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An electronic health record based model predicts statin adherence, LDL cholesterol, and cardiovascular disease in the United States Military Health System.

ABSTRACT: HMG-CoA reductase inhibitors (or "statins") are important and commonly used medications to lower cholesterol and prevent cardiovascular disease. Nearly half of patients stop taking statin medications one year after they are prescribed leading to higher cholesterol, increased cardiovascular risk, and costs due to excess hospitalizations. Identifying which patients are at highest risk for not adhering to long-term statin therapy is an important step towards individualizing interventions to improve adherence. Electronic health records (EHR) are an increasingly common source of data that are challenging to analyze but have potential for generating more accurate predictions of disease risk. The aim of this study was to build an EHR based model for statin adherence and link this model to biologic and clinical outcomes in patients receiving statin therapy. We gathered EHR data from the Military Health System which maintains administrative data for active duty, retirees, and dependents of the United States armed forces military that receive health care benefits. Data were gathered from patients prescribed their first statin prescription in 2005 and 2006. Baseline billing, laboratory, and pharmacy claims data were collected from the two years leading up to the first statin prescription and summarized using non-negative matrix factorization. Follow up statin prescription refill data was used to define the adherence outcome (> 80 percent days covered). The subsequent factors to emerge from this model were then used to build cross-validated, predictive models of 1) overall disease risk using coalescent regression and 2) statin adherence (using random forest regression). The predicted statin adherence for each patient was subsequently used to correlate with cholesterol lowering and hospitalizations for cardiovascular disease during the 5 year follow up period using Cox regression. The analytical dataset included 138 731 individuals and 1840 potential baseline predictors that were reduced to 30 independent EHR "factors". A random forest predictive model taking patient, statin prescription, predicted disease risk, and the EHR factors as potential inputs produced a cross-validated c-statistic of 0.736 for classifying statin non-adherence. The addition of the first refill to the model increased the c-statistic to 0.81. The predicted statin adherence was independently associated with greater cholesterol lowering (correlation = 0.14, p < 1e-20) and lower hospitalization for myocardial infarction, coronary artery disease, and stroke (hazard ratio = 0.84, p = 1.87E-06). Electronic health records data can be used to build a predictive model of statin adherence that also correlates with statins' cardiovascular benefits.

SUBMITTER: Lucas JE

PROVIDER: S-EPMC5695792 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:BackgroundThe rapid expansion in the use of electronic health records (EHR) has increased the number of medical errors originating in health information systems (HIS). The sociotechnical approach helps in understanding risks in the development, implementation, and use of EHR and health information technology (HIT) while accounting for complex interactions of technology within the health care system.ObjectiveThis study addresses two important questions: (1) "which of the common EHR error types are associated with perceived high- and extreme-risk severity ratings among EHR users?", and (2) "which variables are associated with high- and extreme-risk severity ratings?"MethodsThis study was a quantitative, non-experimental, descriptive study of EHR users. We conducted a cross-sectional web-based questionnaire study at the largest hospital district in Finland. Statistical tests included the reliability of the summative scales tested with Cronbach's alpha. Logistic regression served to assess the association of the independent variables to each of the eight risk factors examined.ResultsA total of 2864 eligible respondents provided the final data. Almost half of the respondents reported a high level of risk related to the error type "extended EHR unavailability". The lowest overall risk level was associated with "selecting incorrectly from a list of items". In multivariate analyses, profession and clinical unit proved to be the strongest predictors for high perceived risk. Physicians perceived risk levels to be the highest ( P<.001 in six of eight error types), while emergency departments, operating rooms, and procedure units were associated with higher perceived risk levels ( P<.001 in four of eight error types). Previous participation in eLearning courses on EHR-use was associated with lower risk for some of the risk factors.ConclusionsBased on a large number of Finnish EHR users in hospitals, this study indicates that HIT safety hazards should be taken very seriously, particularly in operating rooms, procedure units, emergency departments, and intensive care units/critical care units. Health care organizations should use proactive and systematic assessments of EHR risks before harmful events occur. An EHR training program should be compulsory for all EHR users in order to address EHR safety concerns resulting from the failure to use HIT appropriately.

