Dissatisfaction-considered waiting time prediction for outpatients with interpretable machine learning.

Long waiting time in outpatient departments is a crucial factor in patient dissatisfaction. We aim to analytically interpret the waiting times predicted by machine learning models and provide patients with an explanation of the expected waiting time. Here, underestimating waiting times can cause patient dissatisfaction, so preventing this in predictive models is necessary. To address this issue, we propose a framework considering dissatisfaction for estimating the waiting time in an outpatient department. In our framework, we leverage asymmetric loss functions to ensure robustness against underestimation. We also propose a dissatisfaction-aware asymmetric error score (DAES) to determine an appropriate model by considering the trade-off between underestimation and accuracy. Finally, Shapley additive explanation (SHAP) is applied to interpret the relationship trained by the model, enabling decision makers to use this information for improving outpatient service operations. We apply our framework in the endocrinology metabolism department and neurosurgery department in one of the largest hospitals in South Korea. The use of asymmetric functions prevents underestimation in the model, and with the proposed DAES, we can strike a balance in selecting the best model. By using SHAP, we can analytically interpret the waiting time in outpatient service (e.g., the length of the queue affects the waiting time the most) and provide explanations about the expected waiting time to patients. The proposed framework aids in improving operations, considering practical application in hospitals for real-time patient notification and minimizing patient dissatisfaction. Given the significance of managing hospital operations from the perspective of patients, this work is expected to contribute to operations improvement in health service practices.

Machine learning-based survival rate prediction of Korean hepatocellular carcinoma patients using multi-center data.

AIM: To predict survival time of Korean hepatocellular carcinoma (HCC) patients using multi-center data as a foundation for the development of a predictive artificial intelligence model according to treatment methods based on machine learning. METHODS: Data of patients who underwent treatment for HCC from 2008 to 2015 was provided by Korean Liver Cancer Study Group and Korea Central Cancer Registry. A total of 10,742 patients with HCC were divided into two groups, with Group I (2920 patients) confirmed on biopsy and Group II (5562 patients) diagnosed as HCC according to HCC diagnostic criteria as outlined in Korean Liver Cancer Association guidelines. The data were modeled according to features of patient clinical characteristics. Features effective in predicting survival rate were analyzed retrospectively. Various machine learning methods were used. RESULTS: Target was overall survival time, which divided into approximately 60 months (= /< 60 m, > 60 m). Target distribution in Group I (total 514 samples) was 28.8%: (148 samples) less than 60 months, 71.2% (366 samples) greater than 60 months, and in Group II (total 757 samples) was 66.6% (504 samples) less than 60 months, 33.4% (253 samples) greater than 60 months. Using NG Boost method, its accuracy was 83%, precision 84%, sensitivity 95%, and F1 score 89% for more than 60 months survival time in Group I with surgical resection. Moreover, its accuracy was 79%, precision 82%, sensitivity 87%, and F1 score 84% for less than 60 months survival time in Group II with TACE. The feature importance with gain criterion indicated that pathology, portal vein invasion, surgery, metastasis, and needle biopsy features could be explained as important factors for prediction in case of biopsy (Group I). CONCLUSION: By developing a predictive model using machine learning algorithms to predict prognosis of HCC patients, it is possible to project optimized treatment by case according to liver function and tumor status.

Development and Effectiveness of a Clinical Decision Support System for Pressure Ulcer Prevention Care Using Machine Learning: A Quasi-experimental Study.

This study was conducted to develop and evaluate the effectiveness of a clinical decision support system for pressure ulcer prevention on clinical (performance, visual discrimination ability, and decision-making ability) and cognitive (knowledge and attitude) workflow. After developing a clinical decision support system using machine learning, a quasi-experimental study was used. Data were collected between January and April 2020. Forty-nine RNs who met the inclusion criteria and worked at seven tertiary and five secondary hospitals participated. A clinical decision support system was provided to the intervention group during the same period. Differences in outcome variables between the two groups were analyzed using t tests. The level of pressure ulcer prevention nursing performance and visual differentiation ability of skin pressure and oral mucosa pressure ulcer showed significantly greater improvement in the experimental group compared with the control group, whereas clinical decision making did not differ significantly. A clinical decision support system using machine learning was partially successful in performance of skin pressure ulcer prevention, attitude, and visual differentiation ability for skin and oral mucosa pressure ulcer prevention. These findings indicated that a clinical decision support system using machine learning needs to be implemented for pressure ulcer prevention.

