Clinical Decision Support Requirements for Ventricular Tachycardia Diagnosis Within the Frameworks of Knowledge and Practice: Survey Study.

BACKGROUND: Ventricular tachycardia (VT) diagnosis is challenging due to the similarity between VT and some forms of supraventricular tachycardia, complexity of clinical manifestations, heterogeneity of underlying diseases, and potential for life-threatening hemodynamic instability. Clinical decision support systems (CDSSs) have emerged as promising tools to augment the diagnostic capabilities of cardiologists. However, a requirements analysis is acknowledged to be vital for the success of a CDSS, especially for complex clinical tasks such as VT diagnosis. OBJECTIVE: The aims of this study were to analyze the requirements for a VT diagnosis CDSS within the frameworks of knowledge and practice and to determine the clinical decision support (CDS) needs. METHODS: Our multidisciplinary team first conducted semistructured interviews with seven cardiologists related to the clinical challenges of VT and expected decision support. A questionnaire was designed by the multidisciplinary team based on the results of interviews. The questionnaire was divided into four sections: demographic information, knowledge assessment, practice assessment, and CDS needs. The practice section consisted of two simulated cases for a total score of 10 marks. Online questionnaires were disseminated to registered cardiologists across China from December 2022 to February 2023. The scores for the practice section were summarized as continuous variables, using the mean, median, and range. The knowledge and CDS needs sections were assessed using a 4-point Likert scale without a neutral option. Kruskal-Wallis tests were performed to investigate the relationship between scores and practice years or specialty. RESULTS: Of the 687 cardiologists who completed the questionnaire, 567 responses were eligible for further analysis. The results of the knowledge assessment showed that 383 cardiologists (68%) lacked knowledge in diagnostic evaluation. The overall average score of the practice assessment was 6.11 (SD 0.55); the etiological diagnosis section had the highest overall scores (mean 6.74, SD 1.75), whereas the diagnostic evaluation section had the lowest scores (mean 5.78, SD 1.19). A majority of cardiologists (344/567, 60.7%) reported the need for a CDSS. There was a significant difference in practice competency scores between general cardiologists and arrhythmia specialists (P=.02). CONCLUSIONS: There was a notable deficiency in the knowledge and practice of VT among Chinese cardiologists. Specific knowledge and practice support requirements were identified, which provide a foundation for further development and optimization of a CDSS. Moreover, it is important to consider clinicians' specialization levels and years of practice for effective and personalized support.

Requirements analysis for an AI-based clinical decision support system for general practitioners: a user-centered design process.

BACKGROUND: As the first point of contact for patients with health issues, general practitioners (GPs) are frequently confronted with patients presenting with non-specific symptoms of unclear origin. This can result in delayed, prolonged or false diagnoses. To accelerate and improve the diagnosis of diseases, clinical decision support systems would appear to be an appropriate tool. The objective of the project 'Smart physician portal for patients with unclear disease' (SATURN) is to employ a user-centered design process based on the requirements analysis presented in this paper to develop an artificial Intelligence (AI)-based diagnosis support system that specifically addresses the needs of German GPs. METHODS: Requirements analysis for a GP-specific diagnosis support system was conducted in an iterative process with five GPs. First, interviews were conducted to analyze current workflows and the use of digital applications in cases of diagnostic uncertainty (as-is situation). Second, we focused on collecting and prioritizing tasks to be performed by an ideal smart physician portal (to-be situation) in a workshop. We then developed a task model with corresponding user requirements. RESULTS: Numerous GP-specific user requirements were identified concerning the tasks and subtasks: performing data entry (open system, enter patient data), reviewing results (receiving and evaluating results), discussing results (with patients and colleagues), scheduling further diagnostic procedures, referring to specialists (select, contact, make appointments), and case closure. Suggested features particularly concerned the process of screening and assessing results: e.g., the system should focus more on atypical patterns of common diseases than on rare diseases only, display probabilities of differential diagnoses, ensure sources and results are transparent, and mark diagnoses that have already been ruled out. Moreover, establishing a means of using the platform to communicate with colleagues and transferring patient data directly from electronic patient records to the system was strongly recommended. CONCLUSIONS: Essential user requirements to be considered in the development and design of a diagnosis system for primary care could be derived from the analysis. They form the basis for mockup-development and system engineering.

