Healthcare Engineering Defined: A White Paper.

Engineering has been playing an important role in serving and advancing healthcare. The term "Healthcare Engineering" has been used by professional societies, universities, scientific authors, and the healthcare industry for decades. However, the definition of "Healthcare Engineering" remains ambiguous. The purpose of this position paper is to present a definition of Healthcare Engineering as an academic discipline, an area of research, a field of specialty, and a profession. Healthcare Engineering is defined in terms of what it is, who performs it, where it is performed, and how it is performed, including its purpose, scope, topics, synergy, education/training, contributions, and prospects.

Integration of Value Stream Map and Healthcare Failure Mode and Effect Analysis into Six Sigma Methodology to Improve Process of Surgical Specimen Handling.

Specimen handling is a critical patient safety issue. Problematic handling process, such as misidentification (of patients, surgical site, and specimen counts), specimen loss, or improper specimen preparation can lead to serious patient harms and lawsuits. Value stream map (VSM) is a tool used to find out non-value-added works, enhance the quality, and reduce the cost of the studied process. On the other hand, healthcare failure mode and effect analysis (HFMEA) is now frequently employed to avoid possible medication errors in healthcare process. Both of them have a goal similar to Six Sigma methodology for process improvement. This study proposes a model that integrates VSM and HFMEA into the framework, which mainly consists of define, measure, analyze, improve, and control (DMAIC), of Six Sigma. A Six Sigma project for improving the process of surgical specimen handling in a hospital was conducted to demonstrate the effectiveness of the proposed model.

Prediagnosis of obstructive sleep apnea via multiclass MTS.

Obstructive sleep apnea (OSA) has become an important public health concern. Polysomnography (PSG) is traditionally considered an established and effective diagnostic tool providing information on the severity of OSA and the degree of sleep fragmentation. However, the numerous steps in the PSG test to diagnose OSA are costly and time consuming. This study aimed to apply the multiclass Mahalanobis-Taguchi system (MMTS) based on anthropometric information and questionnaire data to predict OSA. Implementation results showed that MMTS had an accuracy of 84.38% on the OSA prediction and achieved better performance compared to other approaches such as logistic regression, neural networks, support vector machine, C4.5 decision tree, and rough set. Therefore, MMTS can assist doctors in prediagnosis of OSA before running the PSG test, thereby enabling the more effective use of medical resources.

Data mining techniques for assisting the diagnosis of pressure ulcer development in surgical patients.

Pressure ulcer is a serious problem during patient care processes. The high risk factors in the development of pressure ulcer remain unclear during long surgery. Moreover, past preventive policies are hard to implement in a busy operation room. The objective of this study is to use data mining techniques to construct the prediction model for pressure ulcers. Four data mining techniques, namely, Mahalanobis Taguchi System (MTS), Support Vector Machines (SVMs), decision tree (DT), and logistic regression (LR), are used to select the important attributes from the data to predict the incidence of pressure ulcers. Measurements of sensitivity, specificity, F(1), and g-means were used to compare the performance of four classifiers on the pressure ulcer data set. The results show that data mining techniques obtain good results in predicting the incidence of pressure ulcer. We can conclude that data mining techniques can help identify the important factors and provide a feasible model to predict pressure ulcer development.

Applying HFMEA to prevent chemotherapy errors.

To evaluate risk and vulnerability in the chemotherapy process using a proactive risk analysis method. Healthcare failure mode and effect analysis (HFMEA) was adopted to identify potential chemotherapy process failures. A multidisciplinary team is formed to identify, evaluate, and prioritize potential failure modes based on a chemotherapy process flowchart. Subsequently, a decision tree is used to locate potential failure modes requiring urgent improvement. Finally, some recommended actions are generated and executed to eliminate possible risks. A total of 11 failure modes were identified with high hazard scores in both inpatient and outpatient processes. Computerized physician order entry was adopted to eliminate potential risks in chemotherapy processes. Chemotherapy prescription errors significantly decreased from 3.34% to 0.40%. Chemotherapy is regarded as a high-risk process. Multiple errors can occur during ordering, preparing, compounding, dispensing, and administering medications. Subsequently, these can lead to serious consequences. HFMEA is a useful tool to evaluate potential risk in healthcare processes.

An application of artificial immune recognition system for prediction of diabetes following gestational diabetes.

Diabetes mellitus (DM) is a disease prevalent in population and is not easily perceived in its initial stage but may sway a patient very seriously in later stage. In accordance with the estimation of World Health Organization (WHO), there will be 370 million diabetics which are 5.4% of the global people in 2030, so it becomes more and more important to predict whether a pregnant woman has or is likely to acquire diabetes. This study is conducted with the use of the machine learning-Artificial Immune Recognition System (AIRS)-to assist doctors in predicting pregnant women who have premonition of type 2 diabetes. AIRS is proposed by Andrew Watkins in 2001 and it makes use of the metaphor of the vertebrate immune system to recognize antigens, select clone, and memorize cells. Additionally, AIRS includes a mechanism, limited resource, to restrain the number of memory cells from increasing uncontrollably. It has also showed positive results on problems in which it was applied. The objective of this study is to investigate the feasibility in using AIRS to predict gestational diabetes mellitus (GDM) subsequent DM. The dataset of diabetes has imbalanced data, but the overall classification recall could still reach 62.8%, which is better than the traditional method, logistic regression, and the technique which is thought as one of the powerful classification approaches, support vector machines (SVM).

Multiclass MTS for saxophone timbre quality inspection using waveform-shape-based features.

Under the highly developed automation today, the manufacture of saxophone is still a nonautomatic process and much relies on highly skilled technicians. In order to insure the timbre quality, the sound of finished saxophone must be tested in the final inspection stage. The evaluation of timbre quality mainly depends on the professional musicians' hearing judgment; however, the sensitivity of human perception can be influenced by many factors. To improve the reliability of saxophone timbre quality inspection, an automatic multiclass timbre classification system (AMTCS) is developed and used to assist in the inspection work. The AMTCS is composed of our proposed waveform-shape-based feature extraction method in parameterization phase and multiclass Mahalanobis-Taguchi system in classification phase. The numerical experiments show that the musical instrument classification accuracy obtained by our proposed AMTCS is satisfactory. Through employing the AMTCS, strong assistance was provided to the inspection of saxophone timbre quality, and a perfect identification rate on the saxophones with different timbre quality levels is achieved. Moreover, the significant tones having impact on saxophone timbre quality can also be easily identified by AMTCS.