Data Quality of Coronary Health Disease Surveillance Improvement through Technology Acceptance

Data Quality of Coronary Health Disease Surveillance Improvement through Technology Acceptance | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Data Quality of Coronary Health Disease Surveillance Improvement through Technology Acceptance Arief Hargono, Chatarina Umbul Wahyuni, Hari Basuki Notobroto, and 4 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8546554/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 11 You are reading this latest preprint version Abstract Background and aims: The application of information technology can support decision making and accelerate data recording and information reporting. Surveillance attributes and acceptance of information technology contribute to the implementation of surveillance to achieve its goals. Implementation and intervention of surveillance attributes need to be done in a balanced manner so that the surveillance system can run effectively and efficiently. This study was to develop a model for improving NCD data quality through technology acceptance in NCD surveillance attributes. Methods This research using cross-sectional design. This study uses the total population, namely all health centers in Surabaya to be the target of the study. Data analysis will be conducted using Spearman Correlation Test, Fisher Exact and path analysis. Results The surveillance attribute (Acceptability) and the TAM component (Perceived Usefulness) have the highest impact on the CHD surveillance Data Quality attribute. The quality of CHD surveillance data will improve if supported by the use of information technology that can be perceived by officers to support their performance. Conclusion The use of the NCD Web Portal has an impact on the Data Quality attribute. Information technology will improve the quality of surveillance data if the technology can provide benefits to officers Surveillance Attributes TAM Information Technology Figures Figure 1 Introduction Coronary Heart Disease (CHD) remains one of the leading causes of mortality and morbidity globally, with an increasing burden observed in developing nations due to urbanization, lifestyle changes, and limited access to preventive healthcare services (WHO, 2023).Coronary heart disease is the most common type of heart disease. It killed 371,506 people in 2022 and about 1 out of every 5 deaths from cardiovascular diseases (CVDs) was among adults younger than 65 years old. 1 According to the results of the Basic Health Research (Riskesdas) in 2007, 2013 and 2018 and Indonesia Health Survei (SKI) in 2023, there is an increasing trend in the prevalence of NCDs. In addition, obesity, which is one of the risk factors for non-communicable diseases in Indonesia, is known to continue to increase. In 2017, it was reported that the proportion of obesity (BMI > 27.0) was 10.5% and increased to 14.8% (2013) and 21.8% (2018). According to the 2023 Indonesian Health Survey (SKI), the national prevalence of doctor-diagnosed heart disease among all age groups is 0.85% (95% CI: 0.82%–0.88%). This indicates that approximately 8 to 9 out of every 1,000 people in Indonesia have been diagnosed with heart disease. 2 The increase in the prevalence of non-communicable diseases is a serious threat to development, due to the rapid growth of the national economy. Surveillance systems for CHD are critical in supporting timely monitor health problem, public health responses, informing policy decisions, and guiding resource allocation. 3 However, the utility of these systems is directly linked to the quality of data they collect, which must be accurate, complete, timely, and reliable. 4 In many developing nations, CHD surveillance is constrained by fragmented health information systems, limited infrastructure, and human resource challenges. 5 These limitations compromise data quality and, in turn, weaken the formulation and evaluation of healthcare policies. In Indonesia, CHD Surveillance is a part of NCD Surveillance. The implementation of NCD surveillance in Indonesia is currently supported by information technology which is organized into a web-based NCD Surveillance Information System (SIS PTM). 6 The application of SIS PTM in the form of NCD Web Portal in surveillance activities is expected to improve the quality of surveillance. The use of information technology in public health surveillance allows the surveillance system to perform more complex recording and reporting, but done in a relatively fast time with more valid results. 7 – 8 A study conducted by Pollettini et al showed that the application of information technology can support decision-making and accelerate data recording and information reporting. 9 The quality of NCD surveillance can be assessed based on surveillance attributes. The application of information technology in surveillance systems can affect surveillance attributes including Simplicity, Flexibility, Acceptability, Sensitivity, PPV, Representativeness, Timeliness, Data Quality, and Stability. Regular evaluation of surveillance attributes is expected to improve and maintain the quality of surveillance and ensure that the surveillance system runs according to the expected goals. In recent years, the adoption of health information technologies (HIT) has been promoted as a solution to improve the performance and reliability of health surveillance systems. The successful integration of such technologies depends not only on infrastructure readiness but also on the acceptance of these technologies by healthcare professionals and system users . 10–11 Utility and simplicity are important variables in the implementation of information technology in surveillance. This is in accordance with the Technology Acceptance Model (TAM) theory by Davis, Bagozzi, & Warshaw, TAM theory is a user acceptance model in using information technology. TAM theory states that the willingness to use information technology (actual behavior) is influenced by one's intention.. TAM theory is a theory of technology acceptance that is widely used in research on the implementation of information technology in health services. The variables that make up TAM can estimate and explain the use of information technology by users in the health sector. 10 – 12 Surveillance attributes and acceptance of information technology contribute to the implementation of surveillance to achieve its goals. The combination and relationship between attributes determine the strengths and weaknesses of surveillance systems. 13 Implementation and intervention of surveillance attributes need to be done in a balanced manner so that the surveillance system can run effectively and efficiently. The study aims to develop a model for improving the quality of Coronary Heart Disease (CHD) data through technology acceptance in Coronary Heart Disease (CHD) surveillance attributes. Materials and Methods Research Design This type of research is observational with a cross-sectional design. This study made observations at the same time on the research variables, namely surveillance attributes without providing treatment. The research location was chosen based on the high prevalence of CHDs and the consideration that CHD surveillance and information technology-based NCD information systems have been implemented in all Public Health Centre and have the most NCD Integrated Service Delivery Centers (Puskesmas) and Posbindu (Integrated Post for non-communicable diseases) NCD in East Java Province Participants The study population was Puskesmas Pandu in Surabaya City. This study used the total population, i.e. all Puskesmas in Surabaya became the target of the study. The research respondents were CHD surveillance officers who worked at Puskesmas in Surabaya City, totaling 63 Puskesmas. Puskesmas in Surabaya City have at least one officer who is responsible for NCD surveillance activities in the Puskesmas and its working area. If there is a Puskesmas that has more than one NCD surveillance officer, one is chosen to be in charge of the CHD surveillance program. Inclusion criteria as a condition for respondents were NCD surveillance officers at Puskesmas who had attended NCD surveillance training at least once and had run the CHD surveillance program for at least one year. Instruments The instruments used in this study were developed by adapting the technology acceptance instrument from Davis, Bagozzi, & Warshaw (Davis, 1989) and the surveillance system evaluation from CDC (CDC,2001). Questions and answer choices in the questionnaire were read by the enumerator and not shown to the respondents. Data Analyzed The relationship between the independent variable of respondent characteristics and the dependent variable of Data Quality using Spearman Correlation Test and Fisher Exact. The result of the analysis is a correlation coefficient to measure the closeness of the variable relationship. Data processing and analysis for model development using Path Analysis. The output of the analysis results in the form of path analysis of the relationship between surveillance system attributes. Result Characteristics Respondent Respondent characteristics are the variables examined in this research. Respondent characteristics examined included respondent age, gender, education level, length of service and knowledge of NCD surveillance. Table 1 Characteristics Respondent Variable n % Sex Male 23 36,5 Female 40 63,5 Education Elementary School 0 0,0 Junior High School 0 0,0 SMA 0 0,0 Diploma 36 57,1 Bachelors Degree 27 42,9 Number of years in NCD Surveillance ≤ 5 years 58 92,1 6–10 years 2 3,2 > 10 years 3 4,8 Knowledge Low 0 0,0 Medium 2 3,2 High 61 96,8 The gender of surveillance officers at Puskesmas throughout Surabaya City who became research respondents showed that most of the respondents were female, namely 40 people or 63.5%. Based on the level of education, most respondents have completed at least a diploma, namely 36 people or 57.1%. Respondents with a Bachelor's degree were 27 people or 42.9%. Description of Data Quality Attributes The implementation of CHD surveillance at Puskesmas throughout Surabaya City based on the description of Data Quality Attributes is presented in Table 1 . Assessment of the Data Quality attribute is based on the number of reports with blank answers, both manually and electronically. Table 2 shows that the quality of CHD surveillance data is high. This indicates that most reports were filled in completely. Table 2 Distribution of Data Quality Attributes of Manual and Electronic