Journal articles on the topic 'Medical records Australia Data processing'
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1
Pearce, Christopher, Adam McLeod, Jon Patrick, Jason Ferrigi, Michael Michael Bainbridge, Natalie Rinehart, and Anna Fragkoudi. "Coding and classifying GP data: the POLAR project." BMJ Health & Care Informatics 26, no. 1 (November 2019): e100009. http://dx.doi.org/10.1136/bmjhci-2019-100009.
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BackgroundData, particularly ‘big’ data are increasingly being used for research in health. Using data from electronic medical records optimally requires coded data, but not all systems produce coded data.ObjectiveTo design a suitable, accurate method for converting large volumes of narrative diagnoses from Australian general practice records to codify them into SNOMED-CT-AU. Such codification will make them clinically useful for aggregation for population health and research purposes.MethodThe developed method consisted of using natural language processing to automatically code the texts, followed by a manual process to correct codes and subsequent natural language processing re-computation. These steps were repeated for four iterations until 95% of the records were coded. The coded data were then aggregated into classes considered to be useful for population health analytics.ResultsCoding the data effectively covered 95% of the corpus. Problems with the use of SNOMED CT-AU were identified and protocols for creating consistent coding were created. These protocols can be used to guide further development of SNOMED CT-AU (SCT). The coded values will be immensely useful for the development of population health analytics for Australia, and the lessons learnt applicable elsewhere.
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Joshi, Ranee, Kavitha Madaiah, Mark Jessell, Mark Lindsay, and Guillaume Pirot. "<i>dh2loop</i> 1.0: an open-source Python library for automated processing and classification of geological logs." Geoscientific Model Development 14, no. 11 (November 4, 2021): 6711–40. http://dx.doi.org/10.5194/gmd-14-6711-2021.
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Abstract. A huge amount of legacy drilling data is available in geological survey but cannot be used directly as they are compiled and recorded in an unstructured textual form and using different formats depending on the database structure, company, logging geologist, investigation method, investigated materials and/or drilling campaign. They are subjective and plagued by uncertainty as they are likely to have been conducted by tens to hundreds of geologists, all of whom would have their own personal biases. dh2loop (https://github.com/Loop3D/dh2loop, last access: 30 September 2021) is an open-source Python library for extracting and standardizing geologic drill hole data and exporting them into readily importable interval tables (collar, survey, lithology). In this contribution, we extract, process and classify lithological logs from the Geological Survey of Western Australia (GSWA) Mineral Exploration Reports (WAMEX) database in the Yalgoo–Singleton greenstone belt (YSGB) region. The contribution also addresses the subjective nature and variability of the nomenclature of lithological descriptions within and across different drilling campaigns by using thesauri and fuzzy string matching. For this study case, 86 % of the extracted lithology data is successfully matched to lithologies in the thesauri. Since this process can be tedious, we attempted to test the string matching with the comments, which resulted in a matching rate of 16 % (7870 successfully matched records out of 47 823 records). The standardized lithological data are then classified into multi-level groupings that can be used to systematically upscale and downscale drill hole data inputs for multiscale 3D geological modelling. dh2loop formats legacy data bridging the gap between utilization and maximization of legacy drill hole data and drill hole analysis functionalities available in existing Python libraries (lasio, welly, striplog).
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Morrow, Melanie, Hollie Sekulich, Abigail Trewin, and Peter Archer. "Immunization Readiness of a Deploying Emergency Medical Team." Prehospital and Disaster Medicine 34, s1 (May 2019): s137—s138. http://dx.doi.org/10.1017/s1049023x19003030.
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Introduction:It is a requirement for a World Health Organization verified Emergency Medical Team (EMT) that all members be immunized against common diseases in the deploying region. Most jurisdictions use private suppliers such as travel doctors for immunization services. When a deployment is announced, members are nominated by their jurisdiction under the condition they are fully immunized. It is up to the individual to monitor their immunization status.Aim:To determine how many members nominated for deployment were fully immunized.Methods:Nominated members sent their completed vaccination record to a central location for assessment of their immunization status. The following data were recorded: vaccination status, last-minute booster doses required, and the number of emails sent by the assessor in processing the records. The number of phone calls made and received were not recorded.Results:To complete the skills matrix for a field hospital containing an emergency department and operating theater (an EMT type 2), 61 members were nominated. At the time of assessment, 32 (52%) were fully immunized, requiring no further booster doses (vaccinations or serology tests). Three members were removed from the deployment as they were not fully immunized. Last-minute booster doses were required by 27 (44%) members, with a total of 74 booster doses administered (range 0-5). 19 of the booster doses administered were immunizations required to work in any health facility in Australia. The most common vaccines requiring booster doses were rabies (n=21) and typhoid (n=15). 58 emails were sent over a period of 5 days to 24 members to clarify vaccination status.Discussion:This deployment highlighted a gap in members’ perception of their immunization status, leading to delays in deployment readiness for the team. A new electronic system where vaccine status tracking occurs in real time should address this issue.
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Young, Marcus, Natasha Holmes, Raymond Robbins, Nada Marhoon, Sobia Amjad, Ary Serpa Neto, and Rinaldo Bellomo. "Natural language processing to assess the epidemiology of delirium-suggestive behavioural disturbances in critically ill patients." Critical Care and Resuscitation 23, no. 2 (June 7, 2021): 144–53. http://dx.doi.org/10.51893/2021.2.oa1.
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Background: There is no gold standard approach for delirium diagnosis, making the assessment of its epidemiology difficult. Delirium can only be inferred though observation of behavioural disturbance and described with relevant nouns or adjectives. Objective: We aimed to use natural language processing (NLP) and its identification of words descriptive of behavioural disturbance to study the epidemiology of delirium in critically ill patients. Study design: Retrospective study using data collected from the electronic health records of a university-affiliated intensive care unit (ICU) in Melbourne, Australia. Participants: 12 375 patients Intervention: Analysis of electronic progress notes. Identification using NLP of at least one of a list of words describing behavioural disturbance within such notes. Results: We analysed 199 648 progress notes in 12 375 patients. Of these, 5108 patients (41.3%) had NLP-diagnosed behavioural disturbance (NLP-Dx-BD). Compared with those who did not have NLP-Dx-DB, these patients were older, more severely ill, and likely to have medical or unplanned admissions, neurological diagnosis, chronic kidney or liver disease and to receive mechanical ventilation and renal replacement therapy (P < 0.001). The unadjusted hospital mortality for NLP-Dx-BD patients was 14.1% versus 9.6% for patients without NLP-Dx-BD. After adjustment for baseline characteristics and illness severity, NLP-Dx-BD was not associated with increased risk of death (odds ratio [OR], 0.94; 95% CI, 0.80–1.10); a finding robust to multiple sensitivity, subgroups and time of observation subcohort analyses. In mechanically ventilated patients, NLP-Dx-BD was associated with decreased hospital mortality (OR, 0.80; 95% CI, 0.65–0.99) after adjustment for baseline severity of illness and year of admission. Conclusions: NLP enabled rapid assessment of large amounts of data identifying a population of ICU patients with typical high risk characteristics for delirium. Moreover, this technique enabled identification of previously poorly understood associations. Further investigations of this technique appear justified.
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Musa, Ibrahim, Hyun Park, Lkhagvadorj Munkhdalai, and Keun Ryu. "Global Research on Syndromic Surveillance from 1993 to 2017: Bibliometric Analysis and Visualization." Sustainability 10, no. 10 (September 25, 2018): 3414. http://dx.doi.org/10.3390/su10103414.
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Syndromic Surveillance aims at analyzing medical data to detect clusters of illness or forecast disease outbreaks. Although the research in this field is flourishing in terms of publications, an insight of the global research output has been overlooked. This paper aims at analyzing the global scientific output of the research from 1993 to 2017. To this end, the paper uses bibliometric analysis and visualization to achieve its goal. Particularly, a data processing framework was proposed based on citation datasets collected from Scopus and Clarivate Analytics’ Web of Science Core Collection (WoSCC). The bibliometric method and Citespace were used to analyze the institutions, countries, and research areas as well as the current hotspots and trends. The preprocessed dataset includes 14,680 citation records. The analysis uncovered USA, England, Canada, France and Australia as the top five most productive countries publishing about Syndromic Surveillance. On the other hand, at the Pinnacle of academic institutions are the US Centers for Disease Control and Prevention (CDC). The reference co-citation analysis uncovered the common research venues and further analysis of the keyword cooccurrence revealed the most trending topics. The findings of this research will help in enriching the field with a comprehensive view of the status and future trends of the research on Syndromic Surveillance.
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Hallinan, Christine Mary, Sedigheh Khademi Habibabadi, Mike Conway, and Yvonne Ann Bonomo. "Social media discourse and internet search queries on cannabis as a medicine: A systematic scoping review." PLOS ONE 18, no. 1 (January 20, 2023): e0269143. http://dx.doi.org/10.1371/journal.pone.0269143.
