Swiss medical informatics最新文献

{"title":"Going beyond the relapse peak on social network smoking cessation programmes: ChatBot opportunities","authors":"Fabien Dubosson, Roger Schaer, Roland Savioz, M. Schumacher","doi":"10.4414/SMI.33.00397","DOIUrl":"https://doi.org/10.4414/SMI.33.00397","url":null,"abstract":"Research question: A social network programme called J’arrete de fumer was set up in 2016 in the six French-speaking cantons of Switzerland. It consists of Facebook groups where people agree on a date to quit smoking. A peak of relapse appears during the first three weeks of the programme. This research aims to explore the feasibility of building a Chatbot to help people to get over this peak in future iterations of the programme. Methods: It has been shown that the urge to smoke may be one of the reasons for relapses. Being able to distract users from the idea of smoking during these phases would help them to get through these three first weeks. Due to the large number of participants, a human intervention within the craving time frame is difficult to achieve, but such a constraint would be easier to overcome  with ChatBots. Results: A ChatBot for the Telegram platform has been developed. It offers five different modules to overtake the time frame where the urge to smoke is greatest. Some of these modules, such as motivating comments and factual information, are already well used, but some others are less widely explored, like helping scientific research by classifying images or putting people in touch with each other as another form of distraction. Conclusion: ChatBots offer interesting opportunities for helping smoking cessation communities, as they would help participants during craving time frames and would be able to handle the large number of participants.","PeriodicalId":156842,"journal":{"name":"Swiss medical informatics","volume":"19 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-08-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133033922","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Gamification and mHealth: a model to bolster cardiovascular disease self-management","authors":"K. Blondon, P. Meyer, C. Lovis, Frédéric Ehrler","doi":"10.4414/SMI.33.00398","DOIUrl":"https://doi.org/10.4414/SMI.33.00398","url":null,"abstract":"Cardiovascular disease management, especially after an acute event, requires consideration of cardiovascular risk factors, which are generally lifestyle habits. Current implementation of recommended care is suboptimal, due in part to the difficulty of implementing and maintaining health behaviour changes. mHealth apps offer new approaches to support behavioural changes, in particular with the use of gamification strategies. These strategies have the potential to help maintain engagement over time. Reviews of current health apps that use gamification strategies show low integration of theoretical behavioural models in app design, and low use of gamification for cardiovascular self-management apps in general. We propose the integration of gamification strategies in a validated behaviour-change model, based on a patient survey and focus group. This model can be used to design a future smartphone app to support self-management of cardiovascular disease.","PeriodicalId":156842,"journal":{"name":"Swiss medical informatics","volume":"38 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-08-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115142620","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"What clinicians want from healthcare information systems: A survey-based cross-sectional study at a Swiss tertiary care hospital","authors":"J. Sidler, Marc Strasser, S. Bassetti, B. Hug","doi":"10.4414/SMI.32.00352","DOIUrl":"https://doi.org/10.4414/SMI.32.00352","url":null,"abstract":"Little is known about the key demands of clinicians in regard to healthcare information systems (HIS). We therefore aimed to describe important characteristics of user-friendly HIS from a clinicians’ perspective. In November 2015, we consecutively e-mailed two anonymized web-based surveys to all clinicians working at the Department of Internal Medicine of the University Hospital Basel. The surveys did not focus on a specific HIS. The first survey asked for the single most important characteristic of user-friendly HIS and the second survey requested to further sub-categorize these characteristics. The response rates for the first and second survey were 48.2% (40/83 clinicians) and 39.8% (33/83), respectively. In the first survey, the most