Automatic Data Transfer Research Articles

IDF21-0101 Blood Glucose Control using a Mobile Health Application in Asia – A Retrospective Real-World Data Analysis

Background: Mobile health (mHealth) solutions have the potential to reduce the clinical and psychological burden of diabetes and enhance patient self-management. Previous real world data analyses showed significant improvement in blood glucose (BG) control with the use of a mHealth application for people with diabetes after 6 months. However, there is under-representation of Asian data while user behaviours and outcomes remain little known. Aim: We aimed to investigate the potential impact of mySugr Logbook app usage on parameters of BG control in Asia – Singapore, Philippines and Hong Kong. Method: We analysed changes in BG control [mean BG, tests in range (TeIR), estimated HbA1c (eHbA1c)] in a sample of highly engaged users, defined as ≥2 logs on at least 14 out of 30 days, over a 6-month period. We also analysed measures of glycaemic variability [high blood glucose index (HBGI) and Low blood glucose index (LBGI)]. Data from the first month (t0) of use was compared to month 6 (t1) using paired t-tests in individuals who logged continuously for 6 months. Subgroup analysis was performed on those with suboptimal glycaemic control (eHbA1c >7.0%). Results: A total of 1761 users satisfying criteria above have been using mySugr app over a 6-month period. Of this, 63.7% had Type 2 diabetes (T2D), 14.4% had Type 1 diabetes (T1D), 21.9% was others/unknown. Baseline eHBA1c and mean BG were 6.6% and 142.6 ± 36.4 mg/dl respectively. In 357 individuals who logged continuously, t0 eHbA1c 6.7±0.9% fell 0.2% (p<0.01) at t1. In those with eHbA1c>7% (n=107) at baseline, mean t0 eHbA1c 8.1 ± 1.4%, reduced by 0.5% (p<0.01) with TeIR improving by 10.9% (p<0.01) at t1. Likewise, HBGI decreased from 11 (high risk) to 9 (moderate risk) (p<0.01) whilst the LBGI did not significantly change and remained at low risk level (<2.5) (p=0.300). Discussion: This real-world data shows improvements in BG control in those with higher eHbA1c at baseline over a 6-month period in highly engaged users. A clinically relevant decrease in eHbA1c by 0.5% for those with baseline eHbA1c>7% at 6 months of mHealth application usage was shown to be statistically significant. Although mySugr app supports manual logging of BG data, it was previously observed that users who manually logged data had lower retention rates compared to those who used connected BG meters that enable automatic transfer of data. This preliminary work suggests a positive impact of connected BG meters and mobile health applications in supporting diabetes self-management.

A BIM Based Framework for Damage Segmentation, Modeling, and Visualization Using IFC

Paper-based data acquisition and manual transfer between incompatible software or data formats during inspections of bridges, as done currently, are time-consuming, error-prone, cumbersome, and lead to information loss. A fully digitized workflow using open data formats would reduce data loss, efforts, and the costs of future inspections. On the one hand, existing studies proposed methods to automatize data acquisition and visualization for inspections. These studies lack an open standard to make the gathered data available for other processes. On the other hand, several studies discuss data structures for exchanging damage information among different stakeholders. However, those studies do not cover the process of automatic data acquisition and transfer. This study focuses on a framework that incorporates automatic damage data acquisition, transfer, and a damage information model for data exchange. This enables inspectors to use damage data for subsequent analyses and simulations. The proposed framework shows the potentials for a comprehensive damage information model and related (semi-)automatic data acquisition and processing.

