This was the first study to quantitatively test the use of SEIPS (Systems Engineering Initiative for Patient Safety) model to identify factors influencing a medication safety outcome. By using a SEIPS model, our study developed a comprehensive approach to identifying potential factors influencing adverse drug events (ADEs). The SEIPS work system is composed of five components which include person, organization, technologies and tools, tasks, and environment. SEIPS model successfully explained the work system factors influencing ADEs and falls in nursing homes (NHs). The second important contribution of our study is that it used the CMS (Centers for Medicare and Medicaid Services) ADE Trigger Tool not only to detect actual ADEs, but also to identify specific potential ADEs in NHs. This study had five objectives: 1) calculate actual ADE incidence rate (number of incidents per 100 residents per month) in NHs using the ADE trigger tool, 2) measure potential ADE incidence rate based on abnormal lab data, vital signs and non-harmful falls, 3) identify the classes of medications most likely to cause ADEs, 4) evaluate the relationships between work system characteristics and the incidence of ADEs, and 5) assess the relationships between work system characteristics and resident fall incidents. This study was an observational quantitative study. It included two quantitative methods: retrospective resident medical chart extraction and survey four types of healthcare practitioners. The staff surveys included four categories of NH practitioners at each facility to ensure comprehensive assessment of the work system: Director of nursing (DON), registered nurse (RN), certified nurse assistant (CNA) and consultant pharmacist. The surveys included questions about the facility conditions, environment, technology, task, and staff/practitioners. Both methods were conducted within the same facilities and during the same period. The study was conducted in 11 NHs in nine cities in Iowa. Data collection was conducted over fall 2016 and spring 2017. Binary logistic regression with Generalized Estimated Equation (GEE) was used to measure the association between the ADE incidence (Yes/No) and characteristics of residents and facilities. The secondary outcome was the incidence of falls. We reviewed 755 medical charts and conducted 44 staff surveys. The rate of ADEs was 6.13 incidents per 100 residents per month. Approximately (64.1%) of the ADEs were preventable. More than half of the ADEs were fall-related (51.1%) and half of those harmful falls were due to hypotension. We considered all the harmful falls as ADEs in residents with one or more psychotropic, antihypertensive, opioid and/or anti-diabetic medications, which can cause fall. The most common ADEs included medication (opioid)-induced constipation (24.6%), psychotropic induced confusion, dizziness or drowsiness (6.5%), antibiotic-induced Clostridium difficile diarrhea (4.2%), anticoagulant induced bleeding (3.9%) and antidiabetic induced hypoglycemia (3.2%). The most common fall-related ADEs were bruise (9.7%) and abrasion or laceration (9.4%). Psychotropic medications (74.9%), antidepressants (61.3%), antihypertensive agents (58.7%), and opioids (51.9%) were the most common medications associated with ADEs. The rate of potential ADEs was 48.6 per 100 residents per month. The rate of falls was 23.38 per 100 residents per month. The regression analysis revealed significant associations between the ADEs and opioid analgesics, psychotropic medications, warfarin, skilled care, consultant pharmacist accessibility, nurse-physician collaboration, CNA skills in taking vital signs, number of physician visits to the facility, nurse workload and the use of electronic health records. On the other hand, the regression analysis showed non-significant relationships between ADEs and cardiac arrhythmia (AFib), DON years in the facility and distracting noise during medication administration. The six significant facility characteristics represent five concepts of the SEIPS model: organization, task, environment, person and technology. In the fall regression analysis, twelve of the resident and the facility SEIPS variables had significant relationships with the incidence of resident falls. The significant variables represent four concepts of the SEIPS model: organization, task, environment, and person. Longer DON years in the facility and more nurse time per resident per day were associated with lower number of fall incidents. The CNA skills in taking vital signs have significant negative association with both ADEs and falls. Finally, the variable “CNAs work fast” and the nurse workload also have positive association with the incidence of falls