frequently mentioned characteristics of user-friendly HIS were a “rapid retrieval of relevant clinical data” in 54.3% (19/35), an “easy to use interface” in 17.1% (6/35) and a “simple visual design” in 17.1% (6/35). In the second survey, clinicians divided the main characteristic “rapid retrieval of relevant clinical data” in the following sub-categories: A “simple software architecture” in 97.0% (32/33), the “availability of strong search tools” in 93.9% (31/33), the “presence of only few interface layers” in 72.7% (24/33), an“automatic alerting system” in 63.6% (21/33) and an “overview of important clinical data in one window” in 54.5% (18/33). In conclusion, the clinicians’ top priority in regard to HIS was to quickly find relevant patient information. Developers of HIS should particularly focus on creating intuitive interfaces with powerful search engines and clear visualization of clinical data.","PeriodicalId":156842,"journal":{"name":"Swiss medical informatics","volume":"14 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-10-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115469810","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Adherence to guidelines for therapeutic monitoring of glycopeptide and aminoglycoside antibiotics","authors":"Sascha Karlen","doi":"10.4414/smi.32.00353","DOIUrl":"https://doi.org/10.4414/smi.32.00353","url":null,"abstract":"Introduction Guidelines for therapeutic drug monitoring (TDM) have been established for glycopeptide and aminoglycoside antibiotics due to their narrow therapeutic windows to ensure therapeutic efficacy and to avoid toxic overdosing with nephro- or ototoxicity. The purpose of this quality control study was to determine the adherence to TDM guidelines for the aminoglycoside gentamicin (G) and the glycopeptides vancomycin (V) and teicoplanin (T) in order to assess the need for improvements. Methods We included all inpatients admitted to the University Hospital Zurich over a 3-year-period from 01/01/2012 to 31/12/2014. All electronic orders of intravenously administered G, V, and T and the corresponding TDM-lab-orders were analyzed retrospectively. Medication orders during intensive care stays were not electronically available and were therefore excluded. Institutional guidelines released 2011 provided recommendations for initial monitoring no later than 72 h for G, 60 h for V and 96 h for T, respectively. Shorter initial TDM intervals have been advised for patients with impaired renal function and for thrice in contrast to once daily dosing of G. Guidelines released 2014 propose shorter intervals for G and T. In this analysis, however, these shortened intervals have not been considered. Drug therapies may be prescribed as single or multiple subsequent orders (e.g. for dose adjustments). Therefore, subsequent prescriptions being separated by ≤ 24h were considered as single continuous therapies. To analyze the adherence to guidelines we measured the time period between the start of drug therapy and the initial TDM. Results Drug therapies administered to 115’509 inpatients were analyzed, including 470 G therapies consisting of 1’045 orders, 2’396 V therapies with 6’168 orders and 807 T therapies with 2’184 orders. Therapies were administered for less than 72 h in 40% (188/470) of G, 41% (985/2’396) of V and 26% (209/807) of T. Therapies lasting ≥ 72 h were monitored according to guidelines for G in 72% (203/282), for V in 67% (939/1411) and for T in 63% (379/598). Therapies of ≥ 96 h, were monitored within 72 h for G in 74% (178/241), for V in 74% (907/1’224), and for T in 50% (275/552). Therapies of ≥ 120 h were not monitored at all for G in 7% (14/208), for V in 7% (68/1046), and for T in 13% (66/512), respectively. Conclusion Physicians’ adherence to TDM guidelines was only 72% or below, with the best compliance for G. Overall TDM adherence offers room for improvement and quality assurance actions have to be considered. Educational programs require a continuous effort. As a complementary measure computerized decision support might be implemented. Automated reminders could be displayed in the medical record whenever TDM is overdue. The same algorithms might also be applied to other therapies that have to be monitored according to guidelines. Studies are needed to evaluate the impact of these concepts on patients’ safety, treatment efficacy and physi","PeriodicalId":156842,"journal":{"name":"Swiss medical informatics","volume":"140 