The use of data management technologies in the creation of historical Internet resources

In this article, special attention is paid to the problem of preserving and distributing the results of virtual reconstructions of historical and cultural heritage objects by creating specialized electronic reference books based on the Wordpress website builder. Using the example of the handbook on virtual reconstructions of estates in the Moscow region, the author shows how, with the help of modern data management technologies, it is possible to configure the system in such a way that its filling and administration take place with the least amount of time on the part of the authors of publications and the directory administrator. The main problem solved by such a system is the elimination of the need for manual entry of each article and subsequent approval of the publication in manual mode. The author of the study presents an approbation of the use of data management technologies in historical Internet resources. The systems presented in the article are used in industrial data management systems, but their availability allows them to be used in other fields of activity, including as an auxiliary tool in scientific research. Automatic transfer of data from the user's web form to a new directory page significantly speeds up the task of filling an Internet resource. The proposed version of the directory was created using the MySQL database, the Apache NiFi data orchestrator and the Wordpress website builder. All the listed tools are free and available for download on their official pages.

Acquisition, transmission and management of data in industrial practice in the digital age

Digital technologies permeate all aspects of industrial production and Logistics systems, accelerating the generation of large amounts of industrial data. Industry 4.0 and Industrial Internet of Things offers great development opportunities, new perspectives, a new paradigm of autonomous and decentralized production control. The information on the current status of industrial processes is invaluable in present times. Among others, it enables deliberate and rational management of available resources. Therefore, acquisition, transmission and management of data is an important issue in the systems for collecting data from production lines and a key element in the decision-making process. In industrial practice, the data acquisition process uses the information obtained from various sensors placed directly in production lines. The current level of microelectronics development enables acquisition and transfer of data from technological processes in real time. This article presents selected popular methods of automatic acquisition and transfer of data directly from production lines in the course of implementation of manufacturing processes. A review of all the issues discussed is supported by examples. Particular attention is paid to the nature and importance of data acquisition in the decisionmaking process. It was emphasized that the information itself is very important, but the value of information and the way it is processed and managed are also important. Data management in the unit optimizes the decision-making process and the work of the entire unit. On the other hand, data is a resource, the loss of which may result in the loss of competitive position or the loss of development opportunities.

Automatic Data Transfer from OMOP-CDM to REDCap: A Semantically-Enriched Framework.

Automatic Data Transfer from OMOP-CDM to REDCap: A Semantically-Enriched Framework.

Quality Assurance in Cataract and Lens Surgery with Special Consideration of Subjective Patient Reported Outcome Measures and Clinical Reported Outcome Measures.

Results of medical interventions must be documented and evaluated. In studies, this is done with clinical outcomes data (clinician/clinical reported outcome measure, CROM). In the past, less weight has been given to patient surveys with questionnaires (patient reported outcome measure, PROM). This retrospective study included 104 eyes from 53 patients. Of these, 35 patients had cataract surgery and 15 patients had a refractive lens exchange. The implanted lenses included 62 trifocal IOLs (Asphina trifiocal 839, Zeiss), 34 trifocal toric IOLs (Asphina trifocal toric 939, Zeiss) and 8 bifocal IOLs (Asphina 808, Zeiss) with the same IOL platform. Patients completed a modified questionnaire before surgery and one year after surgery. We made changes to the CatQuest-9SF questionnaire so as to also document side effects. The effort required by the patients to answer the questionnaire was a burden. Transcribing the data into electronic files so as they could be saved and analyzed was a lot of work for the staff. Among the patients, 88.7% were spectacle-independent in everyday life, and 77.5% for reading. 44.4% had a halo problem. 92% reported the operation as a success. 100% had a prediction error of ≤ ± 0.75 dpt. There is a high rate of patient satisfaction with the outcome of the intervention. New questionnaires are needed for new IOLs. The Catquest-9SF is from 2009. Accordingly, revisions and new validation is necessary. Beyond that, only automatic data transfer will reduce the amount of work involved in data input.