<p>The medication errors associated with intravenous (IV) administration may cause severe patient harm. To address this issue, smart infusion pumps now include a built-in dose error reduction system (DERS) to help ensure the safety of IV administration in clinical settings. However, a drug limit alert triggered by DERS may be overridden by the practitioners which can potentially cause patient harm, especially for high-risk medications. Most analytical measures used to estimate the associated risk of harm are frequency-based and only consider the overall drug performance rather than the severity impact from individual alerts. Unlike these other measures, the IV medication harm index attempts to quantify risk of harm for individual alerts. However, it is not known how well these measures describe the risk associated with alert-overridden scenarios. The goal of this research was (1) to quantitatively measure the risk for simulated individual alert-overridden infusions, (2) to compare these assessments against the risk scores obtained among four different analytical methods, and (3) to propose better risk quantification methods with a higher correlation to risk benchmarks than traditional measures, such as the IV Harm index. </p> <p>In this study, 25 domain experts (20 pharmacists and 5 nurses) were recruited to assess the risk (adjusted for risk benchmarks) for representative scenarios created based on hospital alert data. Four analytical methods were applied to quantify risk for the scenarios: the linear mixed models (Method A), the IV harm index (Method B), Huang and Moh’s matrix-based ranking method matrix-based method (Method C), and the analytical hierarchy process method, adjusted by linear mixed models (Method D). Method A used seven alert factors (identified as key risk factors) to build models for risk prediction, and Methods B and C used two out of seven factors to obtain risk scores. Method D used pairwise comparison surveys to calculate the risk priorities. The quantified scores from the four methods were evaluated in comparison to the risk benchmarks.</p> <p>Risk assessment results from the domain experts indicated that overdosing scenarios with continuous and bolus dose field limit types had significantly higher risks than those of bolus dose rate type. About the soft limit type, the expected risk in the group with a large soft maximum limit was significantly higher than the group with a small soft maximum limit. This significant difference could be found in the adult intensive care unit (AICU), but not in adult medical/surgical care unit (AMSU). The comparisons between four analytical methods and risk benchmarks showed that the risk scores from Method A (<i>ρ</i> = 0.94) and Method D (<i>ρ </i>= 0.87) were highly correlated to the risk benchmarks. The risk scores derived from Method B and Method C did not have a positive correlation with the benchmarks.</p> <p>This study demonstrated that the traditional IV harm index should include more risk factors, along with their interaction effects, for increased correlation with risk benchmarks. Furthermore, the linear mixed models and the adjusted AHP method allow for better risk quantification methods where the quantified scores most correlated with the benchmarks. These methods can provide risk-based analytical support to evaluate alert overrides of four high-risk medications, propofol, morphine, insulin, and heparin in the settings of adult intensive care unit (AICU) and adult medical/surgical care unit (AMSU). We believe that healthcare systems can use these analytical methods to efficiently identify the riskiest medication-care unit combinations (e.g. propofol in AICU), and reduce medication error/harm associated with infusions to enhance patient safety.</p> <p> </p>

Indiana University-Purdue University Indianapolis (IUPUI) / Health care automation provides opportunities for health care agencies to save time, save money, and increase patient safety. The Department of Veterans Affairs medical centers use a program, Attachmate Reflections, for pharmacy medication order verification. This program is a command line interface that allows the use of macros, or programmed automated routines, that have the ability to automate repetitive tasks. Through the use of macro programming at the VISN 11 VA medical centers, this author was able to automate converting patients from Combivent MDI inhalers to its successor Combivent Respimat inhalers due to the MDI inhaler being withdrawn from the market. Usage of the macro resulted in a time savings of 649.1 hours, cost savings of $32,748.36, and increased patient safety by providing consistent medication instructions, correct dispense quantities, correct prescription day supply, and correct number of refills remaining on the prescription.

No description available.

