Computer-Based Medical Systems Research Articles

Usefulness of a medical interview support application for residents: A pilot study.

To conduct an appropriate medical interview, education and clinical experience are necessary. The usefulness of computer-based medical diagnostic support systems has been reported in medical interviewing. However, only a few reports have actually applied these systems and noted changes in the quality of the medical interview of residents. We aimed to examine how the use of a medical interview support application changes the medical interviews of residents. The study was conducted on 15 residents (with less than two years post-graduation) and ran from November 2020 to March 2021. Faculty members played the role of simulated patients in 20 cases, and the residents conducted the medical interviews. In 10 of the 20 cases, a medical interview support application was used. After the interview, the residents were asked to list up to 10 differential diseases; the interview was considered appropriate if it included the disease portrayed by the simulated patient. Furthermore, the duration of the medical interview, the number of questions asked, and changes in stress parameters were evaluated. The use of a medical interview support application increased the percentage of appropriate medical interviews. Considering the frequency, the use of a medical interview support application increased the rate of appropriate medical interviews in the rare disease group, as well as the number of questions and duration of the interviews. No stress reduction was observed. The medical interview support application may be a useful tool in identifying appropriate differential diseases during medical interviews by residents.

Statistik Data Administrasi Sensus Data Pasien Raat Inap Di RSE Medan

The daily census of inpatients is the number of inpatients starting at 00.00 to 24.00. In its implementation at RSE Medann involving nurses and data processing officers in the medical record section. However there are problems with medical record officers, completeness of data, effectiveness data processing, and timeliness of information presentation. The purpose of this study is to evaluate data management activities daily census of inpatients per day at the Medan RSE. This research is a descriptive study with quantitative and qualitative approaches, with the objects of daily census data management activities being inpatients, nurses, medical records officers, heads of medical records installations, and heads of inpatient rooms as subjects. Data were collected using questionnaires and checklists, analyzed by quantitative and qualitative analysis. From the results of the study, it is known that the inpatient data information in each room is incomplete, which will affect the effectiveness of data processing. It is concluded that this error occurs from two sides, namely the input side and the output side. On the input side, namely the education of medical record officers is not appropriate, data is incomplete on the length of care, age, debtor, diagnosis, recapitulation has not been completed, from the output side within one month the information cannot be known in the following month. It is hoped that there will be standard operating procedures and computer-based medical record systems.

Clinical Evaluation of 'Computer-Aided Diagnosis InNeuro-Otology (CADINO)' in Terms of Usefulness, Functionality and Effectiveness.

Computer-based medical diagnosis expert systems are considered both accurate and educationally helpful in most cases. Dizziness and vertigo are among the most common complaints however E.N.T. surgeons and neuro-otologists are not available in the peripheral areas. Computer-Aided Diagnosis In NeurOtology (CADINO) can be of immense value for these unprivileged dizzy patients of remote and rural areas. The study aimed to document the strength, weaknesses and capabilities of CADINO in terms of accuracy, educational usefulness, functionality and effectiveness. Design Hospital-based observational study of a diagnostic tool. Settings Otorhinolaryngology Department of a tertiary care medical college hospital. This prospective study was conducted in 70 patients, 24 simulated cases and 6 case reports from journals. The study included even the feedback of the clinicians before and after consultation. Eleven ENT residents, 14 ENT surgeons [8 teachers and 6 consultants] participated in the study. The overall diagnostic accuracy of the CADINO was found 86%. While in the patients, CADINO accuracy was found 84% approximately similar to faculties/consultants (80%) but it was significantly better than that of residents (57%). Most of the clinicians (84%), rated the CADINO consultation as being educationally helpful, and useful for patient management. CADINO was found very effective and convenient as it could be operated in the OPD simultaneously while evaluating the dizzy patients. CADINO provided accurate diagnostic suggestions. It was found improving patient safety and quality of care by enhancing knowledge and cognitive skills of the clinicians.

Wavelet Power Spectral Domain Functional Principal Component Analysis for Feature Extraction of Epileptic EEGs

Feature extraction plays an important role in machine learning for signal processing, particularly for low-dimensional data visualization and predictive analytics. Data from real-world complex systems are often high-dimensional, multi-scale, and non-stationary. Extracting key features of this type of data is challenging. This work proposes a novel approach to analyze Epileptic EEG signals using both wavelet power spectra and functional principal component analysis. We focus on how the feature extraction method can help improve the separation of signals in a low-dimensional feature subspace. By transforming EEG signals into wavelet power spectra, the functionality of signals is significantly enhanced. Furthermore, the power spectra transformation makes functional principal component analysis suitable for extracting key signal features. Therefore, we refer to this approach as a double feature extraction method since both wavelet transform and functional PCA are feature extractors. To demonstrate the applicability of the proposed method, we have tested it using a set of publicly available epileptic EEGs and patient-specific, multi-channel EEG signals, for both ictal signals and pre-ictal signals. The obtained results demonstrate that combining wavelet power spectra and functional principal component analysis is promising for feature extraction of epileptic EEGs. Therefore, they can be useful in computer-based medical systems for epilepsy diagnosis and epileptic seizure detection problems.