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The use of cannabis for medicinal purposes has increased globally over the past decade since patient access to medicinal cannabis has been legislated across jurisdictions in Europe, the United Kingdom, the United States, Canada, and Australia. Yet, evidence relating to the effect of medical cannabis on the management of symptoms for a suite of conditions is only just emerging. Although there is considerable engagement from many stakeholders to add to the evidence base through randomized controlled trials, many gaps in the literature remain. Data from real-world and patient reported sources can provide opportunities to address this evidence deficit. This real-world data can be captured from a variety of sources such as found in routinely collected health care and health services records that include but are not limited to patient generated data from medical, administrative and claims data, patient reported data from surveys, wearable trackers, patient registries, and social media. In this systematic scoping review, we seek to understand the utility of online user generated text into the use of cannabis as a medicine. In this scoping review, we aimed to systematically search published literature to examine the extent, range, and nature of research that utilises user-generated content to examine to cannabis as a medicine. The objective of this methodological review is to synthesise primary research that uses social media discourse and internet search engine queries to answer the following questions: (i) In what way, is online user-generated text used as a data source in the investigation of cannabis as a medicine? (ii) What are the aims, data sources, methods, and research themes of studies using online user-generated text to discuss the medicinal use of cannabis. We conducted a manual search of primary research studies which used online user-generated text as a data source using the MEDLINE, Embase, Web of Science, and Scopus databases in October 2022. Editorials, letters, commentaries, surveys, protocols, and book chapters were excluded from the review. Forty-two studies were included in this review, twenty-two studies used manually labelled data, four studies used existing meta-data (Google trends/geo-location data), two studies used data that was manually coded using crowdsourcing services, and two used automated coding supplied by a social media analytics company, fifteen used computational methods for annotating data. Our review reflects a growing interest in the use of user-generated content for public health surveillance. It also demonstrates the need for the development of a systematic approach for evaluating the quality of social media studies and highlights the utility of automatic processing and computational methods (machine learning technologies) for large social media datasets. This systematic scoping review has shown that user-generated content as a data source for studying cannabis as a medicine provides another means to understand how cannabis is perceived and used in the community. As such, it provides another potential ‘tool’ with which to engage in pharmacovigilance of, not only cannabis as a medicine, but also other novel therapeutics as they enter the market.
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Unwin, Elizabeth, James Codde, Louise Gill, Suzanne Stevens, and Timothy Nelson. "The WA Hospital Morbidity Data System: An Evaluation of its Performance and the Impact of Electronic Data Transfer." Health Information Management 26, no. 4 (December 1996): 189–92. http://dx.doi.org/10.1177/183335839702600407.
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This paper evaluates the performance of the Hospital Morbidity Data System, maintained by the Health Statistics Branch (HSB) of the Health Department of Western Australia (WA). The time taken to process discharge summaries was compared in the first and second halves of 1995, using the number of weeks taken to process 90% of all discharges and the percentage of records processed within four weeks as indicators of throughput. Both the hospitals and the HSB showed improvements in timeliness during the second half of the year. The paper also examines the impact of a recently introduced electronic data transfer system for WA country public hospitals on the timeliness of morbidity data. The processing time of country hospital records by the HSB was reduced to a similar time as for metropolitan hospitals, but the processing time in the hospitals increased, resulting in little improvement in total processing time.
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Mesibov, Robert. "An audit of some processing effects in aggregated occurrence records." ZooKeys 751 (April 20, 2018): 129–46. http://dx.doi.org/10.3897/zookeys.751.24791.
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A total of ca 800,000 occurrence records from the Australian Museum (AM), Museums Victoria (MV) and the New Zealand Arthropod Collection (NZAC) were audited for changes in selected Darwin Core fields after processing by the Atlas of Living Australia (ALA; for AM and MV records) and the Global Biodiversity Information Facility (GBIF; for AM, MV and NZAC records). Formal taxon names in the genus- and species-groups were changed in 13–21% of AM and MV records, depending on dataset and aggregator. There was little agreement between the two aggregators on processed names, with names changed in two to three times as many records by one aggregator alone compared to records with names changed by both aggregators. The type status of specimen records did not change with name changes, resulting in confusion as to the name with which a type was associated. Data losses of up to 100% were found after processing in some fields, apparently due to programming errors. The taxonomic usefulness of occurrence records could be improved if aggregators included both original and the processed taxonomic data items for each record. It is recommended that end-users check original and processed records for data loss and name replacements after processing by aggregators.
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Sun, Wencheng, Zhiping Cai, Yangyang Li, Fang Liu, Shengqun Fang, and Guoyan Wang. "Data Processing and Text Mining Technologies on Electronic Medical Records: A Review." Journal of Healthcare Engineering 2018 (2018): 1–9. http://dx.doi.org/10.1155/2018/4302425.
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Currently, medical institutes generally use EMR to record patient’s condition, including diagnostic information, procedures performed, and treatment results. EMR has been recognized as a valuable resource for large-scale analysis. However, EMR has the characteristics of diversity, incompleteness, redundancy, and privacy, which make it difficult to carry out data mining and analysis directly. Therefore, it is necessary to preprocess the source data in order to improve data quality and improve the data mining results. Different types of data require different processing technologies. Most structured data commonly needs classic preprocessing technologies, including data cleansing, data integration, data transformation, and data reduction. For semistructured or unstructured data, such as medical text, containing more health information, it requires more complex and challenging processing methods. The task of information extraction for medical texts mainly includes NER (named-entity recognition) and RE (relation extraction). This paper focuses on the process of EMR processing and emphatically analyzes the key techniques. In addition, we make an in-depth study on the applications developed based on text mining together with the open challenges and research issues for future work.
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da Rocha, Naila Camila, Abner Macola Pacheco Barbosa, Yaron Oliveira Schnr, Juliana Machado-Rugolo, Luis Gustavo Modelli de Andrade, José Eduardo Corrente, and Liciana Vaz de Arruda Silveira. "Natural Language Processing to Extract Information from Portuguese-Language Medical Records." Data 8, no. 1 (December 29, 2022): 11. http://dx.doi.org/10.3390/data8010011.
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Studies that use medical records are often impeded due to the information presented in narrative fields. However, recent studies have used artificial intelligence to extract and process secondary health data from electronic medical records. The aim of this study was to develop a neural network that uses data from unstructured medical records to capture information regarding symptoms, diagnoses, medications, conditions, exams, and treatment. Data from 30,000 medical records of patients hospitalized in the Clinical Hospital of the Botucatu Medical School (HCFMB), São Paulo, Brazil, were obtained, creating a corpus with 1200 clinical texts. A natural language algorithm for text extraction and convolutional neural networks for pattern recognition were used to evaluate the model with goodness-of-fit indices. The results showed good accuracy, considering the complexity of the model, with an F-score of 63.9% and a precision of 72.7%. The patient condition class reached a precision of 90.3% and the medication class reached 87.5%. The proposed neural network will facilitate the detection of relationships between diseases and symptoms and prevalence and incidence, in addition to detecting the identification of clinical conditions, disease evolution, and the effects of prescribed medications.
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Todorova, Violeta, Veska Gancheva, and Valeri Mladenov. "COVID-19 Medical Data Integration Approach." MOLECULAR SCIENCES AND APPLICATIONS 2 (July 18, 2022): 102–6. http://dx.doi.org/10.37394/232023.2022.2.11.
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The need to create automated methods for extracting knowledge from data arises from the accumulation of a large amount of data. This paper presents a conceptual model for integrating and processing medical data in three layers, comprising a total of six phases: a model for integrating, filtering, sorting and aggregating Covid-19 data. A medical data integration workflow was designed, including steps of data integration, filtering and sorting. The workflow for Covid-19 medical data from clinical records of 20400 potential patients was employed.
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Dewri, Rinku, Toan Ong, and Ramakrishna Thurimella. "Linking Health Records for Federated Query Processing." Proceedings on Privacy Enhancing Technologies 2016, no. 3 (July 1, 2016): 4–23. http://dx.doi.org/10.1515/popets-2016-0013.
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Abstract A federated query portal in an electronic health record infrastructure enables large epidemiology studies by combining data from geographically dispersed medical institutions. However, an individual’s health record has been found to be distributed across multiple carrier databases in local settings. Privacy regulations may prohibit a data source from revealing clear text identifiers, thereby making it non-trivial for a query aggregator to determine which records correspond to the same underlying individual. In this paper, we explore this problem of privately detecting and tracking the health records of an individual in a distributed infrastructure. We begin with a secure set intersection protocol based on commutative encryption, and show how to make it practical on comparison spaces as large as 1010 pairs. Using bigram matching, precomputed tables, and data parallelism, we successfully reduced the execution time to a matter of minutes, while retaining a high degree of accuracy even in records with data entry errors. We also propose techniques to prevent the inference of identifier information when knowledge of underlying data distributions is known to an adversary. Finally, we discuss how records can be tracked utilizing the detection results during query processing.
