Data Mining - Concepts and Techniques.

Sources of Power: How People Make Decisions

The rapidly expanding field of big data analytics has started to play a pivotal role in the evolution of healthcare practices and research. It has provided tools to accumulate, manage, analyze, and assimilate large volumes of disparate, structured, and unstructured data produced by current healthcare systems. Big data analytics has been recently applied towards aiding the process of care delivery and disease exploration. However, the adoption rate and research development in this space is still hindered by some fundamental problems inherent within the big data paradigm. In this paper, we discuss some of these major challenges with a focus on three upcoming and promising areas of medical research: image, signal, and genomics based analytics. Recent research which targets utilization of large volumes of medical data while combining multimodal data from disparate sources is discussed. Potential areas of research within this field which have the ability to provide meaningful impact on healthcare delivery are also examined.

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Big Data Analytics in Healthcare

Process mining in healthcare

The past few years have witnessed an increase in the development of wearable sensors for health monitoring systems. This increase has been due to several factors such as development in sensor technology as well as directed efforts on political and stakeholder levels to promote projects which address the need for providing new methods for care given increasing challenges with an aging population. An important aspect of study in such system is how the data is treated and processed. This paper provides a recent review of the latest methods and algorithms used to analyze data from wearable sensors used for physiological monitoring of vital signs in healthcare services. In particular, the paper outlines the more common data mining tasks that have been applied such as anomaly detection, prediction and decision making when considering in particular continuous time series measurements. Moreover, the paper further details the suitability of particular data mining and machine learning methods used to process the physiological data and provides an overview of the properties of the data sets used in experimental validation. Finally, based on this literature review, a number of key challenges have been outlined for data mining methods in health monitoring systems.

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Data Mining for Wearable Sensors in Health Monitoring Systems: A Review of Recent Trends and Challenges

As a new concept that emerged in the middle of 1990's, data mining can help researchers gain both novel and deep insights and can facilitate unprecedented understanding of large biomedical datasets. Data mining can uncover new biomedical and healthcare knowledge for clinical and administrative decision making as well as generate scientific hypotheses from large experimental data, clinical databases, and/or biomedical literature. This review first introduces data mining in general (e.g., the background, definition, and process of data mining), discusses the major differences between statistics and data mining and then speaks to the uniqueness of data mining in the biomedical and healthcare fields. A brief summarization of various data mining algorithms used for classification, clustering, and association as well as their respective advantages and drawbacks is also presented. Suggested guidelines on how to use data mining algorithms in each area of classification, clustering, and association are offered along with three examples of how data mining has been used in the healthcare industry. Given the successful application of data mining by health related organizations that has helped to predict health insurance fraud and under-diagnosed patients, and identify and classify at-risk people in terms of health with the goal of reducing healthcare cost, we introduce how data mining technologies (in each area of classification, clustering, and association) have been used for a multitude of purposes, including research in the biomedical and healthcare fields. A discussion of the technologies available to enable the prediction of healthcare costs (including length of hospital stay), disease diagnosis and prognosis, and the discovery of hidden biomedical and healthcare patterns from related databases is offered along with a discussion of the use of data mining to discover such relationships as those between health conditions and a disease, relationships among diseases, and relationships among drugs. The article concludes with a discussion of the problems that hamper the clinical use of data mining by health professionals.

Data Mining in Healthcare and Biomedicine: A Survey of the Literature

Objectives: Electronic patient safety event reporting (e-reporting) is an effective mechanism to learn from errors and enhance patient safety. Unfortunately, the value of e-reporting system (a software or web server based platform) in patient safety research is greatly overshadowed by low quality reporting. This paper aims at revealing the current status of system features, detecting potential gaps in system design, and accordingly proposing suggestions for future design and implementation of the system. Methods: Three literature databases were searched for publications that contain informative descriptions of e-reporting systems. In addition, both online publicly accessible reporting forms and systems were investigated. Results: 48 systems were identified and reviewed. 11 system design features and their frequencies of occurrence (Top 5: widgets (41), anonymity or confidentiality (29), hierarchy (20), validator (17), review notification (15)) were identified and summarized into a system hierarchical model. Conclusions: The model indicated the current e-reporting systems are at an immature stage in their development, and discussed their future development direction toward efficient and effective systems to improve patient safety. Citation: Gong Y, Kang H, Wu X, Hua L. Enhancing Patient Safety Event Reporting. Appl Clin Inform 2017; 8: 893–909 https://doi.org/10.4338/ACI-2016-02-R-0023

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Enhancing Patient Safety Event Reporting: A Systematic Review of System Design Features

To learn from errors, electronic patient safety event reporting systems (e-reporting systems) have been widely adopted to collect medical incidents from the frontline practitioners in US hospitals. However, two issues of underreporting and low-quality of reports pervade and thus the system effectiveness remains dubious.
 This study employing semi-structured interviews of health professionals in the Texas Medical Center investigated the perceived benefits and barriers from users who have used e-reporting systems.
 As a result, the perceived benefits include the enhanced convenience in data processing and the assistant functions leading to patient safety enhancement. The perceived barriers to the acceptance and quality use of the system include the lack of instructions, lack of reporter-friendly classifications, lack of time, and lack of feedback. The identified benefits and barriers help design a user-centered e-reporting system where learning and assistant features are discussed during the interviews.
 As a response, the learning and assistant features aiming at enhancing benefits and removing barriers of e-reporting systems should be included for facilitating the acceptance and effective use of the systems.

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Identifying barriers and benefits of patient safety event reporting toward user-centered design

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.

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Text Prediction on Structured Data Entry in Healthcare

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.

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

Lei Hua

Papers

Data Mining in Healthcare and Biomedicine: A Survey of the Literature

Enhancing Patient Safety Event Reporting: A Systematic Review of System Design Features

Identifying barriers and benefits of patient safety event reporting toward user-centered design

Text Prediction on Structured Data Entry in Healthcare

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