Chemoprophylaxis Application for Meningococcal Disease for Android ...

Chemoprophylaxis Application for Meningococcal Disease for Android Devices M. Parejo-Bellido1,2, E. Dorronzoro-Zubiete3, M. Zurbarán4, F.J. Sánchez-Laguna4, L. Fernández-Luque3, and A.A. Muñoz-Macho2 1

Open University of Catalonia, Telemedicine Master, Catalonia, Spain 2 Thotalmed, Seville, Spain 3 Salumedia, Seville, Spain 4 University Hospital Virgen del Rocío, Seville, Spain

Abstract—This paper presents a feasibility study about a Clinical Decision Support System (CDSS) application for Android devices, Chemopro, for meningococcal disease using antimicrobial chemoprophylaxis of close contacts. The application implements an algorithm provided by the Regional Ministry of Health of Andalusia, Spain, and it has been extended to consider pregnant women sensible to ceftriaxone. Having the application on a smartphone or a table device allows physicians to have access to the protocol and could reduce errors when it is applied. All the data introduced on the application is sent and stored in a web, which can be consulted to control use of antibiotics on the study of contacts. The implementation was done using ODK, a free and open-source set of tools for building data collection forms or surveys. The forms have been created using ODK and enriched to support constraints and rules to control the branches of the algorithm. Keywords—CDSS, ODK, chemoprophylaxis, meningococcal disease.

I. INTRODUCTION Each year, an estimated 1,400--2,800 cases of meningococcal disease occur in the United States (CDC, unpublished data, 2004) [1]. Meningococcal disease is caused by the bacterium Neiserria meningitidis. The infection produced by this bacterium can cause meningitis, widespread blood infection (sepsis) or a combination on both. One method of prevention for meningococcal disease is antimicrobial chemoprophylaxis of close contacts of a patient with invasive meningococcal disease. Chemoprophylaxis refers to the administration of a preemptive medication. It must be applied only when the benefits overweigh the risks produced by the side effects of the medication. In order to support physicians to make medication recommendations, we have designed an application for Android devices that implements an algorithm provided by the Regional Ministry of Health of Andalusia, Spain. The application is presented as a CDSS. CDSS is a computerized system that, responding to inputs or generating alarms, provides useful information to support the physician in decision-making [2].

This paper contains the following sections: 1) Introduction, 2) Objective, 3) Materials and methods, 4) Results, 5) Discussion and 6) Conclusions. In the objective section the challenges of this project are detailed. In the materials and methods section, the preliminary study will be presented with the description of the application developed. In the Results section, results of this preliminary study are showed. The state of Chemopro application and the next steps to perform to evaluate its effectiveness are explained in the discussion section. Finally, considerations of the Chemoprophylaxis application are reported in the conclusion section. This paper presents a Knowledge-Based CDSS application that implements a protocol to determine which antibiotic should be administered according to the characteristics of the person who has been in contact with a case of meningococcal disease. II. OBJECTIVE The objective of Chemopro is to make a feasibility study of the implementation of a CDSS for meningococcal disease using ODK, a free and open-source set of tools for building data collection forms and surveys. III. MATERIALS AND METHOD For developing the application, we have used ODK, a free and open source suit of tools, explained below in detail. It provides the tools to implement a fast and effective CDSS to allow physicians to apply decision algorithms. A. Decision Support Systems A CDSS is a computerized system that helps the physicians to make a diagnosis, medication recommendations, provide alarms, etc. The CDSS can be active and provide alarms based on a particular situation or it can be passive and respond to the physician inputs providing a response. It must be the physician who has to decide if the information provided by the CDSS is valid or not.

L.M. Roa Romero (ed.), XIII Mediterranean Conference on Medical and Biological Engineering and Computing 2013, IFMBE Proceedings 41, DOI: 10.1007/978-3-319-00846-2_342, © Springer International Publishing Switzerland 2014

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Chemoprophylaxis Application for Meningococcal Disease for Android Devices