Digitization of Measurement-Based Care Pathways in Mental Health Through REDCap and Electronic Health Record Integration: Development and Usability Study

BackgroundThe delivery of standardized self-report assessments is essential for measurement-based care in mental health. Paper-based methods of measurement-based care data collection may result in transcription errors, missing data, and other data quality issues when entered into patient electronic health records (EHRs).ObjectiveThis study aims to help address these issues by using a dedicated instance of REDCap (Research Electronic Data Capture; Vanderbilt University)—a free, widely used electronic data capture platform—that was established to enable the deployment of digitized self-assessments in clinical care pathways to inform clinical decision making. MethodsREDCap was integrated with the primary clinical information system to facilitate the real-time transfer of discrete data and PDF reports from REDCap into the EHR. Both technical and administrative components were required for complete implementation. A technology acceptance survey was also administered to capture physicians’ and clinicians’ attitudes toward the new system.ResultsThe integration of REDCap with the EHR transitioned clinical workflows from paper-based methods of data collection to electronic data collection. This resulted in significant time savings, improved data quality, and valuable real-time information delivery. The digitization of self-report assessments at each appointment contributed to the clinic-wide implementation of the major depressive disorder integrated care pathway. This digital transformation facilitated a 4-fold increase in the physician adoption of this integrated care pathway workflow and a 3-fold increase in patient enrollment, resulting in an overall significant increase in major depressive disorder integrated care pathway capacity. Physicians’ and clinicians’ attitudes were overall positive, with almost all respondents agreeing that the system was useful to their work. ConclusionsREDCap provided an intuitive patient interface for collecting self-report measures and accessing results in real time to inform clinical decisions and an extensible backend for system integration. The approach scaled effectively and expanded to high-impact clinics throughout the hospital, allowing for the broad deployment of complex workflows and standardized assessments, which led to the accumulation of harmonized data across clinics and care pathways. REDCap is a flexible tool that can be effectively leveraged to facilitate the automatic transfer of self-report data to the EHR; however, thoughtful governance is required to complement the technical implementation to ensure that data standardization, data quality, patient safety, and privacy are maintained.

Applications of DEA and SFA in benchmarking studies in forestry: state-of-the-art and future directions

ABSTRACT The forestry sector is constantly looking for ways for making data-driven decisions and improving efficiency. The application of Data Envelopment Analysis (DEA) and Stochastic Frontier Analysis (SFA) allow the users to go beyond looking at simple key performance indicators. Benchmarking is one of the most common tools in business for improving efficiency and competitiveness. This study searched for benchmarking studies in Web of Science until December 2020. It reviewed 56 benchmarking studies in forestry and discusses the potential advantages of using benchmarking in forestry. More than 80% of the studies apply DEA. This review found that almost half of the benchmarking studies in forestry have attempted to estimate the efficiency of forest management organizations at regional scale, mostly being public or state-owned forest districts. A bit more than one-third of the studies have focused on benchmarking forest industries and one-fifth, benchmarking of forest operations. Forest management organizations mainly applied benchmarking for internal comparison and forest industries entirely focused on competitive benchmarking. Surprisingly, in most cases the studies do not necessarily overlap geographically with forest rich countries (e.g., Russian Federation or Brazil). A number of studies address multiple criteria. The future potential for applying automatic data transfer from harvest machines to interactive benchmarking systems are discussed. Finally, the paper discusses the advantages and weaknesses of benchmarking and future research on improving usefulness and usability of benchmarking in forest businesses.

Telemedicine reduces long term mortality in patients with heart failure with reduced ejection fraction