<p>Integrating laboratory information into prescribing tasks may improve medication safety. This thesis addresses several methodological issues in the progress of two studies: a systematic review of randomized trials addressing the impact of drug-lab safety alerts on adverse drug events and changes in prescribing or lab monitoring and a randomized trial using an electronic survey to compare prescribing decisions in complex clinical scenarios including integrated lab data with those in which the lab data were available on request. The systematic review found 32 studies; 10 addressed multiple drug-lab combinations, and 22 addressed single drug-lab combinations, including 14 targeting anticoagulation. We report a benefit of anticoagulation-related alerts (OR of an adverse event (bleeding or thrombosis) 0.88 (95% CI 0.78-1.00) and improved prescribing in multi-drug studies (OR 2.22, 95% CI 1.19-4.17), but substantial study heterogeneity precluded combining studies of other drugs. Methodological issues addressed in the RCT include medication selection, scenario design, recruitment, and assessment of the representativeness of the sample. We selected medications for study scenarios that are commonly prescribed by Canadian primary care physicians, and are associated with clinically important harm that may be preventable through laboratory monitoring. Data sources included IMS Brogan data on prescribing patterns and the Discharge Abstracts Database (DAD) and the National Ambulatory Care Reporting System (NACRS) from 2006-2007 to 2008-2009. Our study had 148 completed surveys. The study sample differed from the population of Ontario family physicians by gender, and use of electronic medical records. We found no difference in prescribing decisions (OR 1.21, 95% CI 0.84-1.75) between the study groups and no predictors of improved prescribing decisions. The lack of demonstrated impact of integrating lab data into clinical decision-making may be related to the study being underpowered, to a true lack of clinical benefit, or to a lack of discriminatory power in the scenarios.</p> / Master of Science (MSc)

Medication safety and improving communication at care transitions are an international priority. There is vast evidence on the scale of error associated with medicines reconciliation and some evidence of successful interventions to improve reconciliation. However, there is insufficient evidence on the factors that contribute towards medication error at transitions, or the roles of those involved. This thesis examined current UK medicines reconciliation practice within primary and secondary care, and the role of HCPs and patients. Using a mixed-method, multi-centre design, the type and severity of discrepancies at admission to hospital were established and staff undertaking medicines reconciliation across secondary and primary care were observed, using evidence-informed framework, based on a narrative literature review. The overall processes used to reconcile medicines were similar; however, there was considerable inter and intra-organisational variation within primary and secondary care practice. Patients were not routinely involved in discussions about their medication, despite their capacity to do so. Various human factors in reconciliation-related errors were apparent; predominantly inadequate communication, individual factors e.g. variation in approach by HCP, and patient factors e.g. lack of capacity. Areas of good practice which could reduce medicines reconciliation-related errors/discrepancies were identified. There is a need for increased consistency and standardisation of medicines reconciliationrelated policy, procedures and documentation, alongside communication optimisation. This could be achieved through a standardised definition and taxonomy of error, the development of a medicines reconciliation quality assessment framework, increased undergraduate and post-graduate education, improved patient engagement, better utilisation of information technology and improved safety culture.

Background: Prescribing errors are the most frequent type of error in the medication use process. High risk medicines (HRMs) are a sub-class of medications that if used erroneously have potentially devastating consequences which defined by Institute for Safe Medication Practices (ISMP) as the drugs that bear a heightened risk of causing significant patient harm when they are used in error. Therefore, prescribing errors with HRMs are of concern to healthcare professionals that are responsible for ensuring mitigating patient safety. This thesis examines to what extent prescribing errors with HRMs in hospital occur, the causes of prescribing errors with HRMs and the differences to non-HRMs and the prescribing errors with HRMs during the on-call period. Method: The research adopted a mixed methods approach to explore prescribing errors with HRMs in hospitals and three studies were undertaken. The first study was a systematic review of the literature to explore the prevalence and incidence of prescribing errors with HRMs in hospitals. The second study was a secondary analysis of 59 existing interviews with foundation year doctors to explore the causes of prescribing errors with HRMs and compare them to those for non-HRMs reported in the same interviews. The third study was a qualitative study of the challenges of prescribing HRMs safely during the on-call period. This final study involved six focus groups with foundation year doctors (total participants number was 42). Results: Overall, findings demonstrated that there is paucity of studies that explored the prevalence of prescribing errors with HRMs and this literature showed inconsistency in definitions of prescribing errors, HRMs lists, severity scales and study methods (Study One). This resulted in a very wide range of prevalence of prescribing errors with HRMs. In terms of causes of prescribing errors with HRMs (Study Two), prescribing HRMs was considered a complex task for participants, especially those requiring dosage calculations, errors in the legal prescription requirements for controlled medications occurred with HRMs only and the on-call period was a particularly challenging period to prescribe safely especially with HRMs. In Study Three, the reasons found for this include the nature of the on-call period as a fast-paced environment, the methods of communication such as the bleep system, lack of accessibility to patient information and lack of plan from the primary team. Conclusions: HRMs form part of general medications, meaning they share similar traits, but the potentially devastating consequences of HRMs and the complicated task posed by prescribing them makes errors in their prescription profound. Therefore, HRMs need closer attention and more concern from healthcare professionals, researchers and policymakers. Such attention could result in a significant reduction in adverse outcomes and improved patient safety.