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Nurhayati, Yunita Wisda Tumarta Arif, and Ahmad Yusron Yunizar. "Rancang Bangun Website Rekam Medis Elektronik di Fasilitas Pelayanan Kesehatan Praktik Dokter." Infokes: Jurnal Ilmiah Rekam Medis dan Informatika Kesehatan 10, no. 2 (September 28, 2020): 49–54. http://dx.doi.org/10.47701/infokes.v10i2.1033.
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Electronic medical records record electronic medical records which contain personal data, demographic data, social data, clinical / medical data. Processing of medical record documents at doctor's practice health facilities is still done manually, starting from patient registration, writing examination history, and storing medical record documents. One of the efforts to overcome these obstacles is by building an Electronic Medical Record website. The website development method uses the development life cycle system. Medical records are processed from input patient data, diagnostic data, action data, drug data, officer data, registration data, examination data. Then the data is processed to produce reports, including patient data, and examination data. The electronic medical record website used can simplify the processing of medical record data.
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Bernad Julvian Zebua and Lea Sri Ita Br P.A. "Statistik Data Administrasi Sensus Data Pasien Raat Inap Di RSE Medan." MAMEN: Jurnal Manajemen 1, no. 3 (July 30, 2022): 286–93. http://dx.doi.org/10.55123/mamen.v1i3.662.
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The daily census of inpatients is the number of inpatients starting at 00.00 to 24.00. In its implementation at RSE Medann involving nurses and data processing officers in the medical record section. However there are problems with medical record officers, completeness of data, effectiveness data processing, and timeliness of information presentation. The purpose of this study is to evaluate data management activities daily census of inpatients per day at the Medan RSE. This research is a descriptive study with quantitative and qualitative approaches, with the objects of daily census data management activities being inpatients, nurses, medical records officers, heads of medical records installations, and heads of inpatient rooms as subjects. Data were collected using questionnaires and checklists, analyzed by quantitative and qualitative analysis. From the results of the study, it is known that the inpatient data information in each room is incomplete, which will affect the effectiveness of data processing. It is concluded that this error occurs from two sides, namely the input side and the output side. On the input side, namely the education of medical record officers is not appropriate, data is incomplete on the length of care, age, debtor, diagnosis, recapitulation has not been completed, from the output side within one month the information cannot be known in the following month. It is hoped that there will be standard operating procedures and computer-based medical record systems.
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Ogbuju, E., and G. N. Obunadike. "Information Extraction from Electronic Medical Records using Natural Language Processing Techniques." Journal of Applied Sciences and Environmental Management 24, no. 6 (July 17, 2020): 1027–33. http://dx.doi.org/10.4314/jasem.v24i6.13.
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Patients share key information about their health with medical practitioners during clinic consultations. These key information may include their past medications and allergies, current situations/issues, and expectations. The healthcare professionals store this information in an Electronic Medical Record (EMR). EMRs have empowered research in healthcare; information hidden in them if harnessed properly through Natural Language Processing (NLP) can be used for disease registries, drug safety, epidemic surveillance, disease prediction, and treatment. This work illustrates the application of NLP techniques to design and implement a Key Information Retrieval System (KIRS framework) using the Latent Dirichlet Allocation algorithm. The cross-industry standard process for data mining methodology was applied in an experiment with an EMR dataset from PubMed todemonstrate the framework. The new system extracted the common problems (ailments) and prescriptions across the five (5) countries presented in the dataset. The system promises to assist health organizations in making informed decisions with the flood of key information data available in their domain.
Keywords: Electronic Medical Record, BioNLP, Latent Dirichlet Allocation
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Zeng, Jiaming, Imon Banerjee, A. Solomon Henry, Douglas J. Wood, Ross D. Shachter, Michael F. Gensheimer, and Daniel L. Rubin. "Natural Language Processing to Identify Cancer Treatments With Electronic Medical Records." JCO Clinical Cancer Informatics, no. 5 (April 2021): 379–93. http://dx.doi.org/10.1200/cci.20.00173.
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PURPOSE Knowing the treatments administered to patients with cancer is important for treatment planning and correlating treatment patterns with outcomes for personalized medicine study. However, existing methods to identify treatments are often lacking. We develop a natural language processing approach with structured electronic medical records and unstructured clinical notes to identify the initial treatment administered to patients with cancer. METHODS We used a total number of 4,412 patients with 483,782 clinical notes from the Stanford Cancer Institute Research Database containing patients with nonmetastatic prostate, oropharynx, and esophagus cancer. We trained treatment identification models for each cancer type separately and compared performance of using only structured, only unstructured ( bag-of-words, doc2vec, fasttext), and combinations of both ( structured + bow, structured + doc2vec, structured + fasttext). We optimized the identification model among five machine learning methods (logistic regression, multilayer perceptrons, random forest, support vector machines, and stochastic gradient boosting). The treatment information recorded in the cancer registry is the gold standard and compares our methods to an identification baseline with billing codes. RESULTS For prostate cancer, we achieved an f1-score of 0.99 (95% CI, 0.97 to 1.00) for radiation and 1.00 (95% CI, 0.99 to 1.00) for surgery using structured + doc2vec. For oropharynx cancer, we achieved an f1-score of 0.78 (95% CI, 0.58 to 0.93) for chemoradiation and 0.83 (95% CI, 0.69 to 0.95) for surgery using doc2vec. For esophagus cancer, we achieved an f1-score of 1.0 (95% CI, 1.0 to 1.0) for both chemoradiation and surgery using all combinations of structured and unstructured data. We found that employing the free-text clinical notes outperforms using the billing codes or only structured data for all three cancer types. CONCLUSION Our results show that treatment identification using free-text clinical notes greatly improves upon the performance using billing codes and simple structured data. The approach can be used for treatment cohort identification and adapted for longitudinal cancer treatment identification.
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Fanning, Laura, Lilian Vo, Jenni Ilomäki, J. Simon Bell, Rohan A. Elliott, and Pēteris Dārziņš. "Validity of electronic hospital discharge prescription records as a source of medication data for pharmacoepidemiological research." Therapeutic Advances in Drug Safety 9, no. 8 (May 18, 2018): 425–38. http://dx.doi.org/10.1177/2042098618776598.
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Background: The advent of hospital electronic medical records (EMRs) with electronic prescribing provides considerable opportunity for pharmacoepidemiological research. However, validity of EMR prescribing data for research purposes is not well established. Validity concerns the percentage of cases in which medications and characteristics (name, type, formulation, dose) are true when verified with an independent data source. This study evaluated the validity of EMR discharge prescription data within the Eastern Health hospital network in Melbourne, Australia. Methods: A random sample of patients were selected who had a diagnosis of atrial fibrillation (AF) and were prescribed at least five medications. Prescription records from 2012 to 2015 were compared with pharmacy dispensing and hospital medical records (reference standards). Medication name, dose, directions and route of administration were compared. Discrepancies between data sources were categorized as omissions, additions, discrepancies in dose, medication form or route of administration or discrepancies in reordering. Sensitivities and 95% confidence intervals (CIs) for intended medication exposure were estimated for therapeutic classes. Results: A total of 5724 prescription orders for 479 patients for whom reference standards were available were included. There were 163 discrepancies (2.8%) between prescription records and reference standards. Additions were the most common data discrepancy ( n = 65; ~1.1% of total prescriptions evaluated), followed by discrepancies in reordering ( n = 34; 0.59%). Sensitivities for intended patient exposure to a medication for each therapeutic class at the first level of the Anatomical Therapeutic Chemical (ATC) classification system were between 97% and 100%. The genitourinary system and sex hormone level of the ATC system demonstrated the lowest sensitivity, (97.3%; 95% CI 92.0%–100%) and the cardiovascular system level demonstrated the highest sensitivity (99.9%; 95% CI 99.7%–100%). Conclusion: EMR discharge prescription records for patients with AF are a valid information source for conducting pharmacoepidemiological research within Eastern Health in Melbourne, Australia. Further studies in different regions, countries and patient cohorts are required to establish validity of hospital EMR prescription records for pharmacoepidemiological research.
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Helgheim, Berit I., Rui Maia, Joao C. Ferreira, and Ana Lucia Martins. "Merging Data Diversity of Clinical Medical Records to Improve Effectiveness." International Journal of Environmental Research and Public Health 16, no. 5 (March 3, 2019): 769. http://dx.doi.org/10.3390/ijerph16050769.
