Quality Assessment of Meta-Analyses on Cervical Cancer.

Meta-analysis (MA) quantitatively summarizes the findings of independent studies and is considered the highest quality of evidence for evidence-based medicine. However, issues in reporting and methodological rigor of MA hamper reproducibility and create the potential for bias. By applying PRISMA reporting guideline and AMSTAR2 execution guidelines on 40 cervical cancer MA samples covering topics such as interventions and risk factors, we determined the extent to which MA execution adhered to best practice guidelines. The results show that the elements with least adherence include "review methods established before MA" and "principal summary measures defined" (each 32.5% per PRISMA) and "characteristics of included studies" (31.3% per AMSTAR2) which undermine reproducibility and increase the risk of bias. This initial work presents common pitfalls in MA and is intended to improve awareness of these issues for clinicians who are interested in conducting MA and to pave the way toward quality improvement via informatics approaches.

Challenges and Opportunities of Patient Safety Event Reporting.

The World Health Organization (WHO) announced the first-ever World Patient Safety Day on September 17, 2019, which remarks a global campaign to create an awareness of patient safety and urges people to show their commitment to making healthcare safer. Reporting medical incidents or patient safety events (PSE) has been recommended as an effective approach for the detection of patterns, discovery of underlying factors, and generation of solutions. It is believed that PSE reporting systems (e-reporting) could be a good resource to share and to learn from the reporting if the event data are collected in a properly structured format. Unfortunately, the prevalence of underreporting and low quality of the reports have become barriers to ultimately achieve the goal of preventing and reducing medical incidents. This chapter describes the efforts that have been made to improve e-reporting through informatics approaches, including a review of PSE taxonomies and conceptual frameworks, studies of medication events, patient falls, and PSE involved in health information technologies, as well as discussions of design requirements for future e-reporting systems.

Technologies for Medication Adherence Monitoring and Technology Assessment Criteria: Narrative Review.

BACKGROUND: Accurate measurement and monitoring of patient medication adherence is a global challenge because of the absence of gold standard methods for adherence measurement. Recent attention has been directed toward the adoption of technologies for medication adherence monitoring, as they provide the opportunity for continuous tracking of individual medication adherence behavior. However, current medication adherence monitoring technologies vary according to their technical features and data capture methods, leading to differences in their respective advantages and limitations. Overall, appropriate criteria to guide the assessment of medication adherence monitoring technologies for optimal adoption and use are lacking. OBJECTIVE: This study aims to provide a narrative review of current medication adherence monitoring technologies and propose a set of technology assessment criteria to support technology development and adoption. METHODS: A literature search was conducted on PubMed, Scopus, CINAHL, and ProQuest Technology Collection (2010-present) using the combination of keywords medication adherence, measurement technology, and monitoring technology. The selection focused on studies related to medication adherence monitoring technology and its development and use. The technological features, data capture methods, and potential advantages and limitations of the identified technology applications were extracted. Methods for using data for adherence monitoring were also identified. Common recurring elements were synthesized as potential technology assessment criteria. RESULTS: Of the 3865 articles retrieved, 98 (2.54%) were included in the final review, which reported a variety of technology applications for monitoring medication adherence, including electronic pill bottles or boxes, ingestible sensors, electronic medication management systems, blister pack technology, patient self-report technology, video-based technology, and motion sensor technology. Technical features varied by technology type, with common expectations for using these technologies to accurately monitor medication adherence and increase adoption in patients' daily lives owing to their unobtrusiveness and convenience of use. Most technologies were able to provide real-time monitoring of medication-taking behaviors but relied on proxy measures of medication adherence. Successful implementation of these technologies in clinical settings has rarely been reported. In all, 28 technology assessment criteria were identified and organized into the following five categories: development information, technology features, adherence to data collection and management, feasibility and implementation, and acceptability and usability. CONCLUSIONS: This narrative review summarizes the technical features, data capture methods, and various advantages and limitations of medication adherence monitoring technology reported in the literature and the proposed criteria for assessing medication adherence monitoring technologies. This collection of assessment criteria can be a useful tool to guide the development and selection of relevant technologies, facilitating the optimal adoption and effective use of technology to improve medication adherence outcomes. Future studies are needed to further validate the medication adherence monitoring technology assessment criteria and construct an appropriate technology assessment framework.

Defining health data elements under the HL7 development framework for metadata management.

