We identified variables used to predict opioid abuse from electronic health records and administrative data. Medication variables are the recurrent variables in the articles reviewed (33 variables). Age and gender are the most consistent demographic variables in predicting opioid abuse. Overall, there is similarity in the sampling method and inclusion/exclusion criteria (age, number of prescriptions, follow-up period, and data analysis methods). Intuitive research to utilize unstructured data may increase opioid abuse models' accuracy.

Objective To understand the influence of demographics and education levels on awareness levels, and on the prevalence of hesitancy to receive the influenza vaccine among adult patients at King Saud University Medical City (KSUMC). Method A crosssectional study in the outpatient pharmacy area at KSUMC was conducted. Data was collected from January 1 to January 31, 2020. A total of 318 random adult patients were encountered and a predesigned survey was administered. After capturing demographic information, respondents were categorized into 3 groups: group A consisted of respondents who had never heard of the influenza vaccine; group B was comprised of respondents who answered that they had never received the influenza vaccine; and group C included respondents who answered that they had received at least one influenza vaccine. Results Out of the 317 survey respondents, 36 (11%) had never heard of the influenza vaccine (Group A). Of the remaining 281 (89%), 122 (39%) had not received the vaccine (Group B), whereas 159 (50%) had received it (Group C). Chi-square test results indicated a significant association between age group and awareness of the vaccine (p = .023). Moreover, there was a significant association between education level and awareness of the vaccine (p = .002). The prevalence of vaccination hesitancy was 42%. Chi-square test results indicated a significant association between gender and vaccination hesitancy (p < .001), and between education level and vaccination hesitancy (p = .011). Conclusion Influenza vaccination hesitancy is prevalent among the study's population. Further efforts by health care providers and public health services may be necessary to educate the community regarding the influenza vaccine's safety and efficacy.

BackgroundPatients with diabetes often have poor adherence to using medications as prescribed. The reasons why, however, are not well understood. Furthermore, most health care delivery processes do not routinely assess medication adherence or the factors that contribute to poor adherence.ObjectiveThe objective of the study was to assess the feasibility of an integrated informatics approach to aggregating and displaying clinically relevant data with the potential to identify issues that may interfere with appropriate medication utilization and facilitate patient-provider communication during clinical encounters about strategies to improve medication use.MethodsWe developed a clinical dashboard within an electronic health record (EHR) system that uses data from three sources: the medical record, pharmacy claims, and a patient portal. Next, we implemented the dashboard into three community health centers. Health care providers (n=15) and patients with diabetes (n=96) were enrolled in a before-after pilot to test the system’s impact on medication adherence and clinical outcomes. To measure adherence, we calculated the proportion of days covered using pharmacy claims. Demographic, laboratory, and visit data from the EHR were analyzed using pairwise t tests. Perceived barriers to adherence were self-reported by patients. Providers were surveyed about their use and perceptions of the clinical dashboard.ResultsAdherence significantly and meaningfully improved (improvements ranged from 6%-20%) consistently across diabetes as well as cardiovascular drug classes. Clinical outcomes, including HbA1c, blood pressure, lipid control, and emergency department utilization remained unchanged. Only a quarter of patients (n=24) logged into the patient portal and completed psychosocial questionnaires about their barriers to taking medications.ConclusionsIntegrated approaches using advanced EHR, clinical decision support, and patient-controlled technologies show promise for improving appropriate medication use and supporting better management of chronic conditions. Future research and development is necessary to design, implement, and integrate the myriad of EHR and clinical decision support systems as well as patient-focused information systems into routine care and patient processes that together support health and well-being.

Background As opioid prescriptions have risen, there has also been a rise in opioid overdose deaths and substance use disorders. Public health systems have tried to improve their ability to detect and intervene in opioid use disorders to prevent death due to overdose. The objective of this study is to compare two approaches to identify opioid use problems (OUP) using electronic health record data- text mining versus diagnostic codes.Methods Our sample consisted of adults on long-term opioid therapy (LTOT), defined as at least ≥ 70 days of supply within 90 days, and who visited a large multi-hospital network within a two-year period, between 1 January 2013 and 31 December 2014. We excluded patients with active cancer or schizophrenia. Text mining results were validated by a semi-assisted human review process and positive predictive value and level of agreement was reported. Each algorithm sought to identify patients who visited a health care facility due to an opioid poisoning event, opioid abuse, or opioid dependence. Population characteristics for positive OUP identified by text mining and ICD cohorts were compared. Chi-square and Fishers exact test were used for categorical data analysis and independent t-test was used to compare means for continuous variables. We further compared the demographics of the cohorts identified by the two methods.Results We identified 14,298 eligible LTOT patients. Text mining of relevant electronic clinical notes yielded 127 positive OUP cases compared to 45 cases using International Classification of Disease (ICD)-9 codes for the same population. Just eight OUP patients were identified using both methods. The two cohorts differed significantly with respect to age, gender, and other characteristics.Conclusions Compared to diagnostic codes, text mining identified more OUP cases with distinct characteristics. Incorporating text-mining techniques into OUP surveillance methods may support better detection of OUP and more accurate estimates of prevalence.

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