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      Effectiveness of Implementation of Electronic Malaria Information System as the National Malaria Surveillance System in Thailand

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          Abstract

          Background

          In moving toward malaria elimination, one strategy is to implement an active surveillance system for effective case management. Thailand has developed and implemented the electronic Malaria Information System (eMIS) capturing individualized electronic records of suspected or confirmed malaria cases.

          Objective

          The main purpose of this study was to determine how well the eMIS improves the quality of Thailand’s malaria surveillance system. In particular, the focus of the study was to evaluate the effectiveness of the eMIS in terms of the system users’ perception and the system outcomes (ie, quality of data) regarding the management of malaria patients.

          Methods

          A mixed-methods technique was used with the framework based on system effectiveness attributes: data quality, timeliness, simplicity, acceptability, flexibility, stability, and usefulness. Three methods were utilized: data records review, survey of system users, and in-depth interviews with key stakeholders. From the two highest endemic provinces, paper forms matching electronic records of 4455 noninfected and 784 malaria-infected cases were reviewed. Web-based anonymous questionnaires were distributed to all 129 eMIS data entry staff throughout Thailand, and semistructured interviews were conducted with 12 management-level officers.

          Results

          The eMIS is well accepted by system users at both management and operational levels. The data quality has enabled malaria personnel to perform more effective prevention and control activities. There is evidence of practices resulting in inconsistencies and logical errors in data reporting. Critical data elements were mostly completed, except for a few related to certain dates and area classifications. Timeliness in reporting a case to the system was acceptable with a delay of 3-4 days. The evaluation of quantitative and qualitative data confirmed that the eMIS has high levels of simplicity, acceptability, stability, and flexibility.

          Conclusions

          Overall, the system implemented has achieved its objective. The results of the study suggested that the eMIS helps improve the quality of Thailand’s malaria surveillance system. As the national malaria surveillance system, the eMIS’s functionalities have provided the malaria staff working at the point of care with close-to-real-time case management data quality, covering case detection, case investigation, drug compliance, and follow-up visits. Such features has led to an improvement in the quality of the malaria control program; the government officials now have quicker access to both individual and aggregated data to promptly react to possible outbreak. The eMIS thus plays one of the key roles in moving toward the national goal of malaria elimination by the next decade.

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          Most cited references32

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          Electronic health records implementation: an evaluation of information system impact and contingency factors.

          This paper provides a review of EHR (electronic health record) implementations around the world and reports on findings including benefits and issues associated with EHR implementation.
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            Evaluation of reporting timeliness of public health surveillance systems for infectious diseases

            Background Timeliness is a key performance measure of public health surveillance systems. Timeliness can vary by disease, intended use of the data, and public health system level. Studies were reviewed to describe methods used to evaluate timeliness and the reporting timeliness of National Notifiable Diseases Surveillance System (NNDSS) data was evaluated to determine if this system could support timely notification and state response to multistate outbreaks. Methods Published papers that quantitatively measured timeliness of infectious disease surveillance systems operating in the U.S. were reviewed. Median reporting timeliness lags were computed for selected nationally notifiable infectious diseases based on a state-assigned week number and various date types. The percentage of cases reported within the estimated incubation periods for each disease was also computed. Results Few studies have published quantitative measures of reporting timeliness; these studies do not evaluate timeliness in a standard manner. When timeliness of NNDSS data was evaluated, the median national reporting delay, based on date of disease onset, ranged from 12 days for meningococcal disease to 40 days for pertussis. Diseases with the longer incubation periods tended to have a higher percentage of cases reported within its incubation period. For acute hepatitis A virus infection, which had the longest incubation period of the diseases studied, more than 60% of cases were reported within one incubation period for each date type reported. For cryptosporidiosis, Escherichia coli O157:H7 infection, meningococcal disease, salmonellosis, and shigellosis, less than 40% of cases were reported within one incubation period for each reported date type. Conclusion Published evaluations of infectious disease surveillance reporting timeliness are few in number and are not comparable. A more standardized approach for evaluating and describing surveillance system timeliness should be considered; a recommended methodology is presented. Our analysis of NNDSS reporting timeliness indicated that among the conditions evaluated (except for acute hepatitis A infection), the long reporting lag and the variability across states limits the usefulness of NNDSS data and aberration detection analysis of those data for identification of and timely response to multistate outbreaks. Further evaluation of the factors that contribute to NNDSS reporting timeliness is warranted.
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              Surveillance systems evaluation: a systematic review of the existing approaches

