Reducing Warfarin Medication Interactions

Feldstein, Adrianne C.; Smith, David H.; Perrin, Nancy; Yang, Xiuhai; Simon, Steven R.; Krall, Michael A.; Sittig, Dean F.; Ditmer, Diane; Platt, Richard; Soumerai, Stephen B.

doi:10.1001/archinte.166.9.1009

Cited by 101 publications

(28 citation statements)

References 22 publications

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“…The use of large time series can be an excellent method for conducting naturalistic studies of the effects of changes at a systems level, which capitalises on existing data, allows intuitive graphical representation, estimates trends before and after an intervention, and takes into account programmatic and legal variables, such as obligatory schooling, maternal health programs, family planning, and abortion legislation [42], [95], [96]. In addition, effect sizes can be estimated at different times after the interventions, simultaneously controlling for other variables using parallel time series to take advantage of multiple causal pathway modelling.…”

Section: Discussionmentioning

confidence: 99%

Women's Education Level, Maternal Health Facilities, Abortion Legislation and Maternal Deaths: A Natural Experiment in Chile from 1957 to 2007

et al. 2012

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BackgroundThe aim of this study was to assess the main factors related to maternal mortality reduction in large time series available in Chile in context of the United Nations' Millennium Development Goals (MDGs).MethodsTime series of maternal mortality ratio (MMR) from official data (National Institute of Statistics, 1957–2007) along with parallel time series of education years, income per capita, fertility rate (TFR), birth order, clean water, sanitary sewer, and delivery by skilled attendants were analysed using autoregressive models (ARIMA). Historical changes on the mortality trend including the effect of different educational and maternal health policies implemented in 1965, and legislation that prohibited abortion in 1989 were assessed utilizing segmented regression techniques.ResultsDuring the 50-year study period, the MMR decreased from 293.7 to 18.2/100,000 live births, a decrease of 93.8%. Women's education level modulated the effects of TFR, birth order, delivery by skilled attendants, clean water, and sanitary sewer access. In the fully adjusted model, for every additional year of maternal education there was a corresponding decrease in the MMR of 29.3/100,000 live births. A rapid phase of decline between 1965 and 1981 (−13.29/100,000 live births each year) and a slow phase between 1981 and 2007 (−1.59/100,000 live births each year) were identified. After abortion was prohibited, the MMR decreased from 41.3 to 12.7 per 100,000 live births (−69.2%). The slope of the MMR did not appear to be altered by the change in abortion law.ConclusionIncreasing education level appears to favourably impact the downward trend in the MMR, modulating other key factors such as access and utilization of maternal health facilities, changes in women's reproductive behaviour and improvements of the sanitary system. Consequently, different MDGs can act synergistically to improve maternal health. The reduction in the MMR is not related to the legal status of abortion.

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Section: Discussionmentioning

confidence: 99%

Women's Education Level, Maternal Health Facilities, Abortion Legislation and Maternal Deaths: A Natural Experiment in Chile from 1957 to 2007

et al. 2012

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“…In a health maintenance organization with 15 primary care clinics and 450,000 members, electronic medical record (EMR) alerts were implemented to reduce the co-prescribing of interacting medications to warfarin users to guard against potential adverse drug interactions [4]. At the beginning of the study period in January 2000, there were 35 months prior to the intervention (baseline), 4 for implementation of the alerts, and 17 after the intervention.…”

Section: Example: Warfarin Alert Studymentioning

confidence: 99%

“…We use the warfarin alerts study [4] to compare results from the multivariate delta method (MDM) and bootstrapping method (BM). Following standard practice, we first constructed a saturated model containing parameters for level and trend at baseline and in post-intervention segments, as detailed in equation 1 above.…”

Section: Applicationmentioning

confidence: 99%

“…After its introduction to the health services research literature by Gillings [1], interrupted time series analysis has been widely used in assessing the effects of health services and policy interventions, such as implementation of changes in institutional formularies [2], payment restrictions for medications in state programs [3], or educational and administrative interventions to improve prescribing [4]. Interrupted time series designs, especially when they involve analysis of comparison series, are the strongest observational designs [1,5] to evaluate changes due to interventions because they can account for the pre-intervention level and trend of the outcome measures.…”