Abstract Funding Acknowledgements Type of funding sources: None. Background Telemonitoring was introduced with the potential to improve the medical care, quality of life and prognosis of patients with heart failure (HF). The aim of the study was to assess the effect of home telemedicine (HTM) in long-term mortality in patients with heart failure with reduced ejection fraction (HFrEF). Methods This is a retrospective study of 452 consecutive subjects with HFrEF who were referred to HTM service. The HTM service was offered to HFrEF patients who: a) have been recently diagnosed with HF, b) have been recently hospitalized due to HF, c) have worsening HF, d) need frequent medication changes, e) are NYHA class II or III. Most patients (n= 352) accepted HTM (HTM-group), but 100 patients refused and received the usual care (UC-group). The HTM group were assessed daily by body weight, blood pressure and heart rate using electronic devices with automatic transfer of data to an online database. A nurse practitioner evaluated the measurements every day using a dedicated clinical user interface. Clinical alerts are dealt with by the HTM nurse calling the patient and then, if necessary, a clinical responder; either a community HF nurse with prescribing qualifications or a cardiologist if long-term changes in therapy are required. Patients in both groups were seen at a specialist HF clinic and the frequency of clinical follow-up was at the discretion of the HF team. The same cardiologists reviewed the patients in both groups. Follow-up period was 60 months. Higher prevalence of male gender was seen in the UC-group (78% vs 67%, p = 0.03). Otherwise there was no significant difference in the demographic characteristics or primary cause of HF between the two groups. Also no differences were seen between the two groups in the treatment with beta blockers, angiotensin-converting enzyme (ACE) inhibitors/angiotensin receptor blockers and aldosterone antagonists. Results The mean follow-up period for survivors was significantly higher in HTM-group compared with UC-group (50.6 ± 18.2 vs. 37.8 ± 25.2, p &amp;lt; 0.001). After 3 month of follow-up, the all-cause mortality was significantly lower in HTM-group than in UC-group (2.8% vs. 14%; p &amp;lt;0.01). This significantly lower mortality in HTM-group compared to UC-group was further observed in 6 months follow-up ( 4.5% vs. 22%, p &amp;lt; 0.0009); in 12 months follow-up (9% vs. 31.2%, p &amp;lt; 0.0002); in 18 months follow-up (13.4% vs. 38.2%, p &amp;lt; 0.0001); in 24 months follow-up (15.1% vs. 42%, p &amp;lt; 0.0001); in 36 months follow-up (19% vs. 44.5%, p &amp;lt; 0.0002); in 48 months follow-up (23% vs. 46%, p &amp;lt; 0.001); and finally in 60 months follow-up (25.3% vs. 46%, p &amp;lt; 0.003). Conclusion HTM was associated with improved survival. This was observed from the first months of the study and remained present until the end of the study.The reduced mortality in the HTM patients may reflect the fact that HTM improves patient HF knowledge and self-care behaviors.

Generic Architecture for the Automatic Parametrization of Production Machine Assembly Programs

In a high-mix low volume production environment, time to market is a key factor. However, one bottleneck lies in the often times manual parametrization of machines for new or modified designs. A truly flexible manufacturing environment therefore requires a continuous data flow from the design stages to the shop floor. This paper presents a concept for the automated parameterization of machines at a large automotive plant. Therefore, this paper initially discusses the results of a stakeholder analysis. The stakeholders comprise of different departments related to the product design and manufacturing processes. The requirements resulting from the interviews conducted with the stakeholders are grouped and ordered by priority. Secondly a general architecture for the control interface is presented. It includes multiple submodules, which model the continuous data flow between the departments and the production systems. The first main step of the data flow is the transformation of the information which is presented in various styles depending on the source departments to structured and standardized data. Thereafter machine parameters are generated automatically by a submodule using the structured input data and inference rules. Finally, the architecture supports the automatic transfer of the machine readable output data to the assembly line. To test the architecture a prototype comprising of more than 100 robots in a live production environment is implemented. It allows for a continuous data flow from the design and productions planning department to the robots. This enables a flexible process control, which up to today has supported the fast roll out of more than 100 new product variants. In contrast to the conventional manual setup of the machines for operation, the prototype was able to show that the monetary and time expenditure could be reduced by 95 percent.

Zinc deficiency: its prevalence and relationship to renal function in Japan.

