Introduction: Detailed clinical information is important for the Casemix System to
generate valuable Case Based Group (CBG) for malnourished geriatric patients. Clinical
coding for malnutrition provides useful information on the nutritional health of patients
for treatment purposes.
Methods: This cross-sectional study with purposive sampling
involved a total of 130 geriatric patients (>60 years) at Hospital Universiti Sains Malaysia
(USM). Nutritional assessments were performed such as anthropometrics measurement,
Subjective Global Assessment (SGA), and biochemical assessment. The patients’ medical
records and coded data were systematically reviewed to observe the documentation of
nutritional information and coding criteria based on the International Classification for
Diseases (ICD-10).
Results: The prevalence of malnutrition among the geriatric patients
was 35.4%. Proper documentation of required nutritional information was found in less
than 50% of the cases. None of the malnourished patients were documented and coded
with malnutrition diagnosis, despite being given nutritional interventions. The reasons
given for this omission were related to the lack of awareness (50%) and incomplete
medical documentation (50%). Further analysis revealed that uncoded diagnosis,
miscoding, missing, and unavailable codes for nutritional counselling and oral nutritional
supplementation were the main contributors to the incomplete records.
Conclusion:
The quality of clinical coding for malnourished geriatric patients in the hospital should
be improved. A structured assessment and standard documentation is recommended to
improve the quality of healthcare provision for malnourished geriatric patients.
The large population of human congestion in Makkah during Hajj would promote contagious diseases. Thus, the pilgrims require health care services that are efficient, effective, and high quality. The aim of this study is to determine the type of health-related problems among Malaysian pilgrims and to identify the health care services required by them during Hajj in Makkah. A cross-sectional study was conducted in which involved 379 Malaysian pilgrims in 2013/14234H. The survey was conducted after the pilgrims completed their Hajj ritual. A total of 400 sets of questionnaires were distributed at Abraj Janadriyah Hotel, which was occupied by more than 3000 Malaysian pilgrims. The response rate for this survey was 93%. Male respondents were constituted of 49.6% and female respondents were 50.4% with the mean age 52 years old. The underlying disease among Malaysian pilgrims during Hajj was respiratory disease (77.5%). The demands for health personnel (36.1%) and quality medication (34.7%) are among the important healthcare services required by the Malaysian pilgrims in Makkah. Respiratory disease is a common disease experienced by Malaysian pilgrims in Makkah. A certain types of services such as health personnel and quality medicine are strongly demanded by the Malaysian pilgrims to overcome their health problem during Hajj. This research provides a fundamental input to the health care providers, and also benefited the Hajj management authority to improve the quality of hajj management in future.
Hajj and ‘Umrah pilgrimage are a huge congregation performed by Muslims in Makkah, Saudi Arabia. The pilgrimage causes overcrowding and congestion that can lead to a high risk of health problems, especially when pilgrims have health problems. The purpose of this study is to assess the reliability of EQ-5D as a measuring tool to capture the health status of the pilgrims. Data collection was done during Ramadhan’s ‘Umrah in 2014. In this cross-sectional study, a total of 300 self-administered questionnaires attached with the EQ-5D-5L questions were distributed to Malaysian ‘Umrah pilgrims in Makkah and willing to participate in the study. The outcomes from the questionnaires and EQ-5D-5L were systematically analysed by using the SPSS software. The response rate was 64%, involved female (53%) and male (47%) respondents with the mean age of 55 years old. Hypertension (21.5%) and diabetes (16.2%) were the commonest underlying health problems suffered by the respondents in this study. Based on the EQ-5D outcomes, 53.3% of the respondents had no problem in their movement. However, the mean of EQ-VAS (visual analogue scale) presented 83 out of 100 scaling point, which means they might have problems in their health status. In addition, this study revealed, respondents with underlying illnesses had difficulty in some dimensions in EQ-5D. Hypertension was identified as the commonest underlying disease amongst the pilgrims. A contradicted outcome from the objective and subjective measuring scales of EQ-5D and EQ-VAS respectively; presented its sensitivity of EuroQol as a measuring tool for the quality of life among pilgrims living within such congestion.