Exploring Stakeholder Requirements to Enable Research and Development of Artificial Intelligence Algorithms in a Hospital-Based Generic Infrastructure: Results of a Multistep Mixed Methods Study.

BACKGROUND: Legal, controlled, and regulated access to high-quality data from academic hospitals currently poses a barrier to the development and testing of new artificial intelligence (AI) algorithms. To overcome this barrier, the German Federal Ministry of Health supports the "pAItient" (Protected Artificial Intelligence Innovation Environment for Patient Oriented Digital Health Solutions for developing, testing and evidence-based evaluation of clinical value) project, with the goal to establish an AI Innovation Environment at the Heidelberg University Hospital, Germany. It is designed as a proof-of-concept extension to the preexisting Medical Data Integration Center. OBJECTIVE: The first part of the pAItient project aims to explore stakeholders' requirements for developing AI in partnership with an academic hospital and granting AI experts access to anonymized personal health data. METHODS: We designed a multistep mixed methods approach. First, researchers and employees from stakeholder organizations were invited to participate in semistructured interviews. In the following step, questionnaires were developed based on the participants' answers and distributed among the stakeholders' organizations. In addition, patients and physicians were interviewed. RESULTS: The identified requirements covered a wide range and were conflicting sometimes. Relevant patient requirements included adequate provision of necessary information for data use, clear medical objective of the research and development activities, trustworthiness of the organization collecting the patient data, and data should not be reidentifiable. Requirements of AI researchers and developers encompassed contact with clinical users, an acceptable user interface (UI) for shared data platforms, stable connection to the planned infrastructure, relevant use cases, and assistance in dealing with data privacy regulations. In a next step, a requirements model was developed, which depicts the identified requirements in different layers. This developed model will be used to communicate stakeholder requirements within the pAItient project consortium. CONCLUSIONS: The study led to the identification of necessary requirements for the development, testing, and validation of AI applications within a hospital-based generic infrastructure. A requirements model was developed, which will inform the next steps in the development of an AI innovation environment at our institution. Results from our study replicate previous findings from other contexts and will add to the emerging discussion on the use of routine medical data for the development of AI applications. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): RR2-10.2196/42208.

Specifying requirements for collection and analysis of online user feedback.

According to data-driven Requirements Engineering (RE), explicit and implicit user feedback can be considered a relevant source of requirements, thus supporting requirements elicitation. However, limited attention has been paid so far to the role of online feedback in RE tasks, such as requirements validation, and on how to specify what online feedback to collect and analyse. We performed an action research study, together with a company that developed a platform for online training. This paper presents the design and execution of the study, and a discussion of its results. This study provides evidence about the need of practitioners to follow a simple but systematic approach for specifying requirements for data collection and analysis, at design time. Another outcome of this study is a method to tackle this task that leverages goal-oriented requirements modelling combined with Goal-Question-Metric. The applicability of the method has been explored on two industrial evaluations, while the perceived effectiveness, efficiency and acceptance have been assessed with practitioners through a dedicated survey.

Exploring Stakeholder Requirements to Enable the Research and Development of Artificial Intelligence Algorithms in a Hospital-Based Generic Infrastructure: Protocol for a Multistep Mixed Methods Study.

