Search

As hospital information systems are complex and the requirements for interoperability grow with the increasing networking in healthcare, careful planning becomes more and more necessary. The use of standards as described in IHE profiles, for example, are an important prerequisite for enabling interoperability. Enterprise Architecture Planning (EAP) methods should support this, but none of the currently available EAP methods offers the option of using IHE profiles. The 3LGM2IHE project wants to close this gap and implement the support of IHE profiles in the 3LGM 2 tool. This paper describes how requirements for this tool were determined and presents the results.

Aims: To compare the characteristics of scientific publications performed in hospitals that used with those that didn't use an obstetric electronic health record (EHR)., Methods: This study included two reviews (A and B). Review A was an exploratory analysis of all 100 abstracts presented at the Scientific Meeting of the Portuguese Society of Obstetrics and Maternal-Fetal Medicine, in November 2017. Review B was a systematic review of studies in obstetrics, performed in Portugal and published between 2016-18 and indexed in PubMed. In both reviews, the included papers/abstracts were divided into two groups: from hospitals that used ObsCare ® (ObsCare group) and from hospitals without a specific obstetric EHR or that didn't use ObsCare (sObsCare group)., Results: In both reviews, the sample size was significantly higher in hospitals from the ObsCare group. In review B, the length of the study period was also significantly longer in ObsCare group; no significant difference was found in review A., Conclusion: Publications from hospitals that used an obstetric specific EHR (ObsCare), included a higher number of patients and longer study periods.

Background: The use of medical data for research purposes requires an informed consent of the patient that is compliant with the EU General Data Protection Regulation. In the context of multi-centre research initiatives and a multitude of clinical and epidemiological studies scalable and automatable measures for digital consent management are required. Modular form, structure, and contents render a patient's consent reusable for varying project settings in order to effectively manage and minimise organisational and technical efforts., Results: Within the DFG-funded project "MAGIC" (Grant Number HO 1937/5-1) the digital consent management service tool gICS was enhanced to comply with the recommendations published in the TMF data protection guideline for medical research. In addition, a structured exchange format for modular consent templates considering established standards and formats in the area of digital informed consent management was designed. Using the new FHIR standard and the HAPI FHIR library, the first version for an exchange format and necessary import-/export-functionalities were successfully implemented., Conclusions: The proposed exchange format is a "work in progress". It represents a starting point for current discussions concerning digital consent management. It also attempts to improve interoperability between different approaches within the wider IHE-/HL7-/FHIR community. Independent of the exchange format, providing the possibility to export, modify and import templates for consents and withdrawals to be reused in similar clinical and epidemiological studies is an essential precondition for the sustainable operation of digital consent management.

Background: Big data analytics promise insights into healthcare processes and management, improving outcomes while reducing costs. However, data quality is a major challenge for reliable results. Business process discovery techniques and an associated data model were used to develop data management tool, ICU-DaMa, for extracting variables essential for overseeing the quality of care in the intensive care unit (ICU)., Objective: To determine the feasibility of using ICU-DaMa to automatically extract variables for the minimum dataset and ICU quality indicators from the clinical information system (CIS)., Methods: The Wilcoxon signed-rank test and Fisher's exact test were used to compare the values extracted from the CIS with ICU-DaMa for 25 variables from all patients attended in a polyvalent ICU during a two-month period against the gold standard of values manually extracted by two trained physicians. Discrepancies with the gold standard were classified into plausibility, conformance, and completeness errors., Results: Data from 149 patients were included. Although there were no significant differences between the automatic method and the manual method, we detected differences in values for five variables, including one plausibility error and two conformance and completeness errors. Plausibility: 1) Sex, ICU-DaMa incorrectly classified one male patient as female (error generated by the Hospital's Admissions Department). Conformance: 2) Reason for isolation, ICU-DaMa failed to detect a human error in which a professional misclassified a patient's isolation. 3) Brain death, ICU-DaMa failed to detect another human error in which a professional likely entered two mutually exclusive values related to the death of the patient (brain death and controlled donation after circulatory death). Completeness: 4) Destination at ICU discharge, ICU-DaMa incorrectly classified two patients due to a professional failing to fill out the patient discharge form when thepatients died. 5) Length of continuous renal replacement therapy, data were missing for one patient because the CRRT device was not connected to the CIS., Conclusions: Automatic generation of minimum dataset and ICU quality indicators using ICU-DaMa is feasible. The discrepancies were identified and can be corrected by improving CIS ergonomics, training healthcare professionals in the culture of the quality of information, and using tools for detecting and correcting data errors., (Copyright © 2018 Elsevier B.V. All rights reserved.)

