The Experts below are selected from a list of 309 Experts worldwide ranked by ideXlab platform
Liam Donaldson - One of the best experts on this subject based on the ideXlab platform.
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automated classification of primary care patient safety Incident Report content and severity using supervised machine learning ml approaches
Health Informatics Journal, 2020Co-Authors: Huw Prosser Evans, Liam Donaldson, Peter Hibbert, A Edwards, Athanasios Anastasiou, Meredith Makeham, Saturnino Luz, Aziz Sheikh, Andrew CarsonstevensAbstract:Learning from patient safety Incident Reports is a vital part of improving healthcare. However, the volume of Reports and their largely free-text nature poses a major analytic challenge. The object...
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diagnostic error in the emergency department learning from national patient safety Incident Report analysis
BMC Emergency Medicine, 2019Co-Authors: Faris Hussain, Liam Donaldson, Alison Cooper, Andrew Carsonstevens, Peter Hibbert, Thomas Hughes, A EdwardsAbstract:Diagnostic error occurs more frequently in the emergency department than in regular in-patient hospital care. We sought to characterise the nature of Reported diagnostic error in hospital emergency departments in England and Wales from 2013 to 2015 and to identify the priority areas for intervention to reduce their occurrence. A cross-sectional mixed-methods design using an exploratory descriptive analysis and thematic analysis of patient safety Incident Reports. Primary data were extracted from a national database of patient safety Incidents. Reports were filtered for emergency department settings, diagnostic error (as classified by the Reporter), from 2013 to 2015. These were analysed for the chain of events, contributory factors and harm outcomes. There were 2288 cases of confirmed diagnostic error: 1973 (86%) delayed and 315 (14%) wrong diagnoses. One in seven Incidents were Reported to have severe harm or death. Fractures were the most common condition (44%), with cervical-spine and neck of femur the most frequent types. Other common conditions included myocardial infarctions (7%) and intracranial bleeds (6%). Incidents involving both delayed and wrong diagnoses were associated with insufficient assessment, misinterpretation of diagnostic investigations and failure to order investigations. Contributory factors were predominantly human factors, including staff mistakes, healthcare professionals’ inadequate skillset or knowledge and not following protocols. Systems modifications are needed that provide clinicians with better support in performing patient assessment and investigation interpretation. Interventions to reduce diagnostic error need to be evaluated in the emergency department setting, and could include standardised checklists, structured Reporting and technological investigation improvements.
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identifying systems failures in the pathway to a catastrophic event an analysis of national Incident Report data relating to vinca alkaloids
BMJ Quality & Safety, 2014Co-Authors: Bryony Dean Franklin, Sukhmeet S Panesar, Charles Vincent, Liam DonaldsonAbstract:Background Catastrophic errors in healthcare are rare, yet the consequences are so serious that where possible, special procedures are put in place to prevent them. As systems become safer, it becomes progressively more difficult to detect the remaining vulnerabilities. Using inadvertent intrathecal administration of vinca alkaloids as an example, we investigated whether analysis of Incident Report data describing low-harm events could bridge this gap. Methods We studied nine million patient safety Incidents Reported from England and Wales between November 2003 and May 2013. We searched for Reports relating to administration of vinca alkaloids in patients also receiving intrathecal medication, and classified the failures identified against steps in the relevant national protocol. Results Of 38 Reports that met our inclusion criteria, none resulted in actual harm. The stage of the medication process most commonly involved was ‘supply, transport and storage’ (15 cases). Seven cases related to dispensing, six to documentation, and four each to prescribing and administration. Defences most commonly breached related to separation of intravenous vinca alkaloids and intrathecal medication in timing (n=16) and location (n=8); potential for confusion due to inadequate separation of these drugs therefore remains. Problems involved in six cases did not align with the procedural defences in place, some of which represented major hazards. Conclusions We identified areas of concern even within the context of a highly controlled standardised national process. If Incident Reporting systems include and encourage Reports of no-harm Incidents in addition to actual patient harm, they can facilitate monitoring the resilience of healthcare processes. Patient safety Incidents that produce the most serious harm are often rare, and it is difficult to know whether patients are adequately protected. Our approach provides a potential solution.
James Deye - One of the best experts on this subject based on the ideXlab platform.