Factors of quality of care and their association with smartphone based PHR adoption in South Korean hospitals.

BACKGROUND: Healthcare organizations have begun to adopt personal health records (PHR) systems to engage patients, but little is known about factors associated with the adoption of PHR systems at an organizational level. The objective of this study is to investigate factors associated with healthcare organizations' adoption of PHR systems in South Korea. METHODS: The units of analysis were hospitals with more than 100 beds. Study data of 313 hospitals were collected from May 1 to June 30, 2020. The PHR adoption status for each hospital was collected from PHR vendors and online searches. Adoption was then confirmed by downloading the hospital's PHR app and the PHR app was examined to ascertain its available functions. One major outcome variable was PHR adoption status at hospital level. Data were analysed by logistic regressions using SAS 9.4 version. RESULTS: Out of 313 hospitals, 103 (32.9%) hospitals adopted PHR systems. The nurse-patient ratio was significantly associated with PHR adoption (OR 0.758; 0.624 to 0.920, p = 0.005). The number of health information management staff was associated with PHR adoption (OR 1.622; 1.228 to 2.141, p = 0.001). The number of CTs was positively associated with PHR adoption (OR 5.346; 1.962 to 14.568, p = 0.001). Among the hospital characteristics, the number of beds was significantly related with PHR adoption in the model of standard of nursing care (OR 1.003; 1.001 to 1.005, p < 0.001), HIM staff (OR 1.004; 1.002 to 1.006, p < 0.001), and technological infrastructure (OR 1.050; 1.003 to 1.006, p < 0.001). CONCLUSIONS: One-third of study hospitals had adopted PHR systems. Standard of nursing care as well as information technology infrastructure in terms of human resources for health information management and advanced technologies were significantly associated with adoption of PHR systems. A favourable environment for adopting new technologies in general may be associated with the adoption and use of PHR systems.

Risk prediction models for the development of oral-mucosal pressure injuries in intubated patients in intensive care units: A prospective observational study.

PURPOSE: Oral-mucosal pressure injury (PI) is the most commonly encountered medical device-related PIs. This study was performed to identify risk factors and construct a risk prediction model for oral-mucosal PI development in intubated patients in the intensive care unit. METHODS: The study design was prospective, observational with medical record review. The inclusion criteria stipulated that 1) participants should be > 18 years of age, 2) there should be ETT use with holding methods including adhesive tape, gauze tying, and commercial devices. Data of 194 patient-days were analysed. The identification and validation of risk model development was performed using SPSS and the SciKit learn platform. RESULTS: The risk prediction logistic models were composed of three factors (bite-block/airway, commercial ETT holder, and corticosteroid use) for lower oral-mucosal PI development and four factors (commercial ETT holder, vasopressor use, haematocrit, and serum albumin level) for upper oral-mucosal PI development among 10 significant input variables. The sensitivity and specificity for lower oral-mucosal PI development were 85.2% and 76.0%, respectively, and those for upper oral-mucosal PI development were 60.0% and 89.1%, respectively. Based on the results of the machine learning, the upper oral-mucosal PI development model had an accuracy of 79%, F1 score of 88%, precision of 86%, and recall of 91%. CONCLUSIONS: The development of lower oral-mucosal PIs is affected by immobility-related factors and corticosteroid use, and that of upper oral-mucosal PIs by undernutrition-related factors and ETT holder use. The high sensitivities of the two logit models comprise important minimum data for positively predicting oral-mucosal PIs.

Acupuncture for acute postoperative pain after back surgery: a systematic review and meta-analysis of randomized controlled trials.