Impact of COVID-19 pandemic on clinical and surgical breast cancer management.

Impact of COVID-19 pandemic on clinical and surgical breast cancer management.

Development of an Experimental System "Computer-Aided Diagnosis in Neurotology (CADINO)" for Vertigo.

Computer-based medical diagnosis expert systems, when used by clinicians, familiar with the limitations of the system, were found both accurate and educationally helpful. Vertigo is one of the most common complaints of the patients and the specialists who manage these patients are not easily available. Diagnostic expert system can help clinicians including general practitioners in providing reasonable diagnostic suggestions while dealing with patients with vertigo/dizziness when the specialist doctors are not available. To provide an understanding about the method of making an expert system "Computer-Aided Diagnosis in Neurotology (CADINO)" for dizzy patients and highlight its potential uses. CADINO, developed during this original research project, is an expert computer program which is capable of making diagnoses in patients with vertigo. CADINO includes more than 100 causes of vertigo. It was developed in Microsoft Office using hyperlinks. CADINO has the potential of making diagnoses in patients with vertigo. It differs from most other programs for computer-assisted diagnosis in the generality of its approach and knowledge base. Specific deficiencies of CADINO include its inability to explain its thinking. CADINO can broaden the clinicians' scope and awareness regarding the findings which are important for making the differential diagnoses in patients with vertigo/dizziness. Though the CADINO on its face value seems to improve patient safety and quality of care by enhancing knowledge and cognitive skills of the clinicians, yet it remains a research tool and will need clinical evaluation in terms of diagnostic accuracy.

Multi-domain and multi-view networks model for clustering hospital admissions from the emergency department

As the healthcare industry continues to generate a massive amount of medical data, healthcare organizations integrate data-driven insights into their clinical and operational processes to enhance the quality of healthcare services. Our preliminary hospital clustering analysis (Albarakati and Obradovic, in The IEEE 29th international symposium on computer-based medical systems (CBMS), IEEE, 2017) studied hospitals monthly admission behavior for different diseases. Results showed consistent behavior when disease symptoms similarity is considered. This study extends our preliminary work to include other aspects of disease data and the fusion of different views of disease data. It is an original approach that tackles clustering complex networks using a combination of multi-view and multi-domain clustering models while imposing data on the clustering goal from both medical and non-medical domains simultaneously. The objective of the study is to determine the effect of disease networks on characterizing the underlying clustering structure of 145 disease-specific hospital networks, each consisting of up to 152 hospitals. This is achieved by extracting two different views of disease networks. One disease network view based on similarity of symptom profiles was extracted from a 20 million medical bibliographic literature records. Another disease network view based on monthly hospitalization distribution was extracted from over 7 million discharge records data obtained from the California State Inpatient Database for years 2009–2011. Patient admission records included both medical and sociodemographic information. These multiple views were analyzed separately and were also integrated in a joint model that combined the two views. It is shown that the fusion of multi-view disease networks of monthly hospitalization distributions explained the hidden common structure shared among multiple hospital-specific disease networks. The group homogeneity measures for obtained hospital clusters ranged between 33 and 60% with average close to 50%. However, integrating multiple views of disease networks extracted from different domains, i.e., from literature and medical databases, better revealed the underlying clustering structure of disease-specific hospital networks. The group homogeneity measures for this multi-domain setting ranged between 38 and 76% with average close to 60%.

Trends in computer-based medical systems

The 25th IEEE International Symposium on Computer-Based Medical Systems, held June 20--22, 2012 in Rome, Italy, has been a forum for discussing the latest developments in the field of computational medicine, biomedical informatics, and related disciplines. The Symposium has covered a broad range of issues in several areas with accepted submissions gathered into one general track, addressing general questions of computational biomedicine, and twelve special tracks, each focused on a particular related sub-theme. Three keynote speakers have presented an overview of recent research and developments in their particular subareas of mining clinical data for biomedical research, multivariate and multiscale analysis of biomedical signals, and oncology image analysis.