BACKGROUND: Health data from different specialties or domains generallly have diverse formats and meanings, which can cause semantic communication barriers when these data are exchanged among heterogeneous systems. As such, this study is intended to develop a national health concept data model (HCDM) and develop a corresponding system to facilitate healthcare data standardization and centralized metadata management. METHODS: Based on 55 data sets (4640 data items) from 7 health business domains in China, a bottom-up approach was employed to build the structure and metadata for HCDM by referencing HL7 RIM. According to ISO/IEC 11179, a top-down approach was used to develop and standardize the data elements. RESULTS: HCDM adopted three-level architecture of class, attribute and data type, and consisted of 6 classes and 15 sub-classes. Each class had a set of descriptive attributes and every attribute was assigned a data type. 100 initial data elements (DEs) were extracted from HCDM and 144 general DEs were derived from corresponding initial DEs. Domain DEs were transformed by specializing general DEs using 12 controlled vocabularies which developed from HL7 vocabularies and actual health demands. A model-based system was successfully established to evaluate and manage the NHDD. CONCLUSIONS: HCDM provided a unified metadata reference for multi-source data standardization and management. This approach of defining health data elements was a feasible solution in healthcare information standardization to enable healthcare interoperability in China.

Mobile and Connected Health Technology Needs for Older Adults Aging in Place: Cross-Sectional Survey Study.

BACKGROUND: An increasing number of mobile and wearable devices are available in the market. However, the extent to which these devices can be used to assist older adults to age in place remains unclear. OBJECTIVE: This study aimed to assess older adults' perceptions of using mobile and connected health technologies. METHODS: Using a cross-sectional design, a total of 51 participants were recruited from a senior community center. Demographics and usage of mobile or wearable devices and online health communities were collected using a survey questionnaire. Descriptive statistics assessed usage of devices and online health communities. The Fisher exact test was used to examine the relationship between technology usage and having access to a smartphone. RESULTS: The sample was primarily comprised non-Hispanic white (35/51, 69%), educated (39/51, 76% any college), and female (36/51, 71%) participants, with an average age of 70 (SD 8) years. All participants were insured and nearly all lived at home (49/51, 94%). A total of 86% (44/51) of the participants had heard of wearable health devices, but only 18 out of 51 (35%) had ever used them. Over 80% (42/51) expressed interest in using such devices and were interested in tracking exercise and physical activity (46/51, 90%), sleep (38/51, 75%), blood pressure (34/51, 67%), diet (31/51, 61%), blood sugar (28/51, 55%), weight (26/51, 51%), and fall risk (23/51, 45%). The greatest concerns about using wearable devices were cost (31/51, 61%), safety (14/51, 28%), and privacy (13/51, 26%); one-fourth (12/51) reported having no concerns. They were mostly interested in sharing data from mobile and connected devices with their health care providers followed by family, online communities, friends, and no one. About 41% (21/51) of the older adults surveyed reported having ever heard of an online health community, and roughly 40% (20/51) of the participants reported being interested in joining such a community. Most participants reported having access to a smartphone (38/51, 74%), and those with such access were significantly more likely to show interest in using a wearable health device (P<.001) and joining an online health community (P=.05). CONCLUSIONS: Our findings suggest that, although few older adults are currently using mobile and wearable devices and connected health technologies for managing health, they are open to this idea and are mostly interested in sharing such data with their health care providers. Further studies are warranted to explore strategies to balance the data sharing preference of older adults and how to best integrate mobile and wearable device data with clinical workflow for health care providers to promote healthy aging in place.

Pediatric Asthma Care Assessment for the Emergency Department.

In the United States alone, approximately 6.2 million children have asthma. Managing the care of pediatric patients presenting in the emergency department with asthma requires clinicians to access multiple locations within the electronic health record. This wealth of information makes it vital to develop methods for sifting through the clutter to information that is relevant to the specific task to be completed. Unfortunately, current information displays have not been developed to maximize provider efficiency when integrating information across a number of disparate locations. Using proven data display theories, we aimed to create a consolidated acute pediatric asthma display to aid in the patient's care with the goal of improving the efficiency and consistency by which they are treated - ultimately reducing the length of stay, complications, and morbidity in these patients.

Data Quality Assessment of Narrative Medication Error Reports.

Medication errors are preventable adverse events or unsafe conditions caused by inappropriate uses of medication. To collect data of patient safety events (PSE) and to analyze the root causes of PSE, reporting systems have been implemented in healthcare settings and patient safety organizations (PSO). However, the poor data quality of reports impedes the reporting and root cause analysis (RCA) of PSE. Incomplete or missing data is the most prevalent problem in event reports. To assess the data quality of PSE reports, we used an adapted taxonomy as the data evaluation model to evaluate the quality of narrative reports collected by a PSO. Sample reports were extracted based on eight error types and scored by experts. Most structured fields in the reports were ignored by reporters. In contrast, the narrative parts of the reports contain rich and valuable information. The evaluation results show that the adapted taxonomy is a promising tool for report quality assessment and improvement.

Early Usability Assessment of a Conversational Agent for HPV Vaccination.