Although zinc deficiency is common among dialyzed patients, its prevalence among non-dialyzed subjects and its relationship to renal function remain unclear. We selected 816 non-dialyzed subjects (495 males; mean age, 56 ± 18years) who underwent measurement of serum zinc at Jikei University Hospital between April 2018 and March 2019 using the Standardized Structured Medical Information eXchange2 (SS-MIX2) system, a global standard in Japan that enables collection of structured medical records with automatic data transfer to a registry database system. A serum zinc level of 60-80μg/dL was defined as marginal zinc deficiency and a level of < 60μg/dL as absolute zinc deficiency. We investigated factors associated with serum zinc using multiple regression analysis. Marginal and absolute zinc deficiency were present in 52.3% and 30.6% of subjects, respectively. Serum zinc levels tended to decrease with increasing stage of chronic kidney disease (CKD) (P = 0.051). Estimated glomerular filtration rate (eGFR) was not independently associated with serum zinc levels. Instead, serum albumin (t = 4.69, P < 0.01), hemoglobin (t = 2.54, P = 0.01) and mean corpuscular volume (MCV) (t = -2.20, P = 0.03) were independently associated with serum zinc. In sensitivity analyses, serum zinc was not associated with either serum copper- or iron-related parameters. This large-scale study clarified the prevalence of zinc deficiency among non-dialyzed Japanese subjects. In addition, eGFR was not independently associated with serum zinc, probably due to confounding factors, such as nutritional status and degree of anemia. Further investigations are needed to clarify the epidemiology of zinc deficiency and its associations with CKD.

The Research Activities of the Main Astronomical Observatory of the National Academy of Sciences of Ukraine on the Use of GNSS Technology

The current state of the research activities of the Main Astronomical Observatory of the National Academy of Sciences of Ukraine (MAO NASU) on the use of GNSS technology is presented. Today, MAO operates a permanent GNSS network consisting of five stations (two stations are temporarily inactive). All stations are included in EPN and the EPOS network, GLSV, KHAR, and UZHL stations are also integrated to the IGS network. MAO Operational Centre also provides automatic data transfer from 12 stations installed by other Ukrainian organizations included in the international networks. Data from approximately 300 Ukrainian permanent GNSS stations are stored in the MAO Local Data Centre. The MAO Local Analysis Centre uses Bernese GNSS Software for different types of data processing of GNSS observations. Rapid processing is performed on a daily basis for monitoring the stability of the permanent stations from some Ukrainian RTK networks. All available observations data from the Ukrainian stations for the period from December 7, 1997, to January 28, 2017, were processed according EPN standards during the MAO’s second reprocessing campaign and regular processing. As a result, the coordinates in the IGb08 reference frame and tropospheric zenith delays for 233 permanent stations (202 of them are the Ukrainian stations), as well as the velocities for the 128 stations that have observation period more than 3 years, were estimated. The creation and development of local GNSS networks, as well as the long-term filling of databases with high-precision coordinate solutions and estimates of the displacement velocities of GNSS stations, made possible to conduct geodynamic studies at the local level. The strain ellipses and rotation were found based on estimated coordinates and velocities of the GNSS stations. In 2016–2019, Kharkiv National University of Radio Electronics, in cooperation with MAO NASU, performed a number of studies on high-precision GNSS positioning and navigation. New implementations of modern methods and algorithms for the PPP positioning were created and experimentally tested. The errors of the daily floating PPP solutions were 5…8 mm (with a probability of P ≈ 95%) for static mode and 5…8 cm (P ≈ 68%) for kinematic mode. The decimeter/centimeter error of determining the coordinates of current satellites with low Earth orbits (LEO) in the implementation of kinematic floating and fixed PPP solutions was proven. The development of a high-accuracy multiposition phase system of trajectory measurements for field tests of high dynamic aircrafts (HDA) were proposed. The estimated values of the root-mean-square error (RMSE) in determining the HDA motion variables were in the range from 0.05 to 0.40 m for coordinates and from 0.5 to 1.6 cm/s for the velocity vector components.

