Identifying health information technology related safety event reports from patient safety event report databases

Fong, Allan; Adams, Katharine T.; Gaunt, Michael J; Howe, Jessica L.; Kellogg, Kathryn M.; Ratwani, Raj M.

doi:10.1016/j.jbi.2018.09.007

Cited by 26 publications

(36 citation statements)

References 20 publications

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“…Studies used clinical reports from various sources, including patient safety organizations, EHR data from Veterans Health Administration and Berkshire Health Systems, and deidentified notes from the Medical Information Mart for Intensive Care. These studies focused on extracting relevant information [ 74 , 77 , 82 , 84 ] to predict bleeding risks among critically ill patients [ 73 ], postoperative surgical complications [ 78 ], mortality risk [ 83 ], and other factors such as lab test results and vital signs [ 77 ] influencing patient safety outcomes.…”

Section: Resultsmentioning

confidence: 99%

“…These studies used AI and clinical reports to extract safety-related information such as fall risks, pyxis discrepancies, patient misidentification, patient severity, and postoperative surgical complications. Our findings exhibit how, with the help of AI techniques such as natural language processing, clinical notes and reports have been used as a data source to extract patient data regarding a broad range of safety issues, including clinical notes, discharge notes, and other issues [ 69 , 70 , 73 , 84 ]. Our review also indicates that AI has the potential to provide valuable insights to treat patients correctly by identifying future health or safety risks [ 125 ], to improve health care quality, and reduce clinical errors [ 126 ].…”

Section: Discussionmentioning

confidence: 99%

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Role of Artificial Intelligence in Patient Safety Outcomes: Systematic Literature Review

2020

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Background Artificial intelligence (AI) provides opportunities to identify the health risks of patients and thus influence patient safety outcomes. Objective The purpose of this systematic literature review was to identify and analyze quantitative studies utilizing or integrating AI to address and report clinical-level patient safety outcomes. Methods We restricted our search to the PubMed, PubMed Central, and Web of Science databases to retrieve research articles published in English between January 2009 and August 2019. We focused on quantitative studies that reported positive, negative, or intermediate changes in patient safety outcomes using AI apps, specifically those based on machine-learning algorithms and natural language processing. Quantitative studies reporting only AI performance but not its influence on patient safety outcomes were excluded from further review. Results We identified 53 eligible studies, which were summarized concerning their patient safety subcategories, the most frequently used AI, and reported performance metrics. Recognized safety subcategories were clinical alarms (n=9; mainly based on decision tree models), clinical reports (n=21; based on support vector machine models), and drug safety (n=23; mainly based on decision tree models). Analysis of these 53 studies also identified two essential findings: (1) the lack of a standardized benchmark and (2) heterogeneity in AI reporting. Conclusions This systematic review indicates that AI-enabled decision support systems, when implemented correctly, can aid in enhancing patient safety by improving error detection, patient stratification, and drug management. Future work is still needed for robust validation of these systems in prospective and real-world clinical environments to understand how well AI can predict safety outcomes in health care settings.

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Section: Resultsmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Role of Artificial Intelligence in Patient Safety Outcomes: Systematic Literature Review

2020

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“…Despite those limitations and biases, reports from a range of incident monitoring systems have been successfully investigated to analyze patient safety issues with technologies. [25][26][27][28][29][30] By analyzing the free-text descriptions provided in reports, those studies have highlighted that incident reports were a valuable material to identify and categorize the types of issues with technology that affected patient safety. They have even described socio-technical factors affecting the use of technology, including usability flaws that led to incidents.…”

Section: Background and Significancementioning

confidence: 99%

“…As mentioned in the introduction, reporting biases may impact the accuracy of the incident reports. Despite those biases, previous studies pointed out that incident reports were a valuable material to identify the type of technology issues associated with the patient safety issues [25][26][27][28][29][30] and to identify incidentally usability flaws and consequences. 31 Moreover, analyzing a large collection of incidents enables identifying characteristic profiles.…”