OBJECTIVES: Acupuncture is commonly used as a complimentary treatment for pain management. However, there has been no systematic review summarizing the current evidence concerning the effectiveness of acupuncture for acute postoperative pain after back surgery. This systematic review aimed at evaluating the effectiveness of acupuncture treatment for acute postoperative pain (≤1 week) after back surgery. METHODS: We searched 15 electronic databases without language restrictions. Two reviewers independently assessed studies for eligibility and extracted data, outcomes, and risk of bias. Random effect meta-analyses and subgroup analyses were performed. RESULTS: Five trials, including 3 of high quality, met our inclusion criteria. The meta-analysis showed positive results for acupuncture treatment of pain after surgery in terms of the visual analogue scale (VAS) for pain intensity 24 hours after surgery, when compared to sham acupuncture (standard mean difference -0.67 (-1.04 to -0.31), P = 0.0003), whereas the other meta-analysis did not show a positive effect of acupuncture on 24-hour opiate demands when compared to sham acupuncture (standard mean difference -0.23 (-0.58 to 0.13), P = 0.21). CONCLUSION: Our systematic review finds encouraging but limited evidence for the effectiveness of acupuncture treatment for acute postoperative pain after back surgery. Further rigorously designed clinical trials are required.

Hospitals' Adoption of Mobile-Based Personal Health Record Systems and Patients' Characteristics: A Cross-Sectional Study Analyzing National Healthcare Big Data.

Insufficient information exists on the associations between hospitals' adoption of mobile-based personal health record (mPHR) systems and patients' characteristics. This study explored the associations between patients' characteristics and hospitals' adoption of mPHR systems in Korea. This cross-sectional study used 316 hospitals with 100 or more beds as the unit of analysis. Previously collected data on mPHR adoption from May 1 to June 30, 2020 were analyzed. National health insurance claims data for 2019 were also used to analyze patients' characteristics. The dependent variable was mPHR system adoption (0 vs 1) and the main independent variables were the number of patients, age distribution, and proportions of patients with cancer, diabetes, and hypertension among inpatients and outpatients. The number of inpatients was significantly associated with mPHR adoption (adjusted odds ratio [aOR]: 1.174; 1.117-1.233, P < .001), as was the number of outpatients (aOR: 1.041; 1.028-1.054, P < .001). The proportion of inpatients aged 31 to 60 years to those aged 31 years and older was also associated with hospital mPHR adoption (aOR: 1.053; 1.022-1.085, P = .001). mPHR system adoption was significantly associated with the proportion of inpatients (aOR: 1.089; 1.012-1.172, P = .024) and outpatients (aOR: 1.138; 1.026-1.263, P = .015) with cancer and outpatients (aOR: 1.271; 1.101-1.466, P = .001) with hypertension. Although mPHR systems are useful for the management of chronic diseases such as diabetes and hypertension, the number of patients, younger age distribution, and the proportion of cancer patients were closely associated with hospitals' introduction of mPHR systems.

Posttraumatic synostosis between the thyroid cartilage and the cervical spine causing dysphagia.

A 64-year-old man, 7 years after cervical trauma, presented with severe dysphagia of 3-month duration. Computed tomography showed an unusual synostosis between the thyroid cartilage and the cervical spine at C5-6-7 on the right side. A barium swallow study revealed no laryngeal elevation during swallowing. Surgical resection of the bony fusion was performed, and the patient's dysphagia immediately improved without any complications. We report a case of delayed synostosis between the thyroid cartilage and the cervical spine causing severe dysphagia 7 years after cervical trauma. Surgical resection of the bony fusion resulted in immediate improvement of the dysphagia.

Relationships between Nursing Students' Skill Mastery, Test Anxiety, Self-Efficacy, and Facial Expressions: A Preliminary Observational Study.

Test anxiety and self-efficacy significantly influence the mastery of nursing skills. Facial expression recognition tools are central components to recognising these elements. This study investigated the frequent facial expressions conveyed by nursing students and examined the relationships between nursing skill mastery, test anxiety, self-efficacy, and facial expressions in a test-taking situation. Thirty-three second-year nursing students who were attending a university in a Korean metropolitan city participated. Test anxiety, self-efficacy, and facial expressions were collected while the students inserted indwelling catheters. Using Microsoft Azure software, the researchers examined the students' facial expressions. Negative facial expressions, such as anger, disgust, sadness, and surprise, were more common during the test-taking situation than the practice trial. Fear was positively correlated with anxiety. None of the facial expressions had significant relationships with self-efficacy; however, disgust was positively associated with nursing skill mastery. The facial expressions during the practice and test-taking situations were similar; however, fear and disgust may have been indicators of test anxiety and skill mastery. To create a screening tool for detecting and caring for students' emotions, further studies should explore students' facial expressions that were not evaluated in this study.