Health Center CHD Surveillance in Surabaya, 2018. Atribut Data Quality n Prosentase (%) Low 4 6,3 High 59 93,7 Total 63 100,0 Mean ± Manual Standart Deviation 3,41 ± 13,28 Minimum Manual Score 0 Maxsimum Manall Score 67 Mean ± Electronic Standart Deviation 4,30 ± 13,96 Minimum Electronic Score 0 Maxsimum Electronic Score 67 Manual data entry is better than electronic data entry. The average percentage of blank reports using the manual method is lower at 3.41 with a standard deviation of 13.28. While the use of electronic methods obtained an average percentage of blank reports of 4.30 with a standard deviation of 13.96. The good Data Quality category occurs in 60 health centers or 95.2 percent of all health centers. Perceived Ease of Use of the NCD Web Portal for CHD Surveillance A description of the perceived ease of use of the NCD Web Portal for Puskesmas NCD surveillance officers in Surabaya City is presented in the following table: Table 3 Perceived Ease of Use Value of PTM Web Portal in Supporting Puskesmas NCD Surveillance Activities in Surabaya, 2018 Perceived Ease of Use n (%) Low 52 82,5 High 11 17,5 Total 63 100,0 Mean ± Standart Deviation 68,23 ± 10,52 Minimum 41,54 Maximum 93,85 The table above shows that most NCD surveillance officers stated that the NCD Web Portal application was difficult to use. This can be seen based on the perceived ease of use in the high category, which is known to have a prevalence of 82.5% of officers. Respondents still find it difficult to enter and process data using the NCD Web Portal. Information and guidance in the NCD Web Portal application was felt to be lacking. They still need other people to help use the NCD Web Portal application. It takes a long time to learn this application so training is needed to learn it. Association between Respondent Characteristics and Data Quality Attributes The following are the results of the analysis of the relationship between respondent characteristics and Data Quality attributes. The characteristics of respondents studied consisted of respondent age, gender, respondent education level, length of service in NCD surveillance, respondent knowledge about CHD, and respondent knowledge about NCD surveillance to Data Quality attributes. The Spearman test was used for variables that had more than 2 categories, namely age and length of service as a NCD surveillance manager. The Fisher test was used for variables that had 2 categories, namely gender, education level, and knowledge of CHD and CHD surveillance. Table 4 Association between Respondent Characteristics and Quality Data Attributes of NCD Surveillance at Puskesmas in Surabaya, 2018 Dependent Variable Independent Variable Correlation Coefficient Value Atribut Data Quality Age 0,171* Gender 0,170** Education 0,108** Length of Service NCD Surveillance 0,058* CHD Knowledge 0,028** CHD Surveillance Knowledge 0,158** Notes: * Using Spearman Test ** Using Fisher Test Table 4 shows that the respondent characteristics variables generally have a low association to the Data Quality attribute. The respondent characteristic that has the highest value to the Data Quality attribute is the age of the respondent while the lowest association is the respondent's knowledge about CHD. Perceived Usefulness of the NCD Web Portal for NCD Surveillance Puskesmas CHD surveillance uses information technology in the form of the NCD Web Portal to support surveillance recording and reporting. The variable of officer acceptance of the use of the PTM Web Portal is strategic in the implementation of surveillance. Perceived Usefulness or application usability is one of the variables that influence a person's acceptance of using information technology (Davis, 1989). Perceived Uselfuness shows the definition of a person's level of confidence in using an application that will improve performance (Ward, 2013). A description of the usability value of the NCD Web Portal for Puskesmas NCD surveillance officers in Surabaya City is presented in Table 5 . Table 5 Perceived Usefulness Value of the NCD Web Portal in Supporting Puskesmas NCD Surveillance Activities in Surabaya, 2018 Perceived Usefullness n (%) Low 42,9 42,9 High 57,1 57,1 Total 100,0 100,0 Mean ± Standart Deviation 73,91 ± 15,21 Minimum 20 Maximum 100 Table 5 . shows that mostly respondents consider that the PTM Web Portal application is useful for supporting their performance. In addition to improving the quality of work, the NCD Web Portal can also increase the productivity of respondents. Most respondents stated that the use of the NCD Web Portal facilitates CHD surveillance work, especially in the recording and reporting process. Others mentioned that the NCD Web Portal was not yet reliable enough to display data and prepare reports. This application has also not been able to shorten the response time for handling reported health problems. Association between Respondent Characteristics and Data Quality Attributes The following are the results of the analysis of the relationship between respondent characteristics and Data Quality attributes. The characteristics of respondents studied consisted of respondent age, gender, respondent education level, length of service in NCD surveillance, respondent knowledge about CHD, and respondent knowledge about NCD surveillance to Data Quality attributes. The Spearman test was used for variables that had more than 2 categories, namely age and length of service as a NCD surveillance manager. The Fisher test was used for variables that had 2 categories, namely gender, education level, and knowledge of CHD and CHD surveillance. Table 6 Association between Respondent Characteristics and Quality Data Attributes of NCD Surveillance at Puskesmas in Surabaya, 2018 Dependent Variable Independent Variable Correlation Coefficient Value Atribut Data Quality Age 0,171* Gender 0,170** Education 0,108** Length of Service NCD Surveillance 0,058* CHD Knowledge 0,028** CHD Surveillance Knowledge 0,158** Notes: * Using Spearman Test ** Using Fisher Test Table 6 shows that the respondent characteristics variables generally have a low association to the Data Quality attribute. The respondent characteristic that has the highest value to the Data Quality attribute is the age of the respondent while the lowest association is the respondent's knowledge about CHD. Association between Surveillance Attributes and Perceived Ease of Use and Perceived Usefulness of the PTM Web Portal with Data Quality Attributes Path analysis was used to develop a model that aims to determine the cause and effect relationship in each variable. The variables analyzed include surveillance attributes and Perceived Ease of Use and Perceived Usefulness of using the NCD Web Portal with CHD surveillance data quality. The results of the Path Analysis are presented in Fig. 1 Results of Path Analysis of Surveillance Attributes as well as Perceived Ease of Use and Perceived Usefulness of the NCD Web Portal with CHD Surveillance Data Quality Attributes in Surabaya,2018. Figure 1 shows the various pathways of the relationship between surveillance attributes and Perceived Ease of Use and Perceived Usefulness of using the NCD Web Portal in supporting the quality of CHD surveillance data. Path analysis displays the value of the regression coefficient as one of the considerations in determining the best flow and strategic indicators in improving the quality of CHD surveillance data. The results of the path analysis between variables along with the regression coefficient values are presented in Table 7 . Table 7 Results of Path Analysis of Surveillance Attributes and Perceived Ease of Use and Perceived Usefulness with CHD Surveillance Data Quality Attributes in Surabaya, 2018 No. Causalitas Regression Coefficient 1. Simplicity input → Simplicity process 0,376 2. Simplicity process → Simplicity output 0,586 3. Simplicity output → Acceptability 0,098 4. Acceptability → Data Quality 0,225 5. Simplicity output → Flexibility 0,145 6. Flexibility → Timeliness -0,075 7. Timeliness → Data Quality -0,206 8. Stability → Acceptability -0,061 9. Stability → Timeliness -0,140 10. Stability → Data Quality -0,124 11. Perceived Ease of Use → Data Quality -0,012 12. Perceived Usefulness → Data Quality 0,034 Discussion Respondent characteristics are aspects that can have an impact on the implementation of NCD surveillance. Respondent characteristics can also be a strategic factor in the application of information technology in NCD surveillance. The characteristics of respondents studied were age, gender, education level, length of service, and knowledge about NCDs, as well as knowledge about NCD surveillance. The discussion reviews the characteristics of respondents towards the implementation of information technology-based NCD surveillance and its impact on the quality of data produced. Individual age is one of the variables that have an impact on differences in attitudes and behaviors, including the use of information technology. The age of respondents in this study is classified as productive age. Someone with a productive age range has good performance. Productive age will make one's productivity at work better than unproductive age. 14 Productivity will decrease as one gets older and approaches retirement. 15 The use of information technology in generation Y which is in productive age is one of the strategic methods to support CHD surveillance. The results showed that the age of respondents produced a positive value for data quality. The results of the assessment of the data quality attribute also show that the quality of CHD surveillance data using the PTM Web Portal in Surabaya has a high value. Education level is an important variable in human resources. Decree of the Indonesian Minister of Health No. 1116 of 2003 concerning Guidelines for the Implementation of the Health Epidemiological Surveillance System states that epidemiologists are one of the resources for organizing surveillance systems. The presence of epidemiologists and doctors is also an indicator of the implementation of the surveillance system at every administrative level from Puskesmas or Hospitals, Districts / Cities, Provinces to the Center. The results showed a positive relationship between the level of education of respondents and the quality of data produced. This positive relationship indicates that the higher the level of education of surveillance officers, the better the quality of CHD surveillance data. This is in accordance with research conducted by Maringan, Pongtuluran, & Maria, which states that the higher the level of education a person takes, the more productivity and performance achievement will increase. 