At the beginning CDSS, even they were used at healthcare information systems, were focused on assisting in administrative and financial tasks in a retrospective way. As they became more useful they were applied at the clinic domain but keeping its retrospective feature. They were useful for planning (treatment guides, critical pathways). Actual CDSS are able to provide an output to before, during and after the clinical decision has to be made [3]. CDSS include differing areas of care, preventive care, diagnosis, planning or implementing treatment, follow up management, cost reductions and improved patient convenience, etc. They have proved to be useful. The meta-analyses that have been done on CDSS using RCT (Randomized Controlled Trials) reflects that they can alter the decision of the physician, reduce the medication errors, use evidence-based recommendations on medical prescription [4]. There are different types of CDSS. As it has been previously commented, it is important “when” the output is provided or if the output is triggered by an alarm or as a response of an input made by a physician. But the two main categories of the CDSS are: Nonknowledge-Based and Knowledge-Based. • Nonknowledge-Based CDSS: This type of CDSS is designed to learn by itself. It can learn form past experiences or recognize patterns in the clinical data. The approach to these systems has been made using AI (Artificial Intelligence) techniques such as neural networks, fuzzy logic, multi-agent based systems. • Knowledge-Based CDSS: Medical experts generate the knowledge base. Their inference engine uses the knowledge base and/or external input to generate the outputs. This output is displayed to the physicians.

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The algorithm for a CDSS can be built following XForm standards and setting restrictions and conditions that must be satisfied to automatically take a branch or another through the decision tree in order to get the correct result depending of the introduced parameters. C. Chemopro The knowledge base is the algorithm to decide which antibiotic should be prescribed. The algorithm is taken from the Monitoring and Meningococcal Disease Alert Protocol of the Epidemiological Surveillance Service of Andalusia of the Regional Ministry of Health, updated in June 2011. The protocol has been modified to consider pregnant women sensible to ceftriaxone. In these cases, ciprofloxacin is recommended by the NHS guidelines. The protocol is presented in the Fig.1. Based on the inputs of the physician to the different questions the protocol presents the recommended medication. Following these guidelines for a female, pregnant and allergic to penicillin patient the algorithm will recommend Ciprofloxacin 500mg. Implementing the protocol as an application for Android devices eliminates the need to carry the algorithm printed.

B. Open Data Kit ODK is a free and open-source suite of tools that provides an out-of-the-box solution for users to build a data collection form or survey, collect the data on a mobile device, send it to a server, and aggregate the collected data on a server and extract it in useful formats [5]. These tools can be used independently or with each other. ODK use XForm [6] standard, an XML-based form description standard designed by the W3C for the next generation of web forms, implementing the OpenRose [7] subset of XForms. Some tools provided by ODK [8]:

Fig. 1 Chemoprophylaxis Algorithm

• ODK Collect (Smart Phone Client) is a client on Google's open source Android platform written in Java. • ODK Aggregate (Server Storage) is a ready-todeploy server that hosts forms and submitted results. • ODK Build is a form designer with a drag-anddrop user interface. It is an HTML5 web application and works best for designing simple forms.

The physicians can evaluate a scenario just using his smartphone or tablet in an easy and fast way. It also reduces the possibility of making input error as the system inputs are controlled by constraints. The application can send the collected data to a server. With the collected data saved in this server the physician has control about the use of antibiotics on the study of contacts.

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D. Implementation Chemopro has a forward chaining inference engine, also called data-directed inference because inference is triggered by the arrival of new data. When a user answers a question the introduced data triggers a rule whose conditions are matched. These rules perform their actions, asking for more input data to the user. The new data trigger new rules and so on until get a final result. Chemopro has been designed graphically using ODK Build. As ODK Build does not support complex constraints and rules the form created has been modified following XForm standards adding this new feature. Range constraints are used to validate the data introduced by the user. Using constraints input errors are avoided. Relevant attribute is used to control which branch of the algorithm will be taken in each step to reach the correct result. The value of the relevant attribute is an expression that references the values of previously answered questions. If this evaluates to true, the question will be shown; if it evaluates to false, it will be skipped. The code shown in Fig.2 makes that the question "Pregnancy or Breastfeeding?" was only asked if the gender question was answered 'Female'.

Fig. 3 Get chemoprophylaxis form Once the form is in the device there is no need to download it again. It can be used for as many times as it is required. “Fill blank form” starts the form of the CDSS as shown in Fig.4. To answer a question is as easy as introducing the required information and sliding with the finger to the next question. This new question is presented based on the input of the physician and following the previously presented algorithm.