Process Automation in an IoT–Fog–Cloud Ecosystem: A Survey and Taxonomy

The number of IoT sensors and physical objects accommodated on the Internet is increasing day by day, and traditional Cloud Computing would not be able to host IoT data because of its high latency. Being challenged of processing all IoT big data on Cloud facilities, there is not enough study on automating components to deal with the big data and real-time tasks in the IoT–Fog–Cloud ecosystem. For instance, designing automatic data transfer from the fog layer to cloud layer, which contains enormous distributed devices is challenging. Considering fog as the supporting processing layer, dealing with decentralized devices in the IoT and fog layer leads us to think of other automatic mechanisms to manage the existing heterogeneity. The big data and heterogeneity challenges also motivated us to design other automatic components for Fog resiliency, which we address as the third challenge in the ecosystem. Fog resiliency makes the processing of IoT tasks independent to the Cloud layer. This survey aims to review, study, and analyze the automatic functions as a taxonomy to help researchers, who are implementing methods and algorithms for different IoT applications. We demonstrated the automatic functions through our research in accordance to each challenge. The study also discusses and suggests automating the tasks, methods, and processes of the ecosystem that still process the data manually.

A Mobile Production Monitoring System Based on Internet of Thing (IoT) and Random Forest Classification

Production variations are crucial factors that cause the reduction of production efficiency. These variations are often unpredictable and difficult to be interpreted directly from the production activity of the working station. Automated diagnostic of the causes to variations is therefore the key to overcome the issue. The system should also detect and diagnose variations for all the machines which are placed in the same manufacturing line at the same instance to prevent misaligned of production volume. To achieve this, Internet of thing (IoT) technology is proposed. The technology enables automatic data transfer without the need of human intervention. Through IoT, manufacturers are able to keep track the production activity and resolve problems encountered immediately. In addition, a typical random forest classification model is developed to analyze the production patterns and subsequently identify the causes to the unwanted variations. To the best of authors’ knowledge, this paper presents a first-time work on implementation of a mobile production monitoring system based on IoT and random forest classification. The methodology and technical matter to realize the implementation are highlighted and discussed. Overall, the proposed system has been tested accordingly and visualized through a developed mobile application.

Performance of CUDA Unified Memory in CMS Heterogeneous Pixel Reconstruction

The management of separate memory spaces of CPUs and GPUs brings an additional burden to the development of software for GPUs. To help with this, CUDA unified memory provides a single address space that can be accessed from both CPU and GPU. The automatic data transfer mechanism is based on page faults generated by the memory accesses. This mechanism has a performance cost, that can be with explicit memory prefetch requests. Various hints on the inteded usage of the memory regions can also be given to further improve the performance. The overall effect of unified memory compared to an explicit memory management can depend heavily on the application. In this paper we evaluate the performance impact of CUDA unified memory using the heterogeneous pixel reconstruction code from the CMS experiment as a realistic use case of a GPU-targeting HEP reconstruction software. We also compare the programming model using CUDA unified memory to the explicit management of separate CPU and GPU memory spaces.

Critical Steps in Data Management During a Crisis.

Data management is essential in a flow cytometry (FCM) shared resource laboratory (SRL) for the integrity of collected data and its long‐term preservation, as described in the Cytometry publication from 2016, ISAC Flow Cytometry Shared Resource Laboratory (SRL) Best Practices (Barsky et al.: Cytometry Part A 89A(2016): 1017–1030). The SARS‐CoV‐2 pandemic introduced an array of challenges in the operation of SRLs. The subsequent laboratory shutdowns and access restrictions brought to the forefront well‐established practices that withstood the impact of a sudden change in operations and illuminated areas that need improvement. The most significant challenges from a data management perspective were data access for remote analysis and workstation management. Notably, lessons learned from this challenge emphasize the importance of safeguarding collected data from loss in various emergencies such as fire or natural disasters where the physical hardware storing data could be directly affected. Here, we describe two data management systems that have been successful during the current emergency created by the pandemic, specifically remote access and automated data transfer. We will discuss other situations that could arise and lead to data loss or challenges in interpreting data. © 2020 International Society for Advancement of Cytometry

Reaching Zero-Defect Manufacturing by Compensation of Dimensional Deviations in the Manufacturing of Rotating Hollow Parts