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Medicine is a knowledge area continuously experiencing changes. Every day, discoveries and procedures are tested with the goal of providing improved service and quality of life to patients. With the evolution of computer science, multiple areas experienced an increase in productivity with the implementation of new technical solutions. Medicine is no exception. Providing healthcare services in the future will involve the storage and manipulation of large volumes of data (big data) from medical records, requiring the integration of different data sources, for a multitude of purposes, such as prediction, prevention, personalization, participation, and becoming digital. Data integration and data sharing will be essential to achieve these goals. Our work focuses on the development of a framework process for the integration of data from different sources to increase its usability potential. We integrated data from an internal hospital database, external data, and also structured data resulting from natural language processing (NPL) applied to electronic medical records. An extract-transform and load (ETL) process was used to merge different data sources into a single one, allowing more effective use of these data and, eventually, contributing to more efficient use of the available resources.
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McAuley, Elise, Chandana Unnithan, and Sofie Karamzalis. "Implementing Scanned Medical Record Systems in Australia." International Journal of E-Adoption 4, no. 4 (October 2012): 29–54. http://dx.doi.org/10.4018/jea.2012100103.
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In recent years, influenced by the pervasive power of technology, standards and mandates, Australian hospitals have begun exploring digital forms of keeping this record. The main rationale is the ease of accessing different data sources at the same time by varied staff members. The initial step in this transition was implementation of scanned medical record systems, which converts the paper based records to digitised form, which required process flow redesign and changes to existing modes of work. For maximising the benefits of scanning implementation and to better prepare for the changes, Austin Hospital in the State of Victoria commissioned this research focused on elective admissions area. This structured case study redesigned existing processes that constituted the flow of external patient forms and recommended a set of best practices at the same time highlighting the significance of user participation in maximising the potential benefits anticipated. In the absence of published academic studies focused on Victorian hospitals, this study has become a conduit for other departments in the hospital as well as other hospitals in the incursion.
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Osswald, P. M., H. J. Hartung, H. J. Bender, and R. Spier. "Computer Records for Rescue Helicopter Service in West Germany." Journal of the World Association for Emergency and Disaster Medicine 1, no. 1 (1985): 67. http://dx.doi.org/10.1017/s1049023x00032775.
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Data from many rescue missions can be made more comprehensible by computer processing. Exact and systematic processing of data is needed to evaluate the effectiveness of treatments. We put into practice a computer assisted recording system for the rescue helicopter Christoph 5 in Ludwigshafen-Oggersheim, dealing specifically with medical performance.
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Ciora, Radu Adrian, Daniela Gîfu, and Adriana-Lavinia Cioca. "A Solution for Medical Information Management." Acta Medica Transilvanica 26, no. 3 (September 1, 2021): 30–33. http://dx.doi.org/10.2478/amtsb-2021-0045.
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Abstract Nowadays, the amount of data that is being generated by medical devices has exponentially increased. The aim of this paper is to develop an integrated health data management tool, that aggregates data from various sources, which are in various formats. With the aid of artificial intelligence (AI), this data will be processed and will help healthcare professionals be aware of the improvements that could make the healthcare system be more preventive, predictive and personalized. This paper introduces an integrated medical information management system – that intends to manage medical activities in hospitals, clinics and laboratories and describes its development and future directions of improvement. Furthermore, it presents a smart analysis tool that can generate both statistical data, but also infer additional information from the medical records based on natural language processing (NLP), image processing and machine learning. The novelty of the system is that it gives an overview of the patients’ medical record, statistical analysis, examinations results and interpretations. Furthermore, the system is trying to predict the evolution of a disease, based on previous medical records.
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Lenert, L., G. Lopez-Campos, and L. J. Frey. "EHR Big Data Deep Phenotyping." Yearbook of Medical Informatics 23, no. 01 (August 2014): 206–11. http://dx.doi.org/10.15265/iy-2014-0006.
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Summary Objectives: Given the quickening speed of discovery of variant disease drivers from combined patient genotype and phenotype data, the objective is to provide methodology using big data technology to support the definition of deep phenotypes in medical records. Methods: As the vast stores of genomic information increase with next generation sequencing, the importance of deep phenotyping increases. The growth of genomic data and adoption of Electronic Health Records (EHR) in medicine provides a unique opportunity to integrate phenotype and genotype data into medical records. The method by which collections of clinical findings and other health related data are leveraged to form meaningful phenotypes is an active area of research. Longitudinal data stored in EHRs provide a wealth of information that can be used to construct phenotypes of patients. We focus on a practical problem around data integration for deep phenotype identification within EHR data. The use of big data approaches are described that enable scalable markup of EHR events that can be used for semantic and temporal similarity analysis to support the identification of phenotype and genotype relationships. Conclusions: Stead and colleagues’ 2005 concept of using light standards to increase the productivity of software systems by riding on the wave of hardware/processing power is described as a harbinger for designing future healthcare systems. The big data solution, using flexible markup, provides a route to improved utilization of processing power for organizing patient records in genotype and phenotype research.
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Mazza, Danielle, Christopher Pearce, Lyle Robert Turner, Maria De Leon-Santiago, Adam McLeod, Jason Ferriggi, and Marianne Shearer. "The Melbourne East Monash General Practice Database (MAGNET): Using data from computerised medical records to create a platform for primary care and health services research." Journal of Innovation in Health Informatics 23, no. 2 (July 4, 2016): 523. http://dx.doi.org/10.14236/jhi.v23i2.181.
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The Melbourne East MonAsh GeNeral PracticE DaTabase (MAGNET) research platform was launched in 2013 to provide a unique data source for primary care and health services research in Australia. MAGNET contains information from the computerised records of 50 participating general practices and includes data from the computerised medical records of more than 1,100,000 patients. The data extracted is patient-level episodic information and includes a variety of fields related to patient demographics and historical clinical information, along with the characteristics of the participating general practices. While there are limitations to the data that is currently available, the MAGNET research platform continues to investigate other avenues for improving the breadth and quality of data, with the aim of providing a more comprehensive picture of primary care in Australia
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Liu, Sijia, Yanshan Wang, Andrew Wen, Liwei Wang, Na Hong, Feichen Shen, Steven Bedrick, William Hersh, and Hongfang Liu. "Implementation of a Cohort Retrieval System for Clinical Data Repositories Using the Observational Medical Outcomes Partnership Common Data Model: Proof-of-Concept System Validation." JMIR Medical Informatics 8, no. 10 (October 6, 2020): e17376. http://dx.doi.org/10.2196/17376.
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Background Widespread adoption of electronic health records has enabled the secondary use of electronic health record data for clinical research and health care delivery. Natural language processing techniques have shown promise in their capability to extract the information embedded in unstructured clinical data, and information retrieval techniques provide flexible and scalable solutions that can augment natural language processing systems for retrieving and ranking relevant records. Objective In this paper, we present the implementation of a cohort retrieval system that can execute textual cohort selection queries on both structured data and unstructured text—Cohort Retrieval Enhanced by Analysis of Text from Electronic Health Records (CREATE). Methods CREATE is a proof-of-concept system that leverages a combination of structured queries and information retrieval techniques on natural language processing results to improve cohort retrieval performance using the Observational Medical Outcomes Partnership Common Data Model to enhance model portability. The natural language processing component was used to extract common data model concepts from textual queries. We designed a hierarchical index to support the common data model concept search utilizing information retrieval techniques and frameworks. Results Our case study on 5 cohort identification queries, evaluated using the precision at 5 information retrieval metric at both the patient-level and document-level, demonstrates that CREATE achieves a mean precision at 5 of 0.90, which outperforms systems using only structured data or only unstructured text with mean precision at 5 values of 0.54 and 0.74, respectively. Conclusions The implementation and evaluation of Mayo Clinic Biobank data demonstrated that CREATE outperforms cohort retrieval systems that only use one of either structured data or unstructured text in complex textual cohort queries.
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Gusev, A. V., B. V. Zingerman, D. S. Tyufilin, and V. V. Zinchenko. "Electronic medical records as a source of real-world clinical data." Real-World Data & Evidence 2, no. 2 (August 8, 2022): 8–20. http://dx.doi.org/10.37489/2782-3784-myrwd-13.
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Currently, information technologies are being actively introduced in the healthcare of the Russian Federation. The share of state and municipal medical organizations that have implemented various medical information systems increased from 3.9 % in 2007 to 91 % in 2021. One of the key tasks of informatization is the introduction of electronic medical records (EMRs), which accumulate large amounts of Real-World Data (RWD). Despite the importance of EHR as a source of RWD, they have a number of shortcomings, such as the decentralized nature of database management systems, unstructured information storage, etc. The article describes the sequential processes for collecting high-quality RWD based on EHR, including the use of artificial intelligence technologies, for the purposes of scientific research, the creation of decision support systems, statistical analysis, etc. The basis of the proposed methodology is the centralized collection of information from EMR in the so-called data lakes, where as much as possible of raw data on the patient is accumulated and subsequent extraction of data from unstructured records through natural language processing (NLP) models. The proposed technology, subject to continuous improvement, will provide a correct and comprehensive solution for the skilful understanding of any text from any medical record.