Renny Varghese Impact of an Electronic Medical Record Implementation on Drug Allergy Overrides in a Large Southeastern HMO Setting (Under the direction of Russell Toal, Associate Professor) Electronic medical records (EMRs) have become recognized as an important tool for improving patient safety and quality of care. Decision support tools such as alerting functions for patient medication allergies are a key part of reducing the frequency of serious medication problems. Kaiser Permanente Georgia (KPGA) implemented its EMR system in the primary care departments at Kaiser's twelve facilities in the greater metro Atlanta area over a six month period beginning in June 2005 and ending December 2005. The aim of this study is to analyze the impact of the EMR implementation on the number of drug allergy overrides within this large HMO outpatient setting. Research was conducted by comparing the rate of drug allergy overrides during pre and post EMR implementation. The timeline will be six months pre and post implementation. Observing the impact of the incidence rate of drug allergy alerts after the implementation provided insight into the effectiveness of EMRs in reducing contraindicated drug allergies. Results show that the incidence rate of drug allergy overrides per 1,000 filled prescriptions rose by a statistically significant 5.9% (ñ > 0.0002; 95% CI [-1.531, -0.767]) following the implementation. Although results were unexpected, several factors are discussed as to the reason for the increase. Further research is recommended to explore trends in provider behavior, KPGA specific facilities and departments, and in other KP regions and non-KP healthcare settings. INDEX WORDS: electronic medical records, drug allergy overrides, patient safety, medication errors, decision support tools, outpatient setting, primary care, computerized provider order entry

Secondary infusions is a common method to deliver short infusions of intravenous (IV) drugs and fluids. Errors associated with this infusion method have led to patient safety concerns. This study's objective was to empirically evaluate interventions to mitigate secondary infusion risks. Three interventions, including a technology-based intervention (clamp detector on a smart pump), a training-based intervention (educational module), and a practice-based intervention (use of a separate pump for short infusions), were tested in a simulated inpatient unit. The technology-based intervention significantly decreased secondary clamp errors whereas the training-based intervention reduced complex pressure differential errors. The practice-based intervention was the only intervention that significantly decreased both secondary clamp errors and pressure differential errors, but introduced new risks due to mismanagement of residual volume in IV tubing. Study results highlight the need for a combination of mitigation strategies and can help guide the selection of interventions to reduce secondary infusion errors.

Secondary infusions is a common method to deliver short infusions of intravenous (IV) drugs and fluids. Errors associated with this infusion method have led to patient safety concerns. This study's objective was to empirically evaluate interventions to mitigate secondary infusion risks. Three interventions, including a technology-based intervention (clamp detector on a smart pump), a training-based intervention (educational module), and a practice-based intervention (use of a separate pump for short infusions), were tested in a simulated inpatient unit. The technology-based intervention significantly decreased secondary clamp errors whereas the training-based intervention reduced complex pressure differential errors. The practice-based intervention was the only intervention that significantly decreased both secondary clamp errors and pressure differential errors, but introduced new risks due to mismanagement of residual volume in IV tubing. Study results highlight the need for a combination of mitigation strategies and can help guide the selection of interventions to reduce secondary infusion errors.