Improving patient safety via automated laboratory-based adverse event grading

The identification and grading of adverse events (AEs) during the conduct of clinical trials is a labor-intensive and error-prone process. This paper describes and evaluates a software tool developed by City of Hope to automate complex algorithms to assess laboratory results and identify and grade AEs. We compared AEs identified by the automated system with those previously assessed manually, to evaluate missed/misgraded AEs. We also conducted a prospective paired time assessment of automated versus manual AE assessment. We found a substantial improvement in accuracy/completeness with the automated grading tool, which identified an additional 17% of severe grade 3–4 AEs that had been missed/misgraded manually. The automated system also provided an average time saving of 5.5 min per treatment course. With 400 ongoing treatment trials at City of Hope and an average of 1800 laboratory results requiring assessment per study, the implications of these findings for patient safety are enormous

Comparative evaluation of three clinical decision support systems: prospective screening for medication errors in 100 medical inpatients

Purpose: Clinical decision support systems (CDSS) are promoted as powerful screening tools to improve pharmacotherapy. The aim of our study was to evaluate the potential contribution of CDSS to patient management in clinical practice. Methods: We prospectively analyzed the pharmacotherapy of 100 medical inpatients through the parallel use of three CDSS, namely, Pharmavista, DrugReax, and TheraOpt. After expert discussion that also considered all patient-specific clinical information, we selected apparently relevant alerts, issued suitable recommendations to physicians, and recorded subsequent prescription changes. Results: For 100 patients with a median of eight concomitant drugs, Pharmavista, DrugReax, and TheraOpt generated a total of 53, 362, and 328 interaction alerts, respectively. Among those we identified and forwarded 33 clinically relevant alerts to the attending physician, resulting in 19 prescription changes. Four adverse drug events were associated with interactions. The proportion of clinically relevant alerts among all alerts (positive predictive value) was 5.7, 8.0, and 7.6%, and the sensitivity to detect all 33 relevant alerts was 9.1, 87.9, and 75.8% for Pharmavista, DrugReax and TheraOpt, respectively. TheraOpt recommended 31 dose adjustments, of which we considered 11 to be relevant; three of these were followed by dose reductions. Conclusions: CDSS are valuable screening tools for medication errors, but only a small fraction of their alerts appear relevant in individual patients. In order to avoid overalerting CDSS should use patient-specific information and management-oriented classifications. Comprehensive information should be displayed on-demand, whereas a limited number of computer-triggered alerts that have management implications in the majority of affected patients should be based on locally customized and supported algorithm

A Systematic Review of Knowledge Visualization Approaches Using Big Data Methodology for Clinical Decision Support

This chapter reports on results from a systematic review of peer-reviewed studies related to big data knowledge visualization for clinical decision support (CDS). The aims were to identify and synthesize sources of big data in knowledge visualization, identify visualization interactivity approaches for CDS, and summarize outcomes. Searches were conducted via PubMed, Embase, Ebscohost, CINAHL, Medline, Web of Science, and IEEE Xplore in April 2019, using search terms representing concepts of: big data, knowledge visualization, and clinical decision support. A Google Scholar gray literature search was also conducted. All references were screened for eligibility. Our review returned 3252 references, with 17 studies remaining after screening. Data were extracted and coded from these studies and analyzed using a PICOS framework. The most common audience intended for the studies was healthcare providers (n = 16); the most common source of big data was electronic health records (EHRs) (n = 12), followed by microbiology/pathology laboratory data (n = 8). The most common intervention type was some form of analysis platform/tool (n = 7). We identified and classified studies by visualization type, user intent, big data platforms and tools used, big data analytics methods, and outcomes from big data knowledge visualization of CDS applications

Promoting Patient Safety and Preventing Medical Error in Emergency Departments

An estimated 108,000 people die each year from potentially preventable iatrogenic injury. One in 50 hospitalized patients experiences a preventable adverse event. Up to 3% of these injuries and events take place in emergency departments. With long and detailed training, morbidity and mortality conferences, and an emphasis on practitioner responsibility, medicine has traditionally faced the challenges of medical error and patient safety through an approach focused almost exclusively on individual practitioners. Yet no matter how well trained and how careful health care providers are, individuals will make mistakes because they are human. In general medicine, the study of adverse drug events has led the way to new methods of error detection and error prevention. A combination of chart reviews, incident logs, observation, and peer solicitation has provided a quantitative tool to demonstrate the effectiveness of interventions such as computer order entry and pharmacist order review. In emergency medicine (EM), error detection has focused on subjects of high liability: missed myocardial infarctions, missed appendicitis, and misreading of radiographs. Some system-level efforts in error prevention have focused on teamwork, on strengthening communication between pharmacists and emergency physicians, on automating drug dosing and distribution, and on rationalizing shifts. This article reviews the definitions, detection, and presentation of error in medicine and EM. Based on review of the current literature, recommendations are offered to enhance the likelihood of reduction of error in EM practice.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/74930/1/j.1553-2712.2000.tb00466.x.pd

