 2018, Acion et al. used a machine-learning framework with multiple prediction models to determine SAD treatment success. Super learning (SL) is a methodology that facilitates this decision by combining all identified prediction algorithms pertinent to a particular prediction problem. SL generates a final model that is at least as good as any of the other models considered for predicting the outcome. The overarching aim of this work is to introduce SL to analysts and practitioners. Using the prediction model suggested by SL and TMLE, one could answer questions such as “What is the treatment success rate difference between Hispanics with comorbid psychiatric disorders and those without comorbid disorders?” or “Is this difference different from zero?”. Acion et al. (2018) found the most successful model was super learning, which combined all identified prediction algorithms pertinent to a specific problem. The authors compared the performance of logistic regression, penalized regression, random forests, deep learning neural networks, and super learning to predict treatment outcomes. The study used common statistical methods in a realistic setting with valid and applicable results. The data was limited to the population studied (Hispanics) but the results are a useful framework for future prediction model research. Future directions of this work include determining the benefits of applying targeted learning for different effect estimations and inference in the addiction field (Acion et al., 2018).

In 2015, Carrell et al. used natural language processing to identify prescription opioid usage. The researchers combined natural language processing and computer-assisted manual review of clinical notes to identify problem usage in current electronic health records. The study used a large number of patient records (22,142), and produced valid results that demonstrated that natural language processing efficiently and accurately identified evidence of opioid abuse. The methods may increase estimates by as much as one-third when compared to traditional methods.

The Center for Disease Control (CDC), recently published a study (Faul, 2017) on Methadone prescribing, overdose, and the association with Medicaid preferred drug list policies in the U.S. Methadone is a prescribed controlled substance used in SAD treatment for those with an opioid addiction. The study aimed to compare the percentage of deaths involving Methadone with the rate of prescribing Methadone for pain, characterize variation in Methadone prescribing among payers and states, and assess whether an association existed between state Medicaid reimbursement preferred drug list (PDL) policies and Methadone overdose rates. Faul found that Methadone accounted for approximately 1% of all opioids prescribed for pain but accounted for approximately 23% of all prescription opioid deaths in 2014. State drug management practices and reimbursement policies can affect methadone prescribing practices and, in turn, might reduce methadone overdose rates within a state. Drug utilization management policies that reduce the use of risky opioids such as methadone might reduce opioid-related morbidity and mortality. This evidence of decreases in methadone overdoses and use of preferred drug list policies could serve as a model for future decreases in other specific opioid drug-related mortality. The article concludes that Methadone should not be the first choice for an extended-release/long-acting opioid, which contradicts current belief and practice.

Hume et al. (2017) aimed to create accurate definitions and measurable factors to identify drug overdose poisonings in four different states. They utilized four distinct drug poisoning indicators: acute or chronic and drug or opioid. These indicators were compared to principal diagnoses or comprehensive medical histories. The authors concluded that a thorough examination of both diagnosis and medical history was necessary to fully capture the state burden of opioid poisonings.

Another study, spearheaded by Nguyen et al. (2017), focused on creating a model for identifying life-threatening admissions and their associations with drug dependence. The model demonstrated an accurate prediction of death or ICU admission in hospitalized drug users. This model is the first of its kind, allowing for early identification of life-threatening cases in drug users admitted to acute care hospitals. The researchers incorporated patients’ age, sex, entrance model, and diagnosis as predictors of an in-hospital death or ICU admission. With their validation cohort, they were able to devise a method of identifying life-threatening admissions that could potentially enhance patient care management. This model not only improves clinical decision-making but it also aids in managing critically ill patients early on. Such patients contribute significantly to the global financial burden of healthcare. The researchers were able to create a user-friendly method that is easily accessible to most healthcare providers, and could potentially benefit handling of life-threatening drug abuse cases.

Apart from age, sex, entrance model, and diagnosis, psychosocial factors also play a significant role as predictors in substance abuse. Psychosocial risk factors include social determinants of health, mental health disorders, and history of substance abuse disorders. They are less amenable to rapid and systematic data analyses because these factors are not often collected or stored as formatted data. Due to US Health Insurance Portability and Accountability Act (HIPAA) regulations, this data is not available as claims data. According to Oreskovic et al. (2017), the implementation of Electronic Health Record screenings for psychosocial factors needs to be integrated into healthcare and care delivery. The researchers used Medicaid and non-Medicaid patient data to identify 22 psychosocial electronic health record terms and compare them to patient outcomes. They found this number of search terms significantly higher than other studies have used. The study concluded that selected informatics tools like word recognition software could enhance healthcare delivery through the accurate identification of psychosocial risk factors in substance abuse.

In a different approach, bioinformatics researchers, Way et al. (2017), evaluated alcohol dependence in populations with the genetic variant, ALDH1B1. While there was no significant allelic association observed, they suggested that individuals with this gene variant are more prone to abusing drugs. This study uniquely focused on a biomedical factor whereas many others solely consider social and demographic associations.

Lastly, Shah et al. (2017) reviewed rates and identified risk factors for opioid dependence and overdose after urological surgery. By analyzing patient risk factors, they pinpointed five patient risk factors associated with opioid dependence or overdose: younger age, inpatient surgery and longer duration of hospitalization, preexisting depression, tobacco use and presence of chronic obstructive pulmonary disease, and insurance provider type, including Medicaid, Medicare (for patients younger than 65 years), and noninsured status.

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Reviewing the Impact of Informatics on Substance Abuse Disorders and the Opioid Epidemic. (2019, Jun 09). Retrieved from