Conversational Artificial Intelligence for Spinal Pain Questionnaire: Validation and User Satisfaction.

OBJECTIVE: The purpose of our study is to develop a spoken dialogue system (SDS) for pain questionnaire in patients with spinal disease. We evaluate user satisfaction and validated the performance accuracy of the SDS in medical staff and patients. METHODS: The SDS was developed to investigate pain and related psychological issues in patients with spinal diseases based on the pain questionnaire protocol. We recognized patients' various answers, summarized important information, and documented them. User satisfaction and performance accuracy were evaluated in 30 potential users of SDS, including doctors, nurses, and patients and statistically analyzed. RESULTS: The overall satisfaction score of 30 patients was 5.5 ± 1.4 out of 7 points. Satisfaction scores were 5.3 ± 0.8 for doctors, 6.0 ± 0.6 for nurses, and 5.3 ± 0.5 for patients. In terms of performance accuracy, the number of repetitions of the same question was 13, 16, and 33 (13.5%, 16.8%, and 34.7%) for doctors, nurses, and patients, respectively. The number of errors in the summarized comment by the SDS was 5, 0, and 11 (5.2%, 0.0%, and 11.6 %), respectively. The number of summarization omissions was 7, 5, and 7 (7.3%, 5.3%, and 7.4%), respectively. CONCLUSION: This is the first study in which voice-based conversational artificial intelligence (AI) was developed for a spinal pain questionnaire and validated by medical staff and patients. The conversational AI showed favorable results in terms of user satisfaction and performance accuracy. Conversational AI can be useful for the diagnosis and remote monitoring of various patients as well as for pain questionnaires in the future.

Successful Motor Evoked Potential Monitoring in Cervical Myelopathy : Related Factors and the Effect of Increased Stimulation Intensity.

OBJECTIVE: Intraoperative neurophysiological monitoring (IONM) has been widely used during spine surgery to reduce or prevent neurologic deficits, however, its application to the surgical management for cervical myelopathy remains controversial. This study aimed to assess the success rate of IONM in patients with cervical myelopathy and to investigate the factors associated with successful baseline monitoring and the effect of increasing the stimulation intensity by focusing on motor evoked potentials (MEPs). METHODS: The data of 88 patients who underwent surgery for cervical myelopathy with IONM between January 2016 and June 2018 were retrospectively reviewed. The success rate of baseline MEP monitoring at the initial stimulation of 400 V was investigated. In unmonitorable cases, the stimulation intensity was increased to 999 V, and the success rate final MEP monitoring was reinvestigated. In addition, factors related to the success rate of baseline MEP monitoring were investigated using independent t-test, Wilcoxon rank-sum test, chi-squared test, and Fisher's exact probability test for statistical analysis. The factors included age, sex, body mass index, diabetes mellitus, smoking history, symptom duration, Torg-Pavlov ratio, space available for the cord (SAC), cord compression ratio (CCR), intramedullary increased signal intensity (SI) on magnetic resonance imaging, SI length, SI ratio, the Medical Research Council (MRC) grade, the preoperative modified Nurick grade and Japanese Orthopedic Association (JOA) score. RESULTS: The overall success rate for reliable MEP response was 52.3% after increasing the stimulation intensity. No complications were observed to be associated with increased intensity. The factors related to the success rate of final MEP monitoring were found to be SAC (p<0.001), CCR (p<0.001), MRC grade (p<0.001), preoperative modified Nurick grade (p<0.001), and JOA score (p<0.001). The cut-off score for successful MEP monitoring was 5.67 mm for SAC, 47.33% for the CCR, 3 points for MRC grade, 2 points for the modified Nurick grade, and 12 points for the JOA score. CONCLUSION: Increasing the stimulation intensity could significantly improve the success rate of baseline MEP monitoring for unmonitorable cases at the initial stimulation in cervical myelopathy. In particular, the SAC, CCR, MRC grade, preoperative Nurick grade and JOA score may be considered as the more important related factors associated with the success rate of MEP monitoring. Therefore, the degree of preoperative neurological functional deficits and the presence of spinal cord compression on imaging could be used as new detailed criteria for the application of IONM in patients with cervical myelopathy.

Predicting survival time of Korean hepatocellular carcinoma patients using the Cox proportional hazards model: a retrospective study based on big data analysis.