Introduction The new General Data Protection Regulation (GDPR) compels health care institutions and their software providers to properly document all personal data processing and provide clear evidence that their systems are inline with the GDPR. All applications involved in personal data processing should therefore produce meaningful event logs that can later be used for the effective auditing of complex processes. Aim This paper aims to describe and evaluate HS.Register, a system created to collect and securely manage at scale audit logs and data produced by a large number of systems. Methods HS.Register creates a single audit log by collecting and aggregating all kinds of meaningful event logs and data (e.g. ActiveDirectory, syslog, log4j, web server logs, REST, SOAP and HL7 messages). It also includes specially built dashboards for easy auditing and monitoring of complex processes, crossing different systems in an integrated way, as well as providing tools for helping on the auditing and on the diagnostics of difficult problems, using a simple web application. HS.Register is currently installed at five large Portuguese Hospitals and is composed of the following open-source components: HAproxy, RabbitMQ, Elasticsearch, Logstash and Kibana. Results HS.Register currently collects and analyses an average of 93 million events per week and it is being used to document and audit HL7 communications. Discussion Auditing tools like HS.Register are likely to become mandatory in the near future to allow for traceability and detailed auditing for GDPR compliance.

Often in clinical IT departments, many different methods and IT systems are used for task-tracking and project organization. Based on managers' personal preferences and knowledge about project management methods, tools differ from team to team and even from employee to employee. This causes communication problems, especially when tasks need to be done in cooperation with different teams. Monitoring tasks and resources becomes impossible: there are no defined deliverables, which prevents reliable deadlines. Because of these problems, we implemented task-tracking software which is now in use across all seven teams at the University Hospital Erlangen. Over a period of seven months, a working group defined types of tasks (project, routine task, etc.), workflows, and views to monitor the tasks of the 7 divisions, 20 teams and 340 different IT services. The software has been in use since December 2016.

We have previously developed a Patient Data Management System for Intensive Care based on Open Source Software. The aim of this work was to adapt this software to use in Emergency Departments in low resource environments. The new software includes facilities for utilization of the South African Triage Scale and prediction of mortality based on independent predictive factors derived from data from the Tabarre Emergency Trauma Center in Port au Prince, Haiti.

Objective: (1) To define features and data items of a Patient Recruitment System (PRS); (2) to design a generic software architecture of such a system covering the requirements; (3) to identify implementation options available within different Hospital Information System (HIS) environments; (4) to implement five PRS following the architecture and utilizing the implementation options as proof of concept., Methods: Existing PRS were reviewed and interviews with users and developers conducted. All reported PRS features were collected and prioritized according to their published success and user's request. Common feature sets were combined into software modules of a generic software architecture. Data items to process and transfer were identified for each of the modules. Each site collected implementation options available within their respective HIS environment for each module, provided a prototypical implementation based on available implementation possibilities and supported the patient recruitment of a clinical trial as a proof of concept., Results: 24 commonly reported and requested features of a PRS were identified, 13 of them prioritized as being mandatory. A UML version 2 based software architecture containing 5 software modules covering these features was developed. 13 data item groups processed by the modules, thus required to be available electronically, have been identified. Several implementation options could be identified for each module, most of them being available at multiple sites. Utilizing available tools, a PRS could be implemented in each of the five participating German university hospitals., Conclusion: A set of required features and data items of a PRS has been described for the first time. The software architecture covers all features in a clear, well-defined way. The variety of implementation options and the prototypes show that it is possible to implement the given architecture in different HIS environments, thus enabling more sites to successfully support patient recruitment in clinical trials.

A Hospital Information Systems (HIS) have turned a hospital into a gigantic computer with huge computational power, huge storage and wired/wireless local area network. On the other hand, a modern medical device, such as echograph, is a computer system with several functional units connected by an internal network named a bus. Therefore, we can embed such a medical device into the HIS by simply replacing the bus with the local area network. This paper designed and developed two embedded systems, a ubiquitous echograph system and a networked digital camera. Evaluations of the developed systems clearly show that the proposed approach, embedding existing clinical systems into HIS, drastically changes productivity in the clinical field. Once a clinical system becomes a pluggable unit for a gigantic computer system, HIS, the combination of multiple embedded systems with application software designed under deep consideration about clinical processes may lead to the emergence of disruptive innovation in the clinical field.