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overview of the american society for radiation oncology national institutes of health american association of physicists in medicine workshop 2015 exploring opportunities for radiation oncology in the era of big data
International Journal of Radiation Oncology Biology Physics, 2016Co-Authors: Stanley H Benedict, Karen E Hoffman, Mary K Martel, Amy P Abernethy, Anthony L Asher, Jacek Capala, Ronald C Chen, B S Chera, Jennifer Couch, James DeyeAbstract:Big data research refers to the collection and analysis of large sets of data elements and interrelationships that are difficult to process with traditional methods. It can be considered a subspecialty of the medical informatics domain under data science and analytics. This approach has been used in many areas of medicine to address topics such as clinical care and quality assessment (1–3). The need for informatics research in radiation oncology emerged as an important initiative during the 2013 National Institutes of Health (NIH)–National Cancer Institute (NCI), American Society for Radiation Oncology (ASTRO), and American Association of Physicists in Medicine (AAPM) workshop on the topic “Technology for Innovation in Radiation Oncology” (4). Our existing clinical practice generates discrete, quantitative, and structured patient-specific data (eg, images, doses, and volumes) that position us well to exploit and participate in big data initiatives. The well-established electronic infrastructure within radiation oncology should facilitate the retrieval and aggregation of much of the needed data. With additional efforts to integrate structured data collection of patient outcomes and assessments into the clinical workflow, the field of radiation oncology has a tremendous opportunity to generate large, comprehensive patient-specific data sets (5). However, there are major challenges to realizing this goal. For example, existing data are presently housed across different platforms at multiple institutions and are often not stored in a standardized manner or with common terminologies to enable pooling of data. In addition, many important data elements are not routinely discretely captured in clinical practice. There are cultural, structural, and logistical challenges (eg, computer compatibility and workflow demands) that will make the dream of big data research difficult. The big data research workshop provided a forum for leaders in cancer registries, Incident Report quality-assurance systems, radiogenomics, ontology of oncology, and a wide range of ongoing big data and cloud computing development projects to interact with peers in radiation oncology to develop strategies to harness data for research, quality assessment, and clinical care. The workshop provided a platform to discuss items such as data capture, data infrastructure, and protection of patient confidentiality and to improve awareness of the wide-ranging opportunities in radiation oncology, as well as to enhance the potential for research and collaboration opportunities with NIH on big data initiatives. The goals of the workshop were as follows: To discuss current and future sources of big data for use in radiation oncology research, To identify ways to improve our current data collection methods by adopting new strategies used in fields outside of radiation oncology, and To consider what new knowledge and solutions big data research can provide for clinical decision support for personalized medicine.
Hani H Abujudeh - One of the best experts on this subject based on the ideXlab platform.
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Safety Incident Reporting in emergency radiology: analysis of 1717 safety Incident Reports
Emergency Radiology, 2015Co-Authors: Mohammad Mansouri, Shima Aran, Khalid W. Shaqdan, Ali S. Raja, Michael H. Lev, Hani H AbujudehAbstract:The aim of this article is to describe the incidence and types of safety Reports logged in the radiology safety Incident Reporting system in our emergency radiology section over an 8-year period. Electronic Incident Reporting system of our institute was searched for the variables in emergency radiology. All Reports from April 2006 to June 2014 were included and deindentified. The following event classifications were investigated in radiography, CT, and MRI modalities: diagnostic test orders, ID/documentation/consent, safety/security/conduct, service coordination, surgery/procedure, line/tube, fall, medication/IV safety, employee general Incident, environment/equipment, adverse drug reaction, skin/tissue, and diagnosis/treatment. A total of 881,194 emergency radiology examinations were performed during the study period, 1717 (1717/881,194 = 0.19 %) of which resulted in safety Reports. Reports were classified into 14 different categories, the most frequent of which were “diagnostic test orders” (481/1717 = 28 % total Incident Reports), “medication/IV safety” (302/1717 = 18 % total Incident Reports), and “service coordination” (204/1717 = 12 % total Incident Reports). X-ray had the highest Report rate (873/1717 = 50 % total Incident Reports), followed by CT (604/1717 = 35 % total Incident Reports) and MRI (240/1717 = 14 % total Incident Reports). Forty-six percent of safety Incidents (789/1717) caused no harm and did not reach the patient, 36 % (617/1717) caused no harm but reached the patient, 18 % (308/1717) caused temporary or minor harm/ damage, and less than 1 % caused permanent or major harm/ damage or death. Our study shows an overall safety Incident Report rate of 0.19 % in emergency radiology including radiography, CT, and MRI modalities. The most common safety Incidents were diagnostic test orders, medication/IV safety, and service coordination.
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Rates of safety Incident Reporting in MRI in a large academic medical center.