AIM: To predict survival time of Korean hepatocellular carcinoma (HCC) patients by analyzing big data using Cox proportional hazards model. METHODS: Big data of the patients who underwent treatment for HCC from 2008 to 2015, provided by Korea Central Cancer Registry, National Cancer Center, and Ministry of Health and Welfare, were analyzed. A total of 10 742 patients with HCC were divided into two groups, with Group I (3021 patients) confirmed on biopsy and Group II (5563 patients) diagnosed as HCC according to HCC diagnostic criteria as outlined in Korean Liver Cancer Association guidelines. Univariate and multivariate Cox regression analyses were performed to identify independent risk factors of recurrence after treatment and survival status. RESULTS: A total of 3021 patients in Group I and 5563 patients in Group II were included in the study and the difference in survival time between the two groups was statistically significant (P < 0.05). Recurrence was only included in intrahepatic cases, and the rates were 21.2 and 19.8% while the periods from the first treatment to recurrence were 15.57 and 14.19 months, respectively. Age, diabetes, BMI, platelet, alpha-fetoprotein, histologic tumor maximum size, imaging T stage, presence of recurrence, and duration of recurrence were included in multivariate analysis. CONCLUSION: By using nationwide, multicenter big data, it is possible to predict recurrence rate and survival time which can provide the basis for treatment response to develop a predictive program.

Analysis of Platforms and Functions of Mobile-Based Personal Health Record Systems.

OBJECTIVES: Little is known about the platforms and functionalities of mobile-based personal health record (PHR) applications. The objective of this study was to investigate these two features of PHR systems. METHODS: The unit of analysis was general hospitals with more than 100 beds. This study was based on a PHR survey conducted from May 1 to June 30, 2020 and the National Health Insurance administrative data as of March 31, 2020. The study considered the platform, Android and iPhone operation system (iOS), and types of functionalities of PHR systems. Among the 316 target hospitals, 103 hospitals had adopted PHR systems. A logistic regression analysis was used. RESULTS: This study found that 103 hospitals had adopted mobile-based PHR systems for their patients. Sixty-four hospitals (62.1%) were adopting both Android and iOS, but 36 (35.0%) and 3 (2.9%) hospitals were adopting Android only or iOS only, respectively. The PHR systems of hospitals adopting both platforms were more likely to have functions for viewing prescriptions, clinical diagnostic test results, and upcoming appointment status compared to those adopting a single platform (p < 0.001). The number of beds (odds ratio [OR] = 1.004; confidence interval [CI], 1.001-1.007; p = 0.0029) and the number of computed tomography systems (CTs) per 100 beds (OR = 6.350; CI, 1.006-40.084; p = 0.0493) were significantly associated with the adoption of both platforms. CONCLUSIONS: More than 60% of hospitals had adopted both Android and iOS platforms for their patients in Korea. Hospitals adopting both platforms had additional functionalities and significant association with the number of beds and CTs.

Process Mining-Supported Emergency Room Process Performance Indicators.

Emergency room processes are often exposed to the risk of unexpected factors, and process management based on performance measurements is required due to its connectivity to the quality of care. Regarding this, there have been several attempts to propose a method to analyze the emergency room processes. This paper proposes a framework for process performance indicators utilized in emergency rooms. Based on the devil's quadrangle, i.e., time, cost, quality, and flexibility, the paper suggests multiple process performance indicators that can be analyzed using clinical event logs and verify them with a thorough discussion with clinical experts in the emergency department. A case study is conducted with the real-life clinical data collected from a tertiary hospital in Korea tovalidate the proposed method. The case study demonstrated that the proposed indicators are well applied using the clinical data, and the framework is capable of understanding emergency room processes' performance.

Prediction of early neurological deterioration in acute minor ischemic stroke by machine learning algorithms.

