Dataset Information

Electronic Health Record-Based Screening for Substance Abuse.

ABSTRACT: Existing methods of screening for substance abuse (standardized questionnaires or clinician's simply asking) have proven difficult to initiate and maintain in primary care settings. This article reports on how predictive modeling can be used to screen for substance abuse using extant data in electronic health records (EHRs). We relied on data available through Veterans Affairs Informatics and Computing Infrastructure (VINCI) for the years 2006 through 2016. We focused on 4,681,809 veterans who had at least two primary care visits; 829,827 of whom had a hospitalization. Data included 699 million outpatient and 17 million inpatient records. The dependent variable was substance abuse as identified from 89 diagnostic codes using the Agency for Healthcare Quality and Research classification of diseases. In addition, we included the diagnostic codes used for identification of prescription abuse. The independent variables were 10,292 inpatient and 13,512 outpatient diagnoses, plus 71 dummy variables measuring age at different years between 20 and 90 years. A modified naive Bayes model was used to aggregate the risk across predictors. The accuracy of the predictions was examined using area under the receiver operating characteristic (AROC) curve in 20% of data, randomly set aside for the evaluation. Many physical/mental illnesses were associated with substance abuse. These associations supported findings reported in the literature regarding the impact of substance abuse on various diseases and vice versa. In randomly set-aside validation data, the model accurately predicted substance abuse for inpatient (AROC?=?0.884), outpatient (AROC?=?0.825), and combined inpatient and outpatient (AROC?=?0.840) data. If one excludes information available after substance abuse is known, the cross-validated AROC remained high, 0.822 for inpatient and 0.817 for outpatient data. Data within EHRs can be used to detect existing or predict potential future substance abuse.

SUBMITTER: Alemi F

PROVIDER: S-EPMC6154440 | biostudies-literature | 2018 Sep

REPOSITORIES: biostudies-literature

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Electronic Health Record-Based Screening for Substance Abuse.

Alemi Farrokh F Avramovic Sanja S Schwartz Mark D MD

Big data 20180919 3

Existing methods of screening for substance abuse (standardized questionnaires or clinician's simply asking) have proven difficult to initiate and maintain in primary care settings. This article reports on how predictive modeling can be used to screen for substance abuse using extant data in electronic health records (EHRs). We relied on data available through Veterans Affairs Informatics and Computing Infrastructure (VINCI) for the years 2006 through 2016. We focused on 4,681,809 veterans who h ...[more]

PMID: 30283729

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Project description:The clinical value of population-based genetic screening projects depends on the actions taken on the findings. The Healthy Nevada Project (HNP) is an all-comer genetic screening and research project based in northern Nevada. HNP participants with CDC Tier 1 findings of hereditary breast and ovarian cancer syndrome (HBOC), Lynch syndrome (LS), or familial hypercholesterolemia (FH) are notified and provided with genetic counseling. However, the HNP subsequently takes a "hands-off" approach: it is the responsibility of notified participants to share their findings with their healthcare providers, and providers are expected to implement the recommended action plans. Thus, the HNP presents an opportunity to evaluate the efficiency of participant and provider responses to notification of important genetic findings, using electronic health records (EHRs) at Renown Health (a large regional hospital in northern Nevada). Out of 520 HNP participants with findings, we identified 250 participants who were notified of their findings and who had an EHR. 107 of these participants responded to a survey, with 76 (71%) indicating that they had shared their findings with their healthcare providers. However, a sufficiently specific genetic diagnosis appeared in the EHRs and problem lists of only 22 and 10%, respectively, of participants without prior knowledge. Furthermore, review of participant EHRs provided evidence of possible relevant changes in clinical care for only a handful of participants. Up to 19% of participants would have benefited from earlier screening due to prior presentation of their condition. These results suggest that continuous support for both participants and their providers is necessary to maximize the benefit of population-based genetic screening. We recommend that genetic screening projects require participants' consent to directly document their genetic findings in their EHRs. Additionally, we recommend that they provide healthcare providers with ongoing training regarding documentation of findings and with clinical decision support regarding subsequent care.

Project description:Intimate partner violence (IPV) is a significant public health issue, with a 25% lifetime prevalence. Screening for IPV in primary care is a recommended practice whose effectiveness is debated. To assess the effect of an electronic health record (EHR)-based multifactorial intervention screening on the detection of IPV risk in primary care practice. This cluster randomized clinical trial used a stepped-wedge design to assign 15 family medicine primary care clinics in the Medical University of South Carolina Health System in the Charleston region to 3 matched blocks from October 6, 2020, to March 31, 2023. All women aged 18 to 49 years who were seen in these clinics participated in this study. A noninterruptive EHR alert combined with confidential screening by computer questionnaire using the EHR platform followed by risk assessment and a decision support template. The main outcomes were the rate at which patients were screened for IPV across the clinics and the rate at which patients at risk for IPV were detected by screening procedures. The study clinics cared for 8895 unique patients (mean [SD] age, 34.6 [8.7] years; 1270 [14.3%] with Medicaid or Medicare and 7625 [85.7%] with private, military, or other insurance) over the study period eligible for the screening intervention. The intervention had significant effects on the overall rate of screening for IPV, increasing the rate of screening from 45.2% (10 268 of 22 730 patient visits) to 65.3% (22 303 of 34 157 patient visits) when the noninterruptive alert was active (relative risk, 1.46 [95% CI, 1.44-1.49]; P < .001). The confidential screening process was more effective than baseline nurse-led oral screening at identifying patients reporting past-year IPV (130 of 8895 patients [1.5%] vs 9 of 17 433 patients [0.1%]). The intervention was largely effective in increasing screening adherence and the positive detection rate of IPV in primary care. A highly private approach to screening for IPV in primary care may be necessary to achieve adequate detection rates while addressing potential safety issues of patients experiencing IPV. ClinicalTrials.gov Identifier: NCT06284148.