Journal of magnetic resonance imaging : JMRI, 2015Co-Authors: Mohammad Mansouri, Shima Aran, H. Harvey, Khalid W. Shaqdan, Hani H AbujudehAbstract:PURPOSE To describe our multiyear experience in Incident Reporting related to magnetic resonance imaging (MRI) in a large academic medical center. MATERIALS AND METHODS This was an Institutional Review Board (IRB)-approved, Health Insurance Portability and Accountability Act (HIPAA)-compliant study. Incident Report data were collected during the study period from April 2006 to September 2012. The Incident Reports filed during the study period were searched for all Reports related to MRI. Incident Reports were classified with regard to the patient type (inpatient vs. outpatient), primary reason for the Incident Report, and the severity of patient harm resulting from the Incident. RESULTS A total of 362,090 MRI exams were performed during the study period, resulting in 1290 MRI-related Incident Reports. The rate of Incident Reporting was 0.35% (1290/362,090). MRI-related Incident Reporting was significantly higher in inpatients compared to outpatients (0.74% [369/49,801] vs. 0.29% [921/312,288], P < 0.001). The most common reason for Incident Reporting was diagnostic test orders (31.5%, 406/1290), followed by adverse drug reactions (19.1%, 247/1290) and medication/IV safety (14.3%, 185/1290). Approximately 39.6% (509/1290) of Reports were associated with no patient harm and did not affect the patient, followed by no patient harm but did affect the patient (35.8%, 460/1290), temporary or minor patient harm (23.9%, 307/1290), permanent or major patient harm (0.6%, 8/1290) and patient death (0.2%, 2/1290). CONCLUSION MRI-related Incident Reports are relatively infrequent, occur at significantly higher rates in inpatients, and usually do not result in patient harm. Diagnostic test orders, adverse drug reactions, and medication/IV safety were the most frequent safety Incidents.
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characteristics of falls in a large academic radiology department occurrence associated factors outcomes and quality improvement strategies
American Journal of Roentgenology, 2011Co-Authors: Hani H Abujudeh, Rathachai Kaewlai, Baiju Shah, James H ThrallAbstract:OBJECTIVE. The objective of our study was to describe the characteristics of falls in a radiology department. MATERIALS AND METHODS. The departmental Incident Report database was retrospectively searched for fall Incidents that occurred from March 2006 through October 2008. During that period, 1,801,275 radiologic examinations were performed in our department and there were 82 falls, yielding an incidence of 0.46 per 10,000 examinations. We collected patient information, associated factors, specific circumstances surrounding each Incident, the location of each Incident, and patient outcome. RESULTS. Eighty-two falls occurred involving 82 patients (35 males, 47 females; mean age, 58.2 years; range, 3–92 years): 66 falls (80%) involved outpatients; 11, inpatients; and five, visitors accompanying a patient. Radiography and CT-MRI units were the top two most common locations of falls (45/82, 55%). Thirty-six events (36/82, 44%) were directly related to a radiologic examination. Most falls were witnessed (61/8...
Anne Drummond - One of the best experts on this subject based on the ideXlab platform.
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using t ret system to improve Incident Report retrieval
Conference on Intelligent Text Processing and Computational Linguistics, 2004Co-Authors: Joe Carthy, David Wilson, Ruichao Wang, John Dunnion, Anne DrummondAbstract:This papers describes novel research involving the development of Textual CBR techniques and applying them to the problem of Incident Report Retrieval. Incident Report Retrieval is a relatively new research area in the domain of Accident Reporting and Analysis. We describe T-Ret, an Incident Report Retrieval system that incorporates textual CBR techniques and outline preliminary evaluation results.
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CICLing - Using T-Ret System to Improve Incident Report Retrieval
Computational Linguistics and Intelligent Text Processing, 2004Co-Authors: Joe Carthy, David Wilson, Ruichao Wang, John Dunnion, Anne DrummondAbstract:This papers describes novel research involving the development of Textual CBR techniques and applying them to the problem of Incident Report Retrieval. Incident Report Retrieval is a relatively new research area in the domain of Accident Reporting and Analysis. We describe T-Ret, an Incident Report Retrieval system that incorporates textual CBR techniques and outline preliminary evaluation results.
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textual cbr for Incident Report retrieval
International Conference on Computational Science and Its Applications, 2003Co-Authors: David Wilson, Joe Carthy, Ruichao Wang, John Dunnion, Karl Abbey, John Sheppard, Anne DrummondAbstract:Incident Management Systems can play a crucial role in helping to reduce the number of workplace accidents by providing support for Incident analysis. In particular, the retrieval of relevant similar Incident Reports can help safety personnel to identify factors and patterns that have contributed or might potentially contribute to accidents. Incident Report Retrieval is a relatively new research topic in the field of Accident Reporting and Analysis, and we are interested in developing intelligent computational support for retrieving Incident information that leverages both the featural and textual components of Incident Reports. This paper describes InRet-T, an Incident Report Retrieval system that incorporates approaches from textual case-based reasoning to integrate both featural and textual aspects in retrieving civil aviation Incident Reports. It also provides a preliminary evaluation of InRet-T that offers some insight into the use of textual CBR approaches to Incident analysis.
