Guideline for good evaluation practice in health informatics (GEP-HI)

Nykänen, Pirkko; Brender, Jytte; Talmon, Jan L.; Keizer, Nicolette de; Rigby, Michael; Beuscart-Zéphir, Marie-Catherine; Ammenwerth, Elske

doi:10.1016/j.ijmedinf.2011.08.004

Cited by 91 publications

(82 citation statements)

References 33 publications

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“…The anticipation is that electronic data capturing tools may overcome substantial limitations of the paper-based system regarding saving time, improving the data quality, and minimizing overall cost [6,7,9,13,14]. Evaluations of such systems require periodic and setting context evidence to support the growing claim on their efficiency, effectiveness, and impacts [15,16].…”

Section: Scientific Backgroundmentioning

confidence: 99%

Evaluation of Electronic and Paper-Pen Data Capturing Tools for Data Quality in a Public Health Survey in a Health and Demographic Surveillance Site, Ethiopia: Randomized Controlled Crossover Health Care Information Technology Evaluation (Preprint)

Zeleke¹,

Worku²,

Demissie³

et al. 2018

Preprint

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Background: Periodic demographic health surveillance and surveys are the main sources of health information in developing countries. Conducting survey in requires extensive use of paper-pen and manual work and lengthy processes to generate the required information. Despite the rise of popularity in using electronic data collection systems to alleviate the problems, sufficient evidence is not available to support the use of electronic data capture tools in interviewer administered data collection processes.Objective: The purpose of this study was to compare data quality parameters in the data collected using mobile electronic and standard paper-based data capture tools in one of health and demographic surveillance sites in northwest Ethiopia Methods: A randomized controlled crossover Healthcare IT-Evaluation was conducted from May 10 to June 3 rd , 2016 in demographic and surveillance site. Twelve interview administrator, as two individuals in six groups (one with a tablet computer and the other with paper-based questionnaire), were assigned in the six towns of the surveillance premises. Data collectors switch data collection method based on computer generated random order. Data was cleaned using Mysql program and transferred to SPSS and R statistical software for analysis. Descriptive and mixed ordinal logistic analyses employed. The qualitative interview audio record was transcribed, and the usability of this open data kit (ODK) based system was assessed using system usability scale (SUS) and interview mapping in the isometric dialogue principles for system usability.Result: From the submitted 1,251 complete records/questionnaires in each tools, 42 % (522) of the paper and pen data capture (PPDC) and 29 % (285) of the electronic data capture (EDC) tool questionnaires had one or more types of data quality errors. The overall error rates were 1.67% and 0.6% for PPDC and EDC respectively. The chances of more errors on PPDC tool were multiplied by 1.015 for each additional question in the interview compared to EDC. The SUS score of the data collectors was 85.6. In qualitative data response mapping, EDC had more positive suitability of task responses with few error tolerance characteristics. Conclusion:EDC possessed significantly better data quality and efficient compared with PPDC, explained in fewer errors, instant data submission, and easy handling. The EDC proved to be a usable data collection tool in the rural study setting. The consistent power source and decent 2 internet connection, standby technical support, and security assurance for the device users suggested by the data collectors before full-fledged the implementation of the system.

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Section: Scientific Backgroundmentioning

confidence: 99%

Evaluation of Electronic and Paper-Pen Data Capturing Tools for Data Quality in a Public Health Survey in a Health and Demographic Surveillance Site, Ethiopia: Randomized Controlled Crossover Health Care Information Technology Evaluation (Preprint)

Zeleke¹,

Worku²,

Demissie³

et al. 2018

Preprint

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“…Some problems in general dimension include organizational challenges like organizational culture, support of high-level management; technological barriers such as lack of ICT and mobile infrastructure [52]; human challenges, for example, lack of trained and skilled personnel at health care centers in this field [28], user attitudes, technology acceptance [53,54], user characteristics like age, economic, social, and educational status [55]; and threats to confidentiality and privacy, legal, ethical, and administrative barriers, costs of system implementation and maintenance [28], dependence on IT [56], the cost of updating, costly modern systems [57], sufficient investment, delays in implementation and providing electronic devices and software [58]. Some barriers from specific aspects also include problems in interoperability between other health systems and information technology tools, poor and inappropriate design and implementation [59], effect on face-to-face communication between health care providers and patient [60], causes omission of human relationship and the negative effects of technology on relationships between individuals and social processes [56], designing of mHealth services content [55], failure to meet targets [58], virtual information control [61], medical errors due to malfunctioning of system [62], fault documentation [59] like data manipulation and rewriting, misrepresentation, and violation of patients' legal rights. Difficulties related to telecommunication industry such as reliability, sustainability of connections, sudden interruptions of telecommunication networks [63], device and sensor type that can be used, type of data and language presentation [56], scalability in terms of data rate, power and energy consumption; antenna design, quality of service, energy efficiency [64,65] wearable devices weight, type of devices that used for patient monitoring that sometimes lead to problem in data processing, accuracy of gathering information depends on where data were collected, and user training to use wearable system [66].…”

Section: Mhealth Challenges In Patient Monitoringmentioning

confidence: 99%

Mobile Health Monitoring

Mohammadzadeh¹,

Safdari²

2016

Mobile Health Technologies - Theories and Applications

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Chronic diseases impose heavy burden and costs on the health industry in many countries. Suitable health procedures, management, and prevention of disease by continuous monitoring through modern technologies can lead to a decrease in health costs and improve people empowerment. Applying remote medical diagnosis and monitoring system based on mobile health systems can help significantly reduce health care costs and correct performance management particularly in chronic disease management. In this chapter, mHealth opportunities in patient monitoring with the introduction of various systems specifically in chronic disease are expressed. Also mHealth challenges in patient monitoring in general and specific aspects are identified. Some of the general challenges include threats to confidentiality and privacy, and lack of information communication technology (ICT), and mobile infrastructure. In specific aspect, some difficulties include lack of system interoperability with electronic health records and other IT tools, decrease in face-to-face communication between doctor and patient, ill-functioning of system that leads to medical errors and negative effects on care outcomes, patients, and personnel, and factors related to the telecommunication industry include reliability and sudden interruptions of telecommunication networks.

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“…Investeringer i IKT innebaerer store samfunnsøkonomiske utgifter, og prosjektene har et stort ansvar i å forvalte ressursene på en hensiktsmessig måte. Evalueringer av prosjekter underveis er viktig for å sikre at investeringene gir gevinster (8). Å aktivt anvende granskningsinstrument utviklet for å vurdere…”

Section: Epj Implementering Av Nye Epjløsninger I Flere Helseregioner Iunclassified