Knowledge-based best of breed approach for automated detection of clinical events based on German free text digital hospital discharge letters

OBJECTIVES: The secondary use of medical data contained in electronic medical records, such as hospital discharge letters, is a valuable resource for the improvement of clinical care (e.g. in terms of medication safety) or for research purposes. However, the automated processing and analysis of medical free text still poses a huge challenge to available natural language processing (NLP) systems. The aim of this study was to implement a knowledge-based best of breed approach, combining a terminology server with integrated ontology, a NLP pipeline and a rules engine. METHODS: We tested the performance of this approach in a use case. The clinical event of interest was the particular drug-disease interaction "proton-pump inhibitor [PPI] use and osteoporosis". Cases were to be identified based on free text digital discharge letters as source of information. Automated detection was validated against a gold standard. RESULTS: Precision of recognition of osteoporosis was 94.19%, and recall was 97.45%. PPIs were detected with 100% precision and 97.97% recall. The F-score for the detection of the given drug-disease-interaction was 96,13%. CONCLUSION: We could show that our approach of combining a NLP pipeline, a terminology server, and a rules engine for the purpose of automated detection of clinical events such as drug-disease interactions from free text digital hospital discharge letters was effective. There is huge potential for the implementation in clinical and research contexts, as this approach enables analyses of very high numbers of medical free text documents within a short time period

Extracting information from the text of electronic medical records to improve case detection: a systematic review

Background: Electronic medical records (EMRs) are revolutionizing health-related research. One key issue for study quality is the accurate identification of patients with the condition of interest. Information in EMRs can be entered as structured codes or unstructured free text. The majority of research studies have used only coded parts of EMRs for case-detection, which may bias findings, miss cases, and reduce study quality. This review examines whether incorporating information from text into case-detection algorithms can improve research quality. Methods: A systematic search returned 9659 papers, 67 of which reported on the extraction of information from free text of EMRs with the stated purpose of detecting cases of a named clinical condition. Methods for extracting information from text and the technical accuracy of case-detection algorithms were reviewed. Results: Studies mainly used US hospital-based EMRs, and extracted information from text for 41 conditions using keyword searches, rule-based algorithms, and machine learning methods. There was no clear difference in case-detection algorithm accuracy between rule-based and machine learning methods of extraction. Inclusion of information from text resulted in a significant improvement in algorithm sensitivity and area under the receiver operating characteristic in comparison to codes alone (median sensitivity 78% (codes + text) vs 62% (codes), P = .03; median area under the receiver operating characteristic 95% (codes + text) vs 88% (codes), P = .025). Conclusions: Text in EMRs is accessible, especially with open source information extraction algorithms, and significantly improves case detection when combined with codes. More harmonization of reporting within EMR studies is needed, particularly standardized reporting of algorithm accuracy metrics like positive predictive value (precision) and sensitivity (recall)

Geisinger Health System: Achieving the Potential of System Integration Through Innovation, Leadership, Measurement, and Incentives

Presents a case study of a physician-led nonprofit healthcare group exhibiting the attributes of an ideal healthcare delivery system as defined by the Fund. Describes how its ProvenCare model improved clinical outcomes with reduced resource utilization

Exploring the facilitators and barriers towards implementation of electronic prescribing, dispensing, and administration of medicines in hospitals in Ireland.

Limited data exist on the facilitators and barriers to implementing electronic systems for medicines management in hospitals. Whilst numerous studies advocate system use in improved patient safety and efficiency within the health service, their rate of adoption in practice has been slow. The aim of this doctoral research was to explore this under-researched area in three phases. Phase one: - Phase one focused on critically appraising and synthesising the available evidence on healthcare professionals perceptions, attitudes, and views of the facilitators and barriers to implementing electronic prescribing, electronic dispensing, and/or electronic administration of medicines in the hospital setting. The review protocol was registered with the Centre for Reviews and Dissemination and conducted according to best practice. Key facilitators included systems improved patient safety and provided better access to patients drug records and that team leadership and hardware/software availability and reliability were essential for successful implementation. Key barriers consisted of hardware and network problems, altered work practices, and weakened interpersonal communication between healthcare professionals and with patients. Phase two: - This phase employed a qualitative phenomenological design to gain original insight into the perceptions of local key stakeholders towards the facilitators and barriers to implementing prescribing, robotic pharmacy systems, and automated medication storage and retrieval systems in public hospitals in Ireland using Normalization Process Theory as a theoretical framework. Individual face-to-face semi-structured interviews were conducted in three public hospitals in Ireland with 23 consenting participants: nine nurses; four pharmacists; two pharmacy technicians; six doctors; and two hospital Information Technology managers. Enhanced patient safety and efficiency in healthcare delivery emerged as key facilitators to system implementation, as well as the need to have clinical champions and a multidisciplinary implementation team to promote engagement and cognitive participation. Key barriers included inadequate training and organisational support, and the need for ease and confidence in system use to achieve collective action. Phase three: - A similar qualitative methodology was employed in phase three of this research in order to explore the perceptions of national key stakeholders and eHealth leads towards the facilitators and barriers to system implementation. Sixteen consenting invitees participated: eight hospital leads, four government leads, two regulatory leads, and two academics. Key facilitators included enhanced patient safety, workflow efficiencies, improvements in governance, and financial gains. Perceived barriers included the introduction of new drug errors, loss of patient contact, initial time inefficiencies, and issues with the complexity of integration and standardisation of work processes. Overall, adequate technology, stakeholder involvement, and organisational leadership and support are required at a national and local level to drive the eHealth agenda forward. Testing at scale, contingency plans, and ongoing evaluations will assist in determining success or otherwise of system implementation. This research has generated novel findings with many potentially transferable themes identified which extend the evidence base. This will assist organisations to better plan for implementation of medication-related eHealth systems