BACKGROUND: In recent years, research and developments in advancing artificial intelligence (AI) in health care and medicine have increased. High expectations surround the use of AI technologies, such as improvements for diagnosis and increases in the quality of care with reductions in health care costs. The successful development and testing of new AI algorithms require large amounts of high-quality data. Academic hospitals could provide the data needed for AI development, but granting legal, controlled, and regulated access to these data for developers and researchers is difficult. Therefore, the German Federal Ministry of Health supports the Protected Artificial Intelligence Innovation Environment for Patient-Oriented Digital Health Solutions for Developing, Testing, and Evidence-Based Evaluation of Clinical Value (pAItient) project, aiming to install the AI Innovation Environment at the Heidelberg University Hospital in Germany. The AI Innovation Environment was designed as a proof-of-concept extension of the already existing Medical Data Integration Center. It will establish a process to support every step of developing and testing AI-based technologies. OBJECTIVE: The first part of the pAItient project, as presented in this research protocol, aims to explore stakeholders' requirements for developing AI in partnership with an academic hospital and granting AI experts access to anonymized personal health data. METHODS: We planned a multistep mixed methods approach. In the first step, researchers and employees from stakeholder organizations were invited to participate in semistructured interviews. In the following step, questionnaires were developed based on the participants' answers and distributed among the stakeholders' organizations to quantify qualitative findings and discover important aspects that were not mentioned by the interviewees. The questionnaires will be analyzed descriptively. In addition, patients and physicians were interviewed as well. No survey questionnaires were developed for this second group of participants. The study was approved by the Ethics Committee of the Heidelberg University Hospital (approval number: S-241/2021). RESULTS: Data collection concluded in summer 2022. Data analysis is planned to start in fall 2022. We plan to publish the results in winter 2022 to 2023. CONCLUSIONS: The results of our study will help in shaping the AI Innovation Environment at our academic hospital according to stakeholder requirements. With this approach, in turn, we aim to shape an AI environment that is effective and is deemed acceptable by all parties. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): DERR1-10.2196/42208.

Mobile-Based Self-management Application Requirements for Patients With Gastric Cancer: Quantitative Descriptive Study of Specialist and Patient Perspectives.

BACKGROUND: Gastric cancer is one of the most common gastrointestinal cancers. Patients with gastric cancer experience disabilities and complications that lead to reduced quality of life. Empowering these patients by providing them with information and self-management skills can help reduce side effects and improve their quality of life. OBJECTIVE: The aim of this study was to identify the user requirements for developing a mobile-based self-management app to support patients with gastric cancer. METHODS: Data were analyzed using descriptive statistics and frequency distribution reports using IBM SPSS Statistics software. RESULTS: All of the data elements and functional requirements except "data recording times" and "weight changes in graphs" were identified as essential by clinical experts and patients. Among the functional requirements required in a gastric cancer self-management app, the capabilities related to informing, announcing warnings, and reminders are included. In the demographic data section, most patients (14/26, 53%) did not comment on the importance of recording data such as name, surname, and place of residence, and the demographic data section was met with less agreement from patients than clinicians. CONCLUSIONS: Applying the requirements mentioned in this study can improve the self-management of patients with gastric cancer. Such apps can play an important role in empowering patients and improving their quality of life. However, the apps need to be designed and implemented to see how they can meet users' requirements.

A Smartphone Remote Monitoring App to Follow Up Colorectal Cancer Survivors: Requirement Analysis.

