Human errors in medical practice: systematic classification and reduction with automated information systems

Knowledge Graph-Based Convolutional Network Coupled With Sentiment Analysis Towards Enhanced Drug Recommendation

Recommending appropriate drugs to patients based on their history and symptoms is a complex real-world problem. Knowing whether a drug is useful without its consumption by a variety of people followed by proper evaluation is a challenge. Modern-day recommender systems can assist in this provided they receive large data to learn. Public reviews on various drugs are available for knowledge sharing. These reviews assist in recommending the best and most appropriate option to the user. The explicit feedback underpins the entire recommender system. This work develops a novel knowledge graph-based convolutional network for recommending drugs. The knowledge graph is coupled with sentiment analysis extracted from the public reviews on drugs to enhance drug recommendations. For each drug that has been used previously, sentiments have been analyzed to determine which one has the most effective reviews. The knowledge graph effectively captures user-item relatedness by mining its associated attributes. Experiments are performed on public benchmarks and a comparison is made with closely related state-of-the-art works. Based on the obtained results, the current work performs better than the past contributions by achieving up to 98.7% Area Under Curve (AUC) score.

Patient safety classifications, taxonomies and ontologies, part 2: A systematic review on content coverage

BACKGROUND

Content coverage of patient safety ontology and classification systems should be evaluated to provide a guide for users to select appropriate ones for specific applications. In this review, we identified and compare content coverage of patient safety classifications and ontologies.

METHODS

We searched different databases and ontology/classification repositories to identify these classifications and ontologies. We included patient safety-related taxonomies, ontologies, classifications, and terminologies. We identified and extracted different concepts covered by these systems and mapped these concepts to international classification for patient safety (ICPS) and finally compared the content of these systems.

RESULTS

Finally, 89 papers (77 classifications or ontologies) were analyzed. Thirteen classifications have been developed to cover all medical domains. Among specific domain systems, most systems cover medication (16), surgery (8), medical devices (3), general practice (3), and primary care (3). The most common patient safety-related concepts covered in these systems include incident types (41), contributing factors/hazards (31), patient outcomes (29), degree of harm (25), and action (18). However, stage/phase (6), incident characteristics (5), detection (5), people involved (5), organizational outcomes (4), error type (4), and care setting (3) are some of the less covered concepts in these classifications/ontologies.

CONCLUSION

Among general systems, ICPS, World Health Organization's Adverse Reaction Terminology (WHO-ART), and Ontology of Adverse Events (OAE) cover most patient safety concepts and can be used as a gold standard for all medical domains. As a result, reporting systems could make use of these broad classifications, but the majority of their covered concepts are related to patient outcomes, with the exception of ICPS, which covers other patient safety concepts. However, the ICPS does not cover specialized domain concepts. For specific medical domains, MedDRA, NCC MERP, OPAE, ADRO, PPST, OCCME, TRTE, TSAHI, and PSIC-PC provide the broadest coverage of concepts. Many of the patient safety classifications and ontologies are not formally registered or available as formal classification/ontology in ontology repositories such as BioPortal. This study may be used as a guide for choosing appropriate classifications for various applications or expanding less developed patient safety classifications/ontologies. Furthermore, the same concepts are not represented by the same terms; therefore, the current study could be used to guide a harmonization process for existing or future patient safety classifications/ontologies.

Patient safety classification, taxonomy and ontology systems: A systematic review on development and evaluation methodologies

INTRODUCTION

Patient safety classifications/ontologies enable patient safety information systems to receive and analyze patient safety data to improve patient safety. Patient safety classifications/ontologies have been developed and evaluated using a variety of methods. The purpose of this review was to discuss and analyze the methodologies for developing and evaluating patient safety classifications/ontologies.

METHODS

Studies that developed or evaluated patient safety classifications, terminologies, taxonomies, or ontologies were searched through Google Scholar, Google search engines, National Center for Biomedical Ontology (NCBO) BioPortal, Open Biological and Biomedical Ontology (OBO) Foundry and World Health Organization (WHO) websites and Scopus, Web of Science, PubMed, and Science Direct. We updated our search on 30 February 2021 and included all studies published until the end of 2020. Studies that developed or evaluated classifications only for patient safety and provided information on how they were developed or evaluated were included. Systems with covered patient safety terms (such as ICD-10) but are not specifically developed for patient safety were excluded. The quality and the risk of bias of studies were not assessed because all methodologies and criteria were intended to be covered. In addition, we analyzed the data through descriptive narrative synthesis and compared and classified the development and evaluation methods and evaluation criteria according to available development and evaluation approaches for biomedical ontologies.