Section: Introductionmentioning

confidence: 99%

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Methods for estimating confidence intervals in interrupted time series analyses of health interventions

Zhang

Wagner

Soumerai

et al. 2009

Journal of Clinical Epidemiology

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205

170

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Objective Interrupted time series is a strong quasi-experimental research design that is increasingly applied to estimate the effects of health services and policy interventions. We describe and illustrate two methods for estimating confidence intervals around absolute and relative changes in outcomes calculated from segmented regression parameter estimates. Study Design and Setting We used multivariate delta and bootstrapping methods to construct confidence intervals around relative changes in level and trend and around absolute changes in outcome based on segmented linear regression analyses of time series data corrected for auto-correlated errors. Results Using previously published time series data, we estimated confidence intervals around the effect of prescription alerts for interacting medications with warfarin on the rate of prescriptions per 10,000 warfarin users per month. Both the multivariate delta method and the bootstrapping method produced similar results. Conclusion The bootstrapping method is preferred for calculating confidence intervals of relative changes in outcomes of time series studies since it does not require large sample sizes when parameter estimates are obtained correctly from the model. Caution is needed when sample size is small.

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“…It has been suggested that such problems can be mitigated through the integration of rules to recommend against or even prohibit certain actions (e.g., the prescription of two drugs in combination that are known to cause an adverse reaction [27,28]). At the same time, it is recognized that no rules-based system is perfect and that exceptions need to be granted.…”

Section: Introductionmentioning

confidence: 99%

We work with them? Healthcare workers interpretation of organizational relations mined from electronic health records

Chen

Lorenzi

Nyemba

et al. 2014

International Journal of Medical Informatics

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Objective Models of healthcare organizations (HCOs) are often defined up front by a select few administrative officials and managers. However, given the size and complexity of modern healthcare systems, this practice does not scale easily. The goal of this work is to investigate the extent to which organizational relationships can be automatically learned from utilization patterns of electronic health record (EHR) systems. Method We designed an online survey to solicit the perspectives of employees of a large academic medical center. We surveyed employees from two administrative areas: 1) Coding & Charge Entry and 2) Medical Information Services and two clinical areas: 3) Anesthesiology and 4) Psychiatry. To test our hypotheses we selected two administrative units that have work-related responsibilities with electronic records; however, for the clinical areas we selected two disciplines with very different patient responsibilities and whose accesses and people who accessed were similar. We provided each group of employees with questions regarding the chance of interaction between areas in the medical center in the form of association rules (e.g., Given someone from Coding & Charge Entry accessed a patient’s record, what is the chance that someone from Medical Information Services access the same record?). We compared the respondent predictions with the rules learned from actual EHR utilization using linear-mixed effects regression models. Results The findings from our survey confirm that medical center employees can distinguish between association rules of high and non-high likelihood when their own area is involved. Moreover, they can make such distinctions between for any HCO area in this survey. It was further observed that, with respect to highly likely interactions, respondents from certain areas were significantly better than other respondents at making such distinctions and certain areas’ associations were more distinguishable than others. Conclusions These results illustrate that EHR utilization patterns may be consistent with the expectations of HCO employees. Our findings show that certain areas in the HCO are easier than others for employees to assess, which suggests that automated learning strategies may yield more accurate models of healthcare organizations than those based on the perspectives of a select few individuals.

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Reducing Warfarin Medication Interactions

Cited by 101 publications

References 22 publications

Women's Education Level, Maternal Health Facilities, Abortion Legislation and Maternal Deaths: A Natural Experiment in Chile from 1957 to 2007

Women's Education Level, Maternal Health Facilities, Abortion Legislation and Maternal Deaths: A Natural Experiment in Chile from 1957 to 2007

Methods for estimating confidence intervals in interrupted time series analyses of health interventions

We work with them? Healthcare workers interpretation of organizational relations mined from electronic health records

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