Background: The ONCO-i2b2 platform is a bioinformatics tool designed to integrate clinical and research data and support translational research in oncology. It is implemented by the University of Pavia and the IRCCS Fondazione Maugeri hospital (FSM), and grounded on the software developed by the Informatics for Integrating Biology and the Bedside (i2b2) research center. I2b2 has delivered an open source suite based on a data warehouse, which is efficiently interrogated to find sets of interesting patients through a query tool interface., Methods: Onco-i2b2 integrates data coming from multiple sources and allows the users to jointly query them. I2b2 data are then stored in a data warehouse, where facts are hierarchically structured as ontologies. Onco-i2b2 gathers data from the FSM pathology unit (PU) database and from the hospital biobank and merges them with the clinical information from the hospital information system. Our main effort was to provide a robust integrated research environment, giving a particular emphasis to the integration process and facing different challenges, consecutively listed: biospecimen samples privacy and anonymization; synchronization of the biobank database with the i2b2 data warehouse through a series of Extract, Transform, Load (ETL) operations; development and integration of a Natural Language Processing (NLP) module, to retrieve coded information, such as SNOMED terms and malignant tumors (TNM) classifications, and clinical tests results from unstructured medical records. Furthermore, we have developed an internal SNOMED ontology rested on the NCBO BioPortal web services., Results: Onco-i2b2 manages data of more than 6,500 patients with breast cancer diagnosis collected between 2001 and 2011 (over 390 of them have at least one biological sample in the cancer biobank), more than 47,000 visits and 96,000 observations over 960 medical concepts., Conclusions: Onco-i2b2 is a concrete example of how integrated Information and Communication Technology architecture can be implemented to support translational research. The next steps of our project will involve the extension of its capabilities by implementing new plug-in devoted to bioinformatics data analysis as well as a temporal query module.

With the advent of 4G and other long-term evolution (LTE) wireless networks, the traditional boundaries of patient record propagation are diminishing as networking technologies extend the reach of hospital infrastructure and provide on-demand mobile access to medical multimedia data. However, due to legacy and proprietary software, storage and decommissioning costs, and the price of centralization and redevelopment, it remains complex, expensive, and often unfeasible for hospitals to deploy their infrastructure for online and mobile use. This paper proposes the SparkMed data integration framework for mobile healthcare (m-Health), which significantly benefits from the enhanced network capabilities of LTE wireless technologies, by enabling a wide range of heterogeneous medical software and database systems (such as the picture archiving and communication systems, hospital information system, and reporting systems) to be dynamically integrated into a cloud-like peer-to-peer multimedia data store. Our framework allows medical data applications to share data with mobile hosts over a wireless network (such as WiFi and 3G), by binding to existing software systems and deploying them as m-Health applications. SparkMed integrates techniques from multimedia streaming, rich Internet applications (RIA), and remote procedure call (RPC) frameworks to construct a Self-managing, Pervasive Automated netwoRK for Medical Enterprise Data (SparkMed). Further, it is resilient to failure, and able to use mobile and handheld devices to maintain its network, even in the absence of dedicated server devices. We have developed a prototype of the SparkMed framework for evaluation on a radiological workflow simulation, which uses SparkMed to deploy a radiological image viewer as an m-Health application for telemedical use by radiologists and stakeholders. We have evaluated our prototype using ten devices over WiFi and 3G, verifying that our framework meets its two main objectives: 1) interactive delivery of medical multimedia data to mobile devices; and 2) attaching to non-networked medical software processes without significantly impacting their performance. Consistent response times of under 500 ms and graphical frame rates of over 5 frames per second were observed under intended usage conditions. Further, overhead measurements displayed linear scalability and low resource requirements.

A powerful hospital information system (HIS) contains information about diagnostic and disease data of all patients in a hospital. In the university hospital in Frankfurt / Main Germany more than 80% of all wards are completely "paperless". But the identification of patients for clinical trials is very difficult. We developed a special query and reporting tool in the HIS to recognize patients with a specific disease and with basic inclusion and exclusion criteria for a specific clinical trial. With the help of this query tool it is possible to increase significantly the patient numbers for clinical trials in a short period.

