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Aim: The aim of this study is to explore the potential of using electronic health records for assessment of nursing care quality through nursing‐sensitive indicators in acute cardiac care. Background: Nursing care quality is a multifaceted phenomenon, making a holistic assessment of it difficult. Quality assessment systems in acute cardiac care units could benefit from big data‐based solutions that automatically extract and help interpret data from electronic health records. Methods: This is a deductive descriptive study that followed the theory of value‐added analysis. A random sample from electronic health records of 230 patients was analysed for selected indicators. The data included documentation in structured and free‐text format. Results: One thousand six hundred seventy‐six expressions were extracted and divided into (1) established and (2) unestablished expressions, providing positive, neutral and negative descriptions related to care quality. Conclusions: Electronic health records provide a potential source of information for information systems to support assessment of care quality. More research is warranted to develop, test and evaluate the effectiveness of such tools in practice. Implications for Nursing Management: Knowledge‐based health care management would benefit from the development and implementation of advanced information systems, which use continuously generated already available real‐time big data for improved data access and interpretation to better support nursing management in quality assessment. [ABSTRACT FROM AUTHOR]

Introduction: The proportion of patients who are frequent attenders (FAs) varies from few percent to almost 30% of all patients. A small group of patients continued to visit GPs year after year. In previous studies, it has been reported that over 15% of all 1-year FAs were persistent frequent attenders (pFAs). Objectives: This study aimed to identify typical features of pFAs from the textual content in their medical entries, which could help GPs to recognize pFAs easily and facilitated treatment.Methods: A retrospective register study was done, using 10 years of electronic patient records. The data were collected from Finnish primary health care centers and used to analyze chronic symptoms and diagnoses of pFAs and to calculate the inverse document frequency weight (IDF) of words used in the patient records. IDF was used to determine which words, if any, are typical for pFAs. The study group consisted of the 5-year pFAs and control group of 1-year FAs. The main background variables were age, gender, occupation, smoking habits, use of alcohol, and BMI. Results: Out of 4392 frequent attenders, 6.6% were pFAs for 3 years and 1.1% were pFAs for 5 years. Of the pFAs, 65% were female and 35% were male. The study group had significantly more depressive episodes (P =.004), heart failure (P =.019), asthma (P =.032), COPD (P =.036), epilepsy (P =.035), and lumbago (P =.046) compared to the control group. GPs described their 5-year pFAs by words related to lung and breathing issues, but there was no statistical difference to the 1-year FAs' descriptions. Conclusion: A typical pFA seems to be a woman, aged about 55 years with depressive episodes, asthma or COPD, and lower back pain. Physicians describe pFAs with ordinary words in patient records. It was not possible to differentiate pFAs from 1-year FAs in this way. [ABSTRACT FROM AUTHOR]

Objective: A major source of information available in electronic health record (EHR) systems are the clinical free text notes documenting patient care. Managing this information is time-consuming for clinicians. Automatic text summarisation could assist clinicians in obtaining an overview of the free text information in ongoing care episodes, as well as in writing final discharge summaries. We present a study of automated text summarisation of clinical notes. It looks to identify which methods are best suited for this task and whether it is possible to automatically evaluate the quality differences of summaries produced by different methods in an efficient and reliable way.Methods and Materials: The study is based on material consisting of 66,884 care episodes from EHRs of heart patients admitted to a university hospital in Finland between 2005 and 2009. We present novel extractive text summarisation methods for summarising the free text content of care episodes. Most of these methods rely on word space models constructed using distributional semantic modelling. The summarisation effectiveness is evaluated using an experimental automatic evaluation approach incorporating well-known ROUGE measures. We also developed a manual evaluation scheme to perform a meta-evaluation on the ROUGE measures to see if they reflect the opinions of health care professionals.Results: The agreement between the human evaluators is good (ICC=0.74, p<0.001), demonstrating the stability of the proposed manual evaluation method. Furthermore, the correlation between the manual and automated evaluations are high (> 0.90 Spearman's rho). Three of the presented summarisation methods ('Composite', 'Case-Based' and 'Translate') significantly outperform the other methods for all ROUGE measures (p<0.05, Wilcoxon signed-rank test and Bonferroni correction).Conclusion: The results indicate the feasibility of the automated summarisation of care episodes. Moreover, the high correlation between manual and automated evaluations suggests that the less labour-intensive automated evaluations can be used as a proxy for human evaluations when developing summarisation methods. This is of significant practical value for summarisation method development, because manual evaluation cannot be afforded for every variation of the summarisation methods. Instead, one can resort to automatic evaluation during the method development process. [ABSTRACT FROM AUTHOR]

Silicon-germanium heterojunction bipolar transistor (SiGe HBT) heavy ion-induced current transients are measured using Sandia National Laboratories' microbeam and highand low-energy broadbeam sources at the Grand Accélérateur National d'Ions Lourds, Caen, France, and the University of Jyväskyla, Finland. The data were captured using a custom broadband IC package and real-time digital phosphor oscilloscopes with at least 16 GHz of analog bandwidth. These data provide detailed insight into the effects of ion strike location, range, and LET. [ABSTRACT FROM AUTHOR]