BackgroundDigital innovation, introduced across many industries, is a strong force of transformation. Some industries have seen faster transformation, whereas the health care sector only recently came into focus. A context where digital corporations move into health care, payers strive to keep rising costs at bay, and longer-living patients desire continuously improved quality of care points to a digital and value-based transformation with drastic implications for the health care sector.ObjectiveWe tried to operationalize the discussion within the health care sector around digital and disruptive innovation to identify what type of technological enablers, business models, and value networks seem to be emerging from different groups of innovators with respect to their digital transformational efforts.MethodsFrom the Forbes 2000 and CBinsights databases, we identified 100 leading technology, life science, and start-up companies active in the health care sector. Further analysis identified projects from these companies within a digital context that were subsequently evaluated using the following criteria: delivery of patient value, presence of a comprehensive and distinctive underlying business model, solutions provided, and customer needs addressed.ResultsOur methodological approach recorded more than 400 projects and collaborations. We identified patterns that show established corporations rely more on incremental innovation that supports their current business models, while start-ups engage their flexibility to explore new market segments with notable transformations of established business models. Thereby, start-ups offer higher promises of disruptive innovation. Additionally, start-ups offer more diversified value propositions addressing broader areas of the health care sector.ConclusionsDigital transformation is an opportunity to accelerate health care performance by lowering cost and improving quality of care. At an economic scale, business models can be strengthened and disruptive innovation models enabled. Corporations should look for collaborations with start-up companies to keep investment costs at bay and off the balance sheet. At the same time, the regulatory knowledge of established corporations might help start-ups to kick off digital disruption in the health care sector.
BackgroundHealth care systems worldwide are struggling to keep rising costs at bay with only modest outcome improvement among many diseases. Digitization with technologies like Artificial Intelligence or Machine Learning algorithms might address this. Although digital technologies have been successfully applied in clinical studies the effect on the overall health care system so far was limited. The regulatory ecosystem or data privacy might be responsible, but other reasons may also predominate.ObjectiveWe analyzed how the digitization of the German health care market is currently perceived among different stakeholders and investigated reasons for its slow adaption.MethodsThis was a mixed methods study split into a qualitative Part A using the conceptual approach of the Grounded Theory and a quantitative Part B using the Delphi method. For Part A we interviewed experts in the health care system and converted the results into 17 hypotheses. The Delphi method consisted of an online survey which was sent to the participants via email and was available for three months. For the assessment of the 17 hypotheses, the participants were given a six-point Likert scale. The participants were grouped into patients, physicians, and providers of services within the German health care market.ResultsThere was a strong alignment of opinions on the hypotheses between experts (N=21) and survey participants (N=733), with 70.5% overall agreement on 12/17 hypotheses. Physicians demonstrated the lowest level of agreement with the expert panel at 88% (15/17) disagreement, with the hypotheses “H8: Digitization in the health care system will free up jobs,” and “H6: Digitization in the health care system will empower the patients,” perceived to be in profound disagreement (P=.036 and P<.001, respectively).ConclusionsDespite the firm agreement among participants and experts regarding the impact of digitization on the health care system, physicians demonstrated a more negative attitude. We assume that this might be a factor contributing to the slow adoption of digitization in practice. Physicians might be struggling with changing power structures, so future measures to transform the market should involve them to a larger degree.
Background Nonadherence to medication is a driver of morbidity and mortality, and complex medication regimens in patients with chronic diseases foster the problem. Digital technology might help, but despite numerous solutions being developed, none are currently widely used, and acceptance rates remain low, especially among the elderly. Objective This study aimed to better understand and operationalize how new digital solutions can be evaluated. Particularly, the goal was to identify factors that help digital approaches targeting adherence to become more widely accepted. Methods A qualitative study using a conceptual grounded theory approach was conducted. We included patients aged 65 years and older who routinely took new oral anticoagulants. To generate theses about the digital competencies of the target group with daily medication intake, face-to-face interviews were conducted, recorded, and anonymized. After coding the interviews, categories were generated, discussed, and combined with several theses until saturation of the statements was reached. Results The methodological approach led to the finding that after interviews in 20 of 77 potentially available patients, a saturation of statements was reached. The average patient’s age was 75 years, and 50% (10/20) of the subjects were female. The data identified five main coding categories—Diseases and medicine, Technology, Autonomy, Patient narrative, and Attitude toward technologies—each including positive and negative subcategories. Main categories and subcategories were summarized as Adherence Radar, which can be considered as a framework to assess the potential of adherence solutions in the process of prototyping and can be applied to all adherence tools in a holistic manner. Conclusions The Adherence Radar can be used to increase the acceptance rate of digital solutions targeting adherence. For a patient-centric design, an app should be adapted to the individual patient’s needs. According to our results, this application should be based on gender and educational background as well as the individual physician-patient relationship. If used in a proper, individualized manner, digital adherence solutions could become a new cornerstone for the treatment of chronically ill individuals.
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