This paper describes the RAC installation process and some notes. Comparing with HA scheme, RAC Cluster technology can greatly improve the system performance.

In many healthcare organizations, comparative effectiveness research and quality improvement (QI) investigations are hampered by a lack of access to data created as a byproduct of patient care. Data collection often hinges upon either manual chart review or ad hoc requests to technical experts who support legacy clinical systems. In order to facilitate this needed capacity for data exploration at our institution (Duke University Health System), we have designed and deployed a robust Web application for cohort identification and data extraction--the Duke Enterprise Data Unified Content Explorer (DEDUCE). DEDUCE is envisioned as a simple, web-based environment that allows investigators access to administrative, financial, and clinical information generated during patient care. By using business intelligence tools to create a view into Duke Medicine's enterprise data warehouse, DEDUCE provides a Guided Query functionality using a wizard-like interface that lets users filter through millions of clinical records, explore aggregate reports, and, export extracts. Researchers and QI specialists can obtain detailed patient- and observation-level extracts without needing to understand structured query language or the underlying database model. Developers designing such tools must devote sufficient training and develop application safeguards to ensure that patient-centered clinical researchers understand when observation-level extracts should be used. This may mitigate the risk of data being misunderstood and consequently used in an improper fashion., (Copyright © 2010 Elsevier Inc. All rights reserved.)

Working interoperability not only requires harmonized system's architectures, but also the same interpretation of technical specifications in order to guide the development processes. But sometimes a specification has not made the underlying model explicit which would enable a coherent understanding. This paper analyses the structures of the HL7 Version 2.x communication standard's family and presents an UML class diagram for it.

HL7 Version 3 offers a semantically robust method for healthcare interoperability but has been criticized as overly complex to implement. This paper reviews initiatives to simplify HL7 Version 3 messaging and presents a novel approach based on semantic mapping. Based on user-defined definitions, precise transforms between simple and full messages are automatically generated. Systems can be interfaced with the simple messages and achieve interoperability with full Version 3 messages through the transforms. This reduces the costs of HL7 interfacing and will encourage better uptake of HL7 Version 3 and CDA.

Objectives: The aim of this study is to objectify user critique rendering it usable for quality assurance. Based on formative and summative evaluation results we strive to promote software improvements; in our case, the physician discharge letter composition process at the Department of Dermatology, University Hospital Erlangen, Germany., Methods: We developed a novel six-step approach to objectify user critique: 1) acquisition of user critique using subjectivist methods, 2) creation of a workflow model, 3) definition of hypothesis and indicators, 4) measuring of indicators, 5) analyzing results, 6) optimization of the system regarding both subjectivist and objectivist evaluation results. In particular, we derived indicators and workflows directly from user critique/narratives. The identified indicators were mapped onto workflow activities, creating a link between user critique and the evaluated system., Results: Users criticized a new discharge letter system as "too slow" and "too labor-intensive" in comparison with the previously used system. In a stepwise approach we collected subjective user critique, derived a comprehensive process model including deviations and deduced a set of five indicators for objectivist evaluation: processing time, system-related waiting time, number of mouse clicks, number of keyboard inputs, and throughput time. About 3500 measurements have been performed to compare the workflow-steps of both systems, regarding 20 discharge letters. Although the difference of the mean total processing time between both systems was statistically insignificant (2011.7 s vs. 1971.5 s; p = 0.457), we detected a significant difference in waiting times (101.8 s vs. 37.2 s; p <0.001) and number of user interactions (77 vs. 69; p <0.001) in favor of the old system, thus objectifying user critique., Conclusions: Our six-step approach enables objectification of user critique, resulting in objective values for continuous quality assurance. To our knowledge no previous study in medical informatics mapped user critique onto workflow steps. Subjectivist analysis prompted us to use the indicator system-related waiting time for the objectivist study, which was rarely done before. We consider combining subjectivist and objectivist methods as a key point of our approach. Future work will concentrate on automated measurement of indicators.