BACKGROUND: Colorectal cancer survivors face multiple challenges after discharge. eHealth may potentially support them by providing tools such as smartphone apps. They have lots of capabilities to exchange information and could be used for remote monitoring of these patients. OBJECTIVE: In this study, we addressed the required features for apps designed to follow up colorectal cancer patients based on survivors' and clinical experts' views. METHODS: A mixed methods study was conducted. Features of related apps were extracted through the literature; the features were categorized, and then, they were modified. A questionnaire was designed containing the features listed and prioritized based on the MoSCoW (Must have, Should have, Could have, Won't have) technique and an open question for each category. The link to the questionnaire was shared among clinical experts in Iran. The answers were analyzed using the content validity ratio (CVR), and based on the value of this measure, the minimum feature set of a monitoring app to follow up patients with colorectal cancer was addressed. In addition, a telephone interview with colorectal cancer survivors was conducted to collect their viewpoints regarding a remote monitoring system for colorectal cancer cases. RESULTS: The questionnaire contained 10 sections evaluating 9 categories of features. The questionnaire was completed by 18 experts. The minimum set of features in the app was identified as patient information registration, sign and symptom monitoring, education, reminders, and patient evaluation (0.42 < CVR < 0.85). Features including physical activity, personalized advice, and social network did not achieve the minimum score (-0.11 < CVR < 0.39). We interviewed 9 colorectal cancer survivors. Information registration, sign and symptom monitoring, education, and personalized advice were the features with high priority from the survivors' perspectives. Scheduling, shopping, and financial support features were emphasized by survivors in the interview. CONCLUSIONS: The requirement set could be used to design an app for the targeted population or patients affected by other cancers. As the views from both survivors and clinical experts were considered in this study, the remote system may more adequately fulfill the need for follow-up of survivors. This eases the patients' and health care providers' communication and interaction.

Development of a Dashboard for Rare Diseases - A Technical Case Report.

About 30 million people suffer from a rare disease in Europe. Those affected face a variety of problems. These include the lack of information and difficult access to scientific knowledge for physicians. For a higher visibility of rare diseases and high-quality research, effective documentation and use of data are essential. The aim of this work is to optimize the processing, use and accessibility of data on rare diseases and thus increase the added value from existing information. While dashboards are already being used to visualize clinical data, it is unclear what requirements are prevalent for rare diseases and how these can be implemented with available development tools so that a highly accepted dashboard can be designed. For this purpose, based on an analysis of the current situation and a requirements analysis, a prototype dashboard for the visualization of up-to-date key figures on rare diseases was developed at the University Hospital Carl Gustav Carus in Dresden. The development was based on the user-centered design process in order to achieve a high-level user-friendliness. The requirements analysis identified parameters that stakeholders wanted to see, focusing primarily on statistical analyses. The dashboard handles the automated calculation of statistics as well as their preparation and provision. The evaluations showed the prototypical dashboard would be considered valuable and used by potential users. This work demonstrates that stakeholders are interested in access to prepared information and exemplifies a way to implement it. The dashboard can increase the usage of existing information in terms of a higher accessibility and thus improve the knowledge about rare diseases.

A System to Support Diverse Social Program Management.

BACKGROUND: Social programs are services provided by governments, nonprofits, and other organizations to help improve the health and well-being of individuals, families, and communities. Social programs aim to deliver services effectively and efficiently, but they are challenged by information silos, limited resources, and the need to deliver frequently changing mandated benefits. OBJECTIVE: We aim to explore how an information system designed for social programs helps deliver services effectively and efficiently across diverse programs. METHODS: This viewpoint describes the configurable and modular architecture of Social Program Management (SPM), a system to support efficient and effective delivery of services through a wide range of social programs and lessons learned from implementing SPM across diverse settings. We explored usage data to inform the engagement and impact of SPM on the efficient and effective delivery of services. RESULTS: The features and functionalities of SPM seem to support the goals of social programs. We found that SPM provides fundamental management processes and configurable program-specific components to support social program administration; has been used by more than 280,000 caseworkers serving more than 30 million people in 13 countries; contains features designed to meet specific user requirements; supports secure information sharing and collaboration through data standardization and aggregation; and offers configurability and flexibility, which are important for digital transformation and organizational change. CONCLUSIONS: SPM is a user-centered, configurable, and flexible system for managing social program workflows.

Visualization of Similar Patients in a Clinical Decision Support System for Rare Diseases - A Focus Group Study.