OBJECTIVES: A significant proportion of patients with acute minor stroke have unfavorable functional outcome due to early neurological deterioration (END). The purpose of this study was to evaluate the applicability of machine learning algorithms to predict END in patients with acute minor stroke. PATIENTS AND METHODS: We collected clinical and neuroimaging information from patients with acute minor stroke with NIHSS score of ≤ 3. Early neurological deterioration was defined as any worsening of NIHSS score within 3 days after admission. Unfavorable functional outcome was defined as a modified Rankin Scale score of ≥ 2. We also compared clinical and neuroimaging information between patients with and without END. Four machine learning algorithms, i.e., Boosted trees, Bootstrap decision forest, Deep neural network, and Logistic Regression, were selected and trained by our dataset to predict early neurological deterioration RESULTS: A total of 739 patients were included in this study. 78 patients (10.6%) experienced END. Among 78 patients with END, 61 (78.2%) had unfavorable functional outcome at 90 days after stroke onset. On multivariate analysis, the initial NIHSS score (P = 0.003), hemorrhagic transformation (P = 0.010), and stenosis (P = 0.014) or occlusion (P = 0.004) of a relevant artery were independently associated with END. Of the four machine learning algorithms, Boosted trees, Deep neural network, and Logistic Regression can be used to predict END in patients with acute minor stroke (Boosted trees: accuracy = 0.966, F1 score = 0.8 and area under the curve = 0.934, Deep neural network :0.966, 0.8, and 0. 904, and Logistic Regression : 0.966, 0.8, and 0.885). CONCLUSIONS: This study suggests that machine learning algorithms that integrate clinical and neuroimaging information can be used to predict END in patients with acute minor stroke. Further studies based on larger, multicenter datasets are needed to predict END accurately for designing treatment strategies and obtaining favorable functional outcome.

Reduction of ischemia-induced cerebral injury by all-trans-retinoic acid.

Ischemia-induced cerebral injury evolves over a longer period than previously believed through post-ischemic inflammation. Retinoic acid (RA) has been shown to exert cytoprotective effects on several cells, but its effects on ischemia-induced cerebral injury have been poorly characterized. The aim of the present study was to examine the effects of all-trans-RA on ischemia-induced cerebral injury and elucidate the underlying mechanism. All-trans-RA treatment reduced the size of the ischemia-induced cerebral infarct. To elucidate the underlying mechanism, ischemia-induced cerebral inflammation was studied by examination of expressions of interleukin 1beta (IL-1beta) and ED-1. RA treatment significantly reduced the cerebral inflammation. Moreover, cerebral ischemic induction of cyclooxygenase-2 (COX-2) and CCAAT/enhancer binding protein beta (C/EBPbeta), which binds to the COX-2 promoter, was also inhibited by RA. These results suggest that RA can reduce ischemia-induced cerebral injury by an anti-inflammatory action, which may be effected via inhibition of C/EBPbeta-mediated COX-2 induction.

Internet of Things, Digital Biomarker, and Artificial Intelligence in Spine: Current and Future Perspectives.

Recent interest in medical artificial intelligence (AI) has increased with onset of the fourth industrial revolution. Real-time monitoring of patients is an important research area of medical AI. The medical AI is very closely related to the Internet of Things (IoT), a core element of the fourth industrial revolution. Attempts to diagnose and treat patients using IoT have been already applied to patients with chronic disease such as hypertension and arrhythmia. However, in the spine, research on IoT and digital biomarkers are still in the early stages. The digital biomarker obtained by IoT devices is objective and could represent real-time, real-world, and abundant data. Based on its characteristics, IoT and digital biomarkers can also be useful in the spine. Currently, research on real-time monitoring of physical activity or spinal posture is ongoing. Therefore, the authors introduce the basic concepts of IoT and digital biomarkers, their relationship to AI, and recent trends. Current and future perspectives of IoT and digital biomarker in spine are also discussed. In the future, it is expected that IoT, digital biomarkers, and AI will lead to a paradigm shift in the diagnosis and treatment of spinal diseases.

Application of Deep Learning System into the Development of Communication Device for Quadriplegic Patient.

OBJECTIVE: In general, quadriplegic patients use their voices to call the caregiver. However, severe quadriplegic patients are in a state of tracheostomy, and cannot generate a voice. These patients require other communication tools to call caregivers. Recently, monitoring of eye status using artificial intelligence (AI) has been widely used in various fields. We made eye status monitoring system using deep learning, and developed a communication system for quadriplegic patients can call the caregiver. METHODS: The communication system consists of 3 programs. The first program was developed for automatic capturing of eye images from the face using a webcam. It continuously captured and stored 15 eye images per second. Secondly, the captured eye images were evaluated for open or closed status by deep learning, which is a type of AI. Google TensorFlow was used as a machine learning tool or library for convolutional neural network. A total of 18,000 images were used to train deep learning system. Finally, the program was developed to utter a sound when the left eye was closed for 3 seconds. RESULTS: The test accuracy of eye status was 98.7%. In practice, when the quadriplegic patient looked at the webcam and closed his left eye for 3 seconds, the sound for calling a caregiver was generated. CONCLUSION: Our eye status detection software using AI is very accurate, and the calling system for the quadriplegic patient was satisfactory.