15 Length of service in NCD surveillance provides an opportunity to get training on surveillance. The results of this study show that the length of time surveillance officers have worked in NCD surveillance and surveillance is mostly less than 5 years. In addition, there was a positive relationship between the length of service of surveillance officers and the quality of data produced. Length of service is expected to have an impact on the implementation of surveillance. Experience in running surveillance programs can be a provision in running surveillance programs better. According to Kista et al, increasing work experience or length of service will improve the performance of surveillance officers. 16 – 17 Experience in the administration of surveillance programs can substantially augment their efficacy by offering valuable insights, enhancing system design, and promoting adaptability to emerging challenges. The existence of established surveillance systems, such as those utilized by the Centers for Disease Control and Prevention (CDC) for cancer monitoring, underscores the significance of leveraging historical data and monitoring efforts to strategize interventions and assess the effectiveness of public health initiatives . 18 Knowledge of surveillance concepts can be information in carrying out CHD surveillance activities. Available information is one aspect of a person to take an action. This is in accordance with the concept of Theory of Reasoned Action (TRA) from Fishbein & Ajzen which states that humans behave in a conscious way by considering the information available and considering the implications of the actions to be taken. Socialization and training on the implementation of CHD surveillance based on the PTM Web Portal can increase the knowledge of Puskesmas PTM surveillance officers. The knowledge possessed about it becomes the basis for the availability of information to determine the officer's intention in accepting to carry out CHD surveillance based on the NCD Web Portal. Officers will use the PTM Web Portal if the application is easy to use and provides benefits in supporting their performance. 19 Perceived Ease of Use of the NCD Web Portal for CHD Surveillance Perceived Ease of Use or ease of use of applications is one of the variables that influence a person's acceptance of using information technology. The results showed that most officers stated that the PTM Web Portal was a difficult application. Compatibility, perceived usefulness, and ease of use significantly influence health professionals' behavioral intention to use information technology. The components that affect the ease of use of information technology. 12 These components include ease of use, being able to increase productivity, and being able to increase their work effectiveness. Another component that can have an impact on the ease of use of information technology is the existence of applications that can make work easier and faster. The results showed that ease of use has not had a positive result on the quality of CHD surveillance data. This suggests that the use of difficult applications will still produce quality data. CHD surveillance is part of NCD surveillance which is a mandatory program of the Ministry of Health. The implementation of NCD surveillance is stated in the Decree of the Minister of Health of the Republic of Indonesia Number 1479/Menkes/SK/X/2003 concerning Guidelines for the Implementation of an Integrated Epidemiological Surveillance System for Communicable and Non-communicable Diseases. The decree was followed up by the 2015 decree of the Ministry of Health of the Republic of Indonesia on technical guidelines for non-communicable disease surveillance and the issuance of Technical Guidelines for Non-Communicable Disease Surveillance. NCD surveillance performance is also part of the Minimum Service Standards (MSS) of Puskesmas. Perceived Usefulness of the NCD Web Portal for NCD Surveillance The results of the study indicate that officers can perceive the benefits of using the PTM Web Portal in CHD surveillance activities. Research shows that the usefulness of information technology has a positive impact on the use of internet-based applications. 20 An important factor that can affect the usefulness of information technology is the suitability of the technology function with the work that is the responsibility of the officer. Usability variables can have a higher significance in predicting a person's acceptance of the use of information technology than socio-demographic factors. This is especially true for applications that are used regularly. The suitability of the application used with the task being carried out is a component that can affect the usefulness of using information technology. 12 Association between Respondent Characteristics and Data Quality Attributes The Data Quality attribute indicates the validity of surveillance data as indicated by the completeness of surveillance data filling. This attribute is assessed by calculating the percentage of completed surveillance data. The results showed that the Data Quality attribute of CHD surveillance has a high value, but there are still reports that have not been filled in completely.High-quality data are needed to measure and assess public health status and performance. Quality data are also needed for decision-making and evaluating the impact of public health programs. Several data quality attributes can be used as variables to measure health data quality. CDC guidelines for surveillance system evaluation and WHO guidelines for assessing routine reports are examples of widely used data quality assessment methods. 21 Model for improving non-communicable disease data quality through technology acceptance in non-communicable disease surveillance attributes CHD surveillance has used information technology in the form of a web-based NCD information system called the NCD Web Portal. This study includes the factors of patugas' acceptance of the information technology used. The theory of information technology acceptance used in this study is the Technology Acceptance Model or TAM theory. 22 The use of the PTM Web Portal has an impact on the Data Quality attribute. Information technology will improve the quality of surveillance data if the technology can provide benefits to officers. These benefits are in the form of using applications that can speed up and simplify the work of surveillance officers, especially in recording and reporting data. 23 The results of this study revealed a model for improving NCD data quality through technology acceptance in NCD surveillance attributes. The results of this study indicate that the best effort to obtain high Data Quality attributes is through the development of a surveillance system with high Simplicity attributes. The high Simplicity attribute includes simplicity and ease of surveillance in each component of the surveillance system which includes Simplicity in input, process and output. Simplicity attributes at the input include simplicity and ease of case definition and availability of data required to achieve CHD surveillance objectives. Simplicity process attributes include simplicity and ease in collecting, recording and processing CHD surveillance data. Simplicity process attributes include simplicity and ease in reporting, disseminating and storing data. The high Simplicity attribute in the system component is related to the Acceptability attribute. . 24–25 This study includes factors of patugas' acceptance of the information technology used. The theory of information technology acceptance used in this study is the Technology Acceptance Model or TAM theory. The use of the PTM Web Portal has an impact on the Data Quality attribute. Information technology will improve the quality of surveillance data if the technology can provide benefits to officers. Surveillance attributes and TAM components that have the highest impact on CHD surveillance Data Quality attributes, namely Acceptability and Perceived Usefulness attributes. Acceptability attributes are assessed based on the completeness and timeliness of reporting. Complete and timely data is related to the quality of surveillance data. The quality of CHD surveillance data will improve if it is supported by the use of information technology that can be perceived by officers to support their performance. Conclusion The best effort to obtain high Data Quality attributes is through the development of a surveillance system with high Simplicity attributes. High Simplicity attributes include simplicity and ease of surveillance in each surveillance system component (input, process and output). The surveillance attribute (Acceptability) and the TAM component (Perceived Usefulness) have the highest impact on the Data Quality attribute of CHD surveillance. Use of technology is importante. Supportive resources need to be allocated. Data integration could be improve the data quality Declarations Conflict of Interest There is no conflict professional or personal interests that might have affected the research. Ethical Approval This study was approved by Health Research Ethics Committee of the Faculty of Public Health Airlangga University (No. 335-KEPK). Consent to participate : This research was performed in compliance with the Declaration of Helsinki. Before data collection, the research objectives and methodologies were explicitly communicated to all participants. All research subjects supplied their written informed consent to engage in the study. Confidentiality was rigorously upheld, and participants were apprised of their choice to resign from the study at any moment without repercussions. Funding Source This research used personal funding. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-8546554","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":584861034,"identity":"26192c9c-fb9b-430f-ada0-e16a5a277f69","order_by":0,"name":"Arief Hargono","email":"data:image/png;base64,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","orcid":"","institution":"Airlangga University","correspondingAuthor":true,"prefix":"","firstName":"Arief","middleName":"","lastName":"Hargono","suffix":""},{"id":584861035,"identity":"68e5ba65-0dac-421d-8143-a8e05e53d0cd","order_by":1,"name":"Chatarina Umbul Wahyuni","email":"","orcid":"","institution":"Bhakti Wiyata Institute of Health Sciences","correspondingAuthor":false,"prefix":"","firstName":"Chatarina","middleName":"Umbul","lastName":"Wahyuni","suffix":""},{"id":584861036,"identity":"ecc5b515-d882-4f58-8b57-c88148bc4999","order_by":2,"name":"Hari Basuki Notobroto","email":"","orcid":"","institution":"Airlangga University","correspondingAuthor":false,"prefix":"","firstName":"Hari","middleName":"Basuki","lastName":"Notobroto","suffix":""},{"id":584861037,"identity":"2f98c6f9-2ffb-4d1d-aab3-73e38ae72159","order_by":3,"name":"Mursyidul Ibad","email":"","orcid":"","institution":"Universitas Nahdlatul Ulama Surabaya","correspondingAuthor":false,"prefix":"","firstName":"Mursyidul","middleName":"","lastName":"Ibad","suffix":""},{"id":584861038,"identity":"44f038a1-e6b7-4786-8210-6ff1bbd438ec","order_by":4,"name":"Kurnia Dwi Artanti","email":"","orcid":"","institution":"Airlangga University","correspondingAuthor":false,"prefix":"","firstName":"Kurnia","middleName":"Dwi","lastName":"Artanti","suffix":""},{"id":584861039,"identity":"8eb5500a-2e19-42ac-9f32-c96ce3dbd471","order_by":5,"name":"Arina Mufida Ersanti","email":"","orcid":"","institution":"Airlangga University","correspondingAuthor":false,"prefix":"","firstName":"Arina","middleName":"Mufida","lastName":"Ersanti","suffix":""},{"id":584861040,"identity":"0212e7e3-9fb0-430a-be4d-f2713943fe25","order_by":6,"name":"Firman Suryadi Rahman","email":"","orcid":"","institution":"Airlangga University","correspondingAuthor":false,"prefix":"","firstName":"Firman","middleName":"Suryadi","lastName":"Rahman","suffix":""}],"badges":[],"createdAt":"2026-01-08 03:38:52","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-8546554/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-8546554/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":101783117,"identity":"da549dfc-b414-4e41-8d89-fb68bcc741e7","added_by":"auto","created_at":"2026-02-03 15:19:26","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":141802,"visible":true,"origin":"","legend":"\u003cp\u003eResults of Path Analysis of Surveillance Attributes as well as Perceived Ease of Use and Perceived Usefulness of PTM Web Portal with Data Quality Attributes of CHD Surveillance in Surabaya, 2018\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-8546554/v1/0190296987a458f2adf85175.png"},{"id":102962283,"identity":"f04561e6-a9a1-407b-8401-dbe2ead35a86","added_by":"auto","created_at":"2026-02-19 04:06:55","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1182140,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8546554/v1/058bff34-7e68-44e2-8d0a-8d9774d10a2c.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Data Quality of Coronary Health Disease Surveillance Improvement through Technology Acceptance","fulltext":[{"header":"Introduction","content":"\u003cp\u003eCoronary Heart Disease (CHD) remains one of the leading causes of mortality and morbidity globally, with an increasing burden observed in developing nations due to urbanization, lifestyle changes, and limited access to preventive healthcare services (WHO, 2023).Coronary heart disease is the most common type of heart disease. It killed 371,506 people in 2022 and about 1 out of every 5 deaths from cardiovascular diseases (CVDs) was among adults younger than 65 years old.\u003csup\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e \u003cp\u003eAccording to the results of the Basic Health Research (Riskesdas) in 2007, 2013 and 2018 and Indonesia Health Survei (SKI) in 2023, there is an increasing trend in the prevalence of NCDs. In addition, obesity, which is one of the risk factors for non-communicable diseases in Indonesia, is known to continue to increase. In 2017, it was reported that the proportion of obesity (BMI\u0026thinsp;\u0026gt;\u0026thinsp;27.0) was 10.5% and increased to 14.8% (2013) and 21.8% (2018). According to the 2023 Indonesian Health Survey (SKI), the national prevalence of doctor-diagnosed heart disease among all age groups is 0.85% (95% CI: 0.82%\u0026ndash;0.88%). This indicates that approximately 8 to 9 out of every 1,000 people in Indonesia have been diagnosed with heart disease.\u003csup\u003e\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/sup\u003e The increase in the prevalence of non-communicable diseases is a serious threat to development, due to the rapid growth of the national economy.\u003c/p\u003e \u003cp\u003eSurveillance systems for CHD are critical in supporting timely monitor health problem, public health responses, informing policy decisions, and guiding resource allocation.\u003csup\u003e\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u003c/sup\u003e However, the utility of these systems is directly linked to the quality of data they collect, which must be accurate, complete, timely, and reliable.\u003csup\u003e\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u003c/sup\u003e In many developing nations, CHD surveillance is constrained by fragmented health information systems, limited infrastructure, and human resource challenges.\u003csup\u003e\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u003c/sup\u003e These limitations compromise data quality and, in turn, weaken the formulation and evaluation of healthcare policies.\u003c/p\u003e \u003cp\u003eIn Indonesia, CHD Surveillance is a part of NCD Surveillance. The implementation of NCD surveillance in Indonesia is currently supported by information technology which is organized into a web-based NCD Surveillance Information System (SIS PTM).\u003csup\u003e\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u003c/sup\u003e The application of SIS PTM in the form of NCD Web Portal in surveillance activities is expected to improve the quality of surveillance. The use of information technology in public health surveillance allows the surveillance system to perform more complex recording and reporting, but done in a relatively fast time with more valid results.\u003csup\u003e\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u003c/sup\u003e A study conducted by Pollettini et al showed that the application of information technology can support decision-making and accelerate data recording and information reporting.\u003csup\u003e\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e \u003cp\u003eThe quality of NCD surveillance can be assessed based on surveillance attributes. The application of information technology in surveillance systems can affect surveillance attributes including Simplicity, Flexibility, Acceptability, Sensitivity, PPV, Representativeness, Timeliness, Data Quality, and Stability. Regular evaluation of surveillance attributes is expected to improve and maintain the quality of surveillance and ensure that the surveillance system runs according to the expected goals.\u003c/p\u003e \u003cp\u003eIn recent years, the adoption of health information technologies (HIT) has been promoted as a solution to improve the performance and reliability of health surveillance systems. The successful integration of such technologies depends not only on infrastructure readiness but also on the acceptance of these technologies by healthcare professionals and system users .\u003csup\u003e10\u0026ndash;11\u003c/sup\u003e Utility and simplicity are important variables in the implementation of information technology in surveillance. This is in accordance with the Technology Acceptance Model (TAM) theory by Davis, Bagozzi, \u0026amp; Warshaw, TAM theory is a user acceptance model in using information technology. TAM theory states that the willingness to use information technology (actual behavior) is influenced by one's intention.. TAM theory is a theory of technology acceptance that is widely used in research on the implementation of information technology in health services. The variables that make up TAM can estimate and explain the use of information technology by users in the health sector.\u003csup\u003e\u003cspan additionalcitationids=\"CR11\" citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e \u003cp\u003eSurveillance attributes and acceptance of information technology contribute to the implementation of surveillance to achieve its goals. The combination and relationship between attributes determine the strengths and weaknesses of surveillance systems.\u003csup\u003e\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e\u003c/sup\u003e Implementation and intervention of surveillance attributes need to be done in a balanced manner so that the surveillance system can run effectively and efficiently. The study aims to develop a model for improving the quality of Coronary Heart Disease (CHD) data through technology acceptance in Coronary Heart Disease (CHD) surveillance attributes.\u003c/p\u003e"},{"header":"Materials and Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eResearch Design\u003c/h2\u003e \u003cp\u003eThis type of research is observational with a cross-sectional design. This study made observations at the same time on the research variables, namely surveillance attributes without providing treatment. The research location was chosen based on the high prevalence of CHDs and the consideration that CHD surveillance and information technology-based NCD information systems have been implemented in all Public Health Centre and have the most NCD Integrated Service Delivery Centers (Puskesmas) and Posbindu (Integrated Post for non-communicable diseases) NCD in East Java Province\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eParticipants\u003c/h3\u003e\n\u003cp\u003eThe study population was Puskesmas Pandu in Surabaya City. This study used the total population, i.e. all Puskesmas in Surabaya became the target of the study. The research respondents were CHD surveillance officers who worked at Puskesmas in Surabaya City, totaling 63 Puskesmas. Puskesmas in Surabaya City have at least one officer who is responsible for NCD surveillance activities in the Puskesmas and its working area. If there is a Puskesmas that has more than one NCD surveillance officer, one is chosen to be in charge of the CHD surveillance program. Inclusion criteria as a condition for respondents were NCD surveillance officers at Puskesmas who had attended NCD surveillance training at least once and had run the CHD surveillance program for at least one year.\u003c/p\u003e\n\u003ch3\u003eInstruments\u003c/h3\u003e\n\u003cp\u003eThe instruments used in this study were developed by adapting the technology acceptance instrument from Davis, Bagozzi, \u0026amp; Warshaw (Davis, 1989) and the surveillance system evaluation from CDC (CDC,2001). Questions and answer choices in the questionnaire were read by the enumerator and not shown to the respondents.