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-10-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116444585","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"AUTOMATED IDENTIFICATION OF HOSPITAL-ACQUIRED VENOUS THROMBOEMBOLISM","authors":"P. Beeler","doi":"10.4414/SMI.32.00354","DOIUrl":"https://doi.org/10.4414/SMI.32.00354","url":null,"abstract":"Introduction Venous thromboembolism (VTE) as a hospital-acquired condition (HAC) – i.e. not ‘present on admission’ (POA) – is a potentially preventable complication. A decrease of HAC VTE events indicates success of efforts to prevent VTE in hospitalized patients. However, so far, costly chart reviews were needed to identify patients with HAC VTE. We investigated whether electronic health record data such as medication orders and their temporal relations allow for differentiating between HAC and POA. Therefore, we modeled a tree and two random forests and evaluated the automated classification of HAC VTE. Methods All inpatients with a length of stay of ≥24 hours (h), discharged from the Brigham and Women’s Hospital, a large tertiary care hospital in Boston, MA, between January 2009 and April 2014 were searched for ICD-9 diagnosis codes of acute venous thrombosis or pulmonary embolism. Patients were included who had VTE in the admitting diagnosis field – defined as POA VTE – or in one of up to 50 discharge diagnoses. Of those, only patients who received heparin, dalteparin, enoxaparin, alteplase, rivaroxaban or fondaparinux were considered, and the time from admission to the first order was calculated for each drug. Additionally included predictors: dose information, demographics (age, gender, race, language), length of stay, admission service, discharge service, transfer destination of the patient after discharge, and whether the patient was alive or died during the hospitalization or within 30 days after discharge. A single tree and two random forests (each with 5,000 trees) were generated to analyze the predictors and to assess the predictive power of the chosen approach. Since medication orders are electronically available in real time, such prospective predictors may have implications for clinical decision support – therefore, prospective predictors (i.e. demographics, admission service, time to order a drug, route and dose information for each drug) were separately analyzed in the first random forest. Half of the data served as calibration set, half as validation set. Statistical computing was performed using the software R version 3.1.0 (R Foundation for Statistical Computing, Vienna, Austria). Results A total of 5,374 patient stays featured a VTE diagnosis with a defined drug order. If VTE was POA (n=1,262; 23.5%), the median time to order one of the aforementioned drugs was 2.5h (IQR 1.3-5.0h). Among HAC VTE cases without an admitting diagnosis of VTE (n=4,112; 76.5%), the median time to order the drug was 4.2h (IQR 1.7-18.2h). Unsurprisingly, a single tree – after cross-validation and pruning – identified the time from admission to the ordering of intravenous (IV) heparin as the most significant predictor (Fig. 1). This tree’s validation resulted in an accuracy of 78.8% and a positive predictive value (PPV) of 83.3% for the classification of HAC VTE. The first validated random forest used predictors which are available in real time: the ","PeriodicalId":156842,"journal":{"name":"Swiss medical informatics","volume":"102 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-10-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121476878","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"«Perioperatives Charting» Logistik, Plan- und Managementsysteme versus KIS, EPD, PDMS","authors":"Peter Beerten","doi":"10.4414/smi.32.358","DOIUrl":"https://doi.org/10.4414/smi.32.358","url":null,"abstract":"","PeriodicalId":156842,"journal":{"name":"Swiss medical informatics","volume":"86 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-10-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131479258","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"C Tracker for the Swiss Hepatitis C Cohort – Collecting Patient Reported Outcomes via Smartphones","authors":"Pascal B. Pfiffner","doi":"10.4414/SMI.32.00356","DOIUrl":"https://doi.org/10.4414/SMI.32.00356","url":null,"abstract":"Background and Introduction Last year’s introduction of ResearchKit, an open source toolkit for iOS facilitating the creation of smartphone research apps, has sparked renewed interest in smartphone-driven biomedical research. In addition to the initial five research apps, about a dozen more ResearchKit-powered apps are now