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Sahrana, Oka, Safrizal Safrizal, Arfah Husna, and Dian Fera. "Process Evaluation on Medical Record Reporting and Information Usage Iskandar Muda Hospital Nagan Raya Regency." J-Kesmas: Jurnal Fakultas Kesehatan Masyarakat (The Indonesian Journal of Public Health) 8, no. 2 (October 22, 2021): 29. http://dx.doi.org/10.35308/j-kesmas.v8i2.3669.
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Medical records are all records and documents about the patient's identities, examinations, treatments, actions and other services provided to the patient. Reporting medical records at Iskandar Muda hospital still does not follow standards. This is due to the lack of discipline of officers in filling out medical records, lack of medical records of officers and related health workers, then also influenced by the Hospital Management Information System that does not yet exist. The purpose of the study was to evaluate the reporting of medical records at Sultan Iskandar Muda hospital. This study uses qualitative research. The results showed that Sultan Iskandar Muda Hospital has been processing medical record data. The procedure of making a report that is not appropriate is the completion of resumes and daily census pain hospitalization. While the proper methods are a recapitulation of outpatient visits, making reports of hospital activities and making morbidity reports of inpatients and outpatients. The medical records unit has produced internal and external reports following the guidelines, and middle-level hospital management has fully used medical record information. It can be concluded that in processing medical record data, there are some obstacles. The procedure of making a report is not following the guidelines, and medical record information has been fully utilized.
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Gîfu, Daniela, Diana Trandabăț, Kevin Cohen, and Jingbo Xia. "Special Issue on the Curative Power of Medical Data." Data 4, no. 2 (June 14, 2019): 85. http://dx.doi.org/10.3390/data4020085.
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With the massive amounts of medical data made available online, language technologies have proven to be indispensable in processing biomedical and molecular biology literature, health data or patient records. With huge amount of reports, evaluating their impact has long ceased to be a trivial task. Linking the contents of these documents to each other, as well as to specialized ontologies, could enable access to and the discovery of structured clinical information and could foster a major leap in natural language processing and in health research. The aim of this Special Issue, “Curative Power of Medical Data” in Data, is to gather innovative approaches for the exploitation of biomedical data using semantic web technologies and linked data by developing a community involvement in biomedical research. This Special Issue contains four surveys, which include a wide range of topics, from the analysis of biomedical articles writing style, to automatically generating tests from medical references, constructing a Gold standard biomedical corpus or the visualization of biomedical data.
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Harzani, Beni, and Diana Diana. "Aplikasi Filling Rekam Medis Menggunakan Metode Algoritma Turbo Boyer Moore." CESS (Journal of Computer Engineering, System and Science) 5, no. 2 (July 6, 2020): 259. http://dx.doi.org/10.24114/cess.v5i2.17565.
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Nagaswidak Health Center is one of the community health centers that is quite large and has complete facilities. But the problem that is often faced by officers in the puskesmas is the medical record data processing system which is still manual, causing the accumulation of patient medical record file data, in addition to patients who have been checked before and lost their medical records, it is very difficult for officers to find back, so the officer made a new medical record data. To overcome this problem, a Medical Records Filling Application was made at the Nagaswidak Health Center which includes the processing of medical records, patient data, drug data, action data, doctor data, and admin logins. So that the data search problem is not difficult, the turbo boyer moore algorithm method is applied which is expected to later be able to facilitate the search for patient data in the medical record filling application. Based on the test results Boyer Moore's Algorithm successfully applied to search for the beginning of a word, middle word, and final word. And the level of ease and usefulness of medical records application using Boyer Moore's algorithm obtained results that the level of ease is 80% and 100% usability rate.
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Hirani, Kajal, Donald N. Payne, Raewyn Mutch, and Sarah Cherian. "Medical needs of adolescent refugees resettling in Western Australia." Archives of Disease in Childhood 104, no. 9 (July 3, 2018): 880–83. http://dx.doi.org/10.1136/archdischild-2018-315105.
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ObjectiveTo investigate the medical needs and socioeconomic determinants of health among adolescent refugees resettling in Western Australia.DesignComprehensive medical and socioeconomic health data of resettling adolescent refugees aged 12 years and above attending a Refugee Health Service over a 1-year period were analysed.ResultsMedical records of 122 adolescents, median (range) age of 14 (12–17) years, were reviewed. Socioeconomic vulnerabilities included dependence on government financial support (50%), housing issues (27%) and child protection service involvement (11%). Medical concerns included non-communicable disorders (85%), infectious diseases (81%), nutrition/growth (71%) and physical symptoms of non-organic origin (43%). One quarter (27%) of female adolescents had sexual/reproductive health issues. A median (range) of 5 (2–12) health concerns were identified for each adolescent with 49% requiring referral to subspecialty services.ConclusionResettling adolescent refugees are socioeconomically vulnerable with a range of medical issues that frequently require additional subspecialty health referrals.
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Nakatani, Hayao, Masatoshi Nakao, Hidefumi Uchiyama, Hiroyoshi Toyoshiba, and Chikayuki Ochiai. "Predicting Inpatient Falls Using Natural Language Processing of Nursing Records Obtained From Japanese Electronic Medical Records: Case-Control Study." JMIR Medical Informatics 8, no. 4 (April 22, 2020): e16970. http://dx.doi.org/10.2196/16970.
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Background Falls in hospitals are the most common risk factor that affects the safety of inpatients and can result in severe harm. Therefore, preventing falls is one of the most important areas of risk management for health care organizations. However, existing methods for predicting falls are laborious and costly. Objective The objective of this study is to verify whether hospital inpatient falls can be predicted through the analysis of a single input—unstructured nursing records obtained from Japanese electronic medical records (EMRs)—using a natural language processing (NLP) algorithm and machine learning. Methods The nursing records of 335 fallers and 408 nonfallers for a 12-month period were extracted from the EMRs of an acute care hospital and randomly divided into a learning data set and test data set. The former data set was subjected to NLP and machine learning to extract morphemes that contributed to separating fallers from nonfallers to construct a model for predicting falls. Then, the latter data set was used to determine the predictive value of the model using receiver operating characteristic (ROC) analysis. Results The prediction of falls using the test data set showed high accuracy, with an area under the ROC curve, sensitivity, specificity, and odds ratio of mean 0.834 (SD 0.005), mean 0.769 (SD 0.013), mean 0.785 (SD 0.020), and mean 12.27 (SD 1.11) for five independent experiments, respectively. The morphemes incorporated into the final model included many words closely related to known risk factors for falls, such as the use of psychotropic drugs, state of consciousness, and mobility, thereby demonstrating that an NLP algorithm combined with machine learning can effectively extract risk factors for falls from nursing records. Conclusions We successfully established that falls among hospital inpatients can be predicted by analyzing nursing records using an NLP algorithm and machine learning. Therefore, it may be possible to develop a fall risk monitoring system that analyzes nursing records daily and alerts health care professionals when the fall risk of an inpatient is increased.
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Croll, P., B. Li, C. P. Wong, S. Gogia, A. Faud, Y. S. Kwak, S. Chu, et al. "Survey on Medical Records and EHR in Asia-Pacific Region." Methods of Information in Medicine 50, no. 04 (2011): 386–91. http://dx.doi.org/10.3414/me11-02-0002.
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SummaryObjectives: To clarify health record background information in the Asia-Pacific region, for planning and evaluation of medical information systems.Methods: The survey was carried out in the summer of 2009. Of the 14 APAMI (Asia-Pacific Association for Medical Informatics) delegates 12 responded which were Australia, China, Hong Kong, India, Indonesia, Japan, Korea, New Zealand, the Philippines, Singapore, Thailand, and Taiwan.Results: English is used for records and education in Australia, Hong Kong, India, New Zealand, the Philippines, Singapore and Taiwan. Most of the countries/regions are British Commonwealth. Nine out of 12 delegates responded that the second purpose of medical records was for the billing of medical services. Seven out of nine responders to this question answered that the second purpose of EHR (Electronic Health Records) was healthcare cost cutting. In Singapore, a versatile resident ID is used which can be applied to a variety of uses. Seven other regions have resident IDs which are used for a varying range of purposes. Regarding healthcare ID, resident ID is simply used as healthcare ID in Hong Kong, Singapore and Thailand. In most cases, disclosure of medical data with patient’s name identified is allowed only for the purpose of disease control within a legal framework and for disclosure to the patient and referred doctors. Secondary use of medical information with the patient’s identification anonymized is usually allowed in particular cases for specific purposes.Conclusion: This survey on the health record background information has yielded the above mentioned results. This information contributes to the planning and evaluation of medical information systems in the Asia-Pacific region.