RESULTS

We identified 84 articles that met all of the inclusion criteria, resulting in 70 classifications/ontologies, nine of which were for the general medical domain. The most papers were published in 2010 and 2011, with 8 and 7 papers, respectively. The United States (50) and Australia (23) have the most studies. The most commonly used methods for developing classifications/ontologies included the use of existing systems (for expanding or mapping) (44) and qualitative analysis of event reports (39). The most common evaluation methods were coding or classifying some safety report samples (25), quantitative analysis of incidents based on the developed classification (24), and consensus among physicians (16). The most commonly applied evaluation criteria were reliability (27), content and face validity (9), comprehensiveness (6), usability (5), linguistic clarity (5), and impact (4), respectively.

CONCLUSIONS

Because of the weaknesses and strengths of the development/evaluation methods, it is advised that more than one method for development or evaluation, as well as evaluation criteria, should be used. To organize the processes of developing classification/ontologies, well-established approaches such as Methontology are recommended. The most prevalent evaluation methods applied in this domain are well fitted to the biomedical ontology evaluation methods, but it is also advised to apply some evaluation approaches such as logic, rules, and Natural language processing (NLP) based in combination with other evaluation approaches. This research can assist domain researchers in developing or evaluating domain ontologies using more complete methodologies. There is also a lack of reporting consistency in the literature and same methods or criteria were reported with different terminologies.

Artificial intelligence enabled applications in kidney disease

Artificial intelligence (AI) is considered as the next natural progression of traditional statistical techniques. Advances in analytical methods and infrastructure enable AI to be applied in health care. While AI applications are relatively common in fields like ophthalmology and cardiology, its use is scarcely reported in nephrology. We present the current status of AI in research toward kidney disease and discuss future pathways for AI. The clinical applications of AI in progression to end-stage kidney disease and dialysis can be broadly subdivided into three main topics: (a) predicting events in the future such as mortality and hospitalization; (b) providing treatment and decision aids such as automating drug prescription; and (c) identifying patterns such as phenotypical clusters and arteriovenous fistula aneurysm. At present, the use of prediction models in treating patients with kidney disease is still in its infancy and further evidence is needed to identify its relative value. Policies and regulations need to be addressed before implementing AI solutions at the point of care in clinics. AI is not anticipated to replace the nephrologists' medical decision-making, but instead assist them in providing optimal personalized care for their patients.

Chronometric vs. Structural Hypercoagulability

Prolonged tourniquet stasis induced by venepuncture can lead to the release of the plasma of cell lysis products, as well as tissue factor (TF), impairing the quality of coagulation test results. The accidental presence of TF in vitro can trigger the coagulation mechanism, generating a false decrease in prothrombin time (PT). Background and Objectives: Identification of short PT tests below the normal reference value that could suggest a situation of hypercoagulability. The study aimed to compare the results of the shortened PT tests at their first determination with the eventual correction following duplication of the analysis from the same sample. Materials and methods: Identification of the shortened PT tests has been carried out for a period of 4 months, upon 544 coagulation samples referred to the Hematology department of Sf. Spiridon County Clinical Emergency Hospital from Iasi, Romania. Results: Out of the 544 samples of which the results indicated a state of hypercoagulability, by repeating the determination from the same sample, for 200 (36.76%) PT tests (p = 0.001) the value was corrected, falling within the normal reference range. For 344 (63.24%) tests, the results suggested a situation of hypercoagulability. Conclusions: In order to guarantee the highest quality of the laboratory services, a proper interpretation and report of the patients' results must be congruent and harmoniously associated to the actual clinical condition of the patient. Duplication of the PT determination from the same sample would exclude situations of false hypercoagulability and would provide significant improvement for the patient's safety.