Background: Inhaled corticosteroid (ICS) nonadherence is common among patients with asthma; however, interventions to improve adherence have often been complex and not easily applied to large patient populations., Objective: To assess the effect of supplying patient adherence information to primary care providers., Methods: Patients and providers were members of a health system serving southeast Michigan. Providers (88 intervention; 105 control) and patients (1335 intervention; 1363 control) were randomized together by practice. Patients were age 5 to 56 years, had a diagnosis of asthma, and had existing prescriptions for ICS medication. Adherence was estimated by using prescription and fill data. Unlike clinicians in the control arm, intervention arm providers could view updated ICS adherence information on their patients via electronic prescription software, and further details on patient ICS use could be viewed by selecting that option. The primary outcome was ICS adherence in last 3 months of the study period., Results: At the study end for the intention-to-treat analysis, ICS adherence was not different among patients in the intervention arm compared with those in the control arm (21.3% vs 23.3%, respectively; P = .553). However, adherence was significantly higher among patients whose clinician elected to view their detailed adherence information (35.7%) compared with both control arm patients (P = .026) and intervention arm patients whose provider did not view adherence data (P = .002)., Conclusions: Overall, providing adherence information to clinicians did not improve ICS use among patients with asthma. However, patient use may improve when clinicians are sufficiently interested in adherence to view the details of this medication use., (Copyright 2010 American Academy of Allergy, Asthma & Immunology. Published by Mosby, Inc. All rights reserved.)

Health Level Seven (HL7) organization published the Clinical Document Architecture (CDA) for exchanging documents among heterogeneous systems and improving medical quality based on the design method in CDA. In practice, although the HL7 organization tried to make medical messages exchangeable, it is still hard to exchange medical messages. There are many issues when two hospitals want to exchange clinical documents, such as patient privacy, network security, budget, and the strategies of the hospital. In this article, we propose a method for the exchange and sharing of clinical documents in an offline model based on the CDA-the Portable CDA. This allows the physician to retrieve the patient's medical record stored in a portal device, but not through the Internet in real time. The security and privacy of CDA data will also be considered.

Objective: To find a way to view ECG from different manufacturers in electrocardiogram information system., Methods: Different format ECG data were transmitted to ECG center by different ways. Corresponding analysis software was used to make the diagnosis reports in the center. Then we use PDF convert to change all ECG reports into PDF format. The electrocardiogram information system manage these PDF format ECG data for clinic user., Results: The ECG reports form several major ECG manufacturers were transformed to PDF format successfully. In the electrocardiogram information system it is freely to view the ECG figure., Conclusions: PDF format ECG report is a practicable way to solve the compatibility problem in electrocardiogram information system.

Rich Internet Applications (RIAs) are an emerging software platform that blurs the line between web service and native application, and is a powerful tool for handheld device deployment. By democratizing health data management and widening its availability, this software platform has the potential to revolutionize telemedicine, clinical practice, medical education and information distribution, particularly in rural areas, and to make patient-centric medical computing a reality. In this paper, we propose a telemedicine application that leverages the ability of a mobile RIA platform to transcode, organise and present textual and multimedia data, which are sourced from medical database software. We adopted a web-based approach to communicate, in real-time, with an established hospital information system via a custom RIA. The proposed solution allows communication between handheld devices and a hospital information system for media streaming with support for real-time encryption, on any RIA enabled platform. We demonstrate our prototype's ability to securely and rapidly access, without installation requirements, medical data ranging from simple textual records to multi-slice PET-CT images and maximum intensity (MIP) projections.

Cancer staging provides a basis for planning clinical management, but also allows for meaningful analysis of cancer outcomes and evaluation of cancer care services. Despite this, stage data in cancer registries is often incomplete, inaccurate, or simply not collected. This article describes a prototype software system (Cancer Stage Interpretation System, CSIS) that automatically extracts cancer staging information from medical reports. The system uses text classification techniques to train support vector machines (SVMs) to extract elements of stage listed in cancer staging guidelines. When processing new reports, CSIS identifies sentences relevant to the staging decision, and subsequently assigns the most likely stage. The system was developed using a database of staging data and pathology reports for 710 lung cancer patients, then validated in an independent set of 179 patients against pathologic stage assigned by two independent pathologists. CSIS achieved overall accuracy of 74% for tumor (T) staging and 87% for node (N) staging, and errors were observed to mirror disagreements between human experts.

Referring to XDS-I and RIM of ebXML, we have built a registry system for electronic medical records sharing which is introduced, in this paper.