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Keogh, Kevin M., Andrew J. Belli, Monica M. Matta, Kathryn A. Tanenbaum, Kaeleigh Farrish, Michael Mulcahy, Shivam Mathura, et al. "The unforeseen business and medical consequences of EHR data collection for a real-world data multiple myeloma registry." Journal of Clinical Oncology 37, no. 15_suppl (May 20, 2019): e19528-e19528. http://dx.doi.org/10.1200/jco.2019.37.15_suppl.e19528.
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e19528 Background: Record retrieval on behalf of a consenting patient within the context of real-world data is not well understood. As the need for real-world data continues to expand, methods for the efficient transfer of information across consenting parties will be critical to enable research collaborations. This need was highlighted through a partnership between COTA and the Multiple Myeloma Research Foundation (MMRF). As one component of the pilot study, COTA managed the record retrieval of consenting patients before going on to abstract the data for use in a registry. Methods: The pilot study identified 23 patients that consented to the release of medical records at 54 institutions across 20 states. COTA partnered with a retrieval vendor and employed its own outreach efforts for acquisition. Outreach and retrieval techniques were similar across COTA and the vendor, including targeted calls, delivery of IRB-approved consent materials, and on-site requests. The 30-day release parameter for a covered entity under 45 CFR 164.524(b)(2) of HIPAA’s Privacy Rule was used to evaluate the observed return rates. Results: A total of 56 medical records were requested, and 48 records were retrieved. The mean (±SD) retrieval time across all sites was 33 (±58) days. We found that 54% of records were released in < = 30 days, and 32% of records > 30 days. 14% of requested records were never released, despite a median of 19 outreach attempts (range 10 to 43). Cited issues for delay or non-release consisted of 22 institutions questioning the validity of the certified electronic patient signature, 6 requiring a physical signature, and 5 requiring their own authorization forms. In no cases did the records contain structured metadata, such as LOINC, MedDRA, and RxNorm. Conclusions: This pilot showed an unpredictable variance associated with the release of records in the context of real-world data. This variance contributed to barriers and delays in broader research efforts. The lack of accompanying metadata with released records resulted in additional required data processing. Future studies should be conducted to establish best practices in the release and retrieval of medical records used to support real-world data research.
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Li, Na. "Body Sensor Network Processing Mechanism for Micro-Data Security Publishing." Advanced Materials Research 1049-1050 (October 2014): 1536–39. http://dx.doi.org/10.4028/www.scientific.net/amr.1049-1050.1536.
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Individuals’ privacy protection when publishing data for research has recently put great attention on data mining and information resources sharing fields. Privacy preservation is an important and challenging problem in micro-data publishing. This paper aimed to find an available directly way protect patient privacy. Processing numeric values which got from body sensor network (BSN). Firstly, we analyze the characteristics of medical data which collected from BSN, and then the records will be grouped according to the Quasi-identifier. The last step is to inspect the diversity of sensitive attributes.
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Staszewska, Anna, Aleksandra Sierocka, and Michał Marczak. "Ethical and legal aspects of scientific research on data collected in medical records." Advances in Rehabilitation 26, no. 4 (December 1, 2012): 41–46. http://dx.doi.org/10.2478/rehab-2013-0048.
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Abstract Research on medical data is essential to the development of evidence based medicine. The use of medical data for scientific purposes must follow principles of good research practice and all relevant legal requirements. The aim of this article is to discuss ethical and legal aspects of processing, preparation and identification of medical data that were used by one of the authors for her doctoral research. The research deals with the black spots methods as a tool for minimizing risks of adverse events in medical setting. It must be stressed, that analyses presented in this article apply to research conducted by physicians as well as to research on medical data undertaken by representative of other medical professions, in particular physiotherapists.
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White, Saraya, and Warren Kealy-Bateman. "Primary evidence of seton therapy at Tarban Creek, New South Wales, 1839." Australasian Psychiatry 25, no. 3 (September 27, 2016): 293–96. http://dx.doi.org/10.1177/1039856216671666.
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Objective: We aimed to find and explore the earliest available New South Wales asylum medical records to identify any management or therapeutic data that might be of interest to the psychiatric field. Conclusions: The earliest known existing records of New South Wales asylum data are from Tarban Creek Asylum. After almost two centuries the preserved records allow insight into treatment used in early colonial Australia, including the scarcely remembered seton therapy. This finding highlights the importance of preserving historical records. It also demonstrates the necessity and/or evolving wish within the colony to care for patients with perceived mental health difficulties based on a shared medical culture inherited from techniques used in Britain.
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Samsir and Syaiful Zuhri Harahap. "Application Design Resume Medical By Using Microsoft Visual Basic.Net 2010 At The Health Center Appointments." International Journal of Science, Technology & Management 1, no. 1 (May 27, 2020): 14–20. http://dx.doi.org/10.46729/ijstm.v1i1.5.
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In implementing health services, puskesmas must document all actions and treatments that are given to patients in a document called Medical Records. According to Minister of Health Regulation No.269 / MENKES / PER / III / 2008 article 1 (1), medical records are files containing notes and documents about patient identities. Medical records are of good quality if the medical record is accurate, complete, trustworthy, valid and timely. One form of management in Medical Records is reporting. According to Minister of Health Regulation No.269 / MENKES / PER / III / 2008 article 1 (1), Medical Record is a file that contains notes and documents about patient identity, examinations, actions, and other services that have been given to patients. In the statement, all information about a patient has been reflected which will be made the basis for determining further actions in services and other medical actions given to a patient who comes to the community health center. The Medical Record is said to be of high quality if the Medical Record is accurate, complete, trustworthy, valid and timely. The Medical Record Installation has activities such as registration, data processing, and storage. One form of processing data in medical records is the existence of assembling activities. Assembling is an assembling activity compiling empty Medical Record forms and storing them into Medical Records, ready to use neatly arranged both in terms of quality and quality.
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Sheikhtaheri, Abbas, Ali Aliabadi, and Samaneh Saravani Aval. "The Quality of health records used in the processing of Lawsuit Cases, a Study in Zabol, Iran." International Journal of Ayurvedic Medicine 9, no. 2 (July 2, 2018): 79–82. http://dx.doi.org/10.47552/ijam.v9i2.1057.
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Background: Forensics is one of the most important areas for the use of health records. Annually, thousands of people refer to forensic medicine organizations to receive health records relating to assaults, accidents, conflicts, medical malpractice, examining the corpse, and clinical examinations. The purpose of this study was to analyze the quality of health records that were used in Zabol Department of Forensic Medicine.Method: In this descriptive cross-sectional study, five hundred health records in total were examined. Data were extracted from health records archived in the Department of Forensic Medicine. Data was collected using a checklist. Data were analyzed by SPSS18 software program.Results: The results showed that 71.2% of the lawsuits were associated with accidents. As for ineffectiveness of the health records, 415 cases (83%) and 441 cases (88.2%) were associated with inaccuracies and incompletely collected information, while 149 cases (29.8%) were associated with the lack of signature and sealing.Conclusion: The quality of the health records under study was not acceptable and despite the importance of legally correct documentation, information was defective. Medical records’ documentation has numerous weak points in terms of accuracy, completeness, consistency, and being signed and sealed.
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Honda, Masayuki, and Takehiro Matsumoto. "System Replacement to a New HIS and Data Warehouse." Journal of Advanced Computational Intelligence and Intelligent Informatics 16, no. 1 (January 20, 2012): 38–41. http://dx.doi.org/10.20965/jaciii.2012.p0038.
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Large-scale hospital information systems (HIS) generally consist of (i) online transaction processing (OLTP) and (ii) online analytical processing (OLAP) systems. Electronic medical records (EMR) are a major OLTP element. The data warehouse (DWH) assumes many important OLAP roles and maintains an institution’s medical care at a high level by providing EMR with the best practice cases available. This article focuses mainly on why OLTP and OLAP are needed and what roles the DWH plays, which means that the DWH has its own utilities and supplementary merits. The background of this discussion is closely related to the HIS at Nagasaki University Hospital introduced before the DWH is discussed.
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Santoso, Hari, Sugesti Sugesti, and Notatema Anugrah Gea. "RANCANG BANGUN SISTEM INFORMASI REKAM MEDIS BERBASIS WEB." Infotech: Journal of Technology Information 7, no. 1 (June 30, 2021): 1–6. http://dx.doi.org/10.37365/jti.v7i1.100.
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Medical record is a file that contains records about the patient's identity, examination, treatment, actions and other health services to patients. The speed of obtaining data and processing of data is very much needed in the current technological era. With the development of technology makes people think to be able to work more effectively and efficiently. One of them is making a conventional system into a computerized system. By utilizing website facilities that are connected to the internet, medical records can be more effective and efficient in searching and recording medical history. In this research a web-based information system is designed using the PHP programming language and MySQL database using the waterfall method as its research method. With this system, it is expected to be able to overcome the various needs of users to search for patient data and perform data processing as well as facilitate users in making reports. From the results of research and design that has been implemented to produce medical record applications that facilitate the processing of patient data.