Computer-based clinical coding activity analysis for neurosurgical terms.

Background: It is not possible to measure how much activity is required to understand and code a medical data. We introduce an assessment method in clinical coding, and applied this method to neurosurgical terms. Methods: Coding activity consists of two stages. At first, the coders need to understand a presented medical term (informational activity). The second coding stage is about a navigating terminology browser to find a code that matches the concept (code-matching activity). Systematized Nomenclature of Medicine - Clinical Terms (SNOMED CT) was used for the coding system. A new computer application to record the trajectory of the computer mouse and record the usage time was programmed. Using this application, we measured the time that was spent. A senior neurosurgeon who has studied SNOMED CT has analyzed the accuracy of the input coding. This method was tested by five neurosurgical residents (NSRs) and five medical record administrators (MRAs), and 20 neurosurgical terms were used. Results: The mean accuracy of the NSR group was 89.33%, and the mean accuracy of the MRA group was 80% (p=0.024). The mean duration for total coding of the NSR group was 158.47 seconds, and the mean duration for total coding of the MRA group was 271.75 seconds (p=0.003). Conclusion: We proposed a method to analyze the clinical coding process. Through this method, it was possible to accurately calculate the time required for the coding. In neurosurgical terms, NSRs had shorter time to complete the coding and higher accuracy than MRAs.

The Usefulness of a Wearable Device in Daily Physical Activity Monitoring for the Hospitalized Patients Undergoing Lumbar Surgery.

OBJECTIVE: Functional outcomes have traditionally been evaluated and compared using subjective surveys, such as visual analog scores (VAS), the Oswestry disability index (ODI), and Short Form-36 (SF-36), to assess symptoms and quality of life. However, these surveys are limited by their subjective natures and inherent bias caused by differences in patient perceptions of symptoms. The Fitbit Charge® (Fitbit Inc., San Francisco, CA, USA) provides accurate and objective measures of physical activity. The use of this device in patients after laminectomy would provide objective physical measures that define ambulatory function, activity level, and degree of recovery. Therefore, the present study was conducted to identify relationships between the number of steps taken by patients per day and VAS pain scores, prognoses, and postoperative functional outcomes. METHODS: We prospectively investigated 22 consecutive patients that underwent laminectomy for spinal stenosis or a herniated lumbar disc between June 2015 and April 2016 by the same surgeon. When patients were admitted for surgery and first visited after surgery, preoperative and postoperative functional scores were recorded using VAS scores, ODI scores, and SF-36. The VAS scores and physical activities were recorded daily from postoperative day (POD) 1 to POD 7. The relationship between daily VAS scores and daily physical activities were investigated by simple correlation analysis and the relationship between mean number of steps taken and ODI scores after surgery was subjected to simple regression analysis. In addition, Wilcoxon's signed-rank test was used to investigate the significance of pre-to-postoperative differences in VAS, ODI, and SF-36 scores. RESULTS: Pre-to-postoperative VAS (p<0.001), ODI (p<0.001), SF-36 mental composite scores (p=0.009), and SF-36 physical composite scores (p<0.001) scores were found to be significantly different. Numbers of steps taken from POD 1 to POD 7 were negatively correlated with daily VAS scores (r=-0.981, p<0.001). In addition, the mean number of steps from POD 3 to POD 7 and the decrease in ODI conducted one month after surgery were statistically significant (p=0.029). CONCLUSION: Wearable devices are not only being used increasingly by consumers as lifestyle devices, but are also progressively being used in the medical area. This is the first study to demonstrate the usefulness of a wearable device for checking patient physical activity and predicting pain and prognosis after laminectomy. Based on our experience, the wearable device used to provide measures of physical activity in the present study has the potential to provide objective information on pain severity and prognosis.