available to iOS-using participants in the United States. In April 2016, ResearchStack – the Android counterpart to ResearchKit – has been released, enabling researchers to finally include participants using the most popular mobile operating system. The field now has powerful informatics tools at its disposal, but it still needs to prove that the approach of collecting patient data for biomedical research via smartphones is useful and sustainable. Methods The C Tracker study is an apps-based trial, assessing hepatitis C patients’ activity levels over time. The app distributes surveys to study participants on a 2-weekly basis and returns activity data, such as steps taken and time spent exercising, along with survey answers. Users are identified by a random number, all data is de-identified and encrypted before being sent over the internet. The well-known i2b2 research backend serves as data storage. To provide value to participants, the app also contains a dashboard showing their recent activity, resources informing about hepatitis C and its treatment and other tidbits, such as a map of the US, showing participant origin. We are bringing C Tracker to Switzerland, extending its target population from anonymous “in the wild” recruitment to patients already enrolled in the Swiss Hepatitis C Cohort Study (SCCS). The data delivery toolchain, available open source under the name “C3-PRO” and using the upcoming Fast Healthcare Interoperability Resources (FHIR) standard, is extended with a separate backend system storing participant identity data, linking the app’s user identifier to participants’ SCCS study identifier. Circumnavigating the cloudy waters of electronic consent in Switzerland, we collect paper-based consent from participants during their annual clinic visit, at least initially. We are also adapting our toolchain to ResearchStack and hope to port the complete app to Android in a timely manner. Results & Discussion At this early stage in the project, we have identified steps in the original approach in need of adaptation to Switzerland. Most importantly, we have built an “identity manager”, allowing us to collect paper based consent from patients, recording the consent electronically and provide participants with a link to “unlock” the app, allowing access to the research study part of the app as a fully consented user. While this adds another system that research coordinators need to use, its use is straightforward, only requiring entry of five data items. The link to the app can either be established immediately via QR code or by emailing a link to the participant that will open the app. We are in the process of finalizi","PeriodicalId":156842,"journal":{"name":"Swiss medical informatics","volume":"12 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-10-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128050246","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"eThe Infomed Electronic patient record, what the doctors think about?","authors":"A. Gnaegi","doi":"10.4414/SMI.32.361","DOIUrl":"https://doi.org/10.4414/SMI.32.361","url":null,"abstract":"The aim of the Infomed project is to implement a shared electronic patient record for all the healthcare professionals and patients in the canton du Valais. Just before opening Infomed to the patients we ask the participating physicians about their platform’s satisfaction. We also try to get their opinion about the access by the patients to Infomed and the included medical records.","PeriodicalId":156842,"journal":{"name":"Swiss medical informatics","volume":"41 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-10-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115877571","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Digitaler Mutterpass Schweiz","authors":"Michel Murbach, Sabine Martin, S. Nüssli","doi":"10.4414/SMI.32.360","DOIUrl":"https://doi.org/10.4414/SMI.32.360","url":null,"abstract":"Digitaler Mutterpass Schweiz Autoren: Sabine Martin, Michel Murbach, Stephan Nussli Abstract Die Analyse des Dokumentationsworkflows in der Schwangerschaft diente als Grundlage fur die Festlegung eines Mutterpass-Datensatzes. Dieser wird nach der Dokumentation im Praxisinformationssystem (PIS) im schweizspezifischen Austauschformat SMEEX (Swiss Medical Data Exchange) extrahiert und in einer Datenbank abgelegt. Die entwickelten Prototypen einer Web- und einer mobilen Applikation zeigen die Nutzungsmoglichkeiten des digitalen Mutterpasses fur Schwangere und Behandelnde. Einleitung In der Schwangerschaft werden vielfaltige