With the emerging use of speech technology in consumer goods, we experimented with the application of conversational agents for the communication of health information relating to HPV vaccine. Research have stated that one-to-one contact between providers and patients have a variety of positive influences on patients' perception towards vaccines, even leading to uptake, compared to paper-based methods. We implemented a Wizard of Oz experiment that counsels adults with children (n=18) on the HPV vaccine, using an iPad tablet and dialogue script developed by public health collaborators, and for early testing of a prospective conversational agent in this area. Our early results show that non-vaccine hesitant parents believed that agent was easy to use and had capabilities needed, despite the desire for additional features. Our future work will involve developing a dialogue engine to provide automated dialogue interaction and future improvements and experimentation for the speech interface.

An automated pipeline for analyzing medication event reports in clinical settings.

BACKGROUND: Medication events in clinical settings are significant threats to patient safety. Analyzing and learning from the medication event reports is an important way to prevent the recurrence of these events. Currently, the analysis of medication event reports is ineffective and requires heavy workloads for clinicians. An automated pipeline is proposed to help clinicians deal with the accumulated reports, extract valuable information and generate feedback from the reports. Thus, the strategy of medication event prevention can be further developed based on the lessons learned. METHODS: In order to build the automated pipeline, four classic machine learning classifiers (i.e., support vector machine, Naïve Bayes, random forest, and multi-layer perceptron) were compared to identify the event originating stages, event types, and event causes from the medication event reports. The precision, recall and F-1 measure were calculated to assess the performance of the classifiers. Further, a strategy to measure the similarity of medication event reports in our pipeline was established and evaluated by human subjects through a questionnaire. RESULTS: We developed three classifiers to identify the medication event originating stages, event types and causes, respectively. For the event originating stages, a support vector machine classifier obtains the best performance with an F-1 measure of 0.792. For the event types, a support vector machine classifier exhibits the best performance with an F-1 measure of 0.758. And for the event causes, a random forest classifier reaches an F-1 measure of 0.925. The questionnaire results show that the similarity measurement is consistent with the domain experts in the task of identifying similar reports. CONCLUSION: We developed and evaluated an automated pipeline that could identify three attributes from the medication event reports and calculate the similarity scores between the reports based on the attributes. The pipeline is expected to improve the efficiency of analyzing the medication event reports and to learn from the reports in a timely manner.

Toward Reporting Support and Quality Assessment for Learning from Reporting: A Necessary Data Elements Model for Narrative Medication Error Reports.

To understand and prevent medication errors, spontaneous reporting systems are developed and implemented to aggregate medication error reports for root cause analysis (RCA). Despite of the rich relational information in medication error reports, low quality, especially incompleteness, impedes effective utilization of the reports for analyzing and learning. The lack of a completeness evaluation tool for narrative medication error reports is a barrier to improving the quality of reports. Moreover, no effective mechanisms are integrated in reporting systems for knowledge support upon reporting. In this study, we developed a minimal data model which defines necessary elements in narrative medication error reports and utilized it to evaluate patient safety organization (PSO) medication reports. This study holds promise in bridging the gap between the low quality of narrative reports and the needs of analyzing and learning from medication errors.

Developing an Assessment Tool for Enhancing Interprofessional Education of Patient Safety.

The increasing recognition of the value of effective interprofessional collaboration has promoted interprofessional education (IPE) among healthcare professionals. The IPE pilot program initiative at UTHealth matriculated students from the disciplines of biomedical informatics, biomedical sciences, dentistry, medicine, nursing and public health. There has been a pressing need for developing an assessment tool in reflecting how IPE participants recognize and understand the other disciplines including clinical informatics in nowadays' practice. This paper reports our development process of the assessment tool, which is still under an iterative, in-depth refinement and aiming at greater collaborations.

Developing a similarity searching module for patient safety event reporting system using semantic similarity measures.

BACKGROUND: The most important knowledge in the field of patient safety is regarding the prevention and reduction of patient safety events (PSE) during treatment and care. The similarities and patterns among the events may otherwise go unnoticed if they are not properly reported and analyzed. There is an urgent need for developing a PSE reporting system that can dynamically measure the similarities of the events and thus promote event analysis and learning effect. METHODS: In this study, three prevailing algorithms of semantic similarity were implemented to measure the similarities of the 366 PSE annotated by the taxonomy of The Agency for Healthcare Research and Quality (AHRQ). The performance of each algorithm was then evaluated by a group of domain experts based on a 4-point Likert scale. The consistency between the scales of the algorithms and experts was measured and compared with the scales randomly assigned. The similarity algorithms and scores, as a self-learning and self-updating module, were then integrated into the system. RESULTS: The result shows that the similarity scores reflect a high consistency with the experts' review than those randomly assigned. Moreover, incorporating the algorithms into our reporting system enables a mechanism to learn and update based upon PSE similarity. CONCLUSION: In conclusion, integrating semantic similarity algorithms into a PSE reporting system can help us learn from previous events and provide timely knowledge support to the reporters. With the knowledge base in the PSE domain, the new generation reporting system holds promise in educating healthcare providers and preventing the recurrence and serious consequences of PSE.

