Yang Gong

Unintended adverse consequences of introducing electronic health records in residential aged care homes

PURPOSE The aim of this study was to investigate the unintended adverse consequences of introducing electronic health records (EHR) in residential aged care homes (RACHs) and to examine the causes of these unintended adverse consequences. METHOD A qualitative interview study was conducted in nine RACHs belonging to three organisations in the Australian Capital Territory (ACT), New South Wales (NSW) and Queensland, Australia. A longitudinal investigation after the implementation of the aged care EHR systems was conducted at two data points: January 2009 to December 2009 and December 2010 to February 2011. Semi-structured interviews were conducted with 110 care staff members identified through convenience sampling, representing all levels of care staff who worked in these facilities. Data analysis was guided by DeLone and McLean Information Systems Success Model, in reference with the previous studies of unintended consequences for the introduction of computerised provider order entry systems in hospitals. RESULTS Eight categories of unintended adverse consequences emerged from 266 data items mentioned by the interviewees. In descending order of the number and percentage of staff mentioning them, they are: inability/difficulty in data entry and information retrieval, end user resistance to using the system, increased complexity of information management, end user concerns about access, increased documentation burden, the reduction of communication, lack of space to place enough computers in the work place and increasing difficulties in delivering care services. The unintended consequences were caused by the initial conditions, the nature of the EHR system and the way the system was implemented and used by nursing staff members. CONCLUSIONS Although the benefits of the EHR systems were obvious, as found by our previous study, introducing EHR systems in RACH can also cause adverse consequences of EHR avoidance, difficulty in access, increased complexity in information management, increased documentation burden, reduction of communication and the risks of lacking care follow-up, which may cause negative effects on aged care services. Further research can focus on investigating how the unintended adverse consequences can be mitigated or eliminated by understanding more about nursing staffs work as well as the information flow in RACH. This will help to improve the design, introduction and management of EHR systems in this setting.

Data Consistency in a Voluntary Medical Incident Reporting System

Voluntary medical incident reporting systems are a valuable source for studying adverse events and near misses. Unfortunately, such systems usually contain a large amount of incomplete and inaccurate reports which negatively affect their utility for medical error research. To investigate the reporting quality and propose solutions towards quality voluntary reports, we employed a content analysis method to examine one-year voluntary medical incident reports of a University Hospital. Results indicate that there is a large amount of inconsistent records within the reports. About 25% of the reports were labeled as “miscellaneous” and “other”. Through an in-depth analysis, those “miscellaneous” and “other” were substituted by their real incident types or error descriptions. Analysis shows that the pre-defined reporting categories serve well in general for the voluntary reporting need. In some cases, human factors play a key role in selecting accurate categories since reporters lack time or information to complete the report. We suggest that a human-centered, ontology based system design for voluntary reporting is feasible. Such a design could help improve the completeness and accuracy, and interoperability among national and international standards.

Usability Evaluation of a Voluntary Patient Safety Reporting System: Understanding the Difference between Predicted and Observed Time Values by Retrospective Think-Aloud Protocols

The study evaluated the usability of a voluntary patient safety reporting system using two established methods of cognitive task analysis and retrospective think-aloud protocols. Two usability experts and ten end users were employed in two separated experiments, and predicted and observed task execution times were obtained for comparison purpose. According to the results, mental operations contributed to the major effort in reporting. The significant time differences were identified that pointed out the difficulty in human cognition as users interacted with the system. At last, the data collected by retrospective think-aloud technique, e.g. the response consistency on structured questions and the user’s attitudes, revealed the frequent usability problems impeding completion of a quality report.

Understanding effective clinical communication in medical errors.

Clinical Communication failures are considered the leading cause of medical errors [1]. Minimizing communication problems among clinical team members could directly reduce medical errors and hence, increase patient safety and improve health care quality. Our main focus is, through knowledge representation approach, to develop an understanding of communication problems applied to health care settings. This will serve as the foundation to our long term goal of building an ontology-driven educational tool that will be used to educate clinicians about miscommunication issues and as a means to improve it.

Developing a user-centered voluntary medical incident reporting system.

Medical errors are one of leading causes of death among adults in the United States. According to the Institute of Medicine, reporting of medical incidents could be a cornerstone to learn from errors and to improve patient safety, if incident data are collected in a properly structured format which is useful for the detection of patterns, discovery of underlying factors, and generation of solutions. Globally, a number of medical incident reporting systems were deployed for collecting observable incident data in care delivery organizations (CDO) over the past several years. However, few researches delved into design of user-centered reporting system for improving completeness and accuracy of medical incident collection, let alone design models created for other institutes to follow. In this paper, we introduce the problems identified in a current using voluntary reporting system and our effort is being made towards complete, accurate and useful user-centered new reporting system through a usability engineering process.