Daten erfasst. Regelmassige Vorsorgeuntersuchungen bei der Gynakologin oder Hebamme werden, je nach Bedarf, durch Ultraschall-Untersuchungen, pranatale Tests und weitere Ergebnisse erganzt. (1) Die wesentlichen Daten werden im Mutterpass zusammengefasst, der zur institutionsubergreifenden Kommunikation und zum Datenaustausch dient. In diesen werden ab Feststellung der Schwangerschaft Informationen zur werdenden Mutter und ihrem Kind eingetragen, welche auch in Notfallsituationen wichtig sind. Da die papierbasierte Version Nachteile wie eine schlechte Verfugbarkeit mit sich bringt, wurde im Rahmen dieser Bachelorarbeit ein Konzept fur den digitalen Mutterpass entwickelt. Dadurch wird die Verfugbarkeit verbessert und der Aufwand fur Behandelnde verringert. (2) Zur Transformation der Daten aus dem PIS wurde das Austauschformat SMEEX verwendet. (3) Ergebnisse Der Datensatz besteht aus den Kategorien Personalien, pranatale Diagnostik, Serologien, Verlauf, Medikamente und Impfungen, Schwangerschaftsuntersuchungen und Ultraschall. Die Erstellung des Mutterpasses lauft in folgenden Teilschritten ab: 1)      SMEEX Extraktion: die Krankengeschichte des Patienten wird als .smeex-File (Zip-Format) gespeichert. 2)      Upload des Mutterpasses auf der Webseite 3)      Ablegen der Mutterpassdaten in der Datenbank 4)      Abrufen der Daten und Anzeigen des Mutterpasses auf der Webseite oder der mobilen Applikation Die Prufung der Akzeptanz der App erfolgte durch Befragung von 27 Schwangeren, es zeigte sich sehr grosses Interesse. Diskussion Das Interesse an der Arbeit und die positiven Ruckmeldungen bei der Befragung zeigen, dass der Mutterpass gerade fur Schwangere von grosser Bedeutung ist. (4) Die ubersichtliche Darstellung aller personlichen Daten in einer App bietet einen grossen Vorteil gegenuber dem papierbasierten Mutterpass. Zudem werden auch fur den Gynakologen der Aufwand und die Fehleranfalligkeit verringert, da die Daten nicht von Hand ubertragen werden mussen. Voraussetzung dafur ist eine strukturierte und standardisierte Datenerfassung. Das Konzept zur Extraktion der benotigten Daten aus dem PIS muss in einer weiterfuhrenden Analyse ausgearbeitet werden. Ebenfalls ist zu prufen, wie sich der digitale Mutterpass mit dem elektronischen Patientendossier verknupfen lasst. Referenzen x 1. Inselspital Bern, Universitatsklinik fur Frauenh","PeriodicalId":156842,"journal":{"name":"Swiss medical informatics","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-10-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132148361","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Using of Patient-Generated mHealth Data for Patient Care: a Comparison of Four Models","authors":"Frédéric Ehrler, C. Lovis, K. Blondon","doi":"10.4414/SMI.32.00357","DOIUrl":"https://doi.org/10.4414/SMI.32.00357","url":null,"abstract":"The rapid adoption of mobile applications for wellness and health tracking has resulted in vast amounts of patient-generated data. However, these data are often underutilized in the traditional patient care. In this paper, we explore how to use these patient-generated data to improve patient care. Based on a review of healthcare model and recommendations, we propose and compare four models with increasing integration with electronic health records. We also compared the freedom of choice of apps, as well as content validity and expected effectiveness. In the first model, patients have the full range of app choice, and full control over their data, in particular for sharing with healthcare providers. In the second model, patients use a selection of apps to export their data to a repository, which can be accessed by their providers (without integration into the EHR). In the third model, interoperability between the apps and the EHR allows full integration, but restricts app choice. Finally, the last model adds the notion of cost-effectiveness to the previous model. Although the EHR-integrated models limit app choice for patients, the app content is medically validated and patient-generated data is more easily accessed to improve patient care. However, these integrated models require decision support algorithms to avoid overwhelming the healthcare providers with data, and may not necessarily imply better quality patient care.","PeriodicalId":156842,"journal":{"name":"Swiss medical informatics","volume":"99 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-10-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115446566","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}