\u003c/p\u003e\n\u003ch3\u003eData Analyzed\u003c/h3\u003e\n\u003cp\u003eThe relationship between the independent variable of respondent characteristics and the dependent variable of Data Quality using Spearman Correlation Test and Fisher Exact. The result of the analysis is a correlation coefficient to measure the closeness of the variable relationship. Data processing and analysis for model development using Path Analysis. The output of the analysis results in the form of path analysis of the relationship between surveillance system attributes.\u003c/p\u003e"},{"header":"Result","content":"\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eCharacteristics Respondent\u003c/h2\u003e \u003cp\u003eRespondent characteristics are the variables examined in this research. Respondent characteristics examined included respondent age, gender, education level, length of service and knowledge of NCD surveillance.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eCharacteristics Respondent\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003en\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e%\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSex\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e36,5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFemale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e40\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e63,5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEducation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eElementary School\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0,0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eJunior High School\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0,0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSMA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0,0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDiploma\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e36\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e57,1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBachelors Degree\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e42,9\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNumber of years in NCD Surveillance\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026le;\u0026thinsp;5 years\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e58\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e92,1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e6\u0026ndash;10 years\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3,2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;10 years\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e4,8\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eKnowledge\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLow\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0,0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMedium\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3,2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHigh\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e61\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e96,8\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eThe gender of surveillance officers at Puskesmas throughout Surabaya City who became research respondents showed that most of the respondents were female, namely 40 people or 63.5%. Based on the level of education, most respondents have completed at least a diploma, namely 36 people or 57.1%. Respondents with a Bachelor's degree were 27 people or 42.9%.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eDescription of Data Quality Attributes\u003c/h3\u003e\n\u003cp\u003eThe implementation of CHD surveillance at Puskesmas throughout Surabaya City based on the description of Data Quality Attributes is presented in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. Assessment of the Data Quality attribute is based on the number of reports with blank answers, both manually and electronically. Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e shows that the quality of CHD surveillance data is high. This indicates that most reports were filled in completely.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eDistribution of Data Quality Attributes of Manual and Electronic Health Center CHD Surveillance in Surabaya, 2018.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAtribut \u003cem\u003eData Quality\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003en\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eProsentase (%)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLow\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6,3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHigh\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e59\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e93,7\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTotal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e63\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e100,0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMean\u0026thinsp;\u0026plusmn;\u0026thinsp;Manual Standart Deviation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e3,41\u0026thinsp;\u0026plusmn;\u0026thinsp;13,28\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMinimum Manual Score\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMaxsimum Manall Score\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e67\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMean\u0026thinsp;\u0026plusmn;\u0026thinsp;Electronic Standart Deviation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e4,30\u0026thinsp;\u0026plusmn;\u0026thinsp;13,96\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMinimum Electronic Score\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMaxsimum Electronic Score\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e67\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eManual data entry is better than electronic data entry. The average percentage of blank reports using the manual method is lower at 3.41 with a standard deviation of 13.28. While the use of electronic methods obtained an average percentage of blank reports of 4.30 with a standard deviation of 13.96. The good Data Quality category occurs in 60 health centers or 95.2 percent of all health centers.\u003c/p\u003e\n\u003ch3\u003ePerceived Ease of Use of the NCD Web Portal for CHD Surveillance\u003c/h3\u003e\n\u003cp\u003eA description of the perceived ease of use of the NCD Web Portal for Puskesmas NCD surveillance officers in Surabaya City is presented in the following table:\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003ePerceived Ease of Use Value of PTM Web Portal in Supporting Puskesmas NCD Surveillance Activities in Surabaya, 2018\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003ePerceived Ease of Use\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003en\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(%)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLow\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e52\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e82,5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHigh\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e17,5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTotal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e63\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e100,0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eMean\u0026thinsp;\u0026plusmn;\u0026thinsp;Standart Deviation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e68,23\u0026thinsp;\u0026plusmn;\u0026thinsp;10,52\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eMinimum\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e41,54\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eMaximum\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e93,85\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eThe table above shows that most NCD surveillance officers stated that the NCD Web Portal application was difficult to use. This can be seen based on the perceived ease of use in the high category, which is known to have a prevalence of 82.5% of officers. Respondents still find it difficult to enter and process data using the NCD Web Portal. Information and guidance in the NCD Web Portal application was felt to be lacking. They still need other people to help use the NCD Web Portal application. It takes a long time to learn this application so training is needed to learn it.\u003c/p\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eAssociation between Respondent Characteristics and Data Quality Attributes\u003c/h2\u003e \u003cp\u003eThe following are the results of the analysis of the relationship between respondent characteristics and Data Quality attributes. The characteristics of respondents studied consisted of respondent age, gender, respondent education level, length of service in NCD surveillance, respondent knowledge about CHD, and respondent knowledge about NCD surveillance to Data Quality attributes. The Spearman test was used for variables that had more than 2 categories, namely age and length of service as a NCD surveillance manager. The Fisher test was used for variables that had 2 categories, namely gender, education level, and knowledge of CHD and CHD surveillance.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eAssociation between Respondent Characteristics and Quality Data Attributes of NCD Surveillance at Puskesmas in Surabaya, 2018\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDependent Variable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eIndependent Variable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCorrelation Coefficient Value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"5\" rowspan=\"6\"\u003e \u003cp\u003eAtribut \u003cem\u003eData Quality\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAge\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0,171*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGender\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0,170**\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eEducation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0,108**\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLength of Service NCD Surveillance\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0,058*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCHD Knowledge\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0,028**\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCHD Surveillance Knowledge\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0,158**\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eNotes: * Using Spearman Test\u003c/p\u003e \u003cp\u003e** Using Fisher Test\u003c/p\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e shows that the respondent characteristics variables generally have a low association to the Data Quality attribute. The respondent characteristic that has the highest value to the Data Quality attribute is the age of the respondent while the lowest association is the respondent's knowledge about CHD.