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the use of data mining in the design and implementation of an Incident Report retrieval system
Systems and Information Engineering Design Symposium, 2003Co-Authors: D Cassidy, Joe Carthy, John Dunnion, Anne Drummond, John SheppardAbstract:Incident Reporting is becoming increasingly important in large organizations. Legislation is progressively being introduced to deal with this information. One example is the European Directive No. 94/95/EC, which obliges airlines and national bodies to collect and collate Reports of Incidents. Typically these organizations use manual files and standard databases to store and retrieve Incident Reports. However, research has established that database technology needs to be enhanced in order to deal with Incidents. We describe the design and implementation of In-Ret, an Incident Report retrieval system that endeavours to find similarities and patterns between Incidents by combining the strengths of case-based reasoning and information retrieval techniques in an integrated system. Preliminary results from InRet are presented and are encouraging.
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ICCSA (1) - Textual CBR for Incident Report retrieval
Computational Science and Its Applications — ICCSA 2003, 1Co-Authors: David Wilson, Joe Carthy, Ruichao Wang, John Dunnion, Karl Abbey, John Sheppard, Anne DrummondAbstract:Incident Management Systems can play a crucial role in helping to reduce the number of workplace accidents by providing support for Incident analysis. In particular, the retrieval of relevant similar Incident Reports can help safety personnel to identify factors and patterns that have contributed or might potentially contribute to accidents. Incident Report Retrieval is a relatively new research topic in the field of Accident Reporting and Analysis, and we are interested in developing intelligent computational support for retrieving Incident information that leverages both the featural and textual components of Incident Reports. This paper describes InRet-T, an Incident Report Retrieval system that incorporates approaches from textual case-based reasoning to integrate both featural and textual aspects in retrieving civil aviation Incident Reports. It also provides a preliminary evaluation of InRet-T that offers some insight into the use of textual CBR approaches to Incident analysis.
A Edwards - One of the best experts on this subject based on the ideXlab platform.
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automated classification of primary care patient safety Incident Report content and severity using supervised machine learning ml approaches
Health Informatics Journal, 2020Co-Authors: Huw Prosser Evans, Liam Donaldson, Peter Hibbert, A Edwards, Athanasios Anastasiou, Meredith Makeham, Saturnino Luz, Aziz Sheikh, Andrew CarsonstevensAbstract:Learning from patient safety Incident Reports is a vital part of improving healthcare. However, the volume of Reports and their largely free-text nature poses a major analytic challenge. The object...
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diagnostic error in the emergency department learning from national patient safety Incident Report analysis
BMC Emergency Medicine, 2019Co-Authors: Faris Hussain, Liam Donaldson, Alison Cooper, Andrew Carsonstevens, Peter Hibbert, Thomas Hughes, A EdwardsAbstract:Diagnostic error occurs more frequently in the emergency department than in regular in-patient hospital care. We sought to characterise the nature of Reported diagnostic error in hospital emergency departments in England and Wales from 2013 to 2015 and to identify the priority areas for intervention to reduce their occurrence. A cross-sectional mixed-methods design using an exploratory descriptive analysis and thematic analysis of patient safety Incident Reports. Primary data were extracted from a national database of patient safety Incidents. Reports were filtered for emergency department settings, diagnostic error (as classified by the Reporter), from 2013 to 2015. These were analysed for the chain of events, contributory factors and harm outcomes. There were 2288 cases of confirmed diagnostic error: 1973 (86%) delayed and 315 (14%) wrong diagnoses. One in seven Incidents were Reported to have severe harm or death. Fractures were the most common condition (44%), with cervical-spine and neck of femur the most frequent types. Other common conditions included myocardial infarctions (7%) and intracranial bleeds (6%). Incidents involving both delayed and wrong diagnoses were associated with insufficient assessment, misinterpretation of diagnostic investigations and failure to order investigations. Contributory factors were predominantly human factors, including staff mistakes, healthcare professionals’ inadequate skillset or knowledge and not following protocols. Systems modifications are needed that provide clinicians with better support in performing patient assessment and investigation interpretation. Interventions to reduce diagnostic error need to be evaluated in the emergency department setting, and could include standardised checklists, structured Reporting and technological investigation improvements.