Section: Benefits Of Usability-oriented Analysesmentioning

confidence: 99%

Building Usability Knowledge for Health Information Technology: A Usability-Oriented Analysis of Incident Reports

et al. 2019

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Background The contribution of usability flaws to patient safety issues is acknowledged but not well-investigated. Free-text descriptions of incident reports may provide useful data to identify the connection between health information technology (HIT) usability flaws and patient safety. Objectives This article examines the feasibility of using incident reports about HIT to learn about the usability flaws that affect patient safety. We posed three questions: (1) To what extent can we gain knowledge about usability issues from incident reports? (2) What types of usability flaws, related usage problems, and negative outcomes are reported in incidents reports? (3) What are the reported usability issues that give rise to patient safety issues? Methods A sample of 359 reports from the U.S. Food and Drug Administration Manufacturer and User Facility Device Experience database was examined. Descriptions of usability flaws, usage problems, and negative outcomes were extracted and categorized. A supplementary analysis was performed on the incidents which contained the full chain going from a usability flaw up to a patient safety issue to identify the usability issues that gave rise to patient safety incidents. Results A total of 249 reports were included. We found that incident reports can provide knowledge about usability flaws, usage problems, and negative outcomes. Thirty-six incidents report how usability flaws affected patient safety (ranging from incidents without consequence, to death) involving electronic patient scales, imaging systems, and HIT for medication management. The most significant class of involved usability flaws is related to the reliability, the understandability, and the availability of the clinical information. Conclusion Incidents reports involving HIT are an exploitable source of information to learn about usability flaws and their effects on patient safety. Results can be used to convince all stakeholders involved in the HIT system lifecycle that usability should be considered seriously to prevent patient safety incidents.

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“…3 Despite the existence of this structured category, it is a continued challenge to expect frontline providers to recognize when health IT is a contributing factor in a safety event, leading to underreporting in safety event reporting systems. Researchers have employed several analytical approaches to improve identification of these events including a keyword search of EHR vendors and products to identify PSE events that explicitly mentioned an EHR vendor, 3 the development of a text classification model to classify health IT events using the unstructured narrative fields 15,16 and using a natural language processing technique called active learning to improve accurate and efficient annotation of health IT-related PSEs. 17 Often, though, health IT-related events are identified through manual annotation 18,19 or by relying on the structured categories with recognition of the limitations to this approach.…”

Section: Analysis Of Health It Pses In the Literaturementioning

confidence: 99%

Responding to health information technology reported safety events: Insights from patient safety event reports

Adams

Kim

Fong

et al. 2019

Journal of Patient Safety and Risk Management

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Objective: We analyzed the described resolutions of patient safety event reports related to health information technology to determine how healthcare systems responded to these events, recognizing that certain types of solutions such as training and education have a limited impact. Methods: A large database of over 1.7 million patient safety event reports was filtered to include those identified by the reporter as being related to health information technology. The resolution text was manually reviewed and coded into one or more of four categories: No Resolution, Training/Education, Policy, Information Technology-oriented solution. Results: Most events (64%) did not include a resolution. Of those that did, Training/Education was the most commonly reported single or component of a multi-pronged solution (55%), followed by Information Technology (45%). Only 59 events (6% of resolutions) described more than one method of resolution. Conclusion: Health information technology-related patient safety event resolutions most often described a solution that suggested additional training or education for healthcare staff, despite the recognized limitations of training and education in resolving these events. Few events suggested multiple resolution methods. Ensuring health information technology-related events are resolved and incorporate effective solutions should be a continued focus area for healthcare systems.

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Identifying health information technology related safety event reports from patient safety event report databases

Abstract: The feature-constraint model provides a method to identify HIT-related patient safety hazards using a method that is applicable across healthcare systems with variability in their PSE report structures.

Cited by 26 publications

References 20 publications

Role of Artificial Intelligence in Patient Safety Outcomes: Systematic Literature Review

Role of Artificial Intelligence in Patient Safety Outcomes: Systematic Literature Review

Building Usability Knowledge for Health Information Technology: A Usability-Oriented Analysis of Incident Reports

Responding to health information technology reported safety events: Insights from patient safety event reports

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