              Background Regular and relevant evaluations of surveillance systems are essential to improve their performance and cost-effectiveness. With this in mind several organizations have developed evaluation approaches to facilitate the design and implementation of these evaluations. Methods In order to identify and to compare the advantages and limitations of these approaches, we implemented a systematic review using the PRISMA guidelines (Preferred Reporting Items for Systematic Reviews and Meta-Analyses). Results After applying exclusion criteria and identifying other additional documents via citations, 15 documents were retained. These were analysed to assess the field (public or animal health) and the type of surveillance systems targeted; the development process; the objectives; the evaluation process and its outputs; and the attributes covered. Most of the approaches identified were general and provided broad recommendations for evaluation. Several common steps in the evaluation process were identified: (i) defining the surveillance system under evaluation, (ii) designing the evaluation process, (iii) implementing the evaluation, and (iv) drawing conclusions and recommendations. Conclusions A lack of information regarding the identification and selection of methods and tools to assess the evaluation attributes was highlighted; as well as a lack of consideration of economic attributes and sociological aspects.
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                Author and article information

                Contributors
                Journal
                JMIR Public Health Surveill
                JMIR Public Health Surveill
                JPH
                JMIR Public Health and Surveillance
                JMIR Publications (Toronto, Canada )
                2369-2960
                Jan-Jun 2016
                06 May 2016
                : 2
                : 1
                : e20
                Affiliations
                [1] 1Department of Tropical Hygiene (Biomedical and Health Informatics) Faculty of Tropical Medicine Mahidol University BangkokThailand
                [2] 2Center of Excellence for Biomedical and Public Health Informatics (BIOPHICS) Faculty of Tropical Medicine Mahidol University BangkokThailand
                [3] 3Thai Health Information Standards Development Center (THIS) Health Systems Research Institute Ministry of Public Health NonthaburiThailand
                Author notes
                Corresponding Author: Jaranit Kaewkungwal jaranit.kae@ 123456mahidol.ac.th
                Author information
                http://orcid.org/0000-0003-4846-9513
                http://orcid.org/0000-0001-8455-2344
                http://orcid.org/0000-0003-1031-7979
                http://orcid.org/0000-0001-9715-6658
                http://orcid.org/0000-0003-0042-9889
                http://orcid.org/0000-0002-4425-1202
                http://orcid.org/0000-0003-4401-8803
                http://orcid.org/0000-0002-3887-4462
                http://orcid.org/0000-0001-8932-1086
                http://orcid.org/0000-0001-7916-8460
                Article
                v2i1e20
                10.2196/publichealth.5347
                4869224
                27227156
                a5d9430c-7929-4330-a88f-70c0a3339078
                ©Shaojin Ma, Saranath Lawpoolsri, Ngamphol Soonthornworasiri, Amnat Khamsiriwatchara, Kasemsak Jandee, Komchaluch Taweeseneepitch, Rungrawee Pawarana, Sukanya Jaiklaew, Boonchai Kijsanayotin, Jaranit Kaewkungwal. Originally published in JMIR Public Health and Surveillance (http://publichealth.jmir.org), 06.05.2016.

                This is an open-access article distributed under the terms of the Creative Commons Attribution License ( http://creativecommons.org/licenses/by/2.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work, first published in JMIR Public Health and Surveillance, is properly cited. The complete bibliographic information, a link to the original publication on http://publichealth.jmir.org, as well as this copyright and license information must be included.

                History
                : 16 November 2015
                : 11 February 2016
                : 20 February 2016
                : 21 March 2016
                Categories
                Original Paper
                Original Paper

                surveillance system,epidemiology,mixed-methods,evaluation,malaria,emis,data quality,public health informatics,thailand

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