Does Being Ill Improve Acceptance of Medical Technology?-A Patient Survey with the Technology Usage Inventory.

Acceptance of new medical technology may be influenced by social conditions and an individual's background and particular situation. We studied this acceptance by hypothesizing that current and former COVID-19 patients would be more likely to accept an electrocardiogram (ECG) "patch" (attached to the chest) that allows continuous monitoring of the heart than individuals who did not have the disease and thus the respective experience. Currently infected COVID-19 patients, individuals who had recovered from COVID-19, and a control group were recruited online through Facebook (and Instagram) and through general practitioners (GPs). Demographic information and questions tailored to the problem were collected via an online questionnaire. An online survey was chosen in part because of the pandemic conditions, and Facebook was chosen because of the widespread discussions of health topics on that platform. The results confirmed the central hypothesis that people who had experienced a disease are more willing to accept new medical technologies and showed that curiosity about new technologies and willingness to use them were significantly higher in the two groups currently or previously affected by COVID-19, whereas fears of being "monitored" (in the sense of surveillance) were significantly higher among people who had not experienced the disease and threat. Experiencing a serious disease ("patient experience") promotes acceptance of new medical technologies.

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

BACKGROUND: In 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. OBJECTIVE: This study aimed to identify the spread of and experience with new digital technologies in the medical sector in Germany. METHODS: We 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. RESULTS: The 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. CONCLUSIONS: The 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.

Acceptance and Resistance of New Digital Technologies in Medicine: Qualitative Study.

BACKGROUND: This study discusses the acceptance of new medical technologies in health care settings and resistance to these technologies from hospitals, doctors' surgical centers, electronic health (eHealth) centers, and related institutions. We suggest a novel method of identifying factors that influence the acceptance of, and resistance to, new technologies by medical staff and patients. OBJECTIVE: The objective of this study was to determine and evaluate the factors that influence acceptance and resistance to achieve a successful implementation of new technologies. METHODS: The target group was patients residing in Brandenburg and major stakeholders in the local health care structure, for instance, medical institutions and medical professionals. The process relies on 3 models: the technology acceptance model, the unified technology acceptance and use of technology model, and the theory of technical innovation diffusion. Qualitative methodology was employed in this study, and an exploratory design was adopted to gain new insights into a poorly understood phenomenon in the German context. This enabled the researcher to take a flexible approach toward exploring a wide range of secondary data and to choose a different approach when unexpected information emerged. Content analysis was used to identify and interpret the data, and the researcher assured that the meaning associated with the information has concurred with that of the original source. RESULTS: This study confirmed that adoption of new technologies in health care depended on individual opinions of the factors relating to them. Some medical professionals believed that technology would interfere with their ability to make independent diagnoses and their relationships with patients. Doctors also feared that technology was a means of management control. In contrast, other medical staff welcomed technology because it provided them with more opportunities to interact with patients and their carers. Generally, patients were more enthusiastic about technology than medical professionals and health care managers because it allowed them to have greater autonomy in selecting health care options. The need for all groups to be involved in the development of the new health care approach was an important outcome, otherwise resistance to it was likely to be greater. In other words, the strategy for change management was the indicator of success or failure. Therefore, following our analysis, a number of practical precepts emerged that could facilitate user acceptance of digital solutions and innovative medical technologies. CONCLUSIONS: The acceptance of digital solutions and innovative medical technology by patients and professionals relies on understanding their anxieties and feelings of insecurity. The process will take time because individuals accept change at different rates. Hence, the development of an extensive user community to fully and successfully implement eHealth is less likely in the short term; however, this should not prevent the push for changes in health care technology.