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Li, Jing Hua, Ying Hui Wang, Zong You Li, Qi Yu, Ye Tian, Wei Bin Wang, Yi Meng Wang, and Yan Huang. "Exploration of Intelligent Development of Medical Heritage." Advances in Science and Technology 105 (April 2021): 272–81. http://dx.doi.org/10.4028/www.scientific.net/ast.105.272.
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With the rapid development of science and technology, more and more new methods and technologies have been added to the traditional Chinese Medicine Inheritance model, which makes the process of inheritance of famous doctors have more means, and the results of inheritance are more objective, rigorous and intelligent. In the process of inheriting the informationization of famous doctors, there are some bottlenecks, such as data acquisition difficulties, data processing difficulties, algorithm application difficulties, analysis and summary difficulties. Integration of artificial intelligence with big data, deep learning algorithm and knowledge atlas technology has brought technological innovation to the informationization of famous doctors' inheritance. Under this wave, the team of the Intelligent Research and Development Center of Traditional Chinese Medicine, Institute of Traditional Chinese Medicine Information, Chinese Academy of Traditional Chinese Medical Sciences, has developed a series of professional application systems in the field of traditional Chinese medicine around the planning of famous doctors' inheritance and excavation, and has developed ancient Chinese medicine, such as Today's Medical Records Cloud Platform, Medical Records Big Data Analysis Platform, Cloud Medical Records APP, Famous Medical Heritage Workstation. To a certain extent, it can solve the problems of inefficient collection of medical records, lack of objective data support and information barriers in the summary of famous doctors' experience under the limitation of traditional model, so as to promote the inheritance of famous doctors' experience and enhance the teaching ability and efficiency of teachers and apprentices.
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McBryde, Emma S., Judy Brett, Philip L. Russo, Leon J. Worth, Ann L. Bull, and Michael J. Richards. "Validation of Statewide Surveillance System Data on Central Line–Associated Bloodstream Infection in Intensive Care Units in Australia." Infection Control & Hospital Epidemiology 30, no. 11 (November 2009): 1045–49. http://dx.doi.org/10.1086/606168.
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Objective.To measure the interobserver agreement, sensitivity, specificity, positive predictive value, and negative predictive value of data submitted to a statewide surveillance system for identifying central line-associated bloodstream infection (BSI).Design.Retrospective review of hospital medical records comparing reported data with gold standard according to definitions of central line–associated BSI.Setting.Six Victorian public hospitals with more than 100 beds.Methods.Reporting of surveillance outcomes was undertaken by infection control practitioners at the hospital sites. Retrospective evaluation of the surveillance process was carried out by independent infection control practitioners from the Victorian Hospital Acquired Infection Surveillance System (VICNISS). A sample of records of patients reported to have a central line-associated BSI were assessed to determine whether they met the definition of central line–associated BSI. A sample of records of patients with bacteremia in the intensive care unit during the assessment period who were not reported as having central line–associated BSI were also assessed to see whether they met the definition of central line-associated BSI.Results.Records of 108 patients were reviewed; the agreement between surveillance reports and the VICNISS assessment was 67.6% (κ = 0.31). Of the 46 reported central line–associated BSIs, 27 were confirmed to be central line–associated BSIs, for a positive predictive value of 59% (95% confidence interval [CI], 43%–73%). Of the 62 cases of bacteremia reviewed that were not reported as central line–associated BSIs, 45 were not associated with a central line, for a negative predictive value of 73% (95% CI, 60%–83%). Estimated sensitivity was 35%, and specificity was 87%. The positive likelihood ratio was 3.0, and the negative likelihood ratio was 0.72.Discussion.The agreement between the reporting of central line–associated BSI and the gold standard application of definitions was unacceptably low. False-negative results were problematic; more than half of central line–associated BSIs may be missed in Victorian public hospitals.
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Zhao, Sizheng Steven, Chuan Hong, Tianrun Cai, Chang Xu, Jie Huang, Joerg Ermann, Nicola J. Goodson, Daniel H. Solomon, Tianxi Cai, and Katherine P. Liao. "Incorporating natural language processing to improve classification of axial spondyloarthritis using electronic health records." Rheumatology 59, no. 5 (September 19, 2019): 1059–65. http://dx.doi.org/10.1093/rheumatology/kez375.
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Abstract Objectives To develop classification algorithms that accurately identify axial SpA (axSpA) patients in electronic health records, and compare the performance of algorithms incorporating free-text data against approaches using only International Classification of Diseases (ICD) codes. Methods An enriched cohort of 7853 eligible patients was created from electronic health records of two large hospitals using automated searches (⩾1 ICD codes combined with simple text searches). Key disease concepts from free-text data were extracted using NLP and combined with ICD codes to develop algorithms. We created both supervised regression-based algorithms—on a training set of 127 axSpA cases and 423 non-cases—and unsupervised algorithms to identify patients with high probability of having axSpA from the enriched cohort. Their performance was compared against classifications using ICD codes only. Results NLP extracted four disease concepts of high predictive value: ankylosing spondylitis, sacroiliitis, HLA-B27 and spondylitis. The unsupervised algorithm, incorporating both the NLP concept and ICD code for AS, identified the greatest number of patients. By setting the probability threshold to attain 80% positive predictive value, it identified 1509 axSpA patients (mean age 53 years, 71% male). Sensitivity was 0.78, specificity 0.94 and area under the curve 0.93. The two supervised algorithms performed similarly but identified fewer patients. All three outperformed traditional approaches using ICD codes alone (area under the curve 0.80–0.87). Conclusion Algorithms incorporating free-text data can accurately identify axSpA patients in electronic health records. Large cohorts identified using these novel methods offer exciting opportunities for future clinical research.
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Heath, A. M., A. L. Culver, and C. W. Luxton. "Gathering good seismic data from the Otway Basin." Exploration Geophysics 20, no. 2 (1989): 247. http://dx.doi.org/10.1071/eg989247.
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Cultus Petroleum N.L. began exploration in petroleum permit EPP 23 of the offshore Otway Basin in December 1987. The permit was sparsely explored, containing only 2 wells and poor quality seismic data. A regional study was made taking into account the shape of the basin and the characteristics of the major seismic sequences. A prospective trend was recognised, running roughly parallel to the present shelf edge of South Australia. A new seismic survey was orientated over this prospective trend. The parameters were designed to investigate the structural control of the prospects in the basin. To improve productivity during the survey, north-south lines had to be repositioned due to excessive swell noise on the cable. The new line locations were kept in accordance with the structural model. Field displays of the raw 240 channel data gave encouraging results. Processing results showed this survey to be the best quality in the area. An FK filter was designed on the full 240 channel records. Prior to wavelet processing, an instrument dephase was used to remove any influence of the recording system on the phase of the data. Close liaison was kept with the processing centre over the selection of stacking velocities and their relevance to the geological model. DMO was found to greatly improve the resolution of steeply dipping events and is now considered to be part of the standard processing sequence for Otway Basin data. Seismic data of a high enough quality for structural and stratigraphic interpretation can be obtained from this basin.
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Baud, R. H., A. M. Rassinoux, and J. R. Scherrer. "Natural Language Processing and Semantical Representation of Medical Texts." Methods of Information in Medicine 31, no. 02 (1992): 117–25. http://dx.doi.org/10.1055/s-0038-1634865.
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Abstract:For medical records, the challenge for the present decade is Natural Language Processing (NLP) of texts, and the construction of an adequate Knowledge Representation. This article describes the components of an NLP system, which is currently being developed in the Geneva Hospital, and within the European Community’s AIM programme. They are: a Natural Language Analyser, a Conceptual Graphs Builder, a Data Base Storage component, a Query Processor, a Natural Language Generator and, in addition, a Translator, a Diagnosis Encoding System and a Literature Indexing System. Taking advantage of a closed domain of knowledge, defined around a medical specialty, a method called proximity processing has been developed. In this situation no parser of the initial text is needed, and the system is based on semantical information of near words in sentences. The benefits are: easy implementation, portability between languages, robustness towards badly-formed sentences, and a sound representation using conceptual graphs.
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Boyle, Malcolm J., M. ClinEpi, Erin C. Smith, and Frank L. Archer. "Trauma Incidents Attended by Emergency Medical Services in Victoria, Australia." Prehospital and Disaster Medicine 23, no. 1 (February 2008): 20–28. http://dx.doi.org/10.1017/s1049023x00005501.
