Female respondent acceptance of computer-assisted personal interviewing (CAPI) for maternal, newborn and child health coverage surveys in rural Uganda

Mercader, Hannah; Kabakyenga, Jerome; Katuruba, David Tumusiime; Hobbs, Amy; Brenner, Jennifer L

doi:10.1016/j.ijmedinf.2016.11.009

Cited by 16 publications

(10 citation statements)

References 37 publications

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“…A pilot study in female respondents in rural Uganda has shown overall openness among the respondents to accept computer-assisted personal interviewing. The implementation of such a technology can, in the long run, be very beneficial for the community because it would provide a cost-effective and more easily accessible health care alternative [20]. It appears crucial that the anxieties and insecurities of both patients and medical professionals are considered to increase the chances for their acceptance [21].…”

Section: Discussionmentioning

confidence: 99%

Empirical Research on Acceptance of Digital Technologies in Medicine Among Patients and Healthy Users: Questionnaire Study

Safi¹,

Danzer²,

Schmailzl³

2019

JMIR Hum Factors

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BackgroundIn recent years, interest in digital technologies such as electronic health, mobile health, telemedicine, big data, and health apps has been increasing in the health care sector. Acceptance and sustainability of these technologies play a considerable role for innovative health care apps.ObjectiveThis study aimed to identify the spread of and experience with new digital technologies in the medical sector in Germany.MethodsWe analyzed the acceptance of new health care technologies by applying the Technology Acceptance Model to data obtained in the German ePatient Survey 2018. This survey used standardized questionnaires to gain insight into the prevalence, impact, and development of digital health applications in a study sample of 9621 patients with acute and chronic conditions and healthy users. We extracted sociodemographic data and details on the different health app types used in Germany and conducted an evaluation based on the Technology Acceptance Model.ResultsThe average age of the respondents was 59.7 years, with a standard deviation of 16 years. Digital health care apps were generally accepted, but differences were observed among age groups and genders of the respondents. Men were more likely to accept digital technologies, while women preferred coaching and consultation apps. Analysis of the user typology revealed that most users were patients (n=4041, 42%), followed by patients with acute conditions (n=3175, 33%), and healthy users (n=2405, 25%). The majority (n=6542, 68%) discovered coaching or medication apps themselves on the internet, while more than half of the users faced initial difficulties operating such apps. The time of use of the same app or program ranged from a few days (n=1607, 37%) and several months (n=1694, 39%) to ≥1 year (n=1042, 24%). Most respondents (n=6927, 72%) stated that they would like to receive customized health care apps from their physician.ConclusionsThe acceptance of digital technologies in the German health care sector varies depending on age and gender. The broad acceptance of medical digital apps could potentially improve individualized health care solutions and warrants governance.

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Section: Discussionmentioning

confidence: 99%

Empirical Research on Acceptance of Digital Technologies in Medicine Among Patients and Healthy Users: Questionnaire Study

Safi¹,

Danzer²,

Schmailzl³

2019

JMIR Hum Factors

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“…Supporting healthcare workers with data capturing in a more efficient manner is paramount to improving not only the data collection process but improving the quality as this will reduce human errors as well as illegible writing. In Uganda, a study investigated whether the use of a computer-assisted personal interviewing could be useful for survey data collection related to healthcare in rural Uganda (Mercader et al 2017). This shows that Uganda is taking active measures to incorporate digital technologies in various healthcare programs.…”

Section: Digitalization: Case Study Of Africamentioning

confidence: 99%

Digital Transformation and Global Health in Africa

Iyawa¹,

Hamunyela²,

Peters³

et al. 2020

Handbook of Global Health

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“…Reviews of community acceptance of data collection in LMICs suggest that participants may also doubt the confidentiality of their data collected electronically (reviewed in Ref. 8); and the paucity of reports on breaches of confidentiality in LMICs suggests that oversight, breach detection and reporting are lacking 9 10…”

Section: Key Tenets For Data Governancementioning

confidence: 99%

How to use relevant data for maximal benefit with minimal risk: digital health data governance to protect vulnerable populations in low-income and middle-income countries

2019

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Globally, the volume of private and personal digital data has massively increased, accompanied by rapid expansion in the generation and use of digital health data. These technological advances promise increased opportunity for data-driven and evidence-based health programme design, management and assessment; but also increased risk to individuals of data misuse or data breach of their sensitive personal data, especially given how easily digital data can be accessed, copied and transferred on electronic platforms if the appropriate controls are not implemented. This is particularly pertinent in low-income and middle-income countries (LMICs), where vulnerable populations are more likely to be at a disadvantage in negotiating digital privacy and confidentiality given the intersectional nature of the digital divide. The potential benefits of strengthening health systems and improving health outcomes through the digital health environment thus come with a concomitant need to implement strong data governance structures and ensure the ethical use and reuse of individuals’ data collected through digital health programmes. We present a framework for data governance to reduce the risks of health data breach or misuse in digital health programmes in LMICS. We define and describe four key domains for data governance and appropriate data stewardship, covering ethical oversight and informed consent processes, data protection through data access controls, sustainability of ethical data use and application of relevant legislation. We discuss key components of each domain with a focus on their relevance to vulnerable populations in LMICs and examples of data governance issues arising within the LMIC context.

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Female respondent acceptance of computer-assisted personal interviewing (CAPI) for maternal, newborn and child health coverage surveys in rural Uganda

Cited by 16 publications

References 37 publications

Empirical Research on Acceptance of Digital Technologies in Medicine Among Patients and Healthy Users: Questionnaire Study

Empirical Research on Acceptance of Digital Technologies in Medicine Among Patients and Healthy Users: Questionnaire Study

Digital Transformation and Global Health in Africa

How to use relevant data for maximal benefit with minimal risk: digital health data governance to protect vulnerable populations in low-income and middle-income countries

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