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003ePerceived Usefulness of the NCD Web Portal for NCD Surveillance\u003c/h2\u003e \u003cp\u003ePuskesmas CHD surveillance uses information technology in the form of the NCD Web Portal to support surveillance recording and reporting. The variable of officer acceptance of the use of the PTM Web Portal is strategic in the implementation of surveillance. Perceived Usefulness or application usability is one of the variables that influence a person's acceptance of using information technology (Davis, 1989). Perceived Uselfuness shows the definition of a person's level of confidence in using an application that will improve performance (Ward, 2013). A description of the usability value of the NCD Web Portal for Puskesmas NCD surveillance officers in Surabaya City is presented in Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab5\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003ePerceived Usefulness Value of the NCD Web Portal in Supporting Puskesmas NCD Surveillance Activities in Surabaya, 2018\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003ePerceived Usefullness\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003en\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(%)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLow\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e42,9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e42,9\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHigh\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e57,1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e57,1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTotal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e100,0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e100,0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eMean\u0026thinsp;\u0026plusmn;\u0026thinsp;Standart Deviation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e73,91\u0026thinsp;\u0026plusmn;\u0026thinsp;15,21\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eMinimum\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e20\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eMaximum\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e100\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e. shows that mostly respondents consider that the PTM Web Portal application is useful for supporting their performance. In addition to improving the quality of work, the NCD Web Portal can also increase the productivity of respondents. Most respondents stated that the use of the NCD Web Portal facilitates CHD surveillance work, especially in the recording and reporting process. Others mentioned that the NCD Web Portal was not yet reliable enough to display data and prepare reports. This application has also not been able to shorten the response time for handling reported health problems.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003eAssociation between Respondent Characteristics and Data Quality Attributes\u003c/h2\u003e \u003cp\u003eThe following are the results of the analysis of the relationship between respondent characteristics and Data Quality attributes. The characteristics of respondents studied consisted of respondent age, gender, respondent education level, length of service in NCD surveillance, respondent knowledge about CHD, and respondent knowledge about NCD surveillance to Data Quality attributes. The Spearman test was used for variables that had more than 2 categories, namely age and length of service as a NCD surveillance manager. The Fisher test was used for variables that had 2 categories, namely gender, education level, and knowledge of CHD and CHD surveillance.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab6\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 6\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eAssociation between Respondent Characteristics and Quality Data Attributes of NCD Surveillance at Puskesmas in Surabaya, 2018\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDependent Variable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eIndependent Variable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCorrelation Coefficient Value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"5\" rowspan=\"6\"\u003e \u003cp\u003eAtribut \u003cem\u003eData Quality\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAge\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0,171*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGender\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0,170**\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eEducation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0,108**\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLength of Service NCD Surveillance\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0,058*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCHD Knowledge\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0,028**\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCHD Surveillance Knowledge\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0,158**\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"3\"\u003eNotes: * Using Spearman Test\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"3\"\u003e** Using Fisher Test\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab6\" class=\"InternalRef\"\u003e6\u003c/span\u003e shows that the respondent characteristics variables generally have a low association to the Data Quality attribute. The respondent characteristic that has the highest value to the Data Quality attribute is the age of the respondent while the lowest association is the respondent's knowledge about CHD.\u003c/p\u003e \u003cp\u003e \u003cb\u003eAssociation between Surveillance Attributes and Perceived Ease of Use and Perceived Usefulness of the PTM Web Portal with Data Quality Attributes\u003c/b\u003e \u003c/p\u003e \u003cp\u003ePath analysis was used to develop a model that aims to determine the cause and effect relationship in each variable. The variables analyzed include surveillance attributes and Perceived Ease of Use and Perceived Usefulness of using the NCD Web Portal with CHD surveillance data quality. The results of the Path Analysis are presented in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eResults of Path Analysis of Surveillance Attributes as well as Perceived Ease of Use and Perceived Usefulness of the NCD Web Portal with CHD Surveillance Data Quality Attributes in Surabaya,2018. Figure\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e shows the various pathways of the relationship between surveillance attributes and Perceived Ease of Use and Perceived Usefulness of using the NCD Web Portal in supporting the quality of CHD surveillance data. Path analysis displays the value of the regression coefficient as one of the considerations in determining the best flow and strategic indicators in improving the quality of CHD surveillance data. The results of the path analysis between variables along with the regression coefficient values are presented in Table\u0026nbsp;\u003cspan refid=\"Tab7\" class=\"InternalRef\"\u003e7\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab7\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 7\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eResults of Path Analysis of Surveillance Attributes and Perceived Ease of Use and Perceived Usefulness with CHD Surveillance Data Quality Attributes in Surabaya, 2018\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo.\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCausalitas\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eRegression Coefficient\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eSimplicity input → Simplicity process\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0,376\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eSimplicity process → Simplicity output\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0,586\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e3.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eSimplicity output → Acceptability\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0,098\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e4.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eAcceptability → Data Quality\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0,225\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e5.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eSimplicity output → Flexibility\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0,145\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e6.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eFlexibility → Timeliness\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0,075\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e7.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eTimeliness → Data Quality\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0,206\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e8.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eStability → Acceptability\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0,061\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e9.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eStability → Timeliness\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0,140\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e10.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eStability → Data Quality\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0,124\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e11.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003ePerceived Ease of Use → Data Quality\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0,012\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e12.