The diagnosis of patients with rare diseases is often delayed. A Clinical Decision Support System using similarity analysis of patient-based data may have the potential to support the diagnosis of patients with rare diseases. This qualitative study has the objective to investigate how the result of a patient similarity analysis should be presented to a physician to enable diagnosis support. We conducted a focus group with physicians practicing in rare diseases as well as medical informatics researchers. To prepare the focus group, a literature search was performed to check the current state of research regarding visualization of similar patients. We then created software-mockups for the presentation of these visualization methods for the discussion within the focus group. Two persons took independently field notes for data collection of the focus group. A questionnaire was distributed to the participants to rate the visualization methods. The results show that four visualization methods are promising for the visualization of similar patients: "Patient on demand table", "Criteria selection", "Time-Series chart" and "Patient timeline. "Patient on demand table" shows a direct comparison of patient characteristics, whereas "Criteria selection" allows the selection of different patient criteria to get deeper insights into the data. The "Time-Series chart" shows the time course of clinical parameters (e.g. blood pressure) whereas a "Patient timeline" indicates which time events exist for a patient (e.g. several symptoms on different dates). In the future, we will develop a software-prototype of the Clinical Decision Support System to include the visualization methods and evaluate the clinical usage.

Interactive, Visual Simulation of a Spatio-Temporal Model of Gas Exchange in the Human Alveolus.

In interdisciplinary fields such as systems biology, good communication between experimentalists and theorists is crucial for the success of a project. Theoretical modeling in physiology usually describes complex systems with many interdependencies. On one hand, these models have to be grounded on experimental data. On the other hand, experimenters must be able to understand the interdependent complexities of the theoretical model in order to interpret the model's results in the physiological context. We promote interactive, visual simulations as an engaging way to present theoretical models in physiology and to make complex processes tangible. Based on a requirements analysis, we developed a new model for gas exchange in the human alveolus in combination with an interactive simulation software named Alvin. Alvin exceeds the current standard with its spatio-temporal resolution and a combination of visual and quantitative feedback. In Alvin, the course of the simulation can be traced in a three-dimensional rendering of an alveolus and dynamic plots. The user can interact by configuring essential model parameters. Alvin allows to run and compare multiple simulation instances simultaneously. We exemplified the use of Alvin for research by identifying unknown dependencies in published experimental data. Employing a detailed questionnaire, we showed the benefits of Alvin for education. We postulate that interactive, visual simulation of theoretical models, as we have implemented with Alvin on respiratory processes in the alveolus, can be of great help for communication between specialists and thereby advancing research.

Requirements Analysis and Specification for a Molecular Tumor Board Platform Based on cBioPortal.

Clinicians in molecular tumor boards (MTB) are confronted with a growing amount of genetic high-throughput sequencing data. Today, at German university hospitals, these data are usually handled in complex spreadsheets from which clinicians have to obtain the necessary information. The aim of this work was to gather a comprehensive list of requirements to be met by cBioPortal to support processes in MTBs according to clinical needs. Therefore, oncology experts at nine German university hospitals were surveyed in two rounds of interviews. To generate an interview guideline a scoping review was conducted. For visual support in the second round, screenshot mockups illustrating the requirements from the first round were created. Requirements that cBioPortal already meets were skipped during the second round. In the end, 24 requirements with sometimes several conceivable options were identified and 54 screenshot mockups were created. Some of the identified requirements have already been suggested to the community by other users or are currently being implemented in cBioPortal. This shows, that the results are in line with the needs expressed by various disciplines. According to our findings, cBioPortal has the potential to significantly improve the processes and analyses of an MTB after the implementation of the identified requirements.

The SEOSS 33 dataset - Requirements, bug reports, code history, and trace links for entire projects.

This paper provides a systematically retrieved dataset consisting of 33 open-source software projects containing a large number of typed artifacts and trace links between them. The artifacts stem from the projects' issue tracking system and source version control system to enable their joint analysis. Enriched with additional metadata, such as time stamps, release versions, component information, and developer comments, the dataset is highly suitable for empirical research, e.g., in requirements and software traceability analysis, software evolution, bug and feature localization, and stakeholder collaboration. It can stimulate new research directions, facilitate the replication of existing studies, and act as benchmark for the comparison of competing approaches. The data is hosted on Harvard Dataverse using DOI 10.7910/DVN/PDDZ4Q accessible via https://bit.ly/2wukCHc.