A safer place for patients: learning to improve patient safety

1 Every day over one million people are treated successfully by National Health Service (NHS) acute, ambulance and mental health trusts. However, healthcare relies on a range of complex interactions of people, skills, technologies and drugs, and sometimes things do go wrong. For most countries, patient safety is now the key issue in healthcare quality and risk management. The Department of Health (the Department) estimates that one in ten patients admitted to NHS hospitals will be unintentionally harmed, a rate similar to other developed countries. Around 50 per cent of these patient safety incidentsa could have been avoided, if only lessons from previous incidents had been learned. 2 There are numerous stakeholders with a role in keeping patients safe in the NHS, many of whom require trusts to report details of patient safety incidents and near misses to them (Figure 2). However, a number of previous National Audit Office reports have highlighted concerns that the NHS has limited information on the extent and impact of clinical and non-clinical incidents and trusts need to learn from these incidents and share good practice across the NHS more effectively (Appendix 1). 3 In 2000, the Chief Medical Officer’s report An organisation with a memory 1 , identified that the key barriers to reducing the number of patient safety incidents were an organisational culture that inhibited reporting and the lack of a cohesive national system for identifying and sharing lessons learnt. 4 In response, the Department published Building a safer NHS for patients3 detailing plans and a timetable for promoting patient safety. The goal was to encourage improvements in reporting and learning through the development of a new mandatory national reporting scheme for patient safety incidents and near misses. Central to the plan was establishing the National Patient Safety Agency to improve patient safety by reducing the risk of harm through error. The National Patient Safety Agency was expected to: collect and analyse information; assimilate other safety-related information from a variety of existing reporting systems; learn lessons and produce solutions. 5 We therefore examined whether the NHS has been successful in improving the patient safety culture, encouraging reporting and learning from patient safety incidents. Key parts of our approach were a census of 267 NHS acute, ambulance and mental health trusts in Autumn 2004, followed by a re-survey in August 2005 and an omnibus survey of patients (Appendix 2). We also reviewed practices in other industries (Appendix 3) and international healthcare systems (Appendix 4), and the National Patient Safety Agency’s progress in developing its National Reporting and Learning System (Appendix 5) and other related activities (Appendix 6). 6 An organisation with a memory1 was an important milestone in the NHS’s patient safety agenda and marked the drive to improve reporting and learning. At the local level the vast majority of trusts have developed a predominantly open and fair reporting culture but with pockets of blame and scope to improve their strategies for sharing good practice. Indeed in our re-survey we found that local performance had continued to improve with more trusts reporting having an open and fair reporting culture, more trusts with open reporting systems and improvements in perceptions of the levels of under-reporting. At the national level, progress on developing the national reporting system for learning has been slower than set out in the Department’s strategy of 2001 3 and there is a need to improve evaluation and sharing of lessons and solutions by all organisations with a stake in patient safety. There is also no clear system for monitoring that lessons are learned at the local level. Specifically: a The safety culture within trusts is improving, driven largely by the Department’s clinical governance initiative 4 and the development of more effective risk management systems in response to incentives under initiatives such as the NHS Litigation Authority’s Clinical Negligence Scheme for Trusts (Appendix 7). However, trusts are still predominantly reactive in their response to patient safety issues and parts of some organisations still operate a blame culture. b All trusts have established effective reporting systems at the local level, although under-reporting remains a problem within some groups of staff, types of incidents and near misses. The National Patient Safety Agency did not develop and roll out the National Reporting and Learning System by December 2002 as originally envisaged. All trusts were linked to the system by 31 December 2004. By August 2005, at least 35 trusts still had not submitted any data to the National Reporting and Learning System. c Most trusts pointed to specific improvements derived from lessons learnt from their local incident reporting systems, but these are still not widely promulgated, either within or between trusts. The National Patient Safety Agency has provided only limited feedback to trusts of evidence-based solutions or actions derived from the national reporting system. It published its first feedback report from the Patient Safety Observatory in July 2005

Development and validation of the DIabetes Severity SCOre (DISSCO) in 139 626 individuals with type 2 diabetes: a retrospective cohort study

OBJECTIVE: Clinically applicable diabetes severity measures are lacking, with no previous studies comparing their predictive value with glycated hemoglobin (HbA1c). We developed and validated a type 2 diabetes severity score (the DIabetes Severity SCOre, DISSCO) and evaluated its association with risks of hospitalization and mortality, assessing its additional risk information to sociodemographic factors and HbA1c. RESEARCH DESIGN AND METHODS: We used UK primary and secondary care data for 139 626 individuals with type 2 diabetes between 2007 and 2017, aged ≥35 years, and registered in general practices in England. The study cohort was randomly divided into a training cohort (n=111 748, 80%) to develop the severity tool and a validation cohort (n=27 878). We developed baseline and longitudinal severity scores using 34 diabetes-related domains. Cox regression models (adjusted for age, gender, ethnicity, deprivation, and HbA1c) were used for primary (all-cause mortality) and secondary (hospitalization due to any cause, diabetes, hypoglycemia, or cardiovascular disease or procedures) outcomes. Likelihood ratio (LR) tests were fitted to assess the significance of adding DISSCO to the sociodemographics and HbA1c models. RESULTS: A total of 139 626 patients registered in 400 general practices, aged 63±12 years were included, 45% of whom were women, 83% were White, and 18% were from deprived areas. The mean baseline severity score was 1.3±2.0. Overall, 27 362 (20%) people died and 99 951 (72%) had ≥1 hospitalization. In the training cohort, a one-unit increase in baseline DISSCO was associated with higher hazard of mortality (HR: 1.14, 95% CI 1.13 to 1.15, area under the receiver operating characteristics curve (AUROC)=0.76) and cardiovascular hospitalization (HR: 1.45, 95% CI 1.43 to 1.46, AUROC=0.73). The LR tests showed that adding DISSCO to sociodemographic variables significantly improved the predictive value of survival models, outperforming the added value of HbA1c for all outcomes. Findings were consistent in the validation cohort. CONCLUSIONS: Higher levels of DISSCO are associated with higher risks for hospital admissions and mortality. The new severity score had higher predictive value than the proxy used in clinical practice, HbA1c. This reproducible algorithm can help practitioners stratify clinical care of patients with type 2 diabetes