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003ePerceived Usefulness → Data Quality\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0,034\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eRespondent characteristics are aspects that can have an impact on the implementation of NCD surveillance. Respondent characteristics can also be a strategic factor in the application of information technology in NCD surveillance. The characteristics of respondents studied were age, gender, education level, length of service, and knowledge about NCDs, as well as knowledge about NCD surveillance. The discussion reviews the characteristics of respondents towards the implementation of information technology-based NCD surveillance and its impact on the quality of data produced.\u003c/p\u003e \u003cp\u003eIndividual age is one of the variables that have an impact on differences in attitudes and behaviors, including the use of information technology. The age of respondents in this study is classified as productive age. Someone with a productive age range has good performance. Productive age will make one's productivity at work better than unproductive age.\u003csup\u003e\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e\u003c/sup\u003e Productivity will decrease as one gets older and approaches retirement.\u003csup\u003e\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e\u003c/sup\u003e The use of information technology in generation Y which is in productive age is one of the strategic methods to support CHD surveillance. The results showed that the age of respondents produced a positive value for data quality. The results of the assessment of the data quality attribute also show that the quality of CHD surveillance data using the PTM Web Portal in Surabaya has a high value.\u003c/p\u003e \u003cp\u003eEducation level is an important variable in human resources. Decree of the Indonesian Minister of Health No. 1116 of 2003 concerning Guidelines for the Implementation of the Health Epidemiological Surveillance System states that epidemiologists are one of the resources for organizing surveillance systems. The presence of epidemiologists and doctors is also an indicator of the implementation of the surveillance system at every administrative level from Puskesmas or Hospitals, Districts / Cities, Provinces to the Center. The results showed a positive relationship between the level of education of respondents and the quality of data produced. This positive relationship indicates that the higher the level of education of surveillance officers, the better the quality of CHD surveillance data. This is in accordance with research conducted by Maringan, Pongtuluran, \u0026amp; Maria, which states that the higher the level of education a person takes, the more productivity and performance achievement will increase.\u003csup\u003e\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e \u003cp\u003eLength of service in NCD surveillance provides an opportunity to get training on surveillance. The results of this study show that the length of time surveillance officers have worked in NCD surveillance and surveillance is mostly less than 5 years. In addition, there was a positive relationship between the length of service of surveillance officers and the quality of data produced. Length of service is expected to have an impact on the implementation of surveillance. Experience in running surveillance programs can be a provision in running surveillance programs better. According to Kista et al, increasing work experience or length of service will improve the performance of surveillance officers.\u003csup\u003e\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e\u003c/sup\u003e Experience in the administration of surveillance programs can substantially augment their efficacy by offering valuable insights, enhancing system design, and promoting adaptability to emerging challenges. The existence of established surveillance systems, such as those utilized by the Centers for Disease Control and Prevention (CDC) for cancer monitoring, underscores the significance of leveraging historical data and monitoring efforts to strategize interventions and assess the effectiveness of public health initiatives .\u003csup\u003e18\u003c/sup\u003e\u003c/p\u003e \u003cp\u003eKnowledge of surveillance concepts can be information in carrying out CHD surveillance activities. Available information is one aspect of a person to take an action. This is in accordance with the concept of Theory of Reasoned Action (TRA) from Fishbein \u0026amp; Ajzen which states that humans behave in a conscious way by considering the information available and considering the implications of the actions to be taken. Socialization and training on the implementation of CHD surveillance based on the PTM Web Portal can increase the knowledge of Puskesmas PTM surveillance officers. The knowledge possessed about it becomes the basis for the availability of information to determine the officer's intention in accepting to carry out CHD surveillance based on the NCD Web Portal. Officers will use the PTM Web Portal if the application is easy to use and provides benefits in supporting their performance.\u003csup\u003e\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003ePerceived Ease of Use of the NCD Web Portal for CHD Surveillance\u003c/h2\u003e \u003cp\u003ePerceived Ease of Use or ease of use of applications is one of the variables that influence a person's acceptance of using information technology. The results showed that most officers stated that the PTM Web Portal was a difficult application. Compatibility, perceived usefulness, and ease of use significantly influence health professionals' behavioral intention to use information technology.\u003c/p\u003e \u003cp\u003eThe components that affect the ease of use of information technology.\u003csup\u003e\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e\u003c/sup\u003e These components include ease of use, being able to increase productivity, and being able to increase their work effectiveness. Another component that can have an impact on the ease of use of information technology is the existence of applications that can make work easier and faster.\u003c/p\u003e \u003cp\u003eThe results showed that ease of use has not had a positive result on the quality of CHD surveillance data. This suggests that the use of difficult applications will still produce quality data. CHD surveillance is part of NCD surveillance which is a mandatory program of the Ministry of Health. The implementation of NCD surveillance is stated in the Decree of the Minister of Health of the Republic of Indonesia Number 1479/Menkes/SK/X/2003 concerning Guidelines for the Implementation of an Integrated Epidemiological Surveillance System for Communicable and Non-communicable Diseases. The decree was followed up by the 2015 decree of the Ministry of Health of the Republic of Indonesia on technical guidelines for non-communicable disease surveillance and the issuance of Technical Guidelines for Non-Communicable Disease Surveillance. NCD surveillance performance is also part of the Minimum Service Standards (MSS) of Puskesmas.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003ePerceived Usefulness of the NCD Web Portal for NCD Surveillance\u003c/h2\u003e \u003cp\u003eThe results of the study indicate that officers can perceive the benefits of using the PTM Web Portal in CHD surveillance activities. Research shows that the usefulness of information technology has a positive impact on the use of internet-based applications.\u003csup\u003e\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e\u003c/sup\u003e An important factor that can affect the usefulness of information technology is the suitability of the technology function with the work that is the responsibility of the officer.\u003c/p\u003e \u003cp\u003eUsability variables can have a higher significance in predicting a person's acceptance of the use of information technology than socio-demographic factors. This is especially true for applications that are used regularly. The suitability of the application used with the task being carried out is a component that can affect the usefulness of using information technology.\u003csup\u003e\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec17\" class=\"Section2\"\u003e \u003ch2\u003eAssociation between Respondent Characteristics and Data Quality Attributes\u003c/h2\u003e \u003cp\u003eThe Data Quality attribute indicates the validity of surveillance data as indicated by the completeness of surveillance data filling. This attribute is assessed by calculating the percentage of completed surveillance data. The results showed that the Data Quality attribute of CHD surveillance has a high value, but there are still reports that have not been filled in completely.High-quality data are needed to measure and assess public health status and performance. Quality data are also needed for decision-making and evaluating the impact of public health programs. Several data quality attributes can be used as variables to measure health data quality. CDC guidelines for surveillance system evaluation and WHO guidelines for assessing routine reports are examples of widely used data quality assessment methods.\u003csup\u003e\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec18\" class=\"Section2\"\u003e \u003ch2\u003eModel for improving non-communicable disease data quality through technology acceptance in non-communicable disease surveillance attributes\u003c/h2\u003e \u003cp\u003eCHD surveillance has used information technology in the form of a web-based NCD information system called the NCD Web Portal. This study includes the factors of patugas' acceptance of the information technology used. The theory of information technology acceptance used in this study is the Technology Acceptance Model or TAM theory.\u003csup\u003e\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e\u003c/sup\u003e The use of the PTM Web Portal has an impact on the Data Quality attribute. Information technology will improve the quality of surveillance data if the technology can provide benefits to officers. These benefits are in the form of using applications that can speed up and simplify the work of surveillance officers, especially in recording and reporting data.