Needs Assessment and System Design of Nursing Information Operation Simulation Learning System.

This study explored needs and content of nursing information simulation learning system for different nursing staff levels. A questionnaire survey method was conducted in this study. A total of 300 clinical instructors and new nurses were recruited from a medical center through purposive sampling. The results show the core framework of a learning system includes five objectives. The mean scores of the five objectives of clinical instructors all were higher than the new nurses. These findings can serve as a reference for the design framework and system functions, and will function as an important learning resource.

Medical doctors' job specification analysis: A qualitative inquiry.

Purpose: A qualitative inquiry was conducted to investigate the qualification requirements of medical doctors in different professional fields and from different perspectives. The inquiry was part of an empirical workplace analysis. Methods: Seventy-four structured interviews were conducted and analyzed to examine critical incidents and behaviors of medical doctors working in different professional fields (clinical theory, clinical practice, practitioner) and disciplines, and from three different perspectives (medical doctors, non-medical staff, and patients). In addition, the National Competency-based Catalogue of Learning Objectives for Medical Education (Nationaler Kompetenzbasierter Lernzielkatalog Medizin / NKLM) was used. Results: The results revealed eleven relevant competencies, which could be categorized into three superordinate competence clusters: interpersonal, work-related, and self-related. The perspectives of medical doctors and non-medical staff included all eleven competencies. However, the perspective of patients did not include one interpersonal and two self-related competencies. Nearly all of the critical behaviors mentioned are included in the NKLM. However, the NKLM also includes behaviors that were not mentioned in the interviews. Conclusions: The behavior-oriented interviews resulted in a requirement profile that is very similar in structure to other competency models in occupational contexts. Comparisons of the different perspectives predominantly revealed similarities. However, the patient perspective also revealed interesting differences compared to the perspectives of medical doctors and non-medical staff. The behavior-related results of the interviews can be directly used for the development of exercises in selection and personnel development contexts and for potential appraisals specific to different medical disciplines. In future steps, the results of this initial qualitative step are to be replicated and extended using quantitative studies and a representative sample. The main overall objective is the definition of relevant competencies both for the selection and development of medical students and for the design of potential appraisals as part of personnel development programs in different medical disciplines.

Performance Evaluation and Analysis for Gravity Matching Aided Navigation.

Simulation tests were accomplished in this paper to evaluate the performance of gravity matching aided navigation (GMAN). Four essential factors were focused in this study to quantitatively evaluate the performance: gravity database (DB) resolution, fitting degree of gravity measurements, number of samples in matching, and gravity changes in the matching area. Marine gravity anomaly DB derived from satellite altimetry was employed. Actual dynamic gravimetry accuracy and operating conditions were referenced to design the simulation parameters. The results verified that the improvement of DB resolution, gravimetry accuracy, number of measurement samples, or gravity changes in the matching area generally led to higher positioning accuracies, while the effects of them were different and interrelated. Moreover, three typical positioning accuracy targets of GMAN were proposed, and the conditions to achieve these targets were concluded based on the analysis of several different system requirements. Finally, various approaches were provided to improve the positioning accuracy of GMAN.

Electronic Alerts Improve Immunization Rates in Two-month-old Premature Infants Hospitalized in the Neonatal Intensive Care Unit.