Fig. 2 Code for Checking Gender Once Chemopro XForm is designed, it can be uploaded to a server with ODK Aggregate installed. We have used Google Application Engine server (GAE) in which we have configured an application and installed ODK Aggregate. Then, we have uploaded the Chemoprophylaxis Algorithm XForm. From this moment the form is available to be downloaded for Android clients with ODK Collect installed. It is possible to configure the server so that only privileged users can connect and download the form. The application is configured so that the server URL and other configuration parameters are pre-established and no accessible. In this way, users do not have to worry about settings. The application presents an intuitive selection screen where different actions can be performed. The first step when launching the application for the first time is to get the form that implements the Chemoprophylaxis Algorithm. “Get the blank form” button connects to the server and returns all the available forms. The form can be selected and downloaded to the application as shown in Fig. 3. As soon as the download process is finished the form is ready to be used.

Fig. 4 Chemopro algorithm As the last question is answered the application shows on screen the recommended medication. The form can be saved and the application returns to the main screen. Back to the main menu there is an option to send the completed forms to the server, edit previously completed forms and remove them. Physicians can access to the server using a web navigator and study and evaluate the values of all the Chemopro forms sent. These values and results (comparative use of antibiotics) can also be presented as pie charts, bar graphs or maps. It is possible to filter them by any of the parameters defined in Chemo-pro as well, as shown in Fig.5.


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VI. CONCLUSIONS

Fig 5 Pie chart: % gender in Chemopro forms

ODK Aggregate allows exporting all the data saved in different formats such as CVS, KML and JSON. This feature allows exporting the collected data in order to be used in other medical applications. IV. RESULTS We have performed a complete battery of tests to the Chemopro application checking all the possible combinations without patients involved. Physicians from the University Hospital Virgen del Rocio have verified that Chemopro works according to the algorithm taken from the Monitoring and Meningococcal Disease Alert Protocol of the Epidemiological Surveillance Service of Andalusia of the Regional Ministry of Health. V. DISCUSSION Chemopro could help physicians in making-decisions and reduce medication errors. The data is sent to a centralized server so that it is available to do statistical studies and to have control about the use of antibiotics on the study of contacts. Testing Chemopro on real patients could have endangered them by wrong advise to physicians, therefore the application has been tested through laboratory trials. We have verified that Chemopro works according to the algorithm implemented. It is necessary that the application complaints with the medical devices regulations for safety and security, and implements interoperability standards such as those defined in HL7 (Health Level 7) [9] for its integration with the EHR (Electronic Medical Record). It is also very important a pilot controlled phase in order to evaluate the usability and effectiveness of Chemopro.

In this paper a feasibility study of the implementation of a CDSS for meningococcal disease using ODK is presented. The algorithm that has been used is taken from the Monitoring and Meningococcal Disease Alert Protocol of the Epidemiological Surveillance Service of Andalusia of the Regional Ministry of Health. It has been expanded to consider pregnant women sensible to ceftriaxone. The application has been built using ODK, which provides a suit of tools that make possible to implement similar CDSS in a fast and effective way. The form has been created using ODK Builder and it has been enriched to control which one of the different branches of the algorithm to take according to the input data and to provide constraints to the inputs that reduces errors. The application could be useful to support physicians in decision-making and for reducing medication errors. Moreover, all the data is sent to a server were it can be presented in tables and charts to be studied. The data can also be exported in different formats such as CVS, KML and JSON.

REFERENCES 1. Bilukha, O., & Rosenstein, N. E. (2005). Prevention and Control of Meningococcal Disease. 2. Yaw Anokwa. Improving Clinical Decision Support In Low-Income Regions (Slides). Dissertation at University of Washington. 2012. 3. Berner ES. Clinical decision support systems: State of the Art. AHRQ Publication No. 09-0069-EF. Rockville, Maryland: Agency for Healthcare Research and Quality. June 2009. 4. Berner, E. S., & Lande, T. J. (2007). Clinical Decision Support Systems (pp. 3–22). New York, NY: Springer New York. Medical Records, Medical Education and Patient Care. 2nd ed. Cleveland: Case Western Reserve University Press, 2007. 5. Carl Hartung, Yaw Anokwa, Waylon Brunette, Adam Lerer, Clint Tseng, Gaetano Borriello. Open Data Kit: Tools to Build Information Services for Developing Regions. Information and Communication Technologies and Development (ICTD). 2010. 6. XForms at http://www.w3.org/MarkUp/Forms/ 7. OpenRosa Consortium at http://openrosa.org/ 8. Open Data Kit at http://opendatakit.org 9. Health Level 7 at http://www.hl7.org/ The address of the corresponding author: Author: Institute: Street: City: Country: Email:

Mónica Parejo Bellido Universitat Oberta de Catalunya Finlandia, 2, H, 1º D C.P. 41012 Seville Seville [email protected]