Design of a User-Centered Voluntary Reporting System for Patient Safety Events.

As the third leading cause of death in the U.S., patient safety events (PSE) are difficult to control due to multiple inputs from healthcare providers, systems, or even patients. Inspired by the success of reporting systems in other fields, PSE reporting systems could be a good resource to share and to learn from previous cases. However, the success of such systems in healthcare is yet to be seen due to the low report quality and the lack of interoperability and communication. A knowledge-based and user-centered PSE reporting system is needed to organize the scattered knowledge and improve user-friendliness. We described the development of a knowledge base for patient falls, the most frequent PSE. Based on the knowledge base, user-centered design features were incorporated into the system to improve the reporting accuracy, completeness, and timeliness. This prototype holds promise in improving PSE reporting quality and facilitating human-computer communication.

Event Reports Promoting Root Cause Analysis.

Improving health is the sole objective of medical care. Unfortunately, mishaps or patient safety events happen during the care. If the safety events were collected effectively, they would help identify patterns, underlying causes, and ultimately generate proactive and remedial solutions for prevention of recurrence. Based on the AHRQ Common Formats, we examine the quality of patient safety incident reports and describe the initial data requirement that can support and accelerate effective root cause analysis. The ultimate goal is to develop a knowledge base of patient safety events and their common solutions which can be readily available for sharing and learning.

Mobile and Wearable Technology Needs for Aging in Place: Perspectives from Older Adults and Their Caregivers and Providers.

There is an increasing number of wearable trackers and mobile devices in the burgeoning world of digital health, the purpose of the study is to explore the role of these mobile and wearable tools among older adults aging in place. We conducted a cross sectional study using individual interviews with older adults and surveys with their caregivers or providers. We interviewed 29 residents living in a retirement community, and surveyed 6 caregivers or providers. The older adults had an average age of 88 years, most did not express interests on technology and heavily relied on providers for health tracking, while their professional caregivers or providers saw a great need to access older adults' health information collected from these mobile and wearable tools. Educating the older old on the benefits of mobile and wearable tools may address such discrepancy on needs of adopting mobile and wearable tools for aging in place.

Enhancing Patient Safety Event Reporting by K-nearest Neighbor Classifier.

Data quality was placed as a major reason for the low utility of patient safety event reporting systems. A pressing need in improving data quality has advanced recent research focus in data entry associated with human factors. The debate on structured data entry or unstructured data entry reveals not only a trade-off problem among data accuracy, completeness, and timeliness, but also a technical gap on text mining. The present study suggested a text classification method, k-nearest neighbor (KNN), for predicting subject categories as in our proposed reporting system. Our results demonstrated the feasibility of KNN classifier used for text classification and indicated the advantage of such an application to raise data quality and clinical decision support in reporting patient safety events.

On Building an Ontological Knowledge Base for Managing Patient Safety Events.

Over the past decade, improving healthcare quality and safety through patient safety event reporting systems has drawn much attention. Unfortunately, such systems are suffering from low data quality, inefficient data entry and ineffective information retrieval. For improving the systems, we develop a semantic web ontology based on the WHO International Classification for Patient Safety (ICPS) and AHRQ Common Formats for patient safety event reporting. The ontology holds potential in enhancing knowledge management and information retrieval, as well as providing flexible data entry and case analysis for both reporters and reviewers of patient safety events. In this paper, we detailed our efforts in data acquisition, transformation, implementation and initial evaluation of the ontology.

Information gaps in reporting patient falls: the challenges and technical solutions.

The emerging computerized system for patient safety event reporting eases the course of learning from medical errors and adverse events for a safer healthcare environment. To a medical event like patient falls, the course usually involves pre, during and post stages for the prediction, reporting and solution of the event. However, the reporting stage often separates from the other two stages for risk assessment and cause analysis. As this iterative flow of actions falls apart and becomes unintelligible or intangible due to information gaps, it is dubious for users to join and complete the task at all three stages in a high quality. Therefore, in this paper, by referencing studies in aspects of Norman' s task action theory and fall management programs, we proposed a gap-bridging model to describe the process of assisting users in proceeding along the stages by user-centered design approaches. Based upon the model, we also developed a series of interface artifacts served as gap-bridging features, which hold promise in improving the quality of reporting and reporter engagement of the system.

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.