OBJECTIVE: To determine if an electronic alert improves 2 month immunization rates in infants remaining hospitalized in the neonatal intensive care unit. METHODS: Institutional Review Board-approved retrospective chart review of 261 infants with birth weights <2 kg and still hospitalized at ≥ 58 days. Charts were reviewed between 2009 and 2013, before and after the 2011 electronic alert was instituted in the electronic medical record from days 56 to 67 to remind providers that immunizations were due. Order and administration dates of two-month vaccine components (Diphtheria, Haemophilus influenza B, Hepatitis B, Pertussis, Pneumococcal, Polio, Tetanus) were determined, and infants were considered fully immunized, partially immunized, or unimmunized by day 90 or discharge, whichever came first. RESULTS: After the alert, the timing of vaccine orders decreased from day 67 to day 61 (p<0.0001) and vaccine administration decreased from day 71 to day 64 (p<0.0001). Missing vaccine orders decreased from 14% [17/121] to 3% [4/140] (p=0.001) with missing administrations decreasing from 21% [26/121] to 4% [6/140] (p<0.0001). Fully immunized rates increased from 71% [86/121] to 94% [132/140] (p<0.0001). CONCLUSIONS: A significant improvement in immunization rates in two-month-old infants in the neonatal intensive care unit occurred by 90 days after implementing an alert in the electronic medical record.

[Need for youth welfare services following child and adolescent psychiatry treatment- a descriptive analysis]. / Jugendhilfebedarf nach (teil-)stationärer kinder- und jugendpsychiatrischer Behandlung.

OBJECTIVE: Cooperation between health and youth welfare services plays a prominent role in the psychosocial healthcare of children and adolescents with mental disorders. The need analysis presented here measured how many children and adolescents engaged youth welfare services before and after inpatient or daycare treatment. METHOD: The number of completed treatments from 36 Bavarian daycare and inpatient child and adolescent psychiatric clinics were recorded over a period of 6 months. Besides sociodemographic and diagnostic data, information was collected about indicated and subsequently realized measures of youth welfare following clinical and day care treatment. RESULTS: 33 %of the clinically treated children and adolescents participated in a youth-welfare measure after psychiatric treatment. In the run-up to clinical treatment, 38 % of the treated children and adolescents had engaged services of youth welfare. Half of the children and adolescents contacted both youth welfare services as well as child and adolescent psychiatric services. The residential setting of youth care is of major importance to the cooperative treatment. CONCLUSIONS: The provision of both youth welfare services and child and adolescent psychiatry treatment is usually not a matter of going from one system to another, but rather consists of different constellations of complex processes of mutual assistance. The goal is to carry out a structured survey of the common clientele and to develop a crossover system and common care structures in order to improve the overall cooperation.

Performance Evaluation and Requirements Assessment for Gravity Gradient Referenced Navigation.

In this study, simulation tests for gravity gradient referenced navigation (GGRN) are conducted to verify the effects of various factors such as database (DB) and sensor errors, flight altitude, DB resolution, initial errors, and measurement update rates on the navigation performance. Based on the simulation results, requirements for GGRN are established for position determination with certain target accuracies. It is found that DB and sensor errors and flight altitude have strong effects on the navigation performance. In particular, a DB and sensor with accuracies of 0.1 E and 0.01 E, respectively, are required to determine the position more accurately than or at a level similar to the navigation performance of terrain referenced navigation (TRN). In most cases, the horizontal position error of GGRN is less than 100 m. However, the navigation performance of GGRN is similar to or worse than that of a pure inertial navigation system when the DB and sensor errors are 3 E or 5 E each and the flight altitude is 3000 m. Considering that the accuracy of currently available gradiometers is about 3 E or 5 E, GGRN does not show much advantage over TRN at present. However, GGRN is expected to exhibit much better performance in the near future when accurate DBs and gravity gradiometer are available.

The Analysis Based on Quality Function Deployment (QFD) Method of Clinical Trial Management System Requirements / 中华医学科研管理杂志

It is imperative to apply information technology in the area of management of clinical research so as to ensure the quality of clinical trials and to improve management efficiency.In this study,the based analysis method was the quality function deployment (QFD).This methodology is used to analysis the clinical trials management information system on a hospital directly under the Ministry of Health.It ensured user participation,lay a solid foundation for software engineers on system design.