Interstitial Lung Disease (ILD) encompasses a wide array of diseases that share some common radiologic characteristics. When diagnosing such diseases, radiologists can be affected by heavy workload and fatigue thus decreasing diagnostic accuracy. Automatic segmentation is the first step in implementing a Computer Aided Diagnosis (CAD) that will help radiologists to improve diagnostic accuracy thereby reducing manual interpretation. Automatic segmentation proposed uses an initial thresholding and morphology based segmentation coupled with feedback that detects large deviations with a corrective segmentation. This feedback is analogous to a control system which allows detection of abnormal or severe lung disease and provides a feedback to an online segmentation improving the overall performance of the system. This feedback system encompasses a texture paradigm. In this study we studied 48 males and 48 female patients consisting of 15 normal and 81 abnormal patients. A senior radiologist chose the five levels needed for ILD diagnosis. The results of segmentation were displayed by showing the comparison of the automated and ground truth boundaries (courtesy of ImgTracer™ 1.0, AtheroPoint™ LLC, Roseville, CA, USA). The left lung's performance of segmentation was 96.52% for Jaccard Index and 98.21% for Dice Similarity, 0.61 mm for Polyline Distance Metric (PDM), -1.15% for Relative Area Error and 4.09% Area Overlap Error. The right lung's performance of segmentation was 97.24% for Jaccard Index, 98.58% for Dice Similarity, 0.61 mm for PDM, -0.03% for Relative Area Error and 3.53% for Area Overlap Error. The segmentation overall has an overall similarity of 98.4%. The segmentation proposed is an accurate and fully automated system.
Human interaction has become almost mandatory for an automated medical system wishing to be accepted by clinical regulatory agencies such as Food and Drug Administration. Since this interaction causes variability in the gathered data, the inter-observer and intra-observer variability must be analyzed in order to validate the accuracy of the system. This study focuses on the variability from different observers that interact with an automated lung delineation system that relies on human interaction in the form of delineation of the lung borders. The database consists of High Resolution Computed Tomography (HRCT): 15 normal and 81 diseased patients' images taken retrospectively at five levels per patient. Three observers manually delineated the lungs borders independently and using software called ImgTracer™ (AtheroPoint™, Roseville, CA, USA) to delineate the lung boundaries in all five levels of 3-D lung volume. The three observers consisted of Observer-1: lesser experienced novice tracer who is a resident in radiology under the guidance of radiologist, whereas Observer-2 and Observer-3 are lung image scientists trained by lung radiologist and biomedical imaging scientist and experts. The inter-observer variability can be shown by comparing each observer's tracings to the automated delineation and also by comparing each manual tracing of the observers with one another. The normality of the tracings was tested using D'Agostino-Pearson test and all observers tracings showed a normal P-value higher than 0.05. The analysis of variance (ANOVA) test between three observers and automated showed a P-value higher than 0.89 and 0.81 for the right lung (RL) and left lung (LL), respectively. The performance of the automated system was evaluated using Dice Similarity Coefficient (DSC), Jaccard Index (JI) and Hausdorff (HD) Distance measures. Although, Observer-1 has lesser experience compared to Obsever-2 and Obsever-3, the Observer Deterioration Factor (ODF) shows that Observer-1 has less than 10% difference compared to the other two, which is under acceptable range as per our analysis. To compare between observers, this study used regression plots, Bland-Altman plots, two tailed T-test, Mann-Whiney, Chi-Squared tests which showed the following P-values for RL and LL: (i) Observer-1 and Observer-3 were: 0.55, 0.48, 0.29 for RL and 0.55, 0.59, 0.29 for LL; (ii) Observer-1 and Observer-2 were: 0.57, 0.50, 0.29 for RL and 0.54, 0.59, 0.29 for LL; (iii) Observer-2 and Observer-3 were: 0.98, 0.99, 0.29 for RL and 0.99, 0.99, 0.29 for LL. Further, CC and R-squared coefficients were computed between observers which came out to be 0.9 for RL and LL. All three observers however manage to show the feature that diseased lungs are smaller than normal lungs in terms of area.
Lung disease risk stratification is important for both diagnosis and treatment planning, particularly in biopsies and radiation therapy. Manual lung disease risk stratification is challenging because of: (a) large lung data sizes, (b) inter- and intra-observer variability of the lung delineation and (c) lack of feature amalgamation during machine learning paradigm. This paper presents a two stage CADx cascaded system consisting of: (a) semi-automated lung delineation subsystem (LDS) for lung region extraction in CT slices followed by (b) morphology-based lung tissue characterization, thereby addressing the above shortcomings. LDS primarily uses entropy-based region extraction while ML-based lung characterization is mainly based on an amalgamation of directional transforms such as Riesz and Gabor along with texture-based features comprising of 100 greyscale features using the K-fold cross-validation protocol (K = 2, 3, 5 and 10). The lung database consisted of 96 patients: 15 normal and 81 diseased. We use five high resolution Computed Tomography (HRCT) levels representing different anatomy landmarks where disease is commonly seen. We demonstrate the amalgamated ML stratification accuracy of 99.53%, an increase of 2% against the conventional non-amalgamation ML system that uses alone Riesz-based feature embedded with feature selection based on feature strength. The robustness of the system was determined based on the reliability and stability that showed a reliability index of 0.99 and the deviation in risk stratification accuracies less than 5%. Our CADx system shows 10% better performance when compared against the mean of five other prominent studies available in the current literature covering over one decade.