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AbstractIntroduction:International literature describing the profile of trauma patients attended by a statewide emergency medical services (EMS) system is lacking. Most literature is limited to descriptions of trauma responses for a single emergency medical service, or to patients transported to a specific Level-1 trauma hospital. There is no Victorian or Australian literature describing the type of trauma patients transported by a state emergency medical service.Purpose:The purpose of this study was to define a profile of all trauma incidents attended by statewide EMS.Methods:A retrospective cohort study of all patient care records (PCR) for trauma responses attended by Victorian Ambulance Services for 2002 was conducted. Criteria for trauma categories were defined previously, and data were extracted from the PCRs and entered into a secure data repository for descriptive analysis to determine the trauma profile. Ethics committee approval was obtained.Results:There were 53,039 trauma incidents attended by emergency ambulances during the 12-month period. Of these, 1,566 patients were in physiological distress, 11,086 had a significant pattern of injury, and a further 8,931 had an identifiable mechanism of injury. The profile includes minor trauma (n = 9,342), standing falls (n = 20,511), no patient transported (n = 3,687), and deceased patients (n = 459).Conclusions:This is a unique analysis of prehospital trauma. It provides a baseline dataset that may be utilized in future studies of prehospital trauma care. Additionally, this dataset identifies a ten-fold difference in major trauma between the prehospital and the hospital assessments.
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Raja, M., Dr S. Dhanasekaran, and Dr V. Vasudevan. "Light Weight Cryptography based Medical Data and Image Encryption Scheme." Webology 18, no. 2 (December 23, 2021): 88–104. http://dx.doi.org/10.14704/web/v18i2/web18309.
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Many medical companies use cloud technology to collect, distribute and transmit medical records. Given the need for medical information, confidentiality is a key issue. In this study, we propose an encrypted scheme based on encrypted data for an electronic healthcare environment. We use hybrid Attribute based encryption and Triple DES encryption technique (ABETDES) scheme, including identity-based cryptography (IBC), to ensure data privacy through communication channels և to improve the reliability of cloud computing. There are also limited indicators of light processing and storage resources. This solves a serious maintenance problem and ensures that a private key is created where it is not blind. The introduction of a security option, a comprehensive security analysis to protect ciphertext, shows that our program is effective against many known attacks and compared to existing methods.
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Ларченко, Елена, Elena Larchenko, Анатолий Нечепуренко, Anatoliy Nechepurenko, Максим Иринархов, Maksim Irinarhov, Надежда Давидюк, and Nadezhda Davidyuk. "The experience in integrating of medical information systems to help a practicing doctor." Vestnik Roszdravnadzora 2019, no. 6 (November 21, 2019): 66–73. http://dx.doi.org/10.35576/2070-7940-2019-2019-6-66-73.
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The abstract: the paper presents the urgency of the problem of integrating of medical information systems and external specialized software products.
The main goal of the paper is to optimize the process of remote monitoring of the patient’s health with an implanted device. As a result, the integration
module of the hospital information system of the Federal state budget foundation “Federal center of cardiovascular surgery” of the Ministry of Health
of the Russian Federation (Astrakhan) was introduced, in terms of the patient’s Electronic Health Records (EHR) and Medtronic CareLink remote monitoring
system. The ability to integrate various medical systems makes it possible to optimize the processing of electronic medical documents, in particular, routine
data collection and processing operations in the patient’s electronic medical record (Electronic Health Records, EHR) in the daily work of a medical specialist.
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Simpson, Steve, and Richard Turner. "Four decades of anal cancer in Tasmania, Australia: what do the case data tell us?" Sexual Health 9, no. 3 (2012): 213. http://dx.doi.org/10.1071/sh11002.
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Background Anal cancer is a rare cancer analogous to cervical cancer, largely caused by exposure to oncogenic human papillomavirus. We have sought to study this disease in the epidemiologically distinct population of Tasmania. Methods: Medical records at all tertiary and secondary referral centres in Tasmania were audited for records with corresponding International Classification of Diseases (ICD)-10 codes. Statistical significances of trends were evaluated using Fisher’s exact test, logistic regression or linear regression. Results: Of ~1350 screening records, 170 cases of anal cancer were found with patient presentation during 1973–2010, corresponding to 132 patients. This cohort was mostly female (66.7%), with squamous cell histology (81.8%) and anal canal primaries (72.0%). Most cases were detected at Stage II or below and the majority remained disease-free after treatment. Relatively few cases had documentation of typical risk factors for anal cancer, such as HIV seropositivity, a history of cancer or smoking. After 2000, there was a trend towards a lower stage at presentation, correlating with an increased 5-year survival. After 2000, no anal margin tumours presented beyond Stage II; nearly half were detected in situ and none were fatal. For anal canal tumours, there was virtually no change in the mean stage at detection or in survival. Conclusion: This is the first case series of anal cancer in Tasmania. We find that in many ways, including symptoms and pathology at presentation, epidemiology is typical. However, our cohort is distinct in its paucity of known risk groups, including HIV-positive people, those with a history of cancer and smokers.
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Zhao, Boyang. "Clinical Data Extraction and Normalization of Cyrillic Electronic Health Records Via Deep-Learning Natural Language Processing." JCO Clinical Cancer Informatics, no. 3 (December 2019): 1–9. http://dx.doi.org/10.1200/cci.19.00057.
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PURPOSE A substantial portion of medical data is unstructured. Extracting data from unstructured text presents a barrier to advancing clinical research and improving patient care. In addition, ongoing studies have been focused predominately on the English language, whereas inflected languages with non-Latin alphabets (such as Slavic languages with a Cyrillic alphabet) present numerous linguistic challenges. We developed deep-learning–based natural language processing algorithms for automatically extracting biomarker status of patients with breast cancer from three oncology centers in Bulgaria. METHODS We used dual embeddings for English and Bulgarian languages, encoding both syntactic and polarity information for the words. The embeddings were subsequently aligned so that they were in the same vector space. The embeddings were used as input to convolutional or recurrent neural networks to derive the biomarker status of estrogen receptor, progesterone receptor, and human epidermal growth factor receptor 2. RESULTS We showed that we can resolve ambiguity in highly variable medical text containing both Latin and Cyrillic text. Final models incorporating both English and Bulgarian syntax and polarity embeddings achieved F1 scores of 0.90 or higher for all estrogen receptor, progesterone receptor, and human epidermal growth factor receptor 2 biomarkers. The models were robust against human errors originally found in the training set. In addition, such models can be extended for analyzing text containing words not seen during training. CONCLUSION By using several techniques that incorporate dual-word embeddings encoding syntactic and polarity information in two languages followed by deep neural network architectures, we show that researchers can extract and normalize parameters within medical data. The principles described here can be used to analyze Cyrillic or Latin mixed medical text and extract other parameters.
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Allen-Graham, Judith, Lauren Mitchell, Natalie Heriot, Roksana Armani, David Langton, Michele Levinson, Alan Young, Julian A. Smith, Tom Kotsimbos, and John W. Wilson. "Electronic health records and online medical records: an asset or a liability under current conditions?" Australian Health Review 42, no. 1 (2018): 59. http://dx.doi.org/10.1071/ah16095.
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Objective The aim of the present study was to audit the current use of medical records to determine completeness and concordance with other sources of medical information. Methods Medical records for 40 patients from each of five Melbourne major metropolitan hospitals were randomly selected (n=200). A quantitative audit was performed for detailed patient information and medical record keeping, as well as data collection, storage and utilisation. Using each hospital’s current online clinical database, scanned files and paperwork available for each patient audited, the reviewers sourced as much relevant information as possible within a 30-min time allocation from both the record and the discharge summary. Results Of all medical records audited, 82% contained medical and surgical history, allergy information and patient demographics. All audited discharge summaries lacked at least one of the following: demographics, medication allergies, medical and surgical history, medications and adverse drug event information. Only 49% of records audited showed evidence the discharge summary was sent outside the institution. Conclusions The quality of medical data captured and information management is variable across hospitals. It is recommended that medical history documentation guidelines and standardised discharge summaries be implemented in Australian healthcare services. What is known about this topic? Australia has a complex health system, the government has approved funding to develop a universal online electronic medical record system and is currently trialling this in an opt-out style in the Napean Blue Mountains (NSW) and in Northern Queensland. The system was originally named the personally controlled electronic health record but has since been changed to MyHealth Record (2016). In Victoria, there exists a wide range of electronic health records used to varying degrees, with some hospitals still relying on paper-based records and many using scanned medical records. This causes inefficiencies in the recall of patient information and can potentially lead to incidences of adverse drug events. What does this paper add? This paper supports the concept of a shared medical record system using 200 audited patient records across five Victorian metropolitan hospitals, comparing the current information systems in place for healthcare practitioners to retrieve data. This research identifies the degree of concordance between these sources of information and in doing so, areas for improvement. What are the implications for practitioners? Implications of this research are the improvements in the quality, storage and accessibility of medical data in Australian healthcare systems. This is a relevant issue in the current Australian environment where no guidelines exist across the board in medical history documentation or in the distribution of discharge summaries to other healthcare providers (general practitioners, etc).