\u003csup\u003e\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e \u003cp\u003eThe results of this study revealed a model for improving NCD data quality through technology acceptance in NCD surveillance attributes. The results of this study indicate that the best effort to obtain high Data Quality attributes is through the development of a surveillance system with high Simplicity attributes. The high Simplicity attribute includes simplicity and ease of surveillance in each component of the surveillance system which includes Simplicity in input, process and output. Simplicity attributes at the input include simplicity and ease of case definition and availability of data required to achieve CHD surveillance objectives. Simplicity process attributes include simplicity and ease in collecting, recording and processing CHD surveillance data. Simplicity process attributes include simplicity and ease in reporting, disseminating and storing data. The high Simplicity attribute in the system component is related to the Acceptability attribute. .\u003csup\u003e24\u0026ndash;25\u003c/sup\u003e\u003c/p\u003e \u003cp\u003eThis study includes factors of patugas' acceptance of the information technology used. The theory of information technology acceptance used in this study is the Technology Acceptance Model or TAM theory. The use of the PTM Web Portal has an impact on the Data Quality attribute. Information technology will improve the quality of surveillance data if the technology can provide benefits to officers.\u003c/p\u003e \u003cp\u003eSurveillance attributes and TAM components that have the highest impact on CHD surveillance Data Quality attributes, namely Acceptability and Perceived Usefulness attributes. Acceptability attributes are assessed based on the completeness and timeliness of reporting. Complete and timely data is related to the quality of surveillance data. The quality of CHD surveillance data will improve if it is supported by the use of information technology that can be perceived by officers to support their performance.\u003c/p\u003e \u003c/div\u003e"},{"header":"Conclusion","content":" \u003cp\u003eThe best effort to obtain high Data Quality attributes is through the development of a surveillance system with high Simplicity attributes. High Simplicity attributes include simplicity and ease of surveillance in each surveillance system component (input, process and output). The surveillance attribute (Acceptability) and the TAM component (Perceived Usefulness) have the highest impact on the Data Quality attribute of CHD surveillance. Use of technology is importante. Supportive resources need to be allocated. Data integration could be improve the data quality\u003c/p\u003e "},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eConflict of Interest\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThere is no conflict professional or personal interests that might have affected the research.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthical Approval\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study was approved by Health Research Ethics Committee of the Faculty of Public Health Airlangga University (No. 335-KEPK).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent to participate :\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis research was performed in compliance with the Declaration of Helsinki. Before data collection, the research objectives and methodologies were explicitly communicated to all participants. All research subjects supplied their written informed consent to engage in the study. Confidentiality was rigorously upheld, and participants were apprised of their choice to resign from the study at any moment without repercussions.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding Source\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis research used personal funding.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent to Publish declaration\u003c/strong\u003e: Not Applicable\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eClinical Trial Number\u003c/strong\u003e : Not Applicable\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of Data and Materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe datasets used and/or analysed during the current study are available from the corresponding author on reasonable request.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eChanani NK, Hamrick SEG. Cardiovascular Disease, an issue of clinics in perinatology. Elsevier Health Sciences; 2016.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eIndonesia Ministry of Health. Indonesian Health Survey (SKI) 2023. Jakarta: Health Development Policy Agency, Ministry of Health, Republic of Indonesia; 2024.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHirschhorn LR, Langlois EV, Bitton A, Ghaffar A. What kind of evidence do we need to strengthen primary healthcare in the 21st century? BMJ Global Health. 2019;4(Suppl 8):e001668.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAbouZahr C, Boerma T. Health information systems: the foundations of public health. Bull World Health Organ. 2005;83:578\u0026ndash;83.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAbouZahr C, Boerma T. Health information systems: the foundations of public health. Bull World Health Organ. 2005;83:578\u0026ndash;83.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eIndonesia Ministry of Health. 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Quality and utility of information captured by surveillance systems relevant to antimicrobial resistance (AMR): a systematic review. Antibiotics. 2021;10(4):431.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMustafa AS, Garcia MB. Theories integrated with technology acceptance model (TAM) in online learning acceptance and continuance intention: A systematic review. In2021 1st Conference on online teaching for mobile education (OT4ME) 2021 Nov 22 (pp. 68\u0026ndash;72). IEEE.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKakolu S, Faheem MA. Digitization and automation in mobile applications: A catalyst for operational efficiency and user engagement.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKissi J, Annobil C, Tijani A, Kissi AA. Electronic health record impact on data quality: An integrated review. Integr Health Res J. 2023;1(2):77\u0026ndash;85.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMashoufi M, Ayatollahi H, Khorasani-Zavareh D, Boni TT. Data quality in health care: main concepts and assessment methodologies. Methods Inf Med. 2023;62(01/02):005\u0026ndash;18.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"discover-public-health","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"","sideBox":"Learn more about [Discover Public Health](https://link.springer.com/journal/12982)","snPcode":"12982","submissionUrl":"https://submission.springernature.com/new-submission/12982/3","title":"Discover Public Health","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Discover Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Surveillance Attributes, TAM, Information Technology","lastPublishedDoi":"10.21203/rs.3.rs-8546554/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8546554/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground and aims:\u003c/h2\u003e \u003cp\u003eThe application of information technology can support decision making and accelerate data recording and information reporting. Surveillance attributes and acceptance of information technology contribute to the implementation of surveillance to achieve its goals. Implementation and intervention of surveillance attributes need to be done in a balanced manner so that the surveillance system can run effectively and efficiently. This study was to develop a model for improving NCD data quality through technology acceptance in NCD surveillance attributes.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eThis research using cross-sectional design. This study uses the total population, namely all health centers in Surabaya to be the target of the study. Data analysis will be conducted using Spearman Correlation Test, Fisher Exact and path analysis.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eThe surveillance attribute (Acceptability) and the TAM component (Perceived Usefulness) have the highest impact on the CHD surveillance Data Quality attribute. The quality of CHD surveillance data will improve if supported by the use of information technology that can be perceived by officers to support their performance.\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e \u003cp\u003eThe use of the NCD Web Portal has an impact on the Data Quality attribute. Information technology will improve the quality of surveillance data if the technology can provide benefits to officers\u003c/p\u003e","manuscriptTitle":"Data Quality of Coronary Health Disease Surveillance Improvement through Technology Acceptance","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-02-03 15:19:16","doi":"10.21203/rs.3.rs-8546554/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2026-02-17T13:11:16+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-02-10T06:39:04+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"181159564614124544306933221073276686980","date":"2026-02-07T10:34:42+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-02-03T08:02:07+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"141721848842943366975836035056421430071","date":"2026-02-02T06:55:12+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"24021456957695324401824601400696249325","date":"2026-02-02T06:54:17+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2026-02-02T06:45:02+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-01-29T22:34:12+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2026-01-19T09:43:03+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2026-01-19T09:18:29+00:00","index":"","fulltext":""},{"type":"submitted","content":"Discover Public Health","date":"2026-01-19T09:03:08+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"discover-public-health","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"","sideBox":"Learn more about [Discover Public Health](https://link.springer.com/journal/12982)","snPcode":"12982","submissionUrl":"https://submission.springernature.com/new-submission/12982/3","title":"Discover Public Health","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Discover Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"f6c06c8c-c740-424c-99e1-d93e5b117b38","owner":[],"postedDate":"February 3rd, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2026-05-08T07:56:10+00:00","versionOfRecord":[],"versionCreatedAt":"2026-02-03 15:19:16","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8546554","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8546554","identity":"rs-8546554","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}