Project description:BackgroundEHR systems are widely used in hospitals and primary care centres but it is usually difficult to share information and to collect patient data for clinical research. This is partly due to the different proprietary information models and inconsistent data quality. Our objective was to provide a more flexible solution enabling the clinicians to define which data to be recorded and shared for both routine documentation and clinical studies. The data should be possible to reuse through a common set of variable definitions providing a consistent nomenclature and validation of data. Another objective was that the templates used for the data entry and presentation should be possible to use in combination with the existing EHR systems.MethodsWe have designed and developed a template based system (called Julius) that was integrated with existing EHR systems. The system is driven by the medical domain knowledge defined by clinicians in the form of templates and variable definitions stored in a common data repository. The system architecture consists of three layers. The presentation layer is purely web-based, which facilitates integration with existing EHR products. The domain layer consists of the template design system, a variable/clinical concept definition system, the transformation and validation logic all implemented in Java. The data source layer utilizes an object relational mapping tool and a relational database.ResultsThe Julius system has been implemented, tested and deployed to three health care units in Stockholm, Sweden. The initial responses from the pilot users were positive. The template system facilitates patient data collection in many ways. The experience of using the template system suggests that enabling the clinicians to be in control of the system, is a good way to add supplementary functionality to the present EHR systems.ConclusionThe approach of the template system in combination with various local EHR systems can facilitate the sharing and reuse of validated clinical information from different health care units. However, future system developments for these purposes should consider using the openEHR/CEN models with shareable archetypes.

Project description:BackgroundElectronic health record (EHR) systems have been shown to be associated with improvements in care processes, quality of care, and patient outcomes. EHR also has a crucial role in the delivery of substance use disorder (SUD) treatment and is considered important for addressing SUD crises, including the opioid epidemic. However, little is known about the adoption of EHR in SUD treatment programs or the organizational-level factors associated with the adoption of EHR in SUD treatment.ObjectiveWe examined the adoption of EHR in SUD programs, with a focus on changes in adoption from 2014 to 2017, and identified organizational-level factors associated with EHR adoption.MethodsWe used data from the 2014 and 2017 National Drug Abuse Treatment System Surveys. Our analysis included 1027 SUD programs (531 in 2014 and 496 in 2017). We used chi-square and Mann-Whitney U tests for categorical and continuous variables, respectively, to assess changes in EHR adoption, technology use, program, and client characteristics. We also investigated differences in characteristics and barriers to adoption by EHR adoption status (adopted EHR vs had not adopted or were planning to adopt EHR). We then conducted multivariate logistic regressions to examine internal and external factors associated with EHR adoption.ResultsThe adoption of EHR increased significantly from 57.6% (306/531) in 2014 to 69.2% (343/496) in 2017 (P<.001), showing that nearly one-third (153/496, 30.8%) of SUD programs had not yet adopted an EHR system by 2017. We identified a significant increase in technology use and ownership by a parent company (P=.01 and P<.001) and a decrease in the percentage of uninsured patients in 2017 (P<.001), compared to 2014. Our analysis further showed significant differences by adoption status for three major barriers to adoption: (1) start-up costs, (2) ongoing financial costs, and (3) privacy or security concerns (P<.001). Programs that used computerized scheduling (adjusted odds ratio [AOR] 3.02, 95% CI 2.23-4.09) and billing systems (AOR 2.29, 95% CI 1.62-3.25) were more likely to adopt EHR. Similarly, ownership type, such as private nonprofit (AOR 1.86, 95% CI 1.31-2.65) and public (AOR 2.14, 95% CI 1.27-3.67), or interest in participating in a patient-centered medical home (AOR 1.93, 95% CI 1.29-2.92), were associated with an increased likelihood to adopt EHR. Overall, SUD programs were more likely to adopt an EHR system in 2017 compared to 2014 (AOR 1.44, 95% CI 1.07-1.94).ConclusionsOur findings highlighted that SUD programs may be on track to achieve widespread EHR adoption. However, there is a need for focused strategies, resources, and policies explicitly designed to systematically address barriers and tackle obstacles to expanding the adoption of EHR systems. These efforts must be holistic and address factors at multiple organizational levels.

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Dataset Information

Electronic Health Record-Based Screening for Substance Abuse.

Publications

Electronic Health Record-Based Screening for Substance Abuse.

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