Computer-Based Medical Decision Support Systems - history, development, comparison of systems in selected European countries and the United States

Computer-Based Medical Decision Support Systems - history, development, comparison of systems in selected European countries and the United States

Medical Decision Support Systems in Africa

To present an overview of the current state of computer-based medical decision support systems in Africa in the areas of public health, patient care, and consumer support. Scientific and gray literature reviews complemented by expert interviews. Various domains of decision support are developed and deployed in Sub-Saharan Africa: public health information systems, clinical decision-support systems, and patient-centred decision-support systems. Until recently, most of these systems have been deployed by international organizations without a real ownership policy entrusted to the African stakeholders. Many of these endeavours have remained or ceased at the experimentation stage. The multiplicity of organizations has led to the deployment of fragmented systems causing serious interoperability problems. In addition to basic infrastructures, these studies also highlight the importance of good organization, training and support, as key to the success and sustainability of these decision support systems.

Preventing Stroke

Stroke, the third leading cause of death in the United States, is also the chief cause of neurological disability in the elderly. Lifetime risk of stroke at age 65 years is estimated to be 1 in 5 in women and 1 in 6 in men in the Framingham Study population.1 Despite improvements in management of the acute stroke and in rehabilitation poststroke, it is clear that prevention holds the key to decreasing the toll of cerebrovascular disease. In recent years, it has become increasingly clear that clinically inapparent strokes detected on magnetic resonance scan of the brain, so-called “silent” strokes, exert a discernible impact on brain function.2 Measurable reduction in cognitive performance that leads to vascular cognitive impairment, depressed mood, and impaired gait are consequences of these “silent” strokes.3 In addition, persons harboring these infarcts and a large burden of white-matter hyperintensities are predisposed to develop clinically apparent cerebral infarctions. Further, the presence of elevated levels of stroke risk factors exerts a measurable effect on brain structure and function, which results in reduced total cerebral volume and an increased presence and volume of white-matter hyperintensities on magnetic resonance scan of the brain, as well as reduced cognitive performance, particularly on tests of executive function.4 Article p 2157 In this issue of Circulation , Markus et al report on the results of the difficult task of …

A grammar of integrity constraints in medical documentation systems

An essential aspect for the utilization of medical data is their quality, thus a main feature of computer-based medical documentation systems should be to assist the user in complete and plausible data acquisition and maintenance. In this paper we define a grammar for modeling medical documentation systems to increase integrity and completeness of collected data, focusing attention on integrity constraints. An integrity constraint defines requirements that involved entities had to comply with. Furthermore it defines possibly implications in case of failure. The constraints presented in this paper are type constraint, length constraint, domain constraint, key constraint, quantity constraint, reference constraint, search constraint, result constraint, hierarchy constraint, and semantic constraint. Their grammar is declared using a schema in extensible markup language-format (XML-schema). The model introduced here can be used in computer-aided design and implementation of clinical documentation both minimizing effort and ensuring data quality, which was tested by an evaluation based on a specification of a registry for HIV-infected patients.

Guest Editorial Introduction to the Special Section on Mining Biomedical Data

Research in data mining, also known as knowledge discovery in databases, aims at developing intelligent tools to automatically search large volumes of data for patterns. Biomedicine is a data-intensive field that forms one of the most important application domains in data mining and knowledge discovery. This special section contains 15 extended papers, focusing on mining of biomedical data, presented at the 18th IEEE International Symposium on Computer-Based Medical Systems, IEEE CBMS 2005, which was held on June 23–24, 2005, at Trinity College, Dublin, Ireland. This editorial first defines the field and then introduces the papers.

Improving outcomes in radiology: Bringing computer-based decision support and education to the point of care 1

Many computer applications have been developed in radiology and other medical disciplines to help physicians make decisions. Artificial intelligence (AI)--an approach to computer-based manipulation of symbols to simulate human reasoning--forms the basis of many of these systems. This article's goals are to: acquaint the reader with the motivations and opportunities for computer-based medical decision support systems; identify AI techniques and applications in radiology decision making; assess the impact of these technologies; and consider new directions and opportunities for AI in radiology. Among the exciting new directions is the use of AI to integrate radiology reporting, online decision support, and just-in-time learning to provide useful information and continuing education that is embedded within a radiologist's daily workflow.

Cardiology patient pages. Innocent murmurs: a parent's guide.