Terminology in a voluntary medical incident reporting system: a human-centered perspective

To understand the current status of voluntary reporting systems in terms of completeness, accuracy and degrees of expressiveness, and to identify the social-technical barriers toward a human-centered design, we studied a set of voluntary reports acquired from the University of Missouri Health Care System (UMHC). We found three relationships between the free text and pre-defined entries, which direct us to improve reporting efficiency through a human-centered, adaptive data entry interface design that offers learning features during and after incident reporting.

Text prediction on structured data entry in healthcare: A two-group randomized usability study measuring the prediction impact on user performance

BACKGROUND Structured data entry pervades computerized patient safety event reporting systems and serves as a key component in collecting patient-related information in electronic health records. Clinicians would spend more time being with patients and arrive at a high probability of proper diagnosis and treatment, if data entry can be completed efficiently and effectively. Historically it has been proven text prediction holds potential for human performance regarding data entry in a variety of research areas. OBJECTIVE This study aimed at examining a function of text prediction proposed for increasing efficiency and data quality in structured data entry. METHODS We employed a two-group randomized design with fifty-two nurses in this usability study. Each participant was assigned the task of reporting patient falls by answering multiple choice questions either with or without the text prediction function. t-test statistics and linear regression model were applied to analyzing the results of the two groups. RESULTS While both groups of participants exhibited a good capacity of accomplishing the assigned task, the results were an overall 13.0% time reduction and 3.9% increase of response accuracy for the group utilizing the prediction function. CONCLUSION As a primary attempt investigating the effectiveness of text prediction in healthcare, study findings validated the necessity of text prediction to structured date entry, and laid the ground for further research improving the effectiveness of text prediction in clinical settings.

Developing a Nomenclature for EMR Errors

Latent medical errors may occur in electronic medical record (EMR) systems. Analyses of medical errors, including the cognitive theory of action and the systems approach, are described. Key aspects of EMR systems are presented and examples are provided. A nomenclature is suggested to improve reporting and communication about EMR errors. The nomenclature uses concepts of an error state and a precipitating event. The error state comprises an error element, an error condition, and an error context. The precipitating event comprises an event agent, and event task, and an event context. The event task includes a task object, a task action, and task parameters.

Developing VISO: Vaccine Information Statement Ontology for patient education

ObjectiveTo construct a comprehensive vaccine information ontology that can support personal health information applications using patient-consumer lexicon, and lead to outcomes that can improve patient education.MethodsThe authors composed the Vaccine Information Statement Ontology (VISO) using the web ontology language (OWL). We started with 6 Vaccine Information Statement (VIS) documents collected from the Centers for Disease Control and Prevention (CDC) website. Important and relevant selections from the documents were recorded, and knowledge triples were derived. Based on the collection of knowledge triples, the meta-level formalization of the vaccine information domain was developed. Relevant instances and their relationships were created to represent vaccine domain knowledgeResultsThe initial iteration of the VISO was realized, based on the 6 Vaccine Information Statements and coded into OWL2 with Protégé. The ontology consisted of 132 concepts (classes and subclasses) with 33 types of relationships between the concepts. The total number of instances from classes totaled at 460, along with 429 knowledge triples in total. Semiotic-based metric scoring was applied to evaluate quality of the ontology.

Toward A Human-Centered Hyperlipidemia Management System: The Interaction between Internal and External Information on Relational Data Search

In a distributed information search task, data representation and cognitive distribution jointly affect user search performance in terms of response time and accuracy. Guided by UFuRT (User, Function, Representation, Task), a human-centered framework, we proposed a search model and task taxonomy. The model defines its application in the context of healthcare setting. The taxonomy clarifies the legitimate operations for each type of search task of relational data. We then developed experimental prototypes of hyperlipidemia data displays. Based on the displays, we tested the search tasks performance through two experiments. The experiments are of a within-subject design with a random sample of 24 participants. The results support our hypotheses and validate the prediction of the model and task taxonomy. In this study, representation dimensions, data scales, and search task types are the main factors in determining search efficiency and effectiveness. Specifically, the more external representations provided on the interface the better search task performance of users. The results also suggest the ideal search performance occurs when the question type and its corresponding data scale representation match. The implications of the study lie in contributing to the effective design of search interface for relational data, especially laboratory results, which could be more effectively designed in electronic medical records.