Benchmarking of hospital information systems: Monitoring of discharge letters and scheduling can reveal heterogeneities and time trends

<p>Abstract</p> <p>Background</p> <p>Monitoring of hospital information system (HIS) usage can provide insights into best practices within a hospital and help to assess time trends. In terms of effort and cost of benchmarking, figures derived automatically from the routine HIS system are preferable to manual methods like surveys, in particular for repeated analysis.</p> <p>Methods</p> <p>Due to relevance for quality management and efficient resource utilization we focused on time-to-completion of discharge letters (assessed by CT-plots) and usage of patient scheduling. We analyzed these parameters monthly during one year at a major university hospital in Germany.</p> <p>Results</p> <p>We found several distinct patterns of discharge letter documentation indicating a large heterogeneity of HIS usage between different specialties (completeness 51 – 99%, delays 0 – 90 days). Overall usage of scheduling increased during the observation period by 62%, but again showed a considerable variation between departments.</p> <p>Conclusion</p> <p>Regular monitoring of HIS key figures can contribute to a continuous HIS improvement process.</p

Health Access Broker: Secure, Patient-Controlled Management of Personal Health Records in the Cloud

Secure and privacy-preserving management of Personal Health Records (PHRs) has proved to be a major challenge in modern healthcare. Current solutions generally do not offer patients a choice in where the data is actually stored and also rely on at least one fully trusted element that patients must also trust with their data. In this work, we present the Health Access Broker (HAB), a patient-controlled service for secure PHR sharing that (a) does not impose a specific storage location (uniquely for a PHR system), and (b) does not assume any of its components to be fully secure against adversarial threats. Instead, HAB introduces a novel auditing and intrusion-detection mechanism where its workflow is securely logged and continuously inspected to provide auditability of data access and quickly detect any intrusions.Comment: Copy of the paper accepted at 13th International Conference on Computational Intelligence in Security for Information Systems (CISIS

Developing a manually annotated clinical document corpus to identify phenotypic information for inflammatory bowel disease

<p>Abstract</p> <p>Background</p> <p>Natural Language Processing (NLP) systems can be used for specific Information Extraction (IE) tasks such as extracting phenotypic data from the electronic medical record (EMR). These data are useful for translational research and are often found only in free text clinical notes. A key required step for IE is the manual annotation of clinical corpora and the creation of a reference standard for (1) training and validation tasks and (2) to focus and clarify NLP system requirements. These tasks are time consuming, expensive, and require considerable effort on the part of human reviewers.</p> <p>Methods</p> <p>Using a set of clinical documents from the VA EMR for a particular use case of interest we identify specific challenges and present several opportunities for annotation tasks. We demonstrate specific methods using an open source annotation tool, a customized annotation schema, and a corpus of clinical documents for patients known to have a diagnosis of Inflammatory Bowel Disease (IBD). We report clinician annotator agreement at the document, concept, and concept attribute level. We estimate concept yield in terms of annotated concepts within specific note sections and document types.</p> <p>Results</p> <p>Annotator agreement at the document level for documents that contained concepts of interest for IBD using estimated Kappa statistic (95% CI) was very high at 0.87 (0.82, 0.93). At the concept level, F-measure ranged from 0.61 to 0.83. However, agreement varied greatly at the specific concept attribute level. For this particular use case (IBD), clinical documents producing the highest concept yield per document included GI clinic notes and primary care notes. Within the various types of notes, the highest concept yield was in sections representing patient assessment and history of presenting illness. Ancillary service documents and family history and plan note sections produced the lowest concept yield.</p> <p>Conclusion</p> <p>Challenges include defining and building appropriate annotation schemas, adequately training clinician annotators, and determining the appropriate level of information to be annotated. Opportunities include narrowing the focus of information extraction to use case specific note types and sections, especially in cases where NLP systems will be used to extract information from large repositories of electronic clinical note documents.</p

Automation of a problem list using natural language processing

BACKGROUND: The medical problem list is an important part of the electronic medical record in development in our institution. To serve the functions it is designed for, the problem list has to be as accurate and timely as possible. However, the current problem list is usually incomplete and inaccurate, and is often totally unused. To alleviate this issue, we are building an environment where the problem list can be easily and effectively maintained. METHODS: For this project, 80 medical problems were selected for their frequency of use in our future clinical field of evaluation (cardiovascular). We have developed an Automated Problem List system composed of two main components: a background and a foreground application. The background application uses Natural Language Processing (NLP) to harvest potential problem list entries from the list of 80 targeted problems detected in the multiple free-text electronic documents available in our electronic medical record. These proposed medical problems drive the foreground application designed for management of the problem list. Within this application, the extracted problems are proposed to the physicians for addition to the official problem list. RESULTS: The set of 80 targeted medical problems selected for this project covered about 5% of all possible diagnoses coded in ICD-9-CM in our study population (cardiovascular adult inpatients), but about 64% of all instances of these coded diagnoses. The system contains algorithms to detect first document sections, then sentences within these sections, and finally potential problems within the sentences. The initial evaluation of the section and sentence detection algorithms demonstrated a sensitivity and positive predictive value of 100% when detecting sections, and a sensitivity of 89% and a positive predictive value of 94% when detecting sentences. CONCLUSION: The global aim of our project is to automate the process of creating and maintaining a problem list for hospitalized patients and thereby help to guarantee the timeliness, accuracy and completeness of this information

Applicability of DICOM structured reporting for the standardized exchange of implantation plans

PURPOSE: Today's operating room is equipped with different devices supporting the surgeon. Due to the lack of common interfaces between devices, an integrated support of the surgical workflow is missing. In the field of implantation, a smooth exchange of preoperatively planned data between devices is of great interest. Additionally, the availability of standardized preoperative data would facilitate the documentation, especially with regard to Electronic Health Records. METHODS: To analyze whether DICOM Structured Reporting can be the basis for a standardized digital human- and machine-readable implantation plan, we derived all requirements for such a document. Therefore, we examined the conventional implantation plan and future applications of the digital plan. RESULTS: In this paper, we propose to use the mechanisms introduced by DICOM Structured Reporting as storage and communication infrastructure for implantation plans in the surgical domain. DICOM Structured Reporting complies with all requirements of our analysis. Additionally, we introduce a first draft of a standardized implantation plan structure. CONCLUSIONS: A standardized digital implantation plan based on the DICOM Structured Report has the potential to overcome current integration problems in the OR and to facilitate new applications

Health Access Broker: Secure, patient-controlled management of Personal Health Records in the Cloud

Secure and privacy-preserving management of Personal Health Records (PHRs) has proved to be a major challenge in modern healthcare. Current solutions generally do not offer patients a choice in where the data is actually stored, and also rely on at least one fully trusted element that patients must also trust with their data. In this work, we present the Health Access Broker (HAB), a patient-controlled service for secure PHR sharing that (a) does not impose a specific storage location (uniquely for a PHR system), and (b) does not assume any of its components to be fully secure against adversarial threats. Instead, HAB introduces a novel auditing and intrusion-detection mechanism where its workflow is securely logged and continuously inspected to provide auditability of data access and quickly detect any intrusions