Project description:BackgroundPoor medication adherence is common; however, few mechanisms exist in clinical practice to monitor how patients take medications in outpatient settings.ObjectiveThis study aimed to pilot test the Electronic Medication Complete Communication (EMC2) strategy, a low-cost, sustainable approach that uses functionalities within the electronic health record to promote outpatient medication adherence and safety.MethodsThe EMC2 strategy was implemented in 2 academic practices for 14 higher-risk diabetes medications. The strategy included: (1) clinical decision support alerts to prompt provider counseling on medication risks, (2) low-literacy medication summaries for patients, (3) a portal-based questionnaire to monitor outpatient medication use, and (4) clinical outreach for identified concerns. We recruited adult patients with diabetes who were prescribed a higher-risk diabetes medication. Participants completed baseline and 2-week interviews to assess receipt of, and satisfaction with, intervention components.ResultsA total of 100 patients were enrolled; 90 completed the 2-week interview. Patients were racially diverse, 30.0% (30/100) had a high school education or less, and 40.0% (40/100) had limited literacy skills. About a quarter (28/100) did not have a portal account; socioeconomic disparities were noted in account ownership by income and education. Among patients with a portal account, 58% (42/72) completed the questionnaire; 21 of the 42 patients reported concerns warranting clinical follow-up. Of these, 17 were contacted by the clinic or had their issue resolved within 24 hours. Most patients (33/38, 89%) who completed the portal questionnaire and follow-up interview reported high levels of satisfaction (score of 8 or greater on a scale of 1-10).ConclusionsFindings suggest that the EMC2 strategy can be reliably implemented and delivered to patients, with high levels of satisfaction. Disparities in portal use may restrict intervention reach. Although the EMC2 strategy can be implemented with minimal impact on clinic workflow, future trials are needed to evaluate its effectiveness to promote adherence and safety.

Project description:BACKGROUND:Robust evidence is lacking on optimal timing of statin administration and its impact on patient outcomes. OBJECTIVE:This study aims to evaluate among incident statin users the relationship between those prescribed evening vs. daily dosing instructions, medication adherence, and changes in low-density lipoprotein cholesterol (LDL-c). DESIGN:This is an observational cohort study at Sutter Health, a community-based healthcare system, 2010-2016. PARTICIPANTS:Patients were ??35 years of age as of the first statin prescription (baseline), with 12 to 36 months of electronic health record activity before and after baseline. Incident use was defined as no statin prescription in 12 months prior to baseline. MAIN MEASURES:Differences in medication adherence (proportion of days covered ??0.80) over 12 months from baseline and mean change in LDL-c between 12 and 24 months from baseline were measured using regression modeling, adjusting for baseline demographics and clinical, prescriber, and statin characteristics. KEY RESULTS:Among 31,252 patients with valid statin prescriptions between 2010 and 2016, 5099 eligible incident statin users (mean age, 63 years) were identified, of whom 53% were prescribed evening and 47% daily dosing instructions. No difference in likelihood of statin adherence over 12 months was observed for evening vs. daily dosing (adjusted odds ratio [OR] 0.90; 95% CI 0.75, 1.08). No differences were observed in mean change in LDL-c (adjusted mean difference 1.42 mg/dL; 95% CI -?1.02, 3.89) or likelihood of attaining LDL-c <?70 mg/dL (adjusted OR 0.83; 95% CI 0.67, 1.04) for evening vs. daily dosing over a mean of 19 months follow-up. CONCLUSIONS:Among incident statin users from a real-world clinical setting, those with daily and evening dosing instructions had similar adherence rates and mean changes in LDL-c. Given potential clinical equipoise for evening and daily dosing, clinicians should consider patient-tailored statin dosing instructions to reduce potentially unnecessary regimen complexity.

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An electronic health record based model predicts statin adherence, LDL cholesterol, and cardiovascular disease in the United States Military Health System.

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