Murmurs are sounds or noises made by blood as it flows through the heart and blood vessels of the body. Murmurs can be caused by abnormal flow patterns that occur when there are abnormalities of the heart valves, holes within the heart, or abnormal communications between blood vessels or between blood vessels and the heart. In infants and children, most murmurs originate through normal flow patterns, however, and are referred to as innocent, physiological, or normal murmurs. By innocent , we mean that there are no structural (anatomic) or functional (physiological) abnormalities of the heart and that the murmur comes from normal flow within a normal heart. Innocent is the preferred term because it strongly conveys that nothing is wrong, as opposed to the older term functional , which is not always clearly understood as being normal by parents and patients. Although murmurs termed innocent may be heard in virtually anyone, they are most often heard in childhood. Although innocent murmurs may be prevalent mostly in childhood, the presence of murmur does not imply the presence of structural heart disease. Congenital heart defects, which are responsible for most of the heart problems in children in the developed world, are structural abnormalities caused by errors in the development of the heart while the child is still in the womb. The heart starts out as a single tube and, during the first eight …

A proposed hierarchical fuzzy inference system for the diagnosis of arthritic diseases.

Development of computer-based medical inference systems is always confronted with some difficulties. In this paper, difficulties of designing an inference system for the diagnosis of arthritic diseases are described, including variations of disease manifestations under various situations and conditions. Furthermore, the need for a huge knowledge base would result in low efficiency of the inference system. We proposed a hierarchical model of the fuzzy inference system as a possible solution. With such a model, the diagnostic process is divided into two levels. The first level of the diagnosis reduces the scope of diagnosis to be processed by the second level. This will reduce the amount of input and mapping for the whole diagnostic process. Fuzzy relational theory is the core of this system and it is used in both levels to improve the accuracy.

Three decades of research on computer applications in health care: medical informatics support at the Agency for Healthcare Research and Quality.

The Agency for Healthcare Research and Quality and its predecessor organizations-collectively referred to here as AHRQ-have a productive history of funding research and development in the field of medical informatics, with grant investments since 1968 totaling $107 million. Many computerized interventions that are commonplace today, such as drug interaction alerts, had their genesis in early AHRQ initiatives. This review provides a historical perspective on AHRQ investment in medical informatics research. It shows that grants provided by AHRQ resulted in achievements that include advancing automation in the clinical laboratory and radiology, assisting in technology development (computer languages, software, and hardware), evaluating the effectiveness of computer-based medical information systems, facilitating the evolution of computer-aided decision making, promoting computer-initiated quality assurance programs, backing the formation and application of comprehensive data banks, enhancing the management of specific conditions such as HIV infection, and supporting health data coding and standards initiatives. Other federal agencies and private organizations have also supported research in medical informatics, some earlier and to a greater degree than AHRQ. The results and relative roles of these related efforts are beyond the scope of this review.

Evaluating the impact of health care information systems.

Evaluating the impact of computer-based medical information systems requires not only an understanding of computer technology but also an understanding of complex social and behavioral processes. This essay discusses the need for evaluation of health care information systems, a set of evaluation questions based on assumptions about the impact of technology on organizations, and recommendations for reducing barriers to the implementation of health care information systems.

CSMS'93---COMPUTER-BASED MEDICAL SYSTEMS

The Sixth Annual IEEE Symposium on Computer-Based Medical Systems (CBMS '93) was held in Ann Arbor, Michigan, on June 13--16, 1993. Much of the conference focused on how to develop medical systems which are safe and usable. Tutorials covered 1) software quality assurance, including improved processes for software development, 2) industrial design, which included processes for incorporating the needs of customers and users early in the design process, and 3) Quality Function Deployment, which is a structured approach to tracking the relationship between users' needs and technical measures of a product.

Enhancing inter-operability and data sharing in medical information systems

Clinical care generates an immense amount of patient data that has been archived and manipulated by computer-based information systems. Such computer-based medical record systems improved the accessibility of clinical information and made several studies of such information possible. Unfortunately, the care provider's task of retrieving, integrating, and interpreting only those portions of the patient's record that are relevant to a specific clinical problem is actually becoming increasingly difficult. This difficulty can be attributed primarily to the large variety of minimum data sets, the heterogeneous formats used to store the data, the heterogeneous data access methods and procedures, the varying granularity of access to data, the different rigid views of the data, and the lack of inter-operability among the information repositories of such data sets. Recognizing the aforementioned issues, we are engaged in a project to build a multi-database environment tailored for the interoperability of medical information systems. The main building blocks of such a system are a multi-disciplinary minimum data set and a catalogue for the support of interoperability and customization functions. In this paper, we report on the design approach used and describe the general architecture of the system.