A Multi-Constraint Scheme with Authorized Mechanism for the Patient Safety

Many research works have attempted to introduce passive RFID technology into medical systems to reduce medical errors. However, most of these proposed works focused on identifying patients and objects. If an RFID based medical system is only good for identifying patients and medical objects but not capable of halting any medical process immediately, then it is not possible to prevent medical errors from happening. Our research focuses on a mechanism to detect and to avoid medical harm before it occurs to patients. In this paper, we propose to incorporate multiple-constraints into the authorization scheme and used this scheme as a basis for implementing a medical management system avoiding medical errors to assist medical staff. Specifically, our scheme ensures that a medical operation is if and only if enabled when the constraints are being satisfied that an "identified patient" is being treated by a "certified medical staff member" within an "authorized area". In practical environments, our authorization scheme can be applied to various healthcare applications, and we develop a prototype system and test it in three applications: X-ray control, specimen collection, and blood transfusion management. The experimental results show that the system can be used to enable X-ray when the X-ray is in authorized location and operated by authorized operator. For the specimen collection and blood transfusion, the logs showing which medical staff has done specimen or blood transfusion on which patient at authorized location are correctly recorded into Hospital Information System (HIS). The locating process can be performed within 10 to 20 seconds, and the locating error is less than 2 meters.

Managing uncertainty in complex interventional fluoroscopic procedures

There was a concern for potential patient skin burns indicated by air kerma as recorded by fluoroscopy equipment during two interventional fluoroscopic procedures. Two sentinel events as defined by The Joint Commission were indicated. Dose reconstruction based on measurements of machine output showed the air kerma readings were high by approximately a factor of four and no patient injury or sentinel event occurred. The radiation safety program in effect at the time of the incidents allowed discovery of equipment problems before a serious patient injury occurred, but additional controls have been implemented as a result, including quality control checks performed by cardiology, additional training, a database for tracking equipment maintenance, and mandatory entry of patient dose into electronic health records with follow-up orders automatically generated by the electronic health records. Most unexpected injuries are a result of multiple failures, but there are also multiple opportunities to prevent the injury. This paper provides a comprehensive look at patient safety concerns during two interventional cardiology fluoroscopic procedures and offers ways to further reduce risks to patients. It focuses on lessons learned and a systems-based approach to improving and promoting radiation safety during complex interventional fluoroscopic procedures.

A simulation framework for mapping risks in clinical processes: the case of in-patient transfers

OBJECTIVE

To model how individual violations in routine clinical processes cumulatively contribute to the risk of adverse events in hospital using an agent-based simulation framework.

DESIGN

An agent-based simulation was designed to model the cascade of common violations that contribute to the risk of adverse events in routine clinical processes. Clinicians and the information systems that support them were represented as a group of interacting agents using data from direct observations. The model was calibrated using data from 101 patient transfers observed in a hospital and results were validated for one of two scenarios (a misidentification scenario and an infection control scenario). Repeated simulations using the calibrated model were undertaken to create a distribution of possible process outcomes. The likelihood of end-of-chain risk is the main outcome measure, reported for each of the two scenarios.

RESULTS

The simulations demonstrate end-of-chain risks of 8% and 24% for the misidentification and infection control scenarios, respectively. Over 95% of the simulations in both scenarios are unique, indicating that the in-patient transfer process diverges from prescribed work practices in a variety of ways.

CONCLUSIONS

The simulation allowed us to model the risk of adverse events in a clinical process, by generating the variety of possible work subject to violations, a novel prospective risk analysis method. The in-patient transfer process has a high proportion of unique trajectories, implying that risk mitigation may benefit from focusing on reducing complexity rather than augmenting the process with further rule-based protocols.

An analysis tool to calculate permeability based on the Patlak method

Strokes are commonly diagnosed by utilizing images obtained from magnetic resonance imaging (MRI) technology. Nowadays, computer software can play a large role in analyzing these images and arriving at diagnoses quickly and accurately. Additionally, this software can reduce workload for medical personnel and lower misdiagnoses. In this paper a flexible permeability calculation tool called PCT based on the Patlak plot method is presented. Using the PCT we can calculate the permeability co-efficient of the Blood-Brain Barrier (BBB) function. The PCT tool offers both manual and automatic options for diagnosing the regions of the brain affected by stroke. Moreover, the PCT tool supports various extensions such as dicom, nifty and analyze.