In many sectors such as the aerospace industry, the manufacturing of rotating components is based on multi-stage production systems to achieve the complex requirements of high quality products. Even in the presence of Industry 4.0 and the increasing connectivity, these systems are very prone to failure due to the high level of potential influences of both the system and the products, ultimately leading to defects. The project "ForZDM", funded by the EU under Horizon2020, envisions reducing scrap rate by avoiding and compensating defects at an early stage thus guaranteeing a high quality product. This paper presents an approach using an existing manufacturing line to compensate the dimensional deviations of an inner contour of a turbine shaft at an early stage. Based on measurements of the inner contour, a new rotation axis for the subsequent manufacturing processes is calculated in order to avoid unbalances at the end-of-line control. Different algorithms are developed and integrated in a web-based application to find an optimal rotation axis under consideration of the to-be-manufactured outer contour in an operator-friendly usage on the shop floor. The application is connected with the measurement system and the subsequent CNC machine which enables automatic execution and data transfer.

Continuous glucose monitoring in older people with diabetes and memory problems: a mixed-methods feasibility study in the UK

ObjectivesOlder people with diabetes are at increased risk of harm from hypoglycaemia, particularly where there are coexisting memory problems. Continuous glucose monitoring (CGM) offers important benefits in terms of detecting...

Swedish Quality Registry for Caries and Periodontal Diseases – a framework for quality development in dentistry

ObjectivesL There is a need for monitoring dental health and healthcare, as support for quality development, allocation of resources and long-term planning of dental care. The aim of this paper is to describe the concept and implementation of the Swedish Quality Registry for Caries and Periodontal Diseases (SKaPa). Materials and methods: The SKaPa receives information by automatic transfer of data daily from electronic patient dental records via secure connections from affiliated dental care organisations (DCOs). The registry stores information about DCOs, dental professionals and patients. Information on a patient level includes personal identifier, gender, age, living area, dental status, risk assessments for caries and periodontitis, and dental care provided. In addition, data generated from a global question on patient-perceived oral health are uploaded. In total, more than 400 variables are transferred to the registry and updated daily. Results: In 2018, all of the 21 public DCOs and the largest private DCO in Sweden were affiliated to SKaPa, representing a total of 1,089 public and 234 private dental clinics. The accumulated amount of information on dental healthcare covers 6.9 million individuals out of the total Swedish population of 10 million. SKaPa produces reports on de-identified data, both cross-sectional and longitudinal. Conclusion: As a nationwide registry based on automatic retrieval of data directly from patient records, SKaPa offers the basis for a new era of systematic evaluation of oral health and quality of dental care. The registry supports clinical and epidemiological research, data mining and external validation of results from randomised controlled trials

Piloting Electronic Case Reporting for Improved Surveillance of Sexually Transmitted Diseases in Utah.

The Utah Department of Health (UDOH) developed an electronic case reporting (eCR) process to automatically transfer clinical data from a provider to the state health department, with aims of improving sexually transmitted disease (STD) surveillance data quality, decreasing the time spent on STD case investigations, and expanding the process to other diseases and larger healthcare systems. Reportable Conditions Trigger Codes (RCTC) were placed into the electronic health record (EHR) system at Planned Parenthood Association of Utah (PPAU) to trigger the automatic transfer of clinical data to Utah's public health surveillance system. Received data were de-duplicated, processed, and assigned directly to the public health surveillance system, with minimal manual intervention. Eighteen new data elements, important for STD case investigations, were transferred to cases with eCR. Additionally, the clinical time spent transmitting data was vastly reduced. With the new eCR process more complete and timely data is received by public health. Providers, as well as public health, now spend less time manually transmitting clinical data by fax and/or phone. Automated processes are challenging but can be achieved with a robust disease surveillance system, flexible rules engine, skillful programming, on-going analysis, and successful partnerships. The eCR process created for this project can potentially be useful for other conditions outside of STDs. Results of this demonstration project offer an opportunity for readers to learn about eCR and apply lessons learned to improve their existing eCR systems, or future public health informatics initiatives, at any state-level jurisdiction.