An Ontology-Driven Methodology To Derive Cases From Structured And Unstructured Sources

The problem-solving capability of a Case-Based Reasoning (CBR) system largely depends on the richness of its knowledge stored in the form of cases, i.e. the CaseBase (CB). Populating and subsequently maintaining a critical mass of cases in a CB is a tedious manual activity demanding vast human and operational resources. The need for human involvement in populating a CB can be drastically reduced as case-like knowledge already exists in the form of databases and documents and harnessed and transformed into cases that can be operationalized. Nevertheless, the transformation process poses many hurdles due to the disparate structure and the heterogeneous coding standards used. The featured work aims to address knowledge creation from heterogeneous sources and structures. To meet this end, this thesis presents a Multi-Source Case Acquisition and Transformation Info-Structure (MUSCATI). MUSCATI has been implemented as a multi-layer architecture using state-of-the-practice tools and can be perceived as a functional extension to traditional CBR-systems. In principle, MUSCATI can be applied in any domain but in this thesis healthcare was chosen. Thus, Electronic Medical Records (EMRs) were used as the source to generate the knowledge. The results from the experiments showed that the volume and diversity of cases improves the reasoning outcome of the CBR engine. The experiments showed that knowledge found in medical records (regardless of structure) can be leveraged and standardized to enhance the (medical) knowledge of traditional medical CBR systems. Subsequently, the Google search engine proved to be very critical in “fixing” and enriching the domain ontology on-the-fly

M-health review: joining up healthcare in a wireless world

In recent years, there has been a huge increase in the use of information and communication technologies (ICT) to deliver health and social care. This trend is bound to continue as providers (whether public or private) strive to deliver better care to more people under conditions of severe budgetary constraint

To develop a public private partnership model of disease notification as a part of integrated disease surveillance project (IDSP) for private medical practitioners in Mumbai City, India

Background The main objective of Integrated Disease Surveillance Project (IDSP) was early detection of disease outbreaks. This could be possible only when the public health authorities have a strong and effective surveillance system in collaboration with Private Health Sector. Objectives 1) To assess knowledge, attitude & practice about notification of diseases amongst Private Medical Practitioners (PMPs). 2) To find out barriers experienced by PMPs in reporting of diseases under surveillance. 3) To assess feasibility of various alternative ways of reporting convenient for PMPs. 4) To develop a Public Private Partnership Model of disease notification based on feasible options obtained in the study. Materials and Methods This study was a cross-sectional descriptive study conducted in the F South Municipal ward of Mumbai city during April-May 2011. Two stage simple random sampling was used to select 104 PMPs for the study. Results and Conclusions Nearly 98% PMPs felt importance of notification in health system, but only 46% had practiced it. Most common reason for non-reporting was lack of information about reporting system. The convenient way of reporting for PMPs was to report to the nearest health post personally or to District Surveillance Unit through SMS/phone call and both at weekly interval

The Promise of Health Information Technology: Ensuring that Florida's Children Benefit

Substantial policy interest in supporting the adoption of Health Information Technology (HIT) by the public and private sectors over the last 5 -- 7 years, was spurred in particular by the release of multiple Institute of Medicine reports documenting the widespread occurrence of medical errors and poor quality of care (Institute of Medicine, 1999 & 2001). However, efforts to focus on issues unique to children's health have been left out of many of initiatives. The purpose of this report is to identify strategies that can